Department of Computational and Data Sciences (CDS): Recent submissions
Now showing items 61-80 of 111
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Deep Learning in Computer Vision: Studies in Neuro-image Segmentation and Satellite Image Super-resolution
Single image super-resolution (SR) has been a topic of great interest in the computer vision and deep learning community and has found applications in many areas including quality enhancement of satellite images. As the ... -
Epistasis Detection and Phenotype Prediction in GWAS Using Machine Learning Methods
Genome-wide association studies (GWAS) are used to find the association between genetic variants, Single Nucleotide Polymorphisms (SNPs), and phenotypic traits or diseases in a population. The number of GWAS has increased ... -
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 ... -
Development of Fully Data Driven Novel Methods for Improving the micro-Computed Tomography Image Quality for Digital Rock
The Digital Rock workflow is an emerging framework utilizing advances in imaging technologies and state-of-the-art image processing algorithms to construct digital models of reservoir rocks. These digital models become ... -
Advances in High Dynamic Range Imaging Using Deep Learning
Natural scenes have a wide range of brightness, from dark starry nights to bright sunlit beaches. Our human eyes can perceive such a vast range of illumination through various adaptation techniques, thus allowing us to ... -
Novel Deep Learning Methods for Improving Low-Dose Computed Tomography Perfusion Imaging of Brain
Computed Tomography (CT) Perfusion imaging is a non-invasive medical imaging modality that has also established itself as a fast and economical imaging modality for diagnosing cerebrovascular diseases such as acute ischemia, ... -
Algorithms for Estimating Integrals in High Dimensional Spaces
Sampling, estimation and integration in high dimensional continuous spaces is required in diverse areas ranging from modeling multi-particle physical systems and optimization to inference from data. When the number of ... -
Characterization of Divergence resulting from Workload, Memory and Control-Flow behavior in GPGPU Applications
GPGPUs have emerged as high-performance computing platforms and are used for boosting the performance of general non-graphics applications from various scientifi c domains. These applications span varied areas like social ... -
Deep Visual Representations: A study on Augmentation, Visualization, and Robustness
Deep neural networks have resulted in unprecedented performances for various learning tasks. Particularly, Convolutional Neural Networks (CNNs) are shown to learn representations that can efficiently discriminate hundreds ... -
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 ... -
Optimization of Traversal Queries on Distributed Graph Stores
In this era of Big Data Analytics, much of the semi-structured data has inherent interconnectivity between representative entities. These are increasingly being modeled as property graphs because of the semantic advantages ... -
Deep Learning for Hand-drawn Sketches: Analysis, Synthesis and Cognitive Process Models
Deep Learning-based object category understanding is an important and active area of research in Computer Vision. Most work in this area has predominantly focused on the portion of depiction spectrum consisting of ... -
Development of advanced regularization methods to improve photoacoustic tomography
Photoacoustic tomography (PAT) is a scalable imaging modality having huge potential for imaging biological samples at very high depth to resolution ratio, thereby playing pivotal role in the areas of neuroscience, ... -
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 ... -
Efficient and Resilient Stream Processing in Distributed Shared Environment
Internet of Things (IoT) deployments comprising of sensors and actuators collect observational data and provide continuous streams of data, often called streaming data or fast data. Smart Cities use such IoT technologies ... -
Understanding spontaneous emission in the strong coupling regime of an emitter and absorbing matter
This thesis proposes a partition of optical states into radiative and non-radiative parts when an emitter is proximal to resonant absorbing nanostructures. The conventional partition is valid only for the weak coupling ... -
Enhancing Blockchain Implementations
Blockchain technology elegantly provides a spectrum of desirable semantic properties, including immutability, authenticity, verifiability, and data integrity. In recent years, implementing blockchains on relational database ... -
Migrating VM Workloads to Containers: Issues and Challenges
Modern day enterprises are adopting virtualization to leverage the benefits of improved server utilization through workload consolidation. Server consolidation provides this benefit to enterprise applications as many of ... -
A study on Deep Learning Approaches, Architectures and Training Methods for Crowd Analysis
Analyzing large crowds quickly is one of the highly sought-after capabilities nowadays. Especially in terms of public security and planning, this assumes prime importance. But automated reasoning of crowd images or videos ... -
Adaptive charging techniques for Li-ion battery using Reinforcement Learning
Li-ion batteries have become a promising technology in recent years and are used everywhere from low-end devices like mobile phones to high-end ones like electric vehicles. In most applications, the discharge of a battery ...