Dilatation Operator Research Articles

Role of inter- and extra-lesion tissue, transfer learning, and fine-tuning in the robust classification of breast lesions

Accurate and unbiased classification of breast lesions is pivotal for early diagnosis and treatment, and a deep learning approach can effectively represent and utilize the digital content of images for more precise medical image analysis. Breast ultrasound imaging is useful for detecting and distinguishing benign masses from malignant masses. Based on the different ways in which benign and malignant tumors affect neighboring tissues, i.e., the pattern of growth and border irregularities, the penetration degree of the adjacent tissue, and tissue-level changes, we investigated the relationship between breast cancer imaging features and the roles of inter- and extra-lesional tissues and their impact on refining the performance of deep learning classification. The novelty of the proposed approach lies in considering the features extracted from the tissue inside the tumor (by performing an erosion operation) and from the lesion and surrounding tissue (by performing a dilation operation) for classification. This study uses these new features and three pre-trained deep neuronal networks to address the challenge of breast lesion classification in ultrasound images. To improve the classification accuracy and interpretability of the model, the proposed model leverages transfer learning to accelerate the training process. Three modern pre-trained CNN architectures (MobileNetV2, VGG16, and EfficientNetB7) are used for transfer learning and fine-tuning for optimization. There are concerns related to the neuronal networks producing erroneous outputs in the presence of noisy images, variations in input data, or adversarial attacks; thus, the proposed system uses the BUS-BRA database (two classes/benign and malignant) for training and testing and the unseen BUSI database (two classes/benign and malignant) for testing. Extensive experiments have recorded accuracy and AUC as performance parameters. The results indicate that the proposed system outperforms the existing breast cancer detection algorithms reported in the literature. AUC values of 1.00 are calculated for VGG16 and EfficientNet-B7 in the dilation cases. The proposed approach will facilitate this challenging and time-consuming classification task.

Multi-perspective patient representation learning for disease prediction on electronic health records

Patient representation learning based on electronic health records (EHR) is a critical task for disease prediction. This task aims to effectively extract useful information on dynamic features. Although various existing works have achieved remarkable progress, the model performance can be further improved by fully extracting the trends, variations, and the correlation between the trends and variations in dynamic features. In addition, sparse visit records limit the performance of deep learning models. To address these issues, we propose the multi-perspective patient representation Extractor (MPRE) for disease prediction. Specifically, we propose frequency transformation module (FTM) to extract the trend and variation information of dynamic features in the time–frequency domain, which can enhance the feature representation. In the 2D multi-extraction network (2D MEN), we form the 2D temporal tensor based on trend and variation. Then, the correlations between trend and variation are captured by the proposed dilated operation. Moreover, we propose the first-order difference attention mechanism (FODAM) to calculate the contributions of differences in adjacent variations to the disease diagnosis adaptively. To evaluate the performance of MPRE and baseline methods, we conduct extensive experiments on two real-world public datasets. The experiment results show that MPRE outperforms state-of-the-art baseline methods in terms of AUROC and AUPRC.

Improved Image Segmentation using Adaptive Threshold Morphology on CT-Scan Images for Brain Tumor Detection

Diagnosing disease by playing the role of image processing is one form of current medical technology development. The results of image processing performance have been able to provide accurate diagnoses to be used as material for decision-making. This research aims to carry out the process of detecting brain tumor objects in Computed Tomography (CT-Scan) images by developing a segmentation technique using the Adaptive Threshold Morphology (ATM) algorithm. The performance of the ATM algorithm in the segmentation process involves the Extended Adaptive Global Treshold (eAGT) function to produce an optimal threshold value. This research method involves several stages of the process in detecting tumor objects. The preprocessing stage is carried out using the cropping and filtering process which is optimized using the eAGT function. The next stage is the morphological segmentation process involving erosion and dilation operations. The final stage of the segmentation process using the ATM algorithm is labeling objects that have been detected. The research dataset used 187 Computed Tomography-Scan images from 10 brain tumor patients. The results of this study show that the accuracy rate for detecting brain tumor objects in Computed Tomography-Scan images is 93.47%. These results can provide an automatic and effective detection process based on the optimal threshold value that has been generated. Overall, this research contributes to the development of segmentation algorithms in image processing and can be used as an alternative solution in the treatment of brain tumor patients.

Aggregate-aware model with bidirectional edge generation for medical image segmentation

Accurate segmentation of lesion areas plays an important role in medical imaging-assisted diagnosis and treatment. Accurate boundary information can help doctors develop precise surgical plans and improve patient prognosis. However, automatic segmentation methods often struggle to accurately segment edges due to the random shape, size, and location of regions of interest (ROI). This problem is compounded in medical images, where the difference in pixel intensity between foreground and background is significantly smaller than in natural images. In this study, we propose an aggregate-aware model with bidirectional edge generation (Ambeg) for medical image segmentation. To overcome the problem of blurred edges between foreground and background in medical images, we design a deep learning model via a multi-task learning strategy and obtain richer visual features to guide segmentation. Furthermore, an Edge Feature Fusion (EFF) module is developed to combine spatial correlation information of lesion edges between adjacent images for more accurate edge segmentation. Finally, we design a new evaluation metric, the Boundary DSC segmentation consistency measure, to evaluate the edge segmentation accuracy of medical image segmentation methods. We utilize dilation and erosion operations in morphological methods to construct lesion edge labels. In addition, we use expansion and erosion rates to regulate the dimensions of the edge region to assess the requirements of different diseases for edge segmentation accuracy. The proposed approach is particularly noteworthy for achieving state-of-the-art results on medical image segmentation datasets, including BraTS 2022 (MRI), BraTS 2020 (MRI) and COVID-19–20 (CT), which have different modalities of datasets. It has an impressive Hausdorff distance of 4.62 mm and a sensitivity score of 92.45 % on BraTS 2020. Compared with existing assessment methods such as Dice score, Boundary DSC segmentation consistency measure focuses on the hard-to-segment lesion edge region rather than the easy-to-segment lesion center region, which provides a more comprehensive reference for physicians to choose automatic segmentation methods. In addition, since the boundary shapes of medical images are complex and diverse, we utilize morphological methods to obtain the boundary labels to ensure the smoothness of the boundary. Moreover, the approach is easy to implement and has a low computational cost, making it an attractive option for practical medical imaging applications.

DCNet: A lightweight retinal vessel segmentation network

Retinal vessel segmentation is a crucial focus within the realm of medical image analysis, playing a pivotal role in early disease diagnosis, notably retinopathy. Deep learning has exhibited remarkable segmentation capabilities for retinal blood vessels, leveraging the advantages of contextual feature learning. However, there are still some shortcomings in fine retinal vessel segmentation due to the loss of semantic information due to too many pooling operations or limited receptive fields due to fewer pooling operations. In response to the nuanced balance required for expanding the receptive field while preserving information integrity during multiple downsampling operations, this paper introduces DCNet (Dilated Convolution Net), a novel lightweight three-layer dilated-convolution-based network tailored for retinal blood vessel segmentation. This three-layer architecture autonomously extracts crucial segmentation features from various levels of the feature map. Each layer comprises a dilated convolution Positive Sequence Block (PSB) and a dilated convolution Reverse Sequence Block (RSB). The dilated convolution operation is strategically exploited for its capacity to extend the receptive field, facilitating effective feature information extraction. Simultaneously, to alleviate semantic information loss within the deep network's feature map, this paper proposes the Nonlinear Feature Extraction Module (NFEM) to supplement shallow network feature information. Furthermore, to comprehensively leverage information from various scale features, a Feature Fusion Module (FFM) is introduced for multiscale vascular feature extraction, ultimately enhancing segmentation accuracy. DCNet undergoes rigorous evaluation on four publicly accessible retinal vascular datasets – DRIVE, STARE, CHASE_DB1, and HRF. Experimental results unequivocally demonstrate that DCNet achieves superior segmentation performance with fewer model parameters compared to existing state-of-the-art methods. The code for DCNet can be accessed on the following website: https://github.com/ChunhuiYu1/DCNet.

Unmixing the Wilson line defect CFT. Part II. Analytic bootstrap

In this second installment of a series of two papers on the 12\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\frac{1}{2} $$\\end{document}-BPS Wilson line defect CFT in N\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\mathcal{N} $$\\end{document} = 4 super Yang-Mills, we focus on dynamical aspects of the theory, in particular studying four-point functions with analytic bootstrap methods. Relying on the results of [1] for the kinematics and strong coupling spectrum, we consider various four-point functions in the planar limit, in an expansion for large ’t Hooft coupling. Our ultimate goal is to provide a detailed derivation of the four-point function of the displacement supermultiplet at three loops, first presented in [2]. Along the way, we present a large amount of new results including four-point functions with zero, one or two long external supermultiplets. The last two represent a novelty in the analytic bootstrap literature and are instrumental in addressing the problem of operators degeneracy. Such phenomenon leads to the necessity of resolving a mixing problem that is more complicated than those usually encountered in the study of holographic correlators, thus leading us to the development of a new approach that we believe will have a wider range of applicability. Related to this issue, we analyze in some detail the structure of the dilatation operator in this model. Some of the ingredients that we use apply more generally to holographic theories, although a thorough investigation of these aspects is missing, to the best of our knowledge, in most interesting cases.

Efficacy of Mid-Urethral Sling and Urethral Dilation for Stress Urinary Incontinence Combined with Urethral Stricture in Women.

To investigate the potential clinical benefits of mid-urethral sling (MUS) and urethral dilatation (UD) operations for the treatment of stress urinary incontinence (SUI) combined with urethral stricture. Descriptive study. Place and Duration of the Study: Department of Urology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China, from January 2017 to 2022. Patients with Qmax <15ml/s or PVR >50ml, and video urodynamic study (VUDS) capable of confirming the presence and position of urethral stricture were included. The clinical efficacy was evaluated by International Consultation on Incontinence Questionnaire-Short Form (ICIQ-SF) questionnaire, maximum flow rate (Qmax), and postvoid residual (PVR) urine. ICIQ-SF, Qmax, and PVR were measured presurgery, on postoperative 2-week, and 1-month postsurgery. There were total 19 patients with an average age of 61.37 ± 11.28 years (range 39-84) with SUI and urethral stricture. ICIQ-SF scores were decreased significantly at one month postoperatively compared with the preoperative [5.0 (0.0, 7.0) vs. 14.0 (13.0, 15.0), p <0.001]. Qmax was increased dramatically compared with the preoperative [21.3 (14.0, 28.4) vs. 13.0 (8.7,18.0), p <0.001], and PVR was decreased remarkably than the preoperative [0.0 (0.0,0.0) vs. 0.0 (0.0,60.0), p = 0.018]. Of 19 patients primarily managed with MUS and UD, two patients experienced recurrence requiring repetitive dilation till sling excision surgery was conducted, and improvement was evident in one patient after repeating UD. The overall incidence of SUI combined with urethral stricture in women is low. With a success rate of 89.5%, MUS and UD were effective therapies for the co-existence of SUI with urethral stricture, and repeated UD can be performed safely if necessary in long-term follow-up. Stress urinary incontinence, Urethral stricture, Mid-urethral sling, Urethral dilatation.

On the Properties of Quasi-Banach Function Spaces

In this paper we explore some basic properties of quasi-Banach function spaces which are important in applications. Namely, we show that they possess a generalised version of Riesz–Fischer property, that embeddings between them are always continuous, and that the dilation operator is bounded on them. We also provide a characterisation of separability for quasi-Banach function spaces over the Euclidean space. Furthermore, we extend the classical Riesz–Fischer theorem to the context of quasinormed spaces and, as a consequence, obtain an alternative proof of completeness of quasi-Banach function spaces.

Calculation Model of Radar Terrain Masking Based on Tensor Grid Dilation Operator

Calculation Model of Radar Terrain Masking Based on Tensor Grid Dilation Operator

Basic concepts of complete residuated lattice-valued fuzzy mathematical morphology

The aim of this paper is to present basic concepts of lattice-valued fuzzy mathematical morphology. We use a complete residuated lattice as the codomain of fuzzy sets, a pair of fuzzy powerset operators, called the fuzzy erosion operator and the fuzzy dilation operator, is defined and their properties and relationships are studied. The pair of two operators forms a Galois adjunction and so that the induced fuzzy opening operator and fuzzy closing are an interior operator and a closure operator respectively. It is shown that the dilation stable sets and the erosion stable sets are equivalent, which form a fuzzy Alexandrov topology.

Measuring Effectiveness of Metamorphic Relations for Image Processing Using Mutation Testing.

Testing an intricate plexus of advanced software system architecture is quite challenging due to the absence of test oracle. Metamorphic testing is a popular technique to alleviate the test oracle problem. The effectiveness of metamorphic testing is dependent on metamorphic relations (MRs). MRs represent the essential properties of the system under test and are evaluated by their fault detection rates. The existing techniques for the evaluation of MRs are not comprehensive, as very few mutation operators are used to generate very few mutants. In this research, we have proposed six new MRs for dilation and erosion operations. The fault detection rate of six newly proposed MRs is determined using mutation testing. We have used eight applicable mutation operators and determined their effectiveness. By using these applicable operators, we have ensured that all the possible numbers of mutants are generated, which shows that all the faults in the system under test are fully identified. Results of the evaluation of four MRs for edge detection show an improvement in all the respective MRs, especially in MR1 and MR4, with a fault detection rate of 76.54% and 69.13%, respectively, which is 32% and 24% higher than the existing technique. The fault detection rate of MR2 and MR3 is also improved by 1%. Similarly, results of dilation and erosion show that out of 8 MRs, the fault detection rates of four MRs are higher than the existing technique. In the proposed technique, MR1 is improved by 39%, MR4 is improved by 0.5%, MR6 is improved by 17%, and MR8 is improved by 29%. We have also compared the results of our proposed MRs with the existing MRs of dilation and erosion operations. Results show that the proposed MRs complement the existing MRs effectively as the new MRs can find those faults that are not identified by the existing MRs.

Reversible data hiding using morphology based pixel classification

Pixel value ordering (PVO) prediction has become a research hotspot in reversible data hiding community and existing achievements have proved the necessity of predicting more pixels in a block. The increase in predicted pixels is beneficial to fully exploiting the redundancy of the cover image, but a single reference pixel may lead to decreased prediction quality. Given this situation, this paper proposes to develop PVO in correlated pixels construction and introduce morphology as an important theoretical tool. Several largest pixels in a block are first selected as candidate reference pixels which are to be activated in descending order. Except for the last one, each active reference pixel is used to predict all pixels to generate a correlation matrix, on which the dilation operation is carried out to pick out connected pixels that are likely to be strongly correlated with the reference pixel. Whether to activate the next reference pixel depends on the presence of disconnected pixels. Once the last reference pixel is activated, all remaining pixels will be taken as connected pixels. After the first round of embedding ends, several smallest pixels are similarly selected as candidate reference pixels to realize the second round of embedding. Experimental results demonstrate that satisfactory superiority in fidelity is achieved, e.g., the average PSNR for the Kodak image database is 63.85 dB with an embedding capacity of 10,000 bits.

A Multi-Target Identification and Positioning System Method for Tomato Plants Based on VGG16-UNet Model

The axillary buds that grow between the main and lateral branches of tomato plants waste nutrients and lead to a decrease in yield, necessitating regular removal. Currently, these buds are removed manually, which requires substantial manpower and incurs high production costs, particularly on a large scale. Replacing manual labor with robots can lead to cost reduction. However, a critical challenge is the accurate multi-target identification of tomato plants and precise positioning for axillary bud removal. Therefore, this paper proposes a multi-target identification and localization method for tomato plants based on the VGG16-UNet model. The average intersection and pixel accuracies of the VGG16-UNet model after introducing the pretrained weights were 85.33% and 92.47%, respectively, which were 5.02% and 4.08% higher than those of the VGG16-UNet without pretrained weights, achieving the identification of main branches, side branches, and axillary bud regions. Then, based on the multi-objective segmentation of the tomato plants in the VGG16-UNet model, the regions of the axillary buds in the tomato plants were identified by HSV color space conversion and color threshold range selection. Morphological dilation and erosion operations were used to remove noise and connect adjacent regions of the same target. The endpoints and centroids of the axillary buds were identified using the feature point extraction algorithm. The left and right positions of the axillary buds were judged by the relationship between the position of the axillary bud centroid and the position of the main branch. Finally, the coordinate parameters of the axillary bud removal points were calculated using the feature points to determine the relationship between the position of the axillary bud and the position of the branch. Experimental results showed that the average accuracy of the axillary bud pruning point recognition was 85.5%.

Content-aware Nakagami morphing for incremental brain MRI

Within the carcinogenesis mechanism, from the initiation of the very first tumor cell to the preneoplastic and neoplastic cancer cell groups, cancer cells omnidirectionally and unpredictably proliferate in three-dimensional (3D) space during the promotion and progression steps. Whole tumors areas, consisting of edema, necrosis, enhancing and non-enhancing tumor subareas, could easily be identified by the typical sequences of magnetic resonance imaging (MRI), mostly by FLAIR, the fluid attenuated inversion recovery sequence. Depending on the increment value of the scanner, not every scan could be contiguous though, which might create some gaps between the slices and cause some crucial loss of information. Shape-based morphing by erosion and dilation operations might be inadequate when the source or target lesions are not found on the MRI slices, which might emerge due to unpredictable shape of tumors. Furthermore, the real trajectories and genuine shapes of the tumors in spatial view might not be seen in the planar MRI slices due to approximations during the voxel-to-pixel transformations executed in MRI devices. Therefore, we propose a novel content-aware morphing procedure to generate imaginary slices by m-parametric Nakagami imaging. The mathematical and parametric design in our framework not only leads to precise segmentation of the whole tumors according to the ground truth images; but also, to realistic morphing, based on the content of tumor footprints in MRI slices. For testing purposes, a total number of 5133 morphs are created using 1831 genuine images containing at least one lesion taken from the Brats 2012 dataset. Given the average dice score coefficients (DSC), we obtained an average of 88.44% DSC with our morphing framework outperforming the conventional dilation-based morphing result which is 83.25%. Besides being a novel morphing methodology, the outcomes of this research could also be very beneficial in smoother 3D reconstruction and more realistic volumetric calculations of lesions to help the experts working on assessment of lesions.

An efficient algorithm for areal morphological filter

This article discusses morphological filtering for assessing a areal parameters of a texture of engineering products. A data organization structure has been proposed that makes it possible to increase the efficiency of an iterative algorithm for enumerating a surface coordinates. Morphological filters complement the standard Gaussian filter to evaluate the functional properties of a surface. The widespread use of the areal morphological filtering in metrological practice is currently hampered by the lack of an effective algorithm. An algorithm based on a matrix representation of morphological operations on the surface coordinates is presented. For this purpose, different indexing of points on a primary surface and a structuring element has been introduced. A sphere or flat segment is used as the structuring element. The high performance of the developed algorithm is ensured due to the fact that all calculations are carried out by enumerating the surface coordinates in one pass, without nested loops, as in some other algorithms. Algorithms for morphological operations of dilation and erosion are presented, on the basis of which a closing and opening filters are constructed. Simulations carried out for various data sets and comparison with known morphological filtering algorithms confirmed the high efficiency of the designed algorithm. The results obtained can be used to analyze the functional properties of product surfaces.

A deep transfer learning model for green environment security analysis in smart city

Green environmental security refers to the state of human-environment interactions that include reducing resource shortages, pollution, and biological dangers that can cause societal disorder. In IoT-enabled smart cities, due to the advancement of technologies, sensors and actuators collect vast quantities of data that are analyzed to extract potentially useful information. However, due to the noise and diversity of the data generated, only a small portion of the massive data collected from smart cities is used. In sustainable Land Use and Land Cover (LULC) management, environmental deterioration resulting from improper land usage in the digital ecosystem is a global issue that has garnered attention. The deep learning techniques of AI are recognized for their capacity to manage vast amounts of erroneous and unstructured data. In this paper, we propose a morphologically augmented fine-tuned DenseNet-121(MAFDN) LULC classification model to automate the categorization of high spatial resolution scene images for environmental conservation. This work includes an augmentation process (i.e. erosion, dilation, blurring, and contrast enhancement operations) to extract spatial patterns and enlarge the training size of the dataset. A few state-of-the-art techniques are incorporated for contrasting the efficacy of the proposed approach. This facilitates green resource management and personalized provision of services.

Assessment of satellite-derived shorelines automatically extracted from Sentinel-2 imagery using SAET

The definition of the shoreline position from satellite imagery is of great interest among coastal monitoring techniques. Understanding the reality mapped by the resulting shorelines and defining their accuracy is of paramount importance. The assessment described in this paper constitutes a validation of the shorelines obtained by using the novel tool SAET (Shoreline Analysis and Extraction Tool) for automatic shoreline extraction. The resulting shorelines applying the different parameters available in SAET are assessed in 9 test sites with diverse morphology and oceanographic conditions along the Atlantic European and Western Mediterranean coasts. The reference data is obtained along large coastal segments (covering up to about 240 km) from nearly coincident very high-resolution satellite images.Different image processing levels and extraction methods have been tested, showing their key role in the accuracy of shoreline position. When defining the approximate shoreline position the Automated Water Extraction Index for images without shadows (AWEInsh) with a 0 threshold generally constitutes the best segmentation method. In turn, the employment of the mathematical morphological operations of dilation or erosion considerably improves the results in certain coastal typologies. On the contrary, the employment of atmospherically-corrected images has a smaller influence on the accuracy of the SDSs.Results support the idea that the magnitude of the errors is strongly related to the specific coastal conditions- In general, the lowest errors appear in low-energetic microtidal sites, contrary to the energetic and mesotidal coasts with gentle slopes.The shoreline errors range between 3.7 m and 13.5 m RMSE (root-mean-square error) among the different coastal types when selecting the most appropriate extraction parameters. The shoreline position identified with SAET shows a similar or better accuracy to that obtained by other tools.

Reducing the effect of forest canopy to measure distances between trees using UAV image

In the aerial images taken from the forests, the crowns of the trees are recorded. Using DN, texture and crown pattern, the type of trees can be predicted with a certain accuracy and used in forest studies. DN is the amount of light intensity that assigns a number to each component of digital images depending on the number of image bits. The presence of foliage causes the space under the canopy to be affected by shade and semi-shade, and these areas have close to zero DNs. The canopy of trees has less effect on aerial images in autumn and winter, but the need to study the forest throughout the year, in spring and summer will be a problem. On the other hand, some LIDAR and RADAR sensors have the ability to pass through the canopy of trees, but these sensors are not always available and their imaging cost is higher than UAVs that capture in the visible range. The need to use image processing methods is to be able to prevent the loss of information about the crown of trees in visible aerial images, and to extract information about the areas affected by the shadow and semi-shadow of the crown for interpretation. Due to the 8-bit image, in this research, 8 level of transfer have been done. At each step of the transition, the DN of each pixel is compared with the average DN of the image. DNs that are larger than the average are assigned a value of 1 and other DNs are assigned a value of 0. The output of this step is an image with an 8-digit binary number assigned to each pixel. By converting the binary number to decimal, the DNs of the image are reconstructed and the image is improved. Due to the increased structural similarity of the fused image (0.8762) compared to the original image (0.2783), we expect the image to improve. In the second step, the transferred pixels are multiplied by the new mean and then subtracted from the value of the old DNs. This creates an image where the tree trunks are dark and other parts of the image are light. In the next step, we open and close the image. This operation consists of two stages: Erosion and Dilation. Erosion is performed on a binary image and the remaining parts of the crown are shortened or thinned. This morphological operation works evenly on the whole image and makes the trunks of the trees thinner, so the Dilation operation is applied to remove the spots created in the image, creating large small gaps between the crown The trees are shown in the picture, which shows the trunks of the trees. Finally, by forming a graph between the trees, the distances between them are calculated in terms of pixels of the image.

State of health and remaining useful life prediction of lithium-ion batteries with conditional graph convolutional network

Graph convolutional networks (GCNs) have been increasingly used to predict the state of health (SOH) and remaining useful life (RUL) of batteries. However, conventional GCNs have limitations. Firstly, the correlation between features and the SOH or RUL is not considered. Secondly, temporal relationships among features are not considered when projecting aggregated temporal features into another dimensional space. To address these issues, two types of undirected graphs are introduced to simultaneously consider the correlation among features and the correlation between features and the SOH or RUL. A conditional GCN is built to analyze these graphs. A dual spectral graph convolutional operation is introduced to analyze the topological structures of these graphs. Additionally, a dilated convolutional operation is integrated with the conditional GCN to consider the temporal correlation among the aggregated features. Two battery datasets are used to evaluate the effectiveness of the proposed method. Experimental results show that the proposed method outperforms other machine learning methods reported in the literature.

A shadow preservation framework for effective content-aware image retargeting process

&lt;p&gt;In the discipline of image retargeting, the structure of shadows in the image is required to be preserved since it gives structural information about the objects. Many traditional image retargeting techniques do not pay attention to preserve the shadow objects present in the image. In this paper a shadow-preservation framework is proposed in which the saliency map is derived using segmentation-based gPb-owt-ucm approach to emphasize the salient regions. To assess the quality of saliency map a fixation prediction analysis is conducted on each section of the image using the eye tracker technology. Second, to pay more attention on the shadow objects a shadow map is developed after executing image pre-processing, shadow detection, region growing, and region filling operations. To rectify the issue of misclassification of shadow pixels erosion and dilation operations are performed. To obtain an efficient importance map, a gradient map is prepared using canny operator which is further combined with the saliency map and shadow map. The obtained importance map is supplied to the seam diversion based image retargeting (SDIR) technique to resize the image in horizontal and vertical directions. To justify the efficiency of the proposed framework’s the obtained results are compared with the existing state-of-the-art. By preserving shadows, the proposed framework enhances the overall visual quality and maintains the integrity of objects in the retargeted images. The framework’s effectiveness is validated through quantitative evaluations and visual comparisons with existing methods. The results demonstrate its potential for improving content-aware image retargeting applications, paving the way for more realistic and visually appealing image resizing techniques in various domains.&lt;em&gt;&lt;/em&gt;&lt;/p&gt;