Areeb Ahmed

The scope of Thesis is to use automotive industrial grade ultrasonic sensors to perceive the surrounding environment of vehicle. Development of MLP & Convolutional Neural Network library for embedded system to predict the types of object. Features: Training model using Tensorflow/keras. Ultrasonic sensors-based data acquisition system with recording & GUI on Python. Embedded system library in C to optimize Neural Network architecture. Benchmarking different architecture & assessing performance.

Implementation of Real Time Operating System on SMT32 cortex M4 board Interfacing industrial grade ultrasonic sensor using DMA Analog to Digital conversion Implement TCP/IP protocol on STM32F4 Cortex-M4 for data transmission between embedded board and PC Graphical user interface on Python to obtain sensor data via TCP/IP. Display bounding boxes on detected image and overlap sensor data using Tensorflow

- Supporting System Engineering ITC & analysis of customer requirements in RFQ phase. - Developing automated scripts to check requirement coverage. - Developing a searching tool to search text/keywords in large number of files with various file formats using Java - Supporting DOORs import and status tracking.

Atomization of textile machinery using Simatic Step7/TIA portal for (S7-200/300/400), Mitsubishi, Omron & Allen Bradley (Rockwell). Designing HMIs using WinCC Flexible/WinCC SCADA. Implementation of PID on chemical dosing system. Implementation of ProfiBus/Modbus RTU for communication. Database and logging system using WinCC.

Programming on Siemens/Allen Bradley/Omron/Mitsubishi/ Fatek/Delta. Development of HMIs on WinCC/Fiji/UniOP/Siemens. Interfacing Energy meters using Modbus RTU on S7 and logging via VB-Scripting. Use of WinAC on Simatic HMI to program/interface ET stations via ProfiBus. Development of HMIs/PLCs programs using Reverse engineering process. Visual Basic Scripts to automate iterative tasks/Generating reports. Implementing CAN-Bus protocol using CAN-To-Profi converter.

Artificial Intelligence