Search Results (436)

Laser rangefinders that use the fixed-threshold method are subject to walk error, which arises because the moment of signal detection depends on its amplitude. In addition, saturation of the receiver channel at short and medium ranges further distorts the shape of the registered pulse and reduces measurement accuracy. This paper presents a ranging device based on high-speed digitization of the entire echo waveform with a sampling rate of up to 500 MHz. The hardware implementation makes it possible to activate the power-consuming units of the receiver channel immediately before the start of the ranging cycle, which improves the energy efficiency of the device. To compensate for the signal attenuation proportional to the square of the range, dynamic gain control is implemented. To determine the position of the intensity peak, an algorithm is proposed that selects the registered signal by its amplitude and, depending on the presence of saturation, chooses the appropriate calculation method. Experimental comparison of the proposed method with classical algorithms was performed on targets of various reflectivity at ranges from 750 m to 12 km under different meteorological optical ranges. The experimental results demonstrated high measurement repeatability, with standard deviations ranging from 0.15 to 0.22 m, and confirmed the applicability of the proposed algorithm under different atmospheric attenuation conditions. Full article

Boundary reflections are usually treated as interference in underwater acoustic localization, yet their position-dependent delays can form a virtual aperture for a single transceiver. This paper develops a geometry-constrained method for exploiting that information without calibrated reflection amplitudes or phases. Reciprocal direct, surface, and bottom echoes are described by image-source delays, while their complex gains remain nuisance parameters. Variable projection yields a position-dependent joint multipath exploitation (JME) objective. The equivalent Fisher information relates local accuracy to bandwidth, path energy, resolvability, and delay-gradient geometry. Grid-search initialization (GSI) and delay-geometric initialization (DGI) feed a common safeguarded Newton-like refinement (JME-NR). Simulations include a coherent matched-field processing baseline and environmental mismatch tests. Among the JME routes, GSI is more reliable at very low signal-to-noise ratio (SNR), whereas DGI becomes more effective with reliable delay extraction and path association. For a fixed target at $0\mathrm{dB}$, GSI-JME-NR achieves an all-trial root-mean-square error (RMSE) of $0.128\mathrm{m}$, close to the exact position error bound of $0.127\mathrm{m}$. In finite-tank tests with a separated transmitter and receiver, three two-dimensional configurations with 20 measurements each yield refined RMSEs of $11.14$–$39.76\mathrm{mm}$ relative to the target’s geometric center. Three-dimensional tests show that improved waveform fitting does not necessarily reduce the error relative to this center. Full article

One-bit quantization technology effectively simplifies the hardware architecture of synthetic aperture radar (SAR) and significantly reduces the volume of echo data, making it a pivotal research direction in spaceborne SAR systems. However, the high computational cost and latency associated with one-bit SAR signal reconstruction have limited the efficiency of downstream target detection applications. To address the demand for efficient ship target detection in one-bit SAR imagery, a label self-supervised ship detection training method on one-bit non-reconstructed SAR images is proposed in this paper, which enables generic deep learning models to perform detection directly in one-bit SAR images based on the chirp scaling algorithm (CSA) without signal recovery during inference. First, a training framework based on curriculum learning is designed, aiming to discriminate between real ship targets and false targets. Second, a one-bit reconstruction and training label self-supervised correction branch is incorporated into the training framework to autonomously generate false target labels and progressively guide the model to learn false target features. Third, a one-bit non-reconstructed image dataset is proposed to validate the trained model, which maps only reliable ship target annotations from full-precision echo imaging results. Finally, experiments conducted on real SAR data demonstrate that the detection model derived from the proposed training method can directly detect ship targets in the non-reconstructed images. Compared with the classic “reconstruction-and-detection” workflow, the trained model runs inference, bypassing the reconstruction process, and achieves a 20-fold improvement in efficiency without compromising detection accuracy. This method provides a practical solution for the rapid acquisition of target information in large-scale ship monitoring using one-bit SAR. Full article

Pultruded carbon-fiber-reinforced polymer (CFRP) composites are increasingly used in large wind turbine blades, but ultrasonic evaluation of thick pultruded structures is complicated by repetitive layer reflections and prolonged transducer ringing. This study proposes a reference-based Layer Echo Suppression Method (LESM) to suppress deterministic structural responses that can mask internal defect echoes, thereby improving the visibility and identification of damage-related reflectors in thick CFRP components. Ultrasonic A-scan signals were acquired from sound and defect regions of a 100-mm-thick pultruded CFRP specimen containing an artificial defect at approximately 50 mm using a 0.5 MHz single-element contact transducer. LESM combines cross-correlation-based registration, independent wavelet denoising, reference-based Hilbert-envelope differencing, and positive-residual weighting of the denoised defect signal. The normalized cross-correlation coefficient between the reference and defect signals was 0.9941. DBR-based evaluation selected Sym5 at decomposition level 5. The final LESM signal yielded a defect-to-background ratio (DBR) of 34.82 dB, compared with 14.14 dB for the raw signal, an improvement of 20.68 dB. The defect-related waveform near 50 mm was retained while surrounding layer echoes and ringing were substantially reduced. By enhancing localized defect reflections without requiring additional ultrasonic hardware, LESM can support the identification of internal planar damage-like discontinuities in thick CFRP components using conventional pulse-echo measurements. These results demonstrate the proof-of-concept feasibility of the proposed method under the examined condition. Full article

The proliferation of small unmanned aerial vehicles (UAVs) operating at low altitude and in swarms has raised increasing concerns for low-altitude airspace safety and poses a central challenge to ground-based low-altitude radar monitoring. When a vehicle-mounted radar operates while in motion, terrain-induced three-axis attitude perturbations give rise to a time-varying clutter Doppler spectrum within each coherent processing interval (CPI), manifested as spectral peak drift and dynamic spectral broadening. Moreover, the radial velocity of slow-flying low-altitude UAVs frequently overlaps the clutter Doppler band, so such targets tend to be masked by strong ground clutter, which severely degrades the performance of conventional clutter suppression methods. To address this problem, a clutter suppression method that combines dynamic time–frequency (TF) ridge tracking with inertial measurement unit (IMU) auxiliary measurements is proposed, which shifts the clutter suppression problem from static filtering in the range–Doppler (RD) domain to dynamic ridge tracking in the TF domain. The clutter Doppler centroid and spectral spread are first predicted from IMU data through a kinematic inversion chain, yielding an echo-independent prior that remains reliable under low signal-to-clutter conditions. After a short-time Fourier transform (STFT), this prior constrains the search space of the Viterbi tracker, enabling frame-by-frame localization of the clutter ridge and frame-level adaptive adjustment of the notch parameters, thereby achieving accurate suppression of the non-stationary clutter while preserving the energy of slow-moving targets. Simulations of a close-range, slow-moving, and low-altitude UAV-swarm scenario demonstrate that the proposed method attains effective clutter suppression and maintains a high detection probability for slow-moving UAV targets at a low false alarm rate, allowing it to support low-altitude drone swarm monitoring from a moving radar platform. The complete processing chain requires only about $3\times {10}^8$ operations per coherent processing interval, so that real-time operation on an embedded platform is feasible. An error budget analysis shows that this prior requires only a calibrated consumer-grade MEMS inertial sensor and that the installation alignment is the binding requirement rather than the sensor stability. The validation presented here is based on simulated clutter and platform motion; validation against measured radar echoes is left for future work. Full article

Interrupted-sampling repeater jamming (ISRJ) produces coherent false targets after pulse compression and degrades the reliability of frequency-agile radar sensors, particularly at low signal-to-noise ratio (SNR) and high jamming-to-signal ratio (JSR). Existing single-domain suppression methods often provide insufficient separation between jamming and target echoes and may attenuate target-bearing components. This paper proposes a joint time–frequency analysis and multi-domain filtering method for radar waveforms employing intra-pulse and inter-pulse frequency agility. A forwarded jamming segment retains the carrier frequency of the sampled sub-pulse while overlapping a subsequent sub-pulse with a different carrier frequency, producing a joint displacement in time and frequency relative to the genuine target echo. Based on this property, a sub-pulse-wise two-dimensional feature extraction strategy is developed in the short-time Fourier transform (STFT) domain. Empirical probability density criteria are derived from projections along the time and frequency axes to determine adaptive rejection thresholds. The resulting time-selective and frequency-selective rejection regions are combined through logical intersection to construct a two-dimensional notch filter. The retained components are reconstructed using the inverse STFT and processed by segmented pulse compression. Simulations covering direct, periodic, and cyclic ISRJ show that the proposed method suppresses coherent false targets while limiting the loss of pulse-compression gain. It achieves higher peak signal-to-jamming-plus-noise ratios than the reference method over the tested SNR and JSR ranges and remains effective under adverse conditions. The proposed method improves the interference resilience of frequency-agile radar sensors and provides more reliable target echoes for subsequent detection and parameter estimation. Full article

Under strong sea clutter conditions, radar echoes from moving sea-surface targets are easily masked by background clutter. A major limitation of existing mode decomposition and reconstruction approaches is the difficulty in reliably distinguishing target-related modes. To address this challenge, a radar target feature detection method based on Correlational Mode Reconstruction is proposed. The original radar signal is first decomposed into a set of intrinsic mode functions using Variational Mode Decomposition. An ideal target reference model is then constructed by combining time–frequency analysis with morphological image processing. A multi-dimensional correlation metric is then developed to evaluate the degree of match between each mode and the model signal, enabling accurate selection of target modes. Features are subsequently extracted from the reconstructed signal in multiple transform domains, and a relative feature gain metric is used to select discriminative features. The experimental results demonstrate that, irrespective of the feature-based detector used, the proposed reconstruction provides better detection performance than the original signals and the other reconstruction strategies. In addition, feature fusion further improves detection performance over single feature detection. Full article

Pitting corrosion is a localized degradation mechanism that can significantly affect the structural integrity of steel components while remaining difficult to detect and characterize using conventional monitoring approaches. This work investigates the use of pulse-echo ultrasound and data-driven signal analysis for the observability and characterization of localized pitting in low-carbon steel. The ultrasonic response of two 20 MHz single-element contact transducers was investigated through laboratory measurements on artificial pits and physics-based k-Wave simulations spanning a wider range of pit sizes and morphologies. The results show that localized pits modify the received signal through attenuation, scattering, waveform distortion, and the generation of secondary reflections, with the observed ultrasonic response strongly dependent on pit morphology and transducer aperture. A signal-processing and feature-extraction framework was developed to analyze these changes and to evaluate pit observability prior to characterization. Using pit-free measurements, observability limits were quantified with an autoencoder trained on signal descriptors, demonstrating that pit observability is influenced predominantly by pit geometry, normalized pit width, and pit volume rather than by depth alone. For signals exhibiting sufficiently observable and specular pit reflections, time-of-flight measurements enabled pit-depth estimation for defects deeper than 300 µm. To account for propagation-path distortions affecting pit-related echoes, an empirically derived cross-path delay compensation approach was developed using Random Forest models trained on synthetic signals, reducing systematic depth-estimation errors in the synthetic dataset and providing a physics-informed framework for future quantitative characterization studies. Additional Random Forest models employing waveform descriptors and reflection-amplitude indicators were used to assess pit morphology and acoustic surface specularity. The proposed framework was evaluated using laboratory measurements and through preliminary long-term monitoring of two pre-pitted specimens deployed in the HarshLab offshore environment. In these deployments, the methodology enabled simultaneous tracking of localized pit-wall evolution and overall backwall thickness changes from the same pulse-echo measurements, illustrating the potential of the approach for monitoring localized corrosion activity alongside generalized specimen wall loss. Full article

Welded structures are widely used in ships and marine equipment. The ultrasonic longitudinal critically refracted (LCR) wave method is a reliable technique for measuring residual stress in welded structures. Conventional ultrasonic measurement systems have high power consumption and are difficult to operate under the limited power supply conditions of marine environments. Reducing the excitation voltage can lower power consumption, but as the excitation voltage decreases, ultrasonic echo energy weakens and noise interference increases, thereby affecting the accuracy of stress measurement. This paper proposes a complexity-mutation-based adaptive singular spectrum analysis–variational mode decomposition algorithm (CM-SSA-VMD). The algorithm constructs a complexity index using the spectral centroid, waveform roughness, and zero-crossing rate. By identifying abrupt changes in complexity between adjacent singular spectrum analysis (SSA) components, it adaptively determines which components to retain for reconstruction. Then, variational mode decomposition (VMD) is used to further separate the residual high-frequency noise. Finally, the ultrasonic time of flight (TOF) is estimated using the cross-correlation algorithm, and the stress is calculated. The stress measurement accuracy of the proposed algorithm was evaluated at different excitation voltages: 5.2 V, 3.3 V, 1.2 V, and 0.5 V. The results show that when the excitation voltage drops to 0.5 V, after being processed by CM-SSA-VMD, the average relative error of stress measurement remains below 10%, while the average relative errors of a new adaptive denoising method, Grey Wolf Optimization–Variational Mode Decomposition–Wavelet Transform (GWO-VMD-WT), traditional FIR filtering and VMD are approximately 14%, 18% and 16% respectively. Compared with GWO-VMD-WT, FIR filtering and VMD, the measurement accuracy of this method is improved by 33.35%, 46.16% and 41.82% respectively. The CM-SSA-VMD algorithm can effectively suppress noise in ultrasonic signals at low excitation voltages and improve the reliability of stress monitoring. It provides a feasible method for low-power ultrasonic residual stress monitoring in marine engineering. Full article

Background: Carcinogenesis has traditionally been attributed to the accumulation of somatic mutations in nuclear genes regulating cell proliferation, survival, and apoptosis. Although hereditary genetic predispositions account for less than 10% of cases, the extensive heterogeneity of the somatic mechanisms driving tumorigenesis—including subsets of neoplasms lacking canonical nuclear mutations—suggests that non-mutational mechanisms and organelle dynamics significantly influence tumor onset. Emerging evidence increasingly supports the concept that mitochondrial dysfunction and metabolic reprogramming are essential factors in tumor development, rather than merely downstream consequences of nuclear genetic alterations. While current literature establishes mitochondrial alterations as crucial co-initiators and facilitators of early transformation, defining whether metabolic failures may eventually act as primary trigger remains an ongoing area of investigation. Within this perspective, the “Endosymbiotic/Evolutionary Theory” provides a useful heuristic model, since it conceptualizes metabolic rewiring not as a literal genetic reversal, but as an adaptive shift toward ancestral, highly glycolytic survival programs when oxidative microenvironments are compromised. Methods: We conducted a comprehensive theoretical and comparative analysis of mitochondrial biology, interrogating the structural, functional, and evolutionary parallels between mitochondria, intracellular bacteria, and viruses within the conceptual context of the endosymbiotic theory. Particular attention was given to mitochondrial proteins associated with organellogenesis, bioenergetic control, and molecular mechanisms facilitating mitochondrial–nuclear communication, including the integration of mitochondrial DNA (mtDNA) into nuclear DNA (nDNA) as nuclear mitochondrial DNA segments (NUMTs). Results: Under sustained bioenergetic stress, mitochondrial retrograde signaling can achieve regulatory dominance over the nucleus, driving a functional dissociation between organellar metabolic state and nuclear control. While not autonomous in a strict sense, this shift echoes the evolutionary legacy described by the endosymbiotic theory, whereby mitochondria act as semi-independent bioenergetic systems capable of imposing profound effects on nuclear gene expression and cellular fate. The resulting state is characterized by metabolic reconfiguration, loss of differentiation fidelity, and the acquisition of cancer stem cell–like properties. This process may unfold through a sequence of interrelated events: (1) persistent endogenous or exogenous stressors that challenge cellular homeostasis; (2) establishment of a chronic inflammatory state and sustained redox imbalance; (3) progressive disruption of the cellular microenvironment and inter-organelle communication; (4) emergence of a permissive pre-neoplastic niche characterized by impaired oxidative phosphorylation (OXPHOS), altered mitochondrial dynamics, and sustained retrograde signaling; (5) activation of adaptive survival pathways and escape from canonical cell death and checkpoint controls; and (6) progression toward a dedifferentiated, stem-like cellular phenotype associated with metabolic plasticity and increased resilience. Conclusions: We recommend that cancer be reframed as a dedifferentiated cellular state primarily driven by mitochondria-derived signaling, reflecting the organelle’s ancestral autonomy and the reactivation of evolutionarily conserved mechanisms involving transposons, retrotransposons, Long Interspersed Nuclear Element-1 (L1), transposable elements (TEs), reactive oxygen species (ROS), and mtDNA–nDNA crosstalk, including the generation of nuclear mitochondrial DNA segments (NUMTs). This perspective extends the canonical view of predominantly nuclear-driven oncogenesis by reframing carcinogenesis as a bioenergetic and bio-evolutionary process, thereby highlighting mtDNA–nDNA interactions and mitochondrial signaling as central targets for cancer prevention, diagnosis, and therapy. Full article

Post-weld evaluation does not readily capture the dynamic evolution of nugget formation in resistance spot welding. In this study, an embedded ultrasonic probe was used to acquire A-scan signals during resistance spot welding of stainless steel. Consecutive signals were assembled into M-type ultrasonic images to analyze echo changes across the thermal cycle and under different heat inputs. Using 12 specimens with metallographic measurements of both nugget diameter D and thickness H, five- and three-dimensional acoustic features were extracted for D and H prediction, respectively. Random forest (RF) regression models were established and evaluated using leave-one-out cross-validation (LOOCV). The M-type images showed the streak evolution of interface echoes, back-wall echoes, and nugget-related echoes, reflecting differences in acoustic responses across welding stages and nugget states. The coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE) were 0.588, 0.674 mm, and 0.798 mm for D, respectively, with corresponding values of 0.570, 0.150 mm, and 0.198 mm for H. These preliminary results indicate quantitative correlations between the extracted low-dimensional acoustic features and nugget geometry, providing an experimental basis for nugget geometry evaluation using in-process ultrasound. Full article

Target-oriented sensing image processing and analysis are important for intelligent surveillance and dynamic sensing applications, where wireless systems need to recover task-relevant visual and state information from heterogeneous sensing data. This study develops a vision–radar semantic communication system for joint target image reconstruction and dynamic state estimation. The transmitter jointly encodes scene-level visual observations and target-associated radar echoes through vision–radar semantic extraction and fusion, lightweight contextual semantic encoding, and communication-aware semantic transmission. The receiver uses a multi-task semantic decoder to reconstruct the target image and estimate normalized distance, angle, and velocity. Experiments are conducted under adaptive signal-to-noise ratio (SNR)–modulation operating points and evaluated using peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and normalized state-estimation root mean square errors (RMSEs). The developed system achieves an average PSNR of 24.57 dB, SSIM of 0.8896, Normalized Distance RMSE of 0.0120, Normalized Angle RMSE of 0.0059, and Normalized Velocity RMSE of 0.0015, outperforming a continuous-channel training variant on all reported average metrics. Ablation studies show that the composite reconstruction objective and adaptive task-balancing scheme provide favorable visual-sensing tradeoffs. These results indicate that vision–radar semantic communication can support target-oriented visual sensing and dynamic state recovery within a unified sensing-oriented wireless framework. Full article

Conventional full-waveform centroid algorithms for pulsed LiDAR rely on rectangular integration and fixed windows, causing timing errors for asymmetric echo pulses and poor adaptability to varying pulse shapes. This paper proposes an adaptive window-width trapezoidal centroid algorithm (AWTWCA) that replaces rectangular with trapezoidal integration and dynamically determines the optimal window based on the rising-edge half-amplitude interval. Simulations under diverse echo conditions demonstrate that AWTWCA consistently achieves lower mean absolute error than rectangular and fixed-window trapezoidal methods. Experiments on a MEMS LiDAR system further validate its robustness: timing errors remain below 0.2 ns (3 cm ranging) and the detection success rate exceeds 99% under low-reflectivity and APD saturation conditions, significantly outperforming fixed-window algorithms. The algorithm simultaneously generates range and intensity point clouds, enabling clear discrimination of targets with different reflectivities at the same distance. With computational complexity virtually identical to conventional methods, AWTWCA offers a hardware-friendly solution for high-precision LiDAR signal processing in complex environments. Full article

Owing to their exceptional thermal insulation and protective capabilities, thermal barrier coatings (TBCs) are widely applied to critical hot-section components of aero-engines. However, under increasingly harsh service environments, internal defects such as delamination tend to form within the coatings, posing a severe threat to engine operational safety and service life. To effectively evaluate delamination defects in TBCs, this study employs the immersion ultrasonic pulse-echo technique to inspect specimens subjected to various thermal cycling treatments. Four specimens, subjected respectively to 21, 32, 43, and 54 thermal cycles at 1200 °C, were tested. Ultrasonic response data were systematically acquired via normal incidence scanning from both the superalloy substrate side and the ceramic top coat side. Combining Fast Fourier Transform (FFT), Continuous Wavelet Transform (CWT) based on the generalized Morse wavelet, Wavelet Packet Energy Entropy (WPEE), and peak-to-peak amplitude variations of the second echo, multi-dimensional features were extracted from ultrasonic signals across the frequency domain, joint time-frequency domain, and energy distribution profiles. Through comparative analysis, ultrasonic waveform and time-frequency characteristics representing defect evolution were obtained. A significant monotonically decreasing trend of WPEE with the aggravation of interfacial delamination was established, characterizing the acoustic energy confinement process induced by interfacial damage. Furthermore, a multilayer finite element (FE) model reasonably reproduced dynamic acoustic wave propagation; numerical results are in agreement with experimental data, validating the feasibility of the proposed detection method. The detection and evaluation framework established in this study provides a reference for safety monitoring and lifespan prediction of aero-engine TBCs. Full article

One of the most important parameters in fetal monitoring is the fetal heart rate (fHR). However, due to the nature of this signal, several non-invasive techniques have been developed for fetal monitoring, including Cardiotocography (CTG), Doppler Echocardiography (Doppler Echo), fetal electrocardiography (fECG), fetal phonocardiography (fPCG), fetal Magnetocardiography (fMCG), and fetal Photoplethysmography (fPPG). Fetal phonocardiography is a passive, safe, and non-invasive method for estimating fHR, in which an acoustic signal is acquired through a transducer placed on the mother’s abdomen to capture sounds associated with fetal cardiac activity. In this paper, we propose a hybrid framework for fHR estimation that integrates digital signal processing techniques with a deep learning approach. The method employs a Hermite Transform (HT)-based bio-inspired vision model for fPCG signal denoising, the Hilbert transform to extract the signal envelope, and Mel-Frequency Cepstral Coefficients (MFCCs) to generate spectrogram-based features. Then, these features, represented as image-like, are used to train and evaluate a hybrid architecture that synergistically combines convolutional neural networks (CNNs) with Bidirectional Long Short-Term Memory (BiLSTM). BiLSTM is a deep learning model that processes sequential information in both forward and backward directions, thereby extending the memory capabilities of conventional Recurrent Neural Networks (RNNs). Thus, CNNs are used to capture intricate spatial morphologies of the fPCG signals, whereas BiLSTM captures their temporal evolution across consecutive time windows. Additionally, to assess its generalization capability, the proposed method is evaluated on two publicly available fPCG datasets and benchmarked against several state-of-the-art approaches. The experimental results demonstrate that the proposed method achieves competitive performance across different datasets, highlighting its effectiveness and robustness. Full article