Search Results (3,252)

Physics-Informed Neural Networks (PINNs) continue to suffer from limited training efficiency in predicting electromagnetic scattering from three-dimensional (3D) targets, resulting in particularly high retraining costs when target geometries or incident fields vary. To address this issue, we propose a transferable PINN framework constrained by discrete Maxwell operators. By embedding finite-difference electromagnetic operators into the loss function, the predicted fields directly satisfy the discrete Maxwell’s equations on the computational grid, thereby enhancing physical consistency and improving training stability. Furthermore, a layer-freezing strategy is employed to preserve the generalized wave priors learned from the source scenario. Consequently, training under novel geometries or incident conditions no longer relies on full-network retraining; instead, rapid prediction is achieved by fine-tuning a minimal set of task-specific parameters. Numerical results demonstrate that the proposed method significantly reduces the number of iterations and retraining costs while maintaining prediction accuracy, making it an effective tool for rapid electromagnetic simulations and optical device design. Full article

This paper presents a modified two-degree-of-freedom wave buoy with an embedded tunable magnetic-spring electromagnetic energy harvester. The proposed device is modeled as a branched kinematic chain composed of a rotational–rotational buoy mechanism and a rotational–prismatic harvester branch sharing the first revolute joint. Two harvester orientations are considered and compared. The mathematical model is formulated using homogeneous transformations, velocity Jacobians and Lagrange equations. Particular attention is paid to the structure of the inertia matrix and to the way in which its inverse transmits generalized forces between the rotational coordinates and the translational motion of the moving magnet. The model is implemented in MATLAB/Simulink R2024b and evaluated under free-response, regular-wave, and bidirectional frequency-sweep excitation scenarios. Under regular-wave excitation, Config. 1 produces approximately 19.3 and 2.98 times greater average load power than Config. 2 for moving-assembly masses of 5 g and 268 g, respectively. The frequency-sweep results show that the preferred harvester orientation depends on the excitation frequency and moving-assembly mass. No resolved sweep-direction dependence is observed for 5 g, whereas for 268 g the identified hysteresis intervals are approximately 0.53 rad/s for Config. 2 and 0.64 rad/s for Config. 1. These results provide design guidelines for selecting the harvester orientation and moving mass in compact wave-excited buoy systems. Full article

A new class of Ln(III) Schiff base (SB) complexes has been synthesized with the general formula [LnL(hfac)₃], where Ln = Tb (TbL), Eu (EuL), Sm (SmL) or Gd (GdL). The molecular structure of the complex was determined using the X-ray diffraction method. The IR spectra show that the C=O stretching is shifted from 1695 cm⁻¹ in pure PMMA to 1719–1724 cm⁻¹ in LnL-PMMA hybrid materials. This means that LnL materials are successfully embedded into the PMMA backbone in the polymeric films. However, the Ln(III) SB complexes exhibit emission spectra that cover the visible region. The TbL complex displays an intense green emission combined with a large emission lifetime of 0.505 ms. However, the incorporation of Ln-SB complexes into PMMA (polymethylmethacrylate) polymeric films was investigated. The thermal stabilities of the LnL-PMMA hybrid materials increased from 140 to 250 °C when compared with the LnL-SB complexes. Furthermore, their luminescence intensity and lifetimes were also enhanced due to their induced structural rigidity. The molecular interactions of the LnL complexes with the PMMA matrix were monitored by investigating the luminescence properties of EuL-PMMA. Detailed photoluminescence studies showed insights into the non-radiative rates and improved quantum yields (Q_Eu = 23%) from Eu(III). Furthermore, the higher values of Judd–Ofelt parameters Ω₂ and Ω₄ indicate strong hydrogen bonding interactions between L and PMMA. Nevertheless, these significant optical properties enable LnL-PMMA polymeric materials as colored phosphors for the design of opto-electronic devices. Full article

Cross-border trade security risks are often embedded in inconsistencies among trade documents, logistics trajectories, hardware sensor states, and financial settlement activities. Existing methods primarily rely on structured declaration fields, making it difficult to verify digital declarations against actual physical processes or to generate complete evidence suitable for regulatory review. To address these challenges, TradeSense-EQA is proposed as a cross-border trade security anomaly detection and evidence-grounded English question-answering framework. Multisource sensing information, including trade documents, GPS/AIS trajectories, RFID records, electronic seal events, port weighing data, temperature and humidity measurements, vibration signals, container door states, and visual images, is jointly modeled within the framework. The reliability-aware representation module dynamically adjusts sensing-channel weights according to data missingness, sampling intervals, device health states, and communication quality. The trade-process-constrained module identifies anomalies across declaration, packing, transportation, transshipment, arrival, and customs clearance stages and generates process-consistent evidence chains. The evidence-grounded question-answering module answers English trade risk questions on the basis of verified documentary fields and sensor records, while confidence estimation and abstention mechanisms are incorporated to reduce factual hallucinations. Experimental results demonstrate that TradeSense-EQA achieved an Accuracy of $0.918$, a Precision of $0.909$, a Recall of $0.897$, a Macro-F1 of $0.903$, and a ROC-AUC of $0.958$ on the cross-border trade anomaly detection task, outperforming baseline methods including XGBoost, LightGBM, TCN, Transformer, BERT, CLIP, and VisualBERT. On the English trade risk question-answering task, Exact Match, Token-level F1, BLEU, ROUGE-L, and BERTScore reached $0.782$, $0.851$, $0.668$, $0.801$, and $0.934$, respectively. Ablation results further confirmed the effectiveness of hardware sensing input, reliability-aware weighting, declaration–fact alignment, process-graph reasoning, and evidence-constrained generation. The proposed framework provides a reliable, interpretable, and auditable artificial intelligence-driven sensing solution for customs supervision, port security, international logistics review, and trade-background investigation. Full article

Signal interpretation in embedded ultrasonic monitoring depends strongly on the internal transmission path of the sensing element, yet this design factor has received limited systematic attention. This work compares two triangular quartz sensors that differ only in the configuration of their epoxy region: an epoxy-notched triangular quartz (ENTQ) device and an epoxy-solid triangular quartz (ESTQ) device. Their responses were examined during 24 h mortar hydration, controlled heating and cooling, and loading-induced debonding. The notch in ENTQ exposes part of the direct-wave route to changing external media, whereas the continuous epoxy region in ESTQ provides a more uniform internal route. Across the investigated conditions, ESTQ retained a more consistent direct-wave waveform and dominant-frequency pattern, while its later-arriving components remained responsive to changes in the surrounding mortar. Energy and time-frequency features also tracked the evolution of mortars prepared at two mix proportions. The comparison indicates that separation of a stable internal reference path from an environmentally sensitive reflected-wave path can improve the interpretability of embedded ultrasonic measurements. The results support structural-path design as a useful strategy for sensor development. Full article

Fuzzy set theory, proposed by Lotfi Zadeh, is applied with great success in embedded, control-oriented systems based on microcontrollers or programmable logic devices. Algorithms used in these systems operate on fuzzy sets represented by membership functions. In practically implemented control systems, these functions typically possess simple linear shapes, such as trapezoidal or triangular. To compute the function value, the classical method relies on multiplication and division. These operations are complex to implement in software or hardware, consume significant resources, and are time-consuming. This paper proposes a fixed-point computation method to obtain the value of a membership function without relying on these operations. For this purpose, the bisection algorithm was adopted, replacing its complex arithmetic with addition and binary shift operations. This approach allows for a reduction in computation time and the hardware or software overhead required by resource-constrained embedded systems, especially architectures without dedicated hardware accelerators for multiplication and division. Sample software and hardware implementations demonstrate its suitability for real-world systems. Full article

Accurate multi-object tracking and metric localization support traffic monitoring and cooperative intelligent transportation at complex intersections. This study presents a fixed-camera vision-map fusion framework that addresses two practical difficulties: axis-aligned boxes poorly represent turning vehicles, and unconstrained image-plane tracking can produce physically implausible trajectories. A map-aided frontend first generates candidate detections using improved You Only Look Once version 8 nano (YOLOv8n) horizontal bounding box (HBB) branch and an improved YOLOv8 oriented bounding box (OBB) branch. A high-definition (HD) map selector then retains the candidate geometry consistent with the straight-driving or turning region and converts it into a unified detection record. The selected reference point is projected to the ground plane through an offline-estimated homography, whereas the appearance feature bypasses the homography and is passed directly to the association stage. The tracking backend uses a 12-dimensional joint image/metric state, symmetric central-difference evaluations of the process and measurement functions, appearance-motion association, and a feasible-road projection derived from HD-map lane polygons. On the evaluated public sequences, the complete configuration achieved a multiple object tracking accuracy (MOTA) of 74.5%, an identification F1 score (IDF1) of 82.6%, 614 identity switches, and a throughput of 26.8 frames per second (FPS) on an RTX 4090 workstation. In a descriptive Vehicle-in-the-Loop case study involving one instrumented vehicle at one intersection, the overall localization mean absolute error (MAE) was 0.180 m, compared with 0.208 m for the baseline end-to-end configuration. These results indicate the feasibility of combining branch-specific vehicle geometry with map-constrained tracking; controlled same-detector comparisons, repeated multi-vehicle trials, and embedded-device latency and power profiling remain necessary for broader claims. Full article

This paper presents, to our knowledge, the first framework that jointly and provably optimizes communication and computation energy across an entire fleet of wirelessly connected mobile robots, rather than for a single device or under a fixed offloading policy as in prior work. Battery capacity limits the endurance of autonomous mobile robots, and on-board computation and radio communication increasingly rival locomotion in energy draw; across a fleet, the two are further coupled through shared uplink bandwidth and edge computing capacity. We formulate the joint selection of each robot’s task-offloading ratio, DVFS processor frequency, and transmit power, together with the fleet-wide allocation of bandwidth and edge capacity, subject to hard per-task deadlines. Closed-form inner solutions reduce each robot’s problem to a jointly convex program, coupled fleet-wide only through two linear resource constraints. We exploit this structure in JECCO-M, a distributed price-based algorithm that provably converges to the global fleet optimum while exchanging only a few scalars per iteration. A trajectory-conditioned channel-prediction extension handles robot mobility. Evaluated in simulations against optimization-based and learning-based baselines from the literature and on a physical three-robot testbed with embedded GPU compute, an IEEE 802.11ac uplink, and instrumented power rails, JECCO-M substantially reduces combined electronic energy while meeting all deadlines, and the measured hardware behavior tracks the analytical model closely. The results indicate that treating radio energy, processor energy, and shared edge resources as a single optimization domain is a practical route to extending the operating time of connected robot fleets. Full article

Broadband, low-frequency sound absorption within a compact device remains a central unsolved problem in noise control engineering, arising from fundamental trade-offs among resonator volume, absorption bandwidth, panel thickness, and frequency tunability that no passive, linear, time-invariant system can simultaneously circumvent. This review establishes a unified mass–spring–damping (MSD) framework applied systematically across the full spectrum of resonance-based absorber technologies. From first principles, we derive the mass–stiffness coupling result (the mass-disappearing result of Shen and Liu): fixing the resonance frequency imposes $K=M{\omega }_{\mathrm{res}}^2$, so acoustic mass and stiffness cannot be adjusted independently; the half-absorption bandwidth ${\Pi }_1=\eta /\left(M{\omega }_{\mathrm{res}}\right)+V{\omega }_{\mathrm{res}}/\left(c_0{S}_{\mathrm{tar}}\right)$ then depends explicitly on the cavity volume V (system stiffness) and on the damping coefficient $\eta$, rather than on mass as an independent lever. This explains why neck extension, space-coiling, and membrane loading—which merely add mass while leaving the cavity stiffness unchanged—fail to broaden the absorption band at fixed volume, and refocuses the design effort on stiffness reduction and damping control. Five non-dimensional performance metrics are introduced that collapse the scattered literature into a single, scale-independent language for rigorous comparison across all absorber families: normalised half-absorption bandwidth ${\Pi }_1$, volume efficiency ${\Pi }_2$, integral absorption criterion ${\Pi }_3$ tied to the Rozanov causality bound, quality factor $Q=1/{\Pi }_1$, and frequency-thickness ratio ${\Pi }_4$. A two-degree-of-freedom acoustic–structural coupling model yields closed-form effective stiffness and damping, revealing how structural loss augments acoustic damping, how modal veering produces split absorption peaks, and how the anti-resonance frequency becomes a designable parameter. A critical distinction is drawn between mathematical negative stiffness (a fitting artefact) and physical negative stiffness via repulsive magnets, bistable elements, or negative-capacitance piezoelectric shunts, which genuinely reduces cavity stiffness, lowers resonance frequency, and widens bandwidth beyond the passive causality bound. The shunt electromechanical diaphragm further demonstrates $\alpha >0.9$ at nine tonal frequencies spanning three octaves without mechanical modification. Finally, embedding MSD equations and ${\Pi }_1$–${\Pi }_4$ bounds as hard physical priors in AI/LLM-assisted design frameworks is identified as the key step toward provably physically consistent absorber synthesis. Full article

Renewable-powered agricultural water systems are often assessed as solar-pumping devices, but their sustainability depends on a service chain linking crop-water demand, hydraulic duty point, power electronics, storage, water quality, governance, operation and end-of-life management. This structured integrative review synthesizes peer-reviewed and practice-oriented evidence on photovoltaic pumping, hybrid renewable irrigation, grid-interactive pumps, micro-hydro assistance and renewable-powered brackish-water reverse osmosis (PV-RO). Evidence was screened across four source families and coded by service function, energy architecture, hydraulic duty and dominant sustainability pathway; recurring combinations were consolidated using explicit separation and merge rules. It develops an archetype-based screening framework for ex ante appraisal of irrigation, desalination and circularity risks. Seven technology-service archetypes are identified: direct PV pumping, PV-to-tank pumping, PV with electrical buffering, grid-interactive PV pumping, PV–wind hybrid irrigation, micro-hydro-assisted irrigation and PV-RO water making. The framework links each archetype to its operating envelope, evidence maturity, enabling subsystems, sustainability pathways, minimum indicators and ordinal triggers for deeper due diligence. Hydraulic storage is usually the lowest-regret reliability buffer for open-field irrigation, whereas batteries are justified mainly when pressure stability, fertigation timing or night-time operation has high agronomic value. PV-RO is a distinct water-making archetype and is environmentally defensible only where feed-water characterization, energy recovery, pretreatment, product-water agronomy, membrane management and permitted concentrate disposal are embedded in design. Two synthetic applications demonstrate archetype selection and due-diligence escalation. Responsible deployment requires service-oriented screening that integrates hydraulic design, groundwater governance, procurement quality assurance, circularity obligations and social inclusion before field implementation. Full article

The LiDAR sensor embedded in recent iPhone Pro devices offers new opportunities for rapid, low-cost, and spatially detailed assessment of outdoor recreational infrastructure. Framed within mobile sensing and IoT-based environmental monitoring, this proof-of-concept case study evaluates a smartphone-based LiDAR workflow for localized hiking trail assessment under dense canopy conditions. Four independent surveys of a degraded hiking trail section were conducted to assess inter-survey repeatability, agreement with conventional field measurements, and practical field applicability. The resulting three-dimensional models reproduced trail morphology, including incised tread sections, exposed roots, rocky surfaces, and localized irregularities relevant to trail-condition assessment. Repeated registrations demonstrated consistent cloud-to-cloud comparison metrics, while comparisons with field reference measurements showed good agreement in the representation of cross-sectional morphology and exposed root characteristics. Continuous surface reconstruction additionally supported exploratory identification of potential runoff pathways and relative surface depressions. Although limited to a single trail section and one smartphone–application combination (iPhone 13 Pro with Scaniverse), the evaluated workflow demonstrates potential as a rapid, accessible, and relatively low-cost approach for localized trail-condition assessment and provides a foundation for further evaluation in hiking trail monitoring. Full article

Industrial sensor-based fault diagnosis often requires continuous data acquisition, local data processing, and timely model inference on edge devices. Although deep learning-based diagnostic methods have achieved promising performance, many existing approaches rely on cloud-centered processing pipelines that introduce communication overhead and potential data privacy concerns. This paper presents a CPU–NPU heterogeneous edge fault diagnosis framework for industrial sensor data. The framework runs on an RK3588 local edge device and includes SQLite- and RingBuffer-based data management, sliding window generation, micro-batch construction, and model inference. The CPU is responsible for data access and buffering, preprocessing, and micro-batch preparation, while the NPU executes fault diagnosis models using the RKNN runtime environment. By performing inference locally, the framework reduces the continuous transmission of raw sensor data and supports real-time fault diagnosis under resource-constrained edge devices. Experimental results demonstrate high consistency between ONNX-based CPU inference and RKNN-based NPU inference after model conversion. Furthermore, the effects of different data input paths and micro-batch configurations are systematically evaluated. A cross-platform comparison between server-class CPU/GPU execution and embedded NPU deployment is also conducted in terms of latency, throughput, and energy efficiency. The results show that RingBuffer-based streaming input significantly reduces data access overhead, while the effectiveness of NPU acceleration depends on both model structure and micro-batch size. The cross-platform results further demonstrate the energy efficiency advantages of the RK3588 platform, making it more suitable for practical deployment in resource-constrained edge scenarios. These findings provide practical insights for deploying fault diagnosis models on heterogeneous edge devices. Full article

As the construction of new-type power systems continues, the extensive integration of renewable energy and power-electronic devices has significantly increased the risk of cascading failures in power grids, making accurate prediction of cascading-failure evolution paths crucial for grid security. However, real-world fault samples are extremely scarce, and conventional physics-based simulations are too computationally intensive for real-time online early warning. To address these challenges, this paper proposes a cascading failure evolution path prediction method based on massive event chain mining and prior knowledge constraints. First, we construct a refined cascading-failure simulation model that incorporates the action logic of the three defense lines to generate a large standardized event-chain dataset, thereby addressing the data scarcity faced by data-driven models. Second, a sequence-prediction engine that combines word embeddings, LSTM-based temporal modeling, and prior-knowledge constraints is developed; the physical action logic of the power system is explicitly incorporated into the loss function to improve the physical plausibility of the predictions. Finally, an evaluation framework from event prediction to third-defense-line early warning is constructed to assess system-level security risks. Based on simulations of the IEEE 39-bus AC/DC hybrid system, the proposed method is shown to achieve single-step Top-1 accuracies of 97.0% and 96.8% for the system-level critical events of frequency limit violation and system instability, respectively, with corresponding Top-3 accuracies of 99.2% and 99.4%. The overall Top-1 and Top-3 accuracies are 89.8% and 98.2%, respectively; the mean single-inference time is approximately 9.6 ms, corresponding to a speedup of more than 4700 times over conventional time-domain simulation, and the weighted mean early-warning lead time is 1.78 s. Full article

Public cattle re-identification benchmarks remain limited in scale and rarely provide leakage-controlled open-set and cross-camera evaluation. Using 25,209 curated crops of 197 Holstein cows recorded by four synchronized side-view cameras in a passageway of a working farm, we investigate viewpoint-related shifts in the embedding space and evaluate cattle re-identification under camera-disjoint gallery/query construction. The extractor combines an animal-pretrained MegaDescriptor Swin-B backbone with multi-level projections, learned gating, quality weighting, and two-level aggregation. Under the strictly inductive identity-disjoint cross-camera protocol without camera centering, the proposed model achieved Rank-1 of 55.7% (95% CI: 47.7–63.8%), Rank-5 of 90.0% (86.3–93.7%), and mAP of 70.2% (66.3–74.1%). Under the same protocol, TransReID ViT-B/16 achieved 38.6%, 77.9%, and 55.9%, while OSNet-x1.0 achieved 12.9%, 47.9%, and 30.7%, respectively. The comparison with the pure-GAP configuration was not statistically resolved (Holm-adjusted p = 0.91). In held-out open-set evaluation, the validation-selected global cosine threshold obtained the strongest mean AUROC, F1, and AUOSCR (63.2%, 66.9%, and 44.4%); the corresponding results were 60.2%, 64.4%, and 38.8% for Mahalanobis rejection and 56.9%, 63.2%, and 41.7% for EVT. Zero-shot evaluation at a second corridor on the same farm yielded Rank-1 of 26.6%, Rank-5 of 41.9%, and mAP of 35.3%. These results establish cross-camera feasibility under the defined protocols and same-farm cross-installation transfer, but do not establish cross-farm, cross-device, seasonal, longitudinal, or full production robustness. Full article

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 is transmitted 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. A component-level cost analysis is also reported, showing that the LaserFit sensing hardware can be manufactured for a small fraction of the retail price of commercially available direct-force power meters. 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. LaserFit is therefore considered a valid alternative for training and performance measurement during continuous cycling. Full article