Person:
Kumar, Manish

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Name

Manish Kumar

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079-68261678

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Algorithm Development & Performance Optimization in�UAV and�Wireless Sensor Networks; Internet of Things; Ad Hoc Networks;�Next Generation Communication Networks - 5G/B5G

Abstract

Biography

Currently, I am an Associate Professor at Dhirubhai Ambani University (Formerly DA-IICT). Before joining here, I worked as a Lead Engineer in the 5G System Engineering Research & Development Team at Radisys, a wholly-owned subsidiary of Reliance Jio Platforms. I worked towards the proposal and development of algorithms for the downlink and uplink of 5G wireless cellular systems, and validating their feasibility with respect to the 3GPP technical specifications. Prior to this, I worked as a Researcher at Ben-Gurion University, Israel, where I worked on research assignments of Heron 5G consortium and Israel Science Foundation. I received my PhD in Electrical Engineering with specialization in wireless communication from Indian Institute of Technology, Patna, India. I worked towards blind parameter estimation, synchronization of wireless communication systems, and implementation of a practical testbed for a blind wireless transceiver system.

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2021 - 20253

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Now showing 1 - 3 of 3
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    Design and Testbed Implementation of Blind Parameter Estimated OFDM Receiver
    (IEEE, 02-06-2022) Chaudhari, Mahesh S; Kumar, Sushant; Gupta, Rahul; Kumar, Manish; Majhi, Sudhan; Kumar, Manish; Kumar, Manish; Kumar, Manish; Kumar, Manish; Kumar, Manish; DA-IICT, Gandhinagar
    Designing an intelligent or adaptive transceiver system is becoming a promising technology for upcoming generations of wireless communication systems due to its adaptivity, spectrum efficiency, and low-latency characteristics. However, there is no work available until now that characterizes and demonstrates complete adaptation in the physical layer for orthogonal frequency-division multiplexing (OFDM) systems. In this article, we propose and implement sequential blind parameter estimation methods for OFDM signals using radio frequency (RF) testbed setup in a realistic scenario. The estimations include the number of subcarriers, symbol duration, cyclic prefix, oversampling factor, symbol timing offset (STO), and carrier frequency offset (CFO). The proposed algorithms also include blind modulation classification for linearly modulated signals over a frequency-selective fading channel. The parameter estimation has been carried out through a cyclic cumulant process. The modulation formats are classified by using normalized fourth-order cumulant in the frequency domain. The STO and CFO are estimated by a proposed modified maximum likelihood algorithm. The performances of parameter estimations, modulation classification, and synchronization are measured through analytical, simulation, and measurement studies. The overall performance of the OFDM system is provided in terms of the received constellation diagram and bit error rate (BER) over an indoor propagation environment.
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    Soil Sensors-Based Prediction System for Plant Diseases Using Exploratory Data Analysis and Machine Learning
    (IEEE, 15-08-2021) Kumar, Manish; Kumar, Ahlad; Palaparthy, Vinay; DA-IICT, Gandhinagar
    Plant diseases cause losses to agricultural production and hence, the economy. This necessitates a need to develop prediction models for the plant disease detection and assessment. Fungal infection, the most dominant disease, can be controlled by taking appropriate measures if detected at an early stage. The article aims to develop an expert system for the prediction of various fungal diseases (powdery mildew, anthracnose, rust, and root rot/leaf blight). A multi-layered perceptron model is used for the classification of the diseases which not only detects the plant diseases effectively but can also increase the production drastically. The proposed technique incorporates three significant steps of dataset pre-processing, exploratory data analysis, and detection module. Firstly, the real-time data is captured by the soil sensors system installed at agriculture field at Sardarkrushinagar Dantiwada Agricultural University, Gujarat, India, along with the satellite data for other micro-meteorological factors. Next, an extensive exploratory data analysis has been performed to get insights into the collected data. Finally, the proposed machine learning model has been employed to predict plant diseases. The experimental results indicate that the model outperforms several existing methods in terms of accuracy. Average accuracy in predicting each disease has been found more than 98%. This work also proves the feasibility of using this technique for faster plant disease detection at an affordable cost.
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    UASPAR: Utility-based Adaptive Sensor Placement and Reconfiguration for Energy Efficient Wireless Sensor Networks
    (IEEE, 23-07-2025) Sheth, Chaitanya; Devmurari, Kandarp; Kumar, Manish; Rajput, Kunwar Pritiraj; DA-IICT, Gandhinagar