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



Publication Type: Journal Article
Year of Publication Publication Type Title
2016 Journal Article M. Kaviarasan, Geetha, P., and 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 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 N. Mohan, S. Kumar, S., Poornachandran, P., and 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 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, Sundaram, G. A. Shanmugha, and 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 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 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 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 H. T. Suseelan, Sudhakaran, S., Sowmya, V., and Soman, K. P., “Performance Evaluation of Sparse Banded Filter Matrices using content based image retrieval”, Institute of Integrative Omics and Applied Biotechnology, vol. 7, 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. More »»
2016 Journal Article A. M, N, D., Sowmya, V., Mahan, N., and 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 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 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, M Anand Kumar, and 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, Sundaram, G. A. Shanmugha, and 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, Sundaram, G. A. Shanmugha, and 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 »»
2015 Journal Article A. Muralidharan, Sugumaran, V., 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, M Anand Kumar, and 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 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 »»
2014 Journal Article R. Gandhiraj and 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., 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 »»
Publication Type: Conference Paper
Year of Publication Publication Type Title
2016 Conference Paper Y. C. Nair, PV, N., Soman, K. P., and Menon, V. Krishna, “Real Time Vehicular Data Analysis utilising Big Data Platforms on Cost Effective ECU Networks”, in 3rd International Conference on Innovation in Information Embedded and Communication Systems (ICIIECS’16), 2016.
2016 Conference Paper A. S and 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 R. R, Sowmya, V., and Soman, K. P., “Improvement in kernel based Hyperspectral image classification using legendre fenchel denoising”, in International Conference on Innovation in Information Embedded and Communication Systems (ICIIECS’16), , 2016.
2016 Conference Paper A. Balaji S, P, G., and 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. Pradeep V, Sowmya, V., and Soman, K. P., “Variational Mode Decomposition based Multispectral and Panchromatic Image Fusion”, in International Conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems (ICAIECES-2016), 2016.
2016 Conference Paper V. M, P, G., and 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 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 V. P.V, G, R. Devi, Sowmya, V., and Soman, K. P., “Least Square based image denoising using wavelet filters”, in International Conference on Innovation in Information Embedded and Communication Systems (ICIIECS’16), 2016.
2016 Conference Paper S. Rajan, Sowmya, V., and 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, Mohan, N., and 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 A. B, se, S., Sowmya, V., and Soman, K. P., “Performanve Evaluation of Edge Feature Extracted using Sparse Banded Matrix Filter Based on Face Recognition”, in International conference on Soft computing systems (ICSCS’16), 2016.
2016 Conference Paper V. Pradeep V, R, R., Sowmya, V., and Soman, K. P., “Comparative Analysis of sparsity based and kernel based algorithms for Hyperspectral Image Classification”, in International conference on Soft computing systems (ICSCS’16), 2016.
2016 Conference Paper N. PV, Nair, Y. C., variyar, S., and 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 Soman, K. P., “X-ray Image Classification Based On Tumor using GURLS and LIBSVM”, in International Conference on Communications and Signal Processing (ICCSP’16), 2016.
2016 Conference Paper S. M, K.S, G. Krishnan, Sowmya, V., and 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 Soman, K. P., “Low Contrast Satellite Image Restoration based on adaptive Histogram Equalization and Discrete Wavelet Transform”, in International Conference on Communications and Signal Processing (ICCSP’16), 2016.
2016 Conference Paper V. Krishna Menon and Soman, K. P., “A New Evolutionary Parsing Algorithm for LTAG”, in International Conference on Advanced Computing, Networking, and Informatics (ICACNI'16), , 2016.
2016 Conference Paper V. Krishna Menon, Vasireddy, N. Chakravart, Jam, S. Aswin, Pedamallu, V. Teja Navee, Sureshkumar, V., and Soman, K. P., “Bulk Price Forecasting using Spark over NSE Data Set”, in Conference on Data Mining and Big Data, 2016.
2016 Conference Paper , M, Akumar, and Soman, K. P., “Amrita_CEN at SemEval-2016 : Complex Word identification using word embeddings”, in International Workshop on Semantic Evaluation (SemEval 2016), 2016, 2016.
2015 Conference Paper , M Anand Kumar, and 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., M Anand Kumar, and 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., M Anand Kumar, Vinayakumar, R., and 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 V. K. Menon, Rajendran, S., and 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, SCMS Group of Institutions, Corporate Office CampusPrathap Nagar , Muttom, Aluva, Kochi (Ernakulam)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, M Anand Kumar, and 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 , ,, M Anand Kumar, and 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 M Anand Kumar, Rajendran, S., and 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 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., R. Gandhiraj, and 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., R. Gandhiraj, and 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.
Publication Type: Conference Proceedings
Year of Publication Publication Type Title
2016 Conference Proceedings B. Ganesh, M, Akumar, and 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.



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