Department of Computational and Data Sciences (CDS): Recent submissions
Now showing items 61-80 of 86
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A Numerical Method for Modeling Light Scattering from Spherical Particles with a Probability Density in the Parametric Space
With modern computational tools, modeling light scattering from a particle with known physical parameters has become relatively trivial. Evaluation using analytical solutions is highly eficient compared to a full ... -
Vector Extrapolation and Guided Filtering Methods for Improving Photoacoustic and Microscopic Images
Photoacoustic imaging is a noninvasive imaging modality which combines the bene ts of optical contrast and ultrasonic resolution. It is applied widely for monitoring tissue health conditions in the elds of cardiology, ... -
Exploring the Inherent Saliency in Visual Data through Convolutional Neural Networks
Saliency plays a key role in various computer vision tasks. Extracting salient regions from images and videos has been a well-established problem in computer vision. Determining salient regions in an image or video has ... -
Numerical Analysis of Some Preconditioners and Associated Error Estimators for Solving Linear Systems
Convergence of iterative algorithms in solving large linear systems is largely affected by the condition number of the matrix. Preconditioners reduce the condition number of the system matrix, thereby letting the linear ... -
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 ... -
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 ... -
Towards Learning Adversarially Robust Deep Learning Models
Deep learning models have shown impressive performance across a wide spectrum of computer vision applications, including medical diagnosis and autonomous driving. One of the major concerns that these models face is their ... -
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 ... -
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 ... -
A Hierarchical Control Plane Framework for Integrated SDN-SFC Management in Multi-tenant Cloud Datacenters
Cloud data centers represent one of the most complex and dynamic environments in terms of network management. The multitude of hosted applications in such centers share the same fabric and yet demand easy and fast service ... -
Improving photoacoustic imaging with model compensating and deep learning methods
Photoacoustic imaging is a hybrid biomedical imaging technique combining optical ab- sorption contrast with ultrasonic resolution. It is a non-invasive technique that is scalable to reveal structural, functional, and ... -
Global control of mechanics on Riemannian manifolds, and applications to under-actuated aerial vehicles
We consider the problem of designing trajectory tracking feedback control laws for La- grangian mechanical systems in a Riemannian geometric framework. Classical nonlinear control techniques that rely on Euclidean ... -
An Accelerator for Machine Learning Based Classifiers
Artificial Neural Networks (ANNs) are algorithmic techniques that simulate biological neural systems. Typical realization of ANNs are software solutions using High Level Languages (HLLs) such as C, C++, etc. Such solutions ... -
Feasible Path Prescription for Engineering Systems in a High-Index Constrained Dynamics Framework
Constrained dynamic performance and control models of complex engineering systems can be represented in the form of a Differential Algebraic Equation (DAE) system. The high-index of this DAE system poses computational ... -
Benchmarking and Scheduling Strategies for Distributed Stream Processing
(2018-08-20)The velocity dimension of Big Data refers to the need to rapidly process data that arrives continuously as streams of messages or events. Distributed Stream Processing Systems (DSPS) refer to distributed programming and ... -
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 ... -
Numerical Studies of Axially Symmetric Ion Trap Mass Analysers
(2018-05-29)In this thesis we have focussed on two types of axially symmetric ion trap mass analysers viz., the quadrupole ion trap mass analyser and the toroidal ion trap mass analyser. We have undertaken three numerical studies in ... -
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 ... -
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 ... -
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 ...