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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, ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...