Browsing Department of Computational and Data Sciences (CDS) by Title
Now showing items 87-106 of 117
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Relating Representations in Deep Learning and the Brain
Deep Neural Networks (DNN) inspired by the human brain have redefined the state-of-the-art performance in AI during the past decade. Much of the research is still trying to understand and explain the function of these ... -
Reliable and Efficient Application Scheduling on Edge, Fog and Cloud
Cloud computing has emerged in the last decade as a popular distributed computing service offered by commercial providers. Public Clouds offer pay-as-you-go access to elastic resources that can be acquired and released ... -
Scalability Bottleneck Analysis of High Performance Applications
Obtaining high performance and scalability for high performance applications are challenging. There are various bottlenecks including, higher rate of memory access, complex algorithm, high rate of communication, big ... -
A scalable asynchronous discontinuous Galerkin method for massively parallel flow simulations
Accurate simulations of turbulent flows are crucial for understanding numerous complex phenomena in engineered systems and natural processes. Notably, under realistic conditions with high Reynolds numbers and complex ... -
Scalable Asynchrony-Tolerant PDE Solver for Multi-GPU Systems
Partial differential equations (PDEs) are used to model various natural phenomena and engineered systems. At conditions of practical interest, these equations are highly non-linear and demand massive computations. Current ... -
Scalable Distributed Frameworks for Temporal Analysis and Partitioning of Streaming Graphs
The analysis of graph-structured data has become increasingly important as networks in various domains, including science, engineering, and business, grow in size, complexity, and dynamism. While static graph analysis ... -
Scalable Video Data Management and Visual Querying System for Autonomous Camera Networks
Video data has been historically known for its unstructured nature, rich semantic content and scalability issues in terms of storage. With advances in computer vision and Deep Neural Net works (DNNs) it is now possible ... -
Self-Supervised Domain Adaptation Frameworks for Computer Vision Tasks
There is a strong incentive to build intelligent machines that can understand and adapt to changes in the visual world without human supervision. While humans and animals learn to perceive the world on their own, almost ... -
Semi-analytical solution for eigenvalue problems of lattice models with boundary conditions
Closed-form relations for limiting eigenvalues of an infinite k-periodic spatial lattice in any number of dimensions d, and its semi-analytical extensions for any given size n of the lattice with free-free boundary ... -
Sequence Alignment to Cyclic Pangenome Graphs
The growing availability of genome sequences of several species, including humans, has created the opportunity to utilize multiple reference genomes for bioinformatics analyses and improve the accuracy of genome resequencing ... -
Similarity between Scalar Fields
(2017-10-05)Scientific phenomena are often studied through collections of related scalar fields such as data generated by simulation experiments that are parameter or time dependent . Exploration of such data requires robust measures ... -
Some Algebraic Aspects Of Graph Similarity Algorithms
We proposed singular values-based sensitivity analysis and self-similarity studies to compare graph-isomorphism algorithms. SimRank method is found to be an application of power method and is not sensitive to noise in any ... -
Sparsification of Reaction-Diffusion Dynamical Systems on Complex Networks
Graph sparsification is an area of interest in computer science and applied mathematics. Spar- sification of a graph, in general, aims to reduce the number of edges in the network while preserving specific properties of ... -
Spectrum Sensing Receivers for Cognitive Radio
(2018-02-16)Cognitive radios require spectral occupancy information in a given location, to avoid any interference with the existing licensed users. This is achieved by spectrum sensing. Existing narrowband, serial spectrum sensors ... -
Stability Preserving Bisection Algorithms in Reaction-Diffusion Complex Networks
Reaction-Diffusion complex networks are ubiquitous in many pragmatic models of network of interacting nodes with individual dynamics, such as social interactions, neuronal functions, transportation models, ecological ... -
Stabilized finite element schemes for computations of viscoelastic free-surface and two-phase flows
Viscoelastic flows can be found in a wide range of industrial and commercial applications such as enhanced oil recovery, pesticide deposition, medicinal/pharmaceutical sprays, drug delivery, injection molding, polymer ... -
Static Analysis and Dynamic Monitoring of Program Flow on REDEFINE Manycore Processor
Manycore heterogeneous architectures are becoming the promising choice for high-performance computing applications. Multiple parallel tasks run concurrently across different processor cores sharing the same communication ... -
Structural and functional studies on the hypothetical protein TTHA1873 from Thermus thermophilus
This thesis reports a detailed study on the structural and functional characterization of the hypothetical protein (TTHA1873) from Thermus thermophilus. In this study, both the structure and function of TTHA1873 were ... -
Structure-Preserving Physics-Informed Neural Networks for Anisotropic Porous Media with Pressure Dependent Viscosity
Modeling flow through porous media with realistic physical constraints remains a longstanding challenge in subsurface engineering. Anisotropy in permeability, pressure-dependent viscosity, and non-negativity requirements ... -
Studies on Kernel Based Edge Detection an Hyper Parameter Selection in Image Restoration and Diffuse Optical Image Reconstruction
(2018-05-25)Computational imaging has been playing an important role in understanding and analysing the captured images. Both image segmentation and restoration has been in-tegral parts of computational imaging. The studies performed ...