Search Results (8,519)

We report on a novel approach for the monitoring of tangential laser turning with ultrashort laser pulses. By using an ultra-sonic sensor consisting of a membrane-free optical microphone, the current state of the ablation process can be analyzed, potentially enabling a real-time automated regulation. With its high sensitivity, bandwidth, and sampling rate, it is an ideal tool for process monitoring. The material ablation caused by focused femtosecond laser pulses produces distinct sound waves, which can be detected by the optical microphone. The diameter reduction of a rotating cylindrical workpiece during the laser turning process with ultrashort laser pulses results in a variation in the acoustic emissions. From this, properties like the state of the machining progress can be inferred. Full article

Underwater acoustic monitoring is a critical technology for marine resource development and modern aquaculture. The performance of acoustic sensors directly determines the effectiveness of biological behavior tracking in complex marine environments. This paper presents the design, fabrication, and characterization of a custom hydrophone utilizing a polyvinylidene fluoride (PVDF) piezoelectric film configured in a biomimetic tympanic cavity structure. Operating on the direct piezoelectric effect, the device employs a pre-tensioned PVDF diaphragm integrated with a dedicated charge amplifier circuit to condition high-impedance signals. Laboratory calibrations demonstrate a stable frequency response (with a sensitivity variation within ±1 dB) in the low-frequency range (1–200 Hz) with an average acoustic pressure sensitivity of approximately −206 dB (re 1 V/μPa), providing a higher relative voltage gain compared to a commercial reference hydrophone with a nominal sensitivity of −210 dB (re 1 V/μPa). Furthermore, extensive field evaluations were conducted in a marine net pen to analyze acoustic data across multiple fish feeding scenarios (baseline, pre-feeding, active feeding, and post-feeding). The proposed custom hydrophone exhibited a superior dynamic range and successfully locked onto a 24.4 Hz Golden Pompano (Trachinotus blochii) bioacoustic signature, maintaining remarkable feature stability even after active feeding ceased. This study validates the efficacy of the biomimetic PVDF hydrophone for low-frequency acoustic detection, providing a robust hardware foundation for automated behavioral recognition systems in aquaculture. Full article

Background: Despite significant advances in medical image analysis, automated diagnosis of Multiple Sclerosis (MS) from magnetic resonance imaging (MRI) remains challenging due to the complexity of 3D brain data and the variability of lesion appearance. Objective: In this work, we propose an efficient and optimized feature extraction framework for automated MS diagnosis using FLAIR, T1-, and T2-weighted MRI. The approach enhances Decimal Descriptor Patterns (DDP) by integrating local gradient information, producing a 3D texture representation that is more discriminative and expressive. Methods: The study is divided into two main parts: (i) detection of MS, and (ii) assessment of disease progression in affected patients. In each part, features are extracted from the relevant MRI data and classified using multiple classical machine learning classifiers, including Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), k-Nearest Neighbors (KNN), and Logistic Regression. Furthermore, the performance of the proposed handcrafted feature-based approach was compared to features extracted using a deep learning-based model (vision–language model, VLM), specifically CLIP (Contrastive Language–Image Pretraining), enabling a clear comparison of their performance. To assess robustness and generalizability, two complementary validation strategies were adopted: (i) controlled experiments on the BrainWeb dataset under varying T1/T2 contrast conditions, and (ii) validation on a the real-world FLAIR MRI dataset, reflecting clinically relevant lesion visibility. Results:Gradient-DDP features achieve the best overall performance for MS progression, reaching up to 97% accuracy on T2-weighted MRI with SVM, while LDA and Logistic Regression also remain strong with accuracies around 83–96% on T2. For binary MS detection, the proposed method attains near-perfect results, with up to 99% accuracy on FLAIR (SVM/KNN) and 98% on T2-weighted images across SVM, while other classifiers also maintain high performance above 90%. Conclusions: Gradient-DDP provides strong consistency and transparency, offering an interpretable link between texture patterns and diagnostic outcomes. While VLM features perform well when lesion patterns are clearly defined (e.g., in T2), Gradient-DDP demonstrates greater robustness in more challenging modalities such as Flair, where deep representations may be less stable. Full article

Class imbalance among arrhythmia types and electrocardiogram (ECG) signal complexity present significant challenges for automated ECG-based arrhythmia detection. This research proposes an innovative approach that combines Genetic Algorithm (GA) optimization of Convolutional Neural Network (CNN) hyperparameters with morphological skeletonization of ECG images. The MIT-BIH Arrhythmia Database served as the primary data source, with the ECG signal converted to skeletonized representations emphasizing QRS complex geometry. A GA-optimized model was compared against a heuristic (manual design) baseline to determine optimal kernel and filter configurations. Evaluation emphasized not only overall accuracy but also robust metrics for minority classes. The optimized model achieved 97.26% accuracy, with macro recall improving substantially from 77.36% to 83.10% (+5.74%). These results demonstrate that evolutionary optimization enhances detection sensitivity to subtle geometric patterns, effectively mitigating class imbalance without artificial oversampling techniques. Full article

Background/Objectives: Osteonecrosis of the femoral head (ONFH) is difficult to diagnose, particularly in the early stages, because radiological findings may be subtle. Delayed or inaccurate staging may increase the risk of femoral head collapse and functional loss. Although magnetic resonance imaging is highly sensitive for early-stage lesion detection, interpretation may vary depending on observer experience. Therefore, reliable and explainable automated decision support approaches are needed. Methods: In this study, a deep learning-based approach was proposed to classify ONFH into early and late stages according to the Ficat–Arlet staging system. Stage I–II cases were defined as early-stage, whereas Stage III–IV cases were defined as late-stage. Axial and coronal MR images were evaluated separately to investigate plane-dependent classification performance. The images were converted into a three-channel format, resized to a common spatial resolution, normalized, and augmented during training. Feature extraction was performed using transfer learning with modern convolutional neural network architectures. ConvNeXt Tiny was used as the main classification backbone. Weighted loss was applied to reduce the effect of class imbalance, and the decision threshold was optimized on validation data to reduce missed clinically critical late-stage cases. Results: A dataset collected from the Orthopedics and Traumatology Department of Firat University Hospital was used in the experimental evaluation. The dataset was divided into training and test sets using an 80:20 split, and 10-fold cross-validation was additionally performed to assess model stability. In the hold-out test, the axial plane model achieved 94.51% accuracy, 96.80% sensitivity, 93.49% specificity, 0.9162 F1-score, and 0.981 AUC. In the coronal plane model, 92.84% accuracy, 96.13% sensitivity, 90.96% specificity, 0.9072 F1-score, and 0.988 AUC were obtained. The 10-fold cross-validation results provided a more conservative estimate of generalization performance. Conclusions: The findings indicate that deep learning-based plane-wise analysis of MR images can distinguish early- and late-stage ONFH with high performance. Grad-CAM-based visual explanations showed that the model focused mainly on clinically relevant subchondral and weight-bearing regions of the femoral head. The proposed approach may serve as an explainable decision support tool for reducing observer-dependent variability in clinical staging. Future studies should validate the method using external, multicenter datasets and paired patient-level axial–coronal images. Full article

Valve stiction remains a persistent nonlinear phenomenon in industrial control loops, often inducing limit-cycle oscillations that degrade control performance, compromise stability, and reduce process efficiency. Reliable detection of stiction is therefore essential for condition-based maintenance and improved operational performance. This study proposes a Hybrid Wavelet–Convolutional Neural Network (HW-CNN) framework for the detection of valve stiction in closed-loop systems. The approach employs the continuous wavelet transform (CWT) to generate time–frequency scalograms that preserve localized energy distributions associated with stick–slip behavior, including transient release events and sustained oscillatory patterns. These representations are subsequently processed using a fine-tuned deep residual neural network to enable automated feature extraction and classification. Unlike conventional signal-based or generic time–frequency learning approaches, the proposed framework is designed to retain control system-specific dynamics within the feature representation, thereby improving the separability of stiction-induced signatures under varying operating conditions. The methodology is evaluated using both simulated control loop data and real industrial datasets obtained from the International Stiction Database (ISDB), ensuring evaluation under controlled and practical conditions. To enhance reliability, performance metrics are reported as averages over repeated experimental runs. The results demonstrate that the proposed HW-CNN framework achieves an accuracy of 96.1% and an F1-score of 96.0% on simulated datasets, and 90.4% accuracy with an F1-score of 90.0% on industrial data. Additional analysis indicates that the model maintains consistent detection capability despite increased variability in real-world conditions. Furthermore, interpretability is supported through Grad-CAM analysis, which shows that the network focuses on physically meaningful regions within the scalograms corresponding to known stiction dynamics. The findings confirm that the integration of wavelet-based feature encoding with deep residual learning provides a robust and interpretable framework for valve stiction detection. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder primarily caused by the degeneration of dopamine-producing neurons in the substantia nigra, leading to characteristic motor symptoms such as tremors, rigidity, and bradykinesia, as well as non-motor manifestations including depression, sleep disturbances, and speech impairments. Among these symptoms, speech abnormalities affect approximately 90% of individuals with PD, making acoustic analysis a promising non-invasive cue for early detection. However, subtle speech variations are often imperceptible to the human ear, and speech-only analysis may overlook complementary visual manifestations, such as hypomimia—reduced facial expressivity commonly observed in PD patients. To address these limitations, we propose Parkinson’s Detection via Attentional Fusion Network (PDAF-Net), a novel multimodal deep learning framework for early PD detection that jointly models acoustic and facial dynamic features in a binary classification setting. The proposed architecture consists of a Dual-Stream Feature Encoder (DSFE), with an audio branch based on a one-dimensional convolutional neural network (1D-CNN) and bidirectional long short-term memory (BiLSTM), and a visual branch built upon a two-dimensional convolutional neural network (2D-CNN) and a Transformer encoder. Multimodal integration is achieved through a Cross-Attention-guided Attentional Feature Fusion (CA-AFF) module, which explicitly models bidirectional cross-modal interactions and performs adaptive feature recalibration via an iterative attentional fusion mechanism. We conducted experiments on a self-collected Chinese multimodal dataset comprising 100 PD patients and 100 healthy controls. Although the data are balanced at the subject level, sliding-window segmentation introduces sample-level imbalance; to address this issue, a class-balanced focal loss is employed. Model performance was evaluated using subject-wise five-fold cross-validation. The results demonstrate that PDAF-Net consistently outperforms unimodal baselines across multiple evaluation metrics, achieving an accuracy of 89.3%, an F1-score of 0.884, and an AUC of 0.916. These findings highlight the effectiveness of explicit cross-modal interaction modeling and adaptive feature fusion for improving automated early PD screening in real-world clinical settings. Full article

The use of dried matrix spots (DMSs) has recently re-emerged as a useful sample storage technique and analytical platform along with the increased adoption of and general preference for ambient ionization mass-spectrometric methods. However, challenges associated with precise liquid confinement and sample targeting persist. In this paper, we present a laser micromachining-based approach to prepare DMSs on hydrophobic paper substrates that include visual recognition elements, or reticles, around surface energy traps (SETs). This targeted DMS substrate is combined with direct mass spectrometric analyses, namely liquid microjunction–surface sampling probe–mass spectrometry (LMJ-SSP-MS) and flowing atmospheric-pressure afterglow–mass spectrometry (FAPA-MS). With the laser-based micromachining approach, DMSs flanked by crosshairs for enhanced visualization are prepared on SETs as small as 0.55 mm in diameter, which offers an approximately 12-fold reduction in size compared to traditional DMS preparations. The DMSs prepared on these targeting SETs are demonstrated with the detection of caffeine in model aqueous and artificial urine solutions using LMJ-SSP-MS and FAPA-MS, respectively. With further refinement, this approach could be automated using computer vision and robotics to broaden the scope of DMSs and improve the analytical workflow. Full article

Meat serves as a prime medium for the growth of foodborne pathogens due to its rich protein content and high water activity, contributing significantly to the global burden of foodborne illnesses. This review synthesizes current advances in meat-borne bacterial pathogen detection with particular emphasis on emerging artificial intelligence (AI)-enabled applications. Major pathogens of concern, including Salmonella, Listeria monocytogenes, Escherichia coli, Campylobacter, Clostridium, and Staphylococcus aureus, are examined in relation to their relevance across the meat supply chain. Recent progress in biosensors (clustered regularly interspaced short palindromic repeats), CRISPR-based assays, isothermal amplification, and metagenomics is evaluated alongside the growing role of AI in automating signal interpretation, enhancing image-based diagnostics, and supporting early contamination prediction. AI-based systems have proved 96.4–104% recovery and 100% bacterial capture ability. Embedding AI methods in a wet lab demands technical and logical modeling, as well as learning and calibration decorum. Nonetheless, AI readiness and full-scale application for meat-borne pathogens surveillance are on the way. Furthermore, additional focus is aligned on meat-borne bacterial pathogen genomic databases, i.e., (NCBI Pathogen Detection, EnteroBase, VFDB, ComBase, and GenBank), which serve as critical training resources for AI models for outbreak tracking, virulence profiling, and antimicrobial resistance (AMR) prediction. By integrating molecular methods, genomic surveillance, and AI-driven analytics, this review presents a framework for strengthening meat safety systems. This will improve early detection capabilities and support data-driven public health interventions in the future. Full article

Electrocardiogram (ECG) heartbeat classification is an essential component of automated arrhythmia detection and intelligent cardiac monitoring systems. Traditionally, ECG analysis has depended on manual interpretation by clinicians and conventional machine learning approaches based on handcrafted features, which are labor-intensive, noise-sensitive, and inadequate for capturing complex nonlinear morphological and temporal characteristics of ECG signals. Furthermore, real-world ECG datasets are highly imbalanced, noisy, and exhibit overlapping waveform patterns across heartbeat classes, leading to biased learning, poor minority class detection, and unreliable predictions. To address these challenges, this paper presents a calibration-aware, reliability-oriented evaluation framework for ECG heartbeat classification, incorporating hybrid deep learning architectures that combine convolutional feature extraction, bidirectional GRU-based temporal modeling, and attention mechanisms. The framework assesses probabilistic reliability using calibration metrics, such as the Brier Score and Expected Calibration Error (ECE), rather than explicitly modeling predictive uncertainty methods. Experimental results on the ECG Heartbeat dataset show that CNN achieves the highest testing accuracy (98.44%), largely due to strong performance on the majority class in an imbalanced setting. Among hybrid approaches, a representative hybrid CNN + BiGRU + Attention model attains a competitive accuracy of 97.80%, along with a higher macro F1-score (0.9052), improved training stability, and good calibration behavior (Brier Score = 0.0417, ECE = 0.1023). As the experiments are conducted on preprocessed, fixed-length segments, the results reflect performance under controlled conditions rather than real-world clinical deployment conditions and should therefore be interpreted as a benchmark-level evaluation. Furthermore, no single model consistently outperforms others across all evaluation criteria, as different metrics capture distinct aspects of performance. Full article

Machine learning techniques are increasingly used for computer vision applications in manufacturing. Synthetic data, generated through realistic simulations, are utilized to accelerate the data collection process while optimizing accuracy and precision of ML models. However, in manufacturing there is usually the need for the development of several CV applications that support different production steps. This obstacle requires a systematic approach for generating synthetic datasets that can be used for developing effective CV systems. Hence, this work presents a pipeline for generating photorealistic synthetic datasets, using a set of digital tools such as 3D modeling, photorealistic rendering, automated labeling, and ML training tools. The proposed framework is tested and validated in a robot-assisted packaging case in the dairy industry. The industrial use case provides a pilot-level demonstration that the synthetic dataset generation framework can support the development of CV modules across several production steps and thus it can aid in accelerating commissioning and reconfiguration of industrial automation setups. Moreover, the pilot validation indicates that object detection and recognition models trained on synthetic data can provide sufficient performance for the specific requirements of the examined packaging scenario. Full article

Reinforced Concrete (RC) structures experience progressive degradation over their service life due to mechanical loading and environmental exposure, leading to reduced bearing capacity and compromised structural safety. Incorporating discrete fibers into concrete mitigates crack propagation and enhances ductility, resulting in fiber-reinforced concrete (FRC) with superior fracture energy, durability, and sustainability characteristics. Despite these advantages, research on Structural Health Monitoring (SHM) techniques for FRC elements remains limited. The Electromechanical Impedance (EMI) method, which exploits piezoelectric transducers as both actuators and sensors, offers high sensitivity for detecting early-stage damage by monitoring variations in local mechanical impedance. This study investigates the effectiveness of a deep learning-enabled EMI framework for assessing the structural condition of FRC beams under flexural loading. A one-dimensional convolutional neural network (1D-CNN) is proposed to automatically extract salient features from high-frequency EMI signatures and classify structural health into three predefined states. The model is rigorously evaluated using specimen-invariant validation to ensure generalization across different FRC specimens, addressing a critical limitation of conventional cross-validation approaches in SHM research. Experimental tests on FRC beams instrumented with surface-bonded PZT transducers provide a dataset of 264 EMI responses for training and validation, enabling direct comparison between common and specimen-invariant validation schemes. The results demonstrate the superior robustness of the specimen-invariant approach and confirm the capability of the proposed 1D-CNN to identify flexural damage progression in FRC elements accurately. An ablation study further highlights the contribution of each architectural component to overall model performance. The findings underscore the potential of integrating EMI-based sensing with advanced deep learning models for reliable, automated, and scalable SHM of next-generation resilient concrete infrastructures. Full article

Loess landslides in northwestern China are highly unstable and difficult to distinguish due to sparse vegetation and their spectral and morphological similarity to the surrounding terrain. These landslides demonstrate considerable diversity in manifestation, encompassing shallow translational slides, small-scale features, partially obscured formations, and instances with irregular or poorly defined boundaries. To address the above issues, we propose MPES-YOLO, a multi-scale lightweight YOLO-based framework with selective edge enhancement to detect loess landslides. This model is based on the YOLOv8 architecture and incorporates a multi-scale partial convolution and exponential moving average (MPCE) module to improve multi-scale feature representation while reducing computational cost and enhancing small-target sensitivity. Additionally, to address ambiguous boundaries, a selective edge enhancement (SEE) module is introduced to extract authentic object edges from original images and inject them into key training layers, improving boundary perception. Finally, SIoU is adopted to improve geometric consistency for irregular landslide boundary localization. This paper first verified the basic detection performance of MPES-YOLO on the publicly available Bijie landslide dataset. Then, an experimental study was conducted in the loess landslides of Yan’an City, Shaanxi Province. The mAP@0.5 was 91.9%, and the parameter quantity was reduced by 23.3% compared with the baseline model. A generalization experiment was also carried out on the landslides in the Ningxia region, with the mAP@0.5 being 97.4%. The results show that MPES-YOLO achieves a strong balance between detection accuracy and computational efficiency, providing an effective and scalable solution for automated loess landslide detection and geological disaster early warning. Full article

To address the key limitations of traditional automated landslide detection methods—namely their reliance on large training datasets, insufficient detection accuracy, and high false positive rates—this study proposes an InSAR-based automated landslide detection approach integrating multi-weight factor coupling, referred to as an Improved Hot Spot Analysis (IHSA) method. Built upon InSAR-derived surface deformation data, the proposed method optimizes the hotspot detection model through a spatial weighting matrix that incorporates multi-feature fusion. Morphological processing is further applied to refine landslide boundaries. Validation against manually interpreted ground truth data demonstrates that the proposed method achieves a precision of 90.20%, representing an improvement of 53.61 percentage points over the conventional hotspot analysis method, while maintaining a stable recall rate of 92.00%. The extracted landslide boundaries exhibit high consistency with manual interpretation results, effectively overcoming common issues in traditional approaches such as fragmented outputs and internal voids. This study provides an efficient, training-free solution for large-scale early identification of potential landslides, offering critical methodological support and data foundations for regional landslide detection and hazard mitigation. Full article

Submesoscale oceanic eddies play a crucial role in ocean dynamics and climate systems, while Synthetic Aperture Radar (SAR) offers distinct advantages for observing these fine-scale phenomena; the advancement of automated detection algorithms is currently hindered by the lack of publicly available, high-quality benchmark datasets. To address this gap, this paper constructs a universal benchmark dataset for submesoscale eddies and presents an improved anchor-free object detection framework based on Fully Convolutional One-Stage (FCOS). We propose two key innovations: (1) a Deep Fusion Feature Pyramid Network (DF-FPN) that integrates adaptive multi-scale feature fusion directly into the pyramid construction process through deep fusion Adaptive Spatial Feature Fusion (ASFF) modules, enabling bidirectional feature enhancement and global context-aware fusion and (2) a Pixel-level Statistical Description Learning (PSDL) module that enhances feature representation by learning statistical descriptors across multiple scales. The DF-FPN replaces traditional staged optimization with an intrinsic deep fusion paradigm, significantly improving feature quality. Extensive experiments on our constructed dataset demonstrate that our method achieves 66.6% mAP, 91.3% AP₅₀, and 80.5% AP₇₅. These results represent a substantial improvement over the FCOS baseline and outperform other state-of-the-art detectors, providing a robust and efficient solution for operational submesoscale eddy monitoring in SAR imagery. Enhanced detection capacity of this kind offers a critical observational foundation for advancing research on upper-ocean nutrient transport, carbon cycle dynamics, and the dispersion of marine pollutants, thereby supporting broader environmental monitoring and climate-related objectives. Full article