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To address the issue that the existing multidomain fusion methods do not consider data correlation within mini-batch data, the first attempt is made in this letter to propose a method based on a sample intercorrelation learning multidomain fusion network (SIMFNet), which aims to accommodate multivariate domain data and further enhance the aquatic human activity recognition (AHAR) performance. To fully use the radar multidimensional information, the three-branch convolution neural network (CNN) feature extractor is first used to extract domain-specific features from the time–range map (TRM), time–Doppler map (TDM), and cadence velocity diagram (CVD). Then, the multidomain features are fused and fed into the graph construction layer (GCL) to generate instance graphs. Next, a graph aggregation layer (GAL) is applied to aggregate node information from various-hop neighborhood domains. Finally, node-level classification is used to achieve AHAR. The experimental results evaluated on the built AHAR dataset demonstrate that the proposed SIMFNet has better generalization performance than the state-of-the-art multidomain fusion methods. [ABSTRACT FROM AUTHOR]

The aim of pansharpening is to fuse panchromatic (PAN) images and the corresponding low-resolution multispectral (LRMS) images to generate high-resolution multispectral (HRMS) images. In recent years, convolutional neural network (CNN)-based methods have obtained excellent performance in this field. In order to break through the limitations of standard convolution, the dynamic convolution is also proposed for pansharpening. However, the existing dynamic convolution ignores the context along channel dimension and does not consider the mutual enhancement between spatial and spectral information. In this letter, we propose a novel cross-interaction kernel attention network (CIKANet). The proposed model consists of two branches to extract spectral and spatial information. In particular, spatial and spectral kernel attention modules that can dynamically scale the parameters of convolutional kernels are applied. To strengthen the interactions between branches and obtain complementary information, we adopt a cross kernel attention structure. A series of experiments conducted on the WV3 and QB datasets suggest that CIKANet outperforms other state-of-the-art (SOTA) models visually and quantitatively. [ABSTRACT FROM AUTHOR]

Edge detection is a technique used to identify inherent structures within an image, and it is an essential requirement for synthetic aperture radar (SAR) applications. In particular, ratio-based edge detectors have been widely used in SAR image registration because of their ability to extract invariant features and reduce the effects of speckle noise. However, current edge detectors often struggle to accurately detect multiscale objects and low-contrast structures. To address this issue, we present a robust multiscale edge detector that uses a modified convolution kernel to improve the extensibility of edge features and aggregates multiscale feature responses. We also propose a local scale estimation module to enhance edge responses in low-contrast areas and reduce noise effects. The experimental results demonstrate that our proposed method effectively preserves the integrity, continuity, and robustness of multiscale and low-contrast structures. By incorporating our proposed edge detector into feature and template matching frameworks, we are able to significantly improve matching accuracy and outperform state-of-the-art SAR image registration methods. [ABSTRACT FROM AUTHOR]

At present, how to achieve high-precision hyperspectral image classification (HSIC) under the condition of few samples is a hot research issue. Metric-based meta-learning methods have proved to be very successful in this field. However, in terms of quantifying the dependencies between embedded features of hyperspectral samples, previous methods either only model marginal distribution and ignore joint distribution, limiting the expressive capability of feature representation, or bring large computational costs though considering joint distribution. In this letter, we propose a novel few-shot learning (FSL) method based on Brownian distance covariance (BDC) for HSIC, which learns hyperspectral images’ (HSIs) representations by measuring the discrepancy between joint characteristic functions of embedded features and product of the marginals. In addition, a lightweight feature extraction network based on tied block convolution is proposed to better model cross-channel correlation and aggregate global spectral–spatial features across channels. Extensive evaluations of several datasets show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Extensive research works demonstrate that the attention mechanism in convolutional neural networks (CNNs) effectively improves accuracy. Nevertheless, few works design attention mechanisms using large receptive fields. In this work, we propose a novel attention method named Rega-Net to increase CNN accuracy by enlarging the receptive field. To the best of our knowledge, increasing the receptive field of the CNN requires increasing the size of the convolution kernel, which also increases the number of parameters. For solving this problem, we design convolutional kernels to resemble the nonuniformly distributed structure inspired by the mechanism of the human retina. Then, we sample variable-resolution values in the Gabor function distribution and fill these values in retina-like kernels. This distribution allows essential features to be more visible in the center position of the receptive field. We further design an attention module including these retina-like kernels. Experiments demonstrate that our Rega-Net achieves 79.96% Top-1 accuracy on ImageNet-1k for classification and 43.1% mAP on COCO2017 for object detection. The mAP of the Rega-Net increased by up to 3.5% compared to baseline networks. [ABSTRACT FROM AUTHOR]

Object detection in aerial images is a critical and essential task in the fields of geoscience and remote sensing. Despite the popularity of computer vision methods in detecting objects, these methods have been faced with significant limitations of aerial images such as appearance occlusion and variable object sizes. In this letter, we explore the limitations of conventional neck networks in object detection by analyzing information bottlenecks. We propose an enhanced neck network to address the information deficiency issue in current neck networks. Our proposed neck network, which serves as a bridge between the backbone network and the head network, comprises a global semantic network (GSNet) and a feature fusion refinement module (FRM). The GSNet is designed to perceive contextual surroundings and propagate discriminative knowledge through a bidirectional global pattern. The FRM is developed to exploit different levels of features to capture comprehensive location information. We validate the efficacy and efficiency of our approach through experiments conducted on two challenging datasets, DOTA and HRSC2016. Our method outperforms existing approaches in terms of accuracy and complexity, demonstrating the superiority of our proposed method. [ABSTRACT FROM AUTHOR]

Active learning (AL) is one of the popular approaches that can mitigate some of the drawbacks of supervised classification. Although sparse representation classifier (SRC) has already proven to be a robust classifier and successfully used in many applications, it is seldom used jointly with AL. In this letter, we propose a novel AL technique for SRCs. In the proposed model, the query function is designed by combining uncertainty and diversity criteria, both of which are defined by using the SRC in kernel space. The proposed technique outperforms other state-of-the-art methods in terms of classification performance. [ABSTRACT FROM AUTHOR]

In the realm of infrared small-target detection, the weighted local contrast approaches, which seek to improve the targets by the defined weighted factors, have garnered a lot of interest. However, there are several problems with these methods as follows. 1) The vast number of local contrast sliding subwindows restricts the time efficiency. 2) The dim targets in the complicated backgrounds are incorrectly eliminated by the background suppression procedure. 3) The background noise in the complicated environment cannot be effectively muted. A simplified dual-weighted three-layer window local contrast method (SDWTLLCM) is suggested in this work as a solution to these issues. To extract tiny targets and suppress complicated backgrounds, a hierarchical convolution filtering window is first created. Then, even without subwindow division, a simple three-layer sliding window is created for time efficiency enhancement. The dual-weighted local contrast approach is also intended to minimize the background and further highlight tiny objects. Eventually, the tiny targets may be extracted more effectively using the adaptive threshold segmentation procedure. The vast experimental findings show that our suggested strategy is effective and efficient. [ABSTRACT FROM AUTHOR]

In recent years, remote sensing images have attracted a lot of attention because of their special value. However, images acquired by satellite sensors are usually low-resolution (LR), so remote sensing images are much more difficult to infer high-frequency details from compared with ordinary digital images, which means they cannot meet the needs of certain downstream tasks. In this letter, we propose a multiscale enhancement network (MEN), which uses multiscale features of remote sensing images to enhance the network’s reconstruction capability. Specifically, the network extracts the coarse features of LR remote sensing images using convolutional layers. Then, these features are fed into the multiscale enhancement module (MEM) proposed by this network, which uses a combination of convolutional layers with multiple convolutional kernel sizes to refine the extraction of multiscale features, and finally, the final reconstructed image is generated by the reconstruction module. Extensive experiments show that MEN achieves significant reconstruction advantages in both objective and subjective aspects. [ABSTRACT FROM AUTHOR]

Gaussian pyramid (GP) has the property of decomposing the 2-D data into multiple scales. It has been successfully applied in image processing, but rarely used in seismic data processing and interpretation. Specifically, seismic data are not just a 2-D image in one time slice or horizon. Moreover, the pattern of geological body varies rapidly in 3-D spaces. Hence, the classical GP method exists a limitation in processing 3-D seismic data. Especially in fracture detection, the 2-D isotropic Gaussian kernel used in GP tends to blur the fracture details. In this letter, to match the high-dimensional seismic data, we propose a structure-oriented adaptive Gaussian pyramid (SOA-GP) algorithm, which expands the 2-D GP method to the 3-D situation. In this case, to eliminate the influence of transverse change of wave impedance and consider stratigraphic characteristics, the 2-D Gaussian filter is also substituted by the 3-D structure-adaptive anisotropic Gaussian kernel, which is constructed by instantaneous phase-based gradient structure tensor (GST) method. Meanwhile, seismic attributes are applied to the multiresolution seismic data decomposed by the SOA-GP method to realize multiscale fracture detection. Finally, we apply this new 3-D SOA-GP method to field data. It demonstrates that the multiscale decomposition of the new method could produce more details of fracture and less disturbance from noise. [ABSTRACT FROM AUTHOR]

The convolutional sparse coding (CSC) can learn shift-invariant convolution kernels. In deep convolutional neural networks, it takes a lot of time to train the convolution kernels. In this letter, a deep two-stage CSC network (DTCSCNet) is proposed, which can be used to simultaneously extract spatial features and spectral features from hyperspectral image (HSI) without back propagation and fine-tuning process, thus saving a lot of time. Furthermore, to further improve the performance of the network, we incorporate multiscale information. After deep feature extraction using DTCSCNet, we further investigate the classification performance of different classifiers on the extracted features. Experimental results show that the proposed method can obtain better classification performance compared with some closely related HSI classification methods. [ABSTRACT FROM AUTHOR]

Oceanic eddy is the ubiquitous ocean flow phenomenon, which has been the key factor in the transportation of ocean energy and materials. Consequently, oceanographic understanding can be enhanced by the intelligent identification of eddy. State-of-the-art deep learning technologies are gradually improving identification methods. This letter proposes the pyramid split attention (PSA) eddy detection U-Net architecture (PSA-EDUNet) that targets oceanic eddy identification from ocean remote sensing imagery. As for the PSA-EDUNet, its inspiration comes from U-Net, which contains encoder and decoder parts, making the integration of inferior and senior features efficient and ensuring the feature information will not be lost in large quantities through nonlinear connection mode. Meanwhile, the PAS module is introduced to enhance feature extraction. In terms of the fusion data, the sea surface feature is the main criterion of eddy identification, including sea surface temperature (SST) and sea level anomaly (SLA). The experiments are implemented on the Kuroshio Extension (KE) and the South Atlantic regions, the results demonstrate that the proposed method can outperform other methods, especially for eddy edges and small-scale eddies. [ABSTRACT FROM AUTHOR]

Typically, hyperspectral image suffers from redundant information, data scarcity, and class imbalance problems. This letter proposes a hyperspectral image classification framework named a two-stream residual separable convolution (2SRS) network that aims to mitigate these problems. Principal component analysis (PCA) is first employed to reduce the spectral dimension of the hyperspectral image. Subsequently, the data scarcity and class imbalance problems are overcome via spatial and spectral data augmentations. A novel spectral data creation from image patches is proposed. The augmented samples are fed into the proposed 2SRS network for hyperspectral image classification. We evaluated the proposed method on three benchmark datasets, namely, 1) Indian Pines (IP); 2) Pavia University; and 3) Salinas Scene (SA). The proposed method achieved state-of-the-art performance in terms of overall accuracy (OA), average accuracy (AA), and Kappa coefficient (Kappa) for both 30% and 10% training set ratios. [ABSTRACT FROM AUTHOR]

Sparse and low-rank modeling has shown the powerful describing abilities to express small targets; however, low-rank model exists the problem of insufficient rank approximation deviation ability and excessive shrinkage, which will lead to inaccurate background estimation. In this letter, a new nonconvex approximation function using the Gaussian model is built toward deeply excavating low-rank information of the background as much as possible. In the sparse collaborative representation, the local prior confidence (LPC) is integrated into the target structure tensor to maximum to distinguish target region and background edge more accurately. In the process of background restoration, total variation constraint (TVC) is employed apropos of describing gray variation of small targets in complex backgrounds more precisely and improving the accuracy of background restoration to further perfectly recover small targets. The low-rank and sparse recovery algorithm engages alternating direction multiplier method (ADMM) for iterative calculation and solution. Compared with advanced optimal algorithms, a great number of experimental results show that the proposed model improves the adaptability and robustness of the detection algorithm to a variety of complex scenes, and has lasting vitality and high-application value. [ABSTRACT FROM AUTHOR]

The aim of this research is to evaluate the performance of a classification model on nonlinear data. The study utilizes accuracy, precision, sensitivity, and specificity metrics based on data from e-commerce X sellers. The classification model is developed using the Support Vector Machine (SVM) approach, employing different kernel functions such as linear, polynomial, and Radial Basis Function (RBF). By comparing the performance scores of each model, the best model is determined. The results indicate that the SVM model with a linear kernel outperforms the others, demonstrating the highest performance scores. This approach is applied to predict the status of sellers on e-commerce X. [ABSTRACT FROM AUTHOR]

Given a digraph G, a set X ⊆ V (G) is said to be an absorbing set (resp. dominating set) if every vertex in the graph is either in X or is an in-neighbour (resp. out-neighbour) of a vertex in X. A set S ⊆ V (G) is said to be an independent set if no two vertices in S are adjacent in G. A kernel (resp. solution) of G is an independent and absorbing (resp. dominating) set in G. The problem of deciding if there is a kernel (or solution) in an input digraph is known to be NP-complete. Similarly, the problems of computing a minimum cardinality dominating set or absorbing set or kernel, and the problems of computing a maximum cardinality independent set or kernel, are all known to be NP-hard for general digraphs. We explore the algorithmic complexity of these problems in the well known class of interval digraphs. A digraph G is an interval digraph if a pair of intervals (S u , T u) can be assigned to each vertex u of G such that (u , v) ∈ E (G) if and only if S u ∩ T v ≠ ∅ . Many different subclasses of interval digraphs have been defined and studied in the literature by restricting the kinds of pairs of intervals that can be assigned to the vertices. We observe that several of these classes, like interval catch digraphs, interval nest digraphs, adjusted interval digraphs and chronological interval digraphs, are subclasses of the more general class of reflexive interval digraphs—which arise when we require that the two intervals assigned to a vertex have to intersect. We see as our main contribution the identification of the class of reflexive interval digraphs as an important class of digraphs. We show that while the problems mentioned above are NP-complete, and even hard to approximate, on interval digraphs (even on some very restricted subclasses of interval digraphs called point-point digraphs, where the two intervals assigned to each vertex are required to be degenerate), they are all efficiently solvable, in most of the cases linear-time solvable, in the class of reflexive interval digraphs. The results we obtain improve and generalize several existing algorithms and structural results for subclasses of reflexive interval digraphs. In particular, we obtain a vertex ordering characterization of reflexive interval digraphs that implies the existence of an O (n + m) time algorithm for computing a maximum cardinality independent set in a reflexive interval digraph, improving and generalizing the earlier known O(nm) time algorithm for the same problem for the interval nest digraphs. (Here m denotes the number of edges in the digraph not counting the self-loops.) We also show that reflexive interval digraphs are kernel-perfect and that a kernel in such digraphs can be computed in linear time. This generalizes and improves an earlier result that interval nest digraphs are kernel-perfect and that a kernel can be computed in such digraphs in O(nm) time. The structural characterizations that we show for point-point digraphs, apart from helping us construct the NP-completeness/APX-hardness reductions, imply that these digraphs can be recognized in linear time. We also obtain some new results for undirected graphs along the way: (a) We describe an O (n (n + m)) time algorithm for computing a minimum cardinality (undirected) independent dominating set in cocomparability graphs, which slightly improves the existing O (n 3) time algorithm for the same problem by Kratsch and Stewart; and (b) We show that the Red-Blue Dominating Set problem, which is NP-complete even for planar bipartite graphs, is linear-time solvable on interval bigraphs, which is a class of bipartite (undirected) graphs closely related to interval digraphs. [ABSTRACT FROM AUTHOR]

The main goal of this study is to seek optimal eigenvalue intervals of Χ1, Χ2,... Χn-1·Χn for which the iterative system of Riemann-Liouville type nonlinear p-Laplacian fractional boundary value problems has positive solutions under appropriate conditions. To demonstrate the positivity of solutions to these kinds of problems in a Banach space. we present an example as our conclusion. [ABSTRACT FROM AUTHOR]

In this paper, we consider an iterative system of Hadamard fractional boundary value problems. The sufficient conditions are derived for the existence of at least one positive solution by utilizing Guo-Krasnosel'skii fixed point theorem. The obtained results in the paper are illustrated with an example for their feasibility. [ABSTRACT FROM AUTHOR]

King coconut (Cocos nucifera var. aurantiaca) is a tropical nut with notable nutritional and medicinal values. King coconut water (KCW) and king coconut kernel (KCK) are sweet and refreshing. They undergo several changes during maturation. The objective of this study was to assess the physico-chemical changes of KCW and KCK with maturity. Three different maturity stages (6, 7, and 8 months) of the variety aurantica, known locally as 'Nawasi Thembili' were studied for several physico-chemical parameters. Whole nut weight (g), nut-circumference (cm), colour (water/kernel) (*L), nut-water volume (mL), kernel weight (g), and kernel thickness (mm) were assessed as physical parameters, while pH, total soluble solids (oBrix), titratable acidity (as % ascorbic acid), total sugars (g/100g; g/100mL), and minerals (mg/L; mg/kg) were analysed as chemical attributes. Sugars were estimated using Agilent 1260 HPLC and ICP-MS was used to measure the mineral profile. Results revealed that all the tested physical parameters of KCW and KCK increased significantly (p < 0.05) with maturity. The pH, TSS, total sugars, and acidity of KCW increased significantly (p < 0.05), while mineral content decreased significantly (p < 0.05) with maturity. However, the TSS and minerals of KCK decreased significantly (p < 0.05) with maturity and a higher sucrose content was reported in KCK compared to KCW at later maturity. The Ca+2 and Mg+2 were prominent in both KCW and KCK. The study concluded that the physico-chemical characteristics varied widely with the maturity of king coconuts. The selection of optimum harvest maturity to match the preferred attributes of intended applications is suggested. [ABSTRACT FROM AUTHOR]

The fixed point theorem of Guo–Krasnosel'skii is applied in this paper to find the intervals of the parameters that have a positive solution to an iterative system of nth order fractional differential equation with three-point boundary conditions including a nonhomogeneous term. [ABSTRACT FROM AUTHOR]

The increasing interest in convolutional neural networks (CNNs) is driving the study and design of different implementations for a variety of platforms, each intended to optimize performance, power consumption, or latency, according to the application’s needs. While GPUs have dominated the high-performance terrain, FPGAs have proved to be a promising alternative due to their relatively high performance and reduced power consumption and costs, compared with GPUs. The main concern regarding FPGA implementations lies in the effort needed to develop the systems and difficulties reusing or combining designs by different authors, due to the highly heterogeneous architectures used in each project. This work proposes a methodology and a high-level architecture designed for CNN implementations in FPGAs, which eases the development process, allows the reusability of designs, and helps to maximize performance, minimize latency, reduce resource utilization, and avoid possible bottlenecks, while allowing high design flexibility. This proposal is validated by implementing a set of blocks that are later used to build different CNNs. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to study the energy ratios of plane waves on an imperfect interface of elastic half-space (EHS) and orthotropic piezothermoelastic half-space (OPHS). Design/methodology/approach: The dual-phase lag (DPL) theory with memory-dependent derivatives is employed to study the variation of energy ratios at the imperfect interface. Findings: A plane longitudinal wave (P) or transversal wave (SV) propagates through EHS and strikes at the interface. As a result, two waves are reflected, and four waves are transmitted, as shown in Figure 2. The amplitude ratios are determined by imperfect boundaries having normal stiffness and transverse stiffness. The variation of energy ratios is computed numerically for a particular model of graphite (EHS)/cadmium selenide (OPHS) and depicted graphically against the angle of incidence to consider the effect of stiffness parameters, memory and kernel functions. Research limitations/implications: The energy distribution of incident P or SV waves among various reflected and transmitted waves, as well as the interaction of waves for imperfect interface (IIF), normal stiffness interface (NSIF), transverse stiffness interface (TSIF), and welded contact interface (WCIF), are important factors to consider when studying seismic wave behavior. Practical implications: The present model may be used in various disciplines, such as high-energy particle physics, earthquake engineering, nuclear fusion, aeronautics, soil dynamics and other areas where memory-dependent derivative and phase delays are significant. Originality/value: In a variety of technical and geophysical scenarios, wave propagation in an elastic/piezothermoelastic medium with varying magnetic fields, initial stress, temperature, porosity, etc., gives important information regarding the presence of new and modified waves. [ABSTRACT FROM AUTHOR]

Associative memories are a significant topic in pattern recognition, and therefore, throughout history, numerous memory models have been designed due to their usefulness. One such model is the associative memory minmax, which is highly efficient at learning and recalling patterns as well as being tolerant of high levels of additive and subtractive noise. However, it is not efficient when it comes to mixed noise. To solve this issue in the associative memory minmax, we present the generic model of heteroassociative memory max robust to acquisition noise (mixed noise). This solution is based on understanding the behavior of acquisition noise and mapping the location of noise in binary images and gray-scale through a distance transform. By controlling the location of the noise, the associative memories minmax become highly efficient. Furthermore, our proposed model allows patterns to contain mixed noise while still being able to recall the learned patterns completely. Our results show that the proposed model outperforms a model that has already solved this type of problem and has proven to overcome existing methods that show some solution to mixed noise. Additionally, we demonstrate that our model is applicable to all associative minmax memories with excellent results. [ABSTRACT FROM AUTHOR]

In this paper, the coupled phase field and local/nonlocal integral elasticity theories are used for stress-induced martensitic phase transformations (MPTs) at the nanoscale to investigate the limitations and contradictions of the nonlocal integral elasticity, which are due to the fact that the support of the nonlocal kernel exceeds the integration domain, i.e., the boundary effect. Different functions for the nonlocal kernel are compared. In order to compensate the boundary effect, a new nonlocal kernel, i.e., the compensated two-phase kernel, is introduced, in which a local part is added to the nonlocal part of the two-phase kernel to account for the boundary effect. In contrast to the previously introduced modified kernel, the compensated two-phase kernel does not lead to a purely nonlocal behavior in the core region, and hence no singular behavior, and consequently, no computational convergence issue is observed. The nonlinear finite element approach and the COMSOL code are used to solve the coupled system of Ginzburg–Landau and local/nonlocal integral elasticity equations. The numerical implementation of the phase field-local elasticity equations and the 2D nonlocal integral elasticity are verified. Boundary effect is investigated for MPT with both homogeneous and nonhomogeneous stress distributions. For the former, in contrast to the local elasticity, a nonhomogeneous phase transformation (PT) occurs in the nonlocal case with the two-phase kernel. Using the compensated two-phase kernel results in a homogeneous PT similar to the local elasticity. For the latter, the sample transforms to martensite except the adjacent region to the boundary for the local elasticity, while for the two-phase kernel, the entire sample transforms to martensite. The solution of the compensated two-phase kernel, however, is very similar to that of the local elasticity. The applicability of boundary symmetry in phase field problems is also investigated, which shows that it leads to incorrect results within the nonlocal integral elasticity. This is because when the symmetric portions of a sample are removed, the corresponding nonlocal effects on the remaining portion are neglected and the symmetric boundaries violate the normalization condition. An example is presented in which the results of a complete model with the two-phase kernel are different from those of its one-fourth model. In contrast, the compensated two-phase kernel can generate similar solutions for both the complete and one-fourth models. However, in general, none of the nonlocal kernels can overcome this issue. Therefore, the symmetrical models are not recommended for nonlocal integral elasticity based phase field simulations of MPTs. The current study helps for a better study of nonlocal elasticity based phase field problems for various phenomena such as various PTs. [ABSTRACT FROM AUTHOR]

We notice that the compact resist model can be mapped to a simple CNN (convolutional neural network): convolutional layer corresponds to convolutions between input images and resist kernels, and a fully connected layer can model the formation of weighted sum of convolutions followed by the comparison to threshold to determine the development. Resist kernels correspond to convolution filters, so they can be obtained through CNN training, which is a key motivation. A number of challenges are identified and solutions are proposed: (1) We demonstrate a CNN structure that can be mapped to a resist model. (2) Convolution filters are large images and cannot be trained with standard methods. Adaptive learning rate and gradient clipping are applied. (3) Convolution filters may easily be overfitted if training data is not enough. We apply gradient descent for fast initialization of filters. (4) CNN is trained with printability of image pixels rather than CD values. Extraction of pixels and their sampling are addressed. The number of kernels and the number of convolutions can greatly be reduced through the proposed method: 22 standard kernel functions are reduced to only 4 optimized ones, which contributes to 44% faster lithography simulation yet accuracy is improved by 10%. [ABSTRACT FROM AUTHOR]

A study was conducted to evaluate the effect of elevated CO2 on groundnut growth, yield, kernel and haulm nutritional quality in the Open Top Chambers (OTC). Three regimes of CO2, ambient (380 ppm) and two elevated CO2 levels (550 and 700 ppm) are maintained in three different OTCs. Compared to the ambient CO2 conditions, the kernel yield per plant declined by 32.28% and 28.40% at 550 and 700 ppm, respectively, although the pod yield remained unaffected under elevated CO2. Under elevated CO2, plant height, specific leaf area, root area and root volume are reduced, while specific leaf weight increased because of leaf thickening. Root dry weight increased due to allocation of extra fixed carbon to roots for greater support to growth under CO2 enriched conditions. Haulm quality is reduced under elevated CO2, observed as reduced metabolizing energy and in-vitro organic matter digestibility at 550 ppm, and low ash and nitrogen content at 700 ppm. Kernel oil (6.54% and 2.98%) and protein content (7.07% and 4.56%) declined at 550 and 700 ppm as compared to ambient conditions, while fatty acid content remained unaffected. Among minerals, kernel iron and manganese content remained unaffected, while copper (13.93% and 26.19%) and calcium (24.33% and 8.20%) content declined at both elevated CO2 levels. The genotype by trait biplot analysis revealed ICGV 03043 as the best performing genotype with a superior trait profile alongside high kernel yield and oil content across three CO2 levels, making it a candidate for future studies to understand the insensitivity to elevated CO2. [ABSTRACT FROM AUTHOR]

Electromagnetic brain imaging is the reconstruction of brain activity from non-invasive recordings of the magnetic fields and electric potentials. An enduring challenge in this imaging modality is estimating the number, location, and time course of sources, especially for the reconstruction of distributed brain sources with complex spatial extent. Here, we introduce a novel robust empirical Bayesian algorithm that enables better reconstruction of distributed brain source activity with two key ideas: kernel smoothing and hyperparameter tiling. Since the proposed algorithm builds upon many of the performance features of the sparse source reconstruction algorithm - Champagne and we refer to this algorithm as Smooth Champagne. Smooth Champagne is robust to the effects of high levels of noise, interference, and highly correlated brain source activity. Simulations demonstrate excellent performance of Smooth Champagne when compared to benchmark algorithms in accurately determining the spatial extent of distributed source activity. Smooth Champagne also accurately reconstructs real MEG and EEG data.

Colorectal cancer occurs in the rectal of humans, and early detection has been proved to reduce its mortality rate. Colonoscopy is the standard used in detecting the presence of polyps in the rectal, and accurate segmentation of the polyps from colonoscopy images often provides helpful information for early diagnosis and treatment. Although existing deep learning models often achieve high segmentation performance when tested on the same dataset used in model training; still, their performance often degrades when applied to out-of-distribution datasets, leading to low model generalization or overfitting. This challenge is often associated with the quality of the features learnt from the input images. In this work, a novel Context Feature Refinement (CFR) module is proposed to address the challenge of low model generalization and segmentation performance. The CFR module is built to extract contextual information from the incoming feature map by using multiple parallel convolutional layers with progressively increasing kernel sizes. Using multiple parallel convolutions with different kernel sizes helped to extract more efficient multi-scale contextual information and thus enabled the network to effectively identify and segment small and fine details, as well as larger and more complex structures in the input images. Extensive experiments on three public benchmark datasets in CVC-ClinicDB, Kvasir-SEG, and BKAI-NeoPolyp showed that the proposed ConvSegNet model achieved jaccard, dice and F2 scores of 0.8650, 0.9177, and 0.9328 on CVC-ClinicDB, 0.7936, 0.8618, and 0.8855 on Kvasir-SEG, and 0.8045, 0.8747 and 0.8909 on BKAI-NeoPolyp datasets respectively. Also, an improved generalization performance was achieved by the ConvSegNet model, compared to the benchmark polyp segmentation models. Code is available at https://github.com/AOige/ConvSegNet.

In this paper, we prove several new integral inequalities for the k-Hilfer fractional derivative operator, which is a fractional calculus operator. As a result, we have a whole new set of fractional integral inequalities. For the generalized fractional derivative, we also use Young's inequality to find new forms of inequalities. Such conclusions for this novel and generalized fractional derivative are extremely useful and valuable in the domains of differential equations and fractional differential calculus, both of which have a strong connections to real-world situations. These findings may stimulate additional research in a variety of fields of pure and applied sciences.

A smart greenhouse is a scheme with the aim of allowing the plant growth optimally due to the control in temperature, soil moisture, and humidity. It is necessary to go for best possible smart greenhouse controlling system to maneuver the parameters in accordance with the climatic and seasonal changes plant. In this proposed work, the appropriate setting, monitoring of the model parameters like optimal temperature, humidity, and soil moisture in the greenhouse farm available at Modakkurichi near Erode is carried out using fuzzy logic and multiclass Support Vector Machine (SVM) techniques. It is efficiently implemented as the fuzzy trapezoidal membership function for each sample within the hyper-sphere as a linear function of the selected sample’s distance in the non-linear SVM hyperplane. Based on the simulation and experimental results, the fuzzy logic with Multi-class Support Vector Machine method is effective in selection of the rules to make decision. Simulation of the work was carried out in Python using the open source tool COLAB. Statistical parameters are evaluated for the proposed Fuzzy based SVM method. Root Mean Square Error of the proposed controller has the minimum value as 0.962E-02 when compared to other controllers which vary from 1.04 E-02 to 8.27 E-02.

The burning of shells and fiber as boiler fuel in palm oil mills produces waste in the form of ash which is not utilized and managed optimally, resulting in environmental damage. Based on the chemical compounds contained in fly ash, its abundant availability, its cheap price and easy-to-obtain, fly ash can be used as a cheap raw material in claybaths as a substitute for clay and calcium carbonate. This study aims to determine the effective weight of boiler ash in reducing kernel and shell production losses at the kernel processing station. This study used variations in the weight of boiler fly ash, namely 3000 g, 3500 g, 4000 g, 4500 g, and 5000 g which were tested first in the laboratory. The application of boiler ash variations that were close to the norm was tested directly in the claybath. The best weight parameter for using fly ash is found in the weight variation of B5 (5000 g) with a loss of production in the sample of 3.98% or 0.159% of the oil palm fresh fruit bunches. At the time of application, boiler fly ash should be mixed with water at a ratio of 1:2 (fly ash to water) to result the best effect.

The diagnosis of systems is one of the major steps in their control and its purpose is to determine the possible presence of dysfunctions, which affect the sensors and actuators associated with a system but also the internal components of the system itself. On the one hand, the diagnosis must therefore focus on the detection of a dysfunction and, on the other hand, on the physical localization of the dysfunction by specifying the component in a faulty situation, and then on its temporal localization. In this contribution, the emphasis is on the use of software redundancy applied to the detection of anomalies within the measurements collected in the system. The systems considered here are characterized by non-linear behaviours whose model is not known apriori. The proposed strategy therefore focuses on processing the data acquired on the system for which it is assumed that a healthy operating regime is known. Diagnostic procedures usually use this data corresponding to good operating regimes by comparing them with new situations that may contain faults. Our approach is fundamentally different in that the good functioning data allow us, by means of a non-linear prediction technique, to generate a lot of data that reflect all the faults under different excitation situations of the system. The database thus created characterizes the dysfunctions and then serves as a reference to be compared with real situations. This comparison, which then makes it possible to recognize the faulty situation, is based on a technique for evaluating the main angle between subspaces of system dysfunction situations. An important point of the discussion concerns the robustness and sensitivity of fault indicators. In particular, it is shown how, by non-linear combinations, it is possible to increase the size of these indicators in such a way as to facilitate the location of faults.