Search Results (229)

Population aging is driving the need for unobtrusive, continuous monitoring solutions in residential care environments. Radio-frequency (RF)-based technologies such as Ultra-Wideband (UWB) and millimeter-wave (mmWave) radar are particularly attractive for providing detailed information on presence and movement while preserving privacy. Building on a UWB–mmWave localization system deployed in a senior living residence, this paper focuses on the data-processing methodology for extracting quantitative mobility indicators from long-term indoor monitoring data. The system combines a device-free mmWave radar setup in bedrooms and bathrooms with a tag-based UWB positioning system in common areas. For mmWave data, an adaptive short-term average/long-term average (STA/LTA) detector operating on an aggregated, normalized radar energy signal is used to classify micro- and macromovements into bedroom occupancy and non-sedentary activity episodes. For UWB data, a partially constrained Kalman filter with a nearly constant velocity dynamics model and floor-plan information yields smoothed trajectories, from which daily gait- and mobility-related metrics are derived. The approach is illustrated using one-day samples from three users as a proof of concept. The proposed methodology provides individualized indicators of bedroom occupancy, sedentary behavior, and mobility in shared spaces, supporting the feasibility of combined UWB and mmWave radar sensing for longitudinal routine analysis in real-world elderly care environments. Full article

This paper presents an intelligent framework for real-time hand gesture recognition using Frequency-Modulated Continuous-Wave (FMCW) mmWave radar and deep learning. Unlike traditional radar-based recognition methods that rely on Discrete Fourier Transform (DFT) signal representations and focus primarily on classifier optimization, the proposed system introduces a novel pre-processing stage based on the Continuous Wavelet Transform (CWT). The CWT enables the extraction of discriminative time–frequency features directly from raw radar signals, improving the interpretability and robustness of the learned representations. A lightweight convolutional neural network architecture is then designed to process the CWT maps for efficient classification on edge IoT devices. Experimental validation with data collected from 20 participants performing five standardized gestures demonstrates that the proposed framework achieves an accuracy of up to 99.87% using the Morlet wavelet, with strong generalization to unseen users (82–84% accuracy). The results confirm that the integration of CWT-based radar signal processing with deep learning forms a computationally efficient and accurate intelligent system for human–computer interaction in real-time IoT environments. Full article

A novel wideband 1-bit reconfigurable transmitarray (RTA) is proposed, which is based on a substrate-integrated cavity-backed patch (SCIBP) element. The RTA element consists of a pair of SCIBP antennas, achieving wideband operational capability through the optimization of dielectric substrate thickness. To suppress surface-wave propagation between adjacent RTA elements, a substrate-integrated waveguide (SIW) is designed to function as a metallic isolation wall. A 180° phase shift is realized by dynamically manipulating p-i-n diodes embedded within the SCIBP antenna structure. When the dielectric substrate thickness is increased from 6 mm to 10 mm, the 3 dB transmission bandwidth is expanded from 10% to 33.6%. The simulation results confirm that the proposed element realizes a 3 dB transmission bandwidth of 33.6%. A prototype RTA with 100 elements is designed, fabricated, and measured. The prototype achieves a peak gain of 16.6 dBi at 4.6 GHz, accompanied by an aperture efficiency of 17.2% and a 3 dB gain bandwidth of 18.9%. Furthermore, measured scanned beams illustrate that the proposed RTA possesses good beamscanning performance. Owing to its many advantages, such as wideband operation, lightweight design, low cost, simple structure, and easy fabrication, it is particularly suitable for application in intelligent communication systems and radar systems. Full article

This paper addresses the insufficient speed control accuracy observed in traditional seeding systems. This paper proposes an electric drive seeding control method that incorporates a composite control strategy combining the Rime optimization algorithm (RIME) with a backpropagation neural network (BPNN). Firstly, the architecture including radar/proximity switch dual-mode speed measurement, STM32F103 main control, and asymmetric half-bridge drive was constructed. Based on the kinematic model, a motor speed-plant spacing mapping relationship was derived to complete the selection of a brushless DC motor. Secondly, this study addresses the issues of large overshoot in traditional PID control, response lag in fuzzy PID, and local optima in BP-PID. To overcome these challenges, the RIME algorithm is employed to optimize the weight-updating mechanism of the backpropagation neural network (BPNN). The soft RIME search facilitates multi-directional exploration, while the hard RIME puncture enhances global optimization capability, significantly improving the adaptive accuracy of the parameters. The simulation results showed that the adjustment time of the proposed RIME-BP-PID in the step response is 73.8% shorter than the BP-PID, and the overshoot is reduced to 0.23%. The square wave tracking error is 27.8% of the traditional PID. The bench test was carried out at 6–12 km/h speed and 200–300 mm. The results showed that, compared with BP-PID, the qualified index of RIME-BP-PID increased by 1.67–1.94 percentage points, the missed seeding index decreased by 1.25–1.80 percentage points, and the coefficient of variation decreased by 4.90–5.82 percentage points. The algorithm effectively solves the problem of the strong nonlinear time-varying control of a seeding system and provides theoretical support for the research and development of precision agricultural equipment. Full article

In recent years, achieving ultra-wideband electromagnetic absorption has emerged as a critical challenge in confronting advanced broadband electromagnetic detection technologies. This capability is essential for effectively countering sophisticated radar systems. In this study, we present a novel multilayer metamaterial absorber that integrates an FR4 transmission layer, a periodic gradient dielectric structure designed for resonant impedance matching, and a magnetic skin layer for enhanced energy dissipation. By employing asymptotic gradients in both structure and composition, this design achieves dual-gradient electromagnetic parameter modulation, enabling efficient absorption across the X, Ku, and K bands (8.6–26.4 GHz) with a total thickness of 3.5 mm (effective thickness: 2 mm) and a density that is one-third that of conventional magnetic metamaterials. The proposed absorber demonstrates polarization insensitivity, stability across wide incident angles (up to 60°), and an absorption efficiency of 94%, as confirmed by full-wave simulations and experimental validation. Moreover, the fiber-reinforced hierarchical structure addresses the traditional trade-off between broadband absorption performance and mechanical load-bearing capacity. Full article

All-medium metamaterial absorbers (MMAs) have attracted considerable attention for ultra-broadband electromagnetic wave (EMW) absorption. Herein, a lightweight graphene aerogel (GA) was synthesized through a low-temperature, atmospheric-pressure reduction route. Benefiting from its 3D porous network, enriched oxygen-containing functional groups, and improved graphitization, the GA offers diverse intrinsic attenuation pathways and a limited effective absorption bandwidth (EAB) of only 6.46 GHz (11.54–18.00 GHz at 1.95 mm). To clarify its attenuation mechanism, nonlinear least-squares fitting was used to quantitatively separate electrical loss contributions. Compared with graphene, the GA shows markedly superior attenuation capability, making it a more suitable medium for MMA design. Guided by equivalent circuit modeling, a stacked frustum-configured GA-based MMA (GA-MMA) was developed, where structure-induced resonances compensate for the intrinsic absence of magnetic components in the GA, thereby substantially broadening its absorption range. The GA-MMA achieves an EAB of 40.7 GHz (9.1–49.8 GHz, reflection loss < −10 dB) and maintains stable absorption under incident angles up to ± 70°. Radar cross-section simulations further indicate its potential in electromagnetic interference mitigation, human health protection, and defense information security. This work provides a feasible route for constructing ultralight and broadband MMAs by coupling electrical loss with structural effects. Full article

Accurate extrinsic calibration between radar and camera sensors is essential for reliable multi-modal perception in robotics and autonomous navigation. Traditional calibration methods often rely on artificial targets such as checkerboards or corner reflectors, which can be impractical in dynamic or large-scale environments. This study presents a fully targetless calibration framework that estimates the rigid spatial transformation between radar and camera coordinate frames by aligning their observed trajectories of a moving object. The proposed method integrates You Only Look Once version 5 (YOLOv5)-based 3D object localization for the camera stream with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Random Sample Consensus (RANSAC) filtering for sparse and noisy radar measurements. A passive temporal synchronization technique, based on Root Mean Square Error (RMSE) minimization, corrects timestamp offsets without requiring hardware triggers. Rigid transformation parameters are computed using Kabsch and Umeyama algorithms, ensuring robust alignment even under millimeter-wave (mmWave) radar sparsity and measurement bias. The framework is experimentally validated in an indoor OptiTrack-equipped laboratory using a Skydio 2 drone as the dynamic target. Results demonstrate sub-degree rotational accuracy and decimeter-level translational error (approximately 0.12–0.27 m depending on the metric), with successful generalization to unseen motion trajectories. The findings highlight the method’s applicability for real-world autonomous systems requiring practical, markerless multi-sensor calibration. Full article

Ground settlement is a multifaceted geological phenomenon driven by natural and man-made forces, posing a significant impediment to sustainable urban development. Thus, ground settlement susceptibility (GSS) mapping has emerged as a critical tool for understanding and mitigating cascading hazards in seismically active and anthropogenically modified sedimentary basins. Here, we develop an integrated framework for assessing GSS in the Pohang region, South Korea, by integrating Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR)-derived vertical land motion (VLM) data with seismological, geotechnical, and topographic parameters (i.e., peak ground acceleration (PGA), effective shear-wave velocity (V_s³⁰), site period (T_s), general amplification factor (A_F), seismic vulnerability index (K_g), soil depth, topographic slope, and landform classes) through ensemble machine learning models such as Random Forest (RF), XGBoost, and Decision Tree (DT). Analysis of 56 Sentinel-1 SLC images (2017–2023) revealed persistent subsidence concentrated in Quaternary alluvium, reclaimed coastal plains, and basin-fill deposits. Among the tested models, RF achieved the best performance and strongly agreed with field evidence of sand boils, liquefaction, and structural damage from the 2017 Pohang earthquake. The very-high-susceptibility zones exhibited mean subsidence rates of −3.21 mm/year, primarily within soft sediments (V_s³⁰ < 360 m/s) and areas of thick alluvium deposits. Integration of the optimal RF-based GSS index with regional building inventories revealed that nearly 65% of existing buildings fell within high- to very-high-susceptibility zones. The proposed framework demonstrates that integrating PSInSAR and ensemble learning provides a robust and transferable approach for quantifying ground settlement hazards and supporting risk-informed urban planning in seismically active and complex geological coastal environments. Full article

A compact, single-layer W-band microstrip antenna for forward-looking ADAS radar in the 77–79 GHz band is presented. The 16.5 × 22 mm² PCB element integrates a linear microstrip taper, two shorting vias, and a slot-loaded cavity to stabilize input reactance and broaden the in-band match. Full-wave simulations and launcher-based measurements using WR-12 TRL de-embedding and anechoic-chamber substitution confirm S₁₁ ≤ −10 dB across 77–79 GHz. At 77/79 GHz, the antenna achieves end-fire realized gains of ≈9.9/≈11.2 dBi. The main beam is end-fire (peak near θ ≈ 90°), with −3 dB beamwidths of ≈36° in the θ-cut at φ = 0 (pointing ≈ 61°/56°) and ≈11.6° in the φ-cut at θ = 90°. First sidelobes are about −2.3/−2.5 dB (θ-cut) and −3.1/−3.4 dB (φ-cut). Cross-polarization is ≥18 dB below co-polarization, and the simulated radiation efficiency reaches ≈85% at 77 GHz and ≈80% at 79 GHz. A controlled thermal sweep (25–105 °C) yields < 100 MHz resonance drift while maintaining ≥ 10 dB return loss. Due to its planar architecture and clean feed integration, compact module packaging in short- to medium-range automotive radars. Full article

Multiferroic composites of xNi_0.8Zn_0.2Fe₂O₄/(1 − x)BaTiO₃ (x = 0, 0.1, 0.3, 0.5, labeled NZFO/BTO) with ~100 nm particle size were synthesized via high-energy ball milling and thermal annealing. The X-ray diffraction shows a co-existence of the ferromagnetic phase of NZFO and the ferroelectric phase of BTO. Our observations indicate that saturation, remanence, and coercivity progressively increase with increasing NFO content, specifically from x = 0 to x = 0.5. At x = 0.1, the maximum electric polarization, remanent electric polarization, coercivity and electric power loss density reach their maximum values of ~0.057 µC/cm², 0.018 µC/cm², 3.25 kV/cm and 0.222 mJ/cm³, respectively, for an applied electric field less than 10 kV/cm. These multiferroic composites demonstrate excellent electromagnetic wave absorption capabilities from 2 to 18 GHz. With BTNF1 (x = 0.1) sample thickness of 2.5–3.5 mm, a minimum reflection loss of −41.51, −37, −28.72 dB corresponds to frequencies of 12.52 GHz, 11 GHz and 9.32 GHz. The effective absorption bandwidth for this sample is 11.5–16 GHz, indicating optimal impedance and attenuation matching and effective absorption of electromagnetic waves throughout the K_u bands. These outcomes reveal the capability for wideband absorption uses in radar invisibility technology and electromagnetic insulation. Full article

Time-division duplex (TDD) transceivers have found broad utility in millimeter-wave 5G communication, radar and imaging applications. The co-design of the switch and transmit/receive (T/R) amplifiers becomes essential in optimizing the passive loss and chip size. This work presents a Ka-band T/R amplifier with an embedded switch topology. The amplification cores from the TX and RX channels reuse the matching network to the T/R common port, and the full combination of switching and matching structures is enabled within a compact two-winding transformer. Implemented in 40 nm CMOS technology, the proof-of-concept Ka-band T/R amplifier occupies a core chip area of 0.163 mm². Experimental results show that it achieves a peak gain of 17.2 dB with a −3 dB bandwidth of 22.6–30.2 GHz in TX mode and a peak of 17.1 dB with a −3 dB bandwidth of 23.4–31.0 GHz in RX mode. The compact size and wideband gain response make the proposed T/R amplifier suitable for Ka-band TDD applications. Full article

In underground mining environments, 4D mmWave radar performance is severely constrained by ghost noise issues resulting from multipath reflections, metal structure interference, and complex terrain, creating significant challenges for target detection, mapping, and autonomous navigation tasks. Existing research lacks efficient automated methods and technical workflows for ghost point labeling in these scenarios. This paper presents a LiDAR-assisted two-stage ghost noise automatic labeling method. The technical workflow first achieves precise mapping between radar and LiDAR point clouds through multi-sensor spatiotemporal alignment (time synchronization and spatial registration) and then labels ghost points using a two-stage strategy that combines distance threshold filtering with density-based clustering analysis (DBSCAN). Experiments covering three typical underground mining scenarios (straight tunnels, straight tunnels with side tunnels, and cross-tunnel turns) demonstrate that the proposed method significantly outperforms single distance threshold or clustering methods in terms of precision (95.15%, 98.81%, and 98.85%, respectively), recall (97.44%, 94.68%, and 98.03%, respectively, slightly lower than distance threshold methods in straight tunnels and cross-tunnel turns), and F1 Score (95.48%, 96.70%, and 98.01%, respectively). The method exhibits efficient ghost noise detection capability and robustness in underground mining environments, providing a practical solution for optimizing radar data quality in complex confined scenarios, with potential for application in similar industrial settings. Full article

This paper presents a 0–360° phase shifter in 28 nm FD-SOI CMOS technology, suitable for radar applications and mm-wave wireless communication systems, which adopt high-efficiency transmitter architectures. It exploits a novel switching vector modulator based on a double-balanced Gilbert cell, which guarantees high-resolution phase control while exhibiting inherently high robustness against process and temperature variations. The phase control is performed by merely changing the currents in the Gilbert cells using digitally controlled current generators. The proposed phase shifter operates at 39 GHz and provides RMS phase and gain errors of 2.7–4.7° and 0.3–0.5 dB, respectively, while drawing 13 mA from a 1 V supply voltage. Full article

This paper compares three nondestructive methods used to detect and locate defects such as delaminations or voids in externally bonded fiber reinforced polymer (FRP) concrete structures: infrared thermography, ground-penetrating radar, and measurement of acoustic wave velocity. One of the main goals was to check whether it was possible to distinguish overlapping defects. For this purpose, eight concrete samples were made with a bonded carbon fiber reinforced polymer (CFRP) strip with the following dimensions 100 × 100 × 500 mm. Two samples had no defects, four had single defects varying in location (at the edge of the strip or in the centre) simulating delamination or voids in the concrete cover, and the remaining samples had overlapping defects. Both infrared thermography and acoustic wave velocity measurement methods allow the detection of defects/voids in the adhesive layer and a concrete defect (void in the concrete cover). However, ground penetration failed to detect defects in the adhesive layer. Only infrared thermography allows for the differentiation of overlapping defects. On the basis of the conducted research, the methodology, differences, advantages, and limitations of each method were described, along with recommendations based on the authors’ experience. Full article

Millimeter-wave (mm-wave) radar has become a key technology in wireless sensor networks (WSNs) due to its high spatial resolution and penetration capability, enabling applications such as smart traffic control and non-contact health monitoring. Achieving fine-range resolution necessitates wide signal bandwidth, which places stringent demands on power amplifier (PA) performance in terms of bandwidth, efficiency, and output power. Therefore the design of the power amplifier for WSN poses significant challenges. This paper presents a broadband mm-wave PA implemented in a 40 nm CMOS process, utilizing transformer-based power combining to enhance efficiency and bandwidth simultaneously, which can adequately meet the requirements of WSN systems. The PA achieves a 3 dB flat power bandwidth up to 62% from 13 to 24.8 GHz. At 19 GHz, it delivers a saturated output power (Psat) of 12.3 dBm, a 1 dB compression point (P1dB) of 10.15 dBm, and exhibits a peak power-added efficiency (PAE) of 23%, with 17.2% PAE at P1dB. The PA consumes 43 mW from a 1.1 V supply and occupies an active area of only 0.06 mm². These results validate the effectiveness of transformer-based combining for achieving compact, high-performance broadband PAs in CMOS, and demonstrate its suitability for mm-wave radar systems requiring high-range resolution. The amplifier provides a high stability, with output return losses better than −10 dB. Full article