Research Work

Wavelet Transform is one of the most popular tools for time-series analysis. It helps in simultaneous localization of data in both time and frequency. Wavelet transform provides a very efficient representation for a broad range of real-world signals, which makes it a powerful tool for signal compression and denoising. Unlike Fourier transform where the basis is unique, we have a number of wavelets that are either compactly supported or infinitely supported, orthogonal or biorthogonal, discrete or continuous, and so on. In addition, wavelets are connected very naturally with multirate filterbanks that makes them all the more useful in signal processing applications. However, due to the availability of number of wavelets, a question that is always difficult to answer is which wavelet is best suited in a particular application or on a particular signal. In this lab, we are working on signal matched wavelets and its applications.

Functional Magnetic Resonance Imaging (fMRI) is being used extensively to understand brain function pathways for neuroscience research and clinical applications. It is a widely used imaging modality for representing in-vivo brain state with better spatial resolution compared to electroencephalogram (EEG) and better temporal resolution compared to positron emission tomography (PET). Moreover, it is a non-invasive method. It is a powerful tool to examine various neurosurgical and psychiatric disorders and can also be used, effectively, in the application of pre-surgical planning. In this lab, we are working on the analysis of fMRI signal from the point of view of building functional brain networks and resting state networks.

It is well known that Magnetic Resonance Imaging (MRI) scannerssuffer with a limitation of higher computation time with reference to image reconstruction for display. One of the ways to improve upon speed is via scanning less no. of samples in the k-space in scanner,thus, reducing scan time. At the same time, devise a better and faster reconstruction algorithm that can reconstruct full MR images from these less number of samples. Currently, compressedsensing (CS) is being used extensively for the same which utilizesinherent sparsity of MR images. Reconstruction is done using convex or non-convex optimization techniques.This is one of the most active research areas. MRI dataalso suffer with motion corruption due to movement of patients in the MR scanner, which is an active research issue. Microscopic medical image analysis is another area of research in medical imaging. Today's cellular microscopy systems generate volumes of data (BigData). There is a need of using machine learning methods to analyze this data and infer important information out of it, which can help in disease diagnosis. There is also a growing interest in developing robust low cost solutions that are deployable across the board in rural as well as urban areas for clinical diagnosis and treatment.

Underwater acoustic channel estimation is a difficult problem under moderate to rough sea conditions. This is primarily due tothe fact that the channel is highly non-stationary and is rapidly fluctuating multipath fading channel. In this lab, we are working on channel estimation of underwater acoustic channel employing recent signal processing techniques of sparse sensing, compressed Sensing (CS), etc. In addition, we are working on channel estimation for reliable and robust data transmission in vehicular-to-vehicular (V2V) wireless communication channel. IEEE 802.11p standard is a dedicated wireless V2V standard, which has the biggest challenge of robust channel estimation due to rapid fast fading nature of the channel. Devising efficient signal processing techniques for channel estimation for this scenario is an interesting and challenging problem.