Department of Microelectronics
Faculty of Electrical Engineering, Mathematics, and Computer Science

MSc M Hassan

PhD student
Microwave Sensing, Signals and Systems (MS3), Department of Microelectronics

Expertise: Machine Learning algorithms for radar object detection and tracking applications in advanced driver assistance systems.

Themes: XG - Next Generation Sensing and Communication

Biography

Mujtaba received his bachelor’s degree in Electrical Engineering at National University of Science & Technology, Pakistan in 2016. Afterwards, he started his master’s degree in Communications Engineering at Technical University Munich, Germany. He completed his master thesis with NXP Semi-Conductors, Germany where he designed an optimized convolutional neural network for dense optical flow estimation. Afterwards, he joined NXP as a Systems & Application Engineer in 2019. During this period, he had investigated machine learning algorithms involved during different stages of ADAS as well as their requirements for implementation on hardware accelerators. Currently, he is focusing on neural networks together with their requirements relevant to radar perception in ADAS. In December 2021, he joined Microwave Sensing, Signals and Systems Group as an external PhD candidate

AI for SLAM & Tracking techniques in automotive radar

  1. Classification of Tracked Objects Using Multiple Frame Processing for Automotive Radar
    Hassan, Mujtaba; Fioranelli, Francesco; Yarovoy, Alexander; Chen, Lihui; Ravindran, Satish; Wu, Ryan;
    In 2024 21st European Radar Conference (EuRAD),
    pp. 35-38, 2024. DOI: 10.23919/EuRAD61604.2024.10734928

  2. Radar Multi Object Tracking using DNN Features
    Hassan, Mujtaba; Fioranelli, Francesco; Yarovoy, Alexander; Ravindran, Satish;
    In 2023 IEEE International Radar Conference (RADAR),
    pp. 1-6, 2023. DOI: 10.1109/RADAR54928.2023.10371032

  3. Opto-chemical pH detection of Myocardial Ischaemia using Fluorescent Hydrogels
    Ger de Graaf; Maurits Frans Vriesendorp; Amin Hassan; Patrick James French;
    IEEE Sensors Journal,
    Volume 22, Issue 11, pp. 10901-10909, June 2022. DOI: doi: 10.1109/JSEN.2022.3166709
    Keywords: ... Fluorescence, ischaemia, hydrogel, optical pH probe, HPTS, pyranine, fluorescent dye, dual wavelength excitation, ratiometric detection.

    Abstract: ... In this research fluorescent optochemical pH probes for the detection of ischaemia have been investigated. Myocardial ischaemia is the most prominent risk during heart surgery. During open heart surgery the heart is temporarily arrested and, since there no blood flowing, oxygen supply and removal of waste products is stopped and heart cells can be damaged. In this paper we propose a novel method to monitor the condition of the heart by placing optochemical pH sensors on several strategic places around the heart during surgery. Low cost opto-chemical pH sensors, using a HPTS (8-hydroxy-1,3,6-pyrene trisulfonic acid trisodium salt) fluorescent dye encapsulated in a thin bio-compatible hydrogel layer, were investigated for this application. Our research started with an extensive optical characterization of several types of hydrogel layers at different pH levels. Secondly a reflection probe prototype using several of these layers was designed, built and tested. Dual wavelength excitation and ratiometric detection of the fluorescent signals was used to detect the pH level. Typical output signals of 35% to 53% per pH in the range from 6.5-8.0 pH have been measured and a response time of typically 400 seconds was obtained for the prototypes. Finally based on our measurements on the HPTS layers and the reflection probe we propose an improved type of pH probe for the detection of ischaemia during open heart surgery.

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Last updated: 29 Oct 2024

Mujtaba Hassan