Complex Texture Research Articles

Segmentation of stroke lesions using transformers-augmented MRI analysis.

Accurate segmentation of chronic stroke lesions from mono-spectral magnetic resonance imaging scans (e.g., T1-weighted images) is a difficult task due to the arbitrary shape, complex texture, variable size and intensities, and varied locations of the lesions. Due to this inherent spatial heterogeneity, existing machine learning methods have shown moderate performance for chronic lesion delineation. In this study, we introduced: (1) a method that integrates transformers' deformable feature attention mechanism with convolutional deep learning architecture to improve the accuracy and generalizability of stroke lesion segmentation, and (2) an ecological data augmentation technique based on inserting real lesions into intact brain regions. Our combination of these two approaches resulted in a significant increase in segmentation performance, with a Dice index of 0.82 (±0.39), outperforming the existing methods trained and tested on the same Anatomical Tracings of Lesions After Stroke (ATLAS) 2022 dataset. Our method performed relatively well even for cases with small stroke lesions. We validated the robustness of our method through an ablation study and by testing it on new unseen brain scans from the Ischemic Stroke Lesion Segmentation (ISLES) 2015 dataset. Overall, our proposed approach of transformers with ecological data augmentation offers a robust way to delineate chronic stroke lesions with clinically relevant accuracy. Our method can be extended to other challenging tasks that require automated detection and segmentation of diverse brain abnormalities from clinical scans.

Dynamic Loss Weighting for Multiorgan Segmentation in Medical Images.

Deep neural networks often suffer from performance inconsistency for multiorgan segmentation in medical images; some organs are segmented far worse than others. The main reason might be organs with different levels of learning difficulty for segmentation mapping, due to variations such as size, texture complexity, shape irregularity, and imaging quality. In this article, we propose a principled class-reweighting algorithm, termed dynamic loss weighting, which dynamically assigns a larger loss weight to organs if they are discriminated as more difficult to learn according to the data and network's status, for forcing the network to learn from them more to maximally promote the performance consistency. This new algorithm uses an extra autoencoder to measure the discrepancy between the segmentation network's output and the ground truth and dynamically estimates the loss weight of organs per the contribution of the organ to the new updated discrepancy. It can capture the variation in organs' learning difficult during training, and it is neither sensitive to data's property nor dependent on human priors. We evaluate this algorithm in two multiorgan segmentation tasks: abdominal organs and head-neck structures, on publicly available datasets, with positive results obtained from extensive experiments which confirm the validity and effectiveness. Source codes are available at: https://github.com/YouyiSong/Dynamic-Loss-Weighting.

DEEP NEURAL MULTI WAVELET SEGMENTATION AND RESNET-50 IMAGE CLASSIFICATION (DNMWS-RESNET-50IC) BASED EARLY GLAUCOMA DETECTION

Glaucoma, a leading cause of irreversible blindness worldwide, results from progressive damage to the optic nerve, often linked to elevated Intra Ocular Pressure (IOP). Early detection is critical for preventing vision loss, yet traditional diagnostic methods can be limited in accessibility and effectiveness, particularly in the early stages of the disease. Addressing the need for early detection, we propose the Deep Neural Multi-Wavelet Segmentation and ResNet-50 Image Classification (DNMWS-ResNet-50IC) method, specifically designed to detect glaucoma at an early stage using retinal fundus images. In this work, we apply Anisotropic Gaussian Filtering for the preprocessing of retinal fundus images, effectively reducing image noise while preserving the original quality of the image pixels by creating an adaptive window size and scale space. We then utilize multi- wavelet-based image segmentation, leveraging wavelet transforms to analyze and decompose the image into its various frequency components. This technique is particularly advantageous for managing images with complex structures and textures. Subsequently, the segmented features are classified using the ResNet-50 model, which categorizes the images as normal, abnormal, or indicative of early- stage glaucoma. The effectiveness of the proposed method is assessed by measuring three key performance indicators sensitivity, specificity, and accuracy on digital retinal images from the HRF image database. Additionally, the model's performance is further evaluated on a separate test set, considering metrics such as accuracy, precision, recall, and prediction time.

HDhuman: High-Quality Human Novel-View Rendering From Sparse Views.

In this paper, we aim to address the challenge of novel view rendering of human performers that wear clothes with complex texture patterns using a sparse set of camera views. Although some recent works have achieved remarkable rendering quality on humans with relatively uniform textures using sparse views, the rendering quality remains limited when dealing with complex texture patterns as they are unable to recover the high-frequency geometry details that are observed in the input views. To this end, we propose HDhuman, which uses a human reconstruction network with a pixel-aligned spatial transformer and a rendering network with geometry-guided pixel-wise feature integration to achieve high-quality human reconstruction and rendering. The designed pixel-aligned spatial transformer calculates the correlations between the input views and generates human reconstruction results with high-frequency details. Based on the surface reconstruction results, the geometry-guided pixel-wise visibility reasoning provides guidance for multi-view feature integration, enabling the rendering network to render high-quality images at 2k resolution on novel views. Unlike previous neural rendering works that always need to train or fine-tune an independent network for a different scene, our method is a general framework that is able to generalize to novel subjects. Experiments show that our approach outperforms all the prior generic or specific methods on both synthetic data and real-world data. Source code and test data will be made publicly available for research purposes at http://cic.tju.edu.cn/faculty/likun/projects/HDhuman/index.html.

Improved FasterViT model for citrus disease diagnosis

Despite advances in deep learning for plant leaf disease recognition, accurately distinguishing morphological features under varying environmental conditions continues to pose significant challenges. Traditional deep learning models often fail to effectively merge local and global information, especially in small-scale datasets, impairing performance and elevating training costs. Focusing on citrus diseases, we propose an improved FasterViT Model, an advanced hybrid CNN-ViT framework that builds upon the FasterViT model. The proposed model seamlessly integrates CNN's rapid local learning capabilities with ViT's global information processing strength, thereby effectively extracting complex textures and morphological features from images. Cross-stage alternating Mixup and Cutout methods are strategically employed to enhance model robustness and generalization capabilities, particularly valuable for fast learning on small-scale datasets by simulating a more diverse training environment. Triplet Attention and AdaptiveAvgPool mechanisms are utilized to reduce training costs and optimize training performance. The proposed model is tested on both our specially constructed small-scale citrus disease dataset called in-field small dataset and the comprehensive PlantVillage dataset. The experimental results demonstrated that the model exhibits the capability of fast learning and adaptation to small sample training in plant disease detection tasks, and demonstrates the effectiveness of our improvement approach in improving model accuracy and reducing training costs. Additionally, its exemplary performance in transfer learning scenarios underscores its adaptability and broad applicability. This study not only highlights the efficacy of the improved FasterViT model in addressing the complexities of plant disease image recognition but also pioneers a new paradigm for developing efficient, scalable, and robust classification systems.

Border-Enhanced Triple Attention Mechanism for High-Resolution Remote Sensing Images and Application to Land Cover Classification

With the continuous development and popularization of remote sensing technology, remote sensing images have been widely used in the field of land cover classification. Since remote sensing images have complex spatial structure and texture features, it is becoming a challenging problem to accurately categorize them. Land cover classification has practical application value in various fields, such as environmental monitoring and protection, urban and rural planning and management, and climate change research. In recent years, remote sensing image classification methods based on deep learning have been rapidly developed, in which semantic segmentation technology has become one of the mainstream methods for land cover classification using remote sensing image. Traditional semantic segmentation algorithms tend to ignore the edge information, resulting in poor classification of the edge part in land cover classification, and there are numerous attention mechanisms to make improvements for these problems. In this paper, a triple attention mechanism, BETAM (Border-Enhanced Triple Attention Mechanism), for edge feature enhancement of high-resolution remote sensing images is proposed. Furthermore, a new model on the basis of the semantic segmentation network model DeeplabV3+ is also introduced, which is called DeepBETAM. The triple attention mechanism BETAM is able to capture feature dependencies in three dimensions: position, space, and channel, respectively. Through feature importance weighting, modeling of spatial relationships, and adaptive learning capabilities, the model BETAM pays more attention to edge features, thus improving the accuracy of edge detection. A remote sensing image dataset SMCD (Subject Meticulous Categorization Dataset) is constructed to verify the robustness of the attention mechanism BETAM and the model DeepBETAM. Extensive experiments were conducted on the two self-built datasets FRSID and SMCD. Experimental results showed that the mean Intersection over Union (mIoU), mean Pixel Accuracy (mPA), and mean Recall (mRecall) of DeepBETAM are 63.64%, 71.27%, and 71.31%, respectively. These metrics are superior to DeeplabV3+, DeeplabV3+(SENet), DeeplabV3+(CBAM), DeeplabV3+(SAM), DeeplabV3+(ECANet), and DeeplabV3+(CAM), which are network models that incorporate different attention mechanisms. The reason is that BETAM has better edge segmentation results and segmentation accuracy. Meanwhile, on the basis of the self-built dataset, the four main classifications of buildings, cultivated land, water bodies and vegetation were subdivided and detected, and good experimental results were obtained, which verified the robustness of the attention mechanism BETAM and the model DeepBETAM. The method has broad application prospects and can provide favorable support for research and application in the field of surface classification.

A Super-Resolution and 3D Reconstruction Method Based on OmDF Endoscopic Images.

In the field of endoscopic imaging, challenges such as low resolution, complex textures, and blurred edges often degrade the quality of 3D reconstructed models. To address these issues, this study introduces an innovative endoscopic image super-resolution and 3D reconstruction technique named Omni-Directional Focus and Scale Resolution (OmDF-SR). This method integrates an Omnidirectional Self-Attention (OSA) mechanism, an Omnidirectional Scale Aggregation Group (OSAG), a Dual-stream Adaptive Focus Mechanism (DAFM), and a Dynamic Edge Adjustment Framework (DEAF) to enhance the accuracy and efficiency of super-resolution processing. Additionally, it employs Structure from Motion (SfM) and Multi-View Stereo (MVS) technologies to achieve high-precision medical 3D models. Experimental results indicate significant improvements in image processing with a PSNR of 38.2902 dB and an SSIM of 0.9746 at a magnification factor of ×2, and a PSNR of 32.1723 dB and an SSIM of 0.9489 at ×4. Furthermore, the method excels in reconstructing detailed 3D models, enhancing point cloud density, mesh quality, and texture mapping richness, thus providing substantial support for clinical diagnosis and surgical planning.

Neural network-based reversible data hiding for medical image

Pixel prediction is an important issue in the field of reversible data hiding. Neural networks are gradually used to improve the accuracy of pixel prediction owing to their excellent performance. However, current neural network-based pixel predictors are designed for natural images and do not consider the characteristics of medical images. Therefore, in this paper, we propose a dual-branch neural network-based reversible data hiding scheme for medical images. Detailedly, considering the characteristics of medical images, in which complex and smooth regions are more clearly distinguished, we present a clustering method to classify pixels into three classes according to their complexities, and generate masks to assist pixel prediction. Then, in the prediction stage, a dual-branch neural network-based pixel predictor is designed to extract unique and shared features, and a convolutional block attention module is used to optimize the extracted features. Finally, in the embedding stage, considering the characteristics of region of interest (ROI) and region of non-interest (NROI) in medical images, we design a class-based embedding algorithm, which can prioritize embedding data into NROI with low complexity and then sequentially into low texture complexity region and high texture complexity region of ROI. Experimental results show that our scheme can achieve better performance of pixel prediction and data embedding than existing state-of-the-art works.

Image inpainting algorithm based on double curvature-driven diffusion model with P-Laplace operator.

The method of partial differential equations for image inpainting achieves better repair results and is economically feasible with fast repair time. Addresses the inability of Curvature-Driven Diffusion (CDD) models to repair complex textures or edges when the input image is affected by severe noise or distortion, resulting in discontinuous repair features, blurred detail textures, and an inability to deal with the consistency of global image content, In this paper, we have the CDD model of P-Laplace operator term to image inpainting. In this method, the P-Laplace operator is firstly introduced into the diffusion term of CDD model to regulate the diffusion speed; then the improved CDD model is discretized, and the known information around the broken region is divided into two weighted average iterations to get the inpainting image; finally, the final inpainting image is obtained by weighted averaging the two image inpainting images according to the distancing. Experiments show that the model restoration results in this paper are more rational in terms of texture structure and outperform other models in terms of visualization and objective data. Comparing the inpainting images with 150, 1000 and 100 iterations respectively, Total Variation(TV) model and the CDD model inpainting algorithm always has inpainting traces in details, and TV model can't meet the visual connectivity, but the algorithm in this paper can remove the inpainting traces well, TV model and the CDD model inpainting algorithm always have inpainting traces in details, and TV model can't meet the visual connectivity, but the algorithm in this paper can remove the inpainting traces well. Of the images used for testing, the highest PSNR reached 38.7982, SSIM reached 0.9407, and FSIM reached 0.9781, the algorithm not only inpainting the effect and, but also has fewer iterations.

Spatial-frequency gradient fusion based model augmentation for high transferability adversarial attack

In recent years, deep learning has gained widespread application across diverse fields, including image classification and machine translation. Nevertheless, the emergence of adversarial examples has revealed a vulnerability of deep learning techniques to potential attacks. Despite the introduction of diverse adversarial attack methods, they are still constrained by certain limitations. Specifically, global perturbations are easily discernible by humans, resulting in poor imperceptibility. Additionally, current methods encounter limited transferability due to their reliance on attacking specific models. To address these challenges, this paper proposed a spatial-frequency gradient fusion based model augmentation for adversarial attack. First, we utilize a Gaussian convolution kernel to pinpoint regions in images that exhibit significant pixel variation, aiming to generate locally imperceptible perturbations undetectable by humans. These areas, which we consider as complex texture regions, are ideal for adding perturbations. Then, we design a perceptual similarity constraint to regulate the generation of perturbations in smooth texture regions. Subsequently, to further enhance the transferability of our method, we propose a spatial-frequency gradient fusion based model augmentation, applying random spectral transformation to shift into the frequency domain for narrowing the differences between models. Additionally, we design complex region scaling transformations in the spatial domain, aimed at capturing common features shared across models. Finally, we integrate the gradients from both the spatial and frequency domains, leveraging the strengths of both to empower attack models in effectively simulating the target model. Extensive experiments conducted on ImageNet and CIFAR-10 datasets have shown that our method attains a remarkable black-box attack success rate of up to 93.1%, with a perceptual loss reduction of approximately 8.39%, while also exhibiting stronger robustness.

Numerical simulation and thermodynamic test analysis of plate heat exchanger based on topology optimization

Efficient heat transfer in PHEs relies on optimizing flow distribution to minimize thermal dead zones. Existing studies have predominantly focused on single-phase fluids as working media and employed density-based methods for structural optimization of microchannel heat exchangers at small scales. In this study, the Brinkman term from the porous media flow model is introduced in an innovative manner, along with the utilization of a material density interpolation model, which builds upon the density-based approach. A novel two-phase fluid topology optimization method is proposed, specifically tailored for large-scale PHE operating conditions, resulting in the successful design of a new PHE topology. To compare the performance of the two heat exchangers, finite element simulation, visualization experiments, and thermodynamic performance testing are conducted. The simulation results reveal that the heat transfer capacity of the topological PHE is 810 W with pressure drops of 18.8 kPa, while the dimple PHE exhibits heat transfer capacity of 652 W with pressure drops of 25.9 kPa. Compared to the dimple PHE, the topological PHE demonstrates 24.2 % improvement in heat transfer performance and 27.8 % reduction in pressure drop. The dimensionless comprehensive heat transfer coefficient (j/f) indicates that the overall performance of the topological PHE significantly surpasses that of the traditional PHE. Furthermore, the visualization experiment results demonstrate that the complex texture structure of the topological PHE effectively induces fluid motion, compelling the fluid to flow along the texture direction. This significantly mitigates the issue of uneven flow within the PHE, thus enhancing heat transfer efficiency. Specifically, the topological PHE exhibits inlet–outlet temperature difference of 10.3 °C and pressure drops of 11.2 kPa, while the dimple PHE shows inlet–outlet temperature difference of 6 °C and pressure drops of 12.9 kPa. The findings of this study provide guidance for future research endeavors aimed at evaluating potential solutions to advance heat transfer in PHEs and facilitate the implementation of thermal management strategies.

Machine learning‑based radiomics models accurately predict Crohn's disease‑related anorectal cancer.

The radiological diagnosis of Crohn's disease (CD)-related anorectal cancer is difficult; it is often found in advanced stages and has a poor prognosis because of the difficulty of curative surgery. However, there are no studies on predicting the diagnosis of CD-related cancer. The present study aimed to develop a predictive model to diagnose CD cancerous lesions more accurately in a way that can be interpreted by clinicians. Patients with CD who developed anorectal CD lesions at Hyogo Medical University (Nishinomiya, Japan) between March 2009 and June 2022 were included in the present study. T2-weighted and T1-weighted magnetic resonance (MR) images were utilized for our analysis. Images of anorectal lesions were segmented using open-source 3D Slicer software, and radiomic features were extracted using PyRadiomics. Six machine learning models were investigated and compared: i) Support vector machine; ii) naive Bayes; iii) random forest; iv) light gradient boosting machine; v) extremely randomized trees; vi) and regularized greedy forest (RGF). SHapley Additive exPlanations (SHAP) values were calculated to assess the extent to which each radiomic feature contributed to the model's predictions compared to baseline, represented as the average of the model's predictions for all test data. The T2-weighted images of 28 patients with anorectal cancer and 40 non-cancer patients were analyzed and the contrast-enhanced T1-weighted images of 22 cancer and 40 non-cancer patients. The model with the highest area under the curve (AUC) was the RGF-based model constructed using T2-weighted image features, achieving an AUC of 0.944 (accuracy, 0.862; recall, 0.830). The SHAP-based model explanation suggested a strong association between the diagnosis of CD-related anorectal cancer and features such as complex lesion texture; greater pixel separation within the same coronal cross-section; larger, randomly distributed clumps of pixels with the same signal intensity; and a more spherical lesion shape on T2-weighted images. The MRI radiomics-based RGF model demonstrated outstanding performance in predicting CD-related anorectal cancer. These results may affect the diagnosis and surveillance strategies of CD-related colorectal cancer.

Multi-scale image compression and reconstruction algorithm for structural health monitoring system

Image data is increasingly common in structural health monitoring systems. However, due to the limitation of hardware resources such as network bandwidth, the data collected by sensors cannot be transmitted efficiently. In order to solve the problem of inefficient data transmission and ensure data recoverability, an end-to-end compression and reconstruction algorithm with an attention-enhanced residual module and a quantization mechanism is proposed. In order to meet the needs of SHM systems for image diversity, multi-scale images of local and global structural damage are used for research. The performance of the proposed algorithm is compared with traditional image compression standards and baseline models. Experimental results show that the peak signal-to-noise ratio of the reconstructed image is greater than 30 dB at different bit rates, effectively reconstructing the original image information; for images with complex textures, the residual block attention module can improve the rate-distortion performance of the proposed algorithm; when the bit rate is less than 0.44 bit per pixel, the peak signal-to-noise ratio and structural similarity of the reconstructed image of the proposed algorithm are better than those of the autoencoder baseline model and Joint Photographic Experts Group compression method. Structural damage detection examples verify the usability of compressed-reconstructed images in engineering applications.

Image smoothing method based on global gradient sparsity and local relative gradient constraint optimization

Removing texture while preserving the main structure of an image is a challenging task. To address this, this paper propose an image smoothing method based on global gradient sparsity and local relative gradient constraints optimization. To reduce the interference of complex texture details, adopting a multi-directional difference constrained global gradient sparsity decomposition method, which provides a guidance image with weaker texture detail gradients. Meanwhile, using the luminance channel as a reference, edge-aware operator is constructed based on local gradient constraints. This operator weakens the gradients of repetitive and similar texture details, enabling it to obtain more accurate structural information for guiding global optimization of the image. By projecting multi-directional differences onto the horizontal and vertical directions, a mapping from multi-directional differences to bi-directional gradients is achieved. Additionally, to ensure the consistency of measurement results, a multi-directional gradient normalization method is designed. Through experiments, we demonstrate that our method exhibits significant advantages in preserving image edges compared to current advanced smoothing methods.

RADDA-Net: Residual attention-based dual discriminator adversarial network for surface defect detection

Surface defect detection is a critical yet challenging task in the product production process, mainly because of the complex background texture on the material surface, the large intra-class defect differences, and the weak texture defect regions. To address this problem, a Residual Attention-based Dual Discriminator Adversarial Network (RADDA-Net) is developed to accurately detect various types of texture defect regions. Specifically, a residual network-based feature extraction module integrating multi-channel attention mechanism and multi-scale convolutional layers is designed to improve the detection ability of abnormal defects. Besides, to deeply excavate material surface texture information, the pixel attention-based up-sampling blocks are also incorporated into RADDA-Net to enhance the extraction of shallow features. To further improve the detection performance of RADDA-Net, a dual discriminant adversarial training strategy and a comprehensive measurement method are introduced to assist the developed network to identify defects from both macroscopic structure and microscopic edges. Finally, the six evaluation indexes of our proposed method outperformed other state-of-the-art methods on the four defect datasets, among which the highest detection precision, recall, and F-measure are 0.926, 0.916, 0.920, respectively. Therefore, the experimental results consistently verify the detection accuracy and effectiveness of RADDA-Net.

Delineating leachate-groundwater interaction at Gyadi-Gyadi dumpsite, Kano, using natural electromagnetic (EM) field detector and Vertical Electrical Sounding (VES)

Geophysical investigations are mostly used for hydrogeological studies, mining, geotechnical investigation and environmental surveys. Geophysical investigation of groundwater within the dumpsite is highly critical because the extent of interaction between leachate plume/contaminated zone and aquifer zones could significantly reduce groundwater quality. The textural complexity and clay content of soils could change their electrical properties, thus reflecting inconsistent resistivity values using the resistivity method. This research investigates the extent of leachate infiltration from waste dumpsite into groundwater at Gyadi-Gyadi Kano State, using natural Electromagnetic (EM) field and Vertical Electrical Sounding (VES) methods. Six natural EM profiles were obtained in various locations within the study area using PQWT-TC 150 model. Six VES data points were occupied along the EM profile lines using SAS 1000 ABEM resistivity meter. The two techniques employed revealed some intercalations of low resistivity (conductive) as well as very low electrical potential differences in the study area. The low resistivity media are mixtures of leachates into groundwater units, thereby creating formations from the surface to a depth of about 40 m. The first and second layers have experience leachate-aquifer interaction in the northern, southern and eastern parts of the study area to about 40 m depth, and the leachate-aquifer interaction has extended to the deep aquifer of about 70 m depth at the western part. Shallow aquifer has not been infiltrated by leachate at about >100 m away from the waste dumpsite. Therefore, for potable groundwater exploitation in this area, it is advised that boreholes should be sited >100 m away from the dumpsite, where leachate-aquifer interaction has attenuated. The natural EM field detector is evidently constrained by issues of over-generalization and summation of potential values, probably due to its relatively low resolution. Hence it might not be capable of precisely delineating varying geological units. In contrast, the resistivity method is able to delineate and discretize the subsurface into sub-units of different resistive zones. This implies that VES is better suited for accurately defining the true subsurface geology, while the NEF detector is effective for determining the extent of a leachate plume.

Lights off the Image: Highlight Suppression for Single Texture-Rich Images in Optical Inspection Based on Wavelet Transform and Fusion Strategy

A wavelet-transform-based highlight suppression method is presented, aiming at suppressing the highlights of single image with complex texture. The strategy involves the rough extraction of specular information, followed by extracting the high-frequency information in specular information based on multi-level wavelet transform to enhance the texture information in the original images by fusion strategy, and fusing with the same-level specular information to achieve the highlight suppression image. The experimental results demonstrate that the proposed method effectively removed large-area highlights while preserving texture details, and demonstrated the authenticity of the highlight estimation and the ‘lights off’ effect in the highlight-suppressed images. Overall, the method offers a feasibility for addressing the challenges of highlight suppression for visual detection image with rich texture and large-area highlights.

A pyramid Gaussian pooling based CNN and transformer hybrid network for smoke segmentation

AbstractVisual smoke semantic segmentation is a challenging task due to semi‐transparency, variable shapes, and complex textures of smoke. To improve segmentation performance, a convolutional neural network and transformer hybrid network are proposed based on pyramid Gaussian pooling (PGP) for smoke segmentation. In order to utilize low‐pass filtering to suppress noise, a PGP method is designed. Then, the output of PGP is reshaped to construct a set of visual tokens for transformers, thus a PGP‐transformer module is presented to make full use of the self‐attention mechanism. Finally, the PGP‐transformer module is inserted into the U‐shaped architecture with skip connections. A large number of experiments have proved that the method is significantly superior to existing state‐of‐the‐art algorithms on virtual and real smoke datasets, and ablation experiments have also verified the effectiveness of the proposed modules.

Unconventional Magnetic and Magneto-Transport Properties in Quasi-2D Ni0.28TaSeS Single Crystal.

Van der Waals (vdW) magnetic materials have broad application prospects in next-generation spintronics. Inserting magnetic elements into nonmagnetic vdWmaterials can introduce magnetism and enhance various transport properties. Herein, the unconventional magnetic and magneto-transport phenomena is reported in Ni0.28TaSeS crystal by intercalating Ni atoms into nonmagnetic 2H-TaSeS matrix. Magnetic characterization reveals a canted magnetic structure in Ni0.28TaSeS, which results in an antiferromagnetic (AFM) order along the c-axis and a ferromagnetic (FM) moment in the ab-plane. The presence of spin-flop (SF) behavior can also be attributed to the canted magnetic structure. Temperature-dependent resistivity exhibits a metallic behavior with an abrupt decrease corresponding to the magnetic transition. Magneto-transport measurements demonstrate a positive magnetoresistance (MR) with a plateau that is different from conventional magnetic materials. The field-dependent Hall signal exhibits nonlinear field dependence when the material is in magnetically ordered state. These unconventional magneto-transport behaviors are attributed to the field-induced formation of a complex spin texture in Ni0.28TaSeS. In addition, it further investigated the angle dependence of MR and observed an unusual fourfold anisotropic magnetoresistance (AMR) effect. This work inspires future research on spintronic devices utilizing magnetic atom-intercalated quasi-2D materials.

Unveiling Visual Treasures: Harnessing Deep Learning for Content-Based Image Retrieval

Objective: An essential aspect of computer vision is content-based image retrieval (CBIR), which enables users to search for images based on their visual content instead of created annotations. Advances in technology have resulted in a significant rise in the complexity of multimedia content and the emergence of new research fields centered on similar multimedia material retrieval. The efficacy of retrieval is impacted by the limits of the present CBIR systems, which result from overlooked algorithms and computing restrictions. Methods: This research introduces a novel approach employing the Siamese Edge Attention Layered Convonet (SEAL Convonet) for Image Retrieval. We utilize the CBIR image dataset through Gaussian smoothing to enhance image quality for data preprocessing and the Canny Edge Detector (CED) for edge detection, following pre-processing. The Histogram of Oriented Gradients (HOG) is used for feature extraction to extract complex textures and patterns from the images. Findings: This approach is implemented and tested through simulations as well as the results indicate a substantial positive deviation in the performance and retrieval of the images compared to existing methods. The performance metrics are accuracy (97 %), precision (94 %), recall (91 %), F1-Score (97 %), False Positive Rate (FNR) (0.0013), Matthew's correlation coefficient (MCC) (0.85), and False Negative Rate (FPR) (0.0036) show the measurements of this proposed model. Application: The state of the art in this work is researching the influence of optimizers on the accuracy process, as indicated by the findings. Keywords: CBIR, Gaussian smoothing, Canny Edge Detector, Histogram of oriented gradients (HOG), Siamese Edge Attention Layered convonet (SEAL Convonet), Database