activity
20182026
most citedLearning Collision-Free Space Detection from Stereo Images: Homography Matrix Brings Better Data Augmentation

79 citations · 266 across the 22 of their papers we have counts for

collaborators

22 papers

cs.RO2026

Enabling Urgency-aware Robot Swarm Intralogistics using Smart IoT Tags

Youssef Alboraei, Murray Groves, Shane Wen +6

Warehouse items differ in how urgently they must be moved: perishable goods, pharmaceutical shipments, and just-in-time production materials must be delivered sooner than the rest…

cs.ET2025

RIoT Digital Twin: Modeling, Deployment, and Optimization of Reconfigurable IoT System with Optical-Radio Wireless Integration

Alaa Awad Abdellatif, Sergio Silva, Eduardo Baltazar +7

This paper proposes an optimized Reconfigurable Internet of Things (RIoT) framework that integrates optical and radio wireless technologies with a focus on energy efficiency, scala…

cs.NI2025

Towards Automated and Predictive Network-Level Energy Profiling in Reconfigurable IoT Systems

Mohammud J. Bocus, Senhui Qiu, Robert J. Piechocki +1

Energy efficiency has emerged as a defining constraint in the evolution of sustainable Internet of Things (IoT) networks. This work moves beyond simulation-based or device-centric…

cs.ET2025

An Energy-Aware RIoT System: Analysis, Modeling and Prediction in the SUPERIOT Framework

Mohammud J. Bocus, Juha Hakkinen, Helder Fontes +4

This paper presents a comprehensive analysis of the energy consumption characteristics of a Silicon (Si)-based Reconfigurable IoT (RIoT) node developed in the initial phase of the…

cs.LG2023★ 1 cited

Privacy in Multimodal Federated Human Activity Recognition

Alex Iacob, Pedro P. B. Gusmão, Nicholas D. Lane +5

Human Activity Recognition (HAR) training data is often privacy-sensitive or held by non-cooperative entities. Federated Learning (FL) addresses such concerns by training ML models…

cs.LG2023★ 6 cited

Streamlining Multimodal Data Fusion in Wireless Communication and Sensor Networks

Mohammud J. Bocus, Xiaoyang Wang, Robert. J. Piechocki

This paper presents a novel approach for multimodal data fusion based on the Vector-Quantized Variational Autoencoder (VQVAE) architecture. The proposed method is simple yet effect…