Browsing by Title
Now showing items 3850-3869 of 8174
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Lattice Boltzmann Relaxation Schemes for High Speed Flows
The lattice Boltzmann method (LBM) has emerged as a highly efficient model for the simulation of incompressible flows in the last few decades. Its extension to compressible flows is hindered by the fact that the ... -
Lattice Codes for Secure Communication and Secret Key Generation
(2018-05-22)In this work, we study two problems in information-theoretic security. Firstly, we study a wireless network where two nodes want to securely exchange messages via an honest-but-curious bidirectional relay. There is no ... -
Lay Tracks : New approach to automated quadrilateral mesh generation using MAT
This thesis describes Lay Tracks, a new mesh generation algorithm to automatically generate an all quad mesh using the medial axis transform (MAT). LayTracks combines the merits of two popular direct techniques for quad ... -
Layered Metal Dichalcogenides-Based Hybrid Devices for Resistive Sensing
During the past few decades, photodetectors (PDs) are being regarded as the crucial components of many photonic devices which are being used in various important applications. However, the PDs based on the traditional bulk ... -
Layered Oxides And Phosphates Of Bismuth: New Structural Types And Related Properties
(Indian Institute of Science, 2007-05-22)The thesis entitled "Layered Oxides and Phosphates of Bismuth: New Structural Types and Related Properties" consists of three parts and six chapters. It begins with an introductory note providing a brief overview of the ... -
Learing automata algorithms for connectionist systems local and global convergence
Connectionist systems have been studied with much interest as models for the brain and also as systems which work in a parallel and distributed manner. These systems exhibit desirable properties such as learning capability ... -
A Learnable Distillation Approach For Model-agnostic Explainability With Multimodal Applications
Deep neural networks are the most widely used examples of sophisticated mapping functions from feature space to class labels. In the recent years, several high impact decisions in domains such as finance, healthcare, law ... -
Learning Across Domains: Applications to Text-based Person Search and Multi-Source Domain Adaptation
With rapid development in technology and ubiquitous presence of diverse types of sensors, a large amount of data from different modalities (e.g., text, audio, images etc.) describing the same person/ object/event has ... -
Learning Action Priors for Deep Visual Predictions
This thesis addresses the critical challenge of visual prediction in mobile robotics, particularly focusing on scenarios where cameras mounted on autonomous robots must navigate dynamic environments with human presence. ... -
Learning Algorithms for stochastic automata-
Reported in this thesis is the study of nonlinear learning algorithms or reinforcement schemes for multi-state stochastic automata acting in stationary random media with unknown response characteristics. Such stochastic ... -
Learning Algorithms Using Chance-Constrained Programs
(2010-07-08)This thesis explores Chance-Constrained Programming (CCP) in the context of learning. It is shown that chance-constraint approaches lead to improved algorithms for three important learning problems — classification with ... -
Learning Compact Architectures for Deep Neural Networks
(2018-05-22)Deep neural networks with millions of parameters are at the heart of many state of the art computer vision models. However, recent works have shown that models with much smaller number of parameters can often perform just ... -
Learning Decentralized Goal-Based Vector Quantization
(2012-05-04) -
Learning Dynamic Prices In Electronic Markets
(2011-04-19) -
Learning Filters, Filterbanks, Wavelets and Multiscale Representations
The problem of filter design is ubiquitous. Frequency selective filters are used in speech/audio processing, image analysis, convolutional neural networks for tasks such as denoising, deblurring/deconvolution, enhancement, ... -
Learning From Examples Using Hierarchical Counterfactual Expressions
In this study, we develop algorithms for learning concepts from examples. Learning is the capability that allows a system to improve its performance. It involves the ability to correct errors, learn domain knowledge, ... -
Learning from Limited and Imperfect Data
Deep Neural Networks have demonstrated orders of magnitude improvement in capabilities over the years after AlexNet won the ImageNet challenge in 2012. One of the major reasons for this success is the availability of ... -
Learning Invariants for Verification of Programs and Control Systems
Deductive verification techniques in the style of Floyd and Hoare have the potential to give us concise, compositional, and scalable proofs of the correctness of various kinds of software systems like programs and control ...