Search Results (842)

Optical coherence tomography (OCT) is an optical imaging modality that provides high-resolution cross-sectional imaging of biological tissues noninvasively. In Fourier-domain OCT, axial resolution is governed by both the center wavelength and the spectral bandwidth of the light source; therefore, limited or discontinuous bandwidth degrades depth resolution and introduces sidelobes and artifacts in OCT images. To address these issues in OCT image reconstruction from gapped spectra, a sparse parameter estimation approach based on Sparse Iterative Covariance-based Estimation (SPICE) is proposed in this study. By utilizing a sparse parameter estimation framework to directly resolve depth-dependent components from discontinuous interferograms, SPICE enhances axial resolution while suppressing sidelobe artifacts inherent in standard interpolation. Experiments on multi-layered tape, oral epithelium, and finger skin show that SPICE visually suppresses gap-induced sidelobe artifacts and improves structural interpretability under representative gap conditions. Quantitative evaluations on multi-layer tape and biological tissues show that SPICE reduces axial FWHM by 30–45%, increases SSIM by 0.15–0.25, and achieves significantly lower computational cost than GAPES (p < 0.01). Full article

While state-of-the-art massive acoustic arrays typically rely on costly, specialized FPGA architectures or rigid proprietary hardware, there is a growing need for modular, high-density sensing in complex aeroacoustics environments. This paper presents the electronic and acoustic design of a multiplexed, modular, scalable, and low-cost massive acoustic array (MxArray) founded on an embedded Linux system. The AM3358 SoC microprocessor collects audio data through its multichannel audio peripheral, where it simultaneously receives four Time-Division Multiplexing streams of 16 microphones each. This multiplexed scheme enables the handling of 64 microphones per module, whose acquisition synchronization is set with the Precision Time Protocol and a pulse injection hardware. The combination of both BeagleBone Black and microphones based on Micro-Electro-Mechanical Systems yields a cost-effective solution with built-in Ethernet connectivity and accessible software development through an embedded Linux environment with audio libraries for hardware control. Sensors are arranged in an Underbrink Spiral pattern on a four-layer printed-circuit board. The perforated thin layout minimizes any airborne disturbance, exploiting a distribution that simultaneously achieves a low sidelobe level and a narrow main lobe when used with a beamforming algorithm. Measurement results for the developed module are presented, as well as an evaluation of a full-scale system comprising 16 modules (1024 microphones) arranged in a honeycomb pattern. The resulting instrument offers a practical and scalable solution for applications that require a large number of simultaneous microphone measurements, such as beamforming technology for aeroacoustics applications. Full article

Passive reception of spaceborne synthetic aperture radar (SAR) signals is of great significance for acquiring target characteristics and identifying SAR operating states. With the rapidly growing demand for high-quality SAR signal reception, signal-receiving arrays are prone to beam performance deterioration and difficulty in beamforming under wide-angle scanning conditions. Traditional uniform arrays fail to meet practical engineering requirements and cannot balance multiple conflicting performance indicators. To address the above technical bottlenecks, this paper proposes a design method of a non-uniform planar receiving array based on the MOEA/D-HPSO algorithm. Taking maximum sidelobe level (MSL), array gain (G), and beamwidth (BW) as core performance indicators, a multi-objective optimization model of SAR signal-receiving array for wide-angle scanning is established. This method integrates the multi-objective decomposition strategy and hybrid genetic particle swarm optimization mechanism, decomposes complex multi-objective problems into several scalar subproblems, obtains uniformly distributed Pareto fronts, and effectively improves the diversity of solution sets. Simulation experimental results show that the proposed algorithm is superior to traditional mainstream algorithms such as NSGA-II and MOEA/D-DE in terms of convergence accuracy, solution set distribution, and various performance indicators. Typical array design examples verify that the proposed method can adapt to various engineering application scenarios and provide technical support for spaceborne SAR signal reception and spectrum management. Full article

Adaptive Sidelobe Cancellation (ASLC) is a core technology for modern radar systems to suppress active sidelobe jamming. From the perspective of disrupting the ASLC system’s ability to stably track the jamming direction, this paper proposes a distributed jamming method based on random phase perturbation. The method employs two spatially separated jamming sources that simultaneously transmit coherent signals. By actively applying controllable random jumps to the relative phase between the two sources, the equivalent wavefront direction of the synthesized signal at the radar receiver changes rapidly, forming a non-stationary jamming that destroys the null-tracking capability of ASLC. An analytical model of the ASLC cancellation ratio (CR) under random phase perturbation is established, with a focus on analyzing the effects of time synchronization accuracy and phase synchronization accuracy on jamming performance. Monte Carlo simulation results show that the proposed method can reduce the average ASLC CR from 26.80 dB to 20.29 dB (a decrease of 6.51 dB). Under identical conditions, this performance is comparable to asynchronous blinking jamming while requiring no precise timing matching, and outperforms multi-source saturation jamming in resource efficiency (two vs. four jammers). This study provides promising simulation-level evidence for the effectiveness of the proposed jamming method. The quantitative results and sensitivity analyses offer a simulation-level theoretical reference for parameter design of distributed cooperative jamming. Further validation in semi-physical simulations or field trials is necessary before claiming engineering readiness. Full article

Against the background of observed climate change, which increases the risk of glacier-system degradation and the formation of hidden crevasses, the development of lightweight, wideband, and highly directional antenna systems has become a key factor in ensuring the safety of logistics operations and enhancing the spatial resolution and interpretability of ground-penetrating radar monitoring of near-surface snow–ice layers. The effectiveness of such systems is largely determined by the characteristics of the antenna unit, including the operating frequency band, gain, radiation pattern, weight, and resilience under extreme climatic conditions. The aim of this review is to provide a systematic analysis of modern Vivaldi antenna designs and Vivaldi-based antenna arrays, as well as to assess their prospects for application in X-band ground-penetrating radar systems for probing Antarctic snow-ice media. The paper considers the main types of ground-penetrating radar (GPR) antennas, their advantages and limitations, substantiates the priority of detecting hazardous near-surface inhomogeneities, and analyzes the capabilities of the X-band for the high-resolution identification of these inhomogeneities. Particular attention is paid to modern modifications of Vivaldi antennas, including antipodal, balanced, director-loaded, metamaterial-based, and array configurations. The analysis shows that Vivaldi antennas represent a promising basis for lightweight, wideband, and directional GPR systems; however, they require further improvement in terms of gain enhancement, sidelobe and back-lobe suppression, radiation-pattern stabilization, and adaptation to Antarctic operating conditions. Future research should focus on the development of adaptive and phased Vivaldi arrays, the use of metamaterials, Electromagnetic Band-Gap/Frequency-Selective Surfaces (EBG/FSS) structures, and director elements, the creation of lightweight, frost-resistant substrate materials, the advancement of multi-polarization multiple-input multiple-output (MIMO) systems, and the integration of antenna arrays with synthetic aperture radar (SAR) processing adapted to a multilayer snow–ice medium. Full article

Background: Preoperative side-specific identification of extracapsular extension (ECE) is important for selecting an appropriate nerve-sparing strategy at radical prostatectomy. Patients with multiparametric magnetic resonance imaging (mpMRI)-defined capsular contact represent a clinically challenging subgroup because contact raises concern for ECE but does not uniformly indicate extraprostatic disease. We aimed to develop a side-specific nomogram for individualized ECE prediction and perform preliminary internal validation in this selected population. Materials and Methods: We retrospectively analyzed 323 prostate lobes from 286 patients with biopsy-proven, non-metastatic prostate cancer and mpMRI-defined capsular contact who underwent robot-assisted radical prostatectomy between 2015 and 2021 at a single institution. The dataset was randomly split into training (70%) and testing (30%) cohorts. Three multivariable logistic-regression models were developed and compared. Discrimination was assessed using the area under the receiver operating characteristic curve (AUC-ROC), calibration by intercept and slope, and clinical utility by decision curve analysis. A nomogram was derived from the best-performing model in the internal split-sample comparison. Results: Side-specific ECE was present in 110/323 lobes (34.1%). Among the candidate models, the forward-selection model showed the most favorable apparent performance, with an AUC-ROC of 0.85 in training and 0.83 in testing, together with good test-set calibration (intercept 0.24; slope 0.97). The final model included a capsular contact length ≥10 mm, percentage tumor involvement in positive biopsy cores, number of positive biopsy cores, and index lesion size. At a 10% predicted risk threshold, 32% of lobes were classified as low risk, with an observed ECE rate of about 5%. Conclusions: We developed a side-specific nomogram tailored to patients with mpMRI-defined capsular contact and performed preliminary internal validation. The model may aid preoperative side-specific risk assessment relevant to nerve-sparing planning, but external validation and assessment of clinical impact are required before clinical adoption. Full article

Recent advancements in computational hardware and parallel processing are driving developments in metaheuristic algorithms. These advancements help solve increasingly complex real-world optimization problems. As a result, more sophisticated and computationally demanding algorithms can now be implemented. This paper presents a comparative performance evaluation of ten modern metaheuristic algorithms for the optimal design of Electromagnetic Devices (EMDs). It also evaluates seven well-established metaheuristics, which are referred to as reference algorithms. In addition to the comparison, a novel hybrid optimization strategy called Multiple Combined Algorithms for Optimization (MuCAO) is proposed. MuCAO probabilistically combines the best-performing algorithms to leverage their complementary strengths. All algorithms, including MuCAO, were tested on six benchmark problems with various complexities and design variables. These benchmarks include analytical models and problems based on the Finite Element Method (FEM). For validation, the approach was also applied to a real-life application, which is Sidelobe Level Reduction in a Circular Antenna Array (CAA). The results show that MuCAO outperformed all other algorithms and achieved the highest overall ranking. Three modern metaheuristics followed. The best-performing reference algorithm ranked lower, with DE in fifth place. The study confirms that modern metaheuristics generally offer superior performance for EMD design and optimization compared to traditional metaheuristics. Full article

Concrete wall cavities are common hidden defects in construction engineering that seriously reduce structural safety, durability, and construction quality, especially in old buildings and projects without complete design documents. Traditional detection methods have obvious limitations: the manual tapping method relies heavily on subjective experience and lacks quantitative standards, while advanced non-destructive testing methods such as ultrasonic testing and infrared thermography are expensive, complex to operate, and difficult to apply on a large scale. At present, the quantitative correlation between acoustic signal characteristics and cavity defects has not been fully studied. To address these problems, this study combines literature analysis, controlled experiments, and acoustic signal processing to propose a quantitative identification method for concrete wall cavities based on autocorrelation analysis of sound signals. Tapping signals from normal and cavity walls are collected and processed using band-pass filtering and amplitude normalization. The autocorrelation function (ACF) is then used to extract characteristic parameters. The results show that the proposed method exhibits significantly improved accuracy and efficiency compared with traditional manual detection. Obvious differences in autocorrelation characteristics can be observed between normal and cavity walls. The method realizes the transformation from subjective auditory judgment to objective quantitative identification, with low cost, strong anti-interference ability, and high sensitivity to small defects. It provides a reliable technical tool for the rapid and quantitative non-destructive testing of concrete wall cavities in engineering practice. Full article

A linear frequency modulation (LFM) signal is widely used in radar systems. However, its inherently high autocorrelation sidelobes can degrade weak-target detection, while amplitude and phase distortions caused by transmitter systems may further elevate sidelobe levels. To address these issues, a joint pre-compensation and windowing optimization framework is proposed for a transmitter-distorted LFM signal. First, a regularized pre-compensation filter with gain constraints is constructed to compensate for transmitter-induced distortions and restore the waveform. Considering that the system frequency response is difficult to estimate accurately in practice, amplitude and phase perturbations are introduced, and a pre-compensation filter under perturbation is derived to improve robustness. To overcome the limited flexibility of fixed windows, a parameterized cosine-series window is employed, and the firefly algorithm is employed to jointly optimize the window coefficients and width, achieving a better trade-off among peak sidelobe ratio, integral sidelobe ratio, main lobe width, and peak-to-average power ratio. Simulation results demonstrate that the proposed method compensates transmitter distortions, significantly suppresses autocorrelation sidelobes, and maintains favorable performance under perturbations. Full article

Background/Objectives: Children with single-sided deafness (SSD) have normal hearing in one ear and are deaf in the other. Navigating complex auditory environments with SSD may cause reallocation of cognitive resources necessary for executive functioning (EF), adding potential cognitive burden to listening, though this is not well understood. To characterize EF in children with SSD, we compared their test performance and everyday functioning on performance-based and caregiver-rated EF measures to normative values and to a group of children with temporal lobe epilepsy (TLE). Methods: A retrospective review compared children with unaided SSD (n = 45) to a clinically referred TLE group (n = 39), all aged 6–16 years old, on performance-based measures including verbal fluency (letter, category), digit span, coding, and the BRIEF general executive composite. In the SSD group, those with congenital and acquired onset were compared across the same performance-based measures and BASC-3 executive functioning composite, and BRIEF2 indexes (cognitive, emotional, and behavioral regulation). Within this SSD group, performance-based and caregiver-rated measures were correlated. Results: In the SSD group, caregiver-reported EF and test performance were within age expectations. However, SSD participants with congenital onset had poorer caregiver-reported everyday EF. Children with SSD and elevated caregiver-reported EF had greater challenges on performance measures of auditory working memory. EF profiles were similar in the SSD and TLE groups, except the TLE group showed significantly worse performance on semantic fluency. Conclusions: Caregiver-rated EF measures may serve as an important tool for detecting neuropsychological deficits in children with SSD. SSD children with congenital onset may benefit from closer EF monitoring. There was lower performance on digit span backward tasks that require auditory working memory in children with elevated daily EF. More research is needed to determine what factors, such as hearing technology use, contribute to EF in children with SSD. *The term SSD is used throughout this article as a neutral placeholder with respect to the variation of terms used with this population (e.g., deaf, hard of hearing, hearing loss, hearing differences, etc.). SSD is used to be inclusive of all cultural/medical perspectives and identities. Full article

A single-band-notched ultra-wideband (UWB) low-sidelobe planar array antenna for millimeter-wave (mmWave) applications is presented. The antenna element employs a planar dipole excited through an H-shaped coupling slot to achieve broadband impedance matching, while a centrally loaded parasitic patch acts as a half-wavelength resonator to generate a controllable notch band. Additional parasitic patches are introduced to recover the high-frequency matching without degrading the notch response. An $8\times 8$ array is then developed using a Taylor-weighted feed network implemented with three classes of 1-to-4 microstrip power dividers. Measured results show that the array operates from 19.0 to 45.0 GHz with $\mathrm{VSWR}<2$, while providing a rejection band from 35.0 to 38.5 GHz. The notch suppresses the realized gain by about 5 dB around 37.0 GHz, the peak gain reaches 20.5 dBi in the passband, and average sidelobe levels better than $-17$ dB are obtained. The proposed design provides a practical approach for combining ultra-wide bandwidth, in-band interference rejection, and low-sidelobe radiation in a compact mmWave planar array. Full article

This paper proposes a closed-loop iterative automatic initial phase calibration scheme. Under the condition that the direction of the calibration source and the signal frequency are known, the complex readback data of all array elements are obtained. The theoretical phase of each array element is calculated in advance and removed. Then, conjugate multiplication with a reference element is performed to eliminate the common phase error and construct the relative phase residual. Finally, the relative phase residual is fed back to the phase compensation table and iteratively updated, thereby achieving rapid automatic calibration of the phased array. The simulation results show that, for a 256-element array, the digital computation time of the proposed method is approximately 1.6 ms, excluding signal acquisition and readback time, and the phase compensation table can converge rapidly. Based on the simulated results and normalized comparison of the array factors before and after calibration, the peak response in the target main-lobe direction is improved by approximately 14.69 dB, while the highest side-lobe level relative to the main lobe is further reduced by approximately 12.72 dB. These results demonstrate that the proposed algorithm can effectively improve beam focusing performance and side-lobe suppression, providing a new implementation scheme for initial phase calibration of large-scale phased arrays. Full article

Synthetic Aperture Radar (SAR) is one of mainstream remote sensing techniques, offering all-weather, day-and-night operational capabilities. However, throughout the processes of signal transmission, propagation, and reception, it is difficult to ensure that the amplitude and phase of the SAR signal strictly follow a linear frequency modulation (LFM) characteristic. The resulting signal distortion often leads to main lobe broadening and sidelobe elevation, degrading the focusing performance of SAR images. Traditionally, this issue has been addressed primarily through SAR system internal calibration and pre-distortion compensation, which makes it challenging to maintain the signal in an ideal state over the long term. At the same time, many simplified SAR systems also lack an internal calibration design, such as low-cost UAV-borne SAR payloads. In this paper, we propose a novel signal distortion compensation method based on SAR image data. Without relying on SAR system calibration signals, this method estimates and compensates for signal distortion directly using SAR image data, thereby improving SAR image focusing performance, achieving a resolution closer to the theoretical bandwidth and lower sidelobe. The processing and analysis of both manned and unmanned airborne SAR image data and calibration signals demonstrate that the proposed method effectively compensates for signal distortion phases, achieving performance comparable to that of real-time calibration-signal-based methods. Full article

In maritime search and rescue and underwater surveillance missions employing forward-looking sonar, strong reverberation and complex underwater environments often substantially degrade the target signal-to-clutter ratio (SCR), presenting significant challenges for target detection. Existing algorithms typically simplify the point spread function (PSF) into an ideal isotropic model, thereby overlooking the inherent anisotropy induced by its sidelobe structures. This physical model mismatch leads to target energy leakage and severely limits detection performance in complex backgrounds. To overcome the limitations of current target models and detection algorithms, this paper introduces a Gaussian 5 Superposition (G5S) model to accurately characterize the physical features of the PSF and proposes a Laplacian-of-G5S-based Local Adaptive Detection (LoG5S-LAD) method through the construction of a LoG5S filtering operator. Initially, a high-SCR target likelihood map is generated using Hessian-matrix-based geometric gating and LoG5S matched filtering techniques. Subsequently, robust background suppression and the effective preservation of faint targets are achieved through morphological artifact suppression, connected component screening, and a high-energy exemption mechanism. The effectiveness of the proposed framework is validated through model fitting experiments, as well as comprehensive simulations and detection tests across various sonar configurations. Experimental results indicate that the G5S model demonstrates precise fitting capabilities and strong physical adaptability. Furthermore, the proposed LoG5S-LAD algorithm significantly enhances the SCR while maintaining robust detection performance for faint and small-scale targets. Full article

Background and Objectives: Whether post-stroke oral hesitation carries different clinical implications by lesion location is unclear. We compared oral hesitation and its relationship with chewing, cognition, and aspiration risk between frontal and parietal lobe stroke. Materials and Methods: We retrospectively analyzed 242 patients (35 frontal, 207 parietal) from 946 consecutive stroke admissions (2016–2020) with isolated lesions and videofluoroscopic swallowing study within one month. Oral hesitation, chewing, Clinical Dysphagia Scale (CDS), and Mini-Mental State Examination (MMSE) were recorded. Penetration-Aspiration Scale (PAS) scores were categorized as Normal (1), Penetration (2–5), or Aspiration (6–8). Multivariable logistic regression adjusting for age, sex, stroke type, and lesion side was performed. Firth’s penalized estimation was used for models with quasi-separation. Results: Groups were demographically comparable in age (68.1 ± 15.0 vs. 71.7 ± 12.2 years; p = 0.206) and female sex (48.6% vs. 42.0%; p = 0.590). Oral hesitation was significantly more prevalent in the frontal group (liquid: 80.0% vs. 23.2%, p < 0.001; semisolid: 68.6% vs. 26.6%, p < 0.001). Frontal patients scored worse on six of seven CDS subcomponents (p < 0.01), yet chewing was uncorrelated with oral hesitation or residue (p > 0.3), unchanged after MMSE adjustment. In parietal patients, chewing correlated with all outcomes (ρ = 0.19–0.30, p < 0.01). In parietal stroke, oral hesitation was linked with liquid aspiration (64.3% vs. 35.7%; OR = 3.25, p = 0.001) and semisolid airway invasion (OR = 2.70, p = 0.005); these associations remained significant after multivariable adjustment and FDR correction. No such association was detected in the frontal group, although this finding is limited by the smaller sample size. Conclusions: Oral hesitation may carry different clinical implications by lesion site. In parietal stroke, it was associated with chewing impairment and higher aspiration risk, suggesting a possible sensorimotor contribution. Frontal group findings were underpowered and should be considered exploratory. Lesion-specific interpretation warrants larger-cohort confirmation. Full article