Search Results (519)

Termite infestation poses a critical threat to ancient timber structures because hidden in-wood activity can cause progressive structural decay before visible surface symptoms appear. Through-wood termite detection remains challenging because termite-induced electromagnetic responses are weak, small-scale, and vulnerable to timber attenuation and multipath propagation. To address this problem, this study presents one of the first investigations to formulate through-wood termite detection as a multi-frequency wireless sensing and image–wireless fusion problem for non-destructive heritage timber inspection. We propose a Reliability-Aware Image–Wireless Fusion Network (RA-IWFNet), in which the image branch captures high-resolution surface-level visual cues while the dual-band wireless branch integrates complementary mmWave radar micro-motion responses and Wi-Fi Channel State Information (CSI) channel variations. A learnable temperature-scaled fusion gate estimates input-dependent image and wireless contributions and constructs a normalized fused representation for four-class recognition, including Termite, Lyctidae, Human, and None. Here, reliability is operationally defined as learned input-adaptive relative modality contribution rather than explicit uncertainty or signal-quality estimation. RA-IWFNet is evaluated under two complementary protocols: a field-motivated protocol with joint visual and wireless degradation and a synchronized verification protocol using physically co-acquired multimodal samples. Across repeated training runs, RA-IWFNet achieves $81.91\pm 1.33\%$ accuracy and $81.96\pm 1.27\%$ Macro-F1 under field-mixed visual degradation and moderate wireless degradation. On the synchronized verification subset, gated fusion achieves $91.53\pm 2.44\%$ accuracy and $91.62\pm 2.44\%$ Macro-F1, yielding higher mean performance than single-modality and non-adaptive fusion baselines. Feature-space, error-correction, and gate-temperature analyses further support the effectiveness of adaptive modality integration. These results provide controlled laboratory feasibility evidence and suggest that multi-frequency wireless sensing combined with adaptive image–wireless fusion offers a promising non-invasive pathway toward practical through-wood termite inspection in heritage timber structures. Full article

The automotive industry is evolving toward Software-Defined Vehicles (SDVs) enabled by centralized computing, cloud integration, and Over-the-Air (OTA) updates. Yet, prevailing SDV and Internet of Vehicles (IoV) simulators often treat each vehicle as a single monolithic node, obscuring the interplay between internal vehicle modules and the surrounding infrastructure in dense urban scenarios. This work proposes a modular SDV reference architecture embedded in a Software-Defined Internet of Vehicles (SD-IoV) framework together with a Software-in-the-Loop (SiL) co-simulation testbed built on Objective Modular Network Testbed in C++ (OMNeT++), Simulation of Urban MObility (SUMO), and Vehicles in Network Simulation (Veins). The architecture decouples perception, communication, decision, and actuation into typed replaceable modules and instantiates them across six co-existing agent types: an SDV; two human-driver vehicle classes with cognition modelled as a multi-stage Eye–Ear–Brain–Hand–Foot pipeline with reaction-delay sampling; a public transport bus; a Roadside Unit (RSU); and a Traffic Light (TL). Three platform-level mechanisms connect the agents to the infrastructure: a single shared world model with a three-layer line-of-sight funnel that serves visual-sensor queries and reuses the building polygons of the wireless shadowing model; a dual-CPU mobile-fog node implementing a cycles-per-frequency workload model with explicit end-to-end latency decomposition; and a three-plane intersection coordination fabric that combines 802.11p wireless with a wired RSU-to-TL star and a wired peer mesh between adjacent TLs. The initial results confirm that the implemented message paths and module interactions behave as specified, including directional Signal Phase and Timing (SPaT) reception, cross-junction handover, bus-side fog-offload latency accounting, and passive identification of Vehicle-to-Everything (V2X)-silent vehicles. Several architecture elements are specified but deliberately not exercised in the present evaluation and remain design targets for future work: the Roadside Unit (RSU) route planning and fog computing companion (and any multi-tier offloading comparison), non-line-of-sight SPaT reception, and a safety violation detection layer. Within the above scope, the testbed is positioned as a reusable foundation for module-level SDV research and as a basis for future extensions such as Joint Communication and Sensing (JCAS), energy-aware driving, and Hardware-in-the-Loop (HiL) integration. Full article

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

Portable sensing systems are increasingly important in biological and chemical analyses because they can provide analytical information at the point of decision-making. While traditional laboratory methods remain crucial for reference measurements, regulatory validation, and high-precision quantification, portable systems emphasize rapid response, convenience, cost-effectiveness, robustness, and relevance to decision-making. This paper views portable sensing systems as integrated analytical platforms rather than isolated sensing elements. The paper discusses recognition elements, including enzymes, antibodies, nucleic acid probes, aptamers, molecularly imprinted polymers, nanomaterials, and hybrid recognition interfaces, as well as electrochemical, optical, mass-sensitive, thermal, field-effect, and hybrid sensing technologies. Furthermore, this paper reviews platform designs, including paper-based analytical devices, chip lab systems, smartphone-assisted sensors, wearable and flexible sensors, handheld instruments, and wireless sensor networks. It explores their applications in sample handling, calibration, data processing, and field deployment. Applications of this technology include point-of-care diagnostics, pathogen detection, wearable health monitoring, agriculture, veterinary medicine, environmental monitoring, food safety, industrial process control, forensic analysis, public safety, and occupational exposure assessment. The report focuses on sample acquisition, miniaturized preparation, reagent storage, matrix interference, calibration transfer, signal conditioning, machine learning, cloud platforms, analytical validation, and decision support. Furthermore, it identifies key obstacles to translating academic prototypes into industrial products, including reproducibility, stability, manufacturability, ease of use, cybersecurity, regulatory approval, and market acceptance. Future development requires fully integrated sample-to-result systems, multimodal sensing, artificial intelligence, sustainable single-use materials, self-powered devices, and system-level validation under real-world operating conditions. Full article

Integrated sensing and communication (ISAC) technology enables the joint integration of communication and sensing functions through the efficient utilization of spectrum/energy resources. By further incorporating with the reconfigurable intelligent surfaces (RISs), the wireless propagation environment of ISAC systems can be dynamically controlled, thereby enhancing the overall sensing and communication (SAC) performance. In this context, accurate channel estimation is a fundamental prerequisite for the efficient operation of the RIS-assisted ISAC systems. However, the strong coupling between SAC signals from direct and reflected channel links, as well as severe noise interference, limits the SAC channel estimation accuracy. This paper proposes a novel two-stage channel estimation scheme for RIS-assisted ISAC systems, where the direct and reflected SAC channels are respectively estimated in the first and second stages. To mitigate the negative effects of noise interference in each estimation stage, an extreme learning machine-based feature purification module is precisely designed, improving the quality of received SAC signals and generating purified channel features. Then, the dedicated deep neural network adopts the purified channel features to estimate SAC channels. Simulation results demonstrate that, under different signal-to-noise ratio conditions and channel dimensions, the proposed scheme achieves superior estimation accuracy and strong robustness compared to the benchmark methods. Full article

In wireless-powered sensor networks, active uplink (UL) transmissions can support both sensed-data delivery and order-dependent RF energy recycling among energy-constrained devices. This paper studies a single hybrid access point (HAP) wireless-powered sensor network (WPSN), where one wireless device (WD) is selected as the relay, and the remaining WDs act as sources. The HAP first transfers wireless energy to all WDs in the downlink. Then, the source WDs reuse the incident wireless energy signal as a controllable carrier and deliver their information to the relay through passive backscatter communication. In the subsequent active uplink phase, the source WDs transmit according to an optimized order, while later scheduled sources recycle energy from the active signals transmitted by preceding sources. The source order therefore determines not only the transmission sequence but also the directed energy-recycling structure among source WDs, thereby reshaping the energy-causality constraints and the feasible transmit-energy region. The relay finally forwards the decoded source information to the HAP. To improve throughput fairness among sensor devices, a max-min throughput optimization problem is formulated by jointly designing the source transmission order, time allocation, and transmit-power allocation. For a given order, the continuous resource allocation problem is transformed into a convex problem through transmit-energy variables, and the optimal order is obtained by searching over all candidate orders. Numerical results show that the proposed scheme achieves higher minimum throughput than the three representative comparison schemes, demonstrating the benefit of jointly exploiting passive source-to-relay collection, relay-assisted forwarding, and order-aware energy recycling. Full article

Smart manufacturing requires real-time monitoring of multidimensional forces at the interface between the tool and workpiece in computer numerical control (CNC) machining. In this study, an innovative iKIT wireless extrasensory toolholder is introduced that is capable of high-fidelity, in situ, high-frequency sensing and monitoring of the cutting force, torque, and two-way bending moments. The hardware design of the system is outlined, highlighting a high-bandwidth miniature wireless transmission method and noncontact power supply and energy storage solution suitable for rotating machining environments. To assess the system performance, comprehensive milling tests were performed on aluminum alloy materials, and the relationship between the process parameters and changes in multidimensional mechanical loads was thoroughly examined. The experimental findings demonstrate that the smart toolholder detects precisely how parameter variations affect the loads. Multidimensional mechanical signals (torque and two-way bending moments) show a strong positive correlation with the feed rate and axial depth of cut, confirming the impact of the material removal rate on the system loads. Conversely, these signals are negatively correlated with spindle speed, accurately reflecting the effects of thermal softening and a reduced friction coefficient in aluminum alloys during high-speed cutting. This study not only offers a dependable hardware framework for integrating miniaturized sensors into toolholders, but also delivers accurate data to support digital twin models and adaptive control in machining processes. Full article

Self-powered monitoring systems capable of scavenging ambient mechanical energy are a highly desirable solution to eliminate the reliance on batteries and grid power in remote and distributed atmospheric sensing networks. However, the widespread adoption of such systems is severely hindered by the insufficient output power density of current energy harvesters, which struggle to simultaneously drive environmental sensors, data acquisition units, and wireless transmission modules. In this work, we report a highly integrated hybrid power generation system that couples a triboelectric nanogenerator (TENG) and an electromagnetic generator (EMG) to efficiently harvest low-frequency mechanical energy from the surroundings. Through systematic structural optimization and synergistic matching of the two transduction mechanisms, the device achieves an outstanding volumetric power density of 129.9 W·m⁻³, which represents one of the highest values ever reported for hybrid nanogenerators targeting self-powered environmental applications. The output characteristics of both the TENG and EMG units under varying load impedances are thoroughly characterized, revealing the optimal operating points for maximum power extraction. A tailored power management module, consisting of rectification, energy storage, and regulation circuits, is designed to convert the irregular alternating output into a stable direct-current supply. To demonstrate the practical viability of the system, we construct a complete self-powered atmospheric environment monitoring node, which integrates multiple environmental sensors, a data acquisition module, and a wireless transmission module. Driven exclusively by the hybrid TENG–EMG generator under ambient mechanical excitation, the node successfully performs real-time sensing, signal processing, and remote data communication without any external power input. This work not only provides a record-high power density among hybrid generators for environmental monitoring, but also establishes a feasible pathway toward maintenance-free, widely distributed, and truly autonomous atmospheric sensing networks. The presented strategy of maximizing volumetric power density through hybrid design and impedance engineering can be readily extended to other self-powered systems. Full article

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

This paper presents an open-source miniaturised readout device designed for the wireless interrogation of passive LC sensors and wireless power transmission. The system is based on a Sparkfun RedBoard Artemis microcontroller with a custom-printed circuit board as an extension, providing a compact, low-cost alternative to expensive laboratory-grade equipment. The reader coil is excited by a signal that can be tuned digitally in both frequency and amplitude. The resonance frequency of a wirelessly coupled LC tank is detected by monitoring the voltage minimum of a rectified signal envelope, which corresponds to the impedance change of the reader inductance at resonance. Experimental validation demonstrates that the device accurately tracks resonance frequency shifts resulting from variations of the LC tank’s capacitance, performing comparably to laboratory-grade impedance analysers. Testing the influence of axial separation between the two coils up to 25 $\mathrm{m}$$\mathrm{m}$ showed stable and identifiable voltage dips. The programmable excitation signal peak-to-peak voltage ranges from $0.81$ V to $5.35$ V. The device enables fully stand-alone operation with a display and navigation switch, making it suitable for untethered LC wireless sensing and actuation applications. Full article

This paper investigates backscatter-aided relaying for interactive dual-HAP wireless-powered sensor networks (WPSNs), in which two cooperative sensor groups transmit sensed data to opposite hybrid access points (HAPs) using harvested radio-frequency energy. Each group consists of multiple source sensor nodes (SNs) and one relay SN selected according to its proximity to the target HAP. To reduce local cooperation overhead, source SNs reuse the wireless power transfer (WPT) signal as a controllable carrier and convey their information to the relay SN through passive backscatter communication. The collected information is then delivered to the target HAPs through direct source transmission and relay forwarding. A source common-throughput maximization problem is formulated by jointly optimizing time allocation, transmit energy allocation, and dual-HAP energy beamforming, subject to energy-causality and relay minimum-rate constraints. To address the resulting non-convexity, an alternating optimization algorithm is developed, where the time-and-energy allocation subproblem is transformed into a convex form and the energy beamforming matrices are updated through energy-feasibility margin maximization. Numerical results show that the proposed scheme outperforms active cooperation without backscatter and direct transmission, demonstrating the effectiveness of integrating passive local information collection, relay-assisted uplink transmission, and optimized dual-HAP WPT. Full article

Oil and gas pipelines are critical infrastructures that require continuous and reliable monitoring to detect leaks, pressure anomalies, corrosion, and unauthorized activities. Wireless sensor networks (WSNs) have emerged as an effective solution for large-scale pipeline monitoring due to their low deployment cost and real-time sensing capabilities. However, the resource-constrained nature of sensor nodes and the open wireless communication environment expose pipeline monitoring systems to various routing attacks, for example, blackhole, sinkhole, selective forwarding, and false data injection attacks, while simultaneously demanding strict energy efficiency to prolong network lifetime. In this paper, we propose SEER-PM (Secure and Energy-Efficient Routing for Pipeline Monitoring): a novel protocol that integrates an Artificial neural network (ANN)-based trust mechanism with energy-aware routing metrics. SEER-PM dynamically evaluates node trustworthiness based on packet forwarding behavior, residual energy, and signal consistency. By training the ANN on historical behavioral data, the system accurately detects malicious nodes with high precision. Simulation results demonstrate that SEER-PM outperforms existing secure routing protocols (Sec-AODV and T-LEACH) in terms of packet delivery ratio (PDR) by 14%, detection rate by 9.5%, and network lifetime by 12% under heavy attack scenarios. The proposed protocol enhances the reliability, security, and sustainability of pipeline monitoring WSNs operating in harsh and remote environments. Full article

Accurate channel state information is essential for the performance of modern wireless communication systems. Conventional channel estimation typically relies on uplink Sounding Reference Signals (SRSs), which can introduce considerable overhead and power consumption, particularly in high-mobility or resource-constrained scenarios. To alleviate this burden, this paper explores an alternative approach that leverages sensing channel information to assist communication channel reconstruction. A shared cluster concept is introduced to capture the correlation between sensing and communication channels, and a sharing probability function is derived through statistical analysis of ray tracing simulation data across multiple scenarios. The shared cluster parameters extracted from the sensing channels are integrated into a cluster-based channel modeling framework to reconstruct the downlink communication channel. A deterministic simulation platform is developed using the Sionna ray tracing library, and the K-Power-Means algorithm is employed for multipath clustering. Simulation results demonstrate that the reconstructed channel closely matches the original channel in terms of the power delay profile and the root mean square delay spread, with mean values of 84.16 ns and 73.52 ns, respectively. The proposed method offers a promising supplementary approach for channel acquisition in scenarios where frequent SRS transmission is undesirable, and provides insights for future sensing-assisted communication system design. Full article

Smart contact lens sensors integrate biochemical sensing elements, flexible electronics, power modules, and wireless readout components onto optically transparent contact lens platforms, enabling non-invasive and potentially continuous analysis of tear-derived biomarkers and ocular physiological signals. This review focuses on the translation pathway from contact lens materials and fabrication methods to sensing mechanisms, tear biomarker interpretation, and clinical deployment. We synthesize recent progress in substrate engineering, manufacturing processes, power delivery, and representative sensing strategies for intraocular pressure, glucose, electrolytes, pH, cortisol, cholesterol, and inflammatory cytokines. Instead of treating these systems as isolated examples, we compare optical/colorimetric, electrochemical, field-effect transistor, microfluidic, and wireless resonant approaches in terms of sensitivity, response time, power/readout requirements, and clinical relevance. Finally, we discuss persistent barriers, including biocompatibility, interface stability, tear-sample variability, calibration, sterilization, regulatory validation, data privacy, and compatibility with commercial contact lens manufacturing. Full article

Automatic Modulation Classification (AMC) is essential for waveform-level signal characterization. It supports spectrum sensing, signal identification, and adaptive resource allocation in cognitive radio and next-generation wireless systems. However, channel impairments such as multipath propagation, frequency offset, fast fading, and noise degrade modulation signatures, making reliable AMC challenging. Existing deep learning-based approaches often rely on purely data-driven learning, leading to insufficient modeling of modulation-relevant features, loss of transient characteristics, and limited exploitation of hierarchical relationships among modulation types. To address these issues, this paper proposes PHM-Net, a physics-informed hierarchical multi-scale network for robust AMC. The model employs a hierarchical backbone with residual encoder blocks. A Transient Feature Gating (TFG) module enhances modulation-relevant representations, a Cross-Resolution Signal Aggregation (CRSA) module fuses multi-stage features, and a Physics-Informed Hierarchical Loss (PI-HL) enforces consistency between coarse- and fine-grained predictions. Experimental results on three benchmark datasets (RML2016.10a, RML2016.10b, and RML2018.01a) show that PHM-Net consistently achieves the highest average accuracy among all compared models. On RML2018.01a, which contains 1024-sample sequences and 24 classes, PHM-Net achieves an average accuracy of 64.59% and a best-case accuracy of 98.42%, surpassing AMC_Net by 11.14 and 17.09 percentage points and CNN-Transformer by 9.43 and 11.15 percentage points, respectively. PHM-Net provides a robust and interpretable solution for AMC under complex channel conditions. Full article