FMCW LiDAR operating at long range or high sweep rate imposes demanding requirements on ADC sampling rate and data throughput. We demonstrate a six-channel undersampling receiver that recovers aliased beat frequencies using the calibrated responses of parallel low-pass filters (LPFs) with progressively increasing cutoff frequencies. The LPF responses identify the true beat frequency interval, which, together with the observed aliased frequency, resolves the alias ambiguity. A down chirp anchoring strategy further supports reliable recovery near folding boundaries. With the sampling rate reduced from 500 to 50 MS/s, the system achieves a ranging precision of 4.49 mm and a ranging accuracy within 7.46 mm over a maximum tested distance of 18.49 m. The measured resolution is 2.35 cm at 50 MS/s. Simultaneous multi-target ranging and 3D imaging are also demonstrated.

Spatial light modulators (SLMs) are extensively utilized for beam steering and multi-beam generation. However, discrete pixel sampling unavoidably introduces spatial aliasing and periodic spectral replication, leading to unwanted grating lobes that degrade beam quality. To address these issues, we propose a two-dimensional auxiliary random phase (2D-ARP) scheme leveraging auxiliary beams and random phase modulation. By redistributing the interference field and reducing the relative concentration of constructive interference, the proposed scheme lowers grating-lobe peak concentration and enhances main-beam energy. Experimental results show an increase of 42.2% in main-lobe energy and a 3.23 dB improvement in peak sidelobe ratio (PSR). A four-beam free-space optical (FSO) communication system, with each beam carrying a 10 GBaud 16-QAM signal, further demonstrates lower bit error rate (BER) and improved receiver sensitivity. Requiring neither hardware modification nor iterative optimization, the proposed scheme is highly attractive for real-time beam shaping and high-capacity multi-beam FSO communications.

Loose particles inside aerospace-sealed electronics cause circuit short circuits and contact faults. Particle Impact Noise Detection (PIND) relies on piezoelectric acoustic emission (AE) sensors to capture collision pulses, yet raw sensor signals are heavily contaminated by background noise, leading to severe time-frequency feature aliasing and low particle material recognition accuracy. This work proposes a sensing signal optimization method combining pulse endpoint detection and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) decomposition for PIND acoustic-sensing systems. First, a frequency-domain variance dual-threshold algorithm extracts valid collision pulses from noisy sensor output and eliminates invalid noise segments. Second, CEEMDAN reconstruction with kurtosis-based IMF screening suppresses high-frequency impulsive noise and low-frequency trend components, and seven-dimensional time-frequency-fused features are extracted for classification. A two-hidden-layer back-propagation (BP) neural network identifies four typical contaminants: copper particles, solder particles, rubber particles, and epoxy particles. Comparative tests against EMD, EEMD, and wavelet thresholding show that the proposed CEEMDAN-based method raises overall classification accuracy from 71.8% (no denoising) to 85.1%. This approach improves the discrimination performance of PIND acoustic-sensing platforms and supports aerospace-packaging defect tracing.

Detector-multiplexed wide-field infrared imaging reuses detector area by folding several sub-fields of view onto a common focal-plane region, but the branch identity of an aliased small target is then unavailable from intensity alone. We propose a dual-domain optical code that combines a fixed Zemax-derived point-spread-function (PSF) library with a conditionally assigned linear-polarization codebook. To replace the earlier feature-level validation, we developed an image-level radiometric simulator that generates four analyzer images under a fixed incident-photon budget, applies mirror transfer, throughput loss, Poisson shot noise, background, read noise, and physical multi-target PSF superposition, and then re-extracts morphology and Stokes observables. The 1037 PSF samples were divided by sub-field into construction, calibration, and locked test sets of 623, 207, and 207 samples. The polarization-to-field assignment was selected by an exhaustive 9! search on the construction data, whereas the compound weight and separability threshold were fixed from calibration data only. In 25,000 locked target-level tests, the equal-weight-form compound decoder achieved 95.936% accuracy (95% confidence interval of 95.684-96.174%), compared with 10.836%, 45.564%, and 88.720% for intensity-only, shape-only, and polarization-only decoding. At the calibrated threshold, the effective capacities were five, four, and nine for the shape, polarization, and compound code spaces. The results are simulation-phase evidence and require experimental verification with measured infrared throughput and component tolerances.

Sound source localization based on microphone arrays has attracted extensive research interest in the fields of speech communication, detection and tracking. However, conventional near-field beamforming typically assumes free-field propagation and ignores coherent ground reflections, which bias phase estimates at floor-mounted arrays and degrade localization accuracy. To address this, the total Green's function is reformulated by embedding the image-source reflected path directly into the beamforming propagation model. Unlike conventional approaches that assume free-field propagation, this formulation enables phase compensation to jointly account for the direct and ground-reflected wavefronts. The in-phase superposition of signals is then realized through frequency-domain phase compensation, and the sound source position is estimated via the maximum likelihood criterion. Subsequently, a dual-mode operation mechanism of automatic broadband scanning and manual single-frequency analysis is implemented, with frequency-weighted wideband spatial spectrum processing for noise and aliasing suppression. Finally, a real-time sound source localization system based on a 4 × 4 MEMS array is designed. Experimental results show that sound source localization in a broad frequency band can be achieved at standoff distances of 0.1 m-0.3 m from the array plane, with a localization accuracy of less than 0.015 m. The proposed method demonstrates strong robustness against ground-reflection interference and has potential applications in acoustic monitoring, industrial fault diagnosis and other related areas.

The moiré effect, a common artifact when photographing digital screens, degrades images with a complex mixture of geometric aliasing patterns and severe photometric distortions. These degradations collectively impair visual quality and hinder downstream computer vision tasks. While recent learning-based demoiréing methods have achieved promising results, they typically employ a coupled network to address all degradations simultaneously. They thus often fail to disentangle the intricate interplay between geometric artifacts and photometric aberrations, resulting in suboptimal generalization. To address this, we introduce DecMoiré, a novel retinex-inspired framework for image demoiréing. Inspired by Retinex theory, we explicitly decouple the degraded image into two components: an illumination map that captures the spatially varying photometric deviations caused by screen light interference, and a reflectance map containing both the underlying moiré patterns and texture. We then process each component via specialized modules. For robust photometric restoration, we propose an uncertainty-aware illumination modulation network that predicts adaptive kernels to refocus the scattered screen light, while simultaneously leveraging uncertainty estimation to improve the robustness of illumination reconstruction. For moiré removal, a joint spatial-frequency module operates on the reflectance map, synergizing spatial convolutions for local texture refinement with frequency-domain modulation to suppress large-scale periodic patterns. Extensive experiments on datasets demonstrate that our framework surpasses state-of-the-art methods, producing images with superior illumination fidelity, finer details, and exceptional robustness in challenging scenarios.

Accelerated magnetic resonance imaging (MRI) typically relies on fully sampled k-space data to train supervised reconstruction networks, which are difficult to acquire in clinical practice. To eliminate this dependency, we propose a novel dual-domain self-supervised learning (Dual-SSL) framework that rigorously enforces physical consistency in both image and k-space domains. The framework employs a shared-intersection re-undersampling strategy to generate two sub-masks with approximately 50% overlap, preserving essential low-frequency priors, which are subsequently concatenated for efficient single-forward-pass processing. A composite loss function is formulated to enforce image-domain consistency alongside comprehensive k-space consistency, the latter encompassing cross-consistency on mutually unobserved points and self-consistency on originally observed points. To automatically balance these dual-domain constraints without empirical hyperparameter tuning, a homoscedastic uncertainty-based mechanism is incorporated to learn log-variance parameters. The proposed framework was evaluated on fastMRI and MICCAI 2013 datasets under 1D Cartesian and 2D random variable-density undersampling at 4× and 8× accelerations. Results demonstrate that this approach significantly outperforms existing self-supervised techniques in mitigating severe aliasing artifacts and localized error hotspots, thereby yielding superior anatomical fidelity. Crucially, despite training exclusively on undersampled data, the proposed framework achieves reconstruction performance comparable to fully supervised baselines using the identical network architecture, effectively bridging the gap between self-supervised and supervised MRI reconstruction. This provides a robust and scalable solution for clinical MRI acceleration.

Objective.4D cone-beam computed tomography (4DCBCT) is a technique used to address respiratory motion in radiotherapy but is limited by significant view-aliasing artifacts. Recently, deep learning methods have been proposed to reduce view-aliasing. This study investigates the robustness and performance of these methods when variations in patient breathing period affect the noise pattern in the scans used as training data, which potentially affects the ability of the models to reduce view-aliasing.Approach.We evaluated both a supervised and a self-supervised scalable deep learning method. Using a dataset of 328 patients, scans were partitioned into cohorts according to breathing period, and independent training with cross-cohort validation was performed to assess robustness. We also evaluated the impact of a modern UNet-based architecture and data augmentation.Main results.Training and testing on matched breathing-period cohorts did not consistently improve performance, indicating that both methods are robust to clinically observed variation in average breathing period. Instead, increasing the overall dataset size led to the most noticeable improvements. For the cohort with long breathing periods, the supervised model trained on the combined dataset achieved aPSNR improvement over the cohort-specific model and a PSG improvement of.Significance.These findings support the reliability of deep learning-based 4DCBCT denoising across the observed range of average breathing periods. They suggest that large, diverse datasets should be prioritized over breathing-cohort-specific optimization. Finally, PSG provides a quantitative indicator that is sensitive to view-aliasing and may support future development of more robust 4DCBCT enhancement techniques.

This study addresses the issue of insufficient spatial resolution in remote sensing images for farmland boundary identification in precision agriculture. It proposes a framework that combines Real-ESRGAN, a GAN-based blind super-resolution algorithm, with YOLO, a real-time instance segmentation framework, to improve farmland boundary extraction accuracy from medium-resolution satellite imagery. Using GF-2 imagery of the agricultural area of Nanxiong City, Guangdong Province, a manually annotated farmland boundary dataset was constructed. The experiments were conducted in this single study area (Nanxiong City); the generalization of the proposed framework to other regions, crops, and sensor platforms requires further validation. The super-resolution preprocessing restored a 1 m resolution from 4 m input while enhancing boundary-related high-frequency details and mitigating aliasing-induced field merging. In farmland boundary recognition, the super-resolved 1 m images achieved mAP@0.5 of 0.755 and mAP@0.5:0.95 of 0.628, approaching the resampled 1 m reference (0.823 and 0.733) and clearly outperforming the resampled 4 m baseline (zero accuracy). The reported mAP values are validation-set best-checkpoint figures and therefore represent an optimistic upper bound under the current spatially autocorrelated split. The framework provides a cost-effective solution for large-scale farmland boundary extraction and precision agricultural management.

A method is presented for estimating 3-D velocity fields over space-time (tensor velocity fields) by combining 2-D velocity estimates acquired in multiple imaging planes with the continuity equation. The proposed tensor velocity estimator accurately reconstructs flow within the aliasing limit and mitigates motion-related artifacts observed in synthetic aperture (SA) and plane wave imaging (PWI) techniques, which typically reduce estimation accuracy at higher velocities. The estimator is demonstrated using both a short imaging sequence, which estimates the tensor velocity field in a thin volume (effectively a 2-D slice), and a longer sequence for reconstructing flow over a larger volume. A singular value decomposition (SVD) procedure for phase shift noise reduction is also proposed. In addition to improving the estimation precision, the method significantly reduces the required storage space and input size for the reconstruction algorithm: from approximately 20 GB-110 MB in one measurement. Furthermore, the procedure enables quick analysis of the SVD components' contributions to the flow. Flow-rig measurements of parabolic flow were acquired at various combinations of beam-to-flow angles (60°, 75°, and 90°) and flow-rotation angles (50° and 90°), with peak velocities ranging from approximately 10%-95% of the velocity aliasing limit. The average bias for the velocity magnitude was found to be -0.71% $ \pm ~2.96$ %, and the standard deviation (SD) was 0.57% $ \pm ~ 0.19\%$ . From Field II simulations of pulsatile carotid artery flows, the estimator reconstructs the velocity magnitude over a 3.5 s period with a normalized root-mean-square error (nRMSE) of 3.57%. In measurements of flow in a carotid artery phantom, the average velocity magnitude pulse profiles of the mean and peak velocities were estimated with SDs of 0.75% and 2.63%. Similarly, in vivo measurements of the carotid artery bifurcation yielded mean and peak velocity magnitude profiles with estimated SDs of 1.06% and 2.51%. These results demonstrate that the proposed estimator can accurately and precisely reconstruct tensor velocity fields in clinically relevant and high-velocity flow scenarios.

Visual patterns are widely deployed as non-chemical insect deterrents, yet the visual mechanism remains unresolved. We tested whether spatial aliasing, arising from a mismatch between stripe geometry and compound-eye sampling, disrupts landing control. Using trained honey bees (Apis mellifera) and three-dimensional trajectory tracking, we quantified search and landing/entry performance across stripe scale and orientation. Stripe scale produced a non-monotonic behavioural response: disruption was greatest at intermediate stripe widths (~ 0.95-0.475 cm), where search times peaked and performance deteriorated. On a floor-mounted landing target, failures peaked at intermediate widths (up to 80% at 0.475 cm). On a wall-mounted target, horizontal stripe patterns yielded 70-90% off-target entries at these intermediate widths, whereas vertical stripe patterns at the same widths produced only 10-20% off-target entries. In contrast, when stripe width entered an ultra-fine range (≤ 0.23 cm; down to 0.175-0.075 cm), error rates decreased (e.g., wall off-target entries fell to 20%, and floor failures dropped to ~20%), consistent with patterns becoming poorly resolved and thus less disruptive. In the near-field, as viewing distance decreases and the angular scale of the pattern increases, landing performance becomes increasingly constrained by optic-flow quality: orientations that degrade coherent edge-motion cues during descent disrupt approach stability and touchdown/entry localization, increasing unstable approaches and off-target landings or entries. Together, these findings provide experimental evidence that spatial aliasing can impair both target localization and terminal landing manoeuvres, and they establish quantitative design rules for pattern-based, chemical-free deterrents.

Speech perception testing remains the dominant framework for cochlear implant (CI) outcomes assessment, yet its reliance on language introduces two fundamental limitations. Mechanistically, speech recognition performance reflects a composite of peripheral auditory encoding, cognitive processing, and linguistic knowledge, obscuring the contribution of each. Practically, language-dependent testing systematically underserves patients with limited English proficiency, prelingually deafened children, and others for whom verbal response formats are not viable. Language agnostic hearing tests (LAHTs) assess auditory function without requiring linguistic processing, offering a more precise probe of the auditory periphery and central encoding pathways and a more equitable framework for diverse populations. This narrative review was conducted via a systematic search of PubMed, MEDLINE, and Embase and examines the evidence for LAHTs across three domains: psychoacoustic resolution measures (spectral, temporal, and spectrotemporal), naturalistic stimulus measures (environmental sound identification, music perception, and phoneme discrimination), and electrophysiologic measures. Across the adult CI and hearing impairment literature, psychoacoustic measures demonstrate consistent, moderate-to-strong correlations with speech recognition outcomes. Spectral ripple discrimination and the Spectrotemporally Modulated Ripple Test (SMRT) predict speech outcomes in both English-speaking and non-English-speaking populations, though their validity at higher ripple densities is constrained by signal aliasing in CI processors. The Audible Contrast Threshold (ACT) has achieved clinical deployment for hearing aid fitting, and environmental sound identification deficits documented by the Familiar Environmental Sounds Test reveal safety-relevant dimensions of auditory function that speech batteries do not capture. In prelingually deafened children, the expected correlations between psychoacoustic resolution and speech recognition are largely absent, suggesting qualitatively distinct auditory reorganization that current measures incompletely characterize. Across domains, LAHT-speech correlations are real but partial: these measures predict speech outcomes without replacing them, and their relationship to patient-reported quality of life remains uninvestigated. The evidence supports integrating LAHTs as complementary tools in clinical practice and research, with the greatest immediate utility in candidacy screening, outcomes characterization in linguistically diverse populations, and mechanistic investigation of device and patient factors underlying outcome variability.

The large majority of patients with congenital heart disease (CHD) survive into adulthood in the modern era of surgical advancements. However, patients require life-long follow-up and non-invasive imaging is crucial for diagnostic and therapeutic purposes. Cardiovascular magnetic resonance (CMR) plays a major role in patients with CHD as it is a non-invasive and ionizing radiation free imaging tool which overcomes the technical limitations of echocardiography. 4D flow is an evolving technique for flow quantification allowing improved CMR exam quality and reduced scan time. However, comprehensive postprocessing of hemodynamics in CHD may be demanding. This document addresses the needs for an "ideal" post-processing software for 4D flow in patients with CHD. It provides information on general requirements, velocity encoding, offset and aliasing correction, visualization, segmentation and reconstruction as well as output data. This document aims to support stakeholders in the field to develop solutions for improved 4D flow post-processing and visualization in CHD patients and thus improve the accuracy and efficiency of patient care.