I'm Mark, I am a recent Electronic Engineering graduate and I'm currently exploring careers in Software development and Embedded systems engineering. I enjoy learning about hardware, microcontrollers, as well integrating them into practical software applications. I have started learning courses in machine learning using tensorflow, and I am looking forward to learning how to deploy that within the architecture of an embedded system. Software engineering basics and computer Science fundamentals is where my main focus is currently.

I really want to learn how to develop software that is both functional, clean and scalable. I realised that I genuinely enjoy design especially if its a broadly defined problem. Programming feels both flexible and adaptaive but at the same time allows you to create a practical solution or final product. I really do believe that this is my niche and its what I'm suppose to do, even if I did discover it a bit later in life.
There is still a lot I dont know , and a lot I want to learn about. I think that GitHub will be a good place to document the highlights of my findings and experiences.

Hope you enjoy and be kind :)

- Python and Tensorflow : Machine learning, Deep learning, Digital Image processing, Digital Signal Processing

- C# : MS DotNET with MS SQL DB 
- Flutter and Dart: Professional experience with mobile app development and deployment for both ios and android. 
- JS : Professional Experience with Thingsboard and IoT, mainly uses Angular JS for widget and dashboard development using web technologies within the thingsboard framework.
- MQTT: Networking protocols
- Familiar with Windows/ MAC  and LINUX system environments

- C++ : ARM STM microcontrollers/ ESP32 MCU/Arduino MCU/ Teensy 4.1 MCU with Arm Coretx M7 chips
- ROS : Robotic Arm project to work on Hardware and FUSION 360 for 3D printing

IEEE Published Conference Article My research paper focuses on the domain of Acoustic Anomaly Detection (AAD) in industrial machinery using Deep Learning techniques. The primary objective of this study is to develop a reliable system for detecting anomalies in the acoustic patterns of industrial machines. In large-scale industrial environments, the early detection of faults and anomalies is critical to ensure the uninterrupted operation of machinery, minimize downtime, and optimize maintenance efforts. The datasets used in this research encompass a range of Signal-to-Noise Ratios (SNR) to simulate diverse operating conditions for industrial fans and pumps. After preprocessing each audio sample was transformed into 9 segments of shape 128 x 32 log mel-spectrograms. The study encompasses a comprehensive analysis of Convolutional Autoencoders (CAE) and Dense Autoencoders (DAE). These models are trained and evaluated against real-world industrial datasets (MIMII dataset), and their performance is meticulously compared. The results for the DAE and CAE had shown over 0.80 at the 6 dB SNR level and decaying results as the SNR level worsens. This research confirms some of the trends pointed out by the literature and provides detailed insight into how the autoencoders are developed and their properties could be used in order to detect anomalous behavior in audio data. Full Documentation is available on request.

For Detailed Information: Click HERE to view the project's repository

For Detailed Information: Click HERE to view the project's repository

For Detailed Information: Click HERE to view the project's repository

Email: [email protected]