Funded Projects


Sl. No.                                             Title                                                            Funding Agency            Fund Sanction (INR)           Role                Status

1            Development of Signal Processing Methods and ML for Enhancing             DRDO, Govt. of India            55.40 Lakhs                               PI                 Ongoing

              LOFAR Spectra of Passive Underwater Targets


2           India Innovation Centre for Graphene                                                              MeitY, Govt. of India              8600 Lakhs                            Co-PI             Ongoing


3          Tactile-sensing integrated neural chip (Chip-to-startup program)                    MeitY, Govt. of India               85.75 Lakhs                            Co-PI             Ongoing


Research Area

Compressed sensing (CS) based synthetic aperture radar (SAR) image reconstruction aims to generate high-resolution radar images while using significantly fewer samples than traditional Nyquist-based systems. The importance of CS-SAR lies in its practical benefits: it reduces acquisition time, onboard storage, and downlink bandwidth, lowers power consumption, enables wider-area coverage, and can improve resolution when bandwidth or sampling opportunities are limited. These advantages make CS highly attractive for spaceborne and airborne missions, real-time surveillance, and resource-constrained platforms. However, challenges remain in computational complexity, optimal sampling design, and robustness to motion and calibration errors, driving ongoing research into hybrid and learning-based reconstruction methods.

The L- and S-band dual-frequency data from SAR offer powerful capabilities for agricultural monitoring because the two wavelengths interact differently with vegetation and soil. L-band penetrates deeper into the canopy, while S-band is more responsive to upper-canopy scattering and surface characteristics. By combining both, SAR enables more reliable monitoring through time-series backscatter and coherence analysis, capturing phenological stages even under persistent cloud cover.  

In quantum image processing (QIP), pixel values and spatial coordinates are encoded into quantum states, allowing massive parallelism, many image elements can be processed simultaneously through quantum operations. This enables potential advantages such as faster image transforms, encryption, pattern recognition, and denoising, especially for very large or high-dimensional datasets. Quantum algorithms can exploit superposition and entanglement to accelerate tasks like edge detection, compression, and feature extraction compared with classical techniques. However, practical challenges remain in building stable quantum hardware, mitigating noise, designing scalable encoding schemes, and developing algorithms that clearly outperform classical systems end-to-end. As quantum processors mature, QIP is expected to play an important role in areas such as medical imaging, remote sensing, security, and AI-driven vision applications.

Compressed sensing MRI (CS-MRI) is successful in reducing the MRI scan time by two to five times. It takes only a few measurements in the frequency domain and then applies highly nonlinear recovery algorithms to reconstruct high-resolution MR images from the partial data. L1-norm-based optimization algorithms in convex optimization are popular for the reconstruction of MR images from partial Fourier data as they are stable, guarantee convergence at large scale, and are efficient, and standard solvers are readily available. Recently, fast convex optimization-based reconstruction algorithms have been developed which are quite competent to achieve the standards for the use of CS-MRI in clinical practice as well.


Underwater signal processing for target tracking involves using sensors like sonar systems and hydrophones to detect and localize objects underwater. Key techniques include beamforming, matched filtering, and Fourier and wavelet transforms, while tracking algorithms like Kalman and particle filters, and multi-hypothesis tracking, are used to estimate and follow target movements. The field faces challenges such as signal attenuation, multipath propagation, and environmental dynamics but has seen advancements through machine learning, improved sensors, and networked systems. Applications span military, commercial, and environmental sectors, from submarine and mine detection to marine wildlife tracking and underwater infrastructure monitoring.


Wireless body area networks (WBANs) are increasingly used for remote healthcare surveillance in recent times, where electrocardiogram (ECG) signals are continuously acquired and transmitted to a base station or remote hospital for their storage and subsequent analysis. Multichannel ECG (MECG) is preferred over single-channel ECG as it provides more information from diagnostic point of view. One of the biggest challenges is to minimize the energy required for the WBAN network for continuous transmission of MECG data, which in turn demands for efficient data compression. Compressive sensing is an efficient signal processing tool for simultaneous compression and reconstruction of MECG data without visibly no or minimum loss of diagnostic information. 

The emerging technology and advancement to record the ECG signal without the help of the medical experts in home care or ambulatory conditions with minimal complexity have become more common in recent times. To reduce the intricacy and enhance patient comfort, it would be better to have minimal lead sets to derive the standard 12-lead ECG.


In MRI, data acquisition is carried out in k-space, which is equivalent to center-shifted Fourier transform of the corresponding MR image. During CS measurements in the k-space domain, center region of k-space is densely acquired, which represents gross structure of the corresponding anatomical structure. On the other hand, peripheral region of the k-space is sparsely acquired, which represents the high-frequency information i.e. fine details, edges, and boundaries. As a result, the diagnostic quality of the CSMRI reconstructed images are degraded. Another vital issue associated with most of the MR images are due to low resolution of the images. These limitations led to serious difficulties in clinical investigation and inaccurate diagnosis.


Satellite image super-resolution is the process of increasing the resolution or sharpness of satellite images. Joint dictionary learning is a technique used in super-resolution of satellite images that involves learning a common dictionary of image patches from both low-resolution and high-resolution images. This approach aims to capture the underlying structure and features of the images in a way that is shared between both resolutions, enabling the creation of a high-resolution image from a low-resolution input. Joint dictionary learning is a powerful technique for super-resolution of satellite images because it can leverage the shared information between both the low-resolution and high-resolution images, resulting in higher-quality super-resolved images. However, it is computationally expensive and requires a large amount of training data to learn the dictionary.