Person:
Kumar, Pankaj

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Name

Pankaj Kumar

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Faculty

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079-68261705

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Specialization

Bio-Inspired Metasurface/Metamaterial Devices, Terahertz Devices, Semiconductor Device Modeling and Simulation, Emerging Devices, JLT, OFET, TFET, FIN-FET, VLSI Design.

Abstract

Biography

Pankaj Kumar received the B.Tech. degree in Electronics and Communication Engineering from Ujjain Engineering College, Ujjain, India in 2011; his M.Tech degree from PDPM Indian Institute of Information Technology Design and Manufacturing, Jabalpur, India in 2014; his Ph.D. from National Institute of Technology Patna, Bihar, India in 2021 under the supervision of Prof. P. K. Jain (IIT BHU) and Prof. Akhlesh Lakhtakia (The Pennsylvania State University, USA). He has worked at NIT Patna as Assistant Professor almost for five years.

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2021 - 20233

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Now showing 1 - 3 of 3
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    Changes in soil-pores and wheat root geometry due to strategic tillage in a no-tillage cropping system
    (CSIRO, 01-01-2021) Mehra, Promil; Kumar, Pankaj; Bolan, Nanthi; Desbiolles, Jack; Orgill, Susan; Denton, Matthew D; DA-IICT, Gandhinagar
    Tillage management can influence soil physical properties such as soil strength, moisture content, temperature, nutrient and oxygen availability, which in turn can affect crop growth during the early establishment phase. However, a short-term �strategic� conventional tillage (CT) shift in tillage practice in a continuous no-tillage (NT) cropping system may change the soil-pore and root geometry. This study identifies the impact of a tillage regime shift on the belowground soil-pore and root geometry. Micro X-ray computed tomography (�XCT) was used to quantify, measure and compare the soil-pore and root architecture associated with the impact of tillage shift across different plant growth stages. Soil porosity was 12.2% higher under CT in the top 0�100 mm and 7.4% in the bottom 100�200 mm of the soil core compared with NT. Soil-pore distribution, i.e. macroporosity (>75 ?m), was 13.4% higher under CT, but mesoporosity (30�75 ?m) was 9.6% higher under NT. The vertical distributions of root biomass and root architecture measurements (i.e. root length density) in undisturbed soil cores were 9.6% higher under the NT and 8.7% higher under the CT system respectively. These results suggest that low soil disturbance under the continuous NT system may have encouraged accumulation of more root biomass in the top 100 mm depth, thus developing better soil structure. Overall, �XCT image analyses of soil cores indicated that this tillage shift affected the soil total carbon, due to the significantly higher soil-pore (i.e. pore surface area, porosity and average pore size area) and root architecture (i.e. root length density, root surface density and root biomass) measurements under the CT system.
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    Bicontrollable all dielectric metasurface absorber for chemical and biosensing applications
    (Elsevier, 01-05-2023) Niharika, Neha; Singh, Sangeeta; Kumar, Pankaj; DA-IICT, Gandhinagar
    A bi-controllable all�dielectric�metasurface�structure is designed at�terahertz, which consists of an InAs micro-cylindrical array on the gold ground plane. The maximum�absorptance�frequency is tunable with the help of an external�magnetic field�and also by geometrical rotation of the�metasurface�as InAs is a gyrotropic material whose property depends on the magnitude and direction of an�external magnetic field. Simulation results indicate that the proposed�metasurface�can achieve an�absorptance�of more than 99 % at 9.756�THz as well as it can provide a high tunability rate of around 2.55�THz�T?1�in lower band and 3.33�THz�T?1�in the upper band frequency spectrum with the application of the�magnetic field. Additionally, the�metasurface�also acts as polarization insensitive when no external�magnetic field�is applied. Furthermore, the maximum absorptance frequency shows sensitivity towards the change in the�refractive index�value of the surrounding medium, thus the metasurface can be used for chemical and biosensing applications. It shows high chemical sensitivity of around 2727�GHz/RIU for the detection of different chemicals and bio sensitivity of 787�GHz/RIU for the detection of various viruses.
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    Deep convolutional neural networks for computer-aided breast cancer diagnostic: a survey
    (Springer, 01-02-2022) Oza, Parita; Sharma, Paawan; Patel, Samir; Kumar, Pankaj; DA-IICT, Gandhinagar
    Advances in deep learning networks, especially deep convolutional neural networks (DCNNs), are causing remarkable breakthroughs in radiology and imaging sciences. These advances have influenced the development of computer-aided diagnosis (CAD). This study presents applications of DCNNs for computer-aided breast cancer diagnosis. We discuss the recent breakthrough, achievements, and notable advances in CAD for breast cancer. Various key and novel insights and challenges on the use of DCNNs for mammogram analysis have been presented in the paper. The latest deep learning toolkits and libraries that are available and insights for using them have been elaborated. We also point out the possible limitations in the use of DCNNs for breast cancer detection. Finally, give some ideas of future research which can address the existing limitations.