Search Results (382)

With the rapid development of software-defined radio (SDR) technology, a digital, software-reconfigurable, and flexible solution is provided for microwave radiometers, particularly suitable for atmospheric water vapor and oxygen detection with wideband, multi-channel requirements, significantly improving system efficiency. Meanwhile, digitization helps improve channel consistency and address nonlinearity issues, while the digital zero-balancing mechanism implemented through adaptive integration is more suitable for digital platforms. This paper proposes a digital Dicke-type radiometer system based on an SDR platform, using Xilinx RFSoC XCZU47DR (AMD, San Jose, CA, USA) as the core hardware to achieve single-chip integration of RF signal sampling, digital local oscillator generation, and signal processing. The system implements a 46-channel channelized receiver (23 channels each for K-band and V-band) on an FPGA using a polyphase filter bank. The prototype filters achieve 70 dB stopband attenuation and 0.5 dB passband ripple, with each polyphase branch requiring only 25 coefficients, significantly reducing hardware resource consumption. An adaptive integration method is proposed, where an adaptive switch controller dynamically adjusts the hot source injection time ratio by calculating the power difference between adjacent integration periods, enabling the Dicke zero-balancing mechanism to operate entirely in the digital domain. Furthermore, a complete hardware transfer model is established for three signal branches (antenna, hot source, and matched load), and full-chain calibration of all 46 channels is performed using a liquid nitrogen cold source, with calibration reliability verified through blackbody measurements. Experimental results demonstrate brightness temperature consistency better than 0.7 K, with a sensitivity of less than 0.15 K for the K-band and less than 0.21 K for the V-band at 1 s integration time. Full article

Unsupervised anomaly detection in industrial fabric inspection remains a formidable challenge due to the complexity of background textures and the subtle, irregular nature of real-world defects. Although the teacher-student distillation paradigm has demonstrated promising performance without reliance on anomalous data, existing methods still struggle in the presence of complex textures, largely due to limited semantic guidance, insufficient frequency modeling, and inadequate multi-scale representation. To address these limitations, we propose a novel reverse distillation framework tailored for fabric defect detection. The core of our method is the frequency decoupling Feature fusion module (FDFM), which achieves frequency domain alignment between teacher and student features through spatially adaptive and learnable filter banks, namely the adaptive high-pass filter (AHPF) and the adaptive low-pass filter (ALPF). Specifically: (1) the high-frequency pathway employs deconvolutional residual enhancement to emphasize boundary details; (2) the low-frequency pathway leverages the CARAFE operator to Handle these normal fluctuations to prevent the model from mistakenly identifying background changes as abnormal areas. This design not only maintains a lightweight architecture but also significantly improves sensitivity to fine-grained anomalies. Furthermore, we introduce a cross-layer residual alignment mechanism that guides the student network in reconstructing deep semantic representations from the teacher-student feature pairs. To balance detection accuracy and deployment efficiency, we develop two model variants: a high-capacity version optimized for precision, and a lightweight version tailored for real-time industrial applications. Compared with other methods from recent years, the experimental results of FAD-RNet validate its superiority in relevant metrics. It should be noted that this study is conducted based on the data organization and processing protocol of the ZJU-Leaper dataset, which may introduce certain dataset-specific characteristics. Full article

Background: Melanoma is one of the most aggressive forms of skin cancer, making early and accurate diagnosis essential for improving patient outcomes. Methods: In this work, we propose an Evolutionary Gabor-based Melanoma Descriptor (Evo-GMD), a lightweight and interpretable approach designed under the principles of Frugal AI. The method integrates multi-scale Gabor filtering with Differential Evolution to automatically learn discriminative texture patterns using a reduced set of parameters. The proposed approach was evaluated on the PH2 dataset, achieving competitive performance (accuracy above 95%) while maintaining low computational complexity and full interpretability. To further assess its robustness, complementary experiments were conducted on the ISIC 2017 dataset, which presents higher variability, class imbalance, and heterogeneous lesion characteristics. Results: The results reveal that multiple methods—including handcrafted descriptors, convolutional neural networks, and transfer learning models—exhibit significant performance degradation or converge to trivial solutions under these conditions. This behavior highlights that increasing model complexity does not necessarily improve classification performance when data constraints are present. Conclusions: Overall, the findings demonstrate that the proposed method provides a robust and efficient alternative for melanoma classification in low-resource scenarios, where data availability, computational capacity, and interpretability are critical factors. Full article

The U.S. strategies of “friend-shoring” and “near-shoring,” aimed at enhancing supply chain autonomy, are profoundly restructuring global production networks. This study empirically evaluates the impact of these strategies on China’s factor-intensive industries. Utilizing the Asian Development Bank Multi-Regional Input-Output database, we constructed a Global Industrial Value Chain Backbone Network and applied the X-index Filtering Algorithm to identify core trade relationships. Policy impacts were quantified by comparing degree, betweenness, and closeness centralities between null and counterfactual models. The results indicate that “friend-shoring” exerts a significant “squeeze effect” on China, with resource-intensive industries facing severe decoupling risks that cascade into supporting services. Conversely, the impact of “near-shoring” remains limited, as Chinese firms mitigate trade diversion through strategic overseas investment. Scenario analysis further reveals that while new trade remedies targeting re-exports may bolster emerging hubs like Vietnam and Mexico in the short term, they increase the topological distance of global production networks, leading to a systemic decline in efficiency. These findings provide critical quantitative evidence regarding the evolution and systemic risks of global value chains under geopolitical intervention. Full article

In precision agriculture, identifying pests such as the whitefly (Bemisia tabaci) is a significant challenge, as precise knowledge of these insects is essential for developing effective Integrated Pest Management (IPM) strategies. Automated daily monitoring within IPM programs optimizes the diagnostic registration stage by reducing logistical expenses and manual errors, enabling early pest treatment interventions and providing quantitative data for informed decision-making. In this study, an image bank was processed using a Quaternionic Gabor Filter (QGF) algorithmto highlight textural features through hypercomplex correlation. The highlighted objects were then processed by a YOLOv8 pretrained model to identify Bemisia tabaci. Experimental results demonstrate that this combination achieves a precision of 0.868 and an mAP@0.5 of 0.950, while a PSNR of 34.10 dB ensures the structural integrity of the enhanced images. Although the total execution time averages 2.3 s per image due to preprocessing complexity, the GPU inference time of 10.3 ms confirms the potential for high-speed detection. This approach significantly enhanced the morphological features of Bemisia tabaci, increasing the robustness of the detection model and narrowing down processing conditions for yellow trap samples to strengthen precision in the semi-arid regions of Zacatecas, Mexico. Full article

This paper examines the interaction between fiscal and monetary policies in Myanmar under ongoing political and economic uncertainty. We estimate a small open-economy New Keynesian DSGE model using Bayesian methods, combining the Kalman filter with Markov Chain Monte Carlo sampling on quarterly data from 2013Q1 to 2022Q1. The results show a persistent regime of monetary and fiscal policy conflict. While the central bank follows an active anti-inflationary interest rate rule that satisfies the Taylor principle, fiscal policy shows weak responsiveness to public debt, providing limited fiscal backing for monetary stabilization. As a result, monetary tightening aimed at controlling inflation exacerbates fiscal stress through the debt-service channel, undermining the overall effectiveness of macroeconomic stabilization. Political instability emerges as a key structural driver of macroeconomic fragility. Political shocks are highly persistent and are transmitted primarily through increases in the country risk premium, accounting for more than 50% of real exchange rate volatility and generating exchange rate depreciation, higher inflation, and output contraction. Overall, the findings indicate that monetary tightening alone is insufficient to restore macroeconomic stability in fragile and conflict-affected economies. Credible fiscal adjustment and improvements in political stability are necessary to contain external vulnerabilities and restore the effectiveness of monetary policy. Full article

Corner detection is a fundamental task in computer vision that plays a critical role in applications such as image registration, 3D reconstruction, and object tracking. In biological visual systems, simple cells in the primary visual cortex exhibit high selectivity to edge stimuli of specific orientations, while end-stopped cells can detect geometric singular structures such as line segment endpoints and corners. Existing corner detection methods based on visual neural computation typically employ a strategy of densely distributed end-stopped cells for corner localization, which suffers from significant localization deviation under small angle conditions due to mutual interference between responses of adjacent neurons. To address this problem, this paper proposes a bionic corner detection method based on cooperative processing of simple cells and end-stopped cells. The method constructs a two-stage cooperative processing framework: the edge filtering stage employs a Gabor filter bank to simulate the orientation selectivity of simple cells, extracting edge positions and orientation information; the dynamic construction stage builds unilateral end-stopped cells only at filtered edge positions based on local orientation information, fundamentally avoiding computational redundancy and response interference caused by global dense distribution; the corner localization stage determines precise corner coordinates through hierarchical clustering and dual-cluster centroid fusion strategies. Experimental results demonstrate that, in the 15° acute-angle regime where dense end-stopped schemes are most severely affected by response interference, the proposed method reduces the mean localization error from 8.76 to 2.34 pixels, corresponding to a 73.3% improvement; averaged across the eight tested angle levels from 15° to 165°, the improvement is approximately 40.9%, and all per-angle differences are statistically significant (paired t-test, p < 0.01 or below, N = 10 independent runs). On standard test images, the method attains the lowest mean localization error among the eight compared detectors (1.58 pixels, versus 1.68–3.42 pixels for Harris, FAST, COSFIRE, KAZE, SuperPoint, Deep Corner, and Wei et al.), while maintaining competitive detection rate, false-alarm rate, and runtime. Physiological plausibility validation experiments show that the correlation coefficient between the detection deviation of this method and human perceptual deviation reaches 0.923, indicating that the output of the framework aligns with previously reported human perceptual bias patterns and supporting its biological plausibility as a biologically inspired—rather than mechanistic—model of corner perception. The source code, dataset, and experimental results are publicly available (see Data Availability Statement). Full article

The mathematical characterization of non-stationary signals remains a significant challenge, particularly when impulsive components are obscured by high-dimensional noise and structural coupling. This paper proposes an application-driven mathematical methodology for a learnable discrete wavelet transform (LDWT) that combines classical multi-resolution analysis with task-optimized data-driven adaptivity. Rather than introducing entirely new foundational theory, our approach strategically relaxes constraints from orthogonal wavelet theory within the non-perfect reconstruction filter bank framework, enabling controlled spectral decomposition optimized for supervised fault diagnosis. We introduce a specialized regularization term based on the half-band property to ensure spectral complementarity and minimize cross-band correlation, while a Jacobian-based stabilization approach is formulated to ensure the convergence of filter coefficients during optimization. The proposed algorithmic architecture, LDBRFnet, features a dual-branch encoder system designed to capture the mathematical synergy between sub-band-level global statistics and time-domain local morphology. This dual-view representation effectively mitigates feature leakage and overconfidence in classification. Theoretical analysis and numerical experiments demonstrate that the learned filters satisfy the frequency-shift property and maintain robust spectral partitioning even under low signal-to-noise ratios. Validation on complex vibration datasets confirms that the framework achieves superior diagnostic accuracy (over 95.5%) and computational efficiency, reducing model parameters by 96.7% compared to state-of-the-art baselines. This work provides a generalizable mathematical approach for adaptive signal decomposition and robust pattern recognition in interdisciplinary applications. Full article

Stability and sustainability are the operational bottom lines of modern power grids. However, the inherent volatility and non-stationarity of wind energy, particularly in complex terrains, severely threaten power grid stability. To address this challenge, we propose an end-to-end architecture named the Adaptive Multi-scale Routing Wind Power forecasting (AMR-Wind) framework. The framework is principally composed of three sequential modules: an Adaptive Frequency Disentanglement Module (AFDM), an inverted Transformer (iTransformer), and a Scale-Routing Gated Recurrent Unit (SRGRU). The AFDM utilizes a differentiable filter bank to dynamically disentangle complex spectral signatures and mitigate mode mixing. The iTransformer is employed to effectively capture the complex multivariate dependencies between these disentangled modes and exogenous meteorological features. The SRGRU utilizes hierarchical temporal routing to synchronize localized high-frequency ramp events with macroscopic evolutionary trends. Comprehensive evaluations across four diverse wind farms demonstrate that AMR-Wind reduces the RMSE by an average of 8.4% and improves the $R^2$ by at least 1.0% compared to state-of-the-art baselines. Ablation studies further confirm the modules’ strong synergistic effects, yielding a 7.6% reduction in forecasting errors. This framework reduces the error in wind energy prediction, providing a reliable tool for the stability and sustainability of the power grid. Full article

The automatic extraction of seismic reflection events is fundamental to seismic interpretation and structural identification in oil and gas exploration, particularly for large-scale regional surveys and preliminary basin-scale assessments. Although the B-COSFIRE (Bar-Combination of Shifted Filter Responses) method has demonstrated strong capability in detecting ridge-like structures, its application in large-scale seismic processing is limited by high computational cost and complex filter bank configuration. Conventional edge detectors such as the Canny operator are computationally efficient but often produce fragmented and noise-sensitive results in low signal-to-noise ratio (SNR) seismic data because they rely solely on local gradient information and ignore the spatial continuity of geological horizons. To overcome these limitations, this study proposes a lightweight and computationally efficient framework for rapid seismic event extraction. The method simplifies the B-COSFIRE architecture by replacing its configurable filter bank with a Difference-of-Gaussian (DoG) operator, which enhances ridge-like reflection features while suppressing background interference through a center–surround mechanism. Furthermore, a Spatial Consistency Constraint (SCC) module is introduced to enforce lateral continuity using directional morphological closing operations. This strategy reconstructs disrupted reflection segments and converts isolated detection responses into spatially coherent linear structures. Adaptive thresholding and skeletonization are then applied to obtain single-pixel-wide reflection contours suitable for geological interpretation and regional structural analysis. The proposed method was evaluated using both synthetic seismic models (Ricker wavelet convolution with Gaussian noise, σ = 0.15) and real post-stack seismic profiles characterized by low SNR conditions. Experimental results demonstrate that the proposed method achieves a Precision of 0.9527, Recall of 1.0000, and F1-score of 0.9758 on synthetic data, outperforming both the standard Canny detector (F1: 0.8972) and B-COSFIRE (F1: 0.7311). The Continuity Index reaches 261.00 pixels, substantially higher than Canny (223.67 pixels) and B-COSFIRE (66.86 pixels). Notably, B-COSFIRE exhibits a severely imbalanced detection profile (Precision: 0.5762, Recall: 1.000), indicating excessive false positives that undermine its practical utility. The proposed method additionally achieves the lowest runtime (0.024 s per profile), representing a 44× speedup over B-COSFIRE (1.039 s), while requiring no training data. Overall, the proposed framework provides a practical and efficient solution for automated seismic event extraction. With only a small number of geologically interpretable parameters and strong robustness across different datasets, the method is well-suited for large-scale seismic data processing and preliminary structural assessment in underexplored regions, enabling rapid first-pass evaluation of extensive survey areas before detailed interpretation and reservoir characterization. These characteristics make the method particularly suitable for computer-assisted interpretation workflows in industrial oil and gas exploration. Unlike prior approaches that treat seismic event extraction as a generic edge detection problem, the proposed framework explicitly encodes geological prior knowledge—specifically, the lateral continuity of stratigraphic interfaces—as a morphological constraint, bridging the gap between image processing methodology and geophysical interpretation requirements. Full article

This study introduces an automated computational tool to evaluate the phase velocity of the highly dispersive A₀ mode using only two signals measured along the wave propagation path. The algorithm combines the zero-crossing technique with automated spectral decomposition, utilizing a bank of bandpass filters with adaptive bandwidths. Validated through theoretical and experimental analysis of an aluminium plate near 300 kHz, the results demonstrate that using a two-signal set and variable filter widths significantly improves accuracy and extends the measurable frequency range of the dispersion curve. Experimental results demonstrate that by applying various filter widths, the phase velocity dispersion curve segment can be reconstructed over a frequency range exceeding 65% of the signal’s spectral width at the −40 dB level. The reconstruction yielded an average relative error of 0.8% ± 1.2%, while the best-case scenario showed an error of just 0.3% ± 0.4%. Implementing automated filter parameter selection on a signal pair offers a time-efficient alternative to traditional spatial scanning, significantly simplifying data collection while reducing labour and time requirements. Full article

As deep learning-based Anomaly Detection (AD) transitions from theoretical research to industrial application, the focus is shifting towards operational efficiency and economic viability on edge devices. While recent studies have achieved remarkable detection accuracy on standard benchmarks, they often rely on heavy memory banks or complex backbones, which pose challenges for deployment in resource-constrained manufacturing environments. Furthermore, real-world inspection lines often present distinct challenges—such as environmental noise and strict latency requirements—that are not fully addressed by accuracy-centric metrics. To bridge the gap between high-performance research models and practical edge deployment, we introduce WEDGE-Net. Our approach is designed to balance structural precision with extreme memory efficiency. We decouple anomaly detection into two specialized streams: (1) a Frequency Stream (DWT) that physically filters out environmental noise to isolate structural defects, and (2) a Context Stream where a Semantic Module explicitly guides feature extraction to enforce object consistency. By synthesizing these two modalities, WEDGE-Net effectively suppresses high-frequency noise while enhancing structural-feature compactness. To validate operational stability, we conducted a robustness analysis of the ‘Tile’ category, which poses a challenging task for distinguishing defects from high-frequency textures. In this stress test, WEDGE-Net demonstrated superior resistance to environmental noise compared to conventional methods. Experimental results on the MVTec AD dataset demonstrate that WEDGE-Net achieves a mean image-level AUROC of 97.82% and an inference speed of 686.5 FPS (measured on an RTX 4090 GPU) under an extreme 1% memory-compression setting. Notably, our method demonstrates superior efficiency, achieving a 2.1× inference speedup over the widely adopted comparative model (PatchCore-10%) while maintaining competitive detection accuracy (e.g., 100% AUROC on Transistor). We hope this work serves as a practical reference for implementing real-time industrial inspection on resource-constrained edge devices. Full article

To address the challenges associated with identifying causal relationships among event mentions in the event causality identification (ECI) task, ECI has emerged as a pivotal area of research for comprehending event structures. Recent studies have leveraged Transformer-based models, augmented by auxiliary components, to develop effective contextual representations for causality prediction. A critical step in ECI models involves transforming intricate event context representations into causal label representations, thereby facilitating the logical score calculations necessary for both training and inference. However, existing models frequently depend on simplistic feedforward networks for this transformation process, which often struggle to bridge the semantic gap between complex event contexts and target causal labels, particularly in linguistically nuanced scenarios. To address these limitations, we propose Contrastive Learning for Event Causality Identification (CLECI), an innovative ECI framework that enhances representation learning through the integration of contrastive learning techniques, a generator-discriminator mechanism with causal label embeddings. In contrast to traditional direct transformation methods, CLECI generates latent causal label embeddings that filter out irrelevant information while aligning with potential label representations. By incorporating contrastive learning principles, CLECI further augments the discriminative capability of event representations by constructing positive and negative pairs of events. Experimental evaluations conducted on the EventStoryLine (ESL), Causal-TimeBank (CTB), and MECI datasets demonstrate that CLECI achieves competitive performance, with F1-score improvements of 4.3%, 7.9%, and 2.5%, respectively, compared with the strongest baseline methods, while maintaining strong robustness in complex and noisy multilingual event contexts. Full article

Objective: This prospective study investigated the use of H-scan ultrasound (US) imaging as a novel component of a multiparametric radiomic analysis framework for characterizing human breast cancer response to neoadjuvant chemotherapy (NAC) before and early after treatment initiation. Methods: Thirty breast cancer patients scheduled for NAC were scanned using a clinical US system (Logiq E9, GE HealthCare) equipped with a 9L-D linear array transducer. Radiofrequency (RF) data was obtained at baseline (pre-NAC) and after 10% and 30% of the complete dose of chemotherapy. The RF data was analyzed by a bank of 256 frequency-shifted bandpass filters to form H-scan US frequency images. Grayscale texture features were extracted from both B-scan and H-scan US images. In addition, US attenuation coefficient and speckle statistics based on the Nakagami and Burr distributions were estimated from the RF data. Data classification of tumor and peri-tumoral regions was performed using a novel three-dimensional (3D) score map based on support vector machine (SVM) modeling. Unlike conventional classifiers that report only a single prediction score, a 3D score map provides a visual representation of the classifier decision space, enabling interpretation of class separation and treatment-induced shifts in multiparametric US measurements. Results: The dataset was split into 10 disjoint partitions (90% training, 10% testing) to compute area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy measures. Actual patient response to NAC was assessed at surgery and categorized as either pathologic complete response (pCR) or non-pCR. Multiparametric US and data classification results at pre-NAC found AUC values of 0.78 after using only tumor information (p < 0.01), which increased to 0.81 with inclusion of peri-tumoral information (p < 0.01). Significant differences in multiparametric US measures from both cancer response types was found after integration of patient data collected at 10% completion of the NAC regimen (i.e., first NAC cycle), yielding an improved AUC of 0.86 (p < 0.001). Conclusions: Multiparametric US imaging with radiomic features from both the tumor and peri-tumoral regions is a promising noninvasive approach for monitoring early breast cancer response to NAC. Full article

The objective of this study is to explore the time-varying shock transmission mechanism between aggregated financial stress indices (FSIs) of developed economies (the U.S., the U.K., the European Union (EU) and Japan) and the emerging economy of China. We employ a novel Time-Varying Parameter Vector Auto-Regression (TVP-VAR)-based “connectedness approach” to capture dynamic shock spillovers without the limitations of arbitrarily chosen rolling windows, loss of observations, or excessive sensitivity to outliers, as it is grounded in a multivariate Kalman filter structure. The aggregated measures of the FSIs of China, the U.S., the U.K., the EU and Japan are incorporated from the Asian Development Bank’s data repository by using time-series observations from January 2010 to September 2023. The findings indicate that the FSI of China is influenced by financial stress shocks originating from Japan (18.35%) and the U.S. (16.86%) the most, whereas the U.K. (EU) contributes to only 8.42% (6.54%) of FSI shocks in China. This research article significantly captures China’s heightened vulnerability to external financial stress shocks from developed economic systems and underscores the critical importance of reinforcing financial resilience, strengthening macro-prudential regulations and early-warning systems, and expanding financial buffers during episodes of trade uncertainty like restrictions on China’s rare earth exports and solar panels, U.S. restrictions on industrial metal imports, Brexit, supply chain disruptions amid COVID-19, and geopolitical uncertainties like the Russia–Ukraine war. Overall, this study provides actionable guidance for mitigating the impact of global financial stresses, improving risk management, and safeguarding economic stability in an increasingly interconnected and volatile international environment. Full article