Browsing Division of Interdisciplinary Research by Subject "Research Subject Categories::TECHNOLOGY::Information technology::Computer science"
Now showing items 1-20 of 48
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Abstractions and Optimizations for Data-driven Applications Across Edge and Cloud
Modern data driven applications have a novel set of requirements. Advances in deep neural networks (DNN) and computer vision (CV) algorithms have made it feasible to extract meaningful insights from large-scale deployments ... -
Application Service Resilience In Cloud: An End-to-End Perspective
The idea of computing as a utility was realized with the emergence of the cloud computing paradigm. Cloud service providers offer a wide range of services that are delivered over the Internet to cloud service consumers. ... -
An arbitrary lagrangian eulerian volume of fluid method for floating body dynamics simulation
The floating body dynamics is treated as a Fluid-Structure Interaction (FSI) problem. A FSI problem is where the forces from the fluid move/deform the interacting structure, and the movement of the structure, in turn, ... -
Assessing protein contribution to phenotypic change using short, coarse grained molecular dynamics simulations
Understanding the functional mapping between genotype and phenotype is an important problem that has ramifications for various diseases. Various existing computational methods can infer these disease-related functional ... -
Augmenting Hyperspectral Image Unmixing Models Using Spatial Correlation, Spectral Variability, And Sparsity
Hyperspectral imaging sensors sample sunlight reflected from different targets on Earth's surface by utilising a series of contiguous narrow spectral channels. The higher spectral resolution of hyperspectral images (HSIs) ... -
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 ... -
Coarse-grained dynamics derived structural ensemble for prediction of metal binding sites of protein and phenotypic effects of variants
Structures of proteins play a key role in determining their functions. Knowledge of structure, especially the details of specific sites of a protein can help us understand their contribution to the overall activity. ... -
Compiler controlled Task Management in Runtime Systems for Dynamic Data ow Model of Execution
For the past 40 years, relentless focus on Moore's Law transistor scaling has provided ever-increasing transistor performance and density. An ever increasing demand for large scale parallelism has driven hardware designers ... -
Data-Driven Approach to Estimate WCET for Real-Time Systems
Estimating Worst-Case Execution Time (WCET) is paramount for developing Real-Time and Em- bedded systems. The operating system’s scheduler uses the estimated WCET to schedule each task of these systems before the assigned ... -
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 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 ... -
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 ... -
An error correction algorithm for long-read sequencing
Long-read sequencing technologies have transformed genomics by generating longer and suffi- ciently accurate DNA sequences, offering advantages in analysing highly repetitive and complex regions of a genome. A long-read ...