Browsing Division of Electrical, Electronics, and Computer Science (EECS) by Title
Now showing items 1105-1124 of 1262
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A Static Slicing Tool for Sequential Java Programs
(2018-07-28)A program slice consists of a subset of the statements of a program that can potentially affect values computed at some point of interest. Such a point of interest along with a set of variables is called a slicing criterion. ... -
Stationary diesel exhaust treatment by blending discharge plasma/ozone with industry wastes: a study on abatement of NOx and THC
Increased usage of fossil fuels, especially diesel, has made a large impact on the environment in the form of rise in global temperature, increased acidity in the rain water, decreased yield in vegetation and numerous ... -
Statistical Leakage Analysis Framework Using Artificial Neural Networks Considering Process And Environmental Variations
(2013-07-03)Leakage current and process variations are two primary hurdles in modern VLSI design. It depends exponentially on process and environmental parameters and hence small variations in these result in a large spread in leakage ... -
Statistical Network Analysis: Community Structure, Fairness Constraints, and Emergent Behavior
Networks or graphs provide mathematical tools for describing and analyzing relational data. They are used in biology to model interactions between proteins, in economics to identify trade alliances among countries, in ... -
Stochastic Approximation Algorithms with Set-valued Dynamics : Theory and Applications
(2018-07-05)Stochastic approximation algorithms encompass a class of iterative schemes that converge to a sought value through a series of successive approximations. Such algorithms converge even when the observations are erroneous. ... -
Stochastic Approximation with Markov Noise: Analysis and applications in reinforcement learning
Stochastic approximation algorithms are sequential non-parametric methods for finding a zero or minimum of a function in the situation where only the noisy observations of the function values are available. Two time-scale ... -
Stochastic approximation with set-valued maps and Markov noise: Theoretical foundations and applications
Stochastic approximation algorithms produce estimates of a desired solution using noisy real world data. Introduced by Robbins and Monro, in 1951, stochastic approximation techniques have been instrumental in the asymptotic ... -
Stochastic Finite Element Modeling of Material and Geometric Uncertainties in Electromagnetics
Design methodologies for RF/Microwave systems require major changes to cope up with the evolution of faster, high data/rate wireless communication systems using 5G and futuristic 6G technologies. The research on Terahertz ... -
Stochastic Methods in Time-Domain Electromagnetic Computations
The world is advancing towards a highly connected, fast data rate, digital trans- formation through communication technologies like 5G and 6G, high-speed data centres, Terahertz imaging etc. High-frequency RF systems are ... -
Stochastic Newton Methods With Enhanced Hessian Estimation
(2018-05-22)Optimization problems involving uncertainties are common in a variety of engineering disciplines such as transportation systems, manufacturing, communication networks, healthcare and finance. The large number of input ... -
Stochastic Optimization And Its Application In Reinforcement Learning
Numerous engineering fields, such as transportation systems, manufacturing, communication networks, healthcare, and finance, frequently encounter problems requiring optimization in the presence of uncertainty. Simulation-based ... -
Straegies For Rapid MR Imaging
(2010-07-21)In MR imaging, techniques for acquisition of reduced data (Rapid MR imaging) are being explored to obtain high-quality images to satisfy the conflicting requirements of simultaneous high spatial and temporal resolution, ... -
Strategies for Handling Large Vocabulary and Data Sparsity Problems for Tamil Speech Recognition
This thesis focuses on the design and development of every building block of a very large vocabulary, continuous speech recognition (LVCSR) system and various experiments conducted in order to enhance its performance. ... -
Structured Regularization Through Convex Relaxations Of Discrete Penalties
Motivation. Empirical risk minimization(ERM) is a popular framework for learning predictive models from data, which has been used in various domains such as computer vision, text processing, bioinformatics, neuro-biology, ... -
Structured Sparse Signal Recovery for mmWave Channel Estimation: Intra-vector Correlation and Modulo Compressed Sensing
This thesis contributes new theoretical results and recovery algorithms for the area of sparse signal recovery motivated by applications to the problem of channel estimation in mmWave communication systems. The presentation ...