Outputs (421)

Discrete model for discontinuous dynamic recrystallisation applied to grain structure evolution inside adiabatic shear bands (2024)
Journal Article
Borodin, E., Bushuev, O., Bratov, V., & Jivkov, A. P. (2024). Discrete model for discontinuous dynamic recrystallisation applied to grain structure evolution inside adiabatic shear bands. Journal of Materials Research and Technology, 30, 2125-2139. https://doi.org/10.1016/j.jmrt.2024.03.206

Discontinuous dynamic recrystallisation (DDRX) is a well-known phenomenon playing a significant role in the high-temperature processing of metals, including industrial forming and severe plastic deformations. The ongoing discussion on the Zener–Hollo... Read More about Discrete model for discontinuous dynamic recrystallisation applied to grain structure evolution inside adiabatic shear bands.

Hybrid Wi-Fi and PLC network for efficient e-health communication in hospitals: a prototype (2024)
Journal Article
Khan, S. U., Ullah Jan, S., Hwang, T., & Koo, I. (2024). Hybrid Wi-Fi and PLC network for efficient e-health communication in hospitals: a prototype. Bulletin of Electrical Engineering and Informatics, 13(2), 1400-1410. https://doi.org/10.11591/eei.v13i2.5309

E-health is being adapted in modern hospitals as a significant addition to the existing healthcare services. To this end, modern hospitals urgently require a mobile, high-capacity, secure, and cost-effective communication infrastructure. In this pape... Read More about Hybrid Wi-Fi and PLC network for efficient e-health communication in hospitals: a prototype.

Can Generative AI Models Extract Deeper Sentiments as Compared to Traditional Deep Learning Algorithms? (2024)
Journal Article
Anas, M., Saiyeda, A., Sohail, S. S., Cambria, E., & Hussain, A. (2024). Can Generative AI Models Extract Deeper Sentiments as Compared to Traditional Deep Learning Algorithms?. IEEE Intelligent Systems, 39(2), 5-10. https://doi.org/10.1109/mis.2024.3374582

Recent advances in the context of deep learning have led to the development of generative artificial intelligence (AI) models which have shown remarkable performance in complex language understanding tasks. This study proposes an evaluation of tradit... Read More about Can Generative AI Models Extract Deeper Sentiments as Compared to Traditional Deep Learning Algorithms?.

Finite Element Modeling of Electric Field Distribution in a Defective XLPE Cable Insulation Under Different Magnitudes of Stressing Voltage (2024)
Presentation / Conference Contribution
Sulaiman, S. H., Rohani, M. N. K. H., Abdulkarim, A., Abubakar, A. S., Shehu, G. S., Musa, U., Mas'ud, A. A., Rosle, N., & Muhammad-Sukki, F. (2023, August). Finite Element Modeling of Electric Field Distribution in a Defective XLPE Cable Insulation Under Different Magnitudes of Stressing Voltage. Presented at The 12th International Conference on Robotics, Vision, Signal Processing, and Power Applications, Penang, Malaysia

Air voids in solid dielectrics affect the performance and lifespan of high voltage (HV) equipment. In this research, electric field distribution within a cross-linked polyethylene (XLPE) HV cable is analyzed using a finite element analysis (FEA) soft... Read More about Finite Element Modeling of Electric Field Distribution in a Defective XLPE Cable Insulation Under Different Magnitudes of Stressing Voltage.

Pre-Processing-based Fast Design of Multiple EM Structures with One Deep Neural Network (2024)
Journal Article
Wang, P., Li, Z., Luo, C., Wei, Z., Wu, T., Jiang, W., Hong, T., Parchi, N. O., Pedersen, G. F., & Shen, M. (2024). Pre-Processing-based Fast Design of Multiple EM Structures with One Deep Neural Network. IEEE Transactions on Antennas and Propagation, 72(5), https://doi.org/10.1109/tap.2024.3381376

Deep learning plays a vital role in the design of electromagnetic (EM) structures. However, in current research, a single neural network typically supports only one structure design and requires a complex framework to accommodate multiple structure d... Read More about Pre-Processing-based Fast Design of Multiple EM Structures with One Deep Neural Network.

Building an Embodied Musicking Dataset for co-creative music-making (2024)
Presentation / Conference Contribution
Vear, C., Poltronieri, F., Di Donato, B., Zhang, Y., Benerradi, J., Hutchinson, S., Turowski, P., Shell, J., & Malekmohamadi, H. (2024, April). Building an Embodied Musicking Dataset for co-creative music-making. Presented at Evostar 2024: The Leading European Event on Bio‑Inspired Computation, Aberystwyth, Wales, United Kingdom

In this paper, we present our findings of the design, development and deployment of a proof-of-concept dataset that captures some of the physiological, musicological, and psychological aspects of embodied musicking. After outlining the conceptual ele... Read More about Building an Embodied Musicking Dataset for co-creative music-making.

PermutEx: Feature-Extraction-Based Permutation — A New Diffusion Scheme for Image Encryption Algorithms (2024)
Presentation / Conference Contribution
Khan, M. S., Ahmad, J., Al-Dubai, A., Jaroucheh, Z., Pitropakis, N., & Buchanan, W. J. (2023, November). PermutEx: Feature-Extraction-Based Permutation — A New Diffusion Scheme for Image Encryption Algorithms. Presented at 2023 IEEE 28th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), Edinburgh, United Kingdom

Traditional permutation schemes mostly focus on random scrambling of pixels, often neglecting the intrinsic image information that could enhance diffusion in image encryption algorithms. This paper introduces PermutEx, a feature-extractionbased permu... Read More about PermutEx: Feature-Extraction-Based Permutation — A New Diffusion Scheme for Image Encryption Algorithms.

Data-based adaptive predictive bipartite consensus for nonlinear multiagent systems against DoS attacks (2024)
Journal Article
Halimu, Y., Zhao, H., Yu, H., Ding, S., & Qiao, S. (2024). Data-based adaptive predictive bipartite consensus for nonlinear multiagent systems against DoS attacks. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 238(7), 1231 - 1241. https://doi.org/10.1177/09596518241236928

This article investigates a Denial-of-Service (DoS) attack problem for nonlinear unknown discrete-time multiagent systems (MASs) to implement bipartite consensus tracking tasks with fixed and switching topologies. Firstly, an equivalent linearization... Read More about Data-based adaptive predictive bipartite consensus for nonlinear multiagent systems against DoS attacks.

PMNet: a multi-branch and multi-scale semantic segmentation approach to water extraction from high-resolution remote sensing images with edge-cloud computing (2024)
Journal Article
Zhang, Z., Liu, Q., Liu, X., Zhang, Y., Du, Z., & Cao, X. (2024). PMNet: a multi-branch and multi-scale semantic segmentation approach to water extraction from high-resolution remote sensing images with edge-cloud computing. Journal of cloud computing: advances, systems and applications, 13(1), Article 76. https://doi.org/10.1186/s13677-024-00637-5

In the field of remote sensing image interpretation, automatically extracting water body information from high-resolution images is a key task. However, facing the complex multi-scale features in high-resolution remote sensing images, traditional met... Read More about PMNet: a multi-branch and multi-scale semantic segmentation approach to water extraction from high-resolution remote sensing images with edge-cloud computing.

Application of Randomness for Security and Privacy in Multi-Party Computation (2024)
Journal Article
Saha, R., Kumar, G., Geetha, G., Conti, M., & Buchanan, W. J. (online). Application of Randomness for Security and Privacy in Multi-Party Computation. IEEE Transactions on Dependable and Secure Computing, https://doi.org/10.1109/tdsc.2024.3381959

A secure Multi-Party Computation (MPC) is one of the distributed computational methods, where it computes a function over the inputs given by more than one party jointly and keeps those inputs private from the parties involved in the process. Randomi... Read More about Application of Randomness for Security and Privacy in Multi-Party Computation.