Electrical and Electronic Engineering - Research Publications
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ItemAn information-based approach to sensor management in large dynamic networks(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2007-05)
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ItemFeedback control under data rate constraints: An overview(INSTITUTE OF ELECTRICAL ELECTRONICS ENGINEERS (IEEE), 2007)The emerging area of control with limited data rates incorporates ideas from both control and information theory. The data rate constraint introduces quantization into the feedback loop and gives the interconnected system a two-fold nature, continuous and symbolic. In this paper, we review the results available in the literature on data-rate-limited control. For linear systems, we show how fundamental tradeoffs between the data rate and control goals, such as stability, mean entry times, and asymptotic state norms, emerge naturally. While many classical tools from both control and information theory can still be used in this context, it turns out that the deepest results necessitate a novel, integrated view of both disciplines.
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ItemControl of large-scale irrigation networks(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2007-01)
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ItemLicense plate localization based on a probabilistic model(SPRINGER, 2010-04)
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ItemVery high speed, close field, object positioning using tri-linear CCDs(SPRINGER-VERLAG BERLIN, 2006)
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ItemValue Function Based Reinforcement Learning in Changing Markovian Environments(MICROTOME PUBL, 2008-08)
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ItemAdaptive stochastic resource control:: A machine learning approach(AI ACCESS FOUNDATION, 2008)The paper investigates stochastic resource allocation problems with scarce, reusable resources and non-preemtive, time-dependent, interconnected tasks. This approach is a natural generalization of several standard resource management problems, such as scheduling and transportation problems. First, reactive solutions are considered and defined as control policies of suitably reformulated Markov decision processes (MDPs). We argue that this reformulation has several favorable properties, such as it has finite state and action spaces, it is aperiodic, hence all policies are proper and the space of control policies can be safely restricted. Next, approximate dynamic programming (ADP) methods, such as fitted Q-learning, are suggested for computing an efficient control policy. In order to compactly maintain the cost-to-go function, two representations are studied: hash tables and support vector regression (SVR), particularly, nu-SVRs. Several additional improvements, such as the application of limited-lookahead rollout algorithms in the initial phases, action space decomposition, task clustering and distributed sampling are investigated, too. Finally, experimental results on both benchmark and industry-related data are presented.
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ItemPROTEIN SECONDARY STRUCTURE PREDICTION USING SUPPORT VECTOR MACHINES AND A NEW FEATURE REPRESENTATION(WORLD SCIENTIFIC PUBL CO PTE LTD, 2006-12)Knowledge of the secondary structure and solvent accessibility of a protein plays a vital role in the prediction of fold, and eventually the tertiary structure of the protein. A challenging issue of predicting protein secondary structure from sequence alone is addressed. Support vector machines (SVM) are employed for the classification and the SVM outputs are converted to posterior probabilities for multi-class classification. The effect of using Chou–Fasman parameters and physico-chemical parameters along with evolutionary information in the form of position specific scoring matrix (PSSM) is analyzed. These proposed methods are tested on the RS126 and CB513 datasets. A new dataset is curated (PSS504) using recent release of CATH. On the CB513 dataset, sevenfold cross-validation accuracy of 77.9% was obtained using the proposed encoding method. A new method of calculating the reliability index based on the number of votes and the Support Vector Machine decision value is also proposed. A blind test on the EVA dataset gives an average Q3accuracy of 74.5% and ranks in top five protein structure prediction methods. Supplementary material including datasets are available on .