Devang Roopesh Mehta
Angestellt, Computer Vision and Artificial Intelligence Developer, Hydac Software GmbH
Bis 2023, Master of Engineering, Technische Hochschule Rosenheim
Rosenheim, Deutschland
Über mich
Exploring the intersection of software engineering, computer vision, and machine learning 🚀 Open to opportunities in scientific research, computer vision, and software engineering | Collaborative team player 📚 Finalizing an article on unsupervised defect localization in wood furniture manufacturing 🌲 🛠️ Played a pivotal role in Proto_lab - Led the development of a state-of-the-art ML pipeline for precise anomaly detection - Managed the process from requirements gathering through rigorous testing and prototype deployment - Demonstrated unwavering integrity, perseverance, and collaborative teamwork 🎓 Master's Thesis: A testament to my disciplined approach and patience BMW Internship Experience - Developed privacy compliant software solutions - Contributed to a secure computing environment 🔧 Problem Solver | Industry Partner | Collaborator | Effective Software Solutions Provider Let's connect and push the boundaries of technology together! 🌐🤝
Werdegang
Berufserfahrung von Devang Roopesh Mehta
Bis heute 10 Monate, seit Jan. 2024
Computer Vision and Artificial Intelligence Developer
Hydac Software GmbH
7 Monate, März 2023 - Sep. 2023
Research and Teaching Assistant
proto_lab, Technische Hochschule Rosenheim
• Refactored deep learning based anomaly localization code to deploy with azure function on the cloud and on the edge device Nvidia Jetson Orin Nano Development Kit • Benchmarked inference on the edge by quantizing the PyTorch model using Torch-TensorRT python package, starting with standard docker containers and following up to native deployment on the edge • Prepared use-case examples of applications of data science as demonstrations, exercises and assignments
7 Monate, Nov. 2022 - Mai 2023
Master Thesis
proto_lab, Technische Hochschule Rosenheim
Defect localization on wooden boards with autoencoder based deep learning • Implemented sliding window, feature extraction with pre-trained VGG16 neural network and Kmeans clustering to classify images • Implemented Kmeans clustering to segment each image of Describable Textures Dataset for artificial anomalies • Developed a pipeline to generate anomalous data to train the neural network based on the autoencoder architecture • Familiarized with Pytorch framework and its remote execution in Docker container
• Worked with common protocols and libraries (Garbled Circuits, Secret Sharing) in the field of Secure Multiparty Computation (SMC) for industrial use in cross-company data exchange. • Developed a software prototype for a distributed system to perform computation on sensitive data from multiple companies using Docker, Python, and MP-SPDZ • Developed APIs and prototype websites using FastAPI, Jinja2, HTML, and Python
• Developed software for an optimised algorithm to extract foreground in a 3D Point Cloud in C++ • Visualized the colour for the 3D Point Cloud for easier user accessibility in the user interface
• Literature study on BRAM read and write access for use with the Red Pitaya hardware • Modified the FPGA design in Vivado Design Suite to measure and save the frequency of an external source
• Developed test setups and necessary software for capturing images with an associated illumination. • Developed software for the optical inspection of component surfaces in an Anaconda environment with functions in Python of the OpenCV and Tensorflow libraries • Generated synthetic images for training in neural network and modified a neural network • Implemented the U-Net model for 3 class image segmentation
• Optimized the algorithms and the software in C++ in terms of execution time and • Modified an emulation program for testing the communication between the software set up as a service and the PLC • Developed a mobile test stand for testing and demonstrating the system
• Selected and integrated functions of the PCL and the Realsense camera library in C++ • Created a prototype test stand for testing and demonstrating the system • Demonstrated the system in front of the industrial partner TGW using real objects
• Explained the practical exercise to the participants
Detection of Rotational Deviation of Objects based on 3D Camera • Developed a pipeline to acquire point clouds from Intel Realsense 3D camera and applied Iterative Closest Point (ICP) algorithm to compute transformation of objects. • Successful and accurate spatial transformation detection of small objects using computer vision techniques. • Software development using C++, Point Cloud Library (PCL), IntelRealsense SDK 2.0.
1 Jahr und 5 Monate, März 2017 - Juli 2018
CADD Engineer
UIT Group Interenational
• Training for participants in various CAD software packages • Demonstration of the course to the prospective candidates • Focus on part design, assembly design, surface design, sheet metal design and drafting
Ausbildung von Devang Roopesh Mehta
4 Jahre und 10 Monate, Okt. 2018 - Juli 2023
Master of Engineering
Technische Hochschule Rosenheim
3 Jahre und 8 Monate, Juli 2012 - Feb. 2016
Mechanical Engineering
Vellore Institute of Technology (VIT)
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Gujarati
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Englisch
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Deutsch
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