IEEE Kharagpur Section

A survey of some of the recent advances in the field of microwave and millimeter-wave imaging is presented. This technology originally evolved from the approaches used in radar, and has been an active area of research since then, the primary attraction being the possibility of “seeing” objects hidden behind some opaque material such as wood, paper, concrete, fog etc. It is well-known that microwave/mm-wave signals ( usually defined as 0.3 – 300 GHz ) can penetrate such materials to greater or smaller depths depending on frequency , as opposed to visible light or infra-red whose penetration in most cases is negligible. The applications are well-known today and many systems are in everyday use such as security screening , medical imaging , through the wall imaging for surveillance, non-destructive testing etc. Usually imaging systems operate in the “near field” in the sense that the distance to the target is of the same order as the size of the complete sensor set – most imagers use multiple antennas for receiving the microwave signal from the target and the sensors are physically spaced out in a region usually much larger than wavelength. In contrast there are a few imagers which operate in the radar mode where the object is located in the far-field of the antenna array. In this talk we will describe some of the recent systems which have been developed by different researchers and which are likely to form the basis of future imagers used commercially , including for security applications. Co-sponsored by: IEEE AP-MTTS SBC IIT Kharagpur Speaker(s): Ananjan Basu, Bldg: E&ECE Department, NKN Room, IIT Kharagpur, Kharagpur, West Bengal, India, 721302

Understanding the connection between the brain’s structural connectivity and its functional connectivity is of immense interest in computational neuroscience. Some studies have suggested that whole brain functional connectivity is shaped by the underlying structure, the rule by which anatomy constraints brain dynamics remains an open question. In this talk, I will discuss a computational framework that identifies a joint subspace of eigenmodes for both functional and structural connectomes. We further learned the joint eigen spectra of both the connectomes in order to predict the functional connectome of a subject from the structural counterpart. In the second part of the presentation, I will discuss a deep learning framework for classification of tinnitus disease, which is an auditory phantom perceptual disorder. Motivated by the evidence of subtle anatomical or functional morphological information in magnetic resonance images (MRI) of the brain, we developed a data-driven framework for tinnitus classification (tinnitus or healthy subject) and prediction of tinnitus severity. The proposed classification method could be used for early detection, monitoring clinical trials, and tracking the progression of the disease. Speaker(s): Dr. Sanjay Ghosh Room: N208, Bldg: Department of Electrical Engineering, Seminar Room, IIT Kharagpur, Kharagpur, West Bengal, India, 721302, Virtual: https://events.vtools.ieee.org/m/366638