Search Results (3,109)

Human kinematic analysis is an increasingly important tool in biomechanics, human performance, and wearable sensing research. Many emerging sensing modalities utilize custom sensors requiring accurate temporal alignment with ground-truth biomechanical movement data. Optical motion capture systems provide high-fidelity kinematic measurements but operate as closed, self-contained systems, making time synchronization with external sensor data non-trivial, particularly in wireless and mobile contexts. This work presents a wireless analog interface system built using commercially available components that enables alignment between analog sensor data (e.g., from custom wearables and Internet-of-Things devices) and a commercial motion capture system. The proposed architecture consists of a wearable data acquisition node and a receiver node interfaced directly with an optical motion capture system, allowing synchronized recording of analog sensor signals alongside kinematic data. Notably, the system reconstructs signals into the commercial hardware interface rather than relying on triggers or sync outputs, resulting in a single data file containing kinematics and sensor readings. Benchtop testing demonstrated a mean end-to-end frame delay of ~6 ms, with 95% of the sample exhibiting delay within 15 ms. Accounting for the typical offset, this leaves a standard deviation of 4 ms, within one motion capture frame of the true timestamp (at 100 Hz). Voltage reconstruction accuracy was within 30 mV across the tested conditions, with gain compression below 2.7%. Adjacent channel crosstalk remained below −83 dB across all test conditions. The use of commercial off-the-shelf components supports replication and adaptation by other research groups and integration with different optical motion capture systems. Full article

Fish Aggregating Devices (FADs), traditionally used to attract and concentrate fish, can also serve as effective environmental enrichment tools in reservoirs, particularly in those with homogeneous characteristics and scarce refuge habitat, enhancing structural complexity and promoting recreational fishing opportunities. This study aimed to evaluate patterns in the use of prototype fish aggregation devices (FADs) in small size reservoirs. It was conducted at the Nascentes Reservoir (Crato), a small Mediterranean reservoir (ca. 10 ha) located in southern Portugal. These FADs were installed to enhance refuge habitat for fish species of interest to recreational fisheries, particularly largemouth bass (Micropterus salmoides Lacepède, 1802), thereby promoting the occurrence of trophy specimens. Two types of FADs were deployed and tested: (1) bank FADs (TREES), used in shallow waters near the margins; and (2) pelagic FADs (DAPs), suspended in the water column in deeper areas at the center of the reservoir. To monitor movement patterns and habitat use, an acoustic telemetry receiver array was deployed with a design to secure a three-dimensional fine-scale positioning with high accuracy. A total of 20 largemouth bass were tagged with acoustic transmitters equipped with pressure (i.e., depth) sensors. A before–after approach was used with 10 fish tracked before FAD deployment and 10 after. Results of fish behavior analysis provide strong evidence of fish using DAPs, but not TREES. In the presence of FADs, fish reduced their home ranges and movement amplitudes, becoming closely associated with these artificial habitats. Several environmental predictors explained fish behavior in the presence of artificial refuges, namely, diel period, moonlight intensity, and fish depth. The findings of this study are expected to contribute to the development of guidelines for refuge habitat enhancement in small- to medium-sized Mediterranean reservoirs, thereby increasing their recreational fishing attractiveness. Full article

While state-of-the-art massive acoustic arrays typically rely on costly, specialized FPGA architectures or rigid proprietary hardware, there is a growing need for modular, high-density sensing in complex aeroacoustics environments. This paper presents the electronic and acoustic design of a multiplexed, modular, scalable, and low-cost massive acoustic array (MxArray) founded on an embedded Linux system. The AM3358 SoC microprocessor collects audio data through its multichannel audio peripheral, where it simultaneously receives four Time-Division Multiplexing streams of 16 microphones each. This multiplexed scheme enables the handling of 64 microphones per module, whose acquisition synchronization is set with the Precision Time Protocol and a pulse injection hardware. The combination of both BeagleBone Black and microphones based on Micro-Electro-Mechanical Systems yields a cost-effective solution with built-in Ethernet connectivity and accessible software development through an embedded Linux environment with audio libraries for hardware control. Sensors are arranged in an Underbrink Spiral pattern on a four-layer printed-circuit board. The perforated thin layout minimizes any airborne disturbance, exploiting a distribution that simultaneously achieves a low sidelobe level and a narrow main lobe when used with a beamforming algorithm. Measurement results for the developed module are presented, as well as an evaluation of a full-scale system comprising 16 modules (1024 microphones) arranged in a honeycomb pattern. The resulting instrument offers a practical and scalable solution for applications that require a large number of simultaneous microphone measurements, such as beamforming technology for aeroacoustics applications. Full article

With the occurrence of global climate change and the depletion of agricultural water resources, there is a growing need to develop rapid, non-destructive, and autonomous plant health monitoring systems. As an economically valuable crop, Ocimum basilicum L. (basil) is sensitive to changes in water availability and may exhibit stress-related morphological variations under drought and over-irrigation conditions. However, due to the visual similarity of leaf symptoms under drought stress, waterlogging stress, and optimal irrigation conditions, accurately distinguishing these conditions remains challenging in practical applications. To address this challenge, this paper presents an attention-based dual-branch deep learning framework designed to extract both subtle leaf details and channel-related features from high-resolution plant images. By combining the Convolutional Block Attention Module (CBAM) and Squeeze-and-Excitation (SE) mechanism in a parallel structure, the proposed network improves the analysis of high-resolution images with an input size of 720 × 720 pixels. Under controlled environmental conditions, with ground-truth labels obtained using soil moisture sensor measurements, the proposed model was compared with eight deep learning architectures, including DenseNet121, InceptionV3, and VGG16. The proposed model achieved a hold-out evaluation accuracy of 99.54%, outperforming the second-best model, DenseNet121, which achieved 96.43%. In addition, the proposed model reached a class-specific precision value of 100% for the Drought Stress category and achieved an area under the receiver operating characteristic curve of 1.00 under the controlled experimental setting. Taylor Diagram analysis also indicated that the model closely preserved the variability pattern of the reference data. These results suggest that the proposed application-specific framework may support non-destructive basil water-stress detection under controlled conditions. After further validation with larger datasets, different cultivars, variable environmental conditions, and real-world agricultural scenarios, the proposed approach may contribute to precision irrigation management and sustainable agricultural production. The contribution of this study should be interpreted as an application-specific implementation and evaluation of complementary attention mechanisms for controlled-environment basil water-stress classification, rather than as the introduction of a fundamentally new deep learning methodology. Full article

Introduction: Understanding the movement ecology of invasive species such as the European catfish Silurus glanis, with documented strong impacts on freshwater fish communities, is essential to improve the effectiveness of management and containment actions, as detailed knowledge of species spatio-temporal habitat use strongly influences the success of control strategies. Objective: This study aimed to investigate the spatial and temporal behaviour of the S. glanis in a river–reservoir system in Portugal, including the Ponsul River and part of the Tagus River within the Cedillo reservoir, and to provide ecologically relevant insights to support targeted management strategies. Methodology: Acoustic telemetry was used to monitor 27 tagged individuals equipped with depth sensors. Fish movements were tracked using an array of 17 acoustic receivers over one and a half years. Results: Three behavioural profiles were identified: a resident group in the lower Ponsul (n = 4), a group moving between the Tagus River and the lower Ponsul (n = 6), and a larger group primarily migrating within the Ponsul River (n = 12). The remaining five individuals were considered dead, due to illegal fishing in this protected area. Migratory individuals showed a clear seasonal pattern, moving downstream to deeper waters during early winter and returning upstream to shallower areas as temperatures increased in early spring, likely in response to thermal gradients. Distance-based analyses confirmed this trend, with minimum inter-individual distances occurring in winter and early summer. Vertical behaviour supported this pattern, with individuals occupying shallow waters (≤7 m) for most of the year and reaching depths of up to 30 m in winter. Conclusions: The observed preference for shallow habitats during warmer periods and downstream migration in winter indicates that eradication efforts should be spatially and temporally targeted. Control actions should prioritize upstream sections during warm seasons and downstream areas of the Ponsul during winter migration, focusing efforts on traditional methods such as large-mesh multimesh gillnets or new longline techniques. Overall, this study highlights the value of telemetry in supporting targeted, evidence-based management of invasive species. Full article

While rapid digital transformation has significantly optimized sectors such as finance and e-commerce, maintenance management in industrial environments has historically received lower levels of technological and capital investment. This lag creates critical gaps in operational efficiency and asset longevity, particularly within renewable energy infrastructures where sustainability and resilience are paramount. Addressing this technological disparity is essential for minimizing ecological footprints and maximizing the viability of net-zero systems. This paper introduces an advanced multi-platform digital solution designed to optimize the operation and maintenance of renewable energy systems and smart infrastructures. The platform addresses traditional management gaps by implementing standardized protocols that integrate real-time remote monitoring, sensor networks, and cloud-based data acquisition. By centralizing historical and real-time data from solar, wind, and hybrid grids, it facilitates advanced analytics, such as predictive modeling of component degradation. Real-world validation across photovoltaic plants and wind farms demonstrates significant impacts: a 30% reduction in unplanned outages and a 20% to 25% decrease in operational and maintenance costs. The results confirm that digitalizing maintenance processes is a strategic pillar for the energy transition, aligning industrial performance with global low-carbon pathways. Full article

In the development of deep shale gas horizontal wells, precise geo-steering relies heavily on downhole sensor networks to acquire extensive formation and engineering parameters. Coiled tubing (CT) provides a promising acoustic waveguide for downhole sensing systems, but conventional acoustic sources rely on gravity-induced interfacial preload. Under highly deviated or horizontal well conditions, the loss of the axial gravity component may induce contact–nonlinearity instability, resulting in waveform distortion and spectral pollution. To address this limitation, a constant-stiffness preloading method based on elastic compliance control is proposed, together with a modal reconstruction strategy achieved by removing high-density tungsten blocks. A fluid–solid coupled dynamic model incorporating contact nonlinearity is established to reveal the dynamic separation mechanism of the acoustic source interface under varying gravity-vector conditions. Wave spring assemblies are then used to reconstruct the mechanical boundary and physically suppress time-domain clipping. Full-scale ground circulation experiments on a 1500 ft CT string show that the proposed method decouples acoustic-source performance from wellbore trajectory. Waveform asymmetry is reduced from 18.4% to 2.1%, and total harmonic distortion decreases from 12.5% to 1.8%. In addition, the first-order longitudinal natural frequency is shifted from 420 Hz to 2850 Hz, avoiding low-frequency pump noise and achieving a 12 dB SNR improvement. This physical-layer gain provides an optimized signal baseline for receiver-end demodulation algorithms. Ultimately, this study provides a robust physical-layer solution for acoustic telemetry in complex deep-earth environments, advancing the reliability of data interaction in downhole sensing systems. Full article

Wireless sensor networks supported by mobile edge computing are increasingly required to process heterogeneous sensing data under stringent latency, reliability, and energy constraints. However, most existing task-offloading studies are still formulated for generic user equipment and primarily focus on uplink transmission, which is insufficient for practical sensing systems where sensor nodes continuously upload measurements while simultaneously receiving control commands, model updates, and feedback from the edge. To address this gap, this paper reformulates joint computation offloading and power control as a sensor-aware optimization problem in an edge-assisted wireless sensor network. We propose a three-layer architecture consisting of sensor nodes, access points with lightweight edge servers, and a cloud coordination layer. Each sensing task is characterized by data size, computation density, latency deadline, and sensing priority, while the optimization objective jointly minimizes long-term task delay, communication and computation energy, and packet-loss penalty under transmission power, edge resource, and residual-energy constraints. To solve the resulting mixed discrete–continuous problem, we develop a multi-agent reinforcement learning framework in which each sensor node acts as an autonomous agent and learns offloading and transmission policies with clipped proximal policy optimization, while the cloud layer performs coordinated edge-resource allocation through the alternating direction method of multipliers. In addition to delay and energy, network lifetime and sensing delivery performance are incorporated into the evaluation. Simulation results in a sensor-network monitoring scenario demonstrate that the proposed framework consistently reduces latency, lowers energy consumption, and prolongs network lifetime compared with representative baselines, highlighting its effectiveness and practical potential for intelligent sensing applications that require integrated sensing, communication, and edge computing. Full article

Real-time telemetry is essential for performance optimization and safety in motorcycle racing, yet commercial solutions remain proprietary, expensive, and poorly extensible. This paper presents the design, implementation, and experimental evaluation of an open-architecture embedded telemetry unit built around the STM32H745 dual-core microcontroller. The system integrates a u-blox ZED-F9P RTK-GNSS receiver, a Bosch BNO085 9-DoF IMU with on-chip sensor fusion, a CAN-FD interface for powertrain data acquisition, and a SIM7600E-H 4G/LTE module for real-time remote streaming, all housed in a 3D-printed vibration-resistant enclosure. The firmware employs deterministic dual-core task partitioning: the Cortex-M7 core handles sensor fusion and CAN-FD at high frequency, while the Cortex-M4 core manages 4G communication and microSD logging. We explicitly delimit the scope of the evidence presented: CAN-FD powertrain acquisition and end-to-end operational reliability are experimentally validated on real circuit data spanning four campaigns, over 100 laps, and 5.8 h of logging—with sustained acquisition of 13 powertrain channels at speeds up to 185 km/h and zero system resets or data-integrity errors. In contrast, RTK positioning accuracy (2.5 cm CEP), sensor-fusion latency (sub-2 ms at the 99th percentile), 4G-uplink reliability, and thermal margins are characterized through manufacturer specifications, Monte Carlo simulation, and analytical models, with a fully instrumented end-to-end measurement campaign identified as the immediate next step. The 50 Hz effective positioning rate combines 25 Hz GNSS with IMU interpolation. With a bill of materials of approximately EUR 265, the platform offers an order-of-magnitude cost reduction over commercial alternatives while providing full openness and extensibility for distributed intelligence applications. Full article

The NMR resonator is the sensor responsible for transmitting RF pulses and receiving detection signals, and its tuning and matching are crucial to acquiring high-sensitivity NMR signals. Automated tuning and matching (ATM) is therefore essential for rapid, accurate, and continuously efficient testing. Existing NMR ATM methods still primarily rely on iterative search strategies, whose dominant cost arises from repeated hardware measurements and waiting periods, often requiring multiple measurement cycles before convergence. The emergence of in situ NMR detection of high-concentration ionic samples has further increased the demand for real-time, rapid ATM with a large dynamic range, posing a major challenge to conventional approaches. This paper proposes a physics-informed few-shot learning method for automatic tuning and matching over wideband and multi-resonance-frequency NMR scenarios. The tuning-and-matching problem is formulated as a structure and frequency-conditioned function regression task, and a conditional neural process (CNP) is introduced to learn cross-task priors and directly predict the states of tunable components from only a small number of real-machine context measurements. A physics regularizer based on the local sensitivity of the input impedance is further designed to impose stronger penalties on errors under high-Q narrowband operating conditions without relying on proprietary analytical circuit models. Simulation studies and real NMR experiments are conducted on multiple circuit topologies and multiple target frequencies using only a small number of NMR samples. The results demonstrate consistent improvements in key metrics, including accuracy of tuning and matching and the number of collected real-machine samples required per task. In particular, with only 100 sampled tuning/matching capacitor points and 20 on-hardware collected samples, the proposed method already delivers satisfactory tuning-and-matching performance. The method achieves an attractive accuracy–cost tradeoff across both cross-topology and cross-frequency scenarios, and shows strong potential for few-shot, rapid, real-time detection. Full article

Background: Continuous Positive Airway Pressure (CPAP) therapy is the standard treatment for obstructive sleep apnea–hypopnea syndrome (OSAHS), and photoplethysmography (PPG) sensors are commonly used in wearable devices for home sleep apnea testing. The recorded airflow and PPG signals from both sensors capture rich physiological patterns. We hypothesize that by combining information from these signals, we can efficiently estimate sleep dynamics of patients receiving CPAP treatment. Methods: The airflow signals were obtained from CPAP titration devices, denoted as CPAP-airflow, while the PPG signals were collected using the PranaQ TipTraQ (TTQ001), a fingertip-worn wearable device. We separately trained one-dimensional convolutional neural networks for CPAP-airflow and PPG signals and fused their outputs through probabilistic ensembling to predict sleep stages. The ensemble method is a late-fusion soft-voting scheme that computes a linearly weighted combination of synchronized softmax probability vectors from the modality-specific models. Results: For three-stage classification (Wake, REM, NREM), the PPG-based and CPAP-airflow-based models achieved overall Cohen’s kappa scores of 0.511 and 0.452, respectively, while the ensembled model improved the overall kappa to 0.587. The F1-score for the REM stage improved to 0.706 using the ensemble method, compared to 0.685 and 0.532 achieved by the individual models, respectively. In the four-stage classification (Wake, REM, Light, Deep) task, a deep sleep sensitivity of 0.596 was attained through the application of probabilistic ensembling. Conclusions: A fusion scheme of complementary information from the CPAP and PPG enhances the accuracy of sleep stage detection and hence enables more precise sleep monitoring, especially with an improved REM identification. Clinical implications include applying the proposed algorithm to improve in-home auto-CPAP titration by capturing REM-related respiratory instability and avoiding under-titration in REM-dominant OSAHS, better reflecting the patient’s true nocturnal respiratory needs. Full article

Phosphate waste rock piles (PWRPs) generated by open-pit phosphate mining are highly heterogeneous and difficult to characterize using conventional point sampling alone, which limits representative resource assessment, selective recovery, and rehabilitation planning. This study develops an integrated framework combining PRISMA spaceborne hyperspectral imagery, ground-based mineralogical analyses, and spatially constrained machine learning to map lithological heterogeneity at the Benguerir phosphate mining site, Morocco. A three-stage spectral optimization workflow, including atmospheric band masking, Savitzky–Golay filtering, and analysis of variance (ANOVA)-based feature selection, was applied to identify the most discriminative Short-Wave Infrared (SWIR) bands for lithological classification. After removing redundant observations located within shared PRISMA pixel footprints, 127 spatially independent samples were retained for model development. Five supervised classifiers (Random Forest, Extra Trees, XGBoost, Support Vector Machine, and K-Nearest Neighbors) were evaluated under a spatially constrained cross-validation framework aligned with the 30 m native PRISMA pixel size. Ensemble-based models, especially Extra Trees and Random Forest, provided the most stable performance, with balanced accuracies of 0.56–0.69 and area under the receiver operating characteristic curve (AUC) values exceeding 0.95 for carbonate-dominated lithologies. Lower discrimination between phosphate and siliceous facies reflects intrinsic mineralogical mixing and spectral overlap at the sensor scale. Entropy-based uncertainty and posterior probability mapping revealed spatially structured prediction ambiguity concentrated along lithological boundaries and transitional zones, consistent with petrographic evidence of compositional heterogeneity. These results indicate that moderate but stable accuracies likely represent realistic performance limits for spaceborne hyperspectral mapping of complex mining environments under spatial constraints. The proposed framework provides a transferable and uncertainty-aware basis for lithological mapping, selective recovery assessment, and sustainable phosphate waste management. Full article

Vehicle ad hoc networks (VANETs) can help prevent traffic accidents through wireless communication; however, most studies are based on simulations or static evaluations. This research paper presents the design, implementation, and experimental evaluation of a prototype early-warning system for vehicle proximity based on VANETs using ESP-NOW. The prototype utilizes five ESP32-CAM nodes equipped with MaxSonar sensors installed in vehicles and an RSU unit with a Raspberry Pi for vehicle-to-infrastructure (V2I) communication. Field tests were conducted in Quito, Ecuador, at speeds ranging from 10 to 70 km/h, measuring latency, packet loss, and received signal strength (RSSI). The results show average latencies of 9.9 ms at 10 km/h and 114.5 ms at 70 km/h, with packet loss rates of 2% and 60%, respectively. Statistical analysis reveals 95% confidence intervals for latency ranging from ±0.98 ms to ±6.90 ms, while obstacles introduce marginal attenuation (p = 0.051) with significant dispersion (σ = 5.85 dB). The Doppler shift is negligible (155.6 Hz), but the channel coherence time (2.7 ms) explains the observed degradation. Models were obtained that relate speed to latency (R² = 0.994) and packet loss (R² = 0.991). The prototype is viable for early collision warning at urban speeds (up to 60 km/h), outperforming human reaction time (1.5 s). Full article

Distributed seismic sensor networks integrated into the Internet of Things (IoT) infrastructure enable continuous condition monitoring of large-scale engineering structures. During long-term operation, however, measurement channels are subject to sensitivity drift, increased noise, and pulse artifacts that statistically mimic real vibration events. Related deep-learning techniques for noisy and ill-posed inverse problems have demonstrated the value of combining principled physical priors with deep models. Although the application domain differs, the underlying methodological insight—that constrained, physics-aware feature mappings can stabilize learning under noisy and partially observed conditions—directly motivates the use of a parameterized quantum circuit as a nonlinear feature transformer in the present work, where Hilbert space mapping serves as an analogous structural prior for the latent representation. Three principal fault modes are considered in this work, corresponding to the dominant degradation mechanisms observed in long-term seismic instrumentation: sensor drift, increased noise, and sensor failure. Each fault mode produces a distinct signature in the windowed feature space; the proposed model is trained to discriminate between them based on the latent CNN-LSTM-VQC representation. We propose a hybrid quantum-inspired deep-learning model (QC-DL) for the detection and diagnosis of channel-degradation anomalies. The architecture combines a 1D-CNN+LSTM feature extractor with a parameterized variational quantum circuit (VQC) used as a nonlinear feature transformer. All quantum experiments were performed on the QPanda3 CPUQVM simulator. The data were split chronologically prior to windowing to avoid information leakage. On real-world labeled accelerometric data with four operating modes (normal/drift/high-noise/failure), the QC-DL model achieved a macro-averaged F1 score of approximately 0.69 and per-class AUC values in the range 0.88–0.99. The mean early-detection latency was 1.6 s versus 2.1 s for the CNN-LSTM baseline (~24% reduction). An ablation study against a parameter-matched classical MLP showed that the gain is modest and not solely attributable to additional nonlinearity. The reported p-values (p = 0.70, p = 0.29) do not establish statistical significance. The results support the feasibility of hybrid quantum-inspired deep learning for sensor-channel verification, while highlighting the need for evaluation on real NISQ hardware. This paper proposes a hybrid quantum-inspired approach for detecting and diagnosing such anomalies in the time series of distributed seismic networks. The architecture combines a classical temporal feature extraction module based on one-dimensional convolutional layers and a recurrent long short-term memory (LSTM) network, which generates a latent window representation of the signal, with a parameterized variational quantum circuit used as a nonlinear feature processor in a hybrid computational circuit. Experimental validation was performed on real-world labeled data with multiple sensor degradation modes. The evaluation was organized in a scoring framework aligned with autonomous operation through window ranking and threshold alarm generation. In the experiments, the proposed model provided a macro-averaged F1 score of approximately 0.69 and area under the receiver operating characteristic (AUC) curve values in the range of 0.88–0.99 across classes, outperforming baseline deep models. The average early detection latency was 1.6 s versus 2.1 s for the baseline recurrent model (a 24% reduction). An ablative comparison with a control model based on a classical multilayer perceptron of comparable dimension confirmed that the improvement is not limited to the addition of additional nonlinearity. The obtained results indicate the potential of quantum-supported deep learning for improving the reliability of long-term vibration monitoring and verifying the correctness of sensor channels in distributed seismic networks. Full article

The postpartum period in beef cows is characterized by complex physiological and reproductive changes that may influence the postpartum reproductive changes and estrus expression. This exploratory pilot study evaluated postpartum reproductive responses in Angus cows receiving grape extract supplementation while investigating associations among thermographic, behavioral, hormonal, and reticulorumen temperature indicators associated with estrus expression. Nineteen Angus cows were assigned to a control group (C; n = 10) or a treatment group receiving slow-release grape extract boluses (T; n = 9). From calving until artificial insemination, ocular and vulvar thermographic images, blood samples, and physiological measurements were obtained weekly between 10:00 and 11:00 a.m. Reticulorumen temperature and activity data were continuously recorded using intraruminal sensors. Kaplan–Meier survival analysis demonstrated an earlier onset of postpartum estrus in supplemented cows compared with controls (p = 0.010). Mean time to first estrus was 23.88 ± 1.86 days in the T group and 39.82 ± 5.05 days in the C group. No significant differences were observed between groups for most individual physiological or hormonal variables. Exploratory correlation analysis revealed moderate associations among vulvar temperature, ocular temperature, activity, estrus index, and reticulorumen temperature indicators. However, because diagnostic accuracy was not evaluated, these findings should not be interpreted as validation of estrus detection performance. The results suggest that multimodal physiological monitoring may provide complementary information related to postpartum estrus expression in beef cows, while grape extract supplementation may be associated with earlier postpartum reproductive recovery. Due to the exploratory study design and limited sample size, further studies are required to validate these preliminary observations. Full article