Search Results (254)

Autonomous driving has emerged as a rapidly advancing field in both industry and academia over the past decade. Among the enabling technologies, computer vision (CV) has demonstrated high accuracy across various domains, making it a critical component of autonomous vehicle systems. However, CV tasks are computationally intensive and often require hardware accelerators to achieve real-time performance. Field Programmable Gate Arrays (FPGAs) have gained popularity in this context due to their reconfigurability and high energy efficiency. Numerous researchers have explored FPGA-accelerated CV solutions for autonomous driving, addressing key tasks such as lane detection, pedestrian recognition, traffic sign and signal classification, vehicle detection, object detection, environmental variability sensing, and fault analysis. Despite this growing body of work, the field remains fragmented, with significant variability in implementation approaches, evaluation metrics, and hardware platforms. Crucial performance factors, including latency, throughput, power consumption, energy efficiency, detection accuracy, datasets, and FPGA architectures, are often assessed inconsistently. To address this gap, this paper presents a comprehensive literature review of FPGA-accelerated, vision-based autonomous driving systems. It systematically examines existing solutions across sub-domains, categorizes key performance factors and synthesizes the current state of research. This study aims to provide a consolidated reference for researchers, supporting the development of more efficient and reliable next generation autonomous driving systems by highlighting trends, challenges, and opportunities in the field. Full article

Cultural heritage tourism is a vital part of Malaysia’s tourism sector, attracting visitors to iconic UNESCO sites like George Town and Melaka. However, these heritage sites face growing challenges from overcrowding and environmental degradation, which accelerate the deterioration of historic architecture and cultural artifacts. Preservation efforts often require site closures, which negatively impact tourist experiences and satisfaction. Thus, augmented reality (AR) offers a solution by supporting heritage management and preservation, allowing visitors to engage with virtual representations via mobile AR apps, thereby enhancing visitor engagement and travel experience. Despite global adoption, mobile AR apps often suffer from low user retention, with many users abandoning them shortly after downloading them. Understanding what drives continued usage is crucial for successful AR implementation. This study integrates the expectation confirmation model (ECM) and the unified theory of acceptance and use of technology 2 (UTAUT2) to examine the determinants affecting user’s experiential satisfaction and continued usage intention of mobile AR apps. An online survey of 450 domestic tourists in George Town and Melaka was conducted. Data analysis using structural equation modeling with SmartPLS 4.0 revealed that the integrated model offers a stronger predictive power and significantly outperforms ECM and UTAUT2 individually. The findings contribute valuable insights for researchers, app developers, tourism stakeholders, and policymakers. Full article

Air pollution poses major risks to public health, driving the adoption of low-cost sensor (LCS) networks for fine-grained and real-time monitoring. However, the variable accuracy of LCS data compared with reference instruments necessitates robust quality control (QC) frameworks. Over the past decade, machine learning (ML) has emerged as a powerful tool to calibrate sensors, detect anomalies, and mitigate drift in large-scale deployment. This survey reviews advances in three methodological categories: traditional ML models, deep learning architectures, and hybrid or unsupervised methods. We also examine spatiotemporal QC frameworks that exploit redundancies across time and space, as well as real-time implementations based on edge–cloud architectures. Applications include personal exposure monitoring, integration with atmospheric simulations, and support for policy decision making. Despite these achievements, several challenges remain. Traditional models are lightweight but often fail to generalize across contexts, while deep learning models achieve higher accuracy but demand large datasets and remain difficult to interpret. Spatiotemporal approaches improve robustness but face scalability constraints, and real-time systems must balance computational efficiency with accuracy. Broader adoption will also require clear standards, reliable uncertainty quantification, and sustained trust in corrected data. In summary, ML-based QC shows strong potential but is still constrained by data quality, transferability, and governance gaps. Future work should integrate physical knowledge with ML, leverage federated learning for scalability, and establish regulatory benchmarks. Addressing these challenges will enable ML-driven QC to deliver reliable, high-resolution data that directly support science-based policy and public health. Full article

The electrification of agricultural equipment is a critical pathway to address the dual challenges of increasing global food production and ensuring sustainable agricultural development. As the core power unit, the permanent magnet synchronous motor (PMSM) drive system faces severe challenges in achieving high performance, robustness, and reliable control in complex farmland environments characterized by sudden load changes, extreme operating conditions, and strong interference. This paper provides a comprehensive review of key technological advancements in PMSM drive systems for agricultural electrification. First, it analyzes solutions to enhance the reliability of power converters, including high-frequency silicon carbide (SiC)/gallium nitride (GaN) power device packaging, thermal management, and electromagnetic compatibility (EMC) design. Second, it systematically elaborates on high-performance motor control algorithms such as Direct Torque Control (DTC) and Model Predictive Control (MPC) for improving dynamic response; robust control strategies like Sliding Mode Control (SMC) and Active Disturbance Rejection Control (ADRC) for enhancing resilience; and the latest progress in fault-tolerant control architectures incorporating sensorless technology. Furthermore, the paper identifies core challenges in large-scale applications, including environmental adaptability, real-time multi-machine coordination, and high reliability requirements. Innovatively, this review proposes a closed-loop intelligent control paradigm encompassing environmental disturbance prediction, control parameter self-tuning, and actuator dynamic response. This paradigm provides theoretical support for enhancing the autonomous adaptability and operational quality of agricultural machinery in unstructured environments. Finally, future trends involving deep AI integration, collaborative hardware innovation, and agricultural ecosystem construction are outlined. Full article

With the rapid development of the electric vehicle (EV) industry, the regenerative braking system (RBS) has become a pivotal technology for enhancing overall vehicle energy efficiency and safety. This article systematically reviews recent research advances, spanning macro-architecture, drive and energy-storage hardware, control strategies, and evaluation frameworks. It focuses on comparing the mechanisms and performance of six categories of intelligent control algorithms—fuzzy logic, neural networks, model predictive control, sliding-mode control, adaptive control, and learning-based algorithms—and, leveraging the structural advantages of four-wheel independent drive (4WID) electric vehicles, quantitatively analyzes improvements in energy-recovery efficiency and coordinated vehicle-dynamics control. The review further discusses how high-power-density motors, hybrid energy storage, brake-by-wire systems, and vehicle-road cooperation are pushing the upper limits of RBS performance, while revealing current technical bottlenecks in high-power recovery at low speeds, battery thermal safety, high-dimensional real-time optimization, and unified evaluation standards. A closed-loop evolutionary roadmap is proposed, consisting of the following stages: system integration, intelligent control, scenario prediction, hardware upgrading, and standard evaluation. This roadmap emphasizes the central roles of deep reinforcement learning, hierarchical model predictive control (MPC), and predictive energy management in the development of next-generation RBS. This review provides a comprehensive and forward-looking reference framework, aiming to accelerate the deployment of efficient, safe, and intelligent regenerative braking technologies. Full article

Key drivetrain components in wind turbines are prone to misalignment faults due to long-term operation under fluctuating loads and harsh environments. Because misalignment develops gradually rather than occurring instantly, reliable evaluation of structural designs and surface treatments requires long-duration, multi-sensor, and multi-condition experiments that generate massive heterogeneous datasets. Traditional data management relying on manual folders and USB drives is inefficient, redundant, and lacks traceability. To address these challenges, this study presents a dedicated misalignment experimental data management platform specifically designed for wind power applications. The innovation lies in its ability to synchronize vibration, electrostatic, and laser alignment data streams in long-term tests, establish a traceable and reusable data structure linking experimental conditions with sensor outputs, and integrate laboratory results with field SCADA data. Built on Laboratory Information Management System (LIMS) principles and implemented with an MVC + Spring Boot + B/S architecture, the platform supports end-to-end functions including multi-sensor data acquisition, structured storage, automated processing, visualization, secure sharing, and cross-role collaboration. Validation on drivetrain shaft assemblies confirmed its ability to handle multi-terabyte datasets, reduce manual processing time by more than 80%, and directly integrate processed results into fault identification models. Overall, the platform establishes a scalable digital backbone for wind turbine misalignment research, supporting structural reliability evaluation, predictive maintenance, and intelligent operation and maintenance. Full article

Induction motor (IM) drives are widely used in industrial applications, particularly within the renewable energy sector, owing to their fast dynamic response and robust performance. Reliable condition monitoring is essential to ensure uninterrupted operation, minimize unexpected downtime, and avoid associated financial losses. Although numerous studies have introduced advanced fault detection techniques for IMs, early fault identification remains a significant challenge, especially in systems powered by electronic drives. To address the limitations of manual feature extraction, deep learning methods, particularly conventional convolutional neural networks (CNNs), have emerged as promising tools for automated fault diagnosis. However, enhancing their capability to capture a broader spectrum of spatial features can further improve detection accuracy. This study presents a novel fault detection framework based on a multi-wide-kernel convolutional neural network (MWK-CNN) tailored for drive-fed induction motors. By integrating convolutional kernels of varying widths, the proposed architecture effectively captures both fine-grained details and large-scale patterns in the input signals, thereby enhancing its ability to distinguish between normal and faulty operating states. Electrical signals acquired from drive-fed IMs under diverse operating conditions were used to train and evaluate the MWK-CNN. Experimental results demonstrate that the proposed model exhibits heightened sensitivity to subtle fault signatures, leading to superior diagnostic accuracy and outperforming existing state-of-the-art approaches for fault detection in drive-fed IM systems. Full article

The rapid growth of artificial intelligence (AI), data centers, and high-performance computing (HPC) has increased the demand for large bandwidth, high energy efficiency, and high-density optical interconnects. Co-packaged optics (CPO) technology offers a promising solution by integrating photonic integrated circuits (PICs) directly within or close to electronic integrated circuit (EIC) packages. This paper explores the evolution of CPO performance from various perspectives, including fan-out wafer level packaging (FOWLP), through-silicon via (TSV)-based packaging, through-glass via (TGV)-based packaging, femtosecond laser direct writing waveguides, ion-exchange glass waveguides, and optical coupling. Micro ring resonators (MRRs) are a high-density integration solution due to their compact size, excellent energy efficiency, and compatibility with CMOS processes. However, traditional thermal tuning methods face limitations such as high static power consumption and severe thermal crosstalk. To address these issues, non-volatile neuromorphic photonics has made breakthroughs using phase-change materials (PCMs). By combining the integrated storage and computing capabilities of photonic memory with the efficient optoelectronic interconnects of CPO, this deep integration is expected to work synergistically to overcome material, integration, and architectural challenges, driving the development of a new generation of computing hardware with high energy efficiency, low latency, and large bandwidth. Full article

The transport sector significantly contributes to global greenhouse gas emissions, making electromobility crucial in the race toward the United Nations Sustainable Development Goals. In recent years, the increasing competition among manufacturers, the development of cheaper batteries, the ongoing policy support, and people’s greater environmental awareness have consistently increased electric vehicles (EVs) adoption. Nevertheless, EVs charging needs—highly influenced by EV drivers’ behavior uncertainty—challenge their integration into the power grid on a massive scale, leading to potential issues, such as overloading and grid instability. Smart charging strategies can mitigate these adverse effects by using information and communication technologies to optimize EV charging schedules in terms of power systems’ constraints, electricity prices, and users’ preferences, benefiting stakeholders by minimizing network losses, maximizing aggregators’ profit, and reducing users’ driving range anxiety. To this end, accurately forecasting EV charging demand is paramount. Traditionally used forecasting methods, such as model-driven and statistical ones, often rely on complex mathematical models, simulated data, or simplifying assumptions, failing to accurately represent current real-world EV charging profiles. Machine learning (ML) methods, which leverage real-life historical data to model complex, nonlinear, high-dimensional problems, have demonstrated superiority in this domain, becoming a hot research topic. In a scenario where EV technologies, charging infrastructure, data acquisition, and ML techniques constantly evolve, this paper conducts a systematized literature review (SLR) to understand the current landscape of ML-based EV charging demand forecasting, its emerging trends, and its future perspectives. The proposed SLR provides a well-structured synthesis of a large body of literature, categorizing approaches not only based on their ML-based approach, but also on the EV charging application. In addition, we focus on the most recent technological advances, exploring deep-learning architectures, spatial-temporal challenges, and cross-domain learning strategies. This offers an integrative perspective. On the one hand, it maps the state of the art, identifying a notable shift toward deep-learning approaches and an increasing interest in public EV charging stations. On the other hand, it uncovers underexplored methodological intersections that can be further exploited and research gaps that remain underaddressed, such as real-time data integration, long-term forecasting, and the development of adaptable models to different charging behaviors and locations. In this line, emerging trends combining recurrent and convolutional neural networks, and using relatively new ML techniques, especially transformers, and ML paradigms, such as transfer-, federated-, and meta-learning, have shown promising results for addressing spatial-temporality, time-scalability, and geographical-generalizability issues, paving the path for future research directions. Full article

The stomach epithelium is a highly dynamic tissue that undergoes continuous self-renewal and responds robustly to injury through tightly regulated repair processes. Organoids have emerged as powerful tools for modelling gastrointestinal biology. This review focuses on the capacity of gastric organoids to model epithelial homeostasis, injury and repair in the stomach. We examine how organoid systems recapitulate key features of in vivo gastric architecture and stem cell dynamics, enabling detailed interrogation of lineage specification, proliferative hierarchies and regional identity. Gastric organoids have proven particularly useful for studying how environmental factors, such as Helicobacter pylori infection or inflammatory cytokines, disrupt epithelial equilibrium and drive metaplastic transformation. Furthermore, we discuss the emerging use of injury-mimicking conditions, co-cultures and bioengineered platforms to model regeneration and inflammatory responses in vitro. While organoids offer unparalleled accessibility and experimental manipulation, they remain limited by the absence of critical niche components such as immune, stromal and neural elements. Nevertheless, advances in multi-cellular and spatially resolved organoid models are closing this gap, making them increasingly relevant for disease modelling and regenerative medicine. Overall, gastric organoids represent a transformative approach to dissecting the cellular and molecular underpinnings of stomach homeostasis and repair. Full article

Hybrid-electric aircraft require a reliable power distribution architecture. The electrical drive system is connected to the power source via a DC-link composed mostly of capacitors—one of the faultiest power electronic components. In order to ensure the safe operation of the aircraft, DC-link capacitor condition monitoring is needed. The main requirements for such an algorithm are low data consumption and the possibility to use it in generator- or battery-powered systems. The proposed discharge-based repetitive recursive least squares (RRLS) method provides satisfactory estimates utilizing small data packages. Its execution during capacitor discharge makes it independent from the power source type. Based on the capacitor’s physical parameters, the computational complexity of the estimation process is reduced. Simulation validation and experimental tests were conducted. An analysis was carried out in a capacitance range between 705 μF and 1175 μF. The effective range of the algorithm is 881 μF–1044 μF, with an estimation error of less than 5%. Additionally, a range of changes in the time constant of the multiplier of 0.1–10 was tested in the simulation study. Full article

With advancements in artificial intelligence (AI), deep learning (DL) has become crucial for real-time data analytics in areas like autonomous driving, healthcare, and predictive maintenance; however, its computational and memory demands often exceed the capabilities of low-end devices. This paper explores optimizing deep learning architectures for memory efficiency to enable real-time computation in low-power designs. Strategies include model compression, quantization, and efficient network designs. Techniques such as eliminating unnecessary parameters, sparse representations, and optimized data handling significantly enhance system performance. The design addresses cache utilization, memory hierarchies, and data movement, reducing latency and energy use. By comparing memory management methods, this study highlights dynamic pruning and adaptive compression as effective solutions for improving efficiency and performance. These findings guide the development of accurate, power-efficient deep learning systems for real-time applications, unlocking new possibilities for edge and embedded AI. Full article

Recent breakthroughs in artificial intelligence are accelerating the intelligent transformation of vehicles. Vehicle electronic and electrical architectures are converging toward centralized domain controllers. Deep learning, reinforcement learning, and deep reinforcement learning now form the core technologies of domain control. This review surveys advances in deep reinforcement learning in four vehicle domains: intelligent driving, powertrain, chassis, and cockpit. It identifies the main tasks and active research fronts in each domain. In intelligent driving, deep reinforcement learning handles object detection, object tracking, vehicle localization, trajectory prediction, and decision making. In the powertrain domain, it improves power regulation, energy management, and thermal management. In the chassis domain, it enables precise steering, braking, and suspension control. In the cockpit domain, it supports occupant monitoring, comfort regulation, and human–machine interaction. The review then synthesizes research on cross-domain fusion. It identifies transfer learning as a crucial method to address scarce training data and poor generalization. These limits still hinder large-scale deployment of deep reinforcement learning in intelligent electric vehicle domain control. The review closes with future directions: rigorous safety assurance, real-time implementation, and scalable on-board learning. It offers a roadmap for the continued evolution of deep-reinforcement-learning-based vehicle domain control technology. Full article

Sindbis virus (SINV), a prototype of the Alphavirus genus (family Togaviridae), is a globally distributed arbovirus causing febrile rash and debilitating arthritis in humans. Viral structural proteins—capsid (C), E1, and E2—are fundamental to the virion’s architecture, mediating all stages from assembly to host cell entry and pathogenesis, thus representing critical targets for study. This review consolidates the historical and current understanding of SINV structural biology, tracing progress from early microscopy to recent high-resolution cryo-electron microscopy (cryo-EM) and X-ray crystallography. We detail the virion’s precise T = 4 icosahedral architecture, composed of a nucleocapsid core and an outer glycoprotein shell. Key functional roles tied to protein structure are examined: the capsid’s dual capacity as a serine protease and an RNA-packaging scaffold that interacts with the E2 cytoplasmic tail; the E1 glycoprotein’s function as a class II fusion protein driving membrane fusion; and the E2 glycoprotein’s primary role in receptor binding, which dictates cellular tropism and serves as the main antigenic target. Furthermore, we connect these molecular structures to viral evolution and disease, analyzing how genetic variation among SINV genotypes, particularly in the E2 gene, influences host adaptation, immune evasion, and the clinical expression of arthritogenic and neurovirulent disease. In conclusion, the wealth of structural data on SINV offers a powerful paradigm for understanding alphavirus biology. However, critical gaps persist, including the high-resolution visualization of dynamic conformational states during viral entry and the specific molecular determinants of chronic disease. Addressing these challenges through integrative structural and functional studies is paramount. Such knowledge will be indispensable for the rational design of next-generation antiviral therapies and broadly protective vaccines against the ongoing threat posed by SINV and related pathogenic alphaviruses. Full article

Positron emission tomography (PET) has become a powerful tool in Alzheimer’s disease (AD) research by enabling in vivo visualization of pathological biomarkers. Recent efforts have aimed to integrate PET-derived imaging phenotypes with genome-wide association studies (GWASs) to better elucidate the genetic architecture underlying AD. This systematic review examines studies that leverage PET imaging in the context of GWASs (PET-GWASs) to identify genetic variants associated with disease risk, progression, and brain region-specific pathology. A comprehensive search of PubMed and Embase databases was performed on 18 February 2025, yielding 210 articles, of which 10 met pre-defined inclusion criteria and were included in the final synthesis. Studies were eligible if they included AD populations, employed PET imaging alongside GWASs, and reported original full-text findings in English. No formal protocol was registered, and the risk of bias was not independently assessed. The included studies consistently identified APOE as the strongest genetic determinant of amyloid burden, while revealing additional significant loci including ABCA7 (involved in lipid metabolism and amyloid clearance), FERMT2 (cell adhesion), CR1 (immune response), TOMM40 (mitochondrial function), and FGL2 (protective against amyloid deposition in Korean populations). The included studies suggest that PET-GWAS approaches can uncover genetic loci involved in processes such as lipid metabolism, immune response, and synaptic regulation. Despite limitations including modest cohort sizes and methodological variability, this integrated approach offers valuable insight into the biological pathways driving AD pathology. Expanding PET-genomic datasets, improving study power, and applying advanced computational tools may further clarify genetic mechanisms and contribute to precision medicine efforts in AD. Full article