Browsing Department of Computational and Data Sciences (CDS) by Title
Now showing items 25-44 of 84
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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 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 ... -
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 ... -
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 ... -
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 ... -
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 ... -
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 ... -
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 ... -
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 ... -
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 ... -
Image Representation using Attribute-Graphs
(2017-12-13)In a digital world of Flickr, Picasa and Google Images, developing a semantic image represen-tation has become a vital problem. Image processing and computer vision researchers to date, have used several di erent representations ... -
An importance sampling in N Sphere Monte Carlo and its performance analysis for high dimensional integration
Statistical methods for estimating integrals are indispensable when the number of dimensions (parameters) become greater than ~ 10, where numerical methods are unviable in general. Well-known statistical methods like ... -
Improving Data Center Utilisation by Reducing Fragmentation
Virtualization enables better server consolidation and utilisation compared to stand-alone servers running a single workload. This enabled wide-spread cloud adoption among many organizations. Data center utilisation is ... -
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 ... -
Intelligent Methods for Cloud Workload Orchestration in Data Centers
Cloud workload orchestration plays a pivotal role in optimizing the performance, resource utilization, and cost effectiveness of applications in data centers. As modern businesses and IT operations are migrating their ... -
INTERPIN: identifying INtrinsic transcription TERminators, hairPINs in bacteria
The conversion of DNA to RNA through transcription is an important step in the life cycle of every organism. It ensures that the genetic information in DNA is converted through RNA into instructions/blueprints for the ...