Browsing Department of Computational and Data Sciences (CDS) by Title
Now showing items 81-100 of 116
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Optimizing the Interval-centric Distributed Computing Model for Temporal Graph Algorithms
Graphs with temporal characteristics are increasingly becoming prominent. Their vertices, edges and attributes are annotated with a lifespan, allowing one to add or remove vertices and edges. Such graphs can grow to millions ... -
Parallel finite element multigrid solver for incompressible navier-stokes equations
Fluid flows are generally modelled using the Navier-Stokes Equations (NSE). The model can also be applied in the simulation of various practical applications such as weather, ocean currents, air flow over aircrafts, etc. ... -
Parallel Smoothers for Multigrid Method in Heterogeneous CPU-GPU Environment
Real-world applications require the solution of large a sparse system of algebraic equations that arise from the discretization of partial di erential equations with the help of supercomputers. Modern supercomputers are ... -
Prediction of Dynamical Systems by Constraining the Dynamics on the Observational Manifold
Evolution models of dynamical systems posed as differential equations generally do not include all the factors affecting the system. This leads to a mismatch between the model prediction and the observations. In this work, ... -
Projection based Variational Multiscale Methods for Incompressible Navier-Stokes Equations to Model Turbulent Flows in Time-dependent Domains
(2018-06-15)Numerical solution of differential equations having multitude of scales in the solution field is one of the most challenging research areas, but highly demanded in scientific and industrial applications. One of the natural ... -
Quantifying the past and future variability in the Bay of Bengal using statistical and deep learning methods
The Bay of Bengal, the world's largest bay, along with the Andaman Sea, a peripheral sea situated in the southeastern part of the bay, is crucial to the economic and maritime security of India. Understanding the dynamics ... -
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 ...