Search Results (1,904)

Tethered space net robots (TSNRs) offer a flexible and promising configuration for active space debris removal. This study investigates the formation tracking control of TSNR for debris capture in the presence of unknown bounded disturbances. To enhance tracking accuracy, an observer-embedded distributed Lyapunov-based model predictive control (DLMPC) framework is proposed. By integrating real-time estimation errors directly into the predictive model, the framework effectively mitigates the mismatch between predicted and actual system dynamics. In addition, a worst-case contraction constraint is developed to ensure recursive feasibility and robust stability. Numerical experiments demonstrate that the proposed method significantly improves formation tracking precision and enhances resilience against disturbances compared to standard DMPC and auxiliary control strategies. Full article

EmbeddedField-Programmable Gate Array (FPGA)-Advanced RISC Machine (ARM) systems used in industrial and Internet of Things (IoT) environments increasingly operate as network-connected edge devices. While such connectivity enables distributed processing and remote monitoring, it also exposes embedded vision nodes to security threats, including command injection, frame replay, data tampering, and abnormal communication traffic. This paper presents a hardware-assisted security architecture for an FPGA-ARM embedded vision system designed for high-speed image acquisition and network streaming. The proposed solution integrates several lightweight protection mechanisms directly into the FPGA processing pipeline, including frame replay detection, cyclic redundancy check (CRC)-based frame integrity verification, frame sequence monitoring, authenticated command execution, communication anomaly monitoring, and hardware-rooted trust primitives, such as a ring-oscillator physical unclonable function (PUF) and a pseudo-random generator. Optional secure communication is provided via a lightweight ASCON-authenticated encryption core. The architecture was implemented on a Cyclone V System-on-Chip (SoC) platform using an industrial Camera Link camera and evaluated in a low-latency image-acquisition setup operating at 100 fps, with data throughput exceeding 1 Gbps. Experimental results demonstrate that the proposed security architecture introduces only about 1.6% additional FPGA logic utilization while maintaining full real-time acquisition performance. The presented approach demonstrates that practical hardware-level security mechanisms can be integrated into FPGA-based embedded vision nodes with minimal architectural modifications and negligible performance overhead. Full article

Unmanned aerial vehicles (UAVs) for urban and clinical–logistics missions operate under severe constraints in onboard energy, computation, and payload integrity. Addressing these challenges requires not only advanced algorithms but also a tight integration between embedded hardware, energy management, perception, and decision-making. This paper presents a unified UAV platform based on a system-level hardware–software co-design. First, a compact six-layer PCB (85 mm × 55 mm) integrates an NVIDIA Jetson Orin for on-edge artificial intelligence and a dedicated microcontroller for real-time flight control, with explicit power-domain separation, thermal management via arrays, and physical isolation of sensitive sensors. Second, a hybrid energy system combines LiPo batteries with perovskite photovoltaic cells and an MPPT stage with experimentally measured efficiency (94.5%), enabling stable operation under variable irradiance conditions. Third, an autonomous navigation strategy based on a Dueling Double Deep Q Network with Prioritized Experience Replay learns energy-efficient trajectories while explicitly incorporating payload thermal deviation (ΔT) and mechanical jerk into the reward function, thereby supporting clinically safe transport. Experimental validation on the physical platform includes onboard power and latency measurements, statistical evaluation across training and deterministic execution, and mission-level key performance indicators. Results show an average reduction of 18.4% in total energy consumption and a 12.1% increase in operational coverage under representative urban scenarios, with end-to-end decision latency below 50 ms. These findings demonstrate that a tightly integrated design of embedded hardware, hybrid energy management, and clinical-aware reinforcement learning enables robust, efficient, and application-ready UAV systems for urban and healthcare missions. Full article

This study presents a data-driven framework for the mechanistic interpretation of asphalt pavement responses using an integrated smart sensing and monitoring system deployed on a national highway in Thailand. A fully instrumented pavement test section was developed, incorporating a multi-sensor embedded network and a field data acquisition platform integrated with weigh-in-motion (WIM) technology. The system consists of 54 sensors, including strain gauges, pressure cells, moisture sensors, and thermocouples, installed at multiple depths to capture high-resolution stress–strain responses under controlled heavy-vehicle loading. Field measurements were analyzed and compared with classical mechanistic models, including Boussinesq’s theory, Odemark’s equivalent thickness method, and Burmister’s multilayer elastic theory. The results demonstrate good agreement for vertical stress predictions in deeper layers, while significant discrepancies were observed in strain responses, particularly in the asphalt layer, where measured tensile strains were up to 2.5 times higher than theoretical estimates. The findings indicate that conventional elastic models provide useful first-order approximations; however, discrepancies were observed in representing the viscoelastic behavior of asphalt materials under real loading conditions. Furthermore, the integration of sensor data with traffic loading information confirms that axle load magnitude is the dominant factor governing pavement responses, whereas vehicle speed primarily influences load duration. The proposed framework demonstrates the potential of smart sensing systems for enabling automated, data-driven pavement analysis and supporting digital twin-based infrastructure management. Full article

LiDAR is widely used in robotics because it provides reliable range data for navigation and mapping. On a small embedded robot, however, there is a practical conflict between scan resolution and reaction speed. Dense scans provide better environmental detail, but they take too long for fast obstacle avoidance, whereas sparse scans are faster but can miss obstacles if the spacing between adjacent rays is too large. This paper presents an Interleaved Sparse–Dense Scanning method for a servo-actuated single-point time-of-flight LiDAR mounted on an embedded mobile robot. A dense nested pan–tilt sweep is used for three-dimensional mapping, while a sparse forward scan is inserted between dense rows for obstacle detection and motion control. A geometric model is derived to relate sensing range, beam spacing, and minimum detectable object width. That model is then linked to zone-based safety constraints and to the distance the robot can travel before the next obstacle update. For the robot used in this study, the resulting sparse configuration is a 7-point forward scan over a 180${}^{\circ }$ field of view. Experiments in a real indoor environment showed that this configuration reliably detected target blocking obstacles and reduced decision latency by 6.2 times compared with waiting for a complete dense scan before each navigation update. The proposed method provides a practical balance between reactive obstacle avoidance and useful 3D mapping on a low-cost embedded platform, while making the system’s timing and safety limits explicit. Full article

(1) Background: SMART spinal implants combine biomechanical stabilization with embedded sensors for continuous in vivo monitoring of spinal loading and implant behaviour. This systematic review summarizes current SMART implant technologies in spinal surgery and evaluates their potential clinical applications. (2) Methods: A structured literature search was conducted in PubMed and Scopus in December 2025. Two independent reviewers screened studies using predefined criteria, with data extracted on implant design, sensor type, study model, and application; risk of bias was assessed using the Office of Health Assessment and Translation tool. (3) Results: Thirty-four studies met inclusion criteria, including sensor-integrated posterior rods and fixators (n = 16), vertebral body replacements (n = 6), intervertebral cages or disc space sensors (n = 7), and other configurations (n = 5). Devices were tested in human, cadaveric, and animal models. Most systems used strain-based sensors to quantify implant loading, while few employed accelerometers or pressure sensors. Reported results demonstrated activity- and posture-dependent load changes, and several studies indicated potential for monitoring spinal fusion progression by monitoring load trends. (4) Conclusions: Overall, SMART spinal implants primarily support biomechanical monitoring and show promise for real-time assessment of implant performance, though further studies correlating sensor data with clinical outcomes are required. Full article

Pig weighing is an essential procedure for monitoring growth and animal health; however, conventional methods are often labor-intensive, costly, and potentially stressful. In this context, this study proposes a non-invasive approach for estimating the body weight of pigs during the growing stage based on computer vision and the YOLOv11 algorithm, enabling automatic segmentation and individual identification in multi-animal environments. The study used RGB images of 10 group-housed pigs captured throughout the growing phase, in which automatic dorsal segmentation was combined with individual identification through numerical markings. From the generated binary masks, the segmented dorsal area was extracted and used as a predictor variable in Linear Regression and a Multilayer Perceptron (MLP) Artificial Neural Network. The YOLOv11 model showed consistent performance in the segmentation task, achieving test-set metrics of Precision = 0.849, Recall = 0.886, mAP@0.50 = 0.936, and mAP@0.50–0.95 = 0.819, demonstrating good generalization capability in scenarios with intense animal interaction. In the weight prediction stage, Linear Regression and the MLP achieved high coefficients of determination (R² = 0.96 and 0.95, respectively) with low errors (RMSE = 1.52 kg and 1.63 kg; MAE = 1.20 kg and 1.25 kg), indicating a strong correlation between segmented dorsal area and actual body weight. Class-wise analysis revealed superior performance for classes 7 and 9, with R² values up to 0.98 and RMSE below 1.1 kg, whereas class 8 showed greater error dispersion, associated with higher morphological variability and a smaller number of available samples. These results demonstrate that the direct use of morphometric information extracted from segmented masks in 2D images constitutes a robust, accurate, and low-cost approach for automatic pig body-weight estimation. Moreover, this study is among the few addressing this task specifically during the growing stage, highlighting its potential for future deployment in embedded systems and intelligent monitoring platforms for precision pig farming, although further evaluation of computational efficiency and real-time performance is still required. Full article

Computer vision–based weed detection plays a critical role in agricultural robotics, enabling accurate, selective weeding. These systems operate on resource-constrained embedded platforms, which introduces a significant trade-off between accuracy and efficiency. This study presents a comparative evaluation of six detection models (YOLOv11n, YOLOv11s, SSD-Lite, NanoDet, Faster R-CNN, RT-DETR) for agro-robotic applications, measuring precision, recall, mAP@0.5, and runtime on low-power hard-ware. NanoDet achieved the highest detection accuracy (precision 98.6%, recall 94.2%, mAP@0.5 97.7%). YOLOv11s demonstrated similar performance (mAP@0.5: 96.1%) but required more computation. YOLOv11n provides the most favourable balance between accuracy and throughput (mAP@0.5: 94.6%, 207 FPS on a workstation). On Raspberry Pi 5, light models achieved 3–5 FPS. RT-DETR and Faster R-CNN exhibited high latency (3112–6500 ms/frame), which prevents real-time operation. NanoDet excelled in detection, while YOLOv11n provides the best balance between accuracy and efficiency for limited devices. Full article

Post-quantum cryptographic implementations in Internet-of-Things (IoT) devices are significantly threatened by physical side-channel attacks, where practical attack risks are increased by physical accessibility and resource limitations. In particular, recent work has shown that belief propagation-based attacks can recover secret keys from lattice-based digital signatures using only a single side-channel trace of the Number Theoretic Transform (NTT). This work introduces the Quantum-Randomized Number Theoretic Transform (QR-NTT), an implementation-level defense mechanism that integrates quantum-derived entropy directly into the execution flow of lattice-based signature algorithms. Rather than treating randomness as a static input, QR-NTT uses quantum entropy to introduce controlled variability in execution ordering, arithmetic factor usage, and memory access behavior while preserving mathematical correctness and constant-time execution. The proposed framework is designed for embedded platforms and remains compatible with existing post-quantum cryptographic standards and IoT communication protocols. A complete implementation on an ARM Cortex-M4 platform, coupled with commercial quantum random number generator (QRNG) hardware, demonstrates that QR-NTT significantly degrades the effectiveness of template matching and belief propagation attacks. Experimental evaluation shows a reduction in single-trace attack success rates from over 90% to below 3% and an increase of approximately two orders of magnitude in the number of traces required for successful key recovery. These security gains are achieved with moderate overheads of 18.3% in execution time and 1.8 KB of additional memory while remaining well within practical IoT constraints. The results indicate that quantum-derived entropy can be leveraged as a practical implementation-level defense against physical attacks, complementing algorithmic post-quantum security. QR-NTT demonstrates a viable path toward strengthening the real-world resilience of post-quantum IoT systems without sacrificing deployability. Full article

Hybrid CNC and additive manufacturing platforms often rely on host-assisted or otherwise overdimensioned control architectures to achieve deterministic multi-axis motion, increasing system cost and complexity. This paper presents a fully microcontroller-based timer–DMA motion execution architecture that eliminates the need for external processors or FPGA-based execution, enabling deterministic multi-axis synchronization under the tested conditions in a simpler, more cost-effective way. The proposed framework integrates motion planning, precise step-time computation, and hardware-assisted pulse generation within a unified embedded control architecture. The main novelty lies in the systematic use of timer and DMA peripherals to offload time-critical pulse execution from the microcontroller core, allowing it to focus on motion planning and precise step-time computation. Unlike segmentation-based approaches, the duration of each individual step is calculated directly without fixed-interval segmentation, enabling high motion resolution while avoiding per-step interrupts that introduce jitter at high motion speeds. The architecture was validated on a hybrid platform capable of both milling and material extrusion. Experimental results confirmed real-time feasibility within practical on-chip memory limits and demonstrated very small interpolation errors caused mainly by timer quantization, comparable to those observed in host-processor-based motion systems. Machining and additive-manufacturing experiments further confirmed stable execution and accurate trajectory tracking under real operating conditions. Full article

Power converters are fundamental components in vehicle electrification systems. However, their inherently nonlinear and time-varying condition requires complex design procedures when conventional control strategies based on linear small-signal models are employed. This work proposes a simplified and hardware-oriented DC-DC converter control methodology that combines fuzzy logic and Neural Networks in a sequential manner. A fuzzy logic fuzzy controller is first used to generate a dataset of control actions under closed-loop operation. A lightweight neural network is then trained using the obtained data to approximate this mapping and subsequently replace the fuzzy controller in real-time operation. To validate the approach, a bidirectional buck–boost DC-DC converter is designed for applications in the powertrain of electric vehicles with 500 kHz switching frequency and 13 kW power rating. The control algorithm is embedded in an FPGA to demonstrate its suitability for hardware deployment. The experimental results show a reduction in RMSE of 33.7% and a decrease in the settling time of at least 51.7% when compared with a benchmark PID control. Full article

District heating systems play a critical role in urban energy supply; however, secondary networks suffer from frequent sensor failures that undermine thermal balance control. The development of virtual sensors to estimate return-water temperatures offers a promising solution to this challenge. This study investigates the performance of different feature selection methods for developing virtual sensors. The investigated feature selection methods include two engineering experience-based methods, one embedded method, one wrapped-based method, and two filter methods. Using operational data from a real secondary district heating network over an entire heating season, the embedded method’s performance is investigated, and an appropriate machine learning algorithm, paired with the wrapped and filter methods, is selected. For the filter methods, the paper additionally examines the differences between the rank-based and threshold-based filter implementations. The performance of the wrapped and filter methods on accuracy, computational cost, and sensitivity to data volume was compared. The results indicate that the embedded method exhibits relatively unstable performance under the engineering experience-based baselines, but the gradient tree boosting (GTB) method demonstrates better performance in both accuracy and stability. Further tests combining GTB with wrapped and filter methods revealed that both the filter and wrapped methods show an acceptable performance in terms of accuracy. The mean RMSE of both filter and wrapped methods consistently ranges from 0.75 °C to 0.8 °C when the selected feature is more than 6. However, the wrapped method exhibits a higher computational cost and is more sensitive to data volume. The training time of the wrapped method is approximately 136 times that of the fastest filter method. Considering overall performance, the combination of GTB with an HSIC indicator, employing either the rank-based selection of the top 9 features or the threshold-based feature selection, is recommended. These findings provide methodological guidance for the development of virtual sensors in district heating systems. Full article

Photovoltaic (PV) power stations are pivotal for the renewable energy transition, yet their operational efficiency is often compromised by defects such as surface dust accumulation and cracks. Traditional manual inspections are labor-intensive and subjective, while conventional monitoring methods struggle with environmental interference. This study proposes YOLO-MSG, a lightweight framework specifically designed for the automated detection of PV module defects during system operation, including normal panels as well as defective conditions such as dusty and cracked panels. The methodology integrates a Multi-Scale Grouped Convolution (MSGC) module for enhanced feature extraction and a Group-Stem Decoupled Head (GSD-Head) to reduce parameter redundancy. Furthermore, a joint optimization strategy involving LAMP and logits-based knowledge distillation is employed to facilitate edge deployment. Experimental results on a specialized PV defect dataset demonstrate that YOLO-MSG achieves a superior balance between detection accuracy and computational cost. Compared to state-of-the-art models like YOLO11 and YOLOv12, YOLO-MSG significantly reduces GFLOPs and parameter count while maintaining highly competitive mean Average Precision (mAP), with improvements of 1.35% in mAP and 2.37% in mAP50-95 over the baseline models. Specifically, the model achieves an average inference speed of 90.30 FPS on the NVIDIA Jetson AGX platform. These findings confirm the algorithm’s industrial viability, providing a robust and efficient solution for the real-time automated maintenance of photovoltaic infrastructures. Full article

The Controller Area Network (CAN) remains the backbone of in-vehicle communication, but its lack of built-in security exposes safety-critical systems to cyberattacks. This paper presents a real-time, reconfigurable, specification-driven intrusion detection system (IDS) implemented on a custom test bench that emulates an EV powertrain. The CAN traffic captured from the four-ECU setup formed the dataset used in this study. The IDS enforces a compact, reconfigurable ruleset covering timing bounds, jitter envelopes, identifier whitelists, frame format, data length code (DLC) compliance, bus-load thresholds, application-level CRC, and alive-counter verification. The IDS achieves detection times below 2 ms with false positive rates under 1% for injection, denial of service (DoS), and fuzzy attacks, even at CAN bus loads up to 70%, while microcontroller resource usage remains within the constraints of automotive-grade devices, supporting deployment in embedded environments. The main contributions of this study are as follows: (i) a validated and reproducible EV powertrain test bench with millisecond-level timing, (ii) a deployable and easily reconfigurable ruleset with deterministic runtime, and (iii) a latency-oriented evaluation framework that is portable across automotive microcontroller platforms. The EV powertrain dataset v1.0 was released in a public GitHub repository to facilitate reproducible research and enable future benchmarking studies. Full article

With the widespread adoption of electric vehicles (EVs), the deep integration of transportation and power grids has emerged as a significant trend. EV charging stations, acting as dynamic loads, present challenges to real-time power balance and economic dispatch in microgrids, while EVs serving as mobile energy storage units offer new opportunities for system flexibility. To address these issues, this paper proposes a hardware-in-the-loop (HIL) co-design method for vehicle–grid-integrated microgrid energy management systems, covering the entire workflow from simulation to embedded deployment. This method resolves the core challenges of multi-objective optimization algorithm deployment on embedded platforms (i.e., high computational complexity, strict real-time constraints, and heterogeneous communication protocol integration) via deployability analysis, hybrid code generation, real-time task restructuring, and consistency validation. A prototype microgrid system integrating photovoltaic panels, wind turbines, diesel generators, an energy storage system, and EV charging loads was built on the RK3588 embedded platform. An improved multi-objective particle swarm optimization (MOPSO) algorithm is employed to optimize operational costs. Experimental results verify the effectiveness of the proposed co-design method. Compared with traditional rule-based control strategies, the MOPSO algorithm reduces the total daily operating cost of the VGIM system by approximately 50%. After integrating vehicle-to-grid (V2G) scheduling, the operating cost is further reduced. In addition, this method ensures the consistency of algorithm functionality and performance during the migration from HIL simulation to embedded deployment, and the RK3588-based embedded system can complete a single optimization iteration within 60 s, which fully satisfies the real-time requirements of industrial applications. This work provides a feasible technical pathway for the reliable deployment of vehicle–grid-integrated microgrids in practical industrial scenarios. Full article