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Research Work

Artificial Intelligence and Machine Learning in Caner Imaging

Microscopic medical image analysis is an important 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. We are working on blood cancers, particularly, white blood cancers, B-ALL and MM. The work is in collaboration with AIIMS, New Delhi. We are building automated tools for disease diagnosis.

Machine (Deep) Learning and Compressed Sensing

(Yet to be updated).

Sparse Reconstruction

(Yet to be updated).

fMRI/MRI/DTI/ECG Signal and Image Processing

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 both fMRI and EEG signals from the point of view of building functional brain networks and resting state networks. In addition, we are working at the problem of accelerated reconstruction of fMRI signals from a fewer measurements using compressive sensing based methods.

Genomics Signal Processing

(Yet to be updated).

Cancer Genomics

Cancer is a disease that causes uncontrollable growth of abnormal cells in the body. It arises and progresses due to accumulation of multiple genetic mutations within the population of abnormal cells. Next generation sequencing technology like RNA-seq, whole exome sequencing and whole genome sequencing has facilitated the study of the diverse genomic and epigenomic alterations causing tumor cell proliferation in the body. Our work is focused on developing computational methods for addressing problems in cancer genomics. We have developed optimization based methods for addressing the problem of missing values in genomics data. We have also developed AI-enabled methods for risk stratification in newly diagnosed multiple myeloma patients. In addition to it, we are also working on analysing the whole exome data of multiple myeloma patients to infer the mutational landscape and the pattern of clonal evolution in multiple myeloma.

Brain Imaging/Neuroscience

Diffusion MRI (DMRI) is an emerging non-invasive medical imaging technique primarily used to study white matter structures in brain. Based on the principles of Nuclear Magnetic Resonance (NMR), it utilizes the fact that diffusion of water molecules in white matter region is not isotropic. Presence of nerve fibers, membranes and other macro-molecules creates a diffusion pattern that can be used to study microscopic details and fiber tracts. In DMRI, signals are captured along many diffusion directions that is a time intensive process. Ideally, patients are required to remain stationary during this time while the data is being captured and is very inconvenient. At SBILab, we are working on accelerated reconstruction of DMRI, particularly, HARDI signals using compressive sensing methods via collecting fewer data. The CS-based reconstructed signal is aimed to achieve comparable DMRI representations like ODF and FA with lesser signal samples, thereby decreasing the scanning time. In the near future, we would be working on fibre tractography in HARDI. Some results are also available HERE.

Wavelet Transform Learning and Applications

Transform learning (TL) is currently an active research area and is being explored in several applications. It has the advantage that it adapts to signals of interest and is often observed to perform better than the existing sparsifying transforms such as discrete cosine transform (DCT) and discrete wavelet transform (DWT). In general, transform learning is posed as an optimization problem satisfying some constraints, such as transform domain sparsity, that are specific to applications. Joint learning of both the transform basis and the transform domain signal under the constraints renders the optimization problem to be non-convex with no closed form solution. Hence, TL problems are solved using greedy algorithms, wherein a large number of variables are learned. In general, the above requirements and solutions makes TL computationally expensive. Among existing transforms, DWT provides an efficient representation for a variety of multi-dimensional and multiscale signals, capturing signal information into a few significant coefficients. Also, it helps in simultaneous localization of data in both time and frequency. Unlike Fourier transform where the basis is unique, there are 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. This motivates us to explore wavelet transform learning from given signals. We are working on learning DSP hardware friendly wavelets from given signals for applications in inverse problems. Our wavelet transform learning solutions have closed forms leading to fast implementations without the need to look for greedy solution. So far, we have learned dyadic and rational wavelets for both forward as well as inverse problems in signal processing and image processing.

Signal Processing for Communication Engineering

Underwater acoustic channel estimation is a difficult problem under moderate to rough sea conditions. This is primarily due to the 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.

Higher Education and Pedagogy

(Yet to be updated.)

Energy Harvesting

To meet the of 5G wireless communication demands of high coverage and seamless user experience, various technologies such as massive multiple input multiple output (MIMO), device to-device communication and mm-wave communication have been proposed. Although these techniques provide an improvement in the performance, they increase the power consumption as well. Therefore, apart from improving the energy efficiency, harvesting the energy from RF signal has gain lot of popularity over renewable resources such as solar, wind, etc. As the RF signal contains both information and energy, therefore, the device can decode the information as well as harvest the energy. As of today, energy harvesting is being explored largely from either solar, wind, or RF signals. We are working on developing newer methods to harvest the energy from analog signals (at any frequency band).

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