Mohammad Loni is a researcher (Licentiate Degree) at the School of Innovation, Design, and Engineering at Mälardalen University since October 2017. He received his B.Sc. degree in computer hardware engineering and the M.Sc. degree in computer science from Shiraz University in 2017. He had collaboration in domestic industrial projects focusing on FPGA based radar emulators and embedded signal processing.
Currently, he is a member of the Dependable Platforms for Autonomous Systems and Control (DPAC), Deep Learning Accelerator on Commercial Programmable Devices (DeepMaker), and Fast and Sustainable Analysis Techniques for Advanced Real-Time Systems (FAST-ARTS) projects at Mälardalen University. He is mainly working on energy efficient designing neural network architecture for embedded systems.
My research interests are including deep learning, automated machine learning (AutoML), and heterogeneous embedded systems.
Image Synthesisation and Data Augmentation for Safe Object Detection in Aircraft Auto-Landing System (Feb 2021) Najda Vidimlic , Alexandra Levin , Mohammad Loni, Masoud Daneshtalab 16th International Conference on Computer Vision Theory and Applications (VISAPP 2021)
Improving Motion Safety and Efficiency of Intelligent Autonomous Swarm of Drones (Aug 2020) Amin Majd , Mohammad Loni, Golnaz Sahebi , Masoud Daneshtalab Drones (Drones)
DenseDisp: Resource-Aware Disparity Map Estimation by Compressing Siamese Neural Architecture (Jul 2020) Mohammad Loni, Ali Zoljodi , Daniel Maier , Amin Majd , Masoud Daneshtalab, Mikael Sjödin, Ben Juurlink , Reza Akbari IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE (WCCI) 2020 (IEEE WCCI)
A Review on Deep Learning Methods for ECG Arrhythmia Classification (Jun 2020) Zahra Ebrahimi , Mohammad Loni, Masoud Daneshtalab, Arash Ghareh Baghi Expert Systems with Applications (ESWA)
DPAC Newsletter Spring 2020 (May 2020) Kristina Lundqvist, Mikael Sjödin, Saad Mubeen, Håkan Forsberg, Mikael Ekström, Cristina Seceleanu, Nandinbaatar Tsog, Jakob Danielsson, Mohammad Loni, Baran Çürüklü, LanAnh Trinh, Afshin Ameri E., Luciana Provenzano, Kaj Hänninen, Susanne Fronnå, Marjan Sirjani, Rong Gu, Masoud Daneshtalab, Sima Sinaei, Joakim Lindén
|AutoDeep: Automatic Design of Safe, High-Performance and Compact Deep Learning Models for Autonomous Vehicles||active|
|DeepMaker: Deep Learning Accelerator on Commercial Programmable Devices||finished|
|DPAC - Dependable Platforms for Autonomous systems and Control||active|
|FAST-ARTS: Fast and Sustainable Analysis Techniques for Advanced Real-Time Systems||active|
|HERO: Heterogeneous systems - software-hardware integration||active|
|Developing the First Carbon Footprint Indicator for Multi-modal Transport||available|
|Development of the First of its Kind Public Transport Crowdedness Indicator||available|
|Efficient Implementation of Ternary Neural Networks on FPGA||available|
|Energy Efficient Designing a Multi-task Deep Neural Network for Embedded Syetems||available|
|A Framework for Testing Redundant Components In Software and Hardware||in progress|