Search Results (627)

Tactile sensing can enhance robotic grasping by providing contact information unavailable from vision or control signals alone. However, implementing tactile sensing in robotic hands is often constrained by external wiring, data-acquisition hardware, power requirements, and limited fingertip space. This study presents a self-contained capacitive tactile fingertip module for adding wireless multi-point contact sensing to robotic grippers. The module integrates a 3 × 1 array of thin flexible capacitive sensors, a capacitance-to-digital converter, a Bluetooth-enabled microcontroller, and an onboard battery within a compact fingertip-shaped housing. It can be mounted in place of an existing fingertip and operates independently of the robotic hand controller. Individual sensors characterized before module integration responded near-linearly to normal compression up to 2.5 N, with an approximately 8% relative capacitance change at 2.5 N and R² = 0.99, and were evaluated over 100 repeated compression cycles. In proof-of-concept grasping tests with an empty PET bottle, a water-filled PET bottle, and a water-filled aluminum tumbler, the assembled module produced distinguishable capacitance changes at the mid- and proximal-position sensors, providing relative information on contact location and local loading rather than calibrated force. These results demonstrate the feasibility of wireless tactile sensing in robotic grasping using a compact standalone fingertip module. Full article

Large sensor arrays are an increasingly popular sampling method among ecologists. To last in the field, sensor housing needs to be resistant to damage from both weather and animals. The popular AudioMoth acoustic recorder does not have integral weather-resistant housing and is deployed by users in a wide variety of protective cases. One inexpensive way to protect AudioMoths is to deploy them in plastic bags, which offer moderate weather resistance but are susceptible to chewing damage from small mammals. In this study, we test the effectiveness of commercially available mammal deterrents in preventing such chewing damage. We deployed 115 treatment-control pairs across two grids in temperate forests in Pennsylvania. Bag treatments consisted of Liquid Fence, Bonide, and a cayenne and Vaseline mixture. For all deterrents, there was no statistically significant difference in the proportion or severity of mammal chewing damage between treatments and controls. Counter to expectations, for all three treatments, more of the bags treated with a deterrent were damaged by mammal chewing than the paired control bags. Our results strongly suggest that single applications of these three deterrents have no useful effect on preventing mammal chewing damage to sensor housing in the field. Full article

This work presents the design evolution, fabrication, and characterisation of an additively manufactured ring-type thermal airflow sensor for in-pipe flow monitoring, developed employing exclusively Fused Deposition Modelling (FDM) additive manufacturing technology and a commercially available Carbon Nanotube (CNT)-enriched Biopolymer Polylactic Acid (PLA) composite filament. The design evolution proceeds through three progressive stages. In the first stage, a flat heater element is characterised through Constant-Current (CC) Joule heating experiments in order to derive the corresponding Temperature Coefficient of Resistance (TCR) and Thermal Resistance from the obtained experimental data. Consequently, a Finite Element Method (FEM) model implemented in COMSOL Multiphysics^® and calibrated with the extracted material parameters validates the experimental temperature–power relationship and predicts the convective cooling behaviour at various airflow velocities. In the second stage, the geometry is optimised by introducing a conductive trace with a reduced-cross-section central region; as a result, an equivalent thermal localisation is achieved at approximately 26% lower supplied power with respect to the initial heating element, enabled by the design freedom inherent in the FDM process. We should note that the specific sensing geometry can also be directly embedded into any 3D-printed structural component (e.g., a bracket or housing), enabling simultaneous local thermal heating and/or thermal monitoring together with structural functionality within a single printed part. In the third and final stage—the target device—a fully monolithic ring-type airflow sensor is directly integrated into a 3D-printed pipe segment during the printing process. Under constant-current excitation at 40 mA, the device exhibits a monotonically decreasing resistance with increasing airflow (ΔR ≈ 117 Ω over 0–4 m/s) due to convective cooling, while in a single flow-interruption cycle, approximately 79% of the flow-induced resistance change was recovered upon flow removal, with a residual offset of approximately 3% of the heated baseline. A coupled electrothermal FEM model of the device further supports the experimental response by comparing the simulated temperature rise with the values inferred from resistance measurements, while also clarifying the role of the effective internal convective cooling conditions imposed by the pipe geometry. Key features of the proposed device are low raw-consumables cost, fast on-site manufacturing employing a commercially available desktop 3D printer, monolithic construction free of wire-bonded interconnections, and simplicity, indicating its potential for flow monitoring and condition-based maintenance systems aboard vessels as well as in a wide range of industrial sectors. We should note that the present characterisation was performed under laboratory conditions employing a single prototype per design stage; the effects of humidity, salt exposure, vibration, temperature cycling, and material-batch variability remain to be assessed prior to shipboard deployment. Full article

Three-dimensional phenotypic data of pigs provide important information for evaluating growth, nutritional status and health, and they are fundamental to precision feeding, performance assessments, genetic selection and intelligent livestock management. However, point clouds acquired in real pig-house environments are frequently incomplete because of occlusion, limited camera viewpoints, surface reflection, sensor noise, etc. To address local missing structures and geometric discontinuities in pig point clouds, this paper proposes a self-structure-enhanced completion method. The method follows a global-to-local two-stage framework. In the global stage, a self-view fusion network (SVFNet) integrates an incomplete point cloud and its three orthogonal self-projected depth maps to generate a coarse complete shape. In the local stage, a self-structure dual generator (SDG) progressively refines and upsamples the coarse result through a structure analysis and a similarity alignment. To address the physical limitation of distinguishing single-view occlusion from true missingness, this paper proposes a visibility-incompleteness mask (VIM) as the primary contribution, which explicitly models both geometric missingness and multi-view visibility. Furthermore, a prior-adaptive hybrid generation (PAHG) strategy is introduced as a secondary enhancement to combine learnable global shape priors with input-adaptive geometric queries. A dataset containing 1042 complete–incomplete pig point cloud pairs with six typical missing patterns was constructed for model training and evaluation. The proposed method achieved an F-Score@1% of 0.653, a CD-L1 of 9.766, and a CD-L2 of 0.353 on the test set, demonstrating a competitive aggregate performance compared with state-of-the-art completion methods, with trade-offs across different geometric metrics. Full article

This paper describes the design and implementation of an automated irrigation control system that uses data collected by a wireless sensor network. Each sensor node, built on a custom-designed printed circuit board, includes sensors for light intensity, temperature, and a custom soil moisture sensor. Data is transmitted to a central control node via ESP-NOW, where it is processed and compared with configurable thresholds retrieved from Google Sheets over Wi-Fi. Irrigation is triggered automatically when conditions meet the remotely defined thresholds. A key contribution is the development and testing of a custom soil moisture sensor, with results compared to commercial models. The system supports low-power operation through deep sleep modes, enabling long-term field deployment. The novelty lies in the complete integration of hardware, software, and cloud-based control, providing a flexible and low-cost solution for precision agriculture. The system can be deployed in greenhouses or open fields and serves as a platform for future research in smart irrigation. The fundamental aspect is a very user-friendly solution for any farmer attributable to easy accommodation to the Google Sheets interface, no maintenance cost over the cloud account, and up to 45 days of battery life or a built-in alternative for solar power. Full article

This paper presents a differential capacitive horizontal pendulum tiltmeter based on electrostatic feedback force. The system mainly consists of a horizontal pendulum bob, a triangular platform, a pendulum locking motor, a differential capacitive sensing circuit, an electrostatic feedback circuit, and a sealed protective housing. The electrostatic feedback differential capacitive horizontal pendulum tiltmeter does not introduce a novel tilt measurement principle; its pendulum still adopts the classic Zöllner double-suspension-wire configuration. In this study, engineering improvements were implemented on the conventional quartz horizontal pendulum tiltmeter by replacing quartz fibers with tungsten wires, substituting optical lever sensors with capacitive sensors, and incorporating the electrostatic feedback principle. The improved tiltmeter features a compact structure and reduced size, which greatly facilitates deployment and installation while significantly enhancing practical applicability. Moreover, it overcomes the drawback of conventional horizontal pendulums—namely, that the scale factor varies with inclination and requires frequent calibration—and thus holds considerable importance for geophysical ground deformation observation. Full article

Autonomous driving sensor fusion housings (SFHs) integrate LiDAR, radar, camera, and computing modules within a shared mechanical and thermal enclosure. This review examines how coefficient of thermal expansion (CTE) mismatch among housing polymers, aluminum heat spreaders, substrates, and solder joints can contribute to interfacial delamination, solder joint fatigue, optical misalignment, and Thermomechanical Coupling Interference (TMCI). Using a structured narrative review of 99 publications and authoritative standards from primarily 2009 to 2026, the article organizes the evidence into a 4 × 4 taxonomy linking four failure mechanisms with experimental, computational, AI/ML, and qualification-oriented approaches. The review explicitly distinguishes direct literature evidence, transferred package-level evidence, model-based extrapolation, and author-derived conceptual estimates. Accordingly, TMCI temperature increments, sensor spacing values, optical drift estimates, and lifetime projections are discussed only as case-specific screening-level hypotheses unless directly validated in the cited literature. Five research gaps are identified: standardized multi-sensor TMCI validation, aging-corrected material and solder fatigue databases, long-term qualification of thermally conductive nanocomposites, SFH-specific validation of physics-informed digital twins, and integrated multi-failure testing. The contribution of this article is therefore primarily structural and agenda setting: it clarifies what is supported by direct evidence, what is transferred from adjacent domains, and what remains to be validated before robust SFH-level reliability guidance can be established. Full article

Space gravitational wave detection missions demand ultra-stable calibration of inertial sensor capacitive sensing. Conventional dynamic methods suffer from mechanical vibration noise and bias separation difficulties, while large-displacement operation introduces pronounced nonlinearity. This work proposes a static calibration method using an image-based displacement measurement system to establish a vibration-free benchmark. A subpixel edge detection algorithm locates the Test Mass and Electrode Housing edges with a repeatability of approximately 0.05 pixels, and the Test Mass geometry is independently calibrated by a Coordinate Measuring Machine (CMM, ±2 µm, $k=2$) to provide SI traceability. A nonlinear calibration model incorporating higher-order Taylor terms is developed, combined with a forward/reverse connection technique for composite bias modeling. Experimental validation at $x_0=665$ µm ($x_0/d_0\approx 0.665$) demonstrated a gain coefficient repeatability of 0.01658% RMSPER and a combined expanded uncertainty of $U\approx 2.18\times {10}^{-5}$ $1/\mathsf{\mu }\mathrm{m}$ ($k=2$). Intended as a complementary ground-based technique to dynamic calibration, this method avoids dynamic excitation-induced noise while establishing complete SI traceability, offering a reliable solution for ground validation and long-term monitoring of space inertial sensors. Full article

Vibration sensor networks deployed on bridges continuously generate large volumes of unlabelled measurements under healthy operation, whereas labelled damage records on any specific target bridge remain extremely scarce—a chronic data asymmetry that constrains data-driven structural health monitoring (SHM). Existing remedies either require labelled source-bridge data or borrow augmentation pipelines and encoders from computer vision that are poorly matched to one-dimensional vibration signals. This study proposes a two-stage framework—siamese contrastive pretraining followed by few-shot fine-tuning on the target bridge—that learns environment-invariant representations from unlabelled source-side sensor signals and transfers them to a new bridge using only a handful of labelled samples. Three contributions are advanced: (i) a signal-domain augmentation policy that decouples sensor-level corruptions from operational-level fluctuations, including a frequency-band stochastic masking scheme designed to emulate cross-bridge perturbations; (ii) a one-dimensional self-calibrated convolutional encoder embedded in a stop-gradient siamese learner, providing the enlarged receptive field and inter-channel coupling required to capture sparse damage signatures in multi-sensor recordings; and (iii) a transferability analysis that formally links the contrastive invariance objective to a bound on the expected cross-bridge risk. On the Z24 benchmark and an in-house four-configuration laboratory bridge population, the method attains a 5-shot macro-F1 of 0.913 (Z24 → Lab) and 0.892 (Lab → Z24), outperforming eleven baselines by 3.4–37.1 percentage points. Full article

This paper presents a proof-of-concept investigation into a novel hermetically sealed tunable-medium Extrinsic Fabry–Pérot Interferometer (EFPI) temperature sensor architecture. A series of tuneable-sensitivity EFPI temperature sensors is demonstrated, comprising a large-diameter fused silica diaphragm with a 800 $\mathsf{\mu }\mathrm{m}$ diameter, significantly exceeding conventional designs (typically ∼125 $\mathsf{\mu }\mathrm{m}$), with polished diaphragm thicknesses ranging from 28 to 49 $\mathsf{\mu }\mathrm{m}$, housed in hermetically sealed rigid melting point capillaries with a 1.8 mm internal diameter. By exploiting thermally induced pressure differentials generated by a tunable Krytox GPL 105 oil/air fill fraction within the sealed rigid cavity, the sensors demonstrate a continuously tuneable sensitivity design space spanning 0.45 to 190 $\mathrm{n}\mathrm{m}/\mathrm{K}$. An exact nonlinear thermal pressure model is derived and validated, replacing the linearised approximation which is shown to be inapplicable at fill fractions approaching unity. The low-sensitivity configuration (0.45 $\mathrm{n}\mathrm{m}/\mathrm{K}$) was characterised at the National Standards Authority of Ireland (NSAI) National Metrology Laboratory against ITS-90 fixed points: the Triple Point of Water (273.16 K) and the Gallium Fixed Point (302.9146 K), with traceability to the International Temperature Scale of 1990 (ITS-90), yielding an instrument-limited resolution of $<1.1$ mK, consistent with the metrological validation environment. The high-sensitivity configurations (21 and 190 $\mathrm{n}\mathrm{m}/\mathrm{K}$) were characterised on a laboratory bench, achieving instrument-limited theoretical resolutions of <24 $\mathsf{\mu }\mathrm{K}$ and $<2.6$ $\mathsf{\mu }\mathrm{K}$ respectively, pending future metrological validation. The 190 $\mathrm{n}\mathrm{m}/\mathrm{K}$ sensitivity represents an improvement of approximately 21.7× over the closest directly comparable prior Citationutilised fusion splicing and manual polishing. Future development priorities include metrological validation of the high-sensitivity configurations, long-term stability characterisation, thermal cycling, and progression towards an all-glass hermetically sealed construction. Full article

Fuel poverty is shaped by interacting social, environmental and housing conditions, yet these links remain underexplored at city scale. The analysis is framed as an ecological, cross-sectional assessment of spatial associations rather than as a causal proof of a closed feedback mechanism. This study examines the relationship between fuel poverty, deprivation, particulate air pollution and housing typology across 54 wards in Liverpool, UK. Ward-level fuel poverty and Index of Multiple Deprivation (IMD) data were integrated with 2023–2024 annual mean particulate matter (PM_2.5 and PM₁₀) from 58 low-cost air-quality sensors and classified housing types. Regression models were used to compare individual, additive and interaction effects. Fuel poverty ranged from 12.4% to 25.29%, while PM_2.5 and PM₁₀ frequently exceeded World Health Organization guideline values. IMD was the strongest individual predictor of fuel poverty (R² = 0.281, $p<0.001$). The preferred additive model including IMD, PM_2.5, PM₁₀ and housing type explained 43.5% of the variance, with Victorian Terraces emerging as a significant risk factor. Although interaction models suggested pollution-deprivation coupling, model selection and uncertainty diagnostics favoured the simpler additive specification. The findings support targeted retrofit, fuel-poverty and emissions-control policies in deprived urban neighbourhoods where inefficient housing and environmental stressors compound energy insecurity and where local action can contribute to more equitable urban sustainability. Full article

Understanding hand–paddle interaction is essential for optimizing performance and preventing injury in kayaking, yet coaches still lack objective, practical tools. We present a soft, instrumented glove that measures and dynamically maps palmar pressure throughout the stroke cycle. A matrix of piezoresistive sensors is integrated into the glove and connected to dedicated electronics housed in a waterproof enclosure. A viscoelastic model converts sensor resistance into forces, enabling time-resolved 3D mapping of contact mechanics. Data are transmitted via Bluetooth Low Energy (BLE). Experimental validation on a kayak ergometer across multiple cadences demonstrated accurate measurements (per-sensor root mean square error (RMSE) of ±2 N), clear delineation of pull and push phases, evolving pressure distribution over the motion, and a peak total right-hand force of 186 N at high cadence. Beyond feasibility, these results position the glove as a practical training aid: it supports athlete-specific load monitoring and the early detection of potentially problematic movement patterns. Full article

Intensive duck production is shifting from greenhouse/curtain-sided houses toward closed, mechanically ventilated systems, yet low-carbon environmental control for moisture-dominated houses remains insufficiently synthesized. Using the preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews (PRISMA-ScR) framework, this review aimed to identify low-carbon environmental-control pathways by integrating evidence on envelope design, ventilation, heat pump, and moisture management. Scopus, Web of Science, and PubMed were searched for English-language articles published during 2018–2025. Direct duck house evidence was separated from transferable poultry, livestock-building, and building-energy evidence. Synthesis shows that water access, wet litter, stocking density, and climate make houses latent-load-dominated systems, affecting relative humidity (RH), ammonia (NH₃), particulates, heat stress, welfare, and energy demand. Greenhouse-type houses have low energy use but weak environmental stability, whereas closed/windowless houses improve control and biosecurity but increase dependence on electricity, dehumidification, and backup systems. Low-carbon housing requires staged integration of moisture-source control, drainage, litter management, roof solar-load reduction, controlled ventilation, heat recovery, climate-suitable heat pumps, renewable electricity, sensor-based control, and resilience planning. Low-carbon environmental-control packages should be selected according to house type, climate, and management conditions. Future validation should report standardized energy, carbon, air quality, litter condition, welfare, productivity, cost, and outage-resilience metrics. Full article

This study presents an Indoor Environmental Quality (IEQ)-aware framework for residential valuation by integrating low-cost IoT sensing, transparent scoring, and hedonic price modeling. The analysis uses a dataset of 244 apartments across 12 districts in Tehran. It combines indicators of thermal comfort, particulate exposure, lighting, acoustics, stability, exceedance, and uncertainty with conventional housing covariates (area, age, bedrooms, floor level, renovation status, amenities, and accessibility proxies). Results show that pooled IEQ–price relationships are weak and confounded, whereas controlled specifications produce modest but consistent improvements in explanatory fit after IEQ features are introduced. Conventional location and structural attributes remain the dominant determinants of price per square meter. Still, IEQ contributes a non-redundant information layer that improves within-segment differentiation and interpretability for inspection and listing workflows. Methodologically, the framework extends beyond average comfort metrics by incorporating volatility, threshold exceedance duration, and sensor uncertainty, enabling uncertainty-aware reporting rather than single-point scoring. In practice, the workflow supports portable sensing, reproducible analytics, and privacy-preserving edge aggregation, suitable for PropTech deployment. The findings support a cautious but actionable conclusion: IEQ should be treated as an incremental valuation signal rather than a standalone pricing determinant. In this context, IEQ is conceptualized as a supplementary attribute block that may add explanatory value beyond conventional housing covariates rather than as a standalone pricing determinant. 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