Search Results (533)

The performance of optoelectronic devices is affected by various noise sources. A notable factor is the 4.2% lattice mismatch at the Ge/Si interface, which significantly influences the efficiency of Ge-on-Si photodetectors. These noise sources can be analyzed by examining the impact of the Ge/Si interface and deep traps on dark and photocurrents. This study evaluates the impact of these charge traps on key photodetector performance metrics, including responsivity, photo-to-dark current ratio, noise equivalent power (NEP), and specific detectivity (D^*). The trapping effects on charge transport under both forward and reverse bias conditions are monitored through hysteresis analysis. When illuminated with an unmodulated 1550 nm laser, all the key performance metrics exhibit maximum variations at a specific reverse bias. This critical bias marks the transition from saturated to exponential charge transport regimes, where intensified electric fields enhance trap-assisted recombination and thus maximize metric fluctuations. Full article

Glancing Angle Deposition (GLAD) has emerged as a versatile and powerful nanofabrication technique for developing next-generation gas sensors by enabling precise control over nanostructure geometry, porosity, and material composition. Through dynamic substrate tilting and rotation, GLAD facilitates the fabrication of highly porous, anisotropic nanostructures, such as aligned, tilted, zigzag, helical, and multilayered nanorods, with tunable surface area and diffusion pathways optimized for gas detection. This review provides a comprehensive synthesis of recent advances in GLAD-based gas sensor design, focusing on how structural engineering and material integration converge to enhance sensor performance. Key materials strategies include the construction of heterojunctions and core–shell architectures, controlled doping, and nanoparticle decoration using noble metals or metal oxides to amplify charge transfer, catalytic activity, and redox responsiveness. GLAD-fabricated nanostructures have been effectively deployed across multiple gas sensing modalities, including resistive, capacitive, piezoelectric, and optical platforms, where their high aspect ratios, tailored porosity, and defect-rich surfaces facilitate enhanced gas adsorption kinetics and efficient signal transduction. These devices exhibit high sensitivity and selectivity toward a range of analytes, including NO₂, CO, H₂S, and volatile organic compounds (VOCs), with detection limits often reaching the parts-per-billion level. Emerging innovations, such as photo-assisted sensing and integration with artificial intelligence for data analysis and pattern recognition, further extend the capabilities of GLAD-based systems for multifunctional, real-time, and adaptive sensing. Finally, current challenges and future research directions are discussed, emphasizing the promise of GLAD as a scalable platform for next-generation gas sensing technologies. Full article

This paper presents a novel framework for optimizing multimodal large language model (MLLM) inference through task offloading and resource allocation in UAV-assisted satellite edge computing (SEC) networks. MLLMs leverage transformer architectures to integrate heterogeneous data modalities for IoT applications, particularly real-time monitoring in remote areas. However, cloud computing dependency introduces latency, bandwidth, and privacy challenges, while IoT device limitations require efficient distributed computing solutions. SEC, utilizing low-earth orbit (LEO) satellites and unmanned aerial vehicles (UAVs), extends mobile edge computing to provide ubiquitous computational resources for remote IoTDs. We formulate the joint optimization of MLLM task offloading and resource allocation as a mixed-integer nonlinear programming (MINLP) problem, minimizing latency and energy consumption while optimizing offloading decisions, power allocation, and UAV trajectories. To address the dynamic SEC environment characterized by satellite mobility, we propose an action-decoupled soft actor–critic (AD-SAC) algorithm with discrete–continuous hybrid action spaces. The simulation results demonstrate that our approach significantly outperforms conventional deep reinforcement learning methods in convergence and system cost reduction compared to baseline algorithms. Full article

Navigational aids play a vital role in enhancing the mobility and independence of blind and visually impaired (VI) individuals. However, existing solutions often present challenges related to discomfort, complexity, and limited ability to provide detailed environmental awareness. To address these limitations, we introduce SnapStick, an innovative assistive technology designed to improve spatial perception and navigation. SnapStick integrates a Bluetooth-enabled smart cane, bone-conduction headphones, and a smartphone application powered by the Florence-2 Vision Language Model (VLM) to deliver real-time object recognition, text reading, bus route detection, and detailed scene descriptions. To assess the system’s effectiveness and user experience, eleven blind participants evaluated SnapStick, and usability was measured using the System Usability Scale (SUS). In addition to the 94% accuracy, the device received an SUS score of 84.7%, indicating high user satisfaction, ease of use, and comfort. Participants reported that SnapStick significantly improved their ability to navigate, recognize objects, identify text, and detect landmarks with greater confidence. The system’s ability to provide accurate and accessible auditory feedback proved essential for real-world applications, making it a practical and user-friendly solution. These findings highlight SnapStick’s potential to serve as an effective assistive device for blind individuals, enhancing autonomy, safety, and navigation capabilities in daily life. Future work will explore further refinements to optimize user experience and adaptability across different environments. Full article

This review provides a comprehensive analysis of recent advancements in lower limb exoskeleton systems, focusing on applications, control strategies, hardware architecture, sensing modalities, human-robot interaction, evaluation methods, and technical innovations. The study spans systems developed for gait rehabilitation, mobility assistance, terrain adaptation, pediatric use, and industrial support. Applications range from sit-to-stand transitions and post-stroke therapy to balance support and real-world navigation. Control approaches vary from traditional impedance and fuzzy logic models to advanced data-driven frameworks, including reinforcement learning, recurrent neural networks, and digital twin-based optimization. These controllers support personalized and adaptive interaction, enabling real-time intent recognition, torque modulation, and gait phase synchronization across different users and tasks. Hardware platforms include powered multi-degree-of-freedom exoskeletons, passive assistive devices, compliant joint systems, and pediatric-specific configurations. Innovations in actuator design, modular architecture, and lightweight materials support increased usability and energy efficiency. Sensor systems integrate EMG, EEG, IMU, vision, and force feedback, supporting multimodal perception for motion prediction, terrain classification, and user monitoring. Human–robot interaction strategies emphasize safe, intuitive, and cooperative engagement. Controllers are increasingly user-specific, leveraging biosignals and gait metrics to tailor assistance. Evaluation methodologies include simulation, phantom testing, and human–subject trials across clinical and real-world environments, with performance measured through joint tracking accuracy, stability indices, and functional mobility scores. Overall, the review highlights the field’s evolution toward intelligent, adaptable, and user-centered systems, offering promising solutions for rehabilitation, mobility enhancement, and assistive autonomy in diverse populations. Following a detailed review of current developments, strategic recommendations are made to enhance and evolve existing exoskeleton technologies. Full article

This article presents a comprehensive review of assistive devices for synovial joints, addressing their definitions, classifications, and technological advancements. The historical evolution of artificial exoskeletons, orthoses, prostheses, and splints is analyzed, emphasizing their impact on rehabilitation and the enhancement of human mobility. Through a systematic compilation of scientific literature, patents, and medical regulations, the study clarifies terminology and classifications that have often been imprecisely used in scientific discourse. The review examines the biomechanical principles of the musculoskeletal system and the kinematics of synovial joints, providing a reference framework for the optimization and design of these devices. Furthermore, it explores the various types of artificial exoskeletons, and their classification based on structure, mobility, power source, and control system, as well as their applications in medical, industrial, and military domains. Finally, this study highlights the necessity of a systematic approach in the design and categorization of these technologies to facilitate their development, comparison, and effective implementation, ultimately improving users’ quality of life. Full article

Background/Objectives: This study focuses on the motion planning and control of an active ankle–foot orthosis (AFO) that leverages biomechanical insights to mitigate footdrop, a deficit that prevents safe toe clearance during walking. Methods: To adapt the motion of the device to the user’s walking speed, a geometric model was used, together with real-time measurement of the user’s gait cycle. A geometric speed-adaptive model also scales a trapezoidal ankle-velocity profile in real time using the detected gait cycle. The algorithm was tested at three different walking speeds, with a prototype of the AFO worn by a test subject. Results: At walking speeds of 0.44 and 0.61 m/s, reduced tibialis anterior (TA) muscle activity was confirmed by electromyography (EMG) signal measurement during the stance phase of assisted gait. When the AFO was in assistance mode after toe-off (initial and mid-swing phase), it provided an average of 48% of the estimated required power to make up for the deliberate inactivity of the TA muscle. Conclusions: Kinematic analysis of the motion capture data showed that sufficient foot clearance was achieved at all three speeds of the test. No adverse effects or discomfort were reported during the experiment. Future studies should examine the device in populations with footdrop and include a comprehensive evaluation of safety. Full article

The increasing integration of digital technologies in healthcare has expanded the attack surface for privacy violations in critical systems such as electronic health records (EHRs), telehealth platforms, and medical device software. However, current vulnerability detection datasets lack domain-specific privacy annotations essential for compliance with healthcare regulations like HIPAA and GDPR. This study presents C3-VULMAP, a novel and large-scale dataset explicitly designed for privacy-aware vulnerability detection in healthcare software. The dataset comprises over 30,000 vulnerable and 7.8 million non-vulnerable C/C++ functions, annotated with CWE categories and systematically mapped to LINDDUN privacy threat types. The objective is to support the development of automated, privacy-focused detection systems that can identify fine-grained software vulnerabilities in healthcare environments. To achieve this, we developed a hybrid construction methodology combining manual threat modeling, LLM-assisted synthetic generation, and multi-source aggregation. We then conducted comprehensive evaluations using traditional machine learning algorithms (Support Vector Machines, XGBoost), graph neural networks (Devign, Reveal), and transformer-based models (CodeBERT, RoBERTa, CodeT5). The results demonstrate that transformer models, such as RoBERTa, achieve high detection performance (F1 = 0.987), while Reveal leads GNN-based methods (F1 = 0.993), with different models excelling across specific privacy threat categories. These findings validate C3-VULMAP as a powerful benchmarking resource and show its potential to guide the development of privacy-preserving, secure-by-design software in embedded and electronic healthcare systems. The dataset fills a critical gap in privacy threat modeling and vulnerability detection and is positioned to support future research in cybersecurity and intelligent electronic systems for healthcare. Full article

The recent advancements in high-frequency, high-power switching devices require the development of non-invasive, cost-effective sensors for signal diagnostics. In this context, planar sensors have emerged as promising candidates for voltage and current sensing due to their compatibility with printed circuit boards (PCBs). However, previously proposed voltage planar sensors exhibit trade-offs between high bandwidths and responsivity, limiting their usage to sub-GHz applications. This study introduces a planar voltage sensor that leverages geometric optimization using software-assisted design to enhance bandwidth without compromising sensitivity. The optimized sensors demonstrate an extended bandwidth response up to 4 GHz and accurate recovery of fast transient signals validated through experimental measurements, which represents a significant step forward in broadband sensing for high-power applications. Full article

We report an advanced passivation strategy for perovskite solar cells (PSCs) by introducing core–shell structured perovskite quantum dots (PQDs), composed of methylammonium lead bromide (MAPbBr₃) cores and tetraoctylammonium lead bromide (tetra-OAPbBr₃) shells, during the antisolvent-assisted crystallization step. The epitaxial compatibility between the PQDs and the host perovskite matrix enables effective passivation of grain boundaries and surface defects, thereby suppressing non-radiative recombination and facilitating more efficient charge transport. At an optimal PQD concentration of 15 mg/mL, the modified PSCs demonstrated a remarkable increase in power conversion efficiency (PCE) from 19.2% to 22.85%. This enhancement is accompanied by improved device metrics, including a rise in open-circuit voltage (Voc) from 1.120 V to 1.137 V, short-circuit current density (Jsc) from 24.5 mA/cm² to 26.1 mA/cm², and fill factor (FF) from 70.1% to 77%. Spectral response analysis via incident photon-to-current efficiency (IPCE) revealed enhanced photoresponse in the 400–750 nm wavelength range. Additionally, long-term stability assessments showed that PQD-passivated devices retained more than 92% of their initial PCE after 900 h under ambient conditions, outperforming control devices which retained ~80%. These findings underscore the potential of in situ integrated PQDs as a scalable and effective passivation strategy for next-generation high-efficiency and stable perovskite photovoltaics. Full article

This article presents a series of innovative systems developed through student laboratory projects, comprising two autonomous vehicles, a quadrupedal walking robot, an active ankle-foot orthosis, a ball-on-beam balancing mechanism, a ball-on-plate system, and a manipulator arm, all actuated by pneumatic artificial muscles (PAMs). Due to their flexibility, low weight, and compliance, fluidic muscles demonstrate substantial potential for integration into various mechatronic systems, robotic platforms, and manipulators. Their capacity to generate smooth and adaptive motion is particularly advantageous in applications requiring natural and human-like movements, such as rehabilitation technologies and assistive devices. Despite the inherent challenges associated with nonlinear behavior in PAM-actuated control systems, their biologically inspired design remains promising for a wide range of future applications. Potential domains include industrial automation, the automotive and aerospace sectors, as well as sports equipment, medical assistive devices, entertainment systems, and animatronics. The integration of self-constructed laboratory systems powered by PAMs into control systems education provides a comprehensive pedagogical framework that merges theoretical instruction with practical implementation. This methodology enhances the skillset of future engineers by deepening their understanding of core technical principles and equipping them to address emerging challenges in engineering practice. Full article

Recent advances in technologies such as blockchain, the Internet of Things (IoT), Cyber–Physical Systems (CPSs), and the Industrial Internet of Things (IIoT) have driven the digitalization and intelligent transformation of modern industries. However, embedded control devices within power system communication infrastructures have become increasingly susceptible to cyber threats due to escalating software complexity and extensive network exposure. We have seen that symmetric conventional patching techniques—both static and dynamic—often fail to satisfy the stringent requirements of real-time responsiveness and computational efficiency in resource-constrained environments of all kinds of power grids. To address this limitation, we have proposed a hardware-assisted runtime patching framework tailored for embedded systems in critical power system networks. Our method has integrated binary-level vulnerability modeling, execution-trace-driven fault localization, and lightweight patch synthesis, enabling dynamic, in-place code redirection without disrupting ongoing operations. By constructing a system-level instruction flow model, the framework has leveraged on-chip debug registers to deploy patches at runtime, ensuring minimal operational impact. Experimental evaluations within a simulated substation communication architecture have revealed that the proposed approach has reduced patch latency by 92% over static techniques, which are symmetrical in a working way, while incurring less than 3% CPU overhead. This work has offered a scalable and real-time model-driven defense strategy that has enhanced the cyber–physical resilience of embedded systems in modern power systems, contributing new insights into the intersection of runtime security and grid infrastructure reliability. Full article

In this work, we report a facile and tunable electrodeposition approach for engineering polyacrylic acid (PAA)-modified Co₃O₄ electrodes on nickel foam for high-performance asymmetric pouch-type supercapacitors. By systematically varying the PAA concentration (0.5 wt %, 1 wt %, and 1.5 wt %), we demonstrate that the CO-1 sample (1 wt % PAA) exhibited the most optimized structure and electrochemical behavior. The CO-1 electrode delivered a remarkable areal capacitance of 3467 mF/cm² at 30 mA/cm², attributed to its interconnected nanosheet morphology, enhanced ion diffusion, and reversible Co²⁺/Co³⁺/Co⁴⁺ redox transitions. Electrochemical impedance spectroscopy confirmed low internal resistance (0.4267 Ω), while kinetic analysis revealed a dominant diffusion-controlled charge storage contribution of 91.7%. To evaluate practical applicability, an asymmetric pouch-type supercapacitor device was assembled using CO-1 as the positive electrode and activated carbon as the negative electrode. The device operated efficiently within a 1.6 V window, achieving an impressive areal capacitance of 157 mF/cm², an energy density of 0.056 mWh/cm², a power density of 1.9 mW/cm², and excellent cycling stability. This study underscores the critical role of polymer-assisted growth in tailoring electrode architecture and provides a promising route for integrating cost-effective and scalable supercapacitor devices into next-generation energy storage technologies. Full article

This paper presents a novel transposed MRAM architecture (WinEdge) specifically optimized for Winograd convolution acceleration in edge computing devices. Leveraging Magnetic Tunnel Junctions (MTJs) with Spin Hall Effect (SHE)-assisted Spin-Transfer Torque (STT) writing, the proposed design enables a single SHE current to simultaneously write data to four MTJs, substantially reducing power consumption. Additionally, the integration of stacked MTJs significantly improves storage density. The proposed WinEdge efficiently supports both standard and transposed data access modes regardless of bit-width, achieving up to 36% lower power, 47% reduced energy consumption, and 28% faster processing speed compared to existing designs. Simulations conducted in 45 nm CMOS technology validate its superiority over conventional SRAM-based solutions for convolutional neural network (CNN) acceleration in resource-constrained edge environments. Full article

To address the challenges of high missed detection rates for minute transmission line defects, strong complex background interference, and limited computational power on edge devices in UAV-assisted power line inspection, this paper proposes a lightweight improved YOLOv12 real-time detection model. First, a Bidirectional Weighted Feature Fusion Network (BiFPN) is introduced to enhance bidirectional interaction between shallow localization information and deep semantic features through learnable feature layer weighting, thereby improving detection sensitivity for line defects. Second, a Cross-stage Channel-Position Collaborative Attention (CPCA) module is embedded in the BiFPN’s cross-stage connections, jointly modeling channel feature significance and spatial contextual relationships to effectively suppress complex background noise from vegetation occlusion and metal reflections while enhancing defect feature representation. Furthermore, the backbone network is reconstructed using ShuffleNetV2’s channel rearrangement and grouped convolution strategies to reduce model complexity. Experimental results demonstrate that the improved model achieved 98.7% mAP@0.5 on our custom transmission line defect dataset, representing a 3.0% improvement over the baseline YOLOv12, with parameters compressed to 2.31M (8.3% reduction) and real-time detection speed reaching 142.7 FPS. This method effectively balances detection accuracy and inference efficiency, providing reliable technical support for unmanned intelligent inspection of transmission lines. Full article