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Institution

International Institute of Information Technology, Hyderabad

EducationHyderabad, India
About: International Institute of Information Technology, Hyderabad is a education organization based out in Hyderabad, India. It is known for research contribution in the topics: Authentication & Internet security. The organization has 2048 authors who have published 3677 publications receiving 45319 citations. The organization is also known as: IIIT Hyderabad & International Institute of Information Technology (IIIT).


Papers
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Proceedings ArticleDOI
TL;DR: The proposed algorithm additionally incorporates different methods to avoid field local minima problems associated with using potential field functions in planning, and is a comprehensive solution for cooperative obstacle avoidance in the context of multi-robot target tracking.
Abstract: In this work, we consider the problem of decentralized multi-robot target tracking and obstacle avoidance in dynamic environments. Each robot executes a local motion planning algorithm which is based on model predictive control (MPC). The planner is designed as a quadratic program, subject to constraints on robot dynamics and obstacle avoidance. Repulsive potential field functions are employed to avoid obstacles. The novelty of our approach lies in embedding these non-linear potential field functions as constraints within a convex optimization framework. Our method convexifies non-convex constraints and dependencies, by replacing them as pre-computed external input forces in robot dynamics. The proposed algorithm additionally incorporates different methods to avoid field local minima problems associated with using potential field functions in planning. The motion planner does not enforce predefined trajectories or any formation geometry on the robots and is a comprehensive solution for cooperative obstacle avoidance in the context of multi-robot target tracking. We perform simulation studies in different environmental scenarios to showcase the convergence and efficacy of the proposed algorithm. Video of simulation studies: \url{this https URL}

14 citations

Proceedings ArticleDOI
15 Mar 2012
TL;DR: Approaches for linear and nonlinear principal component analysis (PCA) methods for compression of data for seismic signal processing and the distribution capturing ability of five layer AANN model is explored.
Abstract: Seismic data processing to interpret subsurface features is both computationally and data intensive. It is necessary to keep the dimensionality of data as small as possible, for good generalization from limited data. Therefore it is worthwhile exploring methods to compress the size of seismic data. In this paper, we consider approaches for linear and nonlinear principal component analysis (PCA) methods for compression of data for seismic signal processing. Principal component analysis (PCA) can improve seismic interpretations. Linear compression is realized by Karhunen-Loeve transform (KLT) and also by three layer autoassociative neural network (AANN) models. The distribution capturing ability of five layer AANN model is explored for nonlinear principal component analysis for compression of seismic data.

14 citations

Proceedings ArticleDOI
02 Nov 2012
TL;DR: This work tries to explore ways of gathering Indian language tourism and health pages from the web for Sandhan using a language and domain specific focused crawler and uses different evaluation metrics to evaluate the quality of the crawl - precision, recall and harvest ratio.
Abstract: Focused crawling has wide number of applications in the area of Information Retrieval. It is a crucial part in building domain specific search engines, personalized search tools and extending digital libraries. Be it Google Scholar to search for scholarly articles or Google news to search for news articles, domain specific search is the most widely acclaimed application of focused crawling. Unfortunately, there are very few domain specific search engines available for Indian languages.Sandhan is one such project which offers domain specific search for tourism and health domains across 10 major Indian languages. The amount of Indian language content on web is less compared to other languages. When we restrict the search space to a specific domain (say tourism) the probability of finding relevant pages reduces. Hence recall plays a major role in such a scenario. Due to the tendency of Indian language web pages linking to other language pages usually English, traditional crawling methods with well chosen seeds would end up crawling a lot of unnecessary content. This means that to gain a little recall we need to sacrifice precision and lot of resources.In this work we try to explore ways of gathering Indian language tourism and health pages from the web for Sandhan using a language and domain specific focused crawler. With this setup we crawl the web extensively for Indian language tourism and health pages. We use different evaluation metrics to evaluate the quality of our crawl - precision, recall and harvest ratio. Using our approach we save nearly 80% resources (disk space, bandwidth, processing time) while maintaining a recall of 0.74 and 0.58 for tourism and health domains respectively.

14 citations

Proceedings ArticleDOI
01 Nov 2017
TL;DR: This work proposes a recommendation model which uses semantic similarity between words as input to a 3-D Convolutional Neural Network in order to extract the temporal news reading pattern of the users, which improves the quality of recommendations.
Abstract: Deep neural networks have yielded immense success in speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks for content based recommendation has received a relatively less amount of inspection. Also, different recommendation scenarios have their own issues which creates the need for different approaches for recommendation. One of the problems with news recommendation is that of handling temporal changes in user interests. Hence, modelling temporal behaviour in the domain of news recommendation becomes very important. In this work, we propose a recommendation model which uses semantic similarity between words as input to a 3-D Convolutional Neural Network in order to extract the temporal news reading pattern of the users. This in turn improves the quality of recommendations. We compare our model to a set of established baselines and the experimental results show that our model performs better than the state-of-the-art by 5.8% (Hit Ratio@10).

14 citations

Posted Content
TL;DR: This paper proposes a pipeline for understanding vehicle behaviour from a monocular image sequence or video that can classify a variety of vehicle behaviours to high fidelity on datasets that are diverse and include European, Chinese and Indian on-road scenes.
Abstract: Understanding on-road vehicle behaviour from a temporal sequence of sensor data is gaining in popularity. In this paper, we propose a pipeline for understanding vehicle behaviour from a monocular image sequence or video. A monocular sequence along with scene semantics, optical flow and object labels are used to get spatial information about the object (vehicle) of interest and other objects (semantically contiguous set of locations) in the scene. This spatial information is encoded by a Multi-Relational Graph Convolutional Network (MR-GCN), and a temporal sequence of such encodings is fed to a recurrent network to label vehicle behaviours. The proposed framework can classify a variety of vehicle behaviours to high fidelity on datasets that are diverse and include European, Chinese and Indian on-road scenes. The framework also provides for seamless transfer of models across datasets without entailing re-annotation, retraining and even fine-tuning. We show comparative performance gain over baseline Spatio-temporal classifiers and detail a variety of ablations to showcase the efficacy of the framework.

14 citations


Authors

Showing all 2066 results

NameH-indexPapersCitations
Ravi Shankar6667219326
Joakim Nivre6129517203
Aravind K. Joshi5924916417
Ashok Kumar Das562789166
Malcolm F. White5517210762
B. Yegnanarayana5434012861
Ram Bilas Pachori481828140
C. V. Jawahar454799582
Saurabh Garg402066738
Himanshu Thapliyal362013992
Monika Sharma362384412
Ponnurangam Kumaraguru332696849
Abhijit Mitra332407795
Ramanathan Sowdhamini332564458
Helmut Schiessel321173527
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202310
202229
2021373
2020440
2019367
2018364