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Next-generation internet of things (IoT) wireless electronics require antennas that are conformal, transparent, and electromagnetically efficient, yet these attributes form an impossible trinity. Here, we realize the first Ti3C2Tx MXene-based conformal, transparent, and high-performance antenna through an optimized nanoimprint lithography-blading technique. Nanoimprinted MXene grid transparent conductive film (TCF) achieves an exceptional balance of low sheet resistance (4.32 ohms per square) at high transmittance (~89.1%) with promising flexibility. Two representative scenarios in IoT networks are selected to demonstrate the versatility of this TCF. For personal devices, the efficient MXene transparent dipole antenna enables a quasi-transparent wireless wearable device for long-distance and real-time communication (>30 meters). For fixed infrastructure, MXene-based digital coding metasurface enables high-quality wireless communication, maintaining a low bit error rate (~0.15%) even in a curved state. This work establishes MXene as a versatile platform that successfully merges conformal transparency and high electromagnetic performance, paving the way for next-generation imperceptible IoT wireless systems.

Skin temperature is fundamental in characterising human thermoregulatory responses. Wired probes, although accurate, restrict movement and are impractical outside laboratory settings. The iButton (DS1922L; Maxim Integrated, USA) is a widely used wireless alternative but does not meet the precision recommended by ISO 9886 and permits only retrospective data retrieval. A recently developed wireless sensor, the eTemp Performance (BodyCAP, France), claims to address these limitations but has not been validated independently. Here, we compared the eTemp and iButton against a wired thermocouple reference (SST-1; Physitemp, USA) during rest, exercise and recovery in the heat [mean (SD): 35.0 (0.5)°C, 40.6 (1.5)% relative humidity]. Twenty-six adults (10 women) completed seated rest (15 min), cycling at fixed heat production (30 min) and passive recovery (15 min). Mean skin temperature was calculated using the Ramanathan four-site formula. Across all periods, the eTemp showed a small negative bias (mean difference, -0.04°C; 95% limits of agreement, -0.33°C to 0.25°C; concordance correlation coefficient, 0.986), whereas the iButton showed a small positive bias (+0.11°C; 95% limits of agreement, -0.23°C to 0.46°C; concordance correlation coefficient, 0.974). The eTemp met all a priori acceptability thresholds for mean skin temperature. The iButton met the acceptability threshold for mean bias in all periods and was statistically equivalent to the reference; however, the 95% confidence interval on the upper limit of agreement marginally exceeded 0.5°C during exercise, recovery and overall. These findings provide the first independent validation of the eTemp Performance sensor and support the use of both wireless devices for mean skin temperature measurement during exercise-heat stress, notwithstanding the known limitations in accuracy of the iButton against the ISO 9886 standard.

Though machine learning is widely used in wireless edge networks, the transmission of raw data still suffers from security and privacy leakage. Federated learning (FL) addresses these privacy concerns by enabling model training without sharing raw data. However, traditional centralized FL is vulnerable to a single point of failure. Blockchain-based federated learning (BFL) technology can provide FL with a more reliable and secure environment. In wireless edge networks with limited resources, BFL systems encounter challenges related to computing demands and network transmission overhead. To address these issues, we propose a BFL framework for wireless edge networks, which includes local client training, a consensus process, and edge server aggregation. A client selection policy is designed to exclude low-quality clients that could degrade training efficiency and accuracy. Additionally, a joint client selection and resource allocation scheme is implemented to optimize the allocation of computing and bandwidth resources necessary for BFL training and consensus. Simulation results demonstrate that the proposed approach improves BFL system accuracy while reducing delay.

Infrared optical wireless communication (OWC) is a promising candidate for next-generation wireless links due to its high bandwidth and inherent security. However, the limited field-of-view (FOV) of terminals induces great difficulty in establishing line-of-sight (LoS) link between the transmitter and the terminal. In this paper, to facilitate efficient beam alignment, we report an auxiliary strategy integrating an external angle-sensing module with a wide-FOV telecentric receiver architecture, providing the receiver with real-time incident of angle (IoA) information to transform the traditional blind search into a deterministic alignment process. Three different angle-sensing modalities-monocular vision, binocular stereo vision, and LiDAR scanning-are systematically evaluated. Experimental results indicate that the precision of vision-based methods degrades significantly when the detection distance exceeds 1.3 m, whereas the LiDAR-based scheme maintains an angular error below 0.714° across a 3 m range and a 100° FOV, demonstrating superior robustness and reliability. By leveraging the LiDAR-derived IoA, the receiver implements a "coarse-to-fine" two-stage alignment mechanism. Combined with a non-coaxial correction algorithm, this approach bypasses the need for redundant internal search modules and bulky fiber bundles, enabling a single fiber to achieve micron-level auto-coupling with a peak steady-state efficiency of 54.2%. Under these conditions, a 10 Gbps on-off-keying (OOK)-modulated OWC link is successfully maintained within a 100° FOV, with the bit error rate (BER) consistently meeting the forward error correction (FEC) requirement. This research demonstrates that sensor-aided alignment simplifies terminal architecture and accelerates link acquisition, providing a viable pathway for wide-FOV optical wireless networks.

Wireless power transfer (WPT) systems for electric vehicles require reliable foreign object detection (FOD) mechanisms both during and prior to power transfer to ensure operational safety and efficiency. The primary purpose of this study was to develop a foreign object detection system to ensure that no objects are present in the area of magnetic coupling (between primary and secondary coils) prior to initiating power transfer. Conventional FOD techniques based on impedance, visual light, or thermal monitoring provide limited spatial information and are sensitive to coil misalignment. This paper proposes a machine learning-based FOD approach using a planar Magnetic Inductance Tomography (MIT) sensor array that enables spatial electromagnetic sensing for early detection and localisation of conductive foreign objects. A dataset comprising 17,800 measurement frames was collected using a custom STM32-based data acquisition system in the absence of (prior to) power transfer. Likewise, a dataset comprising 300 sets of measurement frames was collected during power transfer, in which each frame contains 120 electromagnetic sensor readings. This capture methodology coincides with the detection requirements of live WPT systems. Four classification models, including Random Forest, Support Vector Machine, XGBoost, and Multi-Layer Perceptron, were evaluated. To enhance robustness against sensor drift and environmental variations, feature-engineering techniques incorporating statistical, temporal, frequency-domain, and derivative-based features were developed. Experimental results demonstrate high detection accuracy under both controlled and real-world conditions. The proposed approach demonstrates the feasibility of integrating machine learning-based MIT sensing into wireless EV charging infrastructure for reliable foreign object detection.

Next-generation optical wireless communication requires photodetectors that offer both high spectral selectivity and strong security against interception. However, conventional broadband devices remain vulnerable to spectral crosstalk and eavesdropping. Here we show a digitally encoded dual-narrowband organic photodetector that intrinsically integrates optical filtering with algorithm-assisted encryption to enable secure, high-fidelity optical wireless communication. Operating without an external power supply, the self-powered device employs a Fabry-Pérot cavity with a carefully designed organic spacer Liq to achieve selective detection at wavelengths of 485 nm and 910 nm, along with an ultrafast response time of 440 ns. By combining chaotic encryption with hardware-level wavelength selectivity, our hardware-software co-design system achieves an ultra-low bit-error rate of 9.17 × 10-5 at 1.25 Mbps while demonstrating strong resilience to eavesdropping and external interference. Furthermore, precise cavity engineering allows the dual-narrowband response to be extended into the short-wave infrared region (>1230 nm), offering a scalable route toward multi-wavelength secure transmission, high-resolution spectroscopy, and intelligent photonic networks.

The effective creation of better, faster, and more efficient electronic circuits particularly in the radio frequency (RF) segment, is more important than ever for the entire world. A prevalent issue in wireless communication systems, such as those used for charging or Vehicle-to-Everything (V2X) communication, interference and signal reception problems are related to the receiver front end of electric vehicles (EV) applications. Modern receiver technologies are one way to address this issue, as they reduce interference and enhance signal reception. To address the above requirements, this paper introduces a down conversion Gilbert mixer with the improvement in conversion gain, power efficiency, linearity and noise performance. Here, the proposed mixer design uses current source helpers and current bleeding technique to enhance the performance of the conventional Gilbert mixer. The proposed mixer is simulated in Cadence Tool with an intermediate frequency (IF) of 100 MHz, which provides a conversion gain of 12 dB with the third order input intercept point (IIP3) of 4.5 dBm and a noise figure (NF) of about 9.86 dB. Also, the design functions at a supply voltage of 1.2 V, with a power consumption of only 0.96 mW. So, this mixer design may be the correct choice as a core component in the receiver front end in applications like HEVs and sensor networks, especially where wireless communication, signal conversion, and sensing are involved.

With the continuous evolution toward sixth-generation (6G) wireless communication systems, emerging scenarios such as terahertz transmission, integrated sensing and communication (ISAC), and ultra-massive multiple-input multiple-output (MIMO) have significantly increased the complexity, nonlinearity, and uncertainty of wireless propagation environments. The conventional model-driven paradigm, established upon Shannon information theory and precise mathematical modeling, is increasingly constrained by model-mismatch issues in real-world deployments. This paper systematically reviews recent advances in deep learning-enabled physical-layer signal processing. We examine intelligent channel estimation, signal detection, and end-to-end communication systems based on autoencoder architectures. We then analyze key technical challenges-including interpretability, data dependence, computational complexity, privacy and security in distributed learning, and system-level performance-overhead trade-offs-along with state-of-the-art solution strategies such as deep unfolding, transfer learning, model compression, federated learning, and lightweight design. Future evolutionary directions toward AI-native 6G networks, integrated sensing-communication-computing architectures, and intelligent reconfigurable wireless environments are discussed. Furthermore, emerging generative AI techniques, including diffusion models, are identified as a promising direction for addressing data scarcity and enhancing system adaptability. The study demonstrates that hybrid intelligence-integrating model-based prior knowledge with data-driven learning-will become the dominant design philosophy for next-generation intelligent physical-layer systems.

Uric acid is a critical metabolic biomarker for gout, kidney dysfunction, and cardiovascular disease. Persistent hyperuricemia promotes monosodium urate crystal deposition, triggering recurrent gout flares, chronic joint damage, and systemic inflammation, while early and continuous uric acid monitoring enables timely therapeutic intervention and improved disease outcomes. However, conventional blood tests and enzymatic sensors, although reliable, remain invasive, laboratory-bound, and unsuitable for continuous or point-of-care monitoring. Herein, we report a sustainable one-step strategy to fabricate a non-enzymatic uric acid sensor by direct laser writing on cobalt-treated paper with 455 nm irradiation, producing cobalt oxide-infused graphene. Unlike conventional metal-functionalized laser-induced graphene (LIG), which typically requires multi-step processing and non-biodegradable polymeric substrates, the present approach employs a biomass-derived paper substrate and simultaneously generates conductive graphene and redox-active cobalt oxide nanostructures in a single photothermal process. Furthermore, the incorporated multivalent Co2⁺/Co3⁺ redox couples act as biomimetic active sites for uric acid oxidation, enabling a flexible low-energy electron-hopping mechanism and enhanced interfacial charge transfer. The resulting porous hybrid electrode provides abundant electroactive sites for efficient sensing performance. Integrated into a flexible near-field communication (NFC) tag, the resulting platform enables wireless, battery-free uric acid monitoring in human sweat. The fabricated sensor achieved a sensitivity of 9.96 µA·μM-1 and a detection limit of 1.08 μM for uric acid sensing. The mechanical robustness is confirmed by minimal resonance frequency variation under bending, shifting only from 13.525 MHz at 0° to 13.575 MHz at 180° (~ 0.37% relative change). This work establishes a low-cost, scalable, and environmentally sustainable route toward metal oxide-carbon hybrid biosensors, offering a promising pathway for wearable uric acid monitoring and next-generation point-of-care diagnostics.

Wireless communication systems are experiencing a rapid evolution driven by the increasing demand for intelligent, adaptive, and highly interconnected services [...].

We demonstrate a comb-based photonic processor that performs temporal convolution in the optical domain and directly converts the analog computation result into a 300 GHz wireless signal. A dissipative Kerr soliton microcomb with a 300-GHz free spectral range provides parallel wavelength channels, in which convolution kernels are encoded as comb-line weights and processed via dispersion-induced delays. The convolution output is optically heterodyned in a uni-traveling-carrier photodiode, generating a 300 GHz carrier whose amplitude envelope represents the convolution result. We experimentally demonstrate real-time optical convolution at 1.2 Gbaud and a successful free-space THz link of the processed signal in the terahertz band. Offloading the convolution to the optical domain reduces the estimated computational cost by 23.1% relative to a purely electronic implementation. A latency estimate further suggests that direct optical-to-terahertz transmission can alleviate the I/O overhead by avoiding local digitization and bus-based data transfer.

The energy hole problem is a significant challenge in wireless sensor networks (WSN) that use multi-hop routing protocols. Nodes near the base station (BS) typically experience higher energy consumption due to higher data traffic, resulting in faster network energy depletion and creating an energy hole near the BS. To address this issue, the paper proposes a solution involving a mobile data collector (MDC) in an unequal grid cluster. The number and size of the clusters are determined based on the radio energy model's threshold transmission value, which provides balanced data traffic distribution in the network. The cluster head (CH) is elected based on the node's distance from the cluster centroid and its residual energy. Additionally, the frequency of CH rotation is optimized through an energy-efficient CH change mechanism. The inclusion of an MDC enables data collection from the CHs along the vertical boundaries, effectively reducing the occurrence of energy holes and extending the network's overall lifespan. Simulation results demonstrate the superior performance of our protocol compared to similar existing schemes. Our proposed work was simulated using OMNeT++, and the results indicate that it achieves approximately 21% less energy consumption than similar existing works.

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Recent advancements in sensor technology have made in-situ crack assessment of structures feasible. To investigate the correlation between the ultrasonic amplitude and metal fatigue life, an aluminum compact tension (C(T)) specimen was fabricated to simulate fatigue damage in thin plate structures. An experimental investigation of fatigue crack propagation was performed, wherein the specimen experienced cyclic uniaxial tensile loading at constant amplitude. The crack propagation behavior was analyzed, and the relationship between crack length and the associated loading cycles was determined. Additionally, the evolution of the ultrasonic signal during crack propagation was investigated, and the quantitative dependence of the ultrasonic characteristic parameter on crack length was revealed. Finally, a model correlating ultrasonic characteristic parameters with loading cycles was developed, enabling fatigue life evaluation. The proposed method demonstrates significant potential for evaluating the fatigue life of thin plate structures.

Wireless capsule endoscopy (WCE) enables painless, minimally invasive visualization of the gastrointestinal tract. Still, its diagnostic potential is limited by incomplete mucosal coverage and poor transferability of existing navigation methods across patient anatomies. We propose a transferable, anatomical landmark-guided deep reinforcement learning framework for robust autonomous gastric navigation. Leveraging a lightweight edge-contour-depth fusion module, our policy operates on stable, low-dimensional landmark coordinates rather than high-dimensional video streams. This design effectively bridges the sim-to-real visual gap and ensures robustness across diverse anatomies, enabling low-cost deployment by reducing computational overhead. In simulations across eight patient-derived models, the method achieves >97% coverage within 50 s, significantly outperforming vanilla Proximal Policy Optimization, Soft Actor-Critic, and Deep Q-Network agents by enhancing coverage and minimizing variance. To ensure deployment reliability, a two-stage sim-to-real pipeline supported by an adaptive dynamic programming controller actively mitigates physical disturbances, including actuator latency and peristalsis. Ex vivo experiments across five independent scans demonstrate high coverage stability, achieving a mean coverage of 87% and a 53% reduction in procedure time compared with expert manual control. This study establishes a scalable paradigm for autonomous, high‑coverage endoscopic navigation, advancing the clinical deployment of intelligent WCE systems for GI diagnostics.

Wireless sensor networks (WSNs) deployed for seismic monitoring must sustain long-term operation under strict energy constraints, where premature node failure degrades spatial coverage and detection reliability. This paper presents a safety-constrained reinforcement learning framework for transmission scheduling in energy-harvesting seismic WSNs. The proposed approach integrates Proximal Policy Optimisation (PPO) with action masking and a runtime guard-layer safety filter that enforces battery-preservation and load-balancing constraints without retraining. The guard layer intercepts policy actions and substitutes safe alternatives when constraint violations are detected, using a scoring function that combines battery headroom with network-wide load equity. Experiments across three network scales (10, 15, and 30 nodes) with solar energy harvesting demonstrate that the guard-enhanced PPO achieves 99.46% transmission success at 30 nodes while maintaining 66.47% node survival-a 58.3% improvement in survival over the highest-reward baseline (Closest) at the cost of only a 6.2% reduction in cumulative reward. Crucially, the guard-enhanced policy outperforms the unconstrained PPO baseline simultaneously on cumulative reward (+11.4%), transmission success (+0.8 pp), and node survival (+15.4%), demonstrating that hard safety constraints, when properly aligned with the system's energy model, provide both performance and safety gains rather than a fundamental trade-off. Sensitivity analysis across event rates (pevent=0.5 and 0.9) confirms that the guard layer's advantage persists under both moderate and extreme monitoring conditions. Analysis across scales reveals distinct operational regimes: at 10 nodes, heuristic baselines are near-optimal; at 30 nodes, learned policies dominate, and safety filtering becomes critical for sustained operation.

Injectable bioelectronics offer a minimally invasive approach to peripheral nerve stimulation but remain limited by onboard energy storage and fragile leads. Here, we present SEED, a leadless, battery-free bioelectronic interface engineered for percutaneous delivery through a standard 14-gauge needle. SEED (Stimulating Electrode for Electroceutical Delivery) operates in the magnetoquasistatic regime using low-frequency (65 kilohertz) resonant inductive coupling, externalizing waveform generation and control, enabling programmable neuromodulation without onboard active electronics. A spiral-helix electrode geometry promotes longitudinal nerve engagement while limiting off-target field spread. Benchtop and ex vivo characterization demonstrates precise, programmable control of stimulation frequency, pulse width, and amplitude under physiologically relevant conditions. In vivo validation in a rat sciatic nerve model confirms frequency-locked motor responses and graded neural recruitment following percutaneous deployment. SEED exhibits strong radiopacity and acoustic contrast, supporting compatibility with ultrasound and computed tomography for image-guided neuromodulation. This platform provides a scalable pathway toward minimally invasive bioelectronic therapies.