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, ...

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 ...

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 ...

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 ...

X-ray Computed Tomography (CT) perfusion imaging is a non-invasive medical imaging modality that has been established as a fast and economical method for diagnosing cerebrovascular diseases such as acute ischemia, sub-arachnoid ...

With rapid technological advances, one can easily find video surveillance systems deployed in public places such as malls, airports etc. as well as across private residential areas. These systems play a critical role in ...

Matrix-matrix multiplication is an important operation for many applications and hence it is required to be parallelized optimally for the architecture the applications will run on. REDE- FINE is a many-core co-processor ...

Analyzing humans and their activities takes a central role in computer vision. This requires machine learning models to encapsulate both the diverse poses and appearances exhibited by humans. Estimating the 3D poses of ...

The exponential rise in the availability of data over the past decade has fuelled research in deep learning. While supervised deep learning models achieve near-human performance using annotated data, it comes with an ...

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 ...