Search Results (1,264)

In Underwater Acoustic Target Recognition (UATR), accurately extracting spectral lines from time–frequency spectra in complex ocean environments faces three critical challenges: low-frequency spectral confusion, line spectrum and noise mixture, and a computational efficiency vs. performance trade-off. To address these, we propose a deep learning-integrated framework based on application-oriented integration and adaptation of established techniques tailored to the underwater acoustic domain. The framework consists of the following: (1) the Line Spectrum Separation Network (LSS-Net), which integrates a Time–Frequency Joint LSTM and a Temporal Gated Cross-Attention (TGCA) module within an encoder–decoder architecture adapted for high-resolution underwater acoustic time–frequency spectra; (2) a physics-informed signal simulation approach that realistically models Doppler frequency drift and intensity fluctuations; and (3) a Peak-Tracking Line Extractor (PTLE) algorithm that leverages underwater acoustic-specific temporal constraints. The proposed framework achieves an MOTA of 0.89 on simulated data and 0.52 on real sea trial data, outperforming existing methods by 0.06-2.14 in MOTA and significantly suppressing high-resolution background noise. Full article

This paper proposes a micro-Doppler flash detection framework for hovering unmanned aerial vehicle (UAV) detection with linear frequency modulated continuous wave (LFMCW) radar under the dual constraints of strong ground clutter and severe thermal noise conditions. In such scenarios, conventional methods fail not only due to the spectral overlap between hovering targets and clutter but also because of the visual disappearance of micro-Doppler features under heavy noise. The framework consists of three sequential modules. A prior-template orthogonal projection (PTOP) module suppresses clutter via a single-step orthogonal projection, preserving the micro-Doppler flash signature without distortion while approximately maintaining the Gaussian noise statistics required for subsequent detection. A flash power spectrum construction module then collapses the periodic blade flash energy onto a sharp spectral peak in a one-dimensional (1D) power spectrum via Gabor transform, power projection, and fast Fourier transform (FFT). A cell-averaging constant false alarm rate (CA-CFAR) detection module with an analytically derived threshold factor finally renders a reliable detection decision. Simulations under a signal-to-clutter ratio (SCR) of $-21$ dB and signal-to-noise ratio (SNR) of $-23$ dB confirm that the proposed framework achieves reliable detection even when the micro-Doppler flash signatures are visually obscured by residual noise in the time–frequency domain. Parametric SNR sweep curves and a two-dimensional (2D) SCR–SNR detection-probability heatmap under a non-stationary clutter model further quantify the practical performance boundaries of the framework. By transforming these concealed periodic features into a sharp spectral peak, the framework provides robust detection performance where conventional range-Doppler and moving target indication (MTI)-based methods both exhibit severe performance degradation. Full article

Waveform agility improves anti-reconnaissance and anti-jamming capability in diverse inverse synthetic aperture radar (ISAR) scenarios, but it also breaks the phase variation assumptions used for conventional coherent processing. For cooperative bistatic ISAR radars, the problem is further complicated by the bistatic geometry and phase evolution induced by synchronization. This paper develops a joint coherent integration method for a cooperative bistatic radar with simultaneous pulse width (PW) and pulse repetition interval (PRI) agility. Firstly, we establish and analyze a bistatic geometric model to reveal key integration problems under agile waveforms, and then derive the coherent processing interval (CPI) local polynomial description for bistatic delay, Doppler and acceleration. On this basis, the matched filter response of each agile pulse is analyzed under the fixed-bandwidth assumption with linear frequency modulation (LFM), showing that PW agility produces a compressed peak displacement and an additional deterministic phase term, whereas PRI agility converts slow-time coherent integration into a nonuniformly sampled spectral estimation problem. To solve this problem, a joint fast and slow-time compensation route is derived, together with a bistatic-specific parameter design method that connects coherent integration tolerances with the bistatic angle and the observable projection vector. Finally, we test the performance of the proposed joint integration method in multiple scenarios and verify its effectiveness and robustness, which enhances detection performance and resolution for target localization. Full article

Large carrier frequency offsets (CFOs) can severely distort the correlation response of the Physical Random Access Channel (PRACH), generating multiple significant peaks even for a single transmitting user equipment (UE), such that CFO-induced pseudo-peaks may exceed the detection threshold and be erroneously identified as valid peaks. This work addresses the problem of peak disambiguation under such conditions by formulating peak selection as a model-consistency validation problem under mismatch. A generalized likelihood ratio test (GLRT) is first formulated to provide a principled statistical validation of each detected candidate peak based on the estimated timing advance (TA) and CFO parameters. While theoretically grounded, this approach is shown to be insufficient under realistic large-CFO conditions, where CFO-induced peak ambiguity is further complicated by multipath-induced model mismatch. To address this limitation, a complementary residual-energy-based criterion is introduced, along with a weighted combination of both metrics, interpreted as a penalized consistency criterion for robust peak selection under model mismatch. The proposed framework enables the selection of a single reliable TA/CFO pair among multiple candidates, improving receiver robustness and reducing spurious updates. Performance is evaluated using precision, recall, and F1-score for both short and long PRACH formats under 3GPP-aligned channel models, including high-CFO and high-Doppler scenarios. Results demonstrate that the proposed weighted strategy generally provides a more robust trade-off than the individual GLRT-only and residual-only criteria. Full article

Magnetostrictive patch transducers (MPTs) are highly efficient for generating and detecting ultrasonic waves for non-destructive evaluation (NDE), though their use on cementitious media and fibre-reinforced concrete has not yet been investigated. In this study, a COMSOL simulation, validated with laser-Doppler vibrometry, was first used to quantify patch deformation for use in subsequent simulation of wave propagation in samples. The MPT system was then validated on thin glass plates, producing tunable A₀, S₀, and SH₀ modes through frequency-wavelength matching. In cementitious mortar plates, SH₀ and SH₁ modes were demonstrated experimentally for the first time using MPTs. The validated COMSOL model was then used to interpret complex signals in quasi-plate and half-space cementitious mortar prisms, showing that MPTs generate Rayleigh, bulk SH, and surface-skimming SH modes. In steel fibre-reinforced concrete, surface-skimming SH wave speed correlated with increases in breaking strength even in the presence of surface features such as notches. Notably, Rayleigh wave speeds could not be measured in the presence of surface features, and the Rayleigh velocities measured in the same sample, but not in the local tested area did not correlate with SH speed. This behaviour is likely due to the non-uniform distribution of material constituents, including fibre-reinforcement and coarse aggregate, combined with the different propagation paths and depth sensitivities of the reported wave modes. Overall, racetrack-coil MPTs enable multimodal inspection of cementitious media, providing information on the presence of geometric features and material properties. Full article

Local impedance variations in structural waveguides partially reflect and absorb incident
flexural waves, motivating wave-based strategies for passive vibration control. This study
develops and experimentally validates a wave-energy framework to quantify and optimize
flexural wave absorption by Kelvin–Voigt attachments on a finite Timoshenko beam.
A finite element model is validated against Scanning Laser Doppler Vibrometry measurements
from a clamped–clamped aluminum beam with a passive damper mounted near
one end, with dashpot parameters identified through two independent approaches and
the discrepancies attributed to parameter uncertainty. Wave decomposition of the simulated
and measured velocity fields yields the power reflection coefficient ρ(ω) and power
absorption coefficient α(ω) over the 0–15.3 kHz band. The spring stiffness and damping
coefficient exhibit frequency-dependent optima and act as complementary, jointly tuned design
variables. Expressing dashpot location in wavelength-normalized coordinates reveals
a recurring spatial pattern in which absorption minima cluster around half-wavelength
multiples, while multiple spanwise positions yield near-peak absorption at any given
frequency. This pattern is governed primarily by the flexural wavelength, decoupling
placement from parameter tuning, and persists across clamped–clamped, clamped–free,
and free–free boundary conditions. Two independently tuned dampers further broaden the
effective absorption band by suppressing local minima in α(ω). These results demonstrate
that measurement-driven wave decomposition provides compact, physically grounded
guidelines for passive damper placement in beam structures. Full article

This paper targets the air-to-ground (A2G) data backhaul scenario of UAVs and proposes a communication system based on coherent optical zero-padding orthogonal frequency division multiplexing (CO-ZP-OFDM), which unifies atmospheric turbulence scintillation, pointing errors, and Doppler frequency shift into a composite channel model. The system employs the Gamma-Gamma (GG) distribution to describe turbulence-induced intensity fluctuations, a Gaussian beam truncation model to characterize pointing errors, and a dual-pilot method to estimate and compensate the Doppler frequency offset. Furthermore, on a polarization-time-frequency (PTF) three-dimensional orthogonal grid pilot structure, we derive theoretical mean square error (MSE) expressions for the zero-forcing (ZF) and minimum mean square error (MMSE) estimators, and analyze their MSE characteristics under the proposed pilot model. Simulation results show that, under moderate turbulence, the shrinkage factor of the MMSE estimator yields only about $0.4$ dB MSE reduction over ZF at $\mathrm{SNR}=10$ dB, whereas the full receiver pipeline that combines coherence-bandwidth pilot averaging with the MMSE and maximum ratio combining (MRC) equalizer reduces the empirical MSE by approximately 15 dB. The bit error rate (BER) performance tests indicate that, under turbulence-free conditions with ideal channel estimation, the system can reduce the BER below ${10}^{-4}$ at an SNR of approximately 12 dB. Under strong turbulence conditions with MMSE channel estimation, the SNR cost required to achieve a BER of ${10}^{-3}$ is approximately 18 dB, which corresponds to a 3 to 5 dB BER gain over the ZF baseline at the same SNR. Further simulation analysis shows that the average pointing loss is highly sensitive to the angular jitter at the 1 km link distance: an angular jitter of 1 mrad incurs about 18 dB of loss, and a sub-mrad pointing stability (i.e., ${\sigma }_{\mathrm{jit}}<0.062$ mrad) is required to keep the average pointing loss below 1 dB. Full article

Parkinson’s disease (PD) is a neurodegenerative disorder with an increasing incidence rate that significantly affects patients’ motor functions and quality of life. Involuntary upper limb tremors (ULTs) commonly manifest unilaterally, affecting either the left or right upper limb. Clinically, ULT frequencies can be categorized into three distinct classes: low-frequency (<4.0 Hz), mid-frequency (4.0–7.0 Hz), and high-frequency (>7.0 Hz) tremors. These tremor motions are characterized by oscillatory or rotational (angular displacement) movements, commonly referred to as the micro-Doppler effect (mDE). This study aims to develop a short-range (<1.0 m) and contactless sensing method for ULT detection based on Doppler millimeter-wave (mm-Wave) radar. The reflected electromagnetic waves indicate time-varying frequency characteristics, which can be analyzed by using time–frequency transform (TFT) methods, such as the Wigner–Ville distribution (WVD) and smoothed pseudo WVD (SPWVD). These TFT methods are employed to extract mDE features, which are subsequently visualized as color-coded spectrograms for ULT classification. Then, a two-dimensional (2D) convolutional neural network (CNN) is employed to automatically recognize the visual feature patterns for ULTs classification based on frequency and amplitude information. In the experimental setup, the W-band (76–81 GHz) Doppler mm-Wave biosensor is implemented for sensing and extracting feature patterns. The proposed classifiers based on “WVD + 2D CNN” and “SPWVD + 2D CNN” are trained and validated by using the collected datasets, with 60% randomly selected for training datasets and 40% for testing datasets in each fold validation. A 10-fold cross-validation method is applied to evaluate the classifier’s performances, achieving an average precision of 95.92 ± 0.60%, average recall of 95.89 ± 0.62%, average F1-score of 0.9588 ± 0.0060, and average accuracy of 95.89 ± 0.62%, respectively. The experimental results demonstrate the feasibility of the proposed classifier for real-time ULTs classification in PD patients using short-range (<1.0 m) and contactless sensing. Full article

Background/Objectives: Primary thyroid lymphoma (PTL), including diffuse large B-cell lymphoma (DLBCL) and mucosa-associated lymphoid tissue (MALT) lymphoma, share substantial overlap in ultrasound appearance with Hashimoto’s thyroiditis (HT), making preoperative differentiation challenging. This study aims to develop and validate a deep learning model based on B-mode ultrasound (BMUS) and color Doppler ultrasound (CDUS) for image-level differentiation of DLBCL, MALT lymphoma, and HT. Methods: This retrospective single-center study included 1294 ultrasound images from 290 patients (313 lesions) who underwent preoperative ultrasound examination at West China Hospital between September 2002 and September 2024. All images from the same lesion were assigned to the same data partition, and the dataset was split at the lesion level into training and test sets at an 8:2 ratio. A Frequency-Adaptive WT-ResNet model incorporating wavelet transform convolution and a frequency-adaptive gating mechanism was developed. The primary analysis was performed at the image level. The performance of the model was compared with that of three ultrasound physicians with different levels of experience. Grad-CAM was used for visual interpretation. An exploratory external validation was performed using an independent dataset from Sun Yat-sen Memorial Hospital. Results: In the test set, the model achieved a macro-average AUC of 0.927 (95% CI: 0.889–0.960), with class-specific AUCs of 0.899 for DLBCL, 0.946 for MALT lymphoma, and 0.937 for HT. The macro-average balanced accuracy was 0.866, compared with 0.827 for that of the best-performing senior physician. The exploratory validation set yielded a macro-average AUC of 0.796 (95% CI: 0.686–0.888), with class-specific AUCs of 0.806 for DLBCL, 0.825 for HT, and 0.756 for MALT lymphoma. Grad-CAM showed that the model focused on lesion-internal echotexture and lesion-transition regions with class-dependent patterns. Conclusions: A deep learning model based on BMUS and CDUS showed promising performance for image-level differentiation of DLBCL, MALT lymphoma and HT in a single-center retrospective cohort. The model outperformed three ultrasound physicians and may serve as a potential decision-support tool. However, the exploratory external validation results should be interpreted as preliminary, and larger multicenter cohorts remain necessary to confirm model generalizability. Full article

With the emergence of mobile sixth-generation (6G) integrated sensing and communication (ISAC) scenarios, conventional multicarrier waveforms face challenges in maintaining reliable communication and robust physical-layer security. In this paper, we propose a multi-layer encryption multiple-input multiple-output (MIMO) affine frequency division multiplexing (AFDM) with index modulation (IM) scheme, which exploits the inherent flexibility of the AFDM modulation parameter $c_2$ and subcarrier IM to construct a multi-dimensional physical-layer security mechanism. To enable sensing and exploit MIMO spatial diversity, a unified downlink MIMO configuration is adopted, where sensing and communication share the same transmit waveform, receive array, and physical propagation environment. The proposed configuration enables multi-dimensional parameter estimation, including delay, Doppler, and angle. The obtained sensing information further assists beamforming design, channel reconstruction, and signal equalization. Furthermore, the base station and user equipment share synchronized secret keys, and a unified detection framework is developed to balance computational complexity and detection accuracy while remaining compatible with the multi-dimensional encryption structure of the MIMO-AFDM-IM system. Simulation results verify the effectiveness of the proposed scheme in mobile scenarios, demonstrating enhanced multi-dimensional sensing accuracy, improved resistance to eavesdropping, and superior communication reliability and energy efficiency (EE). Full article

Understanding the dynamics of the urban atmospheric boundary layer is critical for accurate meteorological and air quality modeling. Utilizing one year of continuous Doppler wind lidar observations, this study investigates the seasonal and diurnal variability of wind fields, low-level jets (LLJs), and mixing-layer height (MLH) at an urban site in Beijing. Results show that horizontal winds are strongest in winter and spring and weaker in summer, with northwesterly flow dominating in winter and more diverse patterns in summer, while the corrected vertical-velocity distributions show seasonally varying structures and are interpreted cautiously as frequency-distribution characteristics. A distinct diurnal phase reversal in wind speed is identified near 0.3 km. LLJs occur predominantly at night, with core heights descending from 1.2–1.6 km in winter to 0.6–0.8 km in summer, and are associated with enhanced vertical shear. MLH reaches its deepest development in spring, with clear-sky peaks exceeding 1.5 km, while summer growth is comparatively limited and is associated with stronger latent heat partitioning. These findings indicate that wind fields, LLJs, and MLH exhibit coherent seasonal and diurnal covariations, while their direct causal relationships require further process-oriented analysis. This study provides a year-long observational basis for evaluating urban ABL parameterizations. Full article

Distributed passive acoustic tracking is an important technique for detecting and localizing a non-cooperative underwater target, in which the frequency difference of arrival (FDOA) is a widely used measurement. FDOA reflects the differences in the target’s radial velocity relative to spatially distributed receiving nodes through Doppler-induced variations in the instantaneous frequencies of line-spectrum components. However, conventional FDOA-based methods rely heavily on the stable and accurate estimation of instantaneous line-spectrum frequencies, and their performance degrades when the line spectrum is affected by frequency fluctuations caused by target operating variations and external disturbances. To address this issue, this paper proposes a new measurement, the cross-correlation frequency difference of arrival (CFDOA), which exploits the temporal correlation of line-spectrum to characterize inter-node radial-velocity differences and reduces the reliance on accurate instantaneous-frequency estimation. To evaluate the effect of the proposed CFDOA measurement on positioning performance, a unified FDOA/CFDOA measurement equation is established within the same target position estimation framework. In addition, for scenarios with a limited number of receiving nodes, a recursive estimation scheme combining constrained initial-state search and particle filtering is developed. The simulation and sea-trial results demonstrate that, in the presence of line-spectrum frequency fluctuations, the proposed CFDOA measurement yields more accurate position estimates than conventional FDOA. Full article

Underwater acoustic (UWA) channels are inherently complex, with pronounced variability arising from multipath propagation, time variability, Doppler effects, and nonstationary ocean conditions. Such variability often leads to unstable communication reliability when conventional single-carrier signaling and fixed reception strategies are employed. In practical UWA environments, performance degradation may occur when channel characteristics deviate from the assumed regime, thereby limiting system robustness. To address this reliability challenge, this study develops an unequal-rate frequency–phase keying (URFPK) signaling strategy that combines a low-rate frequency component with a high-rate phase component. A corresponding receiver structure is designed, employing parallel coherent and noncoherent processing to enhance robustness under dynamic channel conditions. In addition, a reduced-complexity noncoherent procedure is introduced to improve computational efficiency. Simulation results demonstrate substantially improved robustness under severe UWA distortions. Full-scale sea trials further validate the engineering effectiveness of the proposed approach, achieving communication success rate improvements of $18.62$% and $9.39$% over baseline schemes within short intervals and maintaining an overall success rate exceeding 91% over extended transmissions. These results indicate that the URFPK signaling strategy provides a practical and robust mechanism for improving UWA link reliability in dynamic UWA channels. Full article

This paper investigates the viability of utilizing Fused Deposition Modeling (FDM) for the fabrication and follow-up testing of a hemispherical resonator (HR). This form of resonator has several significant applications, including the design of vibratory gyroscopes. While traditional high-precision resonators for this application rely on expensive fused-silica fabrication, this study proposes a macro-scale approach using Polylactic Acid (PLA) to enable accessible lab-scale experimentation. The specimens, featuring a unique central-hole mounting configuration, were designed in SolidWorks and analyzed via finite element methods to establish the modal hierarchy. Experimental Modal Analysis (EMA) was conducted using a Laser Doppler Vibrometer (LDV) to acquire vibration signals, which were then analyzed in NVGate, MATLAB, and MEscope to extract natural frequencies and quality factor. Results for a lab-scale HR specimen identified the n = 2 wine-glass mode with a deviation from theoretical natural frequency predictions largely attributed to inherent defects in the fabrication process. Furthermore, a frequency split of 2.15 Hz was observed due to the inherent asymmetries and mass imbalances of the fabrication method. The quality factor was evaluated via the ring-down method and validated using the half-power bandwidth (HPBW) technique. This work demonstrates that 3D-printed resonators serve as an effective, low-cost platform for isolating modal behaviors and optimizing geometric parameters before advancing to micro-scale fabrication. Full article

Rockfall from slope unstable rock masses is a typical geological hazard induced by brittle failure, with abrupt occurrence, limited macroscopic deformation before failure, and a short warning lead time. Conventional static analysis methods are useful for design-stage stability checks, but they cannot continuously capture structural-plane damage or update the stability state in real time. Dynamic evaluation based on structural dynamics links measurable parameters such as natural frequency, damping ratio, mode shape, vibration trajectory, wave velocity, and energy dissipation to the degradation of structural planes. This review synthesizes the dynamic behavior mechanism, parameter system, theoretical models, sensing technologies, and engineering applications for slope unstable rock masses. Different from previous reviews that mainly summarize rockfall monitoring or conventional slope stability analysis, this paper organizes the literature by failure mode, monitoring scale, model assumptions, field validation, uncertainty sources, and engineering applicability. The single-degree-of-freedom models for sliding-, toppling-, and falling-type rock masses, multi-block chain-collapse models, and data-physics dual-driven surrogate models are compared critically. Contact monitoring based on MEMS sensors, non-contact LDV monitoring, acoustic emission, microseismic monitoring, coda wave interferometry, and cloud-edge early-warning architectures are further reviewed. Key challenges include field-scale validation under heterogeneous and anisotropic geological conditions, environmental compensation, robust threshold calibration, and probabilistic linkage between dynamic indicators and failure probability. The review provides guidance for selecting dynamic evaluation models, designing field monitoring systems, and developing full-life-cycle digital-twin platforms for rockfall risk mitigation. Full article