Browsing Department of Computational and Data Sciences (CDS) by Title
Now showing items 21-40 of 116
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Data-efficient Deep Learning Algorithms for Computer Vision Applications
The performance of any deep learning model depends heavily on the quantity and quality of the available training data. The generalization of the trained deep models improves with the availability of a large number of ... -
Deep Convolutional and Generative Networks for Ocean Synoptic Feature Extraction and Super Resolution from Remotely Sensed Images
Accurate extraction of Synoptic Ocean Features and Downscaling of Ocean Features is crucial for climate studies and the operational forecasting of ocean systems. With the advancement of space and sensor technologies, the ... -
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 ... -
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 ... -
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 ... -
Designing Quality of Service aware Serverless Platforms
Serverless computing is a widely used Cloud computing service offering that provides users with managed runtimes to develop their business logic as functions. It supports eventdriven execution of functions and a powerful ... -
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, ... -
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 ... -
Development of Novel Deep Learning Methods for Fast-MRI: Anatomical Image Reconstruction to Quantitative Imaging
In medical imaging, the task of estimating interpretable anatomical images from raw scanner data - based on underlying physical principles - is known as an "inverse problem". The solution to such inverse problems can be ... -
Development of Novel Deep Learning Models for Quantitative Medical Image Analysis
Medical imaging provides a non-invasive way to visualize tissues and diseases, enabling both qualitative and quantitative assessments that are needed in diagnosing and monitoring a wide range of conditions. Modalities ... -
Development of Novel Deep Learning Models with Improved Generalizability for Medical Image Analysis
Medical imaging is a process of visualization of disease/tissue in a non-invasive manner. Several imaging techniques like computed tomography (CT), magnetic resonance imaging (MRI), optical coherence tomography (OCT), and ... -
A Divide and Conquer Framework For Graph Processing in Distributed Heterogeneous Systems
In many fields of science and engineering graph data structures are used to represent real-world information. As these graphs scale in size, it becomes very inefficient to process these graphs on a single core CPU using ... -
Dynamic hybrid partitioned non partitioned queue configurations based on workloads in supercomputer systems
Supercomputers rely on batch queues to manage parallel job execution, with commercial schedulers offering configurable parameters that influence job routing, execution order, and queue visitation. However, selecting an ... -
Efficient and Effective Algorithms for Improving the Robustness of Deep Neural Networks
Deep Neural Networks achieve near-human performance on several benchmark datasets, yet they are not as robust as humans. Their success relies on the proximity of test samples to the distribution of training data, resulting ... -
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 ... -
Efficient betweenness Centrality Computations on Hybrid CPU-GPU Systems
(2017-10-11)Analysis of networks is quite interesting, because they can be interpreted for several purposes. Various features require different metrics to measure and interpret them. Measuring the relative importance of each vertex ... -
Efficient parallel algorithms to compute A sub-complex of the weighted delaunay triangulation for molecular data
In the field of bio-molecules, it is utmost important to study the behaviour of a molecule while interacting with another. This interaction decides the functionality of the molecule. It is widely accepted fact that the ... -
Efficient quantum dynamics simulations for periodic potentials
Quantum algorithms are designed to efficiently solve many problems that are thought to be difficult classically. A specific case is the efficient simulation of time evolution of quantum systems. This work provides a ... -
EMF: System Design and Challenges for Disaggregated GPUs in datacenters for Efficiency, Modularity and Flexibility
With Dennard Scaling phasing out in the mid-2000s, architectural scaling and hardware specialization take centre stage to provide performance bene fits with already stalling Moore's law. An outcome from this hardware ... -
End-to-end Resiliency Analysis Framework for Cloud Storage Services
Cloud storage service brought the idea of a global scale storage system available on-demand and accessible from anywhere. Despite the benefits, resiliency remains one of the key issues that hinder the wide adaptation of ...