Abstract-High-precision localization of underwater robots in enclosed tunnel environments is challenging due to the absence of satellite signals and the practical difficulty of deploying large-scale acoustic infrastructure. Existing solutions typically fuse inertial navigation with vision, laser, or acoustic sensing, yet their performance is often limited by rapid inertial drift and degraded sensing under turbidity, specular surfaces, and constrained geometries. This paper presents SPLoc, a structural-prior-assisted localization and measurement framework, together with an underwater tunnel pose measurement platform that tightly integrates a blue–green structured-light triangulation sensor and an IMU. The structured-light subsystem acquires dense cross-sectional point clouds with centimeter-scale sampling along both axial and radial directions. From these measurements, tunnel cross-sectional primitives are extracted through feature enhancement, redundancy suppression, and RANSAC-based circle fitting with region-growing verification, and are then parameterized as structural priors. To achieve metrologically consistent fusion, we formulate a least-squares adjustment that explicitly models heading and position states, laser observation residuals, and dominant pose-error terms (including heading/position biases and observation perturbations), and solve it via iterative refinement to provide dynamic pose compensation. Simulation and physical-equivalent experiments on a purpose-built testbed demonstrate that SPLoc achieves < 2 cm localization error and < 0.5° heading error under diverse initial disturbances, while improving robustness against sensing outliers and geometric ambiguities. The proposed platform and estimation model provide a practical measurement route for accurate underwater tunnel robot localization and support engineering deployment where external beacons are unavailable.

Wi-Fi sensing—especially human activity recognition (HAR)—is emerging as a device-free means of understanding human–space interactions for smart healthcare and elder care. However, most learning-centric HAR systems focus on improving feature-extraction efficiency or advancing neural-network architectures while overlooking physical information—such as location—which is crucial for accurate activity recognition and interpretation. To close this gap, we introduceDWSen, a Dual-Path Wavelet-Attention Kolmogorov-Arnold Network (KAN) framework for joint activity and indoor location sensing. Specifically, DWSen is a compact multi-task model that combines two complementary encoders with a lightweight decoder. First, an Attention-Masked ResNet-1D (AMRN) suppresses noisy subcarriers and thus highlights discriminative temporal patterns. Meanwhile, a Dual Laplace Wavelet Convolution and bidirectional GRU (Bi-GRU) branch (DLWCB) injects physics-aware, band-pass time–frequency cues and captures long-range temporal dependencies in CSI sequences, thereby enhancing the accuracy and robustness of joint activity and indoor location recognition. Thereafter, the dual features are fused at a late stage and decoded by Wavelet-based Kolmogorov-Arnold Network (WavKAN) heads, which incorporate analytical wavelet kernels as priors and combine them with a nonlinear path and batch normalization to generate activity and location logits. Moreover, training employs an uncertainty-based Adaptive Weighted Loss (AWL) that dynamically balances the objectives, thus mitigating negative transfer without manual tuning. Additionally, we collected an activity- and location-sensing dataset, ElderAL-CSI, covering six activities at nine locations performed by three participants. Extensive experiments on ARIL, CSIDA, and ElderAL-CSI demonstrate that our method attains 94.24%/98.20%, 94.37%/99.65%, and 99.56%/85.20% accuracy (activity/location), respectively, and offers competitive or superior performance compared with recent baselines across most settings. These results point to a low-cost, privacy-friendly path toward reliable ambient monitoring in real homes.

In this paper, we propose a novel active reconfigurable intelligent surface (RIS)-assisted amplitude-domain reflection modulation (ADRM) transmission scheme, termed as ARIS-ADRM. This innovative approach leverages the additional degree of freedom (DoF) provided by the amplitude domain of the active RIS to perform index modulation (IM), thereby enhancing spectral efficiency (SE) without increasing the costs associated with additional radio frequency (RF) chains. Specifically, the ARIS-ADRM scheme transmits information bits through both the modulation symbol and the index of active RIS amplitude allocation patterns (AAPs). To evaluate the performance of the proposed ARIS-ADRM scheme, we provide an achievable rate analysis and derive a closed-form expression for the upper bound on the average bit error probability (ABEP). Furthermore, we formulate an optimization problem to construct the AAP codebook, aiming to minimize the ABEP. Simulation results demonstrate that the proposed scheme significantly improves error performance under the same SE conditions compared to its benchmarks. This improvement is due to its ability to flexibly adapt the transmission rate by fully exploiting the amplitude domain DoF provided by the active RIS.

Wireless data links and wireless power transfer (WPT) have become emerging topics in IoT applications. A simple-structure high-efficiency rectenna is the key technology for wirelessly powered IoT receivers. Herein, we explore a unified co-design technique that synergizes wideband circular polarization (CP) antennas with high-frequency Gallium Arsenide (GaAs) Schottky diodes across the 10–20-GHz range. Our approach involves a systematic study of the rectifier’s nonlinear behavior over wide frequency ranges, followed by the identification of an effective antenna candidate for co-design. This strategy effectively obviates the need for matching networks, filters, and extra components typically found in conventional wideband rectennas. Consequently, we present a co-design example: the proposed CP antenna, featuring a measured impedance bandwidth of 11.65–16.9 GHz (36.8%), a 3-dB axial ratio bandwidth (ARBW) of 12.55–16.9 GHz (29.54%), and a measured peak gain of 8.5 dBic. This design integrates CP magneto-electric (ME)-dipole units with rectifier topologies. Measured results show an RF-DC efficiency of over 50% within the 11–14.6 GHz (28%) and 10–18 dBm power range, with a maximum conversion efficiency of 61%. This design approach holds broad applicability for all wideband rectennas, offering notable advantages in terms of simplicity, efficiency, and compactness.

Existing Internet of Things (IoT) devices face a significant challenge in terms of power consumption due to their limited battery life. Capturing and utilizing ambient radio frequency (RF) energy emerges as a promising solution for powering low-power sensors and electronic devices, given its unique spatial and temporal distributions. However, the low level of ambient RF power severely hampers the rectenna’s RF-to-direct current (DC) conversion efficiency, making it incapable of generating sufficient DC power. To address this issue and enhance the conversion efficiency of a broadband rectenna at low environmental power levels, this study introduces a novel technique called the meta-lens assisted technique (MAT). This technique leads to a substantial increase in the rectenna’s received RF power by more than 10 dB. As a result, the total conversion efficiency improves by over 30% across a wide frequency band ranging from 2.9 GHz to 3.63 GHz (with a fractional bandwidth of 22.3%), even when the initial RF power received (without the MAT) was as low as -20 dBm, which approaches the real-life ambient RF power level. Notably, the proposed MAT achieves a 40% to 60% efficiency improvement compared to state-of-the-art approaches. These remarkable results demonstrate the promising potential of the MAT rectenna as an alternative for harvesting low-density wireless energy and supporting low-power-required industrial IoT applications.

We present a novel design strategy for complex multiband rectifiers, named the “Rectifying Network Common Items Extraction (RNCIE)”. This approach involves sharing one rectifying network (i.e., the common items) between two or more parallel rectifiers, thereby significantly simplifying the design process. In contrast to the traditional global optimization method for multi-band (e.g.,$>$3 bands) rectifier design, our RNCIE strategy can help realize the modular decomposition of the rectifier, including the rectifying network and impedance matching network (with the help of filter branches), thus leading to a significant reduction in design cost and complexity. Additionally, the unique advantage of the RNCIE contributes to in-depth analysis of the influencing factors on the total efficiency of the rectifier. Consequently, the RF-DC conversion efficiency of an example prototype of the rectifier design can be optimized to 44.6% @ 1.8 GHz, 45.4% @ 2.1 GHz, 41.7% @ 2.6 GHz, 33% @ 3.5 GHz, 30.2% @ 4.9 GHz, and 23% @ 5.8 GHz at an input power level of$-$10 dBm. Meanwhile, our rectifier with a shared rectifying network can improve the total efficiency when multi-tone signal inputs are used (27% @$-$20 dBm, 43% @$-$10 dBm for a six-tone signal). The strategy is verified by experimental measurements and paves the way for the efficient and accurate design of multi-band rectifiers of high efficiency.

Backscatter communication is an emerging paradigm for pervasive connectivity of low-power communication devices. Wirelessly powered backscattering wireless sensor networks (WSNs) become particularly important to meet the upcoming era of the Internet of Things (IoT), which requires the massive deployment of self-sustainable and maintenance-free low-cost sensing and communication devices. This article will introduce the state-of-the-art antenna design and radio frequency (RF) system integration for wirelessly powered backscatter communications, covering both the node and the base unit. We capture the latest development in ultralow-power RF front ends and coding schemes for$\mu \text{W}$-level backscatter modulators, as well as the latest progress in wireless power transfer (WPT) and energy harvesting (EH) techniques. Newly emerged rectenna system, waveform design, and channel optimization are reviewed in light of the opportunities for adaptively optimizing the WPT/EH efficiency for low-power signals with varying conditions. In addition, advanced device packaging and integration technologies in, e.g., additively manufactured RF components and modules for microwave and millimeter-wave ubiquitous sensing and backscattering energy-autonomous RF structures are reported. Inkjet printing for the sustainable and ultralow-cost fabrication of flexible RF devices and sensors will be reviewed to provide a prospective insight into the future packaging of backscatter communications from the chip-level design to complete system integration. Finally, this article will also address the challenges in fully wireless powered backscatter radio networks and discuss the future directions of backscatter communication in terms of “Green IoT” and “Low Carbon” smart home, smart city, smart skin, and machine-to-machine (M2M) applications.

This article presents an innovative belt antenna with an electromagnetic band-gap (EBG) ground plane made of textile materials. The antenna can be applied in a smart belt system to set up a communication link with other electronic devices and/or host a variety of sensors to track human motions. The proposed belt antenna works at 2.45 GHz in the industrial, scientific, and medical radio band for Bluetooth low energy communications. Considering the effect the human body would have on the performance of a belt antenna, a textile ground plane is designed to be integrated into the trouser fabric behind the belt to provide isolation from the body and simultaneously improve antenna radiation characteristics. Through the application of the ground plane, the belt antenna achieves a maximum realized gain of 7.94 dBi and a minimum specific absorption rate of 0.04 W/kg at 0.5 W input power. During the design process, characteristic mode analysis is used to explore the underlining principle and further optimize the antenna performance. Two typical EBG structures are analyzed in detail for this application scenario. The suspended transmission line method is used to evaluate EBG performance variations when the textile ground plane is bent. A prototype of such a system is fabricated and tested. Experimental results shows that the belt antenna, together with the textile EBG ground plane, is an excellent candidate for a smart belt system with desirable radiation pattern, efficiency, and safety limit.