7 citations · 18 across the 27 of their papers we have counts for
28 papers
MirrorDistill: Illumination-Aware Latent Distillation for Efficient Low-Light Restoration
Farida Mohsen, Tala Zaim, Nurul Izni Rusli +3
Low-light image enhancement (LLIE) is an im- portant component of visual sensing systems operating under degraded illumination, including nighttime surveillance, au- tonomous navig…
Mamba-based Selective State Space Modeling Improves the Accuracy-Complexity Tradeoff of SmolVLA Vision-Language-Action Experts
Farida Mohsen, Thowayba Elkaffash, Mohammad Reza Chalak Qazani +3
Vision-language-action (VLA) models face a crucial tradeoff between their task success rate and the policy-call frequency. Executing a single action per inference () enables a…
IAML: Illumination-Aware Mirror Loss for Progressive Learning in Low-Light Image Enhancement Auto-encoders
Farida Mohsen, Tala Zaim, Ali Al-Zawqari +2
This letter presents a novel training approach and loss function for learning low-light image enhancement auto-encoders. Our approach revolves around the use of a teacher-student a…
Real-Time Human-Robot Interaction Intent Detection Using RGB-based Pose and Emotion Cues with Cross-Camera Model Generalization
Farida Mohsen, Ali Safa
Service robots in public spaces require real-time understanding of human behavioral intentions for natural interaction. We present a practical multimodal framework for frame-accura…
OpenMENA: An Open-Source Memristor Interfacing and Compute Board for Neuromorphic Edge-AI Applications
Ali Safa, Farida Mohsen, Zainab Ali +2
Memristive crossbars enable in-memory multiply-accumulate and local plasticity learning, offering a path to energy-efficient edge AI. To this end, we present Open-MENA (Open Memris…
MINT-RVAE: Multi-Cues Intention Prediction of Human-Robot Interaction using Human Pose and Emotion Information from RGB-only Camera Data
Farida Mohsen, Ali Safa
Efficiently detecting human intent to interact with ubiquitous robots is crucial for effective human-robot interaction (HRI) and collaboration. Over the past decade, deep learning…
A Novel Monte-Carlo Compressed Sensing and Dictionary Learning Method for the Efficient Path Planning of Remote Sensing Robots
Alghalya Al-Hajri, Ejmen Al-Ubejdij, Aiman Erbad +1
In recent years, Compressed Sensing (CS) has gained significant interest as a technique for acquiring high-resolution sensory data using fewer measurements than traditional Nyquist…
Learning to Control Dynamical Agents via Spiking Neural Networks and Metropolis-Hastings Sampling
Ali Safa, Farida Mohsen, Ali Al-Zawqari
Spiking Neural Networks (SNNs) offer biologically inspired, energy-efficient alternatives to traditional Deep Neural Networks (DNNs) for real-time control systems. However, their t…
On the Importance of Neural Membrane Potential Leakage for LIDAR-based Robot Obstacle Avoidance using Spiking Neural Networks
Zainab Ali, Lujayn Al-Amir, Ali Safa
Using neuromorphic computing for robotics applications has gained much attention in recent year due to the remarkable ability of Spiking Neural Networks (SNNs) for high-precision y…
Deep Fusion of Ultra-Low-Resolution Thermal Camera and Gyroscope Data for Lighting-Robust and Compute-Efficient Rotational Odometry
Farida Mohsen, Ali Safa
Accurate rotational odometry is crucial for autonomous robotic systems, particularly for small, power-constrained platforms such as drones and mobile robots. This study introduces…
Federated Learning of Low-Rank One-Shot Image Detection Models in Edge Devices with Scalable Accuracy and Compute Complexity
Abdul Hannaan, Zubair Shah, Aiman Erbad +2
This paper introduces a novel federated learning framework termed LoRa-FL designed for training low-rank one-shot image detection models deployed on edge devices. By incorporating…
Benchmarking Online Object Trackers for Underwater Robot Position Locking Applications
Ali Safa, Waqas Aman, Ali Al-Zawqari +1
Autonomously controlling the position of Remotely Operated underwater Vehicles (ROVs) is of crucial importance for a wide range of underwater engineering applications, such as in t…
Co-Design of a Robot Controller Board and Indoor Positioning System for IoT-Enabled Applications
Ali Safa, Ali Al-Zawqari
This paper describes the development of a cost-effective yet precise indoor robot navigation system composed of a custom robot controller board and an indoor positioning system. Fi…
Rotational Odometry using Ultra Low Resolution Thermal Cameras
Ali Safa
This letter provides what is, to the best of our knowledge, a first study on the applicability of ultra-low-resolution thermal cameras for providing rotational odometry measurement…
Automating the Design of Multi-band Microstrip Antennas via Uniform Cross-Entropy Optimization
Ali Al-Zawqari, Ali Safa, Gert Vandersteen
Automating the design of microstrip antennas has been an active area of research for the past decade. By leveraging machine learning techniques such as Genetic Algorithms (GAs) or,…
Continual Learning with Hebbian Plasticity in Sparse and Predictive Coding Networks: A Survey and Perspective
Ali Safa
Recently, the use of bio-inspired learning techniques such as Hebbian learning and its closely-related Spike-Timing-Dependent Plasticity (STDP) variant have drawn significant atten…
Towards Chip-in-the-loop Spiking Neural Network Training via Metropolis-Hastings Sampling
Ali Safa, Vikrant Jaltare, Samira Sebt +4
This paper studies the use of Metropolis-Hastings sampling for training Spiking Neural Network (SNN) hardware subject to strong unknown non-idealities, and compares the proposed ap…
Resource-Efficient Gesture Recognition using Low-Resolution Thermal Camera via Spiking Neural Networks and Sparse Segmentation
Ali Safa, Wout Mommen, Lars Keuninckx
This work proposes a novel approach for hand gesture recognition using an inexpensive, low-resolution (24 x 32) thermal sensor processed by a Spiking Neural Network (SNN) followed…
Active Inference in Hebbian Learning Networks
Ali Safa, Tim Verbelen, Lars Keuninckx +5
This work studies how brain-inspired neural ensembles equipped with local Hebbian plasticity can perform active inference (AIF) in order to control dynamical agents. A generative m…
Open the box of digital neuromorphic processor: Towards effective algorithm-hardware co-design
Guangzhi Tang, Ali Safa, Kevin Shidqi +6
Sparse and event-driven spiking neural network (SNN) algorithms are the ideal candidate solution for energy-efficient edge computing. Yet, with the growing complexity of SNN algori…
FMCW Radar Sensing for Indoor Drones Using Learned Representations
Ali Safa, Tim Verbelen, Ozan Catal +4
Frequency-modulated continuous-wave (FMCW) radar is a promising sensor technology for indoor drones as it provides range, angular as well as Doppler-velocity information about obst…
Fusing Event-based Camera and Radar for SLAM Using Spiking Neural Networks with Continual STDP Learning
Ali Safa, Tim Verbelen, Ilja Ocket +4
This work proposes a first-of-its-kind SLAM architecture fusing an event-based camera and a Frequency Modulated Continuous Wave (FMCW) radar for drone navigation. Each sensor is pr…
Learning to SLAM on the Fly in Unknown Environments: A Continual Learning Approach for Drones in Visually Ambiguous Scenes
Ali Safa, Tim Verbelen, Ilja Ocket +4
Learning to safely navigate in unknown environments is an important task for autonomous drones used in surveillance and rescue operations. In recent years, a number of learning-bas…
Continuously Learning to Detect People on the Fly: A Bio-inspired Visual System for Drones
Ali Safa, Ilja Ocket, André Bourdoux +3
This paper demonstrates for the first time that a biologically-plausible spiking neural network (SNN) equipped with Spike-Timing-Dependent Plasticity (STDP) can continuously learn…