Search Results (16)

Glove-based human–machine interfaces (HMIs) offer a natural, intuitive way to capture finger motions for gesture recognition, virtual interaction, and robotic control. However, many existing systems suffer from complex fabrication, limited sensitivity, and reliance on external power. Here, we present a flexible, self-powered glove HMI based on a minimalist triboelectric nanogenerator (TENG) sensor composed of a conductive fabric electrode and textured Ecoflex layer. Surface micro-structuring via 3D-printed molds enhances triboelectric performance without added complexity, achieving a peak power density of 75.02 μW/cm² and stable operation over 13,000 cycles. The glove system enables real-time LED brightness control via finger-bending kinematics and supports intelligent recognition applications. A convolutional neural network (CNN) achieves 99.2% accuracy in user identification and 97.0% in object classification. By combining energy autonomy, mechanical simplicity, and machine learning capabilities, this work advances scalable, multi-functional HMIs for applications in assistive robotics, augmented reality (AR)/(virtual reality) VR environments, and secure interactive systems. Full article

Carbon-fiber-reinforced polymer (CFRP) preforms are vital for high-performance composite structures, yet the real-time detection of surface yarn alignment defects is hindered by complex textures. This study introduces a novel machine vision framework to enable the precise, real-time identification of such defects in CFRP preforms. We proposed obtaining the frequency spectrum by removing the zero-frequency component from the projection curve of images of carbon fiber fabric, aiding in the identification of the cycle number for warp and weft yarns. A texture structure recognition method based on the artistic conception drawing (ACD) revert is applied to distinguishing the complex and diverse surface texture of the woven carbon fabric prepreg from potential surface defects. Based on the linear discriminant analysis for defect area threshold extraction, a defect boundary tracking algorithm rule was developed to achieve defect localization. Using over 1500 images captured from actual production lines to validate and compare the performance, the proposed method significantly outperforms the other inspection approaches, achieving a 97.02% recognition rate with a 0.38 s per image processing time. This research contributes new scientific insights into the correlation between yarn alignment anomalies and a machine-vision-based texture analysis in CFRP preforms, potentially advancing our fundamental understanding of the defect mechanisms in composite materials and enabling data-driven quality control in advanced manufacturing. Full article

An improved YOLOv8n seamless fabric defect detection model is proposed to solve the current issues in seamless fabric defects in factories in this paper. The improvement in this paper first introduces the SPPF_LSKA module, which not only optimizes the extraction of multi-scale features but also enhances the adaptability of the model in detecting defects of different sizes by improving the feature fusion mechanism, enabling efficient recognition of both large-sized and small-sized defects. Secondly, the CARAFE upsampling method is used to adaptively learn the relationship between pixels, which not only reduces information loss but also improves the reconstruction quality of feature maps, which is crucial for capturing complex textures and subtle defects of seamless fabrics. In addition, adding a small object detection layer particularly improves the detection accuracy of the model for small-sized defects, making it no longer limited to traditional models when dealing with high-density fabrics or small defects. Finally, integrating OREPA technology significantly reduces computational complexity, reduces redundant computing burden, and accelerates the training process by optimizing the model structure. The experimental results show that the precision, recall, and mAP@0.5 of the model on the seamless fabric defect dataset have improved by 7.3%, 8.5%, and 5.1%, respectively, compared to the baseline model YOLOv8n. Future research aims to explore the application of the model further in practical scenarios and complete the actual deployment of the seamless fabric defect detection system. Full article

Humans possess an innate ability to perceive a wide range of objects through touch, which allows them to interact effectively with their surroundings. Similarly, tactile perception in artificial sensory systems enables the acquisition of object properties, human physiological signals, and environmental information. Biomimetic tactile sensors, as an emerging sensing technology, draw inspiration from biological systems and exhibit high sensitivity, rapid response, multimodal perception, and stability. By mimicking biological mechanisms and microstructures, these sensors achieve precise detection of mechanical signals, thereby paving the way for advancements in tactile sensing applications. This review provides an overview of key sensing mechanisms, microstructure designs, and advanced fabrication techniques of biomimetic tactile sensors. The system architecture design of biomimetic tactile sensing systems is also explored. Furthermore, the review highlights significant applications of these sensors in recent years, including texture recognition, human health detection, and human–machine interaction. Finally, the key challenges and future development prospects related to biomimetic tactile sensors are discussed. Full article

In modern knitted garment production, accurate identification of fabric texture is crucial for enabling automation and ensuring consistent quality control. Traditional manual recognition methods not only demand considerable human effort but also suffer from inefficiencies and are prone to subjective errors. Although machine learning-based approaches have made notable advancements, they typically rely on manual feature extraction. This dependency is time-consuming and often limits recognition accuracy. To address these limitations, this paper introduces a novel model, called the Differentiated Leaning Weighted DenseNet (DLW-DenseNet), which builds upon the DenseNet architecture. Specifically, DLW-DenseNet introduces a learnable weight mechanism that utilizes channel attention to enhance the selection of relevant channels. The proposed mechanism reduces information redundancy and expands the feature search space of the model. To maintain the effectiveness of channel selection in the later stages of training, DLW-DenseNet incorportes a differentiated learning strategy. By assigning distinct learning rates to the learnable weights, the model ensures continuous and efficient channel selection throughout the training process, thus facilitating effective model pruning. Furthermore, in response to the absence of publicly available datasets for fabric texture recognition, we construct a new dataset named KF9 (knitted fabric). Compared to the fabric recognition network based on the improved ResNet, the recognition accuracy has increased by five percentage points, achieving a higher recognition rate. Experimental results demonstrate that DLW-DenseNet significantly outperforms other representative methods in terms of recognition accuracy on the KF9 dataset. Full article

In the process of fabric production, various types of defects affect the quality of a fabric. However, due to the wide variety of fabric defects, the complexity of fabric textures, and the concealment of small target defects, current fabric defect detection algorithms suffer from issues such as having a slow detection speed, low detection accuracy, and a low recognition rate of small target defects. Therefore, developing an efficient and accurate fabric defect detection system has become an urgent problem that needs to be addressed in the textile industry. Addressing the aforementioned issues, this paper proposes an improved YOLOv8n-LAW algorithm based on the YOLOv8n algorithm. First, LSKNet attention mechanisms are added to both ends of the C2f module in the backbone network to provide a broader context area, enhancing the algorithm’s feature extraction capability. Next, the PAN-FPN structure of the backbone network is replaced by the AFPN structure, so that the different levels of features of the defects are closer to the semantic information in the progressive fusion. Finally, the CIoU loss is replaced with the WIoU v3 loss, allowing the model to dynamically adjust gradient gains based on the features of fabric defects, effectively focusing on distinguishing between defective and non-defective regions. The experimental results show that the improved YOLOv8n-LAW algorithm achieved an accuracy of 97.4% and a detection speed of 46 frames per second, while effectively increasing the recognition rate of small target defects. Full article

Image processing-based pattern recognition applications often use texture features to identify structural characteristics. Existing algorithms, including statistical, structural, model-based, and transform-based, lack expertise for specialized features extracted around potentially defective regions. This paper proposes an attention-guided deep-learning texture feature extraction algorithm that can learn features at various regions with varying complexities, addressing the lack of expertise in existing techniques. This approach can be used for applications such as minor fabric defects and hairline faults in PCB manufacturing. Full article

This paper presents a turnkey integrated system that can be operated in real time for real textile manufacturers. Eight types of defects in woven fabric, including stain, broken end, broken weft, hole, nep, double pick, kinky weft and float can be recognized and classified. First, an image is captured by a CMOS industrial camera with a pixel size of 4600 × 600 above the batcher at 20 m/min. After that, the four-stage image processing procedure is applied to detect defects and for classification. Stage 1 is image pre-processing; the filtration of the image noise is carried out by a Gaussian filter. The light source is corrected to reduce the uneven brightness resulting from halo formation. The improved mask dodging algorithm is used to reduce the standard deviation of the corrected original image. Afterwards, the background texture is filtered by an averaging filter, and the mean value is corrected for histogram shifting, so that this system is robust to the texture and color changes of woven fabric. The binary segmentation threshold is determined using the mean value and standard deviation of an image with a normal sample. Stage 2 uses adaptive binarization for separation of the background and defects and to filter the noise. In Stage 3, the morphological processing is used before the defect contour is circled, i.e., four features of each block, including the defect area, the aspect ratio of the defect, the average gray level of the defect and the defect orientation, which are calculated according to the range of contour. The image defect recognition dataset consists of 2246 images. The results show that the detection success rate is 96.44%, and the false alarm rate is 3.21%. In Stage 4, the defect classification is implemented. The support vector machine (SVM) is used for classification, 230 defect images are used as training samples, and 206 are used as test samples. The experimental results show that the overall defect recognition rate is 96.60%, providing that the software and hardware equipment designed in this study can implement defect detection and classification for woven fabric effectively. Full article

Thermal comfort is associated with clothing insulation, conveying a level of satisfaction with the thermal surroundings. Besides, clothing insulation is commonly associated with indoor thermal comfort. However, clothing classification in smart homes might save energy when the end-user wears appropriate clothes to save energy and obtain thermal comfort. Furthermore, object detection and classification through Convolutional Neural Networks has increased over the last decade. There are real-time clothing garment classifiers, but these are oriented towards single garment recognition for texture, fabric, shape, or style. Consequently, this paper proposes a CNN model classification for the implementation of these classifiers on cameras. First, the Fashion MNIST was analyzed and compared with the VGG16, Inceptionvv4, TinyYOLOv3, and ResNet18 classification algorithms to determine the best clo classifier. Then, for real-time analysis, a new dataset with 12,000 images was created and analyzed with the YOLOv3 and TinyYOLO. Finally, an Azure Kinect DT was employed to analyze the clo value in real-time. Moreover, real-time analysis can be employed with any other webcam. The model recognizes at least three garments of a clothing ensemble, proving that it identifies more than a single clothing garment. Besides, the model has at least 90% accuracy in the test dataset, ensuring that it can be generalized and is not overfitting. Full article

The microbial communities inferred in silica sinter rocks, based on multiscale morphological features (fabrics and textures) and the presence of lipid biomarkers and their carbon isotopic composition, are evaluated in the Krýsuvík geothermal area of Iceland. Close to vent environments (T > 75 °C and pH 1.7‒3), stream floors are capped with homogeneous vitreous crusts and breccia levels, with no distinct recognizable silicified microbes. About 4 m far from the vents (T 75‒60 °C and pH 3‒6) and beyond (T < 60 °C and pH 6‒7.6), microbial sinters, including wavy and palisade laminated and bubble fabrics, differ between abandoned meanders and desiccated ponds. Fabric and texture variances are related to changes in the ratio of filament/coccoid silicified microbes and associated porosity. Coatings of epicellular silica, less than 2 µm thick, favor identification of individual microbial filaments, whereas coalescence of opal spheres into agglomerates precludes recognition of original microbial textures and silicified microbes. Episodic fluctuations in the physico-chemical conditions of surface waters controlled the acidic hydrolysis of biomarkers. Wavy laminated fabrics from pond margins comprise fatty acids, mono- and dialkyl glycerol, mono- and diethers, monoalkyl glycerol esters and small traces of 10-methyl branched C₁₆ and C₁₈ fatty acids and archaeol, indicative of intergrowths of cyanobacteria, Aquificales, and sulfate reducing bacteria and methanogenic archaea. In contrast, wavy laminated fabrics from abandoned meanders and palisade laminated fabrics from ponds differ in their branched fatty acids and the presence vs. absence of bacteriohopanetetrol, reflecting different cyanobacterial contributions. δ¹³C values of biomarkers range from −22.7 to −32.9‰, but their values in the wavy (pond) and bubble fabrics have much wider ranges than those of the wavy (meander), palisade, and vitreous fabrics, reflecting dissolved inorganic carbon (DIC) sources and a decrease in ¹³C downstream outflow channels, with heavier values closer to vents and depleted values in ponds. Full article

The weave pattern (texture) of woven fabric is considered to be an important factor of the design and production of high-quality fabric. Traditionally, the recognition of woven fabric has a lot of challenges due to its manual visual inspection. Moreover, the approaches based on early machine learning algorithms directly depend on handcrafted features, which are time-consuming and error-prone processes. Hence, an automated system is needed for classification of woven fabric to improve productivity. In this paper, we propose a deep learning model based on data augmentation and transfer learning approach for the classification and recognition of woven fabrics. The model uses the residual network (ResNet), where the fabric texture features are extracted and classified automatically in an end-to-end fashion. We evaluated the results of our model using evaluation metrics such as accuracy, balanced accuracy, and F1-score. The experimental results show that the proposed model is robust and achieves state-of-the-art accuracy even when the physical properties of the fabric are changed. We compared our results with other baseline approaches and a pretrained VGGNet deep learning model which showed that the proposed method achieved higher accuracy when rotational orientations in fabric and proper lighting effects were considered. Full article

Hydrothermal diagenesis during the Zagros Orogeny produced three phases of saddle dolomites (SD1, SD2, and SD3) and two phases of blocky calcites (CI and CII) in the studied sections of Bekhme Formation (Fm) (Campanian–Maastrichtian). Field observations, as well as petrographic, cathodoluminescence (CL), Scanning Elecron Microscope (SEM), and oxygen–carbon isotope analyses, indicated that the unit went through multiple submergence–emergence phases after or during hydrothermal diagenesis. These phases resulted in a characteristic calcretized 2–6-m-thick layer within the Bekhme Fm. Several pedogenic textures (e.g., alveolar, pisolite, and laminar fabric microfeatures) were observed. Strong evidence of microbial alteration and diagenesis in this formation brings new insights into its depositional history. The microbial activities developed on the original mineral surface were associated with a great variety of processes including dissolution, re-precipitation, replacement, open-space fillings, microporosity development, grain bridging, and micritization. Probable oxalate pseudomorphs embedded in these fabrics and regular filaments preserved along crystal boundaries suggest the activity of fungi, while frequent coccoidal, rod-like, and chain-like forms attached to the surfaces of dolomitic and calcitic crystals point to bacterial colonization. Extracellular polymeric substance (EPS) was often visible with fungal and bacterial forms. These features, together with stable isotope data, invoke that near-surface conditions occurred sporadically in the Bekhme Fm after the first generation of hydrothermal dolomitization. These new findings allow recognition of unreported sedimentological phases based on new evidence in the Spelek–Sulauk area during the Upper Cretaceous. Full article

The primary stratigraphic fabric of a chaotic rock unit in the Zermatt Saas ophiolite of the Western Alps was reworked by a polyphase Alpine tectonic deformation. Multiscalar structural criteria demonstrate that this unit was deformed by two ductile subduction-related phases followed by brittle-ductile then brittle deformation. Deformation partitioning operated at various scales, leaving relatively unstrained rock domains preserving internal texture, organization, and composition. During subduction, ductile deformation involved stretching, boudinage, and simultaneous folding of the primary stratigraphic succession. This deformation is particularly well-documented in alternating layers showing contrasting deformation style, such as carbonate-rich rocks and turbiditic serpentinite metasandstones. During collision and exhumation, deformation enhanced the boudinaged horizons and blocks, giving rise to spherical to lozenge-shaped blocks embedded in a carbonate-rich matrix. Structural criteria allow the recognition of two main domains within the chaotic rock unit, one attributable to original broken formations reflecting turbiditic sedimentation, the other ascribable to an original sedimentary mélange. The envisaged geodynamic setting for the formation of the protoliths is the Jurassic Ligurian-Piedmont ocean basin floored by mostly serpentinized peridotites, intensely tectonized by extensional faults that triggered mass transport processes and turbiditic sedimentation. Full article

The occurrence of seep-carbonates associated with shallow gas hydrates is increasingly documented in modern continental margins but in fossil sediments the recognition of gas hydrates is still challenging for the lack of unequivocal proxies. Here, we combined multiple field and geochemical indicators for paleo-gas hydrate occurrence based on present-day analogues to investigate fossil seeps located in the northern Apennines. We recognized clathrite-like structures such as thin-layered, spongy and vuggy textures and microbreccias. Non-gravitational cementation fabrics and pinch-out terminations in cavities within the seep-carbonate deposits are ascribed to irregularly oriented dissociation of gas hydrates. Additional evidences for paleo-gas hydrates are provided by the large dimensions of seep-carbonate masses and by the association with sedimentary instability in the host sediments. We report heavy oxygen isotopic values in the examined seep-carbonates up to +6‰ that are indicative of a contribution of isotopically heavier fluids released by gas hydrate decomposition. The calculation of the stability field of methane hydrates for the northern Apennine wedge-foredeep system during the Miocene indicated the potential occurrence of shallow gas hydrates in the upper few tens of meters of sedimentary column. Full article