Search Results (733)

Adaptive restraint systems require specific occupant information, including head position, anthropometry, and safety-relevant posture states. Existing 3D human pose estimation benchmarks mostly report root-relative pose, while automotive in-cabin studies rarely evaluate these outputs across heterogeneous vehicle sensor sets. We present ICF-Fusion, a five-modality transformer fusion architecture, and evaluate it under leave-one-subject-out (LOSO) validation on the ICF-Body dataset, which includes synchronized near-infrared (NIR) camera, 60 GHz millimeter-wave (mmWave) radar, belt webbing extraction sensor (WES), seat configuration sensor (SCS), and ultra-wideband (UWB) recordings. The model localizes the head with a Mean Root Position Error (MRPE) of 6.10 cm and regresses anthropometry to mean absolute errors (MAE) of 5.36 cm for height, 3.50 cm for torso length, 1.78 cm for shoulder width, and 8.61 kg for weight. Feet-on-dashboard is detected on 9 of 10 evaluable folds without meaningful MRPE degradation. The full sensor fusion outperformed every single modality on all three tasks, but NIR alone nearly matched it for head localization and feet-on-dashboard detection. The fusion advantage was substantial only for the anthropometry estimation task. Full article

Ultra-wideband (UWB) and inertial measurement unit (IMU) fusion is an effective scheme for train and rail-guided target localization because UWB provides absolute ranging results and the IMU provides high-rate motion prediction. In practical rail transportation systems, UWB anchors are usually deployed along the track with large longitudinal intervals and limited lateral separation to reduce installation and maintenance costs. This anchor deployment leads to a large condition number of the observation matrix, so slight ranging errors caused by non-line-of-sight (NLoS) propagation may be amplified into large localization errors. To mitigate LoS/NLoS interference, this article proposes a propagation reliability estimation method for track-constrained UWB/IMU fusion localization. First, rail transportation localization along a narrow path is formulated as a one-dimensional track-constrained problem, and each UWB ranging result is converted into a candidate longitudinal coordinate on the known track centerline. Second, the reliability of each anchor–target propagation is estimated in a sliding window by comparing the motion increments solved by UWB observations with the motion prediction by the IMU. Third, the estimated reliability is incorporated into a reliability-weighted track-domain update before a closed-loop position–velocity Kalman correction. The simulation results show that, under the mixed LoS/NLoS scenario, the proposed method achieves an NLoS-interval RMSE of 0.0090 m. Compared with Track-EKF, Track-Gauss-AUKF, Track-Adaptive KF, and Track-SW-FGO, the proposed method reduces the NLoS-interval RMSE by 92.9%, 62.1%, 92.8%, and 92.6%. A supplementary real-data stress test on the public STAR-loc dataset demonstrates an average longitudinal RMSE of 0.0555 m under a strict online calibrated-range protocol, supporting the algorithm’s practical applicability against real-world lateral sway and asynchronous sensor noise. Full article

Testing advanced driver assistance systems (ADAS) under rare or safety-critical conditions is impractical at full scale: track campaigns are expensive, time-intensive, and cannot easily reproduce low-probability events. Scaled cyber–physical testbeds offer a more accessible path by coupling miniature vehicle platforms with virtual simulation environments, but the field has no unified review that covers co-simulation architectures, indoor localization, and ground truth strategies in a single treatment. This paper addresses that gap with a PRISMA 2020-compliant systematic review of 92 primary sources selected from 984 records identified across IEEE Xplore and Scopus. Three topic areas are examined: real-time co-simulation architectures built on AirSim, CARLA, Gazebo, and LGSVL, compared for ROS 2 integration, synchronisation model, and edge hardware suitability; three indoor localization methods, namely AprilTag fiducial tracking, Visual Simultaneous Localization and Mapping (VSLAM), and Ultra-Wideband (UWB) radio positioning, evaluated against shared accuracy, latency, infrastructure, and robustness criteria; and existing ground truth strategies for indoor localization benchmarking. A consistent finding across the corpus is that no controlled cross-method localization comparison exists for scaled testbeds. To address this, we introduce the Programmable Ground Truth Reference System (PGTRS), which renders spatial references on a programmable LED floor panel at a pixel pitch of approximately 3.9 mm, targeting sub-centimetre ground truth accuracy without dedicated motion-capture infrastructure. The concept is demonstrated within a 1:14 scale Digital Driving Twin (DDT) testbed built at the University of Applied Sciences Wildau at a hardware cost of approximately €6576. Design guidelines and open research challenges are discussed. Full article

Ultra-wideband (UWB) localization is widely used in indoor positioning because it provides high temporal resolution and direct geometric range constraints. In complex indoor environments, however, UWB ranging is affected by non-line-of-sight propagation, multipath reflection, heterogeneous measurement quality, and link-dependent persistent bias, producing long-tailed errors and trajectory drift. This paper proposes HBR-PGD, a heteroscedastic bias-robust projected gradient descent framework for UWB-only indoor localization. Its central idea is a role-separated error-source formulation that assigns packet-level quality degradation, anchor-channel persistent bias, and sparse instantaneous NLOS anomalies to different roles in a unified constrained residual model. Packet-level quality features are mapped to heteroscedastic uncertainty scales, robust loss shape parameters, and non-negative NLOS correction priors; anchor-channel soft gating limits residual-correction freedom; and target trajectories and bounded structural bias states are jointly estimated in overlapping sliding windows. Experiments on a public indoor UWB dataset show that HBR-PGD achieves RMSE values of 0.107 m, 0.068 m, and 0.204 m in residential-apartment, small-apartment, and workshop/industrial environments, respectively. Compared with WLS, MCC-VC-TOA, SR-MCC, and AR-PNN, HBR-PGD consistently improves overall accuracy and high-percentile robustness, with the largest gain in the workshop/industrial environment. Ablation results verify the contributions of heteroscedastic weighting, structural-bias estimation, gated correction, and information-weighted fusion. These results suggest that HBR-PGD is a practical UWB-only localization framework for complex indoor environments with heterogeneous measurement quality, persistent link bias, and sparse NLOS anomalies. Full article

This paper presents the design, implementation, and validation of a mobile Real-Time Location System (RTLS) based on Ultra-Wideband (UWB) technology for acquiring and transforming production and logistics data in an industrial environment. The aim is to obtain accurate real-time information on the movement of workers and material flows to support data-driven optimization within the Industry 4.0 framework. The proposed solution introduces a mobile RTLS architecture enabling flexible deployment without permanent infrastructure changes. The system was experimentally validated in a manufacturing enterprise, where UWB anchors and wearable tags were used to monitor six operators and handling equipment during a 12 h production shift. The collected data were analyzed using trajectory mapping, heatmaps, and worker activity analysis to identify inefficiencies in the existing production layout. The identified bottlenecks and unnecessary worker movements were subsequently used to redesign the production layout in Tecnomatix Process Simulate. Simulation results demonstrated a 51.9% reduction in worker travel distance, while transportation and waiting activities, which accounted for approximately 24% of the original production lead time, were significantly reduced in the proposed layout. The results confirm that UWB-based RTLS provides reliable input data for simulation-driven manufacturing layout redesign and supports objective decision-making in digital manufacturing environments. Full article

Ultra-Wideband (UWB) positioning is a promising technique for underground mine localization, but its accuracy is strongly affected by complex tunnel topology, multipath propagation, and non-line-of-sight (NLOS) ranging errors. To address these challenges, this paper proposes a three-dimensional UWB positioning algorithm based on a Multi-head Attention Feature Fusion Graph Neural Network (MAFF-GNN). The localization problem is formulated as a graph-based node position regression task, where anchors and tags are represented as nodes and UWB ranging links are represented as edges. The proposed model integrates graph message passing, multi-head attention-based feature fusion, global skip connections, and geometry-constrained regularization to learn reliability-aware spatial representations from noisy ranging measurements. A physics-guided simulated underground mine environment is constructed by considering tunnel geometry, wall roughness, coal dust concentration, humidity attenuation, and controlled NLOS conditions. Three mine-like corridor topologies are generated, with 10,500 localization samples in total. Experimental results under controlled simulation conditions show that MAFF-GNN achieves an RMSE of 0.323 ± 0.093 m, an MAE of 0.274 ± 0.024 m, and a P90 error of 0.480 ± 0.038 m. Compared with weighted least squares and support vector regression, the proposed method reduces RMSE by 74.16% and 40.30%, respectively. Robustness tests under different simulated NLOS ratios further indicate that the proposed graph-attention framework maintains a gradual error-growth trend as NLOS severity increases. These results indicate the potential of attention-enhanced graph learning for UWB localization in a challenging mine-like environment. Full article

This paper presents a compact, ultra-wideband (UWB) antenna designed for low-power wireless systems requiring upper WLAN (5.725–5.825 GHz) interference mitigation. The antenna integrates a tapered-slot radiator for broadband impedance matching with a dual C-shaped slot resonator on the ground plane to achieve targeted signal rejection. By leveraging the resonant properties of the slots, interference from the WLAN band is suppressed without requiring any active components, ensuring zero additional power consumption for the filtering function. The fabricated prototype, with a compact size of 31 × 40 mm², demonstrates an operational bandwidth from 2.68 to 16 GHz (S11 < −10 dB) with a stable peak realized gain of 2–10 dB and a radiation efficiency above 80% across the passband. At the notch center frequency of 5.72 GHz, the gain decreases by 6.4 dB and the radiation efficiency falls to 0.6, there is effective signal rejection in the target notch band, and there are stable, omnidirectional H-plane radiation patterns. The reflection coefficient at the notch frequency rises to approximately −2.2 dB, effectively suppressing radiation in the WLAN band while maintaining stable gain and omnidirectional patterns in the remaining UWB spectrum. The proposed design offers a simple, low-cost, and energy-efficient solution for achieving spectral coexistence in power-constrained UWB applications. Full article

Caregiver–patient traceability is essential for measuring care workload and interaction time in shared hospital rooms, where a single caregiver attends multiple patients and manual documentation is intrusive, time-consuming, and prone to errors. This paper proposes a non-invasive smart bedside sensing approach based on a commercial smartwatch with integrated Ultra-Wideband (UWB) radio worn by the caregiver and two compact UWB localization anchors deployed near the monitored beds. The classification approach uses a compact smartwatch feature set comprising three-axis magnetometer measurements and a four-component orientation quaternion provided by the device through Magnetic, Angular Rate, and Gravity (MARG)-based sensor fusion. No device is required to be worn by the patients. The system was evaluated in a shared hospital room measuring approximately $5.0\times 4.8$ m, with two hospital beds separated by 1.3–1.7 m. Three datasets involving two caregiver participants were included in the evaluation. Several supervised learning approaches were evaluated, including Long Short-Term Memory (LSTM) networks, a hybrid Convolutional Neural Network plus LSTM (CNN+LSTM) architecture, Extreme Gradient Boosting (XGBoost), and a non-linear Support Vector Machine (SVM). XGBoost and SVM achieved the best overall performance, reaching a macro F1-score of 0.97. The results demonstrate that compact wrist-worn magnetic and MARG-derived orientation signals can accurately identify the attended bed, supporting scalable and objective caregiver–patient traceability in shared hospital rooms with minimal infrastructure. Full article

Traditional design methods for ultra-wideband (UWB) filters rely on complex electromagnetic (EM) simulations and iterative parameter optimization, consuming significant computational resources and design time. Current inverse design approaches predominantly employ generative adversarial networks (GANs) and convolutional neural networks (CNNs). However, these models often produce blurry topological boundaries, rendering them inadequate for UWB filters that demand extremely precise modeling of intricate features, such as microscopic stubs and exceedingly narrow gaps. To overcome this bottleneck, this paper proposes an inverse design framework integrating a conditional diffusion model with an evolutionary algorithm. The conditional diffusion model is uniquely suited for UWB inverse design, directly incorporating target scattering parameters as explicit conditions during training strictly guides the generation trajectory. This mechanism enables the model to synthesize high-fidelity, fine-grained pixelated patterns that traditional networks fail to achieve. During the inverse design process, the conditional diffusion model generates candidate topology-guided target S-parameters. Subsequently, an evolutionary algorithm, coupled with a pre-trained ResNet fast evaluator, iteratively searches the latent space to pinpoint the optimal geometric structure matching the target EM response. Validation through multiple UWB filter examples demonstrates that simulated S-parameters exhibit excellent agreement with target curves, achieving an efficient and precise design from EM performance to geometric structure. The two final fabricated inverse-designed filters exhibit 3 dB passbands ranging from 5.67 to 12.40 GHz and 7.35 to 14.60 GHz, achieving high fractional bandwidths of 74.53% and 66.06%, respectively. Full article

To address unavailable global navigation satellite system (GNSS) signals, dense metallic equipment, valve-tower occlusion, and the insufficient robustness of single-sensor localization in unmanned aerial vehicle (UAV) inspection of converter valve halls, this paper proposes a non-line-of-sight (NLOS)-aware LiDAR-ultra-wideband (UWB) fusion localization method. The method uses LiDAR odometry to provide continuous local motion constraints and UWB ranging to provide global distance constraints. The geometric relationship among the UAV, UWB anchors, and valve-hall obstacles is used to evaluate the NLOS risk of each UWB link, and the equivalent ranging variance is adaptively adjusted before tight fusion optimization. To avoid overextending simulation conclusions, this study focuses on localization-layer modeling and simulation-based validation rather than full energized valve-hall flight deployment. In the grouped-bushing valve-hall scenario, the proposed method achieves an RMSE of 0.30 m, a mean error of 0.29 m, a P95 error of 0.43 m, and a maximum error of 0.48 m, reducing the RMSE by 50.0% compared with ordinary tight LiDAR-UWB fusion. Additional Monte Carlo tests under different trajectories, anchor layouts, anchor installation errors, and obstacle densities further verify the robustness of the proposed weighting mechanism. The results indicate that the method can suppress LiDAR accumulated drift and reduce the influence of UWB NLOS ranging in GNSS-denied metallic indoor environments, while real converter-valve-hall flight tests under energized electromagnetic conditions remain necessary before engineering deployment. Full article

We present in this paper an ultra-low-power impulse radio ultra-wideband (IR-UWB) transmitter intended for short-range, highly energy-efficient, and compact silicon-area applications, such as implantable brain–computer interfaces (iBCIs). The proposed transmitter features effective spectrum tunability, enabling independent adjustments to both the center frequency and −10 dB bandwidth. Fabricated in TSMC 40 nm CMOS technology, the chip occupies a core area of just 0.001 mm². Experimental results demonstrate an energy efficiency of 2.45 pJ/b across a data rate range of 10 to 200 Mbps. The peak-to-peak output voltage amplitude is approximately 310 mV when driving a 50 Ω load. Furthermore, in vitro wireless measurements demonstrate reliable through-tissue transmission at an implantation depth of 18 mm and achieve a distance range exceeding 0.8 m. Full article

We present in this paper a low-power impulse-radio ultra-wideband (IR-UWB) transmitter architecture for neural implants. It features a duty-cycled phase-locked loop (PLL) and a co-designed compact fractal antenna. To suppress the carrier frequency drift inherent in open-loop ring oscillators while maintaining ultra-low power consumption, a hybrid PLL-oscillator upconversion scheme integrated with a switch-controlled voltage-holding module is proposed. Operating at a 10% duty cycle, the PLL consumes merely 90 $\mu$W and achieves a locking frequency of 4.25 GHz with a peak-to-peak jitter of 2.14 ps. Furthermore, to eliminate the bulky output matching network, an 8 mm × 10 mm coplanar-waveguide-fed fractal antenna is co-designed to present the conjugate impedance required by the power amplifier output, significantly advancing the miniaturization and energy efficiency of the neural implant. The complete transmitter was fabricated in TSMC 40 nm CMOS, with a supply voltage of 1.0 V, and in vitro wireless experiments through 18 mm of porcine tissue validated the design with a total power consumption of 0.58 mW. Full article

As accessibility development in public buildings has gradually shifted from facility compliance toward experience- and performance-oriented evaluation, the quantitative assessment of indoor mobility experiences among blind users still lacks a systematic sensor-supported analytical framework. To address this gap, this study proposes an indoor accessibility assessment approach that integrates multi-sensor data acquisition with explainable machine learning, using a tactile museum as the experimental setting. Sixty-four participants with first-level blindness were recruited to complete a real-world directed walking task. A multimodal database was constructed by integrating objective data collected from an ultra-wideband (UWB) indoor positioning system, an intelligent gait analysis system, and video-based behavioral recording, including spatiotemporal trajectories, gait characteristics, and behavioral events, together with post-task accessibility satisfaction ratings. Based on this dataset, a random forest model was developed using the Overall Accessibility Satisfaction Score (OAS) as the response variable. SHAP, partial dependence analysis, and GAM smoothing were further applied to interpret the associations between key variables and predicted satisfaction. The results showed that walking distance, number of turns, self-reported collision perception, and selected gait indicators made relatively high contributions to the model interpretation, and these variables exhibited certain nonlinear associations with predicted satisfaction. These findings suggest that combining multi-source sensor-based behavioral measurement with explainable machine learning has potential for sensor-supported post-occupancy evaluation of indoor accessibility environments and can provide exploratory references for the quantitative assessment and optimization of accessibility in public buildings. Full article

Power wireless sensor networks (PWSNs) provide essential field-level sensing and communication support for smart grids, where topology authenticity directly affects communication reliability and network operation. However, wormhole attacks can forge false adjacency relationships through low-latency tunnels, thereby disrupting topology consistency and misleading routing decisions. In practical power environments, metallic obstruction, multipath reflection, and non-line-of-sight (NLOS) propagation may further cause normal-ranging anomalies to resemble attack-induced topology distortion, making reliable wormhole attack detection challenging. To address this issue, this paper proposes a topology-stress-based wormhole attack defense method with ultra-wideband (UWB) physical-layer awareness. The first-path power ratio and root-mean-square delay spread extracted from UWB channel impulse responses are used to evaluate link-ranging reliability and construct adaptive stiffness coefficients. Local backbone links are modeled as virtual springs, and a topology stress indicator is derived from the residual deformation after potential-energy minimization to quantify the geometric inconsistency caused by forged adjacency relationships. Furthermore, a Beta-based temporal evidence fusion mechanism is introduced to support graded node access decisions and improve decision stability. Simulation and hardware validation results demonstrate that the proposed method effectively suppresses NLOS-induced false alarms while maintaining high sensitivity to wormhole attacks. Compared with representative baseline methods, it achieves more stable detection performance under increasing ranging errors and different attack intensities. Hardware experiments further show that topology stress can clearly distinguish normal links, NLOS-affected links, and forged wormhole links, confirming its effectiveness for topology-authenticity verification in power wireless sensor networks. Full article

This review analyses and logically structures modern intelligent sensor technologies in the context of animal husbandry, feed production, and veterinary medicine. The main research discussed in the article focuses on machine learning based on modern neural network models, computer vision, and sensor systems that are transforming the methods for assessing the health, behavior, and nutrition of farm animals. The first part examines modern approaches to quality control and optimization of mineral and vitamin premixes, including visual inspection using visual sensors and neural networks. Key roles are played by precise dosing, component stability (minerals, vitamins), and the transition to more bioefficient organic forms of micronutrients to reduce environmental impact. Improvements in feed and premix production are analyzed, including automation, energy management, and the use of machine learning for non-destructive quality control, defect detection, mixing homogeneity assessment, and vitamin stability prediction. The second part analyzes methods for animal location and behavior detection. This article presents computer vision-based systems, including modifications of YOLO, for automatically tracking and classifying key behavioral patterns (lying down, standing, feeding, and aggression) in cattle and pigs, even in crowded conditions. It also discusses the use of ultra-wideband (UWB) systems and accelerometers combined with machine learning for high-precision positioning and detection of specific behavioral anomalies, such as lameness and playfulness. The third section focuses on the application of machine learning in veterinary diagnostics, including the automated interpretation of medical images (X-ray, ultrasound, and MRI) as sensor data streams for the diagnosis of cardiovascular, oncological, and orthopedic diseases in farm and small animals. Furthermore, the article examines the use of machine learning models for proactive disease diagnosis in farm animals and poultry based on multimodal data and image analysis. Considerable attention is given to methods and tools for radiometric diagnosis of animal diseases at an early stage using microwave sensors, as well as laser therapy and surgery in veterinary medicine. The review concludes that the integration of intelligent systems enables a transition to data-driven livestock management, significantly improving animal welfare and, consequently, the efficiency and sustainability of agricultural production. Full article