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Dear Colleagues,

The rapid development of artificial intelligence has fuelled technological innovations in various fields, which have resulted in the development of many advanced, application-driven solutions. These technological solutions have reduced labour requirements, increased productivity and supported innovative development.

The purpose of this Special Issue is to provide a comprehensive overview of the latest applications and advances in AI and microsystem technology and related areas. It especially seeks to highlight innovative research and development efforts in microsystem design and the integration of microsystems and AI technology for end-side applications. Contributions to this Special Issue will explore the characteristics and trends of these technologies in end-side applications and how they can be utilised to address contemporary challenges in various fields.

We welcome the submission of original research articles and reviews. The research areas covered may include (but are not limited to) the following:

End-side lightweight artificial intelligence deployment techniques;
The application of microsystem verification techniques;
Techniques for artificial intelligence-assisted microsystem design;
Techniques to improve microsystems’ architectures, integration, and reliability;
High-speed optical link devices for the interconnection of AI systems;
Multi-agent autonomous control and decision-making;
Artificial intelligence-based image recognition technology.

Prof. Dr. Yimeng Zhang
Dr. Shikai Sun
Guest Editors

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29 pages, 29485 KB

Open AccessArticle

FPGA-Based Dual Learning Model for Wheel Speed Sensor Fault Detection in ABS Systems Using HIL Simulations

by Farshideh Kordi, Paul Fortier and Amine Miled

Electronics 2026, 15(1), 58; https://doi.org/10.3390/electronics15010058 - 23 Dec 2025

Viewed by 303

Abstract

The rapid evolution of modern vehicles into intelligent and interconnected systems presents new complexities in both functional safety and cybersecurity. In this context, ensuring the reliability and integrity of critical sensor data, such as wheel speed inputs for anti-lock brake systems (ABS), is essential. Effective detection of wheel speed sensor faults not only improves functional safety, but also plays a vital role in keeping system resilience against potential cyber–physical threats. Although data-driven approaches have gained popularity for system development due to their ability to extract meaningful patterns from historical data, a major limitation is the lack of diverse and representative faulty datasets. This study proposes a novel dual learning model, based on Temporal Convolutional Networks (TCN), designed to accurately distinguish between normal and faulty wheel speed sensor behavior within a hardware-in-the-loop (HIL) simulation platform implemented on an FPGA. To address dataset limitations, a TruckSim–MATLAB/Simulink co-simulation environment is used to generate realistic datasets under normal operation and eight representative fault scenarios, yielding up to 5000 labeled sequences (balanced between normal and faulty behaviors) at a sampling rate of 60 Hz. Two TCN models are trained independently to learn normal and faulty dynamics, and fault decisions are made by comparing the reconstruction errors (MSE and MAE) of both models, thus avoiding manually tuned thresholds. On a test set of 1000 sequences (500 normal and 500 faulty) from the 5000 sample configuration, the proposed dual TCN framework achieves a detection accuracy of 97.8%, a precision of 96.5%, a recall of 98.2%, and an F1-score of 97.3%, outperforming a single TCN baseline, which achieves 91.4% accuracy and an 88.9% F1-score. The complete dual TCN architecture is implemented on a Xilinx ZCU102 FPGA evaluation kit (AMD, Santa Clara, CA, USA), while supporting real-time inference in the HIL loop. These results demonstrate that the proposed approach provides accurate, low-latency fault detection suitable for safety-critical ABS applications and contributes to improving both functional safety and cyber-resilience of braking systems. Full article

(This article belongs to the Special Issue Artificial Intelligence and Microsystems)

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20 pages, 2508 KB

Open AccessArticle

An Attention-Enhanced Network for Person Re-Identification via Appearance–Gait Fusion

by Zelong Yu, Yixiang Cai, Hanming Xu, Lei Chen, Mingqian Yang, Huabo Sun and Xiangyu Zhao

Electronics 2025, 14(21), 4142; https://doi.org/10.3390/electronics14214142 - 22 Oct 2025

Cited by 1 | Viewed by 686

Abstract

The objective of person re-identification (Re-ID) is to recognize a given target pedestrian across different cameras. However, perspective variations, resulting from differences in shooting angles, often significantly impact the accuracy of person Re-ID. To address this issue, this paper presents an attention-enhanced person Re-ID algorithm based on appearance–gait information interaction. Specifically, appearance features and gait features are first extracted from RGB images and gait energy images (GEIs), respectively, using two ResNet-50 networks. Then, a multimodal information exchange module based on the attention mechanism is designed to build a bridge for information exchange between the two modalities during the feature extraction process. This module aims to enhance the feature extraction ability through mutual guidance and reinforcement between the two modalities, thereby improving the model’s effectiveness in integrating the two types of modal information. Subsequently, to further balance the signal-to-noise ratio, importance weight estimation is employed to map perspective information into the importance weights of the two features. Finally, based on the autoencoder structure, the two features are weighted and fused under the guidance of importance weights to generate fused features that are robust to perspective changes. The experimental results on the CASIA-B dataset indicate that, under conditions of viewpoint variation, the method proposed in this paper achieved an average accuracy of 94.9%, which is 1.1% higher than the next best method, and obtained the smallest variance of 4.199, suggesting that the method proposed in this paper is not only more accurate but also more stable. Full article

(This article belongs to the Special Issue Artificial Intelligence and Microsystems)

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