Outputs (10732)

Dual-blockchain based multi-layer grouping federated learning scheme for heterogeneous data in industrial IoT (2024)
Journal Article
Wang, X., Zhang, H., Wu, H., & Yu, H. (in press). Dual-blockchain based multi-layer grouping federated learning scheme for heterogeneous data in industrial IoT. Blockchain: Research and Applications, Article 100195. https://doi.org/10.1016/j.bcra.2024.100195

Federated Learning (FL) allows data owners to train neural networks together without sharing local data, allowing the Industrial Internet of Things (IIoT) to share a variety of data. However, traditional federated learning frameworks suffer from data... Read More about Dual-blockchain based multi-layer grouping federated learning scheme for heterogeneous data in industrial IoT.

Considering Environmental Effects on Porous Concrete Applications: An Experimental Investigation (2024)
Journal Article
Marinelli, A., & Rasheed, L. P. H. (2024). Considering Environmental Effects on Porous Concrete Applications: An Experimental Investigation. Procedia Structural Integrity, 54, 332-339. https://doi.org/10.1016/j.prostr.2024.01.091

Urban areas worldwide grapple with ecological disruption due to overpopulation, exacerbated by impermeable concrete surfaces that hinder rainwater absorption, curb plant growth and foster urban heat islands. Innovative porous concrete applications we... Read More about Considering Environmental Effects on Porous Concrete Applications: An Experimental Investigation.

Very Accurate Time-Frequency Representation of Induction Motors Harmonics for Fault Diagnosis Under Load Variations (2024)
Journal Article
Bonet-Jara, J., Fernandez-Cavero, V., Vedreno-Santos, F., Morinigo-Sotelo, D., & Pons-Llinares, J. (in press). Very Accurate Time-Frequency Representation of Induction Motors Harmonics for Fault Diagnosis Under Load Variations. IEEE Transactions on Industry Applications, https://doi.org/10.1109/TIA.2024.3371393

Induction motors work under steady-state in many applications. Nevertheless, in some cases they experience periodic load fluctuations, which generate constant frequency harmonics close to variable frequency bar breakage harmonics. In these cases, tim... Read More about Very Accurate Time-Frequency Representation of Induction Motors Harmonics for Fault Diagnosis Under Load Variations.

Data Matters- Storytelling with technologies (2024)
Digital Artefact
Lechelt, S., Panneels, I., Kaye, R., & Disley, M. Data Matters- Storytelling with technologies. [Video]

Watch Dr Susan Lechelt, Dr Inge Panneels, Rebecca Kaye (ploterre) and Martin Disley from Creative Informatics, Edinburgh, present their practice and research.

ASB-CS: Adaptive sparse basis compressive sensing model and its application to medical image encryption (2024)
Journal Article
Jiang, D., Tsafack, N., Boulila, W., Ahmad, J., & Barba-Franco, J. (in press). ASB-CS: Adaptive sparse basis compressive sensing model and its application to medical image encryption. Expert Systems with Applications, 236, Article 121378. https://doi.org/10.1016/j.eswa.2023.121378

Recent advances in intelligent wearable devices have brought tremendous chances for the development of healthcare monitoring system. However, the data collected by various sensors in it are user-privacy-related information. Once the individuals’ priv... Read More about ASB-CS: Adaptive sparse basis compressive sensing model and its application to medical image encryption.

The retailer perspective on the potential for using urban consolidation centres (UCCs) (2024)
Journal Article
Akgün, E. Z., Monios, J., Cowie, J., & Fonzone, A. (2024). The retailer perspective on the potential for using urban consolidation centres (UCCs). Research in Transportation Economics, 103, Article 101413. https://doi.org/10.1016/j.retrec.2024.101413

This paper examines the role of supportive policies and value-added services which may incentivise retailers to use an urban consolidation centre (UCC). The methodology is a case study of the city of Edinburgh, Scotland, based on semi-structured inte... Read More about The retailer perspective on the potential for using urban consolidation centres (UCCs).

Deep Learning-Based Receiver Design for IoT Multi-User Uplink 5G-NR System (2024)
Conference Proceeding
Gupta, A., Bishnu, A., Ratnarajah, T., Adeel, A., Hussain, A., & Sellathurai, M. (2024). Deep Learning-Based Receiver Design for IoT Multi-User Uplink 5G-NR System. In GLOBECOM 2023 - 2023 IEEE Global Communications Conference (4110-4115). https://doi.org/10.1109/globecom54140.2023.10437776

Designing an efficient receiver for multiple users transmitting orthogonal frequency-division multiplexing signals to the base station remain a challenging interference-limited problem in 5G-new radio (5G-NR) system. This can lead to stagnation of de... Read More about Deep Learning-Based Receiver Design for IoT Multi-User Uplink 5G-NR System.

A Deep Transfer Learning-Powered EDoS Detection Mechanism for 5G and Beyond Network Slicing (2024)
Conference Proceeding
Benzaïd, C., Taleb, T., Sami, A., & Hireche, O. (2024). A Deep Transfer Learning-Powered EDoS Detection Mechanism for 5G and Beyond Network Slicing. In GLOBECOM 2023 - 2023 IEEE Global Communications Conference (4747-4753). https://doi.org/10.1109/globecom54140.2023.10436891

Network slicing is recognized as a key enabler for 5G and beyond (B5G) services. However, its dynamic nature and the growing sophistication of DDoS attacks put it at risk of Economical Denial of Sustainability (EDoS) attack, causing economic losses t... Read More about A Deep Transfer Learning-Powered EDoS Detection Mechanism for 5G and Beyond Network Slicing.

Event-Triggered Relearning Modeling Method for Stochastic System with Non-Stationary Variable Operating Conditions (2024)
Journal Article
Liu, J., Zhang, Y., Zhou, Y., & Chen, J. (2024). Event-Triggered Relearning Modeling Method for Stochastic System with Non-Stationary Variable Operating Conditions. Mathematics, 12(5), Article 667. https://doi.org/10.3390/math12050667

This study presents a novel event-triggered relearning framework for neural network modeling, designed to improve prediction precision in dynamic stochastic complex industrial systems under non-stationary and variable conditions. Firstly, a sliding w... Read More about Event-Triggered Relearning Modeling Method for Stochastic System with Non-Stationary Variable Operating Conditions.

Solar Wall Technology and Its Impact on Building Performance (2024)
Journal Article
Ghamari, M., & Sundaram, S. (2024). Solar Wall Technology and Its Impact on Building Performance. Energies, 17(5), Article 1075. https://doi.org/10.3390/en17051075

Solar walls provide transformative solutions by harnessing solar energy to generate electricity, improve thermal comfort, and reduce energy consumption and emissions, contributing to zero-energy buildings and mitigating climate change. In hot and hum... Read More about Solar Wall Technology and Its Impact on Building Performance.