Mohamed Mohaideen Seyed Mohamed

Investigated the Multi-target Tracking Algorithms for Robot Vision with UWB RADAR point clouds. Implemented the EKF Localization, SLAM for localization and mapping, and JPDAF for data association in Python and C++. Carried out a data fusion, performance evaluation, and error analysis on measurement data and simulation framework.

Assisted and guided the software and hardware teams. Integrated the hardware components and interfaced the ML models with the Raspberry Pi 4 and Intel Movidius NCS 2. Presented the Pitch and participated in Investor funding and Accelerator programs.

Optical sensor integration with the development board in Python and validating and evaluating the data in MS Excel. Debugged and developed the LabVIEW interface for the test benches. Designed the mechanical parts and construction of measuring stations for highly integrated optical sensors. Supported the ambient light sensor project for Apple and Samsung, which raised the revenue by 19%.

Outperformed the image processing and object detection algorithms in OpenCV and Python. Assisted the design of the parallel motion two-jaw gripper in Solidworks and printed in the 3D printer. Qualified for the competition Robothon 2021, Munich, and achieved the 8th position.

Developed and implemented the vehicle dynamics and motion planning models for an autonomous LEGO car in MATLAB. Supported and integrated the hardware components and documentation.

Engineered the hardware components and systems to be integrated. Supported the development of methods for optimization of ADAS using data-sharing between connected vehicles and infrastructure. Conducted the Performance Test, interoperability communication measurements, and data analysis.