Browsing Division of Electrical, Electronics, and Computer Science (EECS) by Title
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Lambda Bipolar Transistor (LBT) in Static Random Access Memory Cell
(Indian Institute of Science, 20050707)With a view to reduce the number of components in a Static Random Access Memory (SRAM) cell, the feasibility of use of Lambda Bipolar Transistor (LBT)in the bistable element of the cell has been explored under the present ... 
Language Identification Through Acoustic SubWord Units
(20110926) 
Language Support for Exploiting Software Structure Specifications
(Indian Institute of Science, 20050216)Precise specification of the architecture and design of software is a good practice. Such specifications contain a lot of information about the software that can potentially be exploited by tools, to reduce redundancy ... 
Language Support For Testing CORBA Based Applications
(Indian Institute of Science, 20051207)Component Based Development has emerged as economical, reusable, scalable way of developing enterprise as well as embedded software applications. Testing distributed component based systems is difficult when third party ... 
Large Data Clustering And Classification Schemes For Data Mining
(20090320)Data Mining deals with extracting valid, novel, easily understood by humans, potentially useful and general abstractions from large data. A data is large when number of patterns, number of features per pattern or both are ... 
Large Scale Graph Processing in a Distributed Environment
(20180525)Graph algorithms are ubiquitously used across domains. They exhibit parallelism, which can be exploited on parallel architectures, such as multicore processors and accelerators. However, real world graphs are massive in ... 
Large Scale Implementation Of The Block Lanczos Algorithm
(20100816)Large sparse matrices arise in many applications, especially in the major problems of Cryptography of factoring integers and computing discrete logarithms. We focus attention on such matrices called sieve matrices generated ... 
Large Time Behaviour and Metastability in MeanField Interacting Particle Systems
This thesis studies the large time behaviour and metastability in weakly interacting Markov processes with jumps. Our motivation is to quantify the large time behaviour of various networked systems that arise in practice. The ... 
Lattice Codes for Secure Communication and Secret Key Generation
(20180522)In this work, we study two problems in informationtheoretic security. Firstly, we study a wireless network where two nodes want to securely exchange messages via an honestbutcurious bidirectional relay. There is no ... 
A Learnable Distillation Approach For Modelagnostic 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 Algorithms Using ChanceConstrained Programs
(20100708)This thesis explores ChanceConstrained Programming (CCP) in the context of learning. It is shown that chanceconstraint approaches lead to improved algorithms for three important learning problems — classification with ... 
Learning Decentralized GoalBased Vector Quantization
(20120504) 
Learning Dynamic Prices In Electronic Markets
(20110419) 
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 ... 
Learning Nonlinear Mappings from Data with Applications to Prioritybased Clustering, Prediction, and Detection
With the volume of data generated in today's internetofthings, learning algorithms to extract and understand the underlying relations between the various attributes of data have gained momentum. This thesis is focused ... 
Learning Robust Support Vector Machine Classifiers With Uncertain Observations
(20150819)The central theme of the thesis is to study linear and non linear SVM formulations in the presence of uncertain observations. The main contribution of this thesis is to derive robust classfiers from partial knowledge of ... 
Learning to Adapt Policies for uSD card
Machine Learning(ML) for Systems is a new and promising research area where performance of computer systems is optimized using machine learning methods. ML for Systems has outperformed traditional heuristics methods in ... 
Learning Tournament Solutions from Preferencebased MultiArmed Bandits
We consider the dueling bandits problem, a sequential decision task where the goal is to learn to pick `good' arms out of an available pool by actively querying for and observing relative preferences between selected pairs ... 
Learning with Complex Performance Measures : Theory, Algorithms and Applications
(20171207)We consider supervised learning problems, where one is given objects with labels, and the goal is to learn a model that can make accurate predictions on new objects. These problems abound in applications, ranging from ...