Search Results (998)

A lateral overflow integration capacitor (LOFIC) CMOS image sensor (CIS) can achieve high-dynamic-range (HDR) imaging by combining a low-conversion-gain (LCG) signal with a high-conversion-gain (HCG) signal. However, the signal-to-noise ratio (SNR) drops at the switching point from HCG signal to LCG signal due to the significant pixel noise in the LCG signal. To address this issue, a triple-gain LOFIC CIS with a middle-conversion-gain (MCG) signal has been introduced. In this work, we propose an area-efficient readout circuit for the triple-gain LOFIC CIS, using amplifier and capacitor sharing techniques to process the HCG, MCG, and LCG signals. A test chip of the proposed readout circuit was fabricated using the $0.18\mathsf{\mu }$m CMOS process. The area overhead was only $7.6\%$, and the SNR drop was improved by $8.05$ dB compared to the readout circuit for a dual-gain LOFIC CIS. Full article

This paper introduces CodeDive, a web-based programming environment with real-time behavioral tracking designed to enhance student progress assessment and provide timely support for learners, while also addressing the academic integrity challenges posed by Large Language Models (LLMs). Visibility into the student’s learning process has become essential for effective pedagogical analysis and personalized feedback, especially in the era where LLMs can generate complete solutions, making it difficult to truly assess student learning and ensure academic integrity based solely on the final outcome. CodeDive provides this process-level transparency by capturing fine-grained events, such as code edits, executions, and pauses, enabling instructors to gain actionable insights for timely student support, analyze learning trajectories, and effectively uphold academic integrity. It operates on a scalable Kubernetes-based cloud architecture, ensuring security and user isolation via containerization and SSO authentication. As a browser-accessible platform, it requires no local installation, simplifying deployment. The system produces a rich data stream of all interaction events for pedagogical analysis. In a Spring 2025 deployment in an Operating Systems course with approximately 100 students, CodeDive captured nearly 25,000 code snapshots and over 4000 execution events with a low overhead. The collected data powered an interactive dashboard visualizing each learner’s coding timeline, offering actionable insights for timely student support and a deeper understanding of their problem-solving strategies. By shifting evaluation from the final artifact to the developmental process, CodeDive offers a practical solution for comprehensively assessing student progress and verifying authentic learning in the LLM era. The successful deployment confirms that CodeDive is a stable and valuable tool for maintaining pedagogical transparency and integrity in modern classrooms. Full article

Among various failure causes, lightning overvoltage represents the most significant threat to overhead distribution lines, which serve as critical components in power systems. This study uses the hybrid partial element equivalent circuit (PEEC) multi-conductor transmission line (MTL) method to perform overvoltage simulations and investigate lightning risk distribution along distribution lines developed from a real 10 kV distribution networks in Guizhou, China. The results of the rocket-triggered lightning observation verify the accuracy of the hybrid method for direct lightning simulation. Combining the Monte Carlo method with the electro-geometric model (EGM), the impact of differential protection configurations on annual lightning flashover rates is analyzed. The results demonstrate that lightning strikes on phase wires generate high-magnitude overvoltages but with limited spatial influence, resulting in fewer pole flashovers. Conversely, strikes on poles produce lower overvoltage peaks but affect wider areas, leading to significantly more flashovers. Using annual flashover rates as the risk evaluation metric, the line topologies into high-risk, medium-risk, and other low-risk areas are classified. Targeting an annual flashover rate below 0.4 as the design objective, the configuration schemes of the arresters are progressively optimized. This risk-based approach provides an effective reference framework for differential protection design of distribution line safeguards. Full article

The emergence of 6G wireless systems marks a paradigm shift toward intelligent, context-aware networks that can adapt in real-time to their environment. Within this landscape, Sensing-Assisted Communication (SAC) emerges as a key enabler, integrating perception into the communication control loop to enhance reliability, beamforming accuracy, and system responsiveness. Unlike prior surveys that treat SAC as a subfunction of Integrated Sensing and Communication (ISAC), this work offers the first dedicated review of SAC in Millimeter-Wave (mmWave) and Sub-Terahertz (Sub-THz) systems, where directional links and channel variability present core challenges. SAC encompasses a diverse set of methods that enable wireless systems to dynamically adapt to environmental changes and channel conditions in real time. Recent studies demonstrate up to 80% reduction in beam training overhead and significant gains in latency and mobility resilience. Applications include predictive beamforming, blockage mitigation, and low-latency Unmanned Aerial Vehicle (UAV) and vehicular communication. This review unifies the SAC landscape and outlines future directions in standardization, Artificial Intelligence (AI) integration, and cooperative sensing for next-generation wireless networks. Full article

Electrochemical energy storage technology, primarily lithium-ion batteries, has been widely applied in large-scale energy storage systems. However, differences in assembly structures, manufacturing processes, and operating environments introduce parameter inconsistencies among cells within a pack, producing complex, high-volume datasets with redundant and fragmented charge–discharge records that hinder efficient and accurate system monitoring. To address this challenge, we propose a hybrid ASW-UKF-TRF framework for the classification and compression of battery data collected from energy storage power stations. First, an adaptive sliding-window Unscented Kalman Filter (ASW-UKF) performs online data cleaning, imputation, and smoothing to ensure temporal consistency and recover missing/corrupted samples. Second, a temporally aware TRF segments the time series and applies an importance-weighted, multi-level compression that formally prioritizes diagnostically relevant features while compressing low-information segments. The novelty of this work lies in combining deployment-oriented engineering robustness with methodological innovation: the ASW-UKF provides context-aware, online consistency restoration, while the TRF compression formalizes diagnostic value in its retention objective. This hybrid design preserves transient fault signatures that are frequently removed by conventional smoothing or generic compressors, while also bounding computational overhead to enable online deployment. Experiments on real operational station data demonstrate classification accuracy above 95% and an overall data volume reduction in more than 60%, indicating that the proposed pipeline achieves substantial gains in monitoring reliability and storage efficiency compared to standard denoising-plus-generic-compression baselines. The result is a practical, scalable workflow that bridges algorithmic advances and engineering requirements for large-scale battery energy storage monitoring. Full article

Multi-variant execution (MVX) is an active defense technique that can detect unknown attacks by comparing the outputs of redundant program variants. Despite notable progress in MVX techniques in recent years, current approaches for recovery of abnormal variants still face fundamental challenges, including state inconsistency, low recovery efficiency, and service disruption of an MVX system. Therefore, a record–replay-based state recovery approach for variants in MVX systems is proposed in this paper. First, a Syscall Coordinator (SSC), composed of a recording module, a classification module, and a replay module, is designed to enable state recovery of variants. Then, a synchronization and voting algorithm is presented. When an anomaly is identified through voting, the abnormal variant is handed over to the SSC for state recovery, while the Synchronization Queue is updated accordingly. Furthermore, to ensure uninterrupted system service, we introduce a parallel grouped recovery mechanism, which enables the execution of normal variants and the recovery of abnormal variants to run in parallel. Experimental results on SPEC CPU 2006 benchmark and server applications show that the proposed approach achieves low overhead in both the recording and replay phases while maintaining high state recovery accuracy and supports uninterrupted system service. Full article

The increasing deployment of internet of things(IoT) systems across critical domains has broadened the threat landscape, and being the catalyst for a variety of security concerns, including very stealthy slow denial of service (slow DoS) attacks. These exploit the hypertext transfer protocol’s (HTTP) application-layer protocol to either close down service requests or degrade responsiveness while closely mimicking legitimate traffic. Current available datasets fail to capture the more stealthy operational profiles of slow DoS attacks or account for the presence of genuine slow nodes (SN), which are devices experiencing high latency. These can significantly degrade detection accuracy since slow DoS attacks closely emulate SN. This paper addresses these problems by synthesising a realistic HTTP slow DoS dataset derived from a live IoT network, that incorporates both stealth-tuned slow DoS traffic and legitimate SN traffic, with the three main slow DoS variants of slow GET, slow Read, and slow POST being critically evaluated under these network conditions. A limited packet capture (LPC) strategy is adopted which focuses on just two metadata attributes, namely packet length ($lp$) and packet inter-arrival time ($\mathrm{\Delta }t$). Using a resource lightweight decision tree classifier, the proposed model achieves over 96% accuracy while incurring minimal computational overheads. Experimental results in a live IoT network reveal the negative classification impact of including SN traffic, thereby underscoring the importance of modelling stealthy attacks and SN latency in any slow DoS detection framework. Finally, a MPerf (Modelling Performance) is presented which quantifies and balances detection accuracy against processing costs to facilitate scalable deployment of low-cost detection models in resource-constrained IoT networks. This represents a practical solution to improving IoT resilience against stealthy slow DoS attacks whilst pragmatically balancing the resource-constraints of IoT nodes. By analysing the impact of SN on detection performance, a robust reliable model has been developed which can both measure and fine tune the accuracy-efficiency nexus. Full article

Recent studies have demonstrated that the utilisation of aluminium in electrical applications has increased substantially, particularly in the context of power cables. The substitution of copper with aluminium in cable fabrication is predominantly driven by economic considerations. When designing such cables, it is imperative to ascertain their functional properties, including their electrical conductivity and mechanical properties, and their operational properties, which include rheological, thermal, and material fatigue resistance. This is to ensure that the aluminium and copper cables are compatible. The primary challenge confronting researchers in this domain pertains to predicting and forecasting the failure of overhead cables during their operational lifecycle. One of the most significant and prevalent operational hazards is fatigue damage. This article presents the experimental results of fatigue tests on single Al and Cu wires in various states of mechanical reinforcement. The parameters of the Wöhler curve were determined, and a comparative analysis of the morphology of fatigue damage in single copper and aluminium wires was performed. It was found that copper wires are more fatigue-resistant than aluminium wires. In the case of high-cycle fatigue, this difference can amount to 10⁶ cycles. An analysis of fatigue fracture morphology showed that fractures have a developed surface and that plastic deformation makes a significant contribution in the case of low-cycle fatigue. In the case of high-cycle fatigue, many cracks were observed in the copper wires. No such cracks were observed in the aluminium wires. Full article

Edge computing offers low-latency and distributed processing for IoT applications but poses new security challenges, due to limited resources and decentralized data. Intrusion detection systems (IDSs) are essential for real-time threat monitoring, yet traditional IDS frameworks often struggle in edge environments, failing to meet efficiency requirements. This paper presents an efficient intrusion detection framework that integrates spatiotemporal hashing, federated learning, and fast K-nearest neighbor (KNN) retrieval. A hashing neural network encodes network traffic into compact binary codes, enabling low-overhead similarity comparison via Hamming distance. To support scalable retrieval, multi-index hashing is applied for sublinear KNN searching. Additionally, we propose an attention-guided federated aggregation strategy that dynamically adjusts client contributions, reducing communication costs. Our experiments on benchmark datasets demonstrate that our method achieves competitive detection accuracy with significantly lower computational, memory, and communication overhead, making it well-suited for edge-based deployment. Full article

High-Voltage Direct Current (HVDC) systems are transforming the global energy landscape, distinguished by their efficiency, stability, and low impact on the electrical grid. One of the challenges of HVDC transmission line design is assessing the generated stray magnetic field, as it can have negative effects on human health and the environment. This study presents an analytical methodology for calculating the magnetic field density at any point along an HVDC line corridor. It considers the spatial configuration, the current per pole, and the location of the conductors. The equations allow for the calculation of the horizontal and vertical components of the field, as well as its total magnitude. A practical case study of a ±500 and ±600 kV HVDC two-pole transmission line is presented. The methodology was programmed in MATLAB^® version R2024a to calculate the magnetic field density, and the results are consistent with those obtained with the established methodology. The presented methodology can be applied to monopolar and two-pole HVDC overhead transmission lines, offering speed and accuracy. Full article

Electric drive systems (EDSs) are vital for automotive and industrial applications but remain highly vulnerable to cyber and physical anomalies (CPAs), such as inverter open-circuit faults, sensor failures, and malicious cyberattacks. Ensuring reliable EDS operation requires the controller to receive accurate and uncompromised feedback and reference signals continuously. However, many existing data-driven detection and mitigation strategies rely on large training datasets, impose significant computational overhead, and often lose effectiveness under various abnormal operating conditions. To overcome these limitations, this paper introduces a trust evaluation framework that continuously assesses the reliability of all incoming signals to the EDS controller by combining behavioral analysis with historical reliability records. The proposed scheme offers a lightweight and model-independent approach, enabling reliable, adaptive decision-making by leveraging both current and historical signal behavior. To this end, this paper further integrates the resulting trust values into a torque-split optimization algorithm, enabling adaptive load optimization by dynamically reducing the torque contribution of motors operating under abnormal or low-trust conditions, thereby demonstrating clear applicability for automotive drive systems. The framework is validated in a real-time OPAL-RT environment across multiple CPA scenarios, demonstrating accurate anomaly detection and adaptive torque redistribution. Owing to its simplicity and versatility, the proposed method can be readily extended to other safety-critical drive applications. Full article

Feature map pooling in convolutional neural networks (CNNs) serves the dual purpose of reducing spatial dimensions and enhancing feature invariance. Current pooling approaches face a fundamental trade-off: deterministic methods (e.g., MaxPool and AvgPool) lack regularization benefits, while stochastic approaches introduce beneficial randomness but can suffer from sampling biases and may require careful hyperparameter tuning (e.g., S3Pool). To address these limitations, this paper introduces LDS3Pool, a novel pooling method that leverages low-discrepancy sequences (LDSs) for quasi-random spatial sampling. LDS3Pool first linearizes 2D feature maps to 1D sequences using Hilbert space-filling curves to preserve spatial locality, then applies LDS-based sampling to achieve quasi-random downsampling with mathematical guarantees of uniform coverage. This framework provides the regularization benefits of randomness while maintaining comprehensive feature representation, without requiring sensitive hyperparameter tuning. Extensive experiments demonstrate that LDS3Pool consistently outperforms baseline methods across multiple datasets and a range of architectures, from classic models like VGG11 to modern networks like ResNet18, achieving significant accuracy gains with moderate computational overhead. The method’s empirical success is supported by a rigorous theoretical analysis, including a quantitative evaluation of the Hilbert curve’s superior, size-independent locality preservation. In summary, LDS3Pool represents a theoretically sound and empirically effective pooling method that enhances CNN generalization through a principled, quasi-random sampling framework. Full article

Zucchini growth in greenhouse environments presents significant challenges for fruit recognition and picking point localization due to characteristics such as foliage occlusion, high density, structural complexity, and diverse fruit morphologies. Current recognition and localization algorithms exhibit limitations including low accuracy, restricted applicability, and procedural complexity, falling short of the requirements for precise and robust intelligent harvesting. To address these issues, this study constructs a zucchini dataset of 942 images using an Intel RealSense D455 depth camera and a smartphone, and proposes a novel keypoint detection model named YOLOv8n-Pose-DSW. The model introduces three key enhancements compared with YOLOv8n-Pose. First, the conventional upsample operator is replaced with an adaptive point sampling operator called Dysample, improving detection accuracy while reducing GPU memory consumption. Second, a Slim-Neck structure is designed to decrease computational overhead through lightweight bottleneck architecture, while preserving robust feature representation. Third, the WIoU-v3 loss is adopted to optimize bounding box regression for object detection, thereby enhancing localization accuracy. Experimental results demonstrate that YOLOv8n-Pose-DSW achieves a zucchini detection P, R, mAP@50, and mAP@50–95 of 92.1%, 90.7%, 94.0%, and 71.4%, respectively. These metrics represent improvements of 3.3%, 11.7%, 7.4%, and 15.4%, respectively, over the original model. For picking point localization, the improved model attains a P of 93.1%, R of 89.5%, mAP@50 of 95.6%, and mAP@50–95 of 95.2%, corresponding to gains of 8.8%, 11.0%, 11.3%, and 27.9% over the original model. Further error analysis shows that picking point localization errors are concentrated within the 0–4-pixel range, demonstrating enhanced localization precision critical for practical harvesting applications. The proposed algorithm effectively addresses greenhouse environmental challenges and provides essential technical support for intelligent zucchini harvesting systems. Full article

The rapid expansion of the Internet of Things (IoT) demands scalable, efficient, and user-friendly gateway solutions that seamlessly connect resource-constrained edge devices to cloud services. Low-cost, widely available microcontrollers, such as the ESP32 and its ecosystem peers, offer integrated Wi-Fi/Bluetooth connectivity, low power consumption, and a mature developer toolchain at a bill of materials cost of only a few dollars. For smart-home deployments where budgets, energy consumption, and maintainability are critical, these characteristics make MCU-class gateways a pragmatic alternative to single-board computers, enabling always-on local control with minimal overhead. This paper presents the design and implementation of an embedded IoT gateway powered by the ESP32 microcontroller. By using lightweight communication protocols such as Message Queuing Telemetry Transport (MQTT) and REST APIs, the proposed architecture supports local control, distributed intelligence, and secure on-site data storage, all while minimizing dependence on cloud infrastructure. A real-world deployment in an educational building demonstrates the gateway’s capability to monitor energy consumption, execute control commands, and provide an intuitive web-based dashboard with minimal resource overhead. Experimental results confirm that the solution offers strong performance, with RAM usage ranging between 3.6% and 6.8% of available memory (approximately 8.92 KB to 16.9 KB). The initial loading of the single-page application (SPA) results in a temporary RAM spike to 52.4%, which later stabilizes at 50.8%. These findings highlight the ESP32’s ability to serve as a functional IoT gateway with minimal resource demands. Areas for future optimization include improved device discovery mechanisms and enhanced resource management to prolong device longevity. Overall, the gateway represents a cost-effective and vendor-agnostic platform for building resilient and scalable IoT ecosystems. Full article

The rapid growth of low-power Internet of Things (IoT) applications has created an urgent demand for compact, battery-free power solutions. However, most existing RF energy harvesters rely on active rectifiers, multi-phase topologies, or complex tuning networks, which increase circuit complexity and static power overhead while struggling to maintain high efficiency under microwatt-level inputs. To address this challenge, this work proposes a harmonic-recycling, passive, RF-energy-harvesting system with integrated power management (HR-P-RFEH). The system adopts a planar microstrip architecture compatible with MEMS fabrication, integrating a dual-stage voltage multiplier rectifier (VMR) and a stub-based harmonic suppression–recycling network. The design was verified through combined electromagnetic/circuit co-simulations, PCB prototyping, and experimental measurements. Operating at 915 MHz under a 0 dBm input and a 2 k$\Omega$ load, the HR-P-RFEH achieves a stable 1.4 V DC output and a peak rectification efficiency of 70.7%. Compared with a conventional single-stage rectifier, it improves the output voltage by 22.5% and the efficiency by 16.4%. The rectified power is further regulated by a BQ25570-based unit to provide a stable 3.3 V supply buffered by a 47 mF supercapacitor, ensuring continuous operation under intermittent RF input. In comparison with the state of the art, the proposed fully passive, harmonic-recycling design achieves competitive efficiency without active bias or adaptive tuning while remaining MEMS- and LTCC-ready. These results highlight HR-P-RFEH as a scalable and fabrication-friendly building block for next-generation energy-autonomous IoT and MEMS systems. Full article