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To investigate the effect of gradient direction on mechanical properties and energy absorption capability of Gyroid lattice structures (GLSs), network-based and sheet-based lattice structures (G1-N768, G2-N768, G1-S768, G2-S768) of different gradient directions with an average porosity of 70% were established. The Al-Si10-Mg samples were manufactured through selective laser melting (SLM). Through compression tests and finite element analysis (FEA), the energy absorption, deformation behavior, and mechanical properties of the GLSs were evaluated. The data exhibited good consistency, and the deviations of yield strength, elastic modulus, plateau stress, densification strain and energy absorption could be controlled at about 10%. The results indicated that whether it is network-based or sheet-based GLSs, by changing the gradient direction, the deformation behavior could be transformed from layer-by-layer deformation (G1-GLSs) to uniform deformation (G2-GLSs), and thus realize the regulation of mechanical properties. At the same time, due to different topological configurations, stretch-dominated sheet-based GLSs (G1-S768, G2-S768) exhibited higher energy absorption capability and mechanical properties than bending-dominated network-based GLSs (G1-N768, G2-N768), and the energy absorption, yield strength and elastic modulus increased by 93.7%, 80.8% and 66.7%, respectively. In addition, the introduction of the Johnson–Cook model has effectively simulated the failure behavior of GLSs. This paper can offer theoretical guidance for the subsequent performance regulation and application of functionally graded GLSs. [ABSTRACT FROM AUTHOR]

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Summary: Vehicular ad hoc networks (VANETs) are the ultimate solution for preventing road accidents, which result in the loss of precious human life worldwide. In this context, effective communication between the vehicular nodes is essential due to the varying network topology and high vehicular mobility inherent with VANETs. Cluster‐based routing is identified to be a significant approach for achieving efficient routing and improving communication proficiency in VANETs. In this paper, a beetle colony optimization algorithm–based clustering scheme (BCOACS) is proposed for generating optimized clusters for facilitating reliable data dissemination. This BCOACS algorithm includes two vital strategies such as beetle antenna search (BAS) and swarm intelligence for attaining inter‐cluster and intra‐cluster communications. In specific, BAS strategy that includes random search attributed toward gradient direction is used for intra‐cluster communication without using the complete amount of gradient information. On the other hand, a swarm intelligence strategy that encompasses a collective approach of self‐organized and decentralized agents is used for inter‐cluster communication with the view to minimize the load on each cluster head (CH) and to extend the clusters' lifetime. The simulation outcomes of the proposed BCOACS scheme confirmed improved performance in optimizing the number of constructed clusters independent of the increase in the network grid size, transmission range, and number of vehicular nodes in the network compared to the benchmarked approaches. The results also confirmed that the proposed BCOACS scheme achieved a maximized throughput of 13.42%, with reduced delay and protocol overhead of 18.96% and 19.45%, better than the benchmarked schemes used for investigation. [ABSTRACT FROM AUTHOR]

The porous metal foams are potential to integrate phase change materials (PCMs) to form the shape-stabilized composited PCMs for the applications of thermal energy management and temperature regulation. However, the effect of porosity distribution of foam keeps challenging in designing such PCM-based composites. In this work, the closed-cell graded porosity foams (GPFs) involving various gradient directions and gradient differences of porosity are engineered by shrinking Voronoi tessellations to achieve precisely tunable microstructure. Then the GPF/PW composite is formed by integrating paraffin wax (PW) in each pore and its melting performance is investigated by the computational model, which is validated by the melting experiment of a specific sample. The investigations on the gradient configurations of foams reveal that the gradient direction and gradient difference of porosity have significant influences on the pore-scale melting behavior, liquid fraction variation in time and overall energy storage efficiency of PW. The positive gradient variation along the direction of heating source and the moderate gradient difference, i.e. 20% or 30%, are beneficial to improve the melting behavior of graded models. In contrast to the uniform model, the complete melting time of the graded models can maximum shorten by 5.6%, 3.8% and 3.7% for the bottom, lateral and top heating modes, respectively.

When the number of training samples is reduced, the recognition result of occlusion face recognition algorithm will also decrease. To solve this problem, an improved GD-HASLR algorithm is proposed. Firstly, the algorithm obtains the generalized gradient direction of the face image, and calculates the gradient size and gradient direction of the face image from the first order to the third order. Then, after mapping with the mapping function, the gradient direction vector is obtained, which is used as the input of the hierarchical sparse low-rank model, and the representation coefficient and error of the image are obtained. In this study, a restarted fast algorithm with shrinkage threshold-II is adopted to solve the sparse representation coefficient. Finally, the residuals of the first order to the third order test samples are calculated respectively, and the category with the highest frequency or the lowest average grade is selected as the classification result. Experimental results on AR and Extended Yale B databases show that compared with GD-HASLR and other methods, the recognition effect of the improved method proposed in this study is better. [ABSTRACT FROM AUTHOR]

Contour map has contour lines that are significant in building a Digital Elevation Model (DEM). During the digitization and pre-processing of contour maps, the contour line intersects with each other or break apart resulting in broken contour segments. These broken segments impose a greater risk while building DEM leading to a faulty model. In this project, a simple yet efficient mechanism is used to match and reconnect the endpoints of the broken segments accurately and efficiently. The matching of the endpoints is done using the concept of minimum Euclidean distance and gradient direction while the Cubic Hermite spline interpolation technique is used to reconnect the endpoints by estimating the values using a mathematical function that minimizes overall surface curvature resulting in a smooth curve. The purpose of this work is to reconnect the broken contour lines generated during the digitization of the contour map, to help build the most appropriate digital elevation model for the corresponding contour map.

Multispectral on-board vision systems of aircraft solve a large number of tasks designed to ensure safe flight in difficult visibility conditions and successful flight performance. These tasks are traditionally divided into low-level and high-level tasks. The low-level tasks – or the primary image processing tasks – deal with the topics of noise reduction in processed images, image enhancements and brightness range detection. Problem-solving methods in on-board computers are subject to severe time restrictions in terms of machine time spent for their implementation. Therefore, the entire set of tasks of both low and high level must be solved in real time. This article presents original algorithms for solving two low-level tasks: discrete Gaussian noise reduction and the edge detection. A modified version of the sigma filter, enhanced by an original algorithm of low computational complexity, was applied to estimate noise level in the processed image and to calculate the optimal cut-off value in the sigma filter. A method, that is, on one hand, analogous to the Canny edge detection algorithm, and, on the other, an alternative one. The differences from the Canny edge detection technique are, firstly, in the use of a vector mask to calculate the estimates of private derivatives in the gradient. This mask provides optimal estimates of private derivatives, in terms of the Least Square Method. Formation of smoothed estimator of private derivatives allowed us to refuse from preliminary smoothing of the processed image in conditions of low intensity noise. Secondly, the proposed method uses a different way of forming edges that provide a choice of "strong" and "weak" lines. Unlike the Canny algorithm, the method used to form and use edges is focused on creating a contour image with a minimum number of short contour lines. Short lines make it difficult to analyze the contour image at the stage of solving high-level tasks. The new edge detection method requires 2-3 times less machine time than the Canny edge detection algorithm.

Purpose: Constraints in extended neighborhood system demand the use of a large number of interpolations in directionality‐guided compressed‐sensing nonlinear diffusion MR image reconstruction technique. This limits its practical application in terms of computational complexity. The proposed method aims at multifold improvement in its runtime without compromising the image quality. Theory and Methods: Conventional approach to extended neighborhood computation requires 108 linear interpolations per pixel for 10 sets of neighborhoods. We propose a neighborhood stretching technique that systematically extends the location of neighboring pixels such that 66% to 100% fewer interpolations are required to compute the gradients along multiple directions. A spatial frequency–based deviation measure is then used to choose the most reliable edges from the set of images generated by diffusion along different directions. Results: The semi‐interpolated and interpolation‐free diffusion techniques proposed in this paper are compared with the fully interpolated diffusion‐based reconstruction by reconstruing multiple multichannel in vivo datasets, undersampled using different sampling patterns at various sampling rates. Results indicate a two‐ to fivefold increase in reconstruction speed with a potential to generate 1 to 2 dB improvement in peak SNR measure. Conclusion: The proposed method outperforms the state‐of‐the‐art fully interpolated diffusion model and generates high‐quality reconstructions for different sampling patterns and acceleration factors with a two‐ to fivefold increment in reconstruction speed. This makes it the most suitable candidate for edge‐preserving penalties used in the compressed sensing MRI reconstruction methods. [ABSTRACT FROM AUTHOR]

The problem of searching a similar pattern is an exciting and challenging research field of pattern recognition. The intelligence of humans for vision to read is a crucial phenomenon for machine simulation and has been carried out for a few decades. Therefore, in this article, a recognition system of handwritten Meitei Mayek (Manipuri script) is introduced using a convolutional neural network. Generally, character recognition is performed using the gray scale of the image of characters. However, we have additionally considered the corresponding gradient direction and gradient magnitude images to create three‐channels image for every character so that supplementary information from gradient images can be obtained for efficient recognition. Experiments are conducted on 14 700 sample images collected from various individuals of different age groups and educational backgrounds. A recognition rate of 98.70% is obtained, which is compared with the existing methods, and it is found to be superior performance than other neural network methods on Meitei Mayek. [ABSTRACT FROM AUTHOR]

In this study, the authors propose a method to calculate the consistency of alpha masking to assess the robustness of the matting algorithm. This study evaluates consistent alpha masks based on the Gaussian–Hermite moment in combination with gradient amplitude and gradient direction. The gradient direction describes the appearance and shape of local objects in the image, and the gradient amplitude accurately reflects the contrast and texture changes of small details in the image. They selected Gaussian blur, pretzel noise, and combined noise to destroy the image, and then evaluated the consistency of the original alpha mask and noise alpha mask. To determine the robustness of the matting algorithm, they assessed the degree of consistency of the alpha mask using three different evaluation levels. The experimental results show that noise has a greater impact on the performance of the matting algorithm, which shows a decreasing trend as the noise level in the image deepens. In noisy images, the traditional matting algorithm exhibits better robustness compared to the recently proposed trap matting algorithm. Different matting algorithms present different adaptations to different noises.

The conventional distance regularized level set evolution method has been very popular in image segmentation, but usually it cannot converge to the desired boundary when there are multiple and unwanted boundaries in the image. By observation, the gradient direction between the target boundaries and the unwanted boundaries are usually different in one image. The gradient direction information of the boundaries can guide the orientation of the level set function evolution. In this study, the authors improved the conventional distance regularized level set evolution method, introduced new edge indicator functions and proposed an oriented distance regularized level set evolution method for image segmentation. The experiment results show the proposed method has a better segmentation result in images with multiple boundaries. Moreover, alternately selecting the edge indicator functions we proposed during the level set evolution can lead the zero level set contour to converge to the desired boundaries in complicated images. [ABSTRACT FROM AUTHOR]

In this study, the authors propose a method to calculate the consistency of alpha masking to assess the robustness of the matting algorithm. This study evaluates consistent alpha masks based on the Gaussian–Hermite moment in combination with gradient amplitude and gradient direction. The gradient direction describes the appearance and shape of local objects in the image, and the gradient amplitude accurately reflects the contrast and texture changes of small details in the image. They selected Gaussian blur, pretzel noise, and combined noise to destroy the image, and then evaluated the consistency of the original alpha mask and noise alpha mask. To determine the robustness of the matting algorithm, they assessed the degree of consistency of the alpha mask using three different evaluation levels. The experimental results show that noise has a greater impact on the performance of the matting algorithm, which shows a decreasing trend as the noise level in the image deepens. In noisy images, the traditional matting algorithm exhibits better robustness compared to the recently proposed trap matting algorithm. Different matting algorithms present different adaptations to different noises. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of garbage in the process of identification and classification, this paper proposed a method based on improved Canny algorithm for garbage image edge detection in the process of garbage image preprocessing. The method optimized the edge detection of garbage images from three aspects: traditional Canny algorithm filtering, gradient direction and threshold adaptive. For the poor performance of Gauss filter used by traditional Canny algorithm in limitation of removing gauss noise and loss of edge details, the improved gradient reciprocal weighting method was used to filter. For the problem that the Canny algorithm was easy to detect the false edge, the edge was refined by adding the direction gradient template in the process of calculating the gradient direction of the image. At the same time, the minimum error method was used to solve the limitation of manually setting the threshold, and the threshold could be adaptive. The experimental result showed that this method improved the denoising performance and obtained better edge detection effect, which provided technical support for the subsequent classification and classification of garbage images. [ABSTRACT FROM AUTHOR]

This paper mainly describes studies hydrophone placement strategy in a complex underwater environment model to compute a set of "good" locations where data sampling will be most effective. Throughout this paper it is assumed that a 3 -- D underwater topographic map of a workspace is given as input.Since the negative gradient direction is the fastest descent direction, we fit a complex underwater terrain to a differentiable function and find the minimum value of the function to determine the low-lying area of the underwater terrain.The hydrophone placement strategy relies on gradient direction algorithm that solves a problem of maximize underwater coverage: Find the maximize coverage set of hydrophone inside a 3--D workspace. After finding the maximize underwater coverage set, to better take into account the optimal solution to the problem of data sampling, the finite VC-dimension algorithm computes a set of hydrophone that satisfies hydroacoustic signal energy loss constraints. We use the principle of the maximize splitting subset of the coverage set and the "dual" set of the coverage covering set, so as to find the hitting set, and finally find the suboptimal set (i.e., the sensor suboptimal coverage set).Compared with the random deployment algorithm, although the computed set of hydrophone is not guaranteed to have minimum size, the algorithm does compute with high network coverage quality. [ABSTRACT FROM AUTHOR]

Purpose: Address the shortcomings of edge‐preserving filters to preserve the complex nature of edges, by adapting the direction of diffusion to the local variations in intensity function on a subpixel level, thereby achieving a reconstruction accuracy superior to that of data‐driven learning‐based approaches. Theory and Methods: Rate of diffusion for edges is found to vary in accordance with their gradient direction. Therefore, the edge preservation is strongly dependent on the direction in which the gradient is computed. Since the directionality of edges varies at different regions of the image, the proposed technique computes the gradients in all possible angular directions and uses a spatial‐frequency‐based deviation measure to choose the most reliable edges from the images diffused along different directions. Results: The proposed method is compared with the state‐of‐the‐art data‐driven learning‐based techniques of block matching and 3D filtering (BM3D), patch‐based nonlocal operator (PANO), and dictionary learning MRI (DLMRI). Best results are obtained when directionality of edges is estimated from a prior optimized k‐space and shows an improvement in peak signal‐to‐noise ratio (PSNR) measures by a factor of 2.36 dB, 1.92 dB, and 1.59 dB over BM3D, PANO, and dictionary learning MRI, respectively. Conclusion: The proposed technique prevents the emphasis of false edges and better captures the structural details by a locally varying directionality‐guided diffusion to make the error lower than that of the state‐of‐the‐art reconstruction techniques. In addition, a highly parallelizable form of the proposed model promises a significant gain in the reconstruction speed for practical implementations. [ABSTRACT FROM AUTHOR]

Text detection in video/images is challenging due to the presence of multiple blur caused by defocus and motion. In this paper, we present a new method for detecting texts in blurred/non-blurred images. Unlike the existing methods that use deblurring or classifiers, the proposed method estimates degree of blur in images based on contrast variations in neighbor pixels and a low pass filter, which results in candidate pixels for deblurring. We consider gradient values of each pixel as the weight for the degree of blur. The proposed method then performs K-means clustering on weighted values of candidate pixels to get text candidates irrespective of blur types. Next, Bhattacharyya distance is used to extract symmetry property of texts to remove false text candidates, which provides text components. Further, the proposed method fixes bounding box for each text component based on the nearest neighbor criteria and direction of the text component. Experimental results on defocus, motion, non-blurred images and standard datasets of curved text show that the proposed method outperforms the existing methods. [ABSTRACT FROM AUTHOR]

In this article, the authors propose a concise corner detection algorithm, which is called CCDA. A cascade classifier concept is used to derive a corner detector, which can quickly discard the most non-corner pixels. The ruler of gradient direction is used to get the corner, which can avoid the influence of the light change. The method of second derivative non-maximum suppression is used to get the location of the corner and can get the exact corner point. As a result, CCDA is compare-tested with classical corner detection algorithms by using the same images which include synthetic corner patterns and real images. The result shows that CCDA has a similar speed to the FAST algorithm and better accuracy and robustness than the HARRIS algorithm. [ABSTRACT FROM AUTHOR]