LargeGraph

Many out-of-GPU-memory systems are recently designed to support iterative processing of large-scale graphs. However, these systems still suffer from long time to converge because of inefficient propagation of active vertices’ new states along graph paths. To efficiently support out-of-GPU-memory graph processing, this work designs a system LargeGraph . Different from existing out-of-GPU-memory systems, LargeGraph proposes a dependency-aware data-driven execution approach , which can significantly accelerate active vertices’ state propagations along graph paths with low data access cost and also high parallelism. Specifically, according to the dependencies between the vertices, it only loads and processes the graph data associated with dependency chains originated from active vertices for smaller access cost. Because most active vertices frequently use a small evolving set of paths for their new states’ propagation because of power-law property, this small set of paths are dynamically identified and maintained and efficiently handled on the GPU to accelerate most propagations for faster convergence, whereas the remaining graph data are handled over the CPU. For out-of-GPU-memory graph processing, LargeGraph outperforms four cutting-edge systems: Totem (5.19–11.62×), Graphie (3.02–9.41×), Garaph (2.75–8.36×), and Subway (2.45–4.15×).

Droplets Image Segmentation Method Based on Machine learning and Watershed

Watershed algorithm is used widely in segmentation of droplet overlapped spots on water-sensitive test paper. However, the phenomenon of over-segmentation, however, is often caused by noise and subtle changes of gray levels in images. To further improve segmentation accuracy of watershed algorithm, this paper proposes a cyclic iterative watershed segmentation algorithm. Through statistical analysis and logistic regression, machine learning models were classified to extract overlapping droplets on test papers. Loop iterative processing of seed points segments overlapping droplets with appropriate thresholds. Compared with fixed threshold watershed segmentation, this method has higher precision and efficiency for spray droplet evaluation in pesticide application.

Encryption of accounting data using DES algorithm in computing environment

The more and more developed network has caused more and more impact on people’s life and work, providing convenient channels for people’s information exchange, and then improving people’s living and working conditions. However, when data is transmitted through the network, there are hidden security risks, especially important accounting data. Once intercepted and used by criminals, it may cause serious harm to the owner of the data. Based on the above background, the purpose of this article is to study the use of the DES algorithm to encrypt accounting data in a computing environment. This paper proposes an improved quantum genetic algorithm and applies it to the S-box design of the DES algorithm, which improves the non-linearity of the S-box, reduces the differential uniformity, and enhances the security of the DES algorithm. This improved DES algorithm reduces the number of iterations by increasing the key length and iterative processing using a two-round function, which further increases the security of the algorithm and improves the operation speed of the encryption process. It is found that the 64 ciphertexts of the DES algorithm and the number of changed bits compared to the original ciphertext fluctuates around 32 bits, which explains the problems that should be paid attention to when using the DES algorithm to encrypt accounting data. The validity of key characters should be guaranteed to prevent key loss or leakage. Shorter data encryption regular solution.

A new fast efficient non-maximum suppression algorithm based on image segmentation

<span>In this paper, the problem of finding local extrema in grayscale images is considered. The known non-maximum suppression algorithms provide high speed, but only single-pixel extrema are extracted, skipping regions formed by multi-pixel extrema. Morphological algorithms allow to</span><span>extract all extrema but its maxima and minima are processed separately with high computational complexity by iterative processing based on image reconstruction using image morphological dilation and erosion. In this paper a new fast efficient non-maximum suppression algorithm based on image segmentation and border analysis is proposed. The proposed algorithm considers homogeneous areas, which are formed by multi-pixel extrema and are the local maxima or minima in relation to adjacent areas, eliminating iterative processing of non-extreme pixels and assigning label numbers to local extrema during their search. The proposed algorithm allowed to increase the accuracy of local extremum extraction in comparison with known non-maximum suppression algorithms and reduce the computational complexity and the use of RAM in comparison with the morphological algorithms.</span>

A Scale-Adaptive Matching Algorithm for Underwater Acoustic and Optical Images

Underwater acoustic and optical data fusion has been developed in recent decades. Matching of underwater acoustic and optical images is a fundamental and critical problem in underwater exploration because it usually acts as the key step in many applications, such as target detection, ocean observation, and joint positioning. In this study, a method of matching the same underwater object in acoustic and optical images was designed, consisting of two steps. First, an enhancement step is used to enhance the images and ensure the accuracy of the matching results based on iterative processing and estimate similarity. The acoustic and optical images are first pre-processed with the aim of eliminating the influence of contrast degradation, contour blur, and image noise. A method for image enhancement was designed based on iterative processing. In addition, a new similarity estimation method for acoustic and optical images is also proposed to provide the enhancement effect. Second, a matching step is used to accurately find the corresponding object in the acoustic images that appears in the underwater optical images. In the matching process, a correlation filter is applied to determine the correlation for matching between images. Due to the differences of angle and imaging principle between underwater optical and acoustic images, there may be major differences of size between two images of the same object. In order to eliminate the effect of these differences, we introduce the Gaussian scale-space, which is fused with multi-scale detection to determine the matching results. Therefore, the algorithm is insensitive to scale differences. Extensive experiments demonstrate the effectiveness and accuracy of our proposed method in matching acoustic and optical images.