Qualification: 
Ph.D, MS, BE
ovr_murthy@cb.amrita.edu

Dr. Oruganti Venkata Ramana Murthy currently serves as Assistant Professor at the Department of Electrical and Electronics Engineering, Amrita School of Engineering, Coimbatore Campus.

He received Bachelor's degree in Electrical and Electronics Engineering from Andhra University in 1999. He got his Masters degree by research and Doctorate degree from Indian Institute of Technology Delhi in 2002 and 2012 respectively.

He has more than 9 years of research experience in reputed Universities including National University of Singapore, Nanyang Technological University, Singapore and University of Canberra, Australia. He has more than 20 publications in major international journals and conferences to his credit. He has participated in a number of workshops on Big data, deep learning, statistics. 

His areas of interest include Pattern Recognition, Machine Learning, Soft Computing, Control systems, Embedded systems, Human-computer Interaction and Data analytics. 

Publications

Publication Type: Journal Article

Year of Publication Publication Type Title

2016

Journal Article

R. Goecke and Dr. Oruganti Venkata Ramana Murthy, “Dimensionality reduction of Fisher vectors for human action recognition”, IET Computer Vision, vol. 10, pp. 392-397(5), 2016.[Abstract]


Automatic analysis of human behaviour in large collections of videos is rapidly gaining interest, even more so with the advent of file sharing sites such as YouTube. From one perspective, it can be observed that the size of feature vectors used for human action recognition from videos has been increasing enormously in the last five years, in the order of ∼100–500K. One possible reason might be the growing number of action classes/videos and hence the requirement of discriminating features (that usually end up to be higher-dimensional for larger databases). In this study, the authors review and investigate feature projection as a means to reduce the dimensions of the high-dimensional feature vectors and show their effectiveness in terms of performance. They hypothesise that dimensionality reduction techniques often unearth latent structures in the feature space and are effective in applications such as the fusion of high-dimensional features of different types; and action recognition in untrimmed videos. They conduct all the authors’ experiments using a Bag-of-Words framework for consistency and results are presented on large class benchmark databases such as the HMDB51 and UCF101 datasets. More »»

2015

Journal Article

Dr. Oruganti Venkata Ramana Murthy and Goecke, R., “Harnessing the Deep Net Object Models for Enhancing Human Action Recognition”, CoRR, vol. abs/1512.06498, 2015.[Abstract]


In this study, the influence of objects is investigated in the scenario of human action recognition with large number of classes. We hypothesize that the objects the humans are interacting will have good say in determining the action being performed. Especially, if the objects are non-moving, such as objects appearing in the background, features such as spatio-temporal interest points, dense trajectories may fail to detect them. Hence we propose to detect objects using pre-trained object detectors in every frame statically. Trained Deep network models are used as object detectors. Information from different layers in conjunction with different encoding techniques is extensively studied to obtain the richest feature vectors. This technique is observed to yield state-of-the-art performance on HMDB51 and UCF101 datasets. More »»

2015

Journal Article

Dr. Oruganti Venkata Ramana Murthy and Goecke, R., “Ordered trajectories for human action recognition with large number of classes”, Image and Vision Computing, vol. 42, pp. 22 - 34, 2015.[Abstract]


Abstract Recently, a video representation based on dense trajectories has been shown to outperform other human action recognition methods on several benchmark datasets. The trajectories capture the motion characteristics of different moving objects in space and temporal dimensions. In dense trajectories, points are sampled at uniform intervals in space and time and then tracked using a dense optical flow field over a fixed length of L frames (optimally 15) spread overlapping over the entire video. However, among these base (dense) trajectories, a few may continue for longer than duration L, capturing motion characteristics of objects that may be more valuable than the information from the base trajectories. Thus, we propose a technique that searches for trajectories with a longer duration and refer to these as ‘ordered trajectories’. Experimental results show that ordered trajectories perform much better than the base trajectories, both standalone and when combined. Moreover, the uniform sampling of dense trajectories does not discriminate objects of interest from the background or other objects. Consequently, a lot of information is accumulated, which actually may not be useful. This can especially escalate when there is more data due to an increase in the number of action classes. We observe that our proposed trajectories remove some background clutter, too. We use a Bag-of-Words framework to conduct experiments on the benchmark HMDB51, \{UCF50\} and \{UCF101\} datasets containing the largest number of action classes to date. Further, we also evaluate three state-of-the art feature encoding techniques to study their performance on a common platform. More »»

2014

Journal Article

Dr. Oruganti Venkata Ramana Murthy and Goecke, R., “Uc–hcc submission to thumos 2014”, THUMOS Challenge: Action Recognition with a Large Number of Classes, 2014.[Abstract]


We basically use a Bag-of-Words framework. We compute the improved dense trajectories to compute Fisher vectors that serve as features. Using the training videos, we compute a mapping function which we conjecture to contain the principal information about each action. Given a temporally untrimmed video, we project it’s feature along this mapping. The transformed features are passed to 1-vs all SVM classifiers framework to get the prediction score of each actions in the given video clip. More »»

2013

Journal Article

Dr. Oruganti Venkata Ramana Murthy, Roy, S., Narang, V., Hanmandlu, M., and Gupta, S., “An approach to divide pre-detected Devanagari words from the scene images into characters”, Signal, Image and Video Processing, vol. 7, pp. 1071–1082, 2013.[Abstract]


A methodology to segment the Devanagari words, extracted from the scene images, into characters is presented. Scene images include street signs, shop names, product advertisements, posters on streets, etc. Such words are prone to multiple sources of noise and these make the segmentation very challenging. The problem gets more complicated while developing the text recognition methodologies for different scripts because there is no general solution to this problem and recognizing text in some scripts can be tougher than in others. An indigenous database is created for this purpose. It consists of 130 samples, manually extracted from 200 natural scene images. The results obtained by applying the proposed techniques are encouraging. The average performance is found to be 55.77 {%}. The execution time for a typical word of size 1169 × 353 is found to be 4.76 s. The database and the results can serve as baseline for the future researchers. More »»

2012

Journal Article

Dr. Oruganti Venkata Ramana Murthy, Roy, S., Narang, V., and Hanmandlu, M., “Devanagari character recognition in the wild'”, International Journal of Computer Applications, vol. 38, pp. 38–45, 2012.[Abstract]


This papers examines the issues in recognizing the Devanagari characters in the wild like sign boards, advertisements, logos, shop names, notices, address posts etc. While some works deal with the issues in recognizing the machine printed and the handwritten Devanagari characters, it is not clear if such techniques can be directly applied to the Devanagari characters captured in the wild. Moreover in the recent times a lot of research has been conducted in the field of object categorization and localization. It would be interesting to investigate if the state-of-the-art tools for object categorization can also be applied to the recognition of the Devanagari characters. The idea is to view the isolated characters as objects so as to detect them in the wild. The ability to recognize the Devanagari characters in the wild will be very useful in the Internet services like Google street view and its associated applications. So, a detailed study of the Devanagari character recognition using the state-of-the-art character recognition and object recognition tools has been carried out to compute the best performance. This serve as a baseline for the comparison for the future works. There is no benchmark database to conduct studies on the Devanagari character recognition in the wild. So a database of 40 Devanagari character categories has been created from 200 pictures of the images in the wild. More »»

2012

Journal Article

Dr. Oruganti Venkata Ramana Murthy and Hanmandlu, M., “An approach to offline handwritten Devanagari word segmentation”, International Journal of Computer Applications in Technology, vol. 44, pp. 284–292, 2012.[Abstract]


Hindi is very popular language after Mandarin and English. Its script is Devanagari. Although major work is reported on OCR techniques for machine printed Devanagari script, very few works are beginning to report on offline handwritten Devanagari OCR. Particularly, very few works have been reported so far on segmentation of Devanagari words. This paper paves a step in that direction and open avenues for researchers in that field. A novel segmentation approach is proposed for segmentation of offline handwritten Devanagari words using techniques of Hough transform and connected components. The difficulties for segmentation in Devanagari script and systematic steps to accommodate those difficulties as much as possible have been presented with elaborate results. More »»

2011

Journal Article

Dr. Oruganti Venkata Ramana Murthy and Hanmandlu, M., “Interactive Fuzzy Model Based Recognition of Handwritten numerals”, Journal of Pattern Recognition Research, vol. 6, pp. 154–165, 2011.

2011

Journal Article

Dr. Oruganti Venkata Ramana Murthy and Hanmandlu, M., “Zoning based Devanagari character recognition”, International Journal of Computer Applications, vol. 27, 2011.[Abstract]


In character recognition, zoning based feature extraction is one of the most popular methods. The character image is divided into predefined number of zones and a feature is computed from each of these zones. Usually, this feature is based on the pattern (black) pixels contained in that zone. Some of such features are average pixel density, sum squared distance, histogram. But in such features, say the average pixel density, different combination location of pixels can all give rise to same average pixel density. This often leads to errors in classification. In this paper, a new technique is presented where the pattern pixel location is also taken into account to contribute as much unique feature as possible. The experimental tests, carried out in the field of Devanagari handwritten numeral and character recognition show that the proposed technique leads to improvement over the traditional zoning methods More »»

2011

Journal Article

Dr. Oruganti Venkata Ramana Murthy and Hanmandlu, M., “A Study on the Effect of Outliers in Devanagari Character Recognition”, International Journal of Computer Applications, vol. 32, 2011.[Abstract]


Devanagari is the basic script for many languages of India, including their National language Hindi. Unlike the Latin script used for the English language, it does not have upper case or lowercase. It has only one case of writing. Moreover each alphabet contains more curves than straight lines. Hence handwritten Devanagari character recognition is a challenging task. To capture different handwritten styles of each alphabet, different approaches have been proposed. In this work, we investigate a simple filtering technique on the features. Support Vector Machine (SVM) was used as classifier. It has been applied on two benchmark Devanagari databases and results show an improvement of as much as 5-10%. This improvement is found to be consistent with different sizes of the database. It was studied on pixel density features and GIST features separately. GIST features were found to be more effective and like having the potency of self-containing filtering. More »»

2009

Journal Article

Y. V. Venkatesh, Kassim, A. A., and Dr. Oruganti Venkata Ramana Murthy, “A novel approach to classification of facial expressions from 3D-mesh datasets using modified \PCA\”, Pattern Recognition Letters, vol. 30, pp. 1128 - 1137, 2009.[Abstract]


We propose a novel approach to human facial expression recognition using only the shape information at a finite set of fiducial points, extracted from the 3D neutral and expressive faces. In the course of applying the technique to the facial database, BU-3DFE, which contains facial shape and 2D color (“texture”) information, we extract from the images of neutral and expressive faces, salient contours in the facial interest-regions around the eyebrows, eyes, nose and mouth by invoking an active contour algorithm. The contours are then uniformly sampled and mapped onto the 3D-mesh dataset in order to generate a shape (and color) description of the interest-regions. By a matrix–algebraic operation on the shape of the neutral and expressive faces, a shape feature-matrix is computed for each expression and for each person, which is then subjected to the proposed modified \{PCA\} approach to recognize expressions. Classification results are presented to demonstrate the effectiveness of the proposed approach. It is also found that accuracy estimates compare favorably with those in the literature on facial expression recognition from 3D-mesh datasets. More »»

2007

Journal Article

M. Hanmandlu and Dr. Oruganti Venkata Ramana Murthy, “Fuzzy model based recognition of handwritten numerals”, Pattern Recognition, vol. 40, pp. 1840 - 1854, 2007.[Abstract]


This paper presents the recognition of handwritten Hindi and English numerals by representing them in the form of exponential membership functions which serve as a fuzzy model. The recognition is carried out by modifying the exponential membership functions fitted to the fuzzy sets. These fuzzy sets are derived from features consisting of normalized distances obtained using the Box approach. The membership function is modified by two structural parameters that are estimated by optimizing the entropy subject to the attainment of membership function to unity. The overall recognition rate is found to be 95% for Hindi numerals and 98.4% for English numerals. More »»

2005

Journal Article

S. Suraneni, Kar, I. N., Dr. Oruganti Venkata Ramana Murthy, and Bhatt, R. K. P., “Adaptive stick–slip friction and backlash compensation using dynamic fuzzy logic system”, Applied Soft Computing, vol. 6, pp. 26 - 37, 2005.[Abstract]


A dynamic fuzzy logic-based adaptive algorithm is proposed for reducing the effect of stick–slip friction and for the compensation of backlash. The control scheme proposed is an online identification and indirect adaptive control, in which the control input is adjusted adaptively to compensate the effect of these non-linearities. A tuning algorithm for fuzzy logic parameters is used to ensure stable performance. The efficacy of the proposed algorithm is verified on a one degree of freedom (1-DOF) mechanical mass system with stick–slip friction and on a one-link robot manipulator with backlash. More »»

2004

Journal Article

Dr. Oruganti Venkata Ramana Murthy, Bhatt, R. K. P., and Ahmad, N., “Extended dynamic fuzzy logic system (DFLS) based indirect stable adaptive control of non-linear systems”, Applied Soft Computing, vol. 4, pp. 109 - 119, 2004.[Abstract]


The dynamic fuzzy logic system (DFLS) consists of static fuzzy logic system added with a dynamic element—the integrator—with a feedback constant α. It was shown to possess the important universal approximation capability. Further, Lee and Vukovich developed \{DFLS\} based stable indirect adaptive control scheme via Lyapunov synthesis approach for a class of non-linear systems of the form ẋ=f(X)+bu. In this paper, this is extended so that now, it can be applied to a larger class of non-linear dynamic systems, i.e., of the form ẋ=f(X)+g(X)u. It was successfully investigated on a chaotic system-modified Duffing’s equation. More »»

0

Journal Article

Dr. Oruganti Venkata Ramana Murthy and Goecke, R., “Player Falling Detection in Soccer Matches Videos”.[Abstract]


In recent years, most sports such as Soccer are recorded during the actual game and analysed later on by the coaches for training their teams to perform better in the future. The training can be in several aspects such as avoiding fouls, injuries, improving passes and tactics. Automatic detection/retrieval of such specific events from the video database will be of great assistance to the coaches. In this paper, we analyse Soccer match videos with the objective to detect two events – players playing or falling. Player falling, often leading to injury can be costly for the players’ health and also the team performance as a whole. More »»

Publication Type: Conference Paper

Year of Publication Publication Type Title

2015

Conference Paper

A. Dhall, Dr. Oruganti Venkata Ramana Murthy, Goecke, R., Joshi, J., and Gedeon, T., “Video and Image Based Emotion Recognition Challenges in the Wild: EmotiW 2015”, in Proceedings of the 2015 ACM on International Conference on Multimodal Interaction, New York, NY, USA, 2015.[Abstract]


The third Emotion Recognition in the Wild (EmotiW) challenge 2015 consists of an audio-video based emotion and static image based facial expression classification sub-challenges, which mimics real-world conditions. The two sub-challenges are based on the Acted Facial Expression in the Wild (AFEW) 5.0 and the Static Facial Expression in the Wild (SFEW) 2.0 databases, respectively. The paper describes the data, baseline method, challenge protocol and the challenge results. A total of 12 and 17 teams participated in the video based emotion and image based expression sub-challenges, respectively. More »»

2015

Conference Paper

S. S. Rajagopalan, Dr. Oruganti Venkata Ramana Murthy, Goecke, R., and Rozga, A., “Play with me - Measuring a child's engagement in a social interaction”, in 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), 2015.[Abstract]


Due to the challenges in automatically observing child behaviour in a social interaction, an automatic extraction of high-level features, such as head poses and hand gestures, is difficult and noisy, leading to an inaccurate model. Hence, the feasibility of using easily obtainable low-level optical flow based features is investigated in this work. A comparative study involving high-level features, baseline annotations of multiple modalities and the low-level features is carried out. Optical flow based hidden structure learning of behaviours is strongly discriminatory in predicting a child's engagement level in a social interaction. A two-stage approach of discovering the hidden structures using Hidden Conditional Random Fields, followed by learning an SVM-based model on the hidden state marginals is proposed. This is validated by conducting experiments on the Multimodal Dyadic Behaviour Dataset and the results indicate a state of the art classification performance. The insights drawn from this study indicate the robustness of the low-level feature approach towards engagement behaviour modelling and can be a good substitute in the absence of accurate high-level features. More »»

2015

Conference Paper

Dr. Oruganti Venkata Ramana Murthy and Goecke, R., “Injury Mechanism Classification in Soccer Videos”, in 2015 IEEE International Conference on Computer Vision Workshop (ICCVW), 2015.[Abstract]


Soccer is a very popular sport but also has a high rate of injuries. In this paper, player falling events in soccer videos are classified into five major categories. These categories have been identified by soccer coaches as the major mechanisms behind player injuries. Automatic detection of these events will be useful to coaches to plan specific training modules and to impart individual training to the players that will enhance their physical strength and also their playing style. A Bag-of-Words framework is used and a baseline classification accuracy is established that will serve as a reference point for further work. More »»

2014

Conference Paper

Dr. Oruganti Venkata Ramana Murthy, Radwan, I., and Goecke, R., “Dense body part trajectories for human action recognition”, in 2014 IEEE International Conference on Image Processing (ICIP), 2014.[Abstract]


Several techniques have been proposed for human action recognition from videos. It has been observed that incorporating mid-level viz. human body and/or high-level information viz. pose estimation in the computation of low-level features viz. trajectories yields the best performance in action recognition where full body is presumed. However, in datasets with a large number of classes, where the full body may not be visible at all times, incorporating such mid- and high-level information is unexplored. Moreover, changes and developments in any stage will require a recompute of all low-level features. We decouple mid-level and low-level feature computation and study on benchmark action recognition datasets such as UCF50, UCF101 and HMDB51, containing the largest number of action classes to date. Further, we employ a part-based model for human body part detection in frames statically, thus also investigating classes where the full body is not present. We also track dense regions around the detected human body parts by Hungarian particle linking, thus minimising most of the wrongly detected body parts and enriching the mid-level information. More »»

2014

Conference Paper

Dr. Oruganti Venkata Ramana Murthy and Goecke, R., “The Influence of Temporal Information on Human Action Recognition with Large Number of Classes”, in 2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2014.[Abstract]


Human action recognition from video input has seen much interest over the last decade. In recent years, the trend is clearly towards action recognition in real-world, unconstrained conditions (i.e. not acted) with an ever growing number of action classes. Much of the work so far has used single frames or sequences of frames where each frame was treated individually. This paper investigates the contribution that temporal information can make to human action recognition in the context of a large number of action classes. The key contributions are: (i) We propose a complementary information channel to the Bag-of- Words framework that models the temporal occurrence of the local information in videos. (ii) We investigate the influence of sensible local information whose temporal occurrence is more vital than any local information. The experimental validation on action recognition datasets with the largest number of classes to date shows the effectiveness of the proposed approach. More »»

2013

Conference Paper

Dr. Oruganti Venkata Ramana Murthy and Goecke, R., “Ordered Trajectories for Large Scale Human Action Recognition”, in The IEEE International Conference on Computer Vision (ICCV) Workshops, 2013.[Abstract]


Recently, a video representation based on dense trajectories has been shown to outperform other human action recognition methods on several benchmark datasets. In dense trajectories, points are sampled at uniform intervals in space and time and then tracked using a dense optical flow field. The uniform sampling does not discriminate objects of interest from the background or other objects. Consequently, a lot of information is accumulated, which actually may not be useful. Sometimes, this unwanted information may bias the learning process if its content is much larger than the information of the principal object(s) of interest. This can especially escalate when more and more data is accumulated due to an increase in the number of action classes or the computation of dense trajectories at different scales in space and time, as in the Spatio-Temporal Pyramidal approach. In contrast, we propose a technique that selects only a few dense trajectories and then generates a new set of trajectories termed 'ordered trajectories'. We evaluate our technique on the complex benchmark HMDB51, UCF50 and UCF101 datasets containing 50 or more action classes and observe improved performance in terms of recognition rates and removal of background clutter at a lower computational cost. More »»

2013

Conference Paper

V. Narang, Roy, S., Dr. Oruganti Venkata Ramana Murthy, and Hanmandlu, M., “Devanagari Character Recognition in Scene Images”, in 2013 12th International Conference on Document Analysis and Recognition, 2013.[Abstract]


Character recognition in scene images is an extremely challenging task. Although several techniques are reported performing well, they pertain to English only. This paper focuses on Devanagari character recognition from scene images. Devanagari script is very popular language and has very typical characteristics different from other scripts, particularly English. Combination of basic Devanagari consonants and vowels in multi-variegated ways can yield as many as 100s of characters. Building a classifier to recognize all these classes will be a difficult task. To alleviate this problem, a novel part-based model technique is proposed. 40 basic classes were identified from the Devanagari script for the same purpose. The technique was proposed so as to classify an instance of one these classes in any given test sample. Procuring a large dataset for training is not feasible in the case of scene images. To simultaneously solve this problem, we developed our technique that can use either the machine printed or the handwritten dataset for training. We present our results on the publicly available dataset (DSIW2K) containing images of street scenes taken in New Delhi, India. More »»

2013

Conference Paper

Dr. Oruganti Venkata Ramana Murthy, Radwan, I., Dhall, A., and Goecke, R., “On the Effect of Human Body Parts in Large Scale Human Behaviour Recognition”, in 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2013.[Abstract]


Automatic analysis of human behaviour in large collections of videos is gaining interest, even more so with the advent of file sharing sites such as YouTube. Human behaviour analysis methods can be categorised into three classes based on the type of features. The three representations are local, region of interest and densely sampled based representations. Local feature representation, such as Spatio-Temporal Interest Points (STIP), are quite popular for modelling temporal aspects in human action recognition. Region of Interest (ROI) based feature representations try to capture and represent human body part regions. Densely sampled representations capture information at uniformly spaced intervals spread in space and temporal directions of the given video. In this paper, we investigate the effect of human body part (ROI) information in large scale action recognition. Further, we also investigate the effect of its fusion with Harris 3D points (local representation) information and densely sampled representations. All experiments use a Bag-of-Words framework. We present our results on large class benchmark databases such as the UCF50 and HMDB51 datasets. More »»

2010

Conference Paper

Y. V. Venkatesh, Kassim, A. K., and Dr. Oruganti Venkata Ramana Murthy, “Resampling Approach to Facial Expression Recognition Using 3D Meshes”, in 2010 20th International Conference on Pattern Recognition, 2010.[Abstract]


We propose a novel strategy, based on resampling of 3D meshes, to recognize facial expressions. This entails conversion of the existing irregular 3D mesh structure in the database to a uniformly sampled 3D matrix structure. An important consequence of this operation is that the classical correspondence problem can be dispensed with. In the present paper, in order to demonstrate the feasibility of the proposed strategy, we employ only spectral flow matrices as features to recognize facial expressions. Experimental results are presented, along with suggestions for possible refinements to the strategy to improve classification accuracy. More »»

2010

Conference Paper

Dr. Oruganti Venkata Ramana Murthy, Muthuswamy, K., Rajan, D., and Tien, C. L., “Image Retargeting in Compressed Domain”, in 2010 20th International Conference on Pattern Recognition, 2010.[Abstract]


A simple algorithm for image retargeting in the compressed domain is proposed. Most existing retargeting algorithms work directly in the spatial domain of the raw image. Here, we work on the DCT coefficients of a JPEG-compressed image to generate a gradient map that serves as an importance map to help identify those parts in the image that need to be retained during the retargeting process. Each 8 × 8 block of DCT coefficients is scaled based on the least importance value. Retargeting can be done both in the horizontal and vertical directions with the same framework. We also illustrate image enlargement using the same method. Experimental results show that the proposed algorithm produces less distortion in the retargeted image compared to some other algorithms reported recently. More »»

2007

Conference Paper

M. Hanmandlu, Dr. Oruganti Venkata Ramana Murthy, and Madasu, V. K., “Fuzzy Model Based Recognition of Handwritten Hindi Characters”, in 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (DICTA 2007), 2007.[Abstract]


This paper presents the recognition of handwritten Hindi Characters based on the modified exponential membership function fitted to the fuzzy sets derived from features consisting of normalized distances obtained using the Box approach. The exponential membership function is modified by two structural parameters that are estimated by optimizing an objective function that includes the entropy and error function. A Reuse Policy that provides guidance from the past policies is utilized to improve the reinforcement learning. This relies on the past errors exploiting the past policies. The Reuse Policy improves the speed of convergence of the learning process over the strategies that learn without reuse and combined with the use of the reinforcement learning, there is a 25-fold improvement in training. Experimentation is carried out on a database of 4750 samples. The overall recognition rate is found to be 90.65%.

More »»

2006

Conference Paper

M. Hanmandlu and Dr. Oruganti Venkata Ramana Murthy, “Reinforcement Learning in the Entropy Based Recognition of Handwritten Hindi Numerals”, in Proc. 10th World Multi-conference on Systems, Cybernetics and Informatics, 2006.

2003

Conference Paper

S. Suraneni, Kar, I. N., Bhatt, R. K. P., and Dr. Oruganti Venkata Ramana Murthy, “Adaptive stick-slip friction compensation using dynamic fuzzy logic system”, in TENCON 2003. Conference on Convergent Technologies for Asia-Pacific Region, 2003.[Abstract]


A dynamic fuzzy logic based adaptive algorithm is proposed for reducing the effect of stick slip friction present in 1-DOF (one degree of freedom) mechanical mass system. The control scheme proposed is an online identification and indirect adaptive control, in which the control input is adjusted adaptively to compensate the effect of nonlinearity. Lyapunov stability analysis is used to ensure the boundedness of tracking errors, identification errors etc. The efficacy of the proposed algorithm is verified on a 1-DOF mechanical mass system with stick slip friction. More »»

2003

Conference Paper

Dr. Oruganti Venkata Ramana Murthy, Panda, A. K., Mukherjee, S., and Kar, I. N., “Fault tolerant operation of one dimensional MPBS”, in TENCON 2003. Conference on Convergent Technologies for Asia-Pacific Region, 2003.[Abstract]


A Stewart platform, in its most general form, is a parallel manipulator with six linear actuators, connected to the base and the end-effector with spherical joints. The concept of massively parallel binary system (MPBS) is realized by using legs consisting of a very large number of binary actuators. Current focus is on the design of the one-dimensional MPBS where a set of actuators is connected in parallel to a body along the x-axis only. The paper presents the robustness of a control algorithm for 1D MPBS under the fault condition. It was possible to track a specified trajectory with acceptable performance when some actuators stop functioning in mid-process. More »»

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