Search Results (190)

Conventional acoustic time-of-arrival (TOA) estimation in complex indoor environments is highly susceptible to multipath reflections and occlusions, resulting in unstable measurements and limited physical interpretability. This paper presents a smartphone-based indoor localization method built on vision-assisted acoustic channel modeling, and develops a fusion anchor integrating a pan–tilt–zoom (PTZ) camera and a near-ultrasonic signal transmitter to explicitly perceive indoor geometry, surface materials, and occlusion patterns. First, vision-derived priors are constructed on the anchor side based on line-of-sight reachability, orientation consistency, and directional risk, and are converted into soft anchor weights to suppress the impact of occlusion and pointing mismatch. Second, planar geometry and material cues reconstructed from camera images are used to generate probabilistic room impulse response (RIR) priors that cover the direct path and first-order reflections, where environmental uncertainty is mapped into path-dependent arrival-time variances and prior probabilities. Finally, under the RIR prior constraints, a path-wise posterior distribution is built from matched-filter outputs, and an adaptive fusion strategy is applied to switch between maximum a posteriori (MAP) and minimum mean square error (MMSE) estimators, yielding debiased TOA measurements with calibratable variances for downstream localization filters. Experiments in representative complex indoor scenarios demonstrate mean localization errors of 0.096 m and 0.115 m in static and dynamic tests, respectively, indicating improved accuracy and robustness over conventional TOA estimation. Full article

Brain magnetic resonance imaging (MRI) is highly susceptible to motion artifacts that degrade fine structural details and undermine quantitative analysis. Conventional U-Net-based deep learning approaches for motion artifact reduction typically operate only in the image domain and are often trained on data with simplified motion patterns, thereby limiting physical plausibility and generalization. We propose Sim-DDNet, a simulation-data-based dual-domain network that combines k-space-based motion simulation with a joint image-k-space reconstruction architecture. Motion-corrupted data were generated from T2-weighted Alzheimer’s Disease Neuroimaging Initiative brain MR scans using a k-space replacement scheme with three to five random rotational and translational events per volume, yielding 69,283 paired samples (49,852/6969/12,462 for training/validation/testing). Sim-DDNet integrates a real-valued U-Net-like image branch and a complex-valued k-space branch using cross attention, FiLM-based feature modulation, soft data consistency, and composite loss comprising L1, structural similarity index measure (SSIM), perceptual, and k-space-weighted terms. On the independent test set, Sim-DDNet achieved a peak signal-to-noise ratio of 31.05 dB, SSIM of 0.85, and gradient magnitude similarity deviation of 0.077, consistently outperforming U-Net and U-Net++ across all three metrics while producing less blurring, fewer residual ghost/streak artifacts, and reduced hallucination of non-existent structures. These results indicate that dual-domain, data-consistency-aware learning, which explicitly exploits k-space information, is a promising approach for physically plausible motion artifact correction in brain MRI. Full article

To address the challenges of high computational costs, susceptibility to local optima, and heavy reliance on manual intervention in multi-physics parameter optimization for symmetric acoustic metamaterials, an enhanced Chimp Optimization Algorithm (DADCOA) is proposed in this paper. This algorithm integrates the double chaotic initialization strategy (DCS), adaptive multimodal convergence mechanism (AMC), and dual-weight pinhole imaging update operator (DWPI). It employs a Logistic–Tent composite chaotic mapping strategy for population initialization, significantly enhancing distribution uniformity within high-dimensional parameter spaces. An AMC factor is then introduced to dynamically balance global exploration and local exploitation based on the real-time evolutionary state of the population. A dual-weight population update mechanism, incorporating distance and historical contributions, is integrated with a pinhole imaging opposition-based learning strategy to improve population diversity. Additionally, a composite single objective error feedback local differential mutation operation is introduced to improve optimization accuracy for coupled multi-physics objectives. Experimental validation based on the CEC 2022 test function suite and an acoustic metamaterial parameter optimization model demonstrates that compared to the standard COA algorithm and existing improved algorithms, the DADCOA algorithm reduces simulation time by 28.46% to 60.76% while maintaining high accuracy. This approach effectively addresses the challenges of high computational cost, stringent accuracy requirements, and composite single objective coupling in COMSOL physical parameter optimization, providing an effective solution for the design of acoustic metamaterials based on symmetric structures. Full article

Background and Purpose: Hepatic encephalopathy (HE) is a neuropsychiatric syndrome associated with liver cirrhosis (LC) that often results in cognitive impairment. Minimal HE (mHE), a subtle form of the condition, significantly affects patients’ quality of life. Advanced imaging techniques, such as quantitative susceptibility mapping (QSM), provide new insights into the brain changes associated with HE. Materials and Methods: The study included 28 patients (17 with mHE and 11 without) with alcohol-induced LC and 25 healthy controls. MR imaging, including QSM, was utilized to assess microstructural tissue changes and iron deposition in the brain. Cognitive function was assessed through a neuropsychological test battery. QSM quantified magnetic susceptibility in deep gray matter, while enlarged perivascular spaces (EPVS) were evaluated using T2-weighted images. Statistical analyses, including non-parametric tests and linear regression, assessed differences in susceptibility and their correlation with cognitive performance and EPVS. Results: Significant differences in cognitive performance and brain susceptibility were observed between patients and controls. Patients exhibited lower susceptibility in the caudate nucleus with the accumbens (CNA); mHE patients, in particular, had a significant reduction in CNA susceptibility. Additionally, EPVS grade correlated positively with cognitive decline, suggesting that EPVS may play an essential role in the pathophysiology of mHE. Conclusions: This study demonstrates that QSM can detect subtle brain changes in LC patients, with decreased susceptibility in the CN (caudate nucleus) linked to cognitive impairment in mHE. The role of EPVS in HE warrants further investigation, as it may affect the efficacy of current diagnostic and therapeutic approaches. These findings highlight the potential of QSM to improve HE assessment. Full article

Purpose: This study proposes a novel magnetic resonance (MRI)-based classification of cortical tubers (CTs) in tuberous sclerosis complex (TSC) patients that incorporates intralesional calcifications. We evaluated prevalence, temporal evolution, and genotype correlation of intra-tuberal calcifications in pediatric TSC patients, emphasizing susceptibility-weighted imaging (SWI) for detection. Materials and Methods: We retrospectively analyzed MRI scans of 57 unrelated pediatric TSC patients followed between 2014 and 2024 at a tertiary care center. Inclusion criteria included longitudinal imaging on the same 1.5T scanner, with T1w, T2w/FLAIR, and SWI sequences. CTs were classified into four MRI-based categories (A–D), with calcified tubers subdivided into micro-calcified and macro-calcified. Descriptive statistics, binomial tests, and Chi-square analyses were performed. Results: Calcified CTs were more prevalent than cystic ones. At baseline MRI, 63% of patients had calcified tubers (19% of all CTs), increasing to 77% at follow-up MRI (24% of all CTs). Micro-calcifications predominated at baseline MRI evaluation, though a significant proportion progressed to macro-calcifications over time. Calcified CTs always progressed from lower-grade lesions. Cystic tubers were rare (<1%). Longitudinal analysis showed significant variation in CTs with inner calcification count (p = 0.0000023), but not in CTs with cystic components (p = 0.42072). No significant genotype–radiological phenotype association emerged. Conclusions: Intralesional calcifications in CTs are dynamic and detectable with SWI. The inclusion of calcification patterns in CT classification could offer insights that may prove useful for future prognostic and risk-stratification frameworks in pediatric TSC. Full article

Body temperature serves as a crucial indicator of dairy cow health. Traditional rectal temperature (RT) measurement often induces stress responses in animals. Body temperature detection based on infrared thermography (IRT) offers non-invasive and timely advantages, contributing to welfare-oriented farming practices. However, automated detection and temperature extraction from critical cow regions are susceptible to complex illumination, black-and-white fur texture interference, and region of interest (ROI) deformation, resulting in low detection accuracy and poor robustness. To address this, this paper proposes the EDC-YOLO-World-DB framework to enhance detection and temperature extraction performance under complex illumination conditions. First, URetinex-Net and CLAHE methods are employed to enhance low light and overexposed images, respectively, improving structural information and boundary contour clarity. Subsequently, spatial relationship constraints between LU and AA are established using five-class text priors—lower udder (LU), around the anus (AA), rear udder, hind legs, and hind quarters—to strengthen the spatial localisation capability of the model for ROIs. Subsequently, a Dual Bidirectional Feature Pyramid Network architecture incorporating EfficientDynamicConv was introduced at the neck of the model to achieve dynamic weight allocation across modalities, levels, and scales. Task Alignment Metric, Gaussian soft-constrained centroid sampling, and combined IoU (C_IoU + G_IoU) loss were introduced to enhance sample matching quality and regression stability. Results demonstrate detection confidence improvements by 0.08 and 0.02 in low light and overexposed conditions, respectively; compared to two-text input, five-text input increases P, R, and mAP50 by 3.61%, 3.81%, and 1.67%, respectively; Comprehensive improvements yielded P = 88.65%, R = 85.77%, and mAP50 = 89.33%—further surpassing the baseline by 2.79%, 3.01%, and 1.92%, respectively. Temperature extraction experiments demonstrated significantly reduced errors for $T_{Max}$, $T_{Min}$, and $T_{avg}$. Specifically, for the mean error of LU, $T_{Max}$, $T_{Min}$, and $T_{avg}$ were reduced by 66.6%, 33.5%, and 4.27%, respectively; for AA, $T_{Max}$, $T_{Min}$, and $T_{avg}$ were reduced by 66.6%, 25.4%, and 11.3%, respectively. This study achieves robust detection of LU and AA alongside precise temperature extraction under complex lighting and deformation conditions, providing a viable solution for non-contact, low-interference dairy cow health monitoring. Full article

Object detection plays a crucial role in remote sensing and UAV image technology, but it faces the challenge of speed and accuracy in multi-scale dense small target mission detection scenarios and is susceptible to noise interference, such as weather conditions, lighting changes, and occluded backgrounds in complex backgrounds. In recent years, Mamba-based methods have become hot in the field of object detection, showing great potential in capturing remote dependencies with linear complexity but lacking deep customization of remote sensing targets. Based on this, we propose GMG-LDefmamba-YOLO, which contains two core modules: the Gaussian mask gear convolution module forms a gear-shaped receptive field through improved convolutional splicing to enhance the extraction of small target features and combines the Gaussian mask mechanism to dynamically modulate the feature weights to suppress complex background interference. The linear deformable Mamba module integrates linear deformable sampling, a spatial state dual model, and residual gating MLP components, integrating the advantages of flexible capture of local features and efficient modeling of global dependence, dynamically adapting to target scale changes and spatial distribution, and reducing computational costs. Experiments on DOTA-v1.0, VEDAI, and USOD datasets show that the mAP50 of the model reaches 70.91%, 77.94%, and 90.28%, respectively, which is better than the baseline and mainstream methods, and maintains the lightweight characteristics, providing efficient technical support for remote sensing monitoring, UAV inspection, and other fields. Full article

Background/Objectives: To evaluate whether incorporating both diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI) in pituitary MRI examinations improves differential diagnosis by providing additional diagnostic value. Methods: A retrospective analysis was performed on 88 patients with histologically confirmed sellar or parasellar tumors who underwent 1.5T MRI with DWI and dynamic susceptibility contrast PWI (DSC-PWI) between October 2007 and April 2023. DWI parameters included minimum apparent diffusion coefficient (ADCmin) and relative ADCmin (rADCmin). PWI parameters included mean and maximum relative cerebral blood volume (rCBV, rCBVmax) and relative peak height (rPH, rPHmax), normalized to white matter. Tumor regions of interest were manually segmented, excluding calcified or hemorrhagic areas. Group comparisons and ROC analyses assessed diagnostic performance of individual and combined parameters. Results: Significant differences in diffusion and perfusion metrics were observed among the five tumor types. The combined analysis of DWI and PWI improved diagnostic accuracy in selected comparisons. The greatest benefit occurred in distinguishing meningiomas from solid non-functional pituitary adenomas (pituitary neuroendocrine tumors-PitNET), where the combination of ADCmin and rPHmax yielded an AUC of 0.818, sensitivity of 88%, and specificity of 76%, exceeding the performance of either parameter alone. In other comparisons, including meningiomas versus invasive PitNETs and adamantinomatous craniopharyngiomas, combined analysis did not substantially improve accuracy when single parameters, particularly rCBVmax (AUC = 0.995), already demonstrated excellent performance. Conclusions: Integration of DWI and PWI into pituitary MRI protocols enhances diagnostic performance in selected tumor groups. The additive value is context-dependent, supporting the tailored application of these sequences in the evaluation of sellar and parasellar tumors. Full article

Industrial image anomaly detection (IAD) is crucial for quality control, but its performance often degrades when training data contain label noise. To circumvent the reliance on potentially flawed labels, unsupervised methods that learn from the data distribution itself have become a mainstream approach. Among various unsupervised techniques, student–teacher frameworks have emerged as a highly effective paradigm. Student–Teacher Feature Pyramid Matching (STPM) is a powerful method within this paradigm, yet it is susceptible to such noise. Inspired by STPM and aiming to solve this issue, this paper introduces Meta-Learning Guided STPM (MLG-STPM), a novel framework that enhances STPM’s robustness by incorporating a guidance mechanism inspired by meta-learning. This guidance is achieved through an Evolving Meta-Set (EMS), which dynamically maintains a small high-confidence subset of training samples identified by their low disagreement between student and teacher networks. By training the student network on a combination of the current batch and the EMS, MLG-STPM effectively mitigates the impact of noisy labels without requiring an external clean dataset or complex re-weighting schemes. Comprehensive experiments on the MVTec AD and VisA benchmark datasets with synthetic label noise (0% to 20%) demonstrate that MLG-STPM significantly improves anomaly detection and localization performance compared to the original STPM, especially under higher noise conditions, and achieves competitive results against other relevant approaches. Full article

Imaging in controlled agriculture helps maximize plant growth by saving labor and optimizing resources. By monitoring specific plant traits, growers can prevent crop losses by correcting environmental conditions that lead to physiological disorders like leaf tipburn. This study aimed to identify morphometric and spectral markers for the early detection of tipburn in two Romaine lettuce (Lactuca sativa) cultivars (‘Chicarita’ and ‘Dragoon’) using an image-based system with color and multispectral cameras. By monitoring tipburn in treatments using melatonin, lettuce cultivars, and with and without supplemental lighting, we enhanced our system’s accuracy for high-resolution tipburn symptom identification. Canopy geometrical features varied between cultivars, with the more susceptible cultivar exhibiting higher compactness and extent values across time, regardless of lighting conditions. These traits were further used to compare simple linear, logistic, least absolute shrinkage and selection operator (LASSO) regression, and random forest models for predicting leaf fresh and dry weight. Random forest regression outperformed simpler models, reducing the percentage error for leaf fresh weight from ~34% (LASSO) to ~13% (RMSE: 34.14 g to 17.32 g). For leaf dry weight, the percentage error decreased from ~20% to ~12%, with an explained variance increase to 94%. Vegetation indices exhibited cultivar-specific responses to supplemental lighting. ‘Dragoon’ consistently had higher red-edge chlorophyll index (CIrededge), enhanced vegetation index, and normalized difference vegetation index values than ‘Chicarita’. Additionally, ‘Dragoon’ showed a distinct temporal trend in the photochemical reflectance index, which increased under supplemental lighting. This study highlights the potential of morphometric and spectral traits for early detection of tipburn susceptibility, optimizing cultivar-specific environmental management, and improving the accuracy of predictive modeling strategies. Full article

Mechanical damage like bruises produced during postharvest handling can lower market value, affect nutritional value, and pose food safety risks. The study evaluated bruises on apples using image processing. This research focuses on using computer vision for apple fruit damage detection. The fruits were subjected to three levels of impact using three ball weights (66, 98, and 110 g) dropped from 50 cm height and stored at 22 °C. The overall impact energies generated were 0.323 J (low), 0.480 J (medium), and 0.539 J (high). The bruise area and susceptibility of the damage, surface area of the fruit, and color were measured manually (colorimeter) and by image processing. The study found that the bruise area was significantly affected by impact force, where 110 g (0.539 J) damaged apples showed a bruise area of 4.24 cm² after 21 days of storage at 22 °C. The images showed a significant change in the RGB values (Red, Green, Blue) over 21 days of storage when impacted at 0.539 J. The study showed that the greater the impact energy effect, the higher the weight loss under constant conditions of storage. After 21 days of storage, the 110 g mechanically damaged apples recorded the highest percentage of weight loss (6.362%). The study found a significant decrease in the surface area of 110 g bruised apples, with a smaller decrease in surface area for 66 g bruised fruit. The use of computer vision to detect bruise damage and other quality attributes of Granny Smith apples can be highly recommended to detect their losses. Full article

Background/Objectives: Breast abscesses and necrotic masses often show similar peripheral enhancement and a fluid-containing appearance on breast MRI, leading to diagnostic confusion. Accurate differentiation is critical because biopsies that fail to sample the lesion wall may yield false-negative results, may be misinterpreted as an infectious process, and delay diagnosis. Incorporating SWI into the protocol can provide additional clues to malignancy and, when warranted, prompt a second wall-targeted biopsy, thus reducing the risk of delayed cancer diagnosis. Methods: This single-center prospective diagnostic accuracy study included 42 female patients diagnosed between 2022 and 2025 with either necrotic breast tumors or abscesses, confirmed by histopathology. SWI-based Intralesional Susceptibility Score (ILSS), rim morphology, and mean ADC values were evaluated. Statistical analyses included the Mann–Whitney U test, chi-square test, ROC analysis, DeLong test for comparison of AUCs, and Cohen’s kappa for interobserver agreement. Results: SWI-based ILSS values were significantly higher in necrotic tumors compared to abscesses (mean ILSS: 2.28 vs. 0.85; 95% CI: 1.0–2.0; p < 0.001). Smooth hypointense rims were predominantly observed in abscesses (Sensitivity: 63.1%, 95% CI: 0.38–0.83; Specificity: 88.9%, 95% CI: 0.65–0.98; p = 0.001). Incomplete rim morphology was more frequent in tumors (Sensitivity: 78.9%, 95% CI: 0.54–0.93; Specificity: 77.8%, 95% CI: 0.52–0.93; p < 0.001). The double rim sign was highly specific for abscesses (Specificity: 95.2%, 95% CI: 0.76–0.99 p = 0.002). Conclusions: SWI provides valuable morphological information in differentiating abscesses from necrotic tumors on breast MRI. When used in combination with ADC values, it can enhance diagnostic accuracy. Full article

Background and Objectives: Cerebral microbleeds (CMBs) are increasingly being considered as potential biomarkers of small vessel disease and cerebral vulnerability, particularly in patients with acute ischemic stroke (AIS). Accurate detection is crucial for prognosis and therapeutic decision-making, yet the relative utility of susceptibility-weighted imaging (SWI) versus T2*-weighted imaging (T2*) remains uncertain. Materials and Methods: We conducted a systematic review and meta-analysis (SPOT-CMB, Susceptibility-weighted imaging and Prognostic Outcomes in Acute Stroke—Cerebral Microbleeds study) of 80 studies involving 28,383 AIS patients. Pooled prevalence of CMBs was estimated across imaging modalities (SWI, T2*, and both), and stratified analyses examined variation by demographic, clinical, and imaging parameters. Meta-analytic odds ratios assessed associations between CMB presence and key outcomes: symptomatic intracerebral hemorrhage (sICH), hemorrhagic transformation (HT), and poor functional outcome (modified Rankin Scale score 3–6) at 90 days. Diagnostic performance was assessed using summary receiver operating characteristic curves. Results: Pooled CMB prevalence was higher with SWI (36%; 95% CI 31–41) than T2* (25%; 95% CI 22–28). CMB presence was associated with increased odds of sICH (OR 2.22; 95% CI 1.56–3.16), HT (OR 1.33; 95% CI 1.01–1.75), and poor 90-day outcome (OR 1.61; 95% CI 1.39–1.86). However, prognostic performance was modest, with low sensitivity (e.g., AUC for sICH: 0.29) and low diagnostic odds ratios. SWI outperformed T2* in detection but offered limited prognostic gain. Access to SWI remains limited in many settings, posing challenges for global implementation. Conclusions: SWI detects CMBs more frequently than T2* in AIS patients and shows stronger associations with adverse outcomes, supporting its value for risk stratification. However, prognostic accuracy remains limited, and our GRADE appraisal indicated only moderate certainty for functional outcomes, with lower certainty for diagnostic accuracy due to heterogeneity and imprecision. These findings highlight the clinical utility of SWI but underscore the need for standardized imaging protocols and high-quality prospective studies. Full article

Navigation line extraction is vital for visual navigation with agricultural machinery. The current methods primarily utilize plant canopy detection frames to extract feature points for navigation line fitting. However, this approach is highly susceptible to environmental changes, causing position instability and reduced extraction accuracy. To address this problem, this study aims to develop a robust navigation line extraction method that overcomes canopy-based feature instability. We propose extracting feature points from root detection frames for navigation line fitting. Compared to canopy points, root feature point positions remain more stable under natural interference and less prone to fluctuations. A dataset of corn crop row images under multiple growth environments was collected. Based on YOLOv8n (You Only Look Once version 8, nano model), we proposed the RS-LineNet lightweight model and introduced a root subordination relationship filtering algorithm to further improve detection precision. Compared with the YOLOv8n model, RS-LineNet achieves 4.2% higher precision, 16.2% improved recall, and an 11.8% increase in mean average precision (mAP₅₀), while reducing the model weight and parameters to 32% and 23% of the original. Navigation lines extracted under different environments exhibit an 0.8° average angular error, which is 3.1° lower than canopy-based methods. On Jetson TX2, the frame rate exceeds 12 FPS, meeting practical application requirements. Full article

Overexpression of αvβ3 integrin is found in a diverse group of tumors originating from glial cells in the brain, making this transmembrane receptor a promising biomarker for molecular MRI diagnosis. In the study, we conjugated a monoclonal antibody against the β3 subunit (CD61) of the αvβ3 integrin receptor with carbon-encapsulated iron nanoparticles to yield Fe@C-(CH₂)₂-CONH-anti-CD61 bioconjugates that were used in dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI). Wistar rats bearing C6 gliomas were injected as a single bolus (0.5 mL) through the tail vain with a suspension of Fe@C-(CH₂)₂-CONH-anti-CD61 nanoparticles (200 μg mL⁻¹) and the animals were imaged using the T2*-weighted echo planar imaging (T2* EPI) technique. Results showed that intravenously infused nanoparticles targeting αvβ3 integrin receptors provide strong contrast in rat glioma tissues. No such effects were observed in other rat organs, although some post-contrast effects were also noted in the liver and kidney. The study shows that the as-developed nanoparticles decorated with anti-CD61 monoclonal antibodies might be considered as a novel contrast candidate for noninvasive DSC-MRI diagnosis in CD61-positive gliomas. Full article