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ISCAS-2024

Dr. Abhishek Srivastava and his students presented the following papers at IEEE International Symposium on Circuits and Systems (ISCAS) held in Singapore from 19 – 22 May 2024:

  • Design and Implementation of FPGA based System for Object Detection and Range Estimation used in ADAS Applications utilizing FMCW Radar – Mujeev Khan, M.Tech 2nd Year, Aligarh Muslim University (AMU); Pranjal Mahajan,  MS ECE 2nd Year; Gani Nawaz Khan, M.Tech 2nd Year, AMU  (Aligarh Muslim University); Devansh Chaudhary, B.Tech ECE 4th Year AMU (Aligarh Muslim University); Jewel Benny, B.Tech Honors ECE, 4th year; Mohd Wajid, Associate Professor, ECE, Aligarh Muslim University (AMU) and Dr. Abhishek Srivastava. Summary of the research work as explained by the authors: 

This paper presents the design and implementation of a hardware system for real-time object detection and range estimation utilizing Frequency-Modulated Continuous Wave (FMCW) millimeter wave radar signals, which are commonly used in Advance Driver Assistance Systems (ADAS) and robotic applications. The proposed system utilizes the Fast Fourier Transform (FFT) algorithm to process the FMCW radar signal for estimating the range of an object. For developing a resource-efficient and low latency range-estimation hardware, this paper also presents a comparative analysis for the implementation of three popular FFT architectures (iterative Radix-2, iterative Radix-$2^2$, and pipelined Radix-2) on FPGA. Based on the presented analysis the lowest latency range-estimation architecture has been chosen for FFT implementation and a system prototype is developed as a proof-of-concept by integrating the commercially available Texas Instruments’ 77 GHz AWR1642BOOST FMCW radar module with a FPGA to host the chosen FFT architecture. Measurement results of the proposed system hardware are also presented in this paper, which shows >99.21% range-estimation accuracy. 

 

  • A Point Cloud-Based Non-Intrusive Approach for Human Posture Classification by Utilizing 77 GHz FMCW Radar and Deep Learning Models – Pranjal Mahajan, MS ECE 2nd Year; Devansh Chaudhary, B.Tech ECE 4th Year Aligarh Muslim University (AMU); Mujeev Khan, M.Tech 2nd Year, Aligarh Muslim University (AMU); Mohammed Hammad Khan, MS ECE 1st year; Mohd Wajid,  Associate Professor, ECE, Aligarh Muslim University (AMU) and Dr. Abhishek Srivastava. Summary of the research work as explained by the authors: 

 Human posture analysis has gained significant research interest in recent times. It helps in many applications such as gait analysis for detecting neurological disorders, fall detection of elderly people, and continuous monitoring of severely ill patients. Camera-based vision systems are commonly employed for detecting human postures; however, they cause concerns over the subjects’ privacy. To address this challenge, we present a millimeter wave (mmWave) radar-based, truly non-contact, non-intrusive, and privacy-conscious posture detection and classification system in this research. The proposed system utilizes three-dimensional point cloud data of the subject to comprehensively classify body postures, capturing intricate real-time details. In this work, we also present a custom-designed Convolutional Neural Network (CNN) and its comparison with other models, which are conventionally used for posture classification. We also demonstrate the hardware implementation of the proposed system and present the measurement results using Texas Instruments’ IWR1843BOOST radar module. The proposed CNN model achieves an accuracy of 97.10\% while classifying standing, sitting, lying and bending postures. 

 

June 2024