Engineering

Abstract: This study aims to evaluate multiple feature sets composed of sensor-based biomarkers acquired during walking for the automated estimation of post-stroke motor impairment levels using Fugl-Meyer Lower Extremity Assessment (FMA-LE) derived classes. Sensor-based walking data from the open-source ARRA dataset were combined with data collected at the Hospital of Braga. Data from 32 post-stroke individuals (FMA-LE:24±3) were included. A decision tree classifier was evaluated using stratified 6-fold cross-validation across different feature configurations, including: correlated versus full feature sets; spatiotemporal versus electromyographic (EMG) features; inclusion of demographic variables; and the use of data augmentation. The best performance was achieved using correlated EMG features combined with age, paretic side, and body mass, along with noise-based data augmentation, yielding a validation MCC of 0.85±0.16 and a test MCC of 0.70. EMG features provided improved classification performance compared to spatiotemporal features, and comparable results were obtained using a reduced subset of muscles. These results demonstrate the feasibility of using EMG-based features acquired during walking to classify post-stroke motor impairment levels. Feature reduction and inclusion of demographic variables may support efficient model design, while data augmentation may enhance generalization. Further validation in larger and more diverse datasets is required to assess robustness and clinical applicability.

Abstract: Detecting objects with LiDAR in fog has long been treated as a noise-removal problem: identify the fog returns and throw them away. This work takes the opposite view. The same fog returns that other methods discard are read as a direct measurement of how far the sensor can still see. After an off-the-shelf fog-segmentation step, the near-range fog points are used to estimate the atmosphere's optical thickness, which is then converted into a per-frame Maximum Detection Range (MDR) — a single, human-readable number, in meters, that tells planning and safety modules how far the LiDAR is currently usable. The estimator has no learnable parameter(The physics core — a per-frame Beer–Lambert slope fit followed by the Koschmieder mapping) and runs in real time during driving. We validate it with real-vehicle experiments in controlled heavy-fog scenarios. Across six fog runs the estimated MDR recovers the true first-detection distance with a mean absolute error of 1.99 m and R² = 0.741 — a 58 % error reduction over a no-feature baseline. To rule out that the result is an artefact of our own recording, the same estimate is cross-checked against two independent references used by the wider community: the STF transmissometer dataset and the Hahner et al. automotive fog simulator. Our estimate agrees with both, which is the key differentiator from prior LiDAR-fog work that reports performance only on its own data. The result is a real-time, parameter-free, externally-corroborated visibility readout that fits inside an existing fog-denoising pipeline and gives downstream autonomy a quantitative answer to the question "how far can I see right now?"

Abstract: The increasing penetration of renewable energy sources demands dispatch strategies that balance technical reliability, environmental sustainability, and economic efficiency. While Hybrid Photovoltaic/Hydro/Diesel/Battery Energy Storage (BESS) systems have been studied, most existing works focus on single-objective optimization or genetic multi-objective trade-offs without explicit integration of global sustainability thresholds. This study introduces a novel policy-embedded Mixed Integer Nonlinear Programming (MINLP) dispatch framework that embeds policy-aligned constraints (losses ≤ 8%, CO₂ reduction ≥ 40%, and cost reduction ≥ 20%) directly into the optimization model of the IEEE 30-bus system. Unlike prior studies, the framework establishes a replicable benchmark for hybrid generator placement and sizing, combining renewable-first dispatch logic with explicit emission and cost caps.Results demonstrate that policy thresholds are achievable within technical feasibility, with losses halved, emissions reduced by over 40%, and costs lowered by 20%. Pareto frontier analysis reveals that global policy targets coincide with the frontier of achievable trade-offs, providing new evidence that sustainability agendas can be operationalized in dispatch optimization. This contribution advances hybrid system research by bridging technical modeling with global energy policy, offering actionable insights for grid operators, policymakers, and researchers. By systematically locating PV+BESS at Bus 19/30, Hydropower at Bus 6/11 and Diesel at Bus 2/5, the study provides a reproducible design logic that future researchers can adopt. This benchmark moves beyond abstract optimization to offer a practical system design contribution.

Abstract: Effective dementia care is hindered by fragmented perspectives among patients, caregivers, and clinicians, leading to delayed or suboptimal interventions. We introduce a novel Ambient Assisted Living (AAL) framework that reconceptualizes the home as an active caregiving partner, aiming at safe aging in place. Our key contribution is a privacy-preserving, affordable, holistic system with continuous real-world validation, designed for unobtrusive, zero-interface operation. The system integrates novel low-cost hardware solutions, including a remote audio announcement device, a bidirectional intercom, and a “Zero-Interface Mirror” enabling non-recorded, real-time video co-presence between patients and guardians. A local edge-based AI architecture processes multi-modal inputs without persistent data storage, ensuring privacy by design. The system combines real-time acoustic event detection (via Audio Spectrogram Transformer), automatic speech recognition and diarization, dual-stage fall detection, and extraction of acoustic biomarkers from natural conversations, which are selectively uploaded -depending on Dementia stage- to a clinician-facing dashboard for longitudinal monitoring. A 10-month in-home deployment demonstrates robust detection of clinically relevant patterns revealed through heatmaps, including nocturnal wandering, counting delusional incidents, behavioral disturbances, quantifying silence and TV usage, respiratory irregularities, and nutritional adherence. The proposed framework establishes a scalable, privacy-first paradigm for continuous dementia monitoring, enabling earlier and more informed clinical interventions to push back hospitalizations and decompress elderly care facilities.

Abstract: Infrared object detection from unmanned aerial vehicles (UAVs) is critically challenged by multi-type composite degradation—including noise, blur, and low contrast—which severely undermines feature discriminability and multi-scale target perception. This study proposes SGW-DETR (Spectral-Guided Graph-structured Wavelet Detection Transformer), a novel framework built upon RT-DETR, incorporating three synergistic modules across the backbone, neck, and encoder. FDSANet (Frequency Domain Spectral Awareness Network) replaces the conventional ResNet backbone, integrating the Multi-Scale Frequency Perception Module (MSFPM), Selective Channel Frequency Decomposition (SCFD), and Dynamic Kernel Spectral Modulation (DKSM) to achieve instance-level adaptive spectral feature extraction without degradation-type supervision. The Graph-Structured Fusion Network (GSFN) combines the Adaptive Semantic Fusion Module (ASFM) with the Graph Structure Perception Module (GSPM), employing Gaussian kernel soft membership and two-stage message passing to explicitly model spatial topological dependencies among object components. The Wavelet-guided Contrast Feature Aggregation module (WCFA) restructures the Attention-based Intra-scale Feature Interaction (AIFI) encoder via a Haar-based Frequency Decomposition Unit (HFDU), decomposing features into foreground-edge and background-thermal components and achieving hierarchical foreground–background decoupling through nested dual-path causal contrastive attention. A UAV infrared degradation dataset comprising 4,686 images spanning six degradation types with component-level annotations was constructed for evaluation. SGW-DETR achieves 75.2% mAP50, outperforming RT-DETR by 3.5%, while simultaneously reducing GFLOPs and parameter count by 16.8% and 9.9% at an inference speed of 85.5 FPS. Sustained performance gains on M3FD and IndraEye benchmarks further demonstrate the framework’s cross-domain generalization capability, offering practical value for UAV-based surveillance, search-and-rescue, and border monitoring under adverse imaging conditions.

Abstract: Inhomogeneous magnetic field distributions in high-frequency planar transformers frequently cause severe localized thermal hotspots and elevated leakage inductance. Traditional interleaved winding designs rely heavily on empirical trial-and-error, which becomes computationally prohibitive for multi-layer parallel structures due to the factorial "curse of dimensionality." To address this bottleneck, this paper proposes a universal, data-driven optimization methodology. First, a quantitative one-dimensional prefix-sum model is established to correlate winding arrangements with spatial magnetomotive force (MMF) distributions, effectively simplifying the electromagnetic evaluation. Subsequently, a customized Genetic Algorithm (GA) framework, featuring physical-constraint-preserving operators such as Order Crossover (OX), is introduced to efficiently navigate the high-dimensional discrete search space. Using an extreme 26-layer complex parallel winding configuration (Np:Ns = 9:2) as a primary case study, the proposed GA method effectively bypasses over 1.5 million permutations, converging to the global optimum within 100 generations. The optimized structure achieves profound peak-shaving, drastically reducing both the peak MMF and total uncoupled magnetic energy area. This methodology provides a systematic, computationally lightweight EDA solution that fundamentally replaces empirical trial-and-error in the design of high-frequency magnetic components.

Abstract: This paper quantifies the spatial divergence between 128-channel Light Detection and Ranging (LiDAR) point clouds and Frequency Modulated Continuous Wave (FMCW) radar tracks in high-clutter urban environments using the TiAND dataset. Nearest-neighbor Euclidean distance between radar target centers and raw LiDAR geometry serves as the error metric, chosen because the dataset provides no semantic bounding-box annotations. Across all processed frames the system produced an RMSE of 10.083 m with a median error (P₅₀) of 1.157 m, while the 99th-percentile (P₉₉) deviation reached 43.008 m with the single worst-case ghost target exceeded 217 m. A total of 4,113 detections crossed the 15 m catastrophic threshold—a figure that must be interpreted against the full detection population reported in Section III. Critically, the top anomalies cluster across consecutive frames near fixed infrastructure suggesting persistent multi-path reflection geometry rather than isolated single-frame noise. These findings indicate that raw FMCW radar output without downstream filtering or LiDAR verification cannot be relied upon for spatial localization in unstructured urban traffic.

Abstract: With the large-scale commercialization of 5G and rapid evolution of 6G wireless systems, planar interdigital bandpass filters (BPFs) have become the core passive components for low-power RF front-ends. However, state-of-the-art filter design methods either rely heavily on empirical trial-and-error with 8–10 simulation iterations, or fail to resolve the inherent trade-off between center frequency tuning and stopband performance degradation, which cannot meet the demands of rapid customized design for 5G/6G multi-band scenarios. In this paper, a symmetric five-resonator three-segment patch-type interdigital BPF is taken as the research object. Through theoretical derivation, full-wave electromagnetic simulation, parametric scanning and orthogonal experiments, the quantitative mapping between structural parameters and filter performance is established. Notably, the directional tuning mechanism of the resonator’s narrow segment width on the first stopband is first revealed, which realizes lossless stopband optimization without disturbing the center frequency. On this basis, a three-stage standardized design procedure is proposed, which reduces design iterations from 8–10 to 3, shortens the design cycle by over 70%, and achieves 100% compliance of core design indexes. This work provides an implementable, low-threshold engineering method for rapid customized design of planar interdigital BPFs for 5G/6G RF front-ends.

Abstract: Cycling is one of the most popular sports and recreational activities. Millions of new people start to integrate bicycling into their daily routines every year. Fitness and activity trackers are the most powerful motivation tools for cycling novices and serious cycling enthusiasts. For this purpose, we present LaserFit, a laser-based direct force power meter for fitness and activity tracking during cycling. We developed embedded hardware to collect the torque and the wheel rotation data, which is produced by a laser-based position sensing system mounted on the rear wheel to precisely record the power output produced by the rider during cycling. The sensor data transmits to a smartphone via Bluetooth/ANT+ for data acquisition and analysis. Our device can be produced at low costs and deliver a level of accuracy similar to that obtained with the most expensive systems available on the market. To evaluate the accuracy of our system extensive experiments were conducted. The results of the present study suggest that the LaserFit power meter provides a strong relationship (r = 0.97) across a range of trials in laboratory and field conditions when compared with the SRM power meter. The LaserFit is therefore considered a valid alternative for training and performance measurement during continuous cycling.

Abstract: The growing demand for high-efficiency, high-power-density converters in data centers, electric vehicle chargers, and renewable energy systems has accelerated the adoption of wide bandgap (WBG) devices. Gallium nitride (GaN) transistors offer superior switching speed, lower losses, and higher power density compared with Silicon (Si) devices. Accurate characterization of GaN switching dynamics is essential due to parasitic effects and transient phenomena affecting performance and reliability. The Double Pulse Test (DPT) is widely used to quantify critical parameters, including switching energy losses, dynamic RDS(on) and transient voltage and current waveforms. This paper reviews DPT techniques for GaN devices, focusing on measurement methodologies, parasitic mitigation, and reliability considerations, providing practical guidance for optimizing high-frequency GaN-based power converters.

Abstract: Expanding renewable energy capacity in sub-Saharan transmission systems is a cornerstone of sustainable development, yet weak grid infrastructure and the absence of flexible storage remain principal barriers to reliable and low-carbon energy access. This paper addresses the economic and environmental dimensions of that challenge by proposing a hierarchical multi-objective framework for the optimal siting and sizing of Battery Energy Storage Systems (BESS), applied to the 130-bus Mali transmission network within the EMERGE project. The upper level employs the NSGA-II evolutionary algorithm to simultaneously maximize daily price-arbitrage revenue—the economic sustainability indicator—and minimize active power losses—the environmental efficiency indicator. For each candidate design, the lower level solves a multi-period DC Optimal Power Flow (DC-OPF) via CasADi/IPOPT, with thermal branch constraints embedded as hard linear inequalities through the Power Transfer Distribution Factor (PTDF) matrix, and voltage-corrected loss estimates recovered via a vectorized Extended DC Power Flow (EDCPF) model. Over 500 NSGA-II generations, the framework identifies Bus 91 (SIRAKORO II, 150 kV) as the dominant storage location, achieving maximum daily revenue of approximately € 10,033 at a marginal loss increment of 6.7×10⁻³ MWh. The Pareto front provides Mali system planners with a quantitative tool for balancing private investment returns against grid-level environmental impact, demonstrating that rigorous network-constrained BESS planning is both technically tractable and economically viable in the resource-constrained context of sub-Saharan sustainable energy transitions.

Abstract: Institutional shuttle fleets with fixed routes and predictable terminal parking are well suited to dedicated photovoltaic–battery (PV–BESS) charging infrastructure, yet siting and sizing are usually solved numerically without clear interpretation of the governing constraints. This study develops a closed-form active-constraint sizing rule, derived via Karush–Kuhn–Tucker (KKT) analysis under verified monotonicity of the net-present-value (NPV) objective over the feasible design region, for a 10-van electric academic shuttle fleet operating between the Huay Kaew and Doi Saket campuses of Rajamangala University of Technology Lanna, Chiang Mai, Thailand. One centralized station is compared with two distributed stations under reliability, cost, solar-fraction, autonomy, charger, budget, and rooftop-area constraints. The two-station configuration eliminates 47,600 km/year of dead-run travel and increases system NPV from USD 36,980 to USD 86,293 after the year-10 BESS replacement cost. The KKT analysis identifies two binding constraints—BESS one-day autonomy and PV rooftop area—giving 30 kWp PV and 94.85 kWh BESS per station, rounded to 100 kWh. The full transition achieves IRR = 12.9%, simple payback = 6.1 years, and 95.9% annual CO₂ reduction. Monte Carlo simulation with 5,000 scenarios yields P(NPV > 0) = 100% within the simulated scenario set, VaR_5% = USD 28,959, and CVaR_5% = USD 21,248, confirming financial robustness under the adopted uncertainty ranges.

Abstract: This paper makes a review for the studies of Space Energy Internet. Based on introducing the background of related networks, this paper discusses several key components of the Space Energy Internet (mainly including Space Solar Power Station, Energy Internet, and Artificial Intelligence Data Center), focusing on their corresponding system architectures, main research directions, and related technical challenges. Subsequently, supporting technologies such as discrete signal compression and coding, communication technology, energy transmission, power electronic devices, and artificial intelligence are discussed and analyzed. Furthermore, a highly integrated “data-computing-energy-networks” framework is established based on star computing networks and multi-orbital star link systems, and adopting the technologies like plug-and-play and modular design, which can support many innovative applications further.

Abstract: Both Noise Radar (NR) and Quantum Radar (QR), with alleged common features, aim to use the randomness of the transmitted signal to enhance radar covertness and to reduce mutual interference. While NR has been prototypically developed and successfully tested in many environments by different organizations, research and development investments on QR did not bring to practically operating prototypes. Starting from the well-known fact that radar detection depends on the energy transmitted on the target, the detailed evaluations in this work show that the detection performance of all the QR types proposed in the literature are well below the ones of a much simpler and cheaper equivalent “classical” radar set, for example of the NR type. Moreover, the absence of a “Quantum radar cross section” different from the well-known radar cross section is explained. From these facts it results that, in spite of alleged advantages in some literature, Quantum Radar proposals cannot lead to useful results, including, of course, the detection of stealth targets.

Abstract: Over the past decade, acoustically-actuated magnetoelectric (ME) antennas have been proposed as chip scale radiofrequency (RF) antennas compatible with post Complementary Metal Oxide Semiconductor (CMOS) fabrication processes. These devices have been reported to exhibit antenna gains far exceeding those of conventional electromagnetic (EM) antennas with comparable footprint. However, recent studies have challenged whether this enhanced gain originates from magnetoelastic coupling or from stray radiation sources, like the electric dipole moment in the piezoelectric film or currents in the probing pads. We resolve this controversy through a combined analytical, numerical, and experimental investigation. We model and quantify the radiated power and corresponding gain contributions from (I) magnetoelastic coupling; (II) the strain driven, time-varying electric dipole moment in the piezoelectric layer; and (III) the currents in the probing pads. Our results confirm that the radiation from magnetoelastic coupling exceeds that of the other sources by several orders of magnitude. In addition, we explain how to optimize the return loss and the radiated power of ME antennas when connected to a 50 Ω source, showing that the optimal operating point is the anti-resonance frequency. Based on this finding, we investigate the impact of the electromechanical coupling (k_t²) on gain and-10 dB fractional bandwidth. To corroborate our simulation results, we design, fabricate, and characterize the first two Aluminum Scandium Nitride (AlScN) magnetoelectric Bulk Acoustic Wave (BAW) antennas operating beyond 1.1 GHz. The two prototypes integrate different magnetostrictive materials (FeGaB and FeCoSiB) and exhibit measured realized gains of-31.8 dB and-29.7 dB, with-10 dB fractional bandwidths of 1.28% and 1.27% at 2.62 and 3.08 GHz, respectively. The achieved bandwidths are the highest reported for radiofrequency (RF) ME antennas, owing primarily to the enhanced piezoelectric coefficients of the AlScN. Benchmarking against control structures (unreleased FeGaB and FeCoSiB devices) confirms substantially degraded radiation performance in the absence of a strong magnetoelastic coupling. These results elucidate the working principle of ME antennas and provide RF designers with a rigorous framework for the design and modeling of acoustically actuated ME antennas for wireless communication and sensing.

Abstract: The ambitious roadmap for a sustainable transport system adopted by the European Commission (EC) by 2050 includes the deployment of an extensive Electric Vehicle Charging Stations (EVCSs) infrastructure, which introduces significant challenges for distribution power grids. High power demand, particularly from fast-charging systems, may lead to network overloading and voltage unbalance. In addition, recent measurement campaigns highlight substantial changes in grid impedance and the emergence of resonance phenomena, together with the injection and propagation of high-frequency conducted disturbances. These effects extend over a wide frequency range, up to several hundreds of kHz, causing degradation, aging and malfunction of network assets, in particular Power Line Communications. This paper provides a comprehensive and updated review of the impact of EVCSs on electrical grids, covering power flow, power quality, stability, and impedance-related interactions. Particular attention is given to the role of power-electronic converters, high-frequency emissions, and the associated challenges in measurement and standardization. The analysis highlights that EVCS integration fundamentally alters the nature of electrical loads, requiring new approaches for grid planning, monitoring, and regulation. The study identifies key research gaps and outlines future directions to ensure the reliable and sustainable integration of electromobility into modern power systems.

Abstract: A serious problem facing power systems today is the deterioration of power quality (PQ), driven mainly by the widespread use of non-linear loads, such as power electronic converters and adjustable-speed drives. These loads inject harmonic currents into the network, resulting in voltage distortion, increased losses, overheating, and reduced system efficiency. Therefore, harmonic mitigation and reactive power compensation are necessary to ensure stable and reliable operation. This paper presents the development of a Shunt Active Power Filter (SAPF) to become an intelligent Shunt Active Power Filter Conditioner (SAPFC) that operates under different loading conditions (linear, non-linear, or mixed). As a result, a conventional PI controller is upgraded with a Fuzzy Gain Scheduling (FGS) technique optimized by the Whale Optimization Algorithm (WOA) in order to keep the DC-link capacitor voltage stable. In addition, an adaptive instantaneous dq theory is used to produce accurate reference currents. The proposed intelligent SAPFC is implemented and validated in MATLAB-Simulink, where simulation results show maximizing the SAPFC's effectiveness in minimizing harmonic distortion to less than 0.7% and achieving a near-unity power factor under different operating conditions. The integrated SAPFC, with its intelligent control, offers a robust and adaptive solution for improving power quality in modern electrical systems, thereby increasing efficiency and reducing power losses.

Abstract: p-GaN gate enhancement-mode GaN HEMTs are promising normally-off power devices, but their high-temperature reliability is strongly affected by the gate contact scheme. This study compares Pd-ohmic and Ni-Schottky p-GaN gate HEMTs fabricated on the same GaN-on-Si epitaxial platform by combining temperature-dependent electrical characterization, post-temperature-dependent-test (TDT) room-temperature recovery analysis, and thermoreflectance thermal mapping. Electrical measurements were performed from room temperature to 500 °C using gate leakage, transfer, and output characteristics, while thermal maps were obtained before and after TDT under identical bias conditions. The Pd-ohmic devices exhibited higher initial current drive but larger operating gate-current penalty and stronger degradation of normalized on-state characteristics at elevated temperature. After TDT, reduced transconductance and maximum drain current were accompanied by weaker active-channel heating, indicating degradation-type cooling associated with reduced gate-channel modulation efficiency. In contrast, the Ni-Schottky devices showed lower gate-current penalty and better normalized output retention up to approximately 300 °C; however, post-TDT increases in transconductance and drain current occurred together with degraded subthreshold swing and persistent localized heating, indicating apparent on-state activation with weakened gate/depletion control. These results show that p-GaN gate reliability should be assessed through coupled electrical and thermal signatures rather than single electrical or thermal metrics.

Abstract: This work deals with the time-domain analysis of asymmetrical faults in three-phase systems. Conventional three-phase analysis provides steady-state solutions for asymmetrical faults. Transient analysis, however, is usually performed by resorting either to oversimplified approximate circuits, or to numerical methods. In this paper, a rigorous analytical methodology based on the time-domain Clarke transformation is presented for the most common asymmetrical faults in three-phase systems. In particular, it is shown that asymmetrical faults result in circuit coupling in the Clarke equivalent circuits. Circuit representation of coupling is also derived in the paper. Coupled equivalent circuits allow rigorous analytical solution of transients in case of asymmetrical faults. The analytical results derived in the paper are validated through proper numerical simulation of faulted radial systems.

Abstract: This study proposes a reproducible exploratory framework to link long-term territorial development with electricity demand in data-scarce contexts, and applies it to Ecuador’s Costa region. The pipeline combines three commonly available input streams: periodic census microdata, an official demand series, and macroeconomic aggregates. Socioeconomic heterogeneity across five non-uniform census rounds (1974, 1982, 1990, 2001, 2010) is summarized through Principal Component Analysis (PCA), and territorial indicators are projected to the demand horizon using low-order polynomial functions. Eleven regression specifications are compared on a log-transformed demand variable, and a rollingorigin backtesting scheme plus a 2020–2024 holdout are used for validation. The selected Trend OLS log model attains R2 = 0.551 and MAPE = 6.08%, and projects a regional demand of approximately 6,940 MW by 2050, equivalent to a compound annual growth rate of 3.45%. Beyond the Ecuadorian case, the results show that transparent, low-data pipelines based on harmonized census information, macroeconomic drivers and simple regression models can provide defensible medium- and long-term demand signals for planners in other emerging economies with limited high-frequency data.