Qualification: 
Ph.D
kp_soman@amrita.edu

Dr. Soman K. P. currently serves as Head and Professor at Amrita Center for Computational Engineering and Networking (CEN), Coimbatore Campus.

Projects Undertaken at CEN:

Sponsored :

  • Software Radio applications in implementing Virtual labs for MHRD, New Delhi, India
  • Particle Image Velocimetry and Planar Laser Induced Fluorescence studies for flow visualization and characterization (funded by ISRO)
  • Massively Parallel Support Vector Machines for target identification (funded by DRDO)
  • Fault simulation and analysis of spacecraft structures using wavelets (funded by ISRO)
  • Study of methodologies for detection of digital contents plagiarism and other piracies (funded by DIT), New Delhi, India
  • Implementation of Image Fusion Algorithms for ADE, Bangalore
  • Video summarization for ISRO, Hyderabad
  • English to Tamil Translation system for IT ministry, New Delhi, India
  • English to Dravidian Language Translation system for MHRD
  • Source Code Plagiarism Detection Engine for IT ministry, New Delhi, India

Publications

Publication Type: Journal Article

Year of Publication Publication Type Title

2018

Journal Article

V. Ankarao, Sowmya V., and Dr. Soman K. P., “Multi-sensor data fusion using NIHS transform and decomposition algorithms”, Multimedia Tools and Applications, 2018.[Abstract]


Multi-spectral image fusion is to enhance the details present in multi-spectral bands with the spatial information available in the panchromatic image. Fused images have the effect of spectral distortions and lack of structural similarity. To overcome these limitations, three methods are proposed using intensity, hue, saturation (IHS) and nonlinear IHS (NIHS) transform along with the Dynamic Mode Decomposition (DMD) and 2D-Empirical Mode Decomposition (2D-EMD or IEMD). An intensity plane is calculated from the NIHS transform. The modes are constructed using DMD by considering the variations between the intensity plane computed using NIHS transforms of a low resolution multi-spectral image and a panchromatic image. Similarly, 2D-EMD is also used for image fusion. Modes are subjected to weighted fusion rule to get an intensity plane with spatial and edge information. Finally, the calculated intensity plane is concatenated along with the hue and saturation plane of low-resolution multi-spectral image and transformed into RGB color space. Thus, the fused images have high spatial and edge information on spectral bands. The experiments and its quality assessment assure that proposed methods perform better than the existing methods. More »»

2018

Journal Article

P. V. Veena, Dr. M. Anand Kumar, and Dr. Soman K. P., “Character embedding for language identification in Hindi-English code-mixed social media text”, Computacion y Sistemas, vol. 22, pp. 65-74, 2018.[Abstract]


Social media platforms are now widely used by the people to express their opinion or interest. The language used by the users in social media earlier was purely English. Code-mixed text, i.e., mixing of two or more languages, is commonly seen now. In code-mixed data, one language will be written using another language script. So to process such code-mixed text, identification of language used in each word is important for language processing. The main objective of the work is to propose a technique for identifying the language of Hindi-English code-mixed data used in three social media platforms namely, Facebook, Twitter, and WhatsApp. The classification of Hindi-English code-mixed data into Hindi, English, Named Entity, Acronym, Universal, Mixed (Hindi along with English) and Undefined tags were performed. Popular word embedding features were used for the representation of each word. Two kinds of embedding features were considered - word-based embedding features and character-based context features. The proposed method was done with the addition of context information along with the embedding features. A well-known machine learning classifier, Support Vector Machine was used to train and test the system. The work on Language Identification in code-mixed text using character-based embedding is a novel approach and shows promising results. © 2018 Instituto Politecnico Nacional. All rights reserved.

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2018

Journal Article

R. Vinayakumar, Dr. Soman K. P., and Poornachandran, P., “Detecting malicious domain names using deep learning approaches at scale”, Journal of Intelligent and Fuzzy Systems, vol. 34, pp. 1355-1367, 2018.[Abstract]


Threats related to computer security constantly evolving and attacking the networks and internet all the time. New security threats and the sophisticated methods that hackers use can bypass the detection and prevention mechanisms. A new approach which can handle and analyze massive amount of logs from diverse sources such as network packets, Domain name system (DNS) logs, proxy logs, system/service logs etc. required. This approach can be typically termed as big data. This approach can protect and provide solution to various security issues such as fraud detection, malicious activities and other advanced persistent threats. Apache spark is a distributed big data based cluster computing platform which can store and process the security data to give real time protection. In this paper, we collect only DNS logs from client machines in local area network (LAN) and store it in a server. To find the domain name as either benign or malicious, we propose deep learning based approach. For comparison, we have evaluated the effectiveness of various deep learning approaches such as recurrent neural network (RNN), long short-term memory (LSTM) and other traditional machine learning classifiers. Deep learning based approaches have performed well in comparison to the other classical machine learning classifiers. The primary reason is that deep learning algorithms have the capability to obtain the right features implicitly. Moreover, LSTM has obtained highest malicious detection rate in all experiments in comparison to the other deep learning approaches. © 2018 - IOS Press and the authors. All rights reserved.

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2018

Journal Article

M. Swarna, Sowmya V., and Dr. Soman K. P., “Band selection using variational mode decomposition applied in sparsity-based hyperspectral unmixing algorithms”, Signal, Image and Video Processing, 2018.[Abstract]


In this work, a frequency-based dimensionality reduction technique using variational mode decomposition (VMD) is proposed. Dimensionality reduction is a very important aspect of preprocessing in case of hyperspectral image (HSI) analysis where this step helps in elimination of the lesser informative bands, thereby reducing the size of the data and making its processing computationally less challenging. In contrast to the standard dimensionality reduction methods such as inter-band block correlation (IBBC) where bands are eliminated based on their similarity with the consecutive bands, the proposed method uses frequency information of each band to categorize it as a less or more informative band. In this way, only the topmost informative bands of HSI are selected to form the reduced dataset. In our experiment, in order to verify the efficiency of VMD as a dimensionality reduction technique, the hyperspectral unmixed results obtained for IBBC reduced dataset is compared with those obtained for VMD reduced dataset. From the parametric measures such as classification accuracy, root-mean-square error (RMSE) and visual results obtained after unmixing for both IBBC and VMD reduced datasets, it is noticed that the VMD reduced dataset performs better by achieving higher classification accuracy and lower RMSE than that of the existing IBBC method.

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2017

Journal Article

P. Poornachandran, Praveen, S., Ashok, A., Krishnan, M. R., and Dr. Soman K. P., “Drive-by-download malware detection in hosts by analyzing system resource utilization using one class support vector machines”, Advances in Intelligent Systems and Computing, vol. 516, pp. 129-137, 2017.[Abstract]


Drive-by-Download is an unintentional download of a malware on to a user system. Detection of drive-by-download based malware infection in a host is a challenging task, due to the stealthy nature of this attack. The user of the system is not aware of the malware infection occurred as it happens in the background. The signature based antivirus systems are not able to detect zero-day malware. Most of the detection has been performed either from the signature matching or by reverse engineering the binaries or by running the binaries in a sandbox environment. In this paper, we propose One Class SVM based supervised learning method to detect the drive-by-download infection. The features comprises of system RAM and CPU utilization details. The experimental setup to collect data contains machine specification matching 4 user profiles namely Designer, Gamer, Normal User and Student. The experimental system proposed in this paper was evaluated using precision, recall and F-measure. © Springer Nature Singapore Pte Ltd. 2017. More »»

2017

Journal Article

K. S. Gokul Krishnan, Pooja, A., Dr. M. Anand Kumar, and Dr. Soman K. P., “Character based bidirectional LSTM for disambiguating tamil part of speech categories”, International Journal of Control Theory and Applications, vol. 10, pp. 229-235, 2017.[Abstract]


Part of speech (POS) tagging is the process of labeling a part of speech tag to each and every word in the corpus. In this paper POS tagging for Tamil language is performed by using Bidirectional Long Short Term Memory. A C2W (character to word) model instead of traditional word lookup table for obtaining word embeddings using BLSTM is presented. The C2W model uses characters to form a vector representation of a word. The word embedding from C2W model is used by BLSTM to tag the words in the corpus. This method, when tested with 3723 words produced highest accuracy of 86.45%. © International Science Press. More »»

2017

Journal Article

K. Harikumar and Dr. Soman K. P., “Convex hyperspectral unmixing algorithm using parameterized non-convex penalty function”, Advances in Intelligent Systems and Computing, vol. 516, pp. 209-217, 2017.[Abstract]


Unmixing of hyperspectral data is an area of major research because the information it provides is utilized in plethora of fields. The year of 2006 witnessed the emergence of Compressed Sensing algorithm which was later used to spearhead research in umixing problems. Later, the notion of lp norms 0 < p < 1 and other non-smooth and non-convex penalty function were used in place of the traditional convex l1 penalty. Dealing with optimization problems with non-convex objective function is rather difficult as most methodologies often get stuck at local optima. In this paper, a parameterised non-convex penalty function is used to induce sparsity in the unknown.The parameters of penalty function can be adjusted so as to make the objective function convex, thus resulting in the possibility of finding a global optimal solution. Here ADMM algorithm is utilized to arrive at the final iterative algorithm for the unmixing problem. The algorithm is tested on synthetic data set, generated from the spectral library provided by US geological survey. Different parametric penalty functions like log and arctan are used in the algorithm and is compared with the traditional l1 penalties, in terms of the performance measures RSNR and PoS. It was observed that the non-convex penalty functions out-performs the l1 penalty in terms of the aforementioned measures. © Springer Nature Singapore Pte Ltd. 2017. More »»

2017

Journal Article

Y. C. Nair, Kumar, S., and Dr. Soman K. P., “Real-time automotive engine fault detection and analysis using bigdata platforms”, Advances in Intelligent Systems and Computing, vol. 515, pp. 507-514, 2017.[Abstract]


This paper is aimed at diagnosing automotive engine fault in real-time utilizing BigData framework called spark. An automobile in the present day world is equipped with millions of sensors which are under the command of a central unit the ECU (Electronic Control Unit). ECU holds all information about the engine. A network of ECUs connected across the globe is a source tap of BigData. Leveraging the new sources of BigData by automotive giants boost vehicle performance, enhance loco driver experience, accelerated product designs. A piezoelectric transducer coupled to the ECU captures the vibration signals from the engine. The engine fault is detected by carving the problem into a pattern classification problem under machine learning after extracting cyclostationary features from the vibration signal. Spark-streaming framework, the most versatile BigData framework available today with immense computational capabilities is employed for engine fault detection and analysis. © Springer Nature Singapore Pte Ltd. 2017.

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2017

Journal Article

V. V. Pradeep, Sowmya V., and Dr. Soman K. P., “Application of M-band wavelet in pan-sharpening”, Special Issue in the Journal of Intelligent and Fuzzy Systems, IOS Press, Netherlands, vol. 32, no. 4, pp. 3151-3158, 2017.[Abstract]


Remote sensing satellites are proficient in taking earth images across various regions in visible part of electromagnetic spectrum. The images can be panchromatic image of a single band, multispectral image of three to seven different bands, and hyperspectral image taken from about 220 contiguous spectral bands. These images are used together or on its own, depending on the significance and usage of the preferred application. Pan-sharpening is one method which is used to improve the quality of a low resolution multispectral image by fusion with a high resolution panchromatic image. This paper proposes a method based on M-band wavelets for the pan-sharpening of a low resolution multispectral image. The method tries to improve the spatial characteristics while preserving the spectral quality of the data. The proposed technique uses weighted fusion rule and average fusion rule. The data used for the experiment were acquired by high resolution optical imagers onboard QuickBird, WorldView-3, WorldView-2 and GeoEye-1. A comparison with existing fusion techniques is done based on image quality metrics and visual interpretation. The experimental results and analysis suggests that the proposed pan-sharpening technique outperforms other compared pre-existing pan-sharpening methods. © 2017-IOS Press and the authors. All rights reserved.

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2016

Journal Article

M. Kaviarasan, Geetha, P., and Dr. Soman K. P., “GIS-based ground water quality monitoring in Thiruvannamalai district, Tamil Nadu, India”, Advances in Intelligent Systems and Computing, vol. 397, pp. 685-700, 2016.[Abstract]


Ground water is a vital resource for drinking water around the world. The economic and ecological stability of many countries heavily relay upon groundwater availability. With rapid developments in industrial and agricultural sectors, the need for ground water is greater than ever before. Consequently, the quality of ground water is affected by fertilizers, effluents run off from industries, chemical dumping sites, domestic sewage, etc. Hence, it is necessary to constantly monitor ground water quality as it has a serious impact on human health. In this paper, we have analyzed ground water quality of Thiruvannamalai district of Tamil Nadu, India. The ground water samples are taken from 13 locations per area. Water Quality Index (WQI) is estimated for each area to ascertain for the potability of water. The physicochemical parameters like pH, Electrical Conductivity (EC), nitrates, fluorides, and chlorides sample data are compared against World Health Organization (WHO) standards. Geographical information system (GIS), an effi- cient tool for estimating water quality is used both in spatial and temporal domain. The results are useful in efficient monitoring and assessment of ground water and thus, for taking relevant measures to curb unrestrained exploitation. © Springer India 2016. More »»

2016

Journal Article

S. Moushmi, Sowmya, V., and Dr. Soman K. P., “Empirical wavelet transform for multifocus image fusion”, Advances in Intelligent Systems and Computing, vol. 397, pp. 257-263, 2016.[Abstract]


Image fusion has enormous applications in the fields of satellite imaging, remote sensing, target tracking, medical imaging, and much more. This paper aims to demonstrate the application of empirical wavelet transform for the fusion of multi- focus images incorporating the simple average fusion rule. The method proposed in this paper is experimented on benchmark datasets used for fusing images of different focuses. The effectiveness of the proposed method is evaluated across the existing techniques. The performance comparison of the proposed method is done by visual perception and assessment of standard quality metrics which includes root mean squared error, relative average spectral error, universal image quality index, and spatial information. The experimental result analysis shows that the proposed technique based on the empirical wavelet transform (EWT) outperforms the existing techniques. © Springer India 2016. More »»

2016

Journal Article

Neethu Mohan, S. Kumar, S., Poornachandran, P., and Dr. Soman K. P., “Modified variational mode decomposition for power line interference removal in ECG signals”, International Journal of Electrical and Computer Engineering, vol. 6, pp. 151-159, 2016.[Abstract]


Power line interferences (PLI) occurring at 50/60 Hz can corrupt the biomedical recordings like ECG signals and which leads to an improper diagnosis of disease conditions. Proper interference cancellation techniques are therefore required for the removal of these power line disturbances from biomedical recordings. The non-linear time varying characteristics of biomedical signals make the interference removal a difficult task without compromising the actual signal characteristics. In this paper, a modified variational mode decomposition based approach is proposed for PLI removal from the ECG signals. In this approach, the central frequency of an intrinsic mode function is fixed corresponding to the normalized power line disturbance frequency. The experimental results show that the PLI interference is exactly captured both in magnitude and phase and are removed. The proposed approach is experimented with ECG signal records from MIT-BIH Arrhythmia database and compared with traditional notch filtering. Copyright © 2016 Institute of Advanced Engineering and Science. All rights reserved.

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2016

Journal Article

M. Kavinandhini, Geetha, P., and Dr. Soman K. P., “Climatic impacts and reliability of large scale wind farms in Tamil Nadu”, Indian Journal of Science and Technology, vol. 9, 2016.[Abstract]


<p>Objective: The main objective of this paper describes how the large scale windfarms affect the climate of south west monsoon region. Methods/Analysis: Method used for analysing climatic parameters of before and after installation of wind farms is Gaussian mixture model. ArcGIS and QGIS software is used for image and geo-information analysis. Data from the commercial wind turbine of south west monsoon region like temperature, relative humidity, precipitation, wind speed is used to find the climatic variation. Findings: Large scale wind farms significantly affect the various climatic parameters. These impacts depends on the static stability, increase or decrease in the climatic parameters. Conclusion/ Application: Improvements can be made by taking the ground temperature measured by satellite image and identify the warming effect of night and day time warming effect of large scale wind farm area of southwest monsoon regions.</p>

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2016

Journal Article

Ca Anjana, Sundaresan, Sb, Dr. Shanmugha Sundaram G. A., and Dr. Soman K. P., “Impact Analysis of Wind Farms on Air Traffic Control Radar”, Journal of Chemical and Pharmaceutical Sciences, vol. 9, pp. 580-584, 2016.[Abstract]


These days, wind vitality use gets to be gigantic which prompts an increment in number of wind turbine establishments. Because of these wind turbines, the electromagnetic waves get impedance and are scattered which brings about the loss of correspondence. In this paper, the unfriendly impacts of wind farms on radar framework is displayed and discussed for Air Traffic Control (ATC) Radar. Coimbatore domestic (ATC) radar working in S-band recurrence gets influenced by wind farms located in Palakkad gap area. Demonstrating of the wind turbines and estimation of Radar Cross-Section (RCS) is done utilizing high frequency EM solver viz., XGtd tool. The investigation of RCS dispersing plot, examination of improved RCS and air traffic issues revels that the wind farms exhibit in viewable pathway and those near to radar influences the framework and results in loss of information which leads to poor air traffic monitoring. More »»

2016

Journal Article

L. S. Kiran, Sowmya V., and Dr. Soman K. P., “Enhanced Variational Mode Features for Hyperspectral Image Classification”, Journal of Chemical and Pharmaceutical Sciences, vol. 9, pp. 502-505, 2016.[Abstract]


Variational Mode Decomposition (VMD) is a recent method and is gaining popularity in the area of signal and image processing. The use of this decomposition technique in hyper spectral image classification is discussed in detail in this paper. The role of VMD as a feature extraction technique is exploited here. The proposed method includes an initial stage of dimensionality reduction so as to reduce the computational complexity. A final stage of recursive filtering is also added to further enhance the results. Results obtained by the proposed method on two hyper spectral image datasets 'Indian Pines and Salinas-A, suggests that VMD is a promising method in the area of image analysis and classification. Quality indices used for experimental analysis include overall accuracy (OA), average accuracy (AA) and kappa coefficient. Notable classification accuracy has been obtained for both the datasets and a final stage of recursive filtering has further improved the results (more than 98% accuracy in the case of Indian Pines). More »»

2016

Journal Article

S. Se, Pradeep, D., Sowmya V., and Dr. Soman K. P., “Fourier Descriptor features for Shape Deformation Classification using Random Kitchen Sink”, Journal of Chemical and Pharmaceutical Sciences, vol. 9, pp. 554-558, 2016.[Abstract]


This paper deals with the Fourier descriptor features for shape deformation classification using Random Kitchen Sink algorithm accessed through GURLS library. Shape recognition is an important method used in all industrial environments which are mostly concerned with robots. It is a highly essential task to make the robot understand the shape of an object. The object may have many deformed shapes and so it is necessary to train the classifier accordingly. Recognition methods based on polar coordinates and probabilistic models are already developed, but its accuracy for finding the deformed shape of the object is low. In this context, Random Kitchen Sink algorithm is used and the classification is done through GURLS in which, regularized least square method is used, which leads to better shape recognition. More »»

2016

Journal Article

N. Nechikkat, Sowmya V., and Dr. Soman K. P., “Low dimensional variational mode features for hyperspectral image classification”, Journal of Chemical and Pharmaceutical Sciences, vol. 9, pp. 565-570, 2016.[Abstract]


High Dimensionality is always a great concern while working with hyperspectral images. The high dimension of hyperspectral image increases the computational complexity, creates data storage issues and decrease the performance and accuracy of hyperspectral image analysis algorithms. This paper focuses on low dimensional Variational Mode features for hyperspectral image classification. The proposed method consist of three stages: preprocessing using Inter Band Block Correlation (IBBC) technique, feature extraction using Variational Mode Decomposition (VMD) and dimensionality reduction using Singular Value Decomposition (SVD). The efficiency of the proposed method based on the low dimensional feature extraction using VMD is evaluated by one of the sparsity based classification algorithms namely Orthogonal Matching Pursuit (OMP). The proposed work is experimented on the standard dataset namely Indian pines acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). The experimental analysis shows that our proposed technique produces 90.88% overall accuracy with 40% of training which is greater than the classification accuracy obtained without feature extraction. More »»

2016

Journal Article

Reshma R, Sowmya V., and Dr. Soman K. P., “Improvement in kernel based Hyperspectral image classification using legendre fenchel denoising”, Indian Journal of Science and Technology, vol. 9, no. 33, 2016.[Abstract]


Hyperspectral images have bulk of information which are widely used in the field of remote sensing. One of the main problems faced by these images is noise. This emphasizes the importance of denoising techniques for enhancing the image quality. In this paper, Legendre Fenchel Transformation (LFT) is used for preprocessing the Indian Pines Dataset. LFT reduces the noise of each band of the hyperspectral image without affecting the edge information. Signal to noise ratio is computed which helps to evaluate the performance of denoising. Further, the denoised image is classified using GURLS and LibSVM and the various accuracies are estimated. The experimental analysis shows that the overall and classwise accuracies are more for the preprocessed data classification when compared to the classification without preprocessing. The classification accuracy is improved with denoising of hyperspectral image.

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2016

Journal Article

V. P.V, G, R. Devi, Sowmya V., and Dr. Soman K. P., “Least Square based image denoising using wavelet filters”, Indian Journal of Science and Technology, vol. 9, no. 30, 2016.[Abstract]


Background/Objectives: Noise in a digital image, is unwanted information that degrades the quality of an image. The main aim of the proposed method is to denoise a noisy image based on least square approach using wavelet filters. Methods/ Statistical Analysis: One dimensional least square approach proposed by Selesnick is extended to two dimensional image denoising. In our proposed technique of least square problem formulation for image denoising, the matrix constructed using second order filter coefficients is replaced by wavelet filter coefficients. Findings: The method is experimented on standard digital images namely Lena, Cameraman, Barbara, Peppers and House. The images are subjected to different noise types such as Gaussian, Salt and Pepper and Speckle with varying noise level ranging from 0.01db to 0.5db. The wavelet filters used in the proposed approach of denoising are Haar, Daubechies, Symlet, Coiflet, Biorthogonal and Reverse biorthogonal. The outcome of the experiment is evaluated in terms of Peak Signal to Noise Ratio (PSNR). The analysis of the experiment results reveals that performance of the proposed method of least square based image denoising by wavelet filters are comparable to denoising using existing second order sparse matrix. Applications/Improvements: Digital images are often prone to noise; hence, proceeding with further processing of such an image requires denoising. This work can be extended in future to m-band wavelet filters.

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2016

Journal Article

H. T. Suseelan, Sudhakaran, S., Sowmya V., and Dr. Soman K. P., “Performance Evaluation of Sparse Banded Filter Matrices using content based image retrieval”, Institute of Integrative Omics and Applied Biotechnology, vol. 7, no. 3, pp. 11-18, 2016.[Abstract]


Content Based Image Retrieval (CBIR) is an extensively used application in the field of Image Processing. It is used to search through a massive database and retrieve the images that have similarity with the query image. In this paper, performance is evaluated for Sparse Banded Filter matrices (ABfilter) against the standard edge detection filters through Content Based Image Retrieval. Performance factor of ABfilter directly relates to its edge detection capabilities. Here, edge detection followed by the Singular Value Decomposition (SVD) is done for feature extraction for both the query and images in database. Query image feature and database image features are matched and those having similar values are retrieved. Similarity measurement is done by computing the distance between corresponding features. Experimental results indicate that retrieval results using ABfilter is much better than using standard edge detection filters for the same, which in turn establishes its superiority in edge detection.

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2016

Journal Article

A. M, N, D., Sowmya V., Mahan, N., and Dr. Soman K. P., “Least Square Based approach for Image Inpainting”, Institute of Integrative Omics and Applied Biotechnology, vol. 7, pp. 44-59, 2016.[Abstract]


Images are widely used over various applications under the aegis of various domains like Computer vision, Biomedical, etc. The problem of missing data identification is of great concern in various fields involving image processing. Least square can be used for missing sample estimation for 1-D signals. The proposed system extends the missing sample estimation in 1-D using least square to 2-D, applied for image inpainting. The paper also draws a comparison between the Total Variation (TV) algorithm and the proposed method. The experiments were conducted on standard images and the standard metrics namely PSNR and SSIM are used to compare the image quality obtained using the proposed method (least square based) and TV algorithm. More »»

2016

Journal Article

D. P. Kuttichira, Sowmya V., and Dr. Soman K. P., “Digit recognition using multiple feature extraction”, IIOAB Journal, vol. 7, pp. 37-43, 2016.[Abstract]


Digit Recognition is one of the classic problems in pattern classification. It has ten labels which are digits from 0-9 and each prototypes in the test set has to be classified under these labels. In this paper, we have used MNIST data for training and testing. MNIST database is a standard database for digit classification. A number of neural network algorithms have been used on MNIST to get high accuracy outputs. These algorithms are computationally costly. Here, we have used multiple feature extraction based on SVD and histogram to create testing and training matrix. To the feature vector formed by SVD, histogram values along x-axis and y-axis of an image is appended. These vectors are mapped to hyperplane using polynomial and Gaussian kernel. For classification open source software like GURLS and LIBSVM is used to obtain a fairly good accuracy. © 2016, Institute of Integrative Omics and Applied Biotechnology. All rights reserved.

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2016

Journal Article

S. Chandran, Variyar, V. V. Sajith, Prabhakar, T. V. Nidhin, and Dr. Soman K. P., “Aerial image classification using regularized least squares classifier”, Journal of Chemical and Pharmaceutical Sciences, vol. 9, pp. 889-895, 2016.[Abstract]


The land cover classification and urban analysis of remotely sensed images has become a challenging problem, hence efficient classifiers are required in order to combat the problem of classifying the huge remote sensing aerial datasets. In this paper we have proposed the use of Random Kitchen Sink (RKS) algorithm and Regularized Least Squares (RLS) classifier for the classification of aerial image. The new machine learning algorithm RKS, primarily engages in mapping the feature data to a higher dimensional space and thereby generates random features. These randomized data are then adopted by RLS classifier for the classification task. It is observed that the randomization of the data reduces the computation time needed for training. The experiment is performed on five classes of the UC Merced Land Use Aerial Imagery Dataset. The efficiency of the proposed method is estimated by comparing the accuracy results with the conventional classifier namely, Support Vector Machine (SVM). Experimental result shows that the proposed method produces a high degree of classification accuracy i.e. 94.4%, when RBF kernel with LOO (Leave One Out) cross-validation was used, when compared to SVM. In this paper, statistical features show better precision and accuracy in classifying different set of classes, compared to textural features in both the classification approaches. Hence, better accuracies could be attained for multi class classification when compared to other classification technique like, SVM since, the random features reduces computation time and enhance the performance of kernel machines.

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2016

Journal Article

S. S. Kumar, Dr. M. Anand Kumar, and Dr. Soman K. P., “Experimental analysis of malayalam pos tagger using epic framework in scala”, ARPN Journal of Engineering and Applied Sciences, vol. 11, pp. 8017-8023, 2016.[Abstract]


In Natural Language Processing (NLP), one of the well-studiedproblems under constant exploration is part-ofspeech tagging or POS tagging or grammatical tagging. The task is to assign labels or syntactic categories such as noun, verb, adjective, adverb, preposition etc. to the words in a sentence or in an un-annotated corpus. This paper presents a simple machine learning based experimental study for POS tagging using a new structured prediction framework known as EPIC, developed in scale programming language. This paper is first of its kind to perform POS tagging in Indian Language using EPIC framework. In this framework, the corpus contains labelled Malayalam sentences in domains like health, tourism and general (news, stories). The EPIC framework uses conditional random field (CRF) for building tagged models. The framework provides several parameters to adjust and arrive at improved accuracy and thereby a better POS tagger model. The overall accuracy were calculated separately for each domains and obtained a maximum accuracy of 85.48%, 85.39%, and 87.35% for small tagged data in health, tourism and general domain. More »»

2016

Journal Article

P. R. Sugatha Kumari, Dr. Shanmugha Sundaram G. A., and Dr. Soman K. P., “A case study on cirrus clouds using PWV measurement”, Journal of Chemical and Pharmaceutical Sciences, vol. 9, pp. 559-564, 2016.[Abstract]


Cirrus (Ci) clouds are high level clouds at an altitude above 6 Km. Ci clouds are composed of ice crystals and responsible for optical phenomenon such as mock suns and halos. Though Ci clouds are non-precipitating in nature, it has considerable role in case of weather forecasting. This paper explores a study of Ci clouds as indicators of fair weather and high altitude wind direction based on Precipitable Water Vapor (PWV) measurement. Brightness Temperature (TB) values from data obtained using satellite water vapor channel, along with pressure and temperature from Global Forecast System (GFS) are used in this study for PWV calculation. Data collected over a period of one year from 1st of January, 2014 to 31st of December, 2014 is considered here. More »»

2016

Journal Article

S. Archana, Dr. Shanmugha Sundaram G. A., and Dr. Soman K. P., “Analysis of precipitating clouds using precipitable water vapour”, Journal of Chemical and Pharmaceutical Sciences, vol. 9, pp. 542-547, 2016.[Abstract]


The detection of precipitating clouds is necessary for weather prediction and climate researches. Precipitation occurs by condensation when the atmosphere is saturated with water vapour. The aim of the study is to discuss about the variations of Precipitable Water Vapour (PWV) in the case of such clouds. The brightness temperature data of 6.7ìm water vapour channel is primarily used in this study. The saturation mixing ratio can be calculated using temperature and pressure profile data. The study on PWV enhances the weather forecasting models and meteorological studies. In this study, the PWV for different precipitating clouds are estimated for a period of January-December 2014. More »»

2016

Journal Article

Sowmya V., Divu, G., and Dr. Soman K. P., “Significance of incorporating chrominance information for effective color-to-grayscale image conversion”, Signal, Image and Video Processing, vol. 11, 2016.[Abstract]


This paper provides an alternative framework for color-to-grayscale image conversion by exploiting the chrominance information present in the color image using singular value decomposition (SVD). In the proposed technique of color-to-grayscale image conversion, a weight matrix corresponds to the chrominance components is derived by reconstructing the chrominance data matrix (planes a* and b*) from the eigenvalues and eigenvectors computed using SVD. The final grayscale converted image is obtained by adding the weighted chrominance data to the luminous intensity which is kept intact for the CIEL*a*b* color space of the given color image. The effectiveness of the proposed grayscale conversion is confirmed by the comparative analysis performed on the color-to-gray benchmark dataset across 10 existing algorithms based on the standard objective measures, namely normalized cross-correlation, color contrast preservation ratio, color content fidelity ratio, E score and subjective evaluation. More »»

2015

Journal Article

A. Muralidharan, Sugumaran, V., Dr. Soman K. P., and Amarnath, M., “Fault diagnosis of helical gear box using variational mode decomposition and random forest algorithm”, SDHM Structural Durability and Health Monitoring, vol. 10, pp. 55-80, 2015.[Abstract]


Gears are machine elements that transmit motion by means of successively engaging teeth. In purely scientific terms, gears are used to transmit motion. A faulty gear is a matter of serious concern as it affects the functionality of a machine to a great extent. Thus it is essential to diagnose the faults at an initial stage so as to reduce the losses that might be incurred. This necessitates the need for continuous monitoring of the gears. The vibrations produced by gears from good and simulated faulty conditions can be effectively used to detect the faults in these gears. The introduction of Variational Mode Decomposition (VMD) as a new signal pre-processing technique along with the different decision trees have provided good classification performance. VMD allows decomposition of the signal into various modes by identifying a compact frequency support around its central frequency, such that adding all the modes reconstructs the original signal. Alternating direction multiplier method (ADMM) is used by VMD to find the intrinsic mode functions on central frequencies. Meaningful statistical features can be extracted from VMD processed signals. J48 decision tree algorithm was used to identify the useful features and the selected features were used for classification using the decision trees namely, Random Forest, REP Tree and Logistic Model Tree algorithms. The performance analyses of various algorithms are discussed in detail. Copyright © 2014 Tech Science Press.

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2015

Journal Article

S. N. Vinithra, Dr. M. Anand Kumar, and Dr. Soman K. P., “Analysis of sentiment classification for Hindi movie reviews: A comparison of different classifiers”, International Journal of Applied Engineering Research, vol. 10, 2015.[Abstract]


To decide on anything in our day to day life, it is important to have an opinion. Every opinion has a sentiment which helps in carrying decisions easier. There is a huge amount of data on the web which needs to be mined in order to find its sentiment. This paper aims at classifying labelled textual Hindi movie reviews with different classifiers. The dataset has been segregated into positive and negative reviews before processing. The goal of this paper is to predict the sentiment of the online movie review which is in form of documents with varied size. A 10-fold-cross-validation is done in order to check the calibre of the classifier used. The test accuracy is checked using the F1 score considering both precision and recall. A detailed comparison of the unigram and bigram feature‟s accuracy of all the mentioned models is done. The proposed model is classified on the following classifiers Naïve Bayes, Logistic Regression and Random Kitchen Sink algorithm. Each one of these algorithms gave better accuracy when bigram was performed. Out of these four classifying algorithms, it is observed that Naive Bayes Multinomial model has the best accuracy with a 70.37%. Hence, this sentiment analysis model which is a developing big data application is suggested for industrial applications wherein predicting the sentiment is a vital component. More »»

2015

Journal Article

P. Maya, Dhivya, N., Kartikga, C., and Dr. Soman K. P., “Discrimination between inrush and internal fault currents in a power transformer using Variational Mode Decomposition Method”, International Journal of Applied Engineering Research, vol. 10, no. 55, pp. 3298-3301, 2015.[Abstract]


Transformers, which are critical and expensive components of a power system, require suitable measures for their protection to ensure reliable operation. Identification between in rush current and internal fault current is important in the design of transformer protection relay. Often nuisance tripping of protection relay occurs when inrush current flows in the system. Identification methods based on higher second harmonic content present in inrush current has limitations in its application. This work investigates the scope of classification method based on Variation Mode Decomposition (VMD) and Support Vector Machine (SVM) in distinguishing internal fault current and inrush current in a power transformer. Validation of this method is done using synthetic data from MATLAB/SIMULINK. Choice of various kernel functions for SVM for better accuracy is also investigated. © Research India Publications. More »»

2015

Journal Article

A. C, Haridas, N., Sowmya V., and Dr. Soman K. P., “Effect of AB filter denoising on ADMM based Hyperspectral Image Classification”, International Journal of Applied Engineering Research (IJAER), vol. 10, no. 73, pp. 127-131, 2015.[Abstract]


In recent years, hyperspectral remote sensing has emerged as a prominent area of research. This has developed a lot of practical solutions to solve the various challenges faced in the field. Noise is one of such issues which deteriorate the quality of information present in the hyperspectral images. In order to address this problem, various preprocessing (denoising) techniques are applied prior to data analysis. In this paper, the proposed method evaluates the effect of Hyperspectral Image (HSI) denoising employing AB filter on optimization based classification which uses Basis Pursuit solved by Alternating Direction Method of Multipliers (ADMM). AVIRIS Indian Pines dataset is used for the experimental study. The efficiency of the proposed technique is proved by a comparative study with other existing preprocessing methods. The experimental result analysis based on visual interpretation and quantitative assessment shows that the proposed method provides better classification results compared to the existing methods. The classification results are assessed by Overall Accuracy, Average accuracy and Kappa coefficient.

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2014

Journal Article

Gandhiraj R. and Dr. Soman K. P., “Modern analog and digital communication systems development using GNU Radio with USRP”, Telecommunication Systems, vol. 56, pp. 367-381, 2014.[Abstract]


In this modern world many communication devices are highly intelligent and interconnected between each other. Any up-gradation of the hardware in the existing communication devices is not easier one. Compatibility of the new hardware with existing hardware is highly essential. But the new protocols may or may not support the older one. The solution for these problems can be provided by using the reconfigurable hardware design. The hardware can be reprogrammed according to the new change in technology up-gradation. The cost of commercially available hardware and software requirements for setting up such a module is very high. This can be solved by using Open source hardware and software such as Universal Software Radio Peripheral (USRP) and GNU Radio. This work demonstrates how the modern analog communication system like Community Radio Schemes and Radio Data System (RDS) and digital communication systems such as Simple Digital Video Broadcasting (DVB) and OFDM based data communication can be developed using the Open source hardware USRP1. This work will be helpful even for first year level of engineering students to easily implement any communication and control applications with cheaper cost. © 2013 Springer Science+Business Media New York. More »»

2014

Journal Article

A. M. Kumar, Dhanalakshmi, V., Dr. Soman K. P., and Rajendran, S., “Factored statistical machine translation system for English to Tamil language”, Pertanika Journal of Social Science and Humanities, vol. 22, pp. 1045-1061, 2014.[Abstract]


This paper proposes a morphology based Factored Statistical Machine Translation (SMT) system for translating English language sentences into Tamil language sentences. Automatic translation from English into morphologically rich languages like Tamil is a challenging task. Morphologically rich languages need extensive morphological pre-processing before the SMT training to make the source language structurally similar to target language. English and Tamil languages have disparate morphological and syntactical structure. Because of the highly rich morphological nature of the Tamil language, a simple lexical mapping alone does not help for retrieving and mapping all the morpho-syntactic information from the English language sentences. The main objective of this proposed work is to develop a machine translation system from English to Tamil using a novel pre-processing methodology. This pre-processing methodology is used to pre-process the English language sentences according to the Tamil language. These pre-processed sentences are given to the factored Statistical Machine Translation models for training. Finally, the Tamil morphological generator is used for generating a new surface word-form from the output factors of SMT. Experiments are conducted with nine different type of models, which are trained, tuned and tested with the help of general domain corpora and developed linguistic tools. These models are different combinations of developed pre-processing tools with baseline models and factored models and the accuracies are evaluated using the well known evaluation metric BLEU and METOR. In addition, accuracies are also compared with the existing online "Google-Translate" machine translation system. Results show that the proposed method significantly outperforms the other models and the existing system. © Universiti Putra Malaysia Press More »»

2014

Journal Article

S. S. Kumar, Manjusha, K., and Dr. Soman K. P., “Novel SVD based character recognition approach for Malayalam language script”, Advances in Intelligent Systems and Computing, vol. 235, pp. 435-442, 2014.[Abstract]


The research on character recognition for Malayalam script dates back to 1990’s. Compared to other Indian languages the research and developments on OCR reported for Malayalam script is very less. The character level and word level accuracy of the existing OCR tools for Indian languages can be improved by implementing robust character recognition and post-processing algorithms. In this paper, we are proposing a character recognition procedure based on Singular Value Decomposition (SVD) and k- Nearest Neighbor classifier (k-NN). The proposed character recognition scheme tested with the dataset created from Malayalam literature books and it could classify 94% of character images accurately. © Springer International Publishing Switzerland 2014 More »»

2014

Journal Article

P. Prabha, Sikha, O. K., Suchithra, M., and Dr. Soman K. P., “Accelerating the performance of DES on GPU and a visualization tool in Microsoft Excel Spreadsheet”, Advances in Intelligent Systems and Computing, vol. 246, pp. 405-411, 2014.[Abstract]


Graphic processing units (GPU) have attained a greater dimension based on their computational efficiency and flexibility compared to that of classical CPU systems. By utilizing the parallel execution capability of GPU, traditional CPU systems can handle complex computations effectively. In this work, we exploit the parallel structure of GPU and provide an improved parallel implementation for data encryption standard (DES), one of the famous symmetric key cryptosystems. We also developed a visualization tool for DES in Microsoft Excel Spreadsheet which helps the students to understand the primitive operations that constitute the DES cryptosystem clearly. The main objective of this work is to investigate the strength of parallel implementation, on the basis of execution time on GPU as well as on CPU systems. © Springer India 2014. More »»

2014

Journal Article

R. Jegadeeshwaran, Sugumaran, V., and Dr. Soman K. P., “Vibration based fault diagnosis of a hydraulic brake system using Variational Mode Decomposition (VMD)”, SDHM Structural Durability and Health Monitoring, vol. 10, pp. 81-97, 2014.[Abstract]


In automobile, brake system is an essential part responsible for control of the vehicle. Vibration signals of a rotating machine contain the dynamic information about its health condition. Many research papers have reported the suitability of vibration signals for fault diagnosis applications. Many of them are based on (Fast Fourier Transform) FFT, which have their own drawback with non-stationary signals. Hence, there is a need for development of new methodologies to infer diagnostic information from such non stationary signals. This paper uses vibration signals acquired from a hydraulic brake system under good and simulated faulty conditions for the purpose of fault diagnosis. A new approach called Variational mode decomposition (VMD) was used in this study. VMD decomposes the signal into various modes by identifying a compact frequency support around its central frequency, such that adding all the modes reconstructs the original signal. VMD finds intrinsic mode functions on central frequencies using alternating direction multiplier method (ADMM). Descriptive statistical features were extracted from VMD processed signals and classified using a machine learning algorithm. For classification J48 decision tree algorithm was used. The results were compared with the statistical features extracted from raw signal using decision tree classifier. Copyright © 2014 Tech Science Press. More »»

2014

Journal Article

N. G. Resmi and Dr. Soman K. P., “Multiresolution analysis of source code using discrete wavelet transform”, International Journal of Applied Engineering Research, vol. 9, pp. 13341-13360, 2014.[Abstract]


In this paper, we propose a method to analyze source code files at multiple resolutions using Discrete Wavelet Transform (DWT) and hence detect plagiarisms in source code files written in C, C++ and Java. Multiresolution analysis of source code files using DWT helps to identify files which are highly similar. On applying DWT distinct clusters of potentially plagiarized files are identified. Further comparison of the potentially plagiarized files can be done using a more reliable and structure-based code similarity detection technique to isolate plagiarized files. Selection of proper wavelet and an optimum level of decomposition can improve the performance of the system to a great extent. © Research India Publications. More »»

2013

Journal Article

Gandhiraj R. and Dr. Soman K. P., “Modern analog and digital communication systems development using GNU Radio with USRP”, Telecommunication Systems, pp. 1-15, 2013.[Abstract]


In this modern world many communication devices are highly intelligent and interconnected between each other. Any up-gradation of the hardware in the existing communication devices is not easier one. Compatibility of the new hardware with existing hardware is highly essential. But the new protocols may or may not support the older one. The solution for these problems can be provided by using the reconfigurable hardware design. The hardware can be reprogrammed according to the new change in technology up-gradation. The cost of commercially available hardware and software requirements for setting up such a module is very high. This can be solved by using Open source hardware and software such as Universal Software Radio Peripheral (USRP) and GNU Radio. This work demonstrates how the modern analog communication system like Community Radio Schemes and Radio Data System (RDS) and digital communication systems such as Simple Digital Video Broadcasting (DVB) and OFDM based data communication can be developed using the Open source hardware USRP1. This work will be helpful even for first year level of engineering students to easily implement any communication and control applications with cheaper cost. © 2013 Springer Science+Business Media New York.

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2012

Journal Article

S. M Manikandan and Dr. Soman K. P., “A novel method for detecting R-peaks in electrocardiogram (ECG) signal”, Biomedical Signal Processing and Control, vol. 7, pp. 118–128, 2012.

2012

Journal Article

M. S. Manikandan and Dr. Soman K. P., “A novel method for detecting R-peaks in electrocardiogram (ECG) signal”, vol. 7, no. 2, pp. 118 - 128, 2012.[Abstract]


The R-peak detection is crucial in all kinds of electrocardiogram (ECG) applications. However, almost all existing R-peak detectors suffer from the non-stationarity of both QRS morphology and noise. To combat this difficulty, we propose a new R-peak detector, which is based on the new preprocessing technique and an automated peak-finding logic. In this paper, we first demonstrate that the proposed preprocessor with a Shannon energy envelope (SEE) estimator is better able to detect R-peaks in case of wider and small QRS complexes, negative QRS polarities, and sudden changes in QRS amplitudes over that using the absolute value, energy value, and Shannon entropy features. Then we justify the simplicity and robustness of the proposed peak-finding logic using the Hilbert-transform (HT) and moving average (MA) filter. The proposed R-peak detector is validated using the first-channel of the 48 ECG records of the MIT-BITH arrhythmia database, and achieves average detection accuracy of 99.80%, sensitivity of 99.93% and positive predictivity of 99.86%. Various experimental results show that the proposed R-peak detection method significantly outperforms other well-known methods in case of noisy or pathological signals. More »»

2012

Journal Article

, Dr. Soman K. P., Kurian, A. P., Kartha, M. M., and Mohan, L., “Modified Wavelet image fusion based on OSVD”, IJERT, 2012.

2012

Journal Article

G. Xavier, Dr. Soman K. P., TVN, D., and Philip, T. Erlin, “An Efficient algorithm for the segmentation of Astronomical images”, IVSRJCE, 2012.

2011

Journal Article

J. Amudha, Dr. Soman K. P., and Kiran, Y., “Feature Selection in Top-Down Visual Attention Model using WEKA.”, International Journal of Computer Applications, vol. 24, no. 4, pp. 38-43, 2011.

2011

Journal Article

J. Amudha, Dr. Soman K. P., and S Reddy, P., “A Knowledge Driven Computational Visual Attention Model”, International Journal of Computer Science Issues, vol. 8, no. 3, 2011.[Abstract]


Computational Visual System face complex processing problems as there is a large amount of information to be processed and it is difficult to achieve higher efficiency in par with human system. In order to reduce the complexity involved in determining the saliency region, decomposition of image into several parts based on specific location is done and decomposed part is passed for higher level computations in determining the saliency region with assigning priority to the specific color in RGB model depending on application. These properties are interpreted from the user using the Natural Language Processing and then interfaced with vision using Language Perceptional Translator (LPT). The model is designed for a robot to search a specific object in a real time environment without compromising the computational speed in determining the Most Salient Region. More »»

2011

Journal Article

R. Harshawardhan, Augustine, M. Sara, and Dr. Soman K. P., “Phrase based English–Tamil Translation System by Concept Labeling using Translation Memory”, International Journal of Computer Applications (0975–8887), vol. 20, 2011.[Abstract]


In this paper, we present a novel framework for phrase based translation system using translation memory by concept labeling. The concepts are labeled on the input text, followed by the conversion of text into phrases. The phrase is searched throughout the translation memory, where the parallel corpus is stored. The translation memory displays all source and target phrases, wherever the input phrase is present in them. Target phrase corresponding to the output source phrase having the same concept as that of input source phrase, is chosen as the best translated phrase. The system is implemented for English to Tamil translation. More »»

2011

Journal Article

P. Kathirvel, M Manikandan, S., and Dr. Soman K. P., “Automated Referee Whistle Sound Detection for Extraction of Highlights from Sports Video”, International Journal of Computer Applications, vol. 12, pp. 0975–8887, 2011.[Abstract]


This paper proposes a simple and automated referee whistle sound detection (RWSD) for sports highlights extraction and video summarization. The proposed method is based on preprocessor, linear phase bandpass finite impulse response (FIR) filter shorttime energy estimator and decision logic. At the processing stage the discrete audio sequence is divided into non-overlapping blocks and then amplitude normalization is performed. Then, a bandpass filter is designed to accentuate referee whistle sound and suppress other audio events. Then, the filtered signal is fed to short-time energy (STE) estimator which includes amplitude squarer and linear filter to obtain a positive signal. In this work, we use decision rules based on the amplitude-dependent threshold and time-dependent threshold for detecting of referee whistle sound regions. The performance of the proposed design is tested using a large scale audio database including American football, soccer, and basket ball. The total duration of the test audio signal is approximately 12 hours and 11 minutes. The proposed method results in time-instants of boundaries of whistle sounds and then time instants are used to automatically extract the sports highlights from the unscripted video. Then, audio perception of the extracted sound segments is performed to indentify the false positive (FP) and false negative (FN). The proposed method has a detection failure rate of 19.4 % (42 FP and 26 FN) and detects 324 whistle sounds successfully. The sensitivity and reliability of the proposed design are 92.5 % and 80.5%, respectively. More »»

2011

Journal Article

S. R Narayanan and Dr. Soman K. P., “DATA DRIVEN SUFFIX LIST AND CONCATENATION ALGORITHM FOR TELUGU MORPHOLOGICAL GENERATOR.”, International Journal of Engineering Science & Technology, vol. 3, 2011.

2011

Journal Article

S. S. Prasad, Gandhiraj R., and Dr. Soman K. P., “Multi-User Spectrum Sensing based on Multi-Taper Method for Cognitive Environments”, International Journal of Computer Applications (IJCA), vol. 22, no. 9, pp. 2613–1093, 2011.[Abstract]


This paper gives a brief but comprehensive review of the Multitaper spectrum estimation method that uses the data tapers or windows in digital signal processing. Instead of using a single kind of window functions, here a cluster of window functions are mentioned, which is known as Slepian tapers. This taper family minimize leakage also, and computing them requires solving eigenvalue problems that are large for long time series. However, the eigenvalue problems have a special structure that makes a fast algorithm possible.Secondly, the enabling of the algorithmic method with Cognitive Radio (CR) Technology, More »»

2010

Journal Article

S. M Manikandan and Dr. Soman K. P., “Robust Heart Sound Activity Detection in Noisy Environments”, Electronics letters, vol. 46, pp. 1100–1102, 2010.[Abstract]


A novel and robust method for heart sound activity detection (HSAD) is presented. In this method, a new discriminative feature is developed based on the lag-1 autocorrelation coefficient and feature smoothing is introduced to identify the endpoints of low-level heart murmurs. Several experiments on a large scale phonocardiogram database show that the method significantly outperforms the other HSAD methods under varying levels of heart sounds and different types of noise. For a signal-to-noise ratio of 5 dB, the method ... More »»

2010

Journal Article

A. M Kumar, Rekha, R. U., Dr. Soman K. P., Rajendran, S., and Dhanalakshmi, V., “A Novel Data Driven Algorithm for Tamil Morphological Generator”, International Journal of Computer Applications, vol. 6, pp. 52–56, 2010.[Abstract]


Tamil is a morphologically rich language with agglutinative nature. Being agglutinative language most of the word features are postpositionally affixed to the root word. The morphological generator takes lemma, POS category and morpho-lexical description as input and gives a word-form as output. It is a reverse process of morphological analyzer. In any natural language generation system, morphological generator is an essential component in post processing stage. Morphological generator system implemented here is based on a new algorithm, which is simple, efficient and does not require any rules and morpheme dictionary. A paradigm classification is done for noun and verb based on Dr.S.Rajendran’s paradigm classification. Tamil verbs are classified into 32 paradigms with 1884 inflected forms. Like verbs, nouns are classified into 25 paradigms with 325 word forms. This approach requires only minimum amount of data. So this approach can be easily implemented to less resourced and morphologically rich languages. More »»

2010

Journal Article

A. M Kumar, Dhanalakshmi, V., Dr. Soman K. P., and Rajendran, S., “A sequence labeling approach to morphological analyzer for Tamil language”, IJCSE) International Journal on Computer Science and Engineering, vol. 2, pp. 1944–195, 2010.[Abstract]


Morphological analysis is the basic process for any Natural Language Processing task. Morphology is the study of internal structure of the word. Morphological analysis retrieves the grammatical features and properties of a morphologically inflected word. Capturing the agglutinative structure of Tamil words by an automatic system is a challenging job. Generally rule based approaches are used for building morphological analyzer. In this paper we propose a novel approach to solve the morphological analyzer problem using machine learning methodology. Here morphological analyzer problem is redefined as classification problem. This approach is based on sequence labeling and training by kernel methods that captures the non linear relationships of the morphological features from training data samples in a better and simpler way. Keywords- morphology; morphological analyzer; machine learning; sequence labeling... More »»

2010

Journal Article

V. Dhanalakshmi, Rajendran, S., M Kumar, A., and Dr. Soman K. P., “Natural Language processing Tools for Tamil grammar Learning and Teaching”, International journal of Computer Applications (0975-8887), vol. 8, 2010.[Abstract]


Today we are living in the world of communication. The world of communication interlinks everyone through its various media. In this aspect Computers play a major role by bringing the world under the user's finger tip. Grammar is the legal advocacy to the human art of communication. But learners get annoyed with the language rules and the old teaching methodology. Interlinking the computer to the language through Natural language Processing (NLP) paves a way to solve this problem. The innovative NLP applications are used to generate language learning and teaching tools which enhance the teaching and learning of Grammar. In this paper we present the Grammar teaching tools for analyzing and learning character, word and sentence of Tamil Language. Tools like Character Analyzer for analyzing character, Morphological Analyzer and Generator and Verb Conjugator for the word level analysis and Parts of Speech Tagger, Chunker and Dependency parser for the sentence level analysis were developed using machine learning based technology. These tools are very useful for second language learners to understand the character, word and sentence construction of Tamil language in a non-conceptual way... More »»

2010

Journal Article

P. J. Antony, M Kumar, A., and Dr. Soman K. P., “A Paradigm based Morphological Analyzer for English to Kannada Using a Machine Learning Approach.”, Advances in Computational Sciences & Technology, Research India Publication(RIP), vol. 3, 2010.[Abstract]


The role of morphological analyzer is very significant in the field of natural language processing (NLP) applications like machine translation (MT), information extraction (IE), information retrieval (IR), spell checker, lexicography etc. So from a serious computational perspective the creation and availability of a morphological analyzer for a language is important. The morphological analyzer maps an inflected word into its stem, parts of speech and feature equations corresponding to inflectional information. The morphological structure of an agglutinative language is unique and capturing its complexity in a machine analyzable and generatable format is a challenging job. This paper presents a paradigm based morphological analyzer, for the complex agglutinative Kannada language using the machine learning approach. The proposed morphological analyzer is designed using sequence labeling approach and training, testing and evaluations are done by support vector method (SVM) algorithms. The system captures the various non-linear relationships and morphological features of Kannada language in a better and simpler way. We also compared the efficiency of our system with the existing morphological analyzers which are publically available in the internet. From the experiment we found that the performance of our system significantly outperforms the existing morphological analyzer and achieves a very competitive accuracy of 96.25% for Kannada verbs. More »»

2010

Journal Article

Dr. Ramanathan R., Dr. Soman K. P., and , “A Novel Methodology for Designing Linear Phase IIR Filters”, Aceee International Journal on Communication, vol. 1, 2010.[Abstract]


This paper presents a novel technique for designing an Infinite Impulse Response (IIR) Filter with Linear Phase Response. The design of IIR filter is always a challenging task due to the reason that a Linear Phase Response is not realizable in this kind. The conventional techniques involve large number of samples and higher order filter for better approximation resulting in complex hardware for implementing the same. In addition, an extensive computational resource for obtaining the inverse of huge matrices is required... More »»

2009

Journal Article

V. Dhanalakshmi, Shivapratap, G., Dr. Soman K. P., Rajendran, S., and M Kumar, A., “Tamil POS tagging using Linear Programming”, International Journal of Recent Trends in Engineering, vol. 1, 2009.[Abstract]


Part of speech (POS) tagging is the process of annotating syntactic categories for each word in a corpus. This paper presents an SVM methodology based on Linear Programming for implementing automatic Tamil POS tagger. We have designed our own tagset consisting of 32 tags for preparing the annotated corpus for Tamil. The features are extracted from a corpus of twenty five thousand sentences and trained with linear programming based SVM. This method, when tested with 10,000 sentences, gave an ... More »»

Publication Type: Book

Year of Publication Publication Type Title

2018

Book

D. J. Ratnam, Kumar, M. A., Premjith, B., Dr. Soman K. P., and Rajendran, S., Sense disambiguation of English simple prepositions in the context of English-Hindi machine translation system. Springer Singapore, 2018, pp. 245-268.[Abstract]


In the context of developing a Machine Translation System, the identification of the correct sense of each and every word in the document to be translated is extremely important. Adpositons play a vital role in the determination of the sense of a particular word in a sentence as they link NPs with the VPs. In the context of developing English to Hindi Machine Translation system, the transfer of the senses of each Preposition into the target langue needs done with much attention. The linguistic and grammatical role of a preposition is to express a variety of syntactic and semantic relationships between nouns, verbs, adjectives, and adverbs. Here we have selected the most important and most frequently used English simple prepositions such as ‘at’, ‘by’, ‘from’, ‘for’, ‘in’, ‘of’, ‘on’, ‘to’ and ‘with’ for the sake of contrast. A supervised machine learning approach called Support Vector Machine (SVM) is used for disambiguating the senses of the simple preposition ‘at’ in contrast with Hindi postpositions. © Springer Nature Singapore Pte Ltd. 2018.

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2010

Book

Dr. Soman K. P. and Dr. K. I. Ramachandran, Insight into wavelets: From theory to practice. PHI Learning Pvt. Ltd., 2010.[Abstract]


Wavelet theory has matured and has entered into its second phase of development and evolution in which practitioners are finding newer applications in ever-widening scientific domains such as bio-informatics, computational drug discovery and nano-material simulation. Parallelly, the theory of wavelets got more and more demystified and has become an everyday tool for signal and image processing. Postgraduate courses in mathematics and physics now include a subject on wavelet theory either as a separate... More »»

2009

Book

Dr. Soman K. P., Loganathan, R., and Ajay, V., Machine Learning with SVM and other Kernel methods. PHI Learning Pvt. Ltd., 2009.[Abstract]


Support vector machines (SVMs) represent a breakthrough in the theory of learning systems. It is a new generation of learning algorithms based on recent advances in statistical learning theory. Designed for the undergraduate students of computer science and engineering, this book provides a comprehensive introduction to the state-of-the-art algorithm and techniques in this field. It covers most of the well known algorithms supplemented with code and data. One Class, Multiclass and hierarchical SVMs are included which will help the students to... More »»

2006

Book

Dr. Soman K. P., Dr. Shyam Diwakar, and Ajay, V., DATA MINING: THEORY AND PRACTICE . PHI Learning Pvt. Ltd., 2006.

Publication Type: Conference Proceedings

Year of Publication Publication Type Title

2018

Conference Proceedings

Dr. Soman K. P., B. Gowri, G., and Dr. Govind D., “Improved Epoch Extraction from Telehonic Speech Signals using Chebfun and zero frequency filtering”, Accepted for publication in INTERSPEECH 2018. INTERSPEECH 2018, Hyderabad, INDIA, 2018.

2017

Conference Proceedings

Dr. Govind D., Sowmya, V., Sachin, R., and Dr. Soman K. P., “Dependency of Various Color and Intensity Planes on CNN Based Image Classification”, International Symposium on Signal Processing and Intelligent Recognition Systems. 2017.[Abstract]


Scene classification systems have become an integral part of computer vision. Recent developments have seen the use of deep scene networks based on convolutional neural networks (CNN), trained using millions of images to classify scenes into various categories. This paper proposes the use of one such pre-trained network to classify specific scene categories. The pre-trained network is combined with the simple classifiers namely, random forest and extra tree classifiers to classify scenes into 8 different scene categories. Also, the effect of different color spaces such as RGB, YCbCr, CIEL*a*b* and HSV on the performance of the proposed CNN based scene classification system is analyzed based on the classification accuracy. In addition to this, various intensity planes extracted from the said color spaces coupled with color-to-gray image conversion techniques such as weighted average, and singular value decomposition (SVD) are also taken into consideration and their effects on the performance of the proposed CNN based scene classification system are also analyzed based on the classification accuracy. The experiments are conducted on the standard Oliva Torralba (OT) scene data set which comprises of 8 classes. The analysis of classification accuracy obtained for the experiments conducted on OT scene data shows that the different color spaces and the intensity planes extracted from various color spaces and color-to-gray image conversion techniques do affect the performance of proposed CNN based scene classification system. More »»

2016

Conference Proceedings

B. Ganesh, M, Akumar, and Dr. Soman K. P., “Amrita_CEN at SemEval-2016 Task Semantic Textual Similarity : Semantic Relation from Word Embeddings in Higher Dimension”, International Workshop on Semantic Evaluation (SemEval 2016). 2016.

2013

Conference Proceedings

M. Suchithra, Sukanya, P., Prabha, P., Sikha, O. K., Sowmya V., and Dr. Soman K. P., “An experimental study on application of orthogonal matching pursuit algorithm for image denoising”, 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013. IEEE, Kochi, Kerala, pp. 729-736, 2013.[Abstract]


<p>Signal or image reconstruction has now become a common task in many applications. According to linear algebra perspective, the number of measurements made or the number of samples taken for reconstruction must be greater than or equal to the dimension of signal or image. Also reconstruction follows the Shanon's sampling theorem which is based on the Nyquist sampling rate. The reconstruction of a signal or image using the principle of compressed sensing is an exception which makes use of only few number of samples which is below the sampling limit. Compressive sensing also known as sparse recovery aims to provide a better data acquisition and reduces computational complexities that occur while solving problems. The main goal of this paper is to provide clear and easy way to understand one of the compressed sensing greedy algorithm called Orthogonal Matching Pursuit (OMP). The OMP algorithm involves the concept of overcomplete dictionary that is formulated based on different thresholding methods. The proposed method gives the simplified approach for image denoising by using OMP only. The experiment is performed on few standard image data set simulated with different types of noises such as Gaussian noise, salt and pepper noise, exponential noise and Poisson noise. The performance of the proposed method is evaluated based on the image quality metric, Peak Signal-to-Noise Ratio (PSNR). © 2013 IEEE.</p>

More »»

2013

Conference Proceedings

G. Aarthy, Amitha, P. L., Krishnan, T., Pillai, G. S., Sowmya V., and Dr. Soman K. P., “A comparative study of spike and smooth separation from a signal using different overcomplete dictionary”, 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013. IEEE, Kochi, Kerala, pp. 590-595, 2013.[Abstract]


Most of the natural signals are complex and are highly time varying, since they are non stationary in nature. In this paper, a comparative study for separating spikes and smooth signal components from a non-stationary signal are performed based on different overcomplete dictionaries. The experiment is evaluated using the sparse representation with different bases such as the Discrete Cosine Transform (DCT), Walsh-Hadamard, Orthogonal and Biorthogonal wavelet basis. The primary focus of this paper is to use L1 minimization for retrieving the smooth and spikes component of the signal using different overcomplete dictionary. The experimental results reveals out the dictionary that delivers a better separation without distorting temporal and spectral characteristics. © 2013 IEEE.

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2013

Conference Proceedings

B. G. Gowri, Hariharan, V., Thara, S., Sowmya V., Kumar, S. S., and Dr. Soman K. P., “2D Image data approximation using Savitzky Golay filter - Smoothing and differencing”, 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013. IEEE, Kochi, Kerala, pp. 365-371, 2013.[Abstract]


Smoothing and differencing is one of the major important and necessary step in the field of signal processing, image processing and also in the field on analytical chemistry. The search for an efficient image smoothing and edge detection method is a challenging task in image processing sector. Savitzky Golay Filters are one among the widely used filters for analytical chemistry. Even though they have exceptional features, they are rarely used in the field of image processing. The designed filter is applied for image smoothing and a mathematical model based on partial derivatives is proposed to extract the edges in images. The smoothing technique of SG filter offers an extremely simple aid in extracting the edge information. An approach using SG filter which can be applied in preserving edge information is one of the major tasks involved in the classification process in the domain of Optical Character Recognition. The paper is focused on designing the Savitzky Golay filter by using the concepts of linear algebra. The main objective of the paper is to portray a clear cut idea about Savitzky Golay filter and to study the design of Savitsky Golay filters based on the concepts of Linear Algebra. © 2013 IEEE.

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2013

Conference Proceedings

M. B. Sruthi, Abirami, M., Manikkoth, A., Gandhiraj R., and Dr. Soman K. P., “Low cost digital transceiver design for software defined radio using RTL-SDR”, Proceedings - 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013. Kerala, pp. 852-855, 2013.[Abstract]


The field of wireless communication has become the hottest area and Software Defined Radio (SDR) is revolutionizing it. By bringing much functionality as software, SDR reduces the cost of hardware maintenance and up-gradation. Open source hardware such as USRP (Universal Software Radio Peripheral) and software called GNU Radio-Companion are commonly used to do experiments in SDR. Since the cost of USRP is high, a low cost set up is needed which is affordable by the student community. In this paper a low cost alternative to USRP is proposed using RTL-SDR (Realtek Software Defined Radio) which is only used for reception. For transmitting purpose, a mixer circuit can be used to map the baseband signal to the band that can be received by RTL-SDR on the other end on Linux / Windows platform. Initially, the experiment is done in simulation. After that, it is tested with low cost hardware such as mixer and RTL-SDR. The cost for total transceiver system can be less than USD 100 which is 10 times less than the existing one. © 2013 IEEE.

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2013

Conference Proceedings

Dr. Padmavathi S., Dr. Soman K. P., and Aarthi, R., “Image restoration using knowledge from the image”, Advances in Intelligent Systems and Computing, vol. 177 AISC. Chennai, pp. 19-25, 2013.[Abstract]


There are various real world situations where, a portion of the image is lost or damaged which needs an image restoration. A Prior knowledge of the image may not be available for restoring the image, which demands for a knowledge derivation from the image itself. Restoring the lost portions of the image based on the knowledge obtained from the image area surrounding the lost area is called as Digital Image Inpainting. The information content in the lost area could contain structural information like edges or textural information like repeating patterns. This knowledge is derived from the boundary area surrounding the lost area. Based on this, the lost area is restored by looking at similar information in the same image. Experimentation have been done on various images and observed that the algorithm restores the image in a visually plausible way. © 2013 Springer-Verlag.

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2012

Conference Proceedings

A. V. Sreedhanya and Dr. Soman K. P., “Secrecy of cryptography with compressed sensing”, Proceedings - 2012 International Conference on Advances in Computing and Communications, ICACC 2012. Cochin, pp. 207-210, 2012.[Abstract]


This paper deals with a new image encryption scheme which employs both compressive sensing and Arnold scrambling method. The compressed sensing(CS) paradigm unifies sensing and compression of sparse signals in a simple linear measurement step. Compressed measurements are scrambled using Arnold transform. So this system provides more security to the data. © 2012 IEEE.

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2012

Conference Proceedings

K. Syama, George, N., Sekhar, S., Neethu, C. S., Manikandan, M. S., and Dr. Soman K. P., “Performance study of active contour model based character segmentation with nonlinear diffusion”, Proceedings - 2012 International Conference on Advances in Computing and Communications, ICACC 2012. Cochin, pp. 118-121, 2012.[Abstract]


In this paper, we present the combined character segmentation algorithm based on the active contour model and nonlinear diffusion techniques. The active contour model is used to perform segmentation of printed characters. The coherence enhancing diffusion technique is proposed to smooth out artifacts and background noises without destroying the edges. The performance of the two character segmentation methods: i) the combined ACM-FGM and CED algorithm, and ii) the ACM-FGM algorithm have been validated using a large scale printed documents in Hindi, Malayalam and Telugu text. The combined algorithm achieves an average segmentation accuracy of 89.08% whereas the ACM-FGM algorithm alone had an average accuracy of 52.63%. The whole character segmentation process time is lesser than that of the ACM-FGM algorithm alone. Experiments show that the combined algorithm provides promising results under scanned documents with different font-size and fond-style characters, and the different artifacts and background noises caused by the aging of the paper and diffusion. © 2012 IEEE.

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2012

Conference Proceedings

N. V. Varghees, Manikandan, M. S., Gini, R., and Dr. Soman K. P., “A new framework to automatically select noise model for Rician noise estimation in MR images”, Proceedings - 2012 International Conference on Advances in Computing and Communications, ICACC 2012. Cochin, pp. 82-85, 2012.[Abstract]


In this paper, we study a set of histogram and higher-order statistical (HOS) features for automatically identifying the presence of large background in the magnitude MR images. The robustness and discriminative power of each individual feature and combining feature sets are investigated using different MR images including brain, cardiac, breast, spine, stomach and noisy images corrupted by Rician noise with different standard deviations, σ={5,10,15,20,25,30,35}. The performances of the identification approaches are evaluated in terms of sensitivity, specificity, and accuracy. Experimental results obtained on 2544 MR images show that an approach based on the kurtosis and histogram peak ratio (HPR) features outperforms significantly as compared to that of other approaches reported in this work. The proposed approach can be used for selection of distribution model (Rayleigh or Gaussian) for accurate estimation of Rician noise level in MR images having large or little background regions. © 2012 IEEE.

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2012

Conference Proceedings

P. K. Indukala, Lakshmi, K., Sowmya V., and Dr. Soman K. P., “Implementation of ℓ 1 magic and one bit compressed sensing based on linear programming using excel”, International Conference on Advances in Computing and Communications, ICACC 2012. IEEE, Kochi, Kerala, pp. 69-72, 2012.[Abstract]


<p>Compressed sensing helps in the reconstruction of sparse or compressible signals from small number of measurements. The sparse representation has great importance in modern signal processing. The main objective is to provide a strong understanding of the concept behind the theory of compressed sensing by using the key ideas from linear algebra. In this paper, the concept of compressed sensing is explained through an experiment formulated based on linear programming and solved using l1 magic and One bit compressed sensing methods in Excel. © 2012 IEEE.</p>

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2012

Conference Proceedings

R. Anand, Prabha, P., Sikha, O. K., Suchithra, M., Dr. Soman K. P., and Sowmya V., “Visualization of OFDM using Microsoft Excel spreadsheet in Linear Algebra Perspective”, International Conference on Advances in Computing and Communications, ICACC 2012. IEEE, Kochi, Kerala, pp. 58-64, 2012.[Abstract]


Orthogonal Frequency Division Multiplexing (OFDM) is one of the leading technology that is ruling the communication field. But unfortunately, it is shrouded in mystery. A good knowledge in Linear Algebra is required to appreciate the technology in a better way. So the work focuses on explaining OFDM system from linear algebra point of view. Also, OFDM model communication system is simulated using Excel which makes ease for anyone experiment with OFDM and understand the underlying principle. The paper aims to provide strong foundation on the concept behind OFDM without the need of having much knowledge in electronics field. © 2012 IEEE.

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2012

Conference Proceedings

P. Prabha, Sikha, O. K., Suchithra, M., Sukanya, P., Sowmya V., and Dr. Soman K. P., “Computation of continuous wavelet transform using microsoft excel spreadsheet”, Proceedings - 2012 International Conference on Advances in Computing and Communications, ICACC 2012. IEEE, Kochi, Kerala, pp. 73-77, 2012.[Abstract]


Wavelet theory has become an essential and significant tool for signal and image processing applied in the analysis of various real time signals. It is thus necessary to include wavelet transform and its application in multifractal analysis as a part of the engineering curriculum. In this paper, we present simple and effective way of computing Continuous Wavelet Transform (CWT) using Microsoft Excel Spreadsheet which serves as an user friendly mathematical tool for beginners. The motivation of this paper is to prove the computational power of excel, using which students can have better understanding of the basic concept behind the computation of Continuous Wavelet Transform. The plot of Continuous Wavelet Transform of Brownian signal computation in Excel is compared with that of the result in the Matlab Toolbox. The singularities present in the signal can be inferred from the wavelet modulus maxima plot. The visual interpretation proves that Excel tool provides computational power comparable to that of the Matlab software. The codes for the implementation of CWT in Excel are available on nlp.amrita.edu:8080/sisp/wavelet/cwt/cwt.xlsm, nlp.amrita.edu:8080/sisp/wavelet/cwt/modmax.xlsm, nlp.amrita.edu:8080/sisp/ wavelet/cwt/thermo.xlsm © 2012 IEEE.

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2012

Conference Proceedings

Dr. Padmavathi S., Rajalaxmi, C., and Dr. Soman K. P., “Texel identification using K-means clustering method”, Advances in Intelligent and Soft Computing, vol. 167 AISC. New Delhi, pp. 285-294, 2012.[Abstract]


Identifying the smallest portion of the image that represents the entire image is a basic need for its efficient storage. Texture can be defined as a pattern that is repeated in a specific manner. The basic pattern that is repeated is called as Texel(Texture Element). This paper describes a method of extracting a Texel from the given textured image using K means clustering algorithm and validating it with the entire image. The number of gray levels in an image is reduced using a linear transformation function. The image is then divided in to sub windows of certain size. These sub windows are clustered together using K-means algorithm. Finally a heuristic algorithm is applied on the cluster labels to identify the Texel, which results in more than one candidate for Texel. The best among them is then chosen based on its similarity with the overall image. The similarity between the Texel and the image is calculated based on then Normalized Gray level co-occurrence matrix in the maximum gradient direction. Experiments are conducted on various texture images for various block sizes and the results are summarized. © 2012 Springer-Verlag GmbH.

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2012

Conference Proceedings

K. Lakshmi, Parvathy, R., Soumya, S., and Dr. Soman K. P., “Image denoising solutions using heat diffusion equation”, 2012 International Conference on Power, Signals, Controls and Computation, EPSCICON 2012. Thrissur, Kerala, 2012.[Abstract]


The idea of this paper is to model image denoising using an approach based on partial differential equations (PDE), which describes two dimensional heat diffusion. The two dimensional image function is taken to be the harmonic, when it can be obtained as the solution to the equation describing the the heat diffusion. To achieve this, image denoising is formulated as an optimization problem, in which a function with two terms is to be minimized. The first term is called the regularization term, which is some form of energy of the image (like Sobolev energy) and the second term is called the data fidelity term, which measures the similarity between the original image and the processed image. The two terms are combined using a control parameter whose value decides which term has to be minimized more. Image denoising problem could then be solved by a simple iterative equation, derived based on the Gradient Descent method. © 2012 IEEE.

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2012

Conference Proceedings

H. Aravind, Gandhiraj R., Dr. Soman K. P., Manikandan, M. S., and Peter, R., “Spectrum sensing implementations for software defined radio in simulink”, Procedia Engineering, vol. 30. Coimbatore, pp. 1119-1128, 2012.[Abstract]


The lack of spectrum for communication and for research is a bottleneck as far as technology and business development is considered. It is a fact that the availability of useful spectrum is limited by hardware constraints. The studies conducted by the Federal Communications Commission found that that there are many areas of the radio spectrum which are not fully utilized in different geographical areas of the country and FCC recommended locating and utilizing these unused spectrum spaces by other users. This is where spectrum sensing comes into use. From then on different spectrum sensing algorithms were developed. The paper implements four of those major sensing spectrum algorithms in MATLAB-Simulink and also does a performance comparison among them.

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2012

Conference Proceedings

Gandhiraj R., Ram, R., and Dr. Soman K. P., “Analog and digital modulation toolkit for software defined radio”, Procedia Engineering, vol. 30. Coimbatore, pp. 1155-1162, 2012.[Abstract]


This work is a small tutorial for the new users in the field of software defined radio. Applications are build up using graphical user interface called the GNU radio companion (GRC). The idea behind developing such a tool kit is to give practical exposure in the communication concepts like basic signal generations, signal operations, multi-rate concepts, analog and digital modulation schemes and finally multiplexing schemes with the help of GNU radio. Unlike MATLAB Simulink or Labview GNU radio is open source i.e. free of cost and the concepts can be easily reached to the normal people without much of programming concepts using the pre written blocks. And programmers also have the chance to write their own applications.

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2012

Conference Proceedings

A. M., Dr. Soman K. P., R., G., M.B., S., and Manikkoth, A., “Low cost digitaltransceiver design for software defined radio using Rtl-Sdr (2012)”, IBM Conference (poster presentation). 2012.

2010

Conference Proceedings

B. D. Bhushan, Sowmya V., and Dr. Soman K. P., “Super resolution blind reconstruction of low resolution images using framelets based fusion”, International Conference on Recent Trends in Information, Telecommunication, and Computing ITC 2010. IEEE, Kochi, Kerala, pp. 100-104, 2010.[Abstract]


In this paper, we propose a fusion technique based on framelets to obtain super resolution image from sub-pixel shifted, noisy, blurred low resolution images. This method has high advantages over all existing methods. A Tight frame filter bank provides symmetry and has a redundancy that allows for approximate shift invariance which leads to clear edges, high spatial information with effective denoising which was lacked in critically sampled discrete wavelet transform. They are also shorter and results in smoother scaling and wavelet functions. The reconstructed super resolution image obtained by this technique has high peak signal to noise ratio (PSNR) and low mean square error (MSE) than that obtained by wavelet based fusion method, which is evident through the experimental results. © 2010 IEEE.

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2010

Conference Proceedings

P. J. Antony, Ajith, V. P., and Dr. Soman K. P., “Statistical method for English to Kannada transliteration”, International Conference on Recent Trends in Business Administration and Information Processing, BAIP 2010. Springer, Trivandrum, Kerala, India, pp. 356–362, 2010.[Abstract]


Language transliteration is one of the important area in natural language processing. Machine Transliteration is the conversion of a character or word from one language to another without losing its phonological characteristics. It is an orthographical and phonetic converting process. Therefore, both grapheme and phoneme information should be considered. Accurate transliteration of named entities plays an important role in the performance of machine translation and cross-language information retrieval processes. The transliteration model must be design in such a way that the phonetic structure of words should be preserve as closely as possible. This paper address the problem of transliterating English to Kannada language using a publically available translation tool called Statistical Machine Translation (SMT).This transliteration technique was demonstrated for English to Kannada Transliteration and achieved exact Kannada transliterations for 89.27% of English names. The result of proposed model is compared with the SVM based transliteration system as well as Google Indic transliteration system. More »»

2009

Conference Proceedings

A. M Kumar, Dhanalakshmi, V., Dr. Soman K. P., and Rajendran, S., “A Novel Approach for Tamil Morphological Analyzer”, Proceedings of the 8th Tamil Internet Conference . Koeln, Germany, 2009.

Publication Type: Conference Paper

Year of Publication Publication Type Title

2017

Conference Paper

N. S. Deve, Jasmineniketha, M., Geetha, P., and Dr. Soman K. P., “Agricultural drought analysis for Thuraiyur taluk of Tiruchirappali District using NDVI and land surface temperature data”, in Proceedings of 2017 11th International Conference on Intelligent Systems and Control, ISCO 2017, 2017, pp. 155-159.[Abstract]


Drastic changes in temperature and rainwater leads to the significant impact on drought which affects agricultural growth. Agricultural drought is a term which explains about reduction in the yield of crops due to abnormalities in rainfall as well as decline in soil moisture that affects agriculture, economy, social aspect, and environment. A trivial variation in the monsoon mainly affects the yield as well as the crops significantly. With the help of remote sensing data agricultural monitoring, management and assessment is done to calculate vegetation and temperature variations. Thuraiyur taluk in Tiruchirappalli District, of Tamilnadu (India) lies in a plain region between 11° 10′ N latitude and 78° 37′ E longitude. It depends mainly on the agriculture therefore the influence of drought affects the yield and the living of humans. The current study deals with the vegetation stress in the Thuraiyur taluk of Tiruchirappalli district with the usage of the Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI). The Landsat data is utilized for the computation of LST and NDVI. The mixture of LST and NDVI, helps to monitor agricultural drought and also as a counsel for farmers. By computing the relationship between LST and NDVI, it is noted that they have a high negative correlation. The correlation between LST and NDVI is -0.763 for the year 2013 and -0.685 for the year 2016. The LST when interrelated with the vegetation index helps to identify the agricultural drought, as demonstrated in the current study. © 2017 IEEE. More »»

2017

Conference Paper

Rahul K Pathinarupothi, Vinaykumar R, Ekanath Srihari Rangan, Dr. E. A. Gopalakrishnan, and Dr. Soman K. P., “Instantaneous heart rate as a robust feature for sleep apnea severity detection using deep learning”, in IEEE International Conference on Biomedical and Health Informatics, Orlando, Florida, 2017, pp. 293-296.[Abstract]


Automated sleep apnea detection and severity identification has largely focused on multivariate sensor data in the past two decades. Clinically too, sleep apnea is identified using a combination of markers including blood oxygen saturation, respiration rate etc. More recently, scientists have begun to investigate the use of instantaneous heart rates for detection and severity measurement of sleep apnea. However, the best-known techniques that use heart rate and its derivatives have been able to achieve less than 85% accuracy in classifying minute-to-minute apnea data. In our research reported in this paper, we apply a deep learning technique called LSTM-RNN (long short-term memory recurrent neural network) for identification of sleep apnea and its severity based only on instantaneous heart rates. We have tested this model on multiple sleep apnea datasets and obtained perfect accuracy. Furthermore, we have also tested its robustness on an arrhythmia dataset (that is highly probable in mimicking sleep apnea heart rate variability) and found that the model is highly accurate in distinguishing between the two.

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2017

Conference Paper

Sowmya V., Dr. Govind D., and Dr. Soman K. P., “Significance of contrast and structure features for an improved color image classification system”, in 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), 2017, pp. 12-14 .[Abstract]


In general, the three main modules of color image classification systems are: color-to-grayscale image conversion, feature extraction and classification. The color-to-grayscale image conversion is the important pre-processing step which must incorporate the significant and discriminative contrast and structure information in the converted grayscale images as in the original color image. All the existing techniques for color-to-grayscale image conversion preserves the significant contrast and structure information in the converted grayscale images in different manners. Hence, the present work is to analyze the significant and discriminative contrast and structure information preserved in the converted grayscale images using two different decolorization techniques called rgb2gray and singular value decomposition based color-to-grayscale image conversion (SVD) applied in the color image classification systems using the three different proposed features. The three different features for color image classification systems are proposed based on the combination of the existing dense SIFT features and the contrast & structure content computed using color-to-gray structure similarity index (C2G-SSIM) metric. More »»

2017

Conference Paper

Sowmya V., Ajay, A., Dr. Govind D., and Dr. Soman K. P., “Improved color scene classification system using deep belief networks and support vector machines”, in 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), 2017, pp. 12-14 .[Abstract]


In general, the three main modules of the color scene classification systems are image decolorization, feature extraction and classification. The work presented in this paper focuses on image decolorization and classification as two stages. The first stage or objective of this paper is to improve the performance of the color scene classification system using deep belief networks (DBN) and support vector machines (SVM). Therefore, color scene classification system termed as AGMM-DBN-SVM is proposed using the existing feature extraction technique called bags of visual words (BoW) derived from the dense scale-invariant feature transform (SIFT) and adapted gaussian mixture models (AGMM). The second stage of the presented work is to combine the proposed AGMM-DBN-SVM classification models obtained for the two different image decolorization methods called rgb2gray and singular value decomposition (SVD) based color-to-grayscale image mapping techniques to significantly increase the performance of the proposed color scene classification system. The effectiveness of the proposed framework is experimented on Oliva Torralba (OT) scene dataset containing 8 different classes. The classification rate of the proposed color scene classification system applied on OT 8 scene dataset is significantly greater than the one of the existing benchmarks color scene classification system developed using AGMM and SVM. More »»

2017

Conference Paper

Rahul K Pathinarupothi, Dhara Prathap J, Ekanath Srihari Rangan, Gopalakrishnan E A, Vinaykumar R, and Dr. Soman K. P., “Single Sensor Techniques for Sleep Apnea Diagnosis Using Deep Learning”, in IEEE International Conference on Healthcare Informatics (ICHI 2017), Park City, Utah, USA, 2017.[Abstract]


A large number of obstructive sleep apnea (OSA) cases are under-diagnosed due unavailability, inconvenience or expense of sleep labs. Hence, an automated detection by applying computational techniques to multivariate signals has already become a well-researched subject. However, the best-known techniques that use various features have not achieved the gold standard of polysomnography (PSG) tests. In this paper, we substantiate the medical conjecture that OSA directly impacts body parameters such as Instantaneous Heart Rate (IHR) and blood oxygen saturation (SpO2). We then use a deep learning technique called LSTM-RNN (long short-term memory recurrent neural networks) to experimentally prove that OSA severity detection can be solely based on either IHR or SpO2 signals, which can be easily, obtained using off-the-shelf non-intrusive wearable single sensors. The results obtained from LSTM-RNN model shows an area under curve (AUC) of 0.98 associated with very high accuracy on a dataset of more than 16,000 apnea non-apnea minutes. These results have encouraged our collaborating doctors to further come up with a diagnostic protocol that is based on LSTM-RNN, SpO2, and IHR, thereby increasing the chances of larger adoption among medical community.

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2016

Conference Paper

Y. C. Nair, PV, N., Dr. Soman K. P., and Vijay Krishna Menon, “Real Time Vehicular Data Analysis utilising Big Data Platforms on Cost Effective ECU Networks”, in International Conference on Innovation in Information Embedded and Communication Systems (ICIIECS’16), 2016.

2016

Conference Paper

A. S and Dr. Soman K. P., “Automatic Modulation Classification using Convolutional Neural Network”, in International Conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems (ICAIECES-2016), 2016.

2016

Conference Paper

A. Balaji S, P, G., and Dr. Soman K. P., “Change Detection of Forest Vegetation using Remote Sensing and GIS Techniques in Kalakkad Mundanthurai Tiger Reserve (A Case Study)”, in International Conference on Innovation in Information Embedded and Communication Systems (ICIIECS’16), 2016.

2016

Conference Paper

V. M, P, G., and Dr. Soman K. P., “Study of Diurnal Temperature Changes Caused by Anthropogenic Activity Using Meteorological Data in Coimbatore District ”, in International Conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems (ICAIECES-2016), 2016.

2016

Conference Paper

P. R, V, S., and Dr. Soman K. P., “Least Square based signal denoising using wavelet filters”, in International Conference on Innovation in Information Embedded and Communication Systems (ICIIECS’16), , 2016.

2016

Conference Paper

S. Rajan and Dr. Soman K. P., “Low Contrast Image Enchancement using Adaptive Histogram, Discrete Wavelet and Cosine Transform”, in International Conference on Innovation in Information Embedded and Communication Systems (ICIIECS’16), 2016.

2016

Conference Paper

D. Pankaj, S, S. Kumar, Neethu Mohan, and Dr. Soman K. P., “Image Fusion Using Variational Mode Decomposition”, in International Conference on Innovation in Information Embedded and Communication Systems (ICIIECS’16), 2016.

2016

Conference Paper

N. PV, Nair, Y. C., variyar, S., and Dr. Soman K. P., “Environment Monitoring Multimode OPerable Autonomous Rover”, in International conference on Soft computing systems (ICSCS’16), , 2016.

2016

Conference Paper

P. A, R, M., Sowmya V., and Dr. Soman K. P., “X-ray Image Classification Based On Tumor using GURLS and LIBSVM”, in International Conference on Communications and Signal Processing (ICCSP’16), Adhiparasakthi Engineering College, Melmaruvathur , 2016.[Abstract]


In today's world, X-ray imaging is the low cost diagnostic technique when compared with all other medical imaging techniques. In this paper, the proposed method is to classify X-ray images based on tumor. The features are extracted using Singular Value Decomposition (SVD) and classified using different kernels in Library for Support Vector Machine (Lib-SVM) and Grand Unified Regularized Least Squares (GURLS). The proposed method is experimented on X-ray image dataset which is approved by an Oncologist. The effectiveness of proposed method is validated based on classification parameters. The experiment result analysis shows that Gaussian-ho in GURLS provides 95% classification accuracy which is 5% higher than RBF kernel in LibSVM. The performance of the proposed system is validated by an Oncologist.

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2016

Conference Paper

S. M, K.S, G. Krishnan, and Dr. Soman K. P., “Image denoising based on weighted Regularized Least Squares”, in International conference on Soft computing systems (ICSCS’16), 2016.

2016

Conference Paper

M. P, M, S., Sowmya V., and Dr. Soman K. P., “Low Contrast Satellite Image Restoration based on adaptive Histogram Equalization and Discrete Wavelet Transform”, in - 5th IEEE International Conference on Communication and Signal Processing-ICCSP'15, Adhiparasakthi Engineering College, Melmaruvathur , 2016.[Abstract]


Normally images obtained from satellites are of low-contrast type which hides major information carried by the image. Hence, image restoration is necessary in the image processing domain to extract all the information present in the images. The low contrast satellite image restoration based on adaptive histogram equalization combined with Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) is proposed in this paper. The proposed technique is experimented on three different satellite images. The effectiveness of the method introduced in this paper is shown by comparing it against the existing techniques based on gamma correction and histogram equalization combined with DCT and DWT. The comparison is done based on the standard parameters called Peak Signal to Noise Ratio (PSNR) and Standard Deviation. The result and analysis on the basis of PSNR values shows that adaptive histogram equalization combined with DWT is more effective approach compared to adaptive histogram equalization combined with DCT.

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2016

Conference Paper

Vijay Krishna Menon and Dr. Soman K. P., “A New Evolutionary Parsing Algorithm for LTAG”, in International Conference on Advanced Computing, Networking, and Informatics (ICACNI'16), , Rourkela, Odisha, 2016.

2016

Conference Paper

Vijay Krishna Menon, Vasireddy, N. Chakravart, Jam, S. Aswin, Pedamallu, V. Teja Navee, Sureshkumar, V., and Dr. Soman K. P., “Bulk Price Forecasting using Spark over NSE Data Set”, in International Conference on Data Mining and Big Data, Bali, Indonesia, 2016.[Abstract]


Financial forecasting is a widely applied area, making use of statistical prediction using ARMA, ARIMA, ARCH and GARCH models on stock prices. Such data have unpredictable trends and non-stationary property which makes even the best long term predictions grossly inaccurate. The problem is countered by keeping the prediction shorter. These methods are based on time series models like auto regressions and moving averages, which require computationally costly recurring parameter estimations. When the data size becomes considerable, we need Big Data tools and techniques, which do not work well with time series computations. In this paper we discuss such a finance domain problem on the Indian National Stock Exchange (NSE) data for a period of one year. Our main objective is to device a light weight prediction for the bulk of companies with fair accuracy, useful enough for algorithmic trading. We present a minimal discussion on these classical models followed by our Spark RDD based implementation of the proposed fast forecast model and some results we have obtained.

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2016

Conference Paper

, M, Akumar, and Dr. Soman K. P., “Amrita_CEN at SemEval-2016 : Complex Word identification using word embeddings”, in International Workshop on Semantic Evaluation (SemEval 2016), 2016, 2016.

2016

Conference Paper

R. G. Devi, Veena, P. V., Dr. M. Anand Kumar, and Dr. Soman K. P., “AMRITA-CEN@FIRE 2016: Code-mix entity extraction for Hindi-English and Tamil-English tweets”, in CEUR Workshop Proceedings, 2016, vol. 1737, pp. 304-308.[Abstract]


Social media text holds information regarding various important aspects. Extraction of such information serves as the basis for the most preliminary task in Natural Language Processing called Entity extraction. The work is submitted as a part of Shared task on Code Mix Entity Extraction for Indian Languages(CMEE-IL) at Forum for Information Retrieval Evaluation (FIRE) 2016. Three different methodology is proposed in this paper for the task of entity extraction for code-mix data. Proposed systems include approaches based on the Embedding models and feature based model. Creation of trigram embedding and BIO tag formatting were done during feature extraction. Evaluation of the system is carried out using machine learning based classifier, SVM-Light. Overall accuracy through cross validation has proven that the proposed system is efficient in classifying unknown tokens too

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2016

Conference Paper

S. V. Skanda, Singh, S., G. Devi, R., Veena, P. V., Dr. M. Anand Kumar, and Dr. Soman K. P., “CEN@Amrita FIRE 2016: Context based character embeddings for entity extraction in code-mixed text”, in CEUR Workshop Proceedings, 2016, vol. 1737, pp. 321-324.[Abstract]


This paper presents the working methodology and results on Code Mix Entity Extraction in Indian Languages (CMEE-IL) shared the task of FIRE-2016. The aim of the task is to identify various entities such as a person, organization, movie and location names in a given code-mixed tweets. The tweets in code mix are written in English mixed with Hindi or Tamil. In this work, Entity Extraction system is implemented for both Hindi-English and Tamil-English code-mix tweets. The system employs context based character embedding features to train Support Vector Machine (SVM) classifier. The training data was tokenized such that each line containing a single word. These words were further split into characters. Embedding vectors of these characters are appended with the I-O-B tags and used for training the system. During the testing phase, we use context embedding features to predict the entity tags for characters in test data. We observed that the cross-validation accuracy using character embedding gave better results for Hindi-English twitter dataset compare to Tamil-English twitter dataset.

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2016

Conference Paper

P. V. Veena, G. Devi, R., Dr. M. Anand Kumar, and Dr. Soman K. P., “AMRITA-CEN@FIRE 2016: Consumer Health Information Search using keyword and word embedding features”, in CEUR Workshop Proceedings, 2016, vol. 1737, pp. 197-200.[Abstract]


This work is submitted to Consumer Health Information Search (CHIS) Shared Task in Forum for Information Retrieval Evaluation (FIRE) 2016. Information retrieval from any part of web should include informative content relevant to the search of web user. Hence the major task is to retrieve only relevant documents according to the users query. The given task includes further refinement of the classification process into three categories of relevance such as support, oppose and neutral. Any user reading an article from web must know whether the content of that article supports or opposes title of the article. This seems to be a big challenge to the system. Our proposed system is developed based on the combination of Keyword based features and Word embedding based features. Classification of sentences is done by machine learning based classifier, Support Vector Machine (SVM).

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2016

Conference Paper

H. B. Barathi Ganesh, Dr. M. Anand Kumar, and Dr. Soman K. P., “Distributional semantic representation in health care text classification”, in CEUR Workshop Proceedings, 2016, vol. 1737, pp. 201-204.[Abstract]


This paper describes about the our proposed system in the Consumer Health Information Search (CHIS) task. The objective of the task 1 is to classify the sentences in the document into relevant or irrelevant with respect to the query and task 2 is analysing the sentiment of the sentences in the documents with respect to the given query. In this proposed approach distributional representation of text along with its statistical and distance measures are carried over to perform the given tasks as a text classification problem. In our experiment, Non - Negative Matrix Factorization utilized to get the distributed representation of the document as well as queries, distance and correlation measures taken as the features and Random Forest Tree utilized to perform the classification. The proposed approach yields 70.19% in task 1 and 34.64% in task 2 as an average accuracy.

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2016

Conference Paper

H. B. Barathi Ganesh, Dr. M. Anand Kumar, and Dr. Soman K. P., “Conditional random fields for code mixed Entity Recognition”, in CEUR Workshop Proceedings, 2016, vol. 1737, pp. 309-312.[Abstract]


Entity Recognition is an essential part of Information Extraction, where explicitly available information and relations are extracted from the entities within the text. Plethora of information is available in social media in the form of text and due to its nature of free style representation, it introduces much complexity while mining information out of it. This complexity is enhanced more by representing the text in more than one language and the usage of transliterated words. In this work we utilized sequential modeling algorithm with hybrid features to perform the Entity Recognition on the corpus given by CMEE-IL (Code Mixed Entity Extraction - Indian Language) organizers. The experimented approach performed great on both the Tamil-English and Hindi-English tweet corpus by attaining nearly 95% against the training corpus and 45.17%, 31.44% against the testing corpus.

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2016

Conference Paper

S. Singh, Dr. M. Anand Kumar, and Dr. Soman K. P., “CEN@Amrita: Information retrieval on CodeMixed Hindi English tweets using vector space models”, in CEUR Workshop Proceedings, 2016, vol. 1737, pp. 131-134.[Abstract]


One of the major challenges nowadays is Information retrieval from social media platforms. Most of the information on these platforms is informal and noisy in nature. It makes the Information retrieval task more challenging. The task is even more difficult for twitter because of its character limitation per tweet. This limitation bounds the user to express himself in condensed set of words. In the context of India, scenario is little more complicated as users prefer to type in their mother tongue but lack of input tools force them to use Roman script with English embeddings. This combination of multiple languages written in the Roman script makes the Information retrieval task even harder. Query processing for such CodeMixed content is a difficult task because query can be in either of the language and it need to be matched with the documents written in any of the language. In this work, we dealt with this problem using Vector Space Models which gave significantly better results than the other participants. The Mean Average Precision (MAP) for our system was 0.0315 which was second best performance for the subtask. More »»

2016

Conference Paper

Dr. M. Anand Kumar, Singh, S., Kavirajan, B., and Dr. Soman K. P., “DPIL@FIRE 2016: Overview of shared task on detecting paraphrases in Indian Languages (DPIL)”, in CEUR Workshop Proceedings, 2016, vol. 1737, pp. 233-238.[Abstract]


This paper explains the overview of the shared task "Detecting Paraphrases in Indian Languages" (DPIL) conducted at FIRE 2016. Given a pair of sentences in the same language, participants are asked to detect the semantic equivalence between the sentences. The shared task is proposed for four Indian languages namely Tamil, Malayalam, Hindi and Punjabi. The dataset created for the shared task has been made available online and it is the first open-source paraphrase detection corpora for Indian languages.

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2016

Conference Paper

H. B. Barathi Ganesh, Dr. M. Anand Kumar, and Dr. Soman K. P., “Distributional semantic representation for text classification and information retrieval”, in CEUR Workshop Proceedings, 2016, vol. 1737, pp. 126-130.[Abstract]


The objective of this experiment is to validate the performance of the distributional semantic representation of text in the classification (Question Classification) task and the Information Retrieval task. Followed by the distributional representation, first level classification of the questions is performed and relevant tweets with respect to the given queries are retrieved. The distributional representation of text is obtained by performing Non - Negative Matrix Factorization on top of the Document - Term Matrix in the training and test corpus. To improve the semantic representation of the text, phrases are also considered along with the words. This proposed approach achieved 80% as a F-1 measure and 0.0377 as a mean average precision against the its respective Mixed Script Information Retrieval task1 and task 2 test sets.

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2016

Conference Paper

Dr. M. Anand Kumar, Dr. Soman K. P., and Dr. Soman K. P., “Amrita-CEN@MSIR-FIRE2016: Code-mixed question classification using BoWs and RNN Embeddings”, in CEUR Workshop Proceedings, 2016, vol. 1737, pp. 122-125.[Abstract]


Question classification is a key task in many question answering applications. Nearly all previous work on question classification has used machine learning and knowledge-based methods. This working note presents an embedding based Bag-of-Words method and Recurrent Neural Network to achieve an automatic question classification in the code-mixed Bengali-English text. We build two systems that classify questions mostly at the sentence level. We used a recurrent neural network for extracting features from the questions and Logistic regression for classification. We conduct experiments on Mixed Script Information Retrieval (MSIR) Task 1 dataset at FIRE20161. The experimental result shows that the proposed method is appropriate for the question classification task.

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2015

Conference Paper

, Dr. M. Anand Kumar, and Dr. Soman K. P., “Deep Belief Network based Part of Speech Tagger for Telugu Language”, in 2nd IC3T International Conference on Computer and Communication Technologies, 2015.

2015

Conference Paper

M. S., Dr. M. Anand Kumar, and Dr. Soman K. P., “Paraphrase Detection for Tamil language using Deep learning algorithms”, in International Conference on Big Data and Cloud Computing (ICBDCC-2015), 2015.

2015

Conference Paper

H. B. Barathi Ganesh, Abinaya, N., Dr. M. Anand Kumar, Vinayakumar, R., and Dr. Soman K. P., “AMRITA - CEN@NEEL : Identification and linking of twitter entities”, in CEUR Workshop Proceedings, Florence; Italy, 2015, vol. 1395, pp. 64-65.[Abstract]


A short text gets updated every now and then. With the global upswing of such micro posts, the need to retrieve information from them also seems to be incumbent. This work focuses on the knowledge extraction from the micro posts by having entity as evidence. Here the extracted entities are then linked to their relevant DBpedia source by featurization, Part Of Speech (POS) tagging, Named Entity Recognition (NER) and Word Sense Disambiguation (WSD). This short paper encompasses its contribution to #Micropost2015 - NEEL task by experimenting existing Machine Learning (ML) algorithms. Copyright © 2015 held by author(s More »»

2015

Conference Paper

Vijay Krishna Menon, Rajendran, S., and Dr. Soman K. P., “A synchronised tree adjoining Grammar for English to Tamil Machine Translation”, in 2015 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2015 (Fourth International Symposium on Natural Language Processing (NLP'15)), SCMS Group of Institutions, Corporate Office CampusPrathap Nagar , Muttom, Aluva, Kochi, Kerala; India, 2015, pp. 1497-1501.[Abstract]


Tree adjoining Grammar (TAG) is a rich formalism for capturing syntax and some limited semantics of Natural languages. The XTAG project has contributed a very comprehensive TAG for English Language. Although TAGs have been proposed nearly 40 years ago by Joshi et al, 1975, their usage and application in the Indian Languages have been very rare, predominantly due to their complexity and lack of resources. In this paper we discuss a new TAG system and methodology of development for Tamil Language that can be extended for other Indian languages. The trees are developed synchronously with a minimalistic grammar obtained by careful pruning of XTAG English Grammar. We also apply Chomskian minimalism on these TAG trees, so as to make them simple and easily parsable. Furthermore we have also developed a parser that can parse simple sentences using the above mentioned grammar, and generating a TAG derivation that can be used for dependency resolution. Due to the synchronous nature of these TAG pairs they can be readily adapted for Formalism based Machine Translation (MT) from English to Tamil and vice versa. © 2015 IEEE. More »»

2014

Conference Paper

P. Sanjanaashree, Dr. M. Anand Kumar, and Dr. Soman K. P., “Language learning for visual and auditory learners using scratch toolkit”, in 2014 International Conference on Computer Communication and Informatics: Ushering in Technologies of Tomorrow, Today, ICCCI 2014, https://www.scopus.com/record/display.uri?eid=2-s2.0-84911391150&origin=inward&txGid=0, 2014.[Abstract]


In recent years, with the development of technology, life has become very easy. Computers have become the life line of today's high-tech world. There is no work in our whole day without the use of computers. When we focus particularly in the field of education, people started preferring to e-books than carrying textbooks. In the phase of learning, visualization plays a major role. When the visualization tool and auditory learning comes together, it brings the in-depth understanding of data and their phoneme sequence through animation and with proper pronunciation of the words, which is far better than the people learning from the textbooks and imagining in their perspective and have their own pronunciation. Scratch with its visual, block-based programming platform is widely used among high school kids to learn programming basics. We investigated that in many schools around the world uses this scratch for students to learn programming basics. Literature review shows that students find it interesting and are very curious about it. This made us anxious towards natural language learning using scratch because of its interesting visual platform. This paper is based on the concept of visual and auditory learning. Here, we described how we make use of this scratch toolkit for learning the secondary language. We also claim that this visual learning will help people remember easily than to read as texts in books and the auditory learning helps in proper pronunciation of words rather than expecting someone's help. We have developed a scratch based tool for learning simple sentence construction of secondary language through primary language. In this paper, languages used are English (secondary language) and Tamil (primary language). This is an enterprise for language learning tool in scratch. This is applicable for other language specific exercises and can be adopted easily for other languages too. © 2014 IEEE. More »»

2014

Conference Paper

, Anirudh Nair, Dr. M. Anand Kumar, and Dr. Soman K. P., “AMRITA@ FIRE-2014: Named Entity Recognition for Indian languages (Working notes)”, in International Workshop: "NER shared Task" Forum for Information Retrieval Evaluation (FIRE-2014), Bengaluru, 2014.

2014

Conference Paper

Dr. M. Anand Kumar, Rajendran, S., and Dr. Soman K. P., “AMRITA@ FIRE-2014: Morpheme Extraction for Tamil using Machine Learning (Working notes)”, in International Workshop: "MET shared Task" Forum for Information Retrieval Evaluation (FIRE- 2014), Bengaluru , 2014.

2014

Conference Paper

S. Ravindranath, Ram, S. R. N., Subhashini, S., Reddy, A. V. S., Janarth, M., Aswathvignesh, R., Gandhiraj R., and Dr. Soman K. P., “Compressive sensing based image acquisition and reconstruction analysis”, in Proceeding of the IEEE International Conference on Green Computing, Communication and Electrical Engineering, ICGCCEE 2014, 2014.[Abstract]


Compressive sensing is a technique by which images are acquired and reconstructed from a relatively fewer measurements than what the Nyquist rate suggests. Compressive sensing is applicable when the signals under consideration are sparse, and most of the images are sparse in wavelet or frequency domain. In this paper, the mathematical formulation of compressive sensing is explained where in various notations and parameters like measurement matrices and sparsity-inducing matrices are dealt in detail. A deterministic measurement matrix, known as chess measurement matrix is implemented in an aperture assembly. Several reconstruction algorithms are analysed and the images reconstructed with PSNR plotted for every case. Based upon the results, it is proved that OMP is the efficient reconstruction algorithm among all. © 2014 IEEE More »»

2014

Conference Paper

M. Sreethivya, Dhanya, M. G., Nimisha, C., Gandhiraj R., and Dr. Soman K. P., “Radiation Pattern of Yagi-Uda antenna using GNU Radio Platform”, in International Conference on Trends in Technology for Convergence (TITCON '14), AVS Engineering College, Salem, 2014.

2014

Conference Paper

D. Rajendra D. Kumar, Ambika, P. S., Anju, S., Gandhiraj R., and Dr. Soman K. P., “Simultaneous Access of RF Front End from more clients using GNU Radio for Low Cost Platform”, in International Confernece on Recent Trends in Engineering and Technology (ICRTET-2014), CSI Institue of Technolgy, Kanyakumari, 2014.

2014

Conference Paper

S. Santhosh, Abinaya, N., Rashmi, G., Sowmya V., and Dr. Soman K. P., “A novel approach for denoising coloured remote sensing image using Legendre Fenchel Transformation”, in 2014 International Conference on Recent Trends in Information Technology, ICRTIT 2014, https://www.scopus.com/record/display.uri?eid=2-s2.0-84921058967&origin=inward&txGid=0, 2014.[Abstract]


Data acquired from remote sensing satellites are processed in order to retrieve the information from an image. Those images are preprocessed using image processing techniques such as noise removal. Satellite images are assumed to be corrupted with white Gaussian noise of zero mean and constant variance. Three planes of the noisy image are denoised separately through Legendre Fenchel Transformation. Later, these three planes are concatenated and compared with results obtained by Euler-Lagrange ROF model. Simulation results show that Legendre Fenchel ROF is highly convergent and less time consuming. To add evidence to the outcomes, quality metrics such as variance and PSNR for noisy and denoised images are calculated. The qualitative analysis of an image is analysed using MSSIM calculations, which clarifies the Structural Similarity between denoised images with original image. © 2014 IEEE.

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2013

Conference Paper

N. Prasannan, Xavier, G., Manikkoth, A., Gandhiraj R., Peter, R., and Dr. Soman K. P., “OpenBTS based microtelecom model: A socio-economic boon to rural communities”, in Proceedings - 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013, Kerala, 2013, pp. 856-861.[Abstract]


This paper proposes a low cost, low power, reconfigurable and flexible Open BTS (Base Transceiver Station) model based on SDR (Software Defined Radio) using USRP. A microtelecom model serves people at the "bottom of the pyramid" along with ensuring the ROI (Return on Investment) for MNO's (Mobile Network Operators). Thus a new telecom revolution that clubs Microtelecom business model with Open BTS concept would positively affect the socio-economic progress of rural communities, thereby ensuring the overall growth of developing nations. The success of a proposed model of this kind would encourage government to take initiatives and confidently invest on policies that would benefit the low-waged and less-privileged rural communities. © 2013 IEEE.

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2013

Conference Paper

P. Sukanya, Suchithra, M., Sikha, O. K., Prabha, P., and Dr. Soman K. P., “Understanding CDMA in linear algebra point of view and its simulation in Excel”, in Proceedings - 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013, Kerala, 2013, pp. 78-83.[Abstract]


Code Division Multiple Access (CDMA) is one of the famous channel access method, mainly used in radio communication technologies. Unfortunately this concept is less understood by the student community due to the lack of understanding the mathematical rules behind it. This paper is intended to provide a linear algebra point of explanation of the concepts behind CDMA. The CDMA concept which was otherwise analyzed in spectral point of view is explained using the orthogonality of the bases. The Microsoft Excel Spread Sheet is used as an aid for the simulation since every one can go deep in to the basic concepts. © 2013 IEEE.

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2012

Conference Paper

R. Anand, Xavier, G., Hariharan, V., Prasannan, N., Peter, R., and Dr. Soman K. P., “GNU radio based control system”, in Proceedings - 2012 International Conference on Advances in Computing and Communications, ICACC 2012, Cochin, 2012, pp. 259-262.[Abstract]


This paper is an attempt to reveal the untapped immense power of the GNU Radio - an open source software, in control and monitoring systems, both real time applications and class-room demonstrations. GNU Radio has already gained wide acceptance and glory in communication and signal processing. As a novel attempt to bring the controlling capability of GNU Radio to limelight, an experiment for temperature control, out of its innumerable applications, and the possibility of a revolution by this free open source software is predicted and explained through this paper. This paper describes the hardware and software requirements for the temperature control experiment with SBHS (Single Board Heater System) using GNU Radio and describes the recommended classroom demonstration. © 2012 IEEE.

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2010

Conference Paper

P. J. Antony and Dr. Soman K. P., “Kernel based part of speech tagger for kannada”, in Machine Learning and Cybernetics (ICMLC), 2010 International Conference on, Qingdao, 2010, vol. 4, pp. 2139-2144.[Abstract]


The proposed paper presents the development of a part-of-speech tagger for Kannada language that can be used for analyzing and annotating Kannada texts. POS tagging is considered as one of the basic tool and component necessary for many Natural Language Processing (NLP) applications like speech recognition, natural language parsing, information retrieval and information extraction of a given language. In order to alleviate problems for Kannada language, we proposed a new machine learning POS tagger approach. Identifying the ambiguities in Kannada lexical items is the challenging objective in the process of developing an efficient and accurate POS Tagger. We have developed our own tagset which consist of 30 tags and built a part-of-speech Tagger for Kannada Language using Support Vector Machine (SVM). A corpus of texts, extracted from Kannada news papers and books, is manually morphologically analyzed and tagged using our developed tagset. The performance of the system is evaluated and we found that the result obtained was more efficient and accurate compared with earlier methods for Kannada POS tagging. More »»

2010

Conference Paper

K. Palanisamy, Kumar, A. Manoj, Chinnappa, M., M Manikandan, S., and Dr. Soman K. P., “Audio visual based pronunciation dictionary for Indian languages”, in Technology for Education (T4E), 2010 International Conference on, Mumbai, 2010, pp. 82-84.[Abstract]


The quality of pronunciation of a letter or word is most important for better conversations. We can accurately perceive the contextual information only when the speech sounds are produced clearly. The quality of any speech sound depends on the movements of organs of the human speech-production system. Nowadays, communication disorders are challenging the carrier development of individuals and growth of the nation. In this paper, we present an effective audio-visual based Tamil pronunciation dictionary by incorporating the visual actions of the organs for improving communication skills. More »»

2010

Conference Paper

P. J. Antony, Mohan, S. P., and Dr. Soman K. P., “SVM Based Part of Speech Tagger for Malayalam”, in Recent Trends in Information, Telecommunication and Computing (ITC), 2010 International Conference on, Kochi, Kerala, 2010, pp. 339-341.[Abstract]


This paper presents the building of part-of-speech Tagger for Malayalam Language using Support Vector Machine (SVM). POS tagger plays an important role in Natural language applications like speech recognition, natural language parsing, information retrieval and information extraction. This supervised machine learning POS tagging approach requires a large amount of annotated training corpus to tag properly. At initial stage of POS-tagging for Malayalam, the model is trained with a very limited resource of annotated corpus. We tried to maximize the performance with this a substantial amount of annotated corpus. The objective of this project was to identify the ambiguities in Malayalam lexical items and develop an efficient and accurate POS Tagger. We have developed our own tagset for training and testing the POS-tagger generators. The present tagset consists of 29 tags. A corpus size of one hundred and eighty thousand words was used for training and testing the accuracy of the tagger generators. We found that the result obtained was more efficient and accurate compared with earlier methods for Malayalam POS tagging. More »»

2010

Conference Paper

P. J. Antony, Ajith, V. P., and Dr. Soman K. P., “Kernel Method for English to Kannada Transliteration”, in Recent Trends in Information, Telecommunication and Computing (ITC), 2010 International Conference on, Kochi, Kerala, 2010, pp. 336-338.[Abstract]


Language transliteration is one of the important area in natural language processing. Accurate transliteration of named entities plays an important role in the performance of machine translation and cross-language information retrieval processes. The transliteration model must be design in such a way that the phonetic structure of words should be preserve as closely as possible. This paper addresses the problem of transliterating English to Kannada language using a publicly available structured output Support Vector Machines (SVM). The proposed transliteration scheme uses sequence labeling method to model the transliteration problem. This transliteration technique was demonstrated for English to Kannada Transliteration and achieved exact Kannada transliterations for 87.28% of English names. More »»

2010

Conference Paper

R. U. Rekha, M Kumar, A., Dhanalakshmi, V., Rajendran, S., and Dr. Soman K. P., “Morphological Generator for Tamil a New Data Driven Approach”, in 9th Tamil Internet Conference, Chemmozhi Maanaadu, Coimbatore, India, 2010.

2010

Conference Paper

R. U. Rekha, M Kumar, A., Dhanalakshmi, V., Rajendran, S., and Dr. Soman K. P., “A Novel Approach to Morphological Generator for Tamil”, in 2nd International Conference on Data Engineering and Management (ICDEM 2010), Trichy, India, 2010.

2010

Conference Paper

V. P. Abeera, Aparna, S., Dhanalakshmi, V., M Kumar, A., Rajendran, S., Dr. Soman K. P., and Rekha, R. U., “Morphological Analyzer for Malayalam using Machine Learning”, in Second International Conference, ICDEM , Tiruchirappalli, India, 2010.[Abstract]


An efficient and reliable method for implementing Morphological Analyzer for Malayalam using Machine Learning approach has been presented here. A Morphological Analyzer segments words into morphemes and analyze word formation. Morphemes are smallest meaning bearing units in a language. Morphological Analysis is one of the techniques used in formal reading and writing. Rule based approaches are generally used for building Morphological Analyzer. The disadvantage of using rule based approaches are that if one rule fails it will affect the entire rule that follows, that is each rule works on the output of previous rule. The significance of using machine learning approach arises from the fact that rules are learned automatically from data, uses learning and classification algorithms to learn models and make predictions. The result shows that the system is very effective and after learning it predicts correct grammatical features even forwords which are not in the training set.

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2010

Conference Paper

Dr. M. Anand Kumar, Dhanalakshmi, V. V., Rajendran, S., Dr. Soman K. P., and Rekha, K. U., “A novel algorithm for Tamil morphological generator (Best Second Paper)”, in 8th International Conference on Natural Language Processing ( ICON2010), IIT Kharagpur, India, 2010.[Abstract]


Tamil is a morphologically rich language with agglutinative nature. Being agglutinative language most of the word features are postpositionally affixed to the root word. The morphological generator takes lemma, POS category and morpholexical description as input and gives a word-form as output. It is a reverse process of morphological analyzer. In any natural language generation system, morphological generator is an essential component in post processing stage. Morphological generator system implemented here is based on a new algorithm, which is simple, efficient and does not require any rules and morpheme dictionary. A paradigm classification is done for noun and verb based on S.Rajendran’s paradigm classification. Tamil verbs are classified into 32 paradigms with 1884 inflected forms. Like verbs, nouns are classified into 25 paradigms with 325 word forms. This approach requires only minimum amount of data. So this approach can be easily implemented to less resourced and morphologically rich languages. More »»

2010

Conference Paper

S. Rajendran, Shivapratap, G., Dhanlakshmi, V., and Dr. Soman K. P., “Building a WordNet for Dravidian Languages”, in Proceedings of the Global WordNet Conference (GWC 10), Indian Institute of Technology, Mumbai, India, 2010.[Abstract]


This paper attempts to emphasize the need for a standalone and independent Dravidian WordNet. Since the morphology and lexical concepts of Dravidian languages are closer to each other than to a language from a different family, it is proposed to base the Dravidian WordNet on a Dravidian Language. A signifi-cant amount of work has already been done in Tamil language to understand the ontological structure and vocabulary. Based on the find-ings of these studies, it is proposed to build a Tamil WordNet first and then extend it to complete the Dravidian WordNet. A prototype model for the Tamil WordNet is also proposed in this paper. More »»

2010

Conference Paper

Dr. Soman K. P. and Menon, A. G., “English to Tamil Machine Translation System”, in 9th Tamil Internet Conference (INFITT ), Chemmozhi Maanaadu, Coimbatore, India, 2010.

Publication Type: Book Chapter

Year of Publication Publication Type Title

1995

Book Chapter

Dr. Soman K. P., “Fuzzy Sets and Probability Theory in Reliability and Safety Related Problem”, in Reliability and Safety Analyses under Fuzziness, 1995.

207
PROGRAMS
OFFERED
5
AMRITA
CAMPUSES
15
CONSTITUENT
SCHOOLS
A
GRADE BY
NAAC, MHRD
8th
RANK(INDIA):
NIRF 2018
150+
INTERNATIONAL
PARTNERS