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The current all Android APP stores only check the known malware APPs and can not detect the new and dynamically loading malware APPs and behaviors. This paper proposed a graph pattern and memory footprint based malware application detection algorithm. Firstly,it acquired memory information of the APP,analyzed the footprint and series number to detect the dynamically packed malware code and new malware APPs. Then, it abstracted the co-occurring permissions required by the APP,and modeled the permissions as graph structure,and used the metrics of graph to analyze the classify pattern and centric permission of the graph. It determined the optimal graph measurement for class representation according to the centric permission. Lastly,it calculated the privacy score and risk threshold of the APPs,and detected malware APPs and behaviors with that risk threshold. Simulation experimental results show that the proposed algorithm performs a high detection ratio to different types of malware APPs and a good detection ration to unknown malware APPs. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Particle-based methods are widely applied in the resolving of complex multi-scale physical phenomena in various science and engineering areas. In order to handle the challenge of increasing computational complexity and declining concurrency for the pair-wised particle searching procedure in massive multi-scale particle-based simulations, a new parallel fast neighbor searching algorithm, which features high-concurrency and low memory footprint, is developed and demonstrated on both many-core CPU and GPU architectures. An inter-level interaction strategy based on the concept of hierarchical nested data structure is proposed to resolve the issue of racing condition in cross-level particle search. An asymmetric mapping method is developed to eliminate the full mapping of particles on each level, which reduces the memory consumption. A set of numerical experiments show that, the proposed algorithm can handle multi-scale problems with particle volume ratio up to 108. Compared with traditional algorithm, the proposed algorithm can achieve 2x-8x speedups and lower memory consumption. The GPUbased implementation of the algorithm achieves state-of-the-art computational efficiency. [ABSTRACT FROM AUTHOR]

Visualizing large-scale scientific data requires high data transmission bandwidth and a large amount of memory. Efficient processing of visualization data poses a significant challenge. To improve the efficiency of scientific visualization, the most common and direct method is to reduce the amount of data that needs to be processed. A novel visualization scheme for large-scale volumes of data is proposed by designing a new data structure called EMRI-Tree as well as a flexible rendering workflow. The characteristics of our scheme can be summarized as follows. Firstly, the proposed EMRI-Tree supports memory-efficient data queries and ROI-data (ROI, region of interest) fetching on large 3D models, thus reducing the memory footprint significantly. Secondly, data blocks at different resolution levels in the EMRI-Tree are stored in a key-value (KV) storage system with variable-length indices, which improves the scalability of storage and the concurrency of reading. Lastly, a prefetching scheme is proposed, which supports progressive rendering based on ray marching to render a more accurate model as interact. By combining the above optimizations, the proposed scheme facilitates the visualization of large volumes of high-resolution data with limited memory overhead. Evaluating the approach by using 80 GB of synthetic data in 10 simulated read tests. The experimental results demonstrate that the scheme has the characteristics of 2 000+ QPS (queries per second) and linear growth in memory consumption, making it a robust and memory-efficient solution. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As the drought occurred frequently, and remote sensing technology matures, the drought remote sensing monitoring has become an important means of drought monitoring, and the vegetation supply water index(VSWI) products is an important reference for drought monitoring, but its production involves large amount of data, and the processing cycle is long, which influence the timeliness seriously. Based on OSGI service, a distributed production model of VSWI is proposed, which turn the VSWI algorithm into an OSGI module called Bundle. This Bundle can be deployed and installed dynamically in a distributed OSGI platform, which can make full use of the resources in LAN, and be helpful to raise up the operating rate of image processing, as well as lessen the memory footprint,which have been proved in this paper. This model will play an important role in massive remote sensing data processing and emergency monitoring. [ABSTRACT FROM AUTHOR]

[Objective] Traditional methods for detecting mold are time-consuming, labor-intensive, and vulnerable to environmental influences, highlighting the need for a swift, precise, and dependable detection approach. Researchers have utilized visible-near infrared (NIR) spectroscopy for the non-destructive, rapid assessment of wheat moisture content, crude protein content, concealed pests, starch content, dry matter, weight, hardness, origin, and other attributes. However, most of these studies rely on research-grade Visible-NIR spectrometers typically found in laboratories. While these spectrometers offer superior detection accuracy and stability, their bulky size, lack of portability, and high cost hinder their widespread use and adoption across various agricultural product distribution channels. [Methods] A low-resolution Visible-NIR spectrometer (VNIAPD, with a resolution of 1.6 nm) was utilized to gather wheat data. The aim was to enhance the accuracy of moldy wheat detection by identifying suitable spectral data preprocessing methods using corre‐ sponding algorithms. A high-resolution Visible-NIR spectrometer (SINO2040, with a resolution of 0.19 nm) served as a control to validate the instrument and method's effectiveness. The Zhoumai (No. 22) wheat variety was adopted, with a total of 100 samples pre‐ pared. The spectra of fresh wheat were scanned and then placed in a constant temperature chamber at 35°C to replicate the appropriate conditions for mold growth, thereby accelerating the reproduction of naturally occurring mold in the wheat. The degree of mold was categorized based on the cultivation time in the constant temperature chamber, with wheat classified as mildly, moderately, or se‐ verely moldy after 3, 6, and 9 days of cultivation, respectively. A total of 400 wheat spectral data points were collected, including 100 samples each of fresh wheat, wheat cultured for 3 days, wheat cultured for 6 days, and wheat cultured for 9 days. Preprocessing methods such as standard deviation normalization (SDN), standard normal variation (SNV), mean centrality (MC), first-order derivatives (1ST), Savitzky-Golay smoothing (SG), and multiple scattering correction (MSC) were applied to the spectral data. Outliers were iden‐ tified and eliminated using the local outlier factor (LOF) method. Following this, the sequential projection algorithm (SPA) and Least absolute shrinkage and selection operator (LASSO) were used to extract characteristic wavelengths from the preprocessed spectra. Subsequently, six algorithms, including k-nearest neighbors (KNN), support vector machines (SVM), random forests (RF), NaïveBayes, back propagation neural networks (BPNN), and deep neural networks (DNN), were employed to model and analyze the feature wavelength spectra, differentiating moldy wheat and classifying the degree of mold. Evaluation criteria encompassed accuracy, modeling time, and model size to aid in selecting the most suitable model for specific application scenarios. [Results and discussions] Regarding accuracy, even when utilizing the computationally slower and more memory-demanding neural net‐ work models BPNN and DNN, both the VNIAPD and SINO2040 achieved a perfect 100% accuracy in the binary classification task of distinguishing between fresh and moldy wheat. They also maintained a faultless 100% accuracy in the ternary classification task that differentiates three varying levels of mold growth. Adopting faster and more memory-efficient shallow models such as KNN, SVM, RF, and Naïve-Bayes, the VNIAPD yielded a top test set accuracy of 97.72% when combined with RF for binary classification. Conversely, SINO2040 achieved 100% accuracy using Naïve-Bayes. In the ternary classification scenario, the VNIAPD hit the mark at 100% accuracy with both KNN and RF, while SINO2040 demonstrated 97.72% accuracy with KNN and SVM. Regarding modeling speed, the shallow machine learning algorithms, including KNN, SVM, RF, and Naïve-Bayes, exhibited quicker training times, with Naïve-Bayes being the swiftest at just 3 ms. In contrast, the neural network algorithms BPNN and DNN required more time for train‐ ing, taking 3 293 and 18 614 ms, respectively. Regarding memory footprint, BPNN had the largest model size, occupying 4 028 kb, whereas SVM was the most memory-efficient, with a size of only 4 kb. Overall, the VNIAPD matched the SINO2040 in detection ac‐ curacy despite having lower optical parameters: A slightly lesser optical resolution of 1.6 nm compared to the SINO2040's 0.19 nm— and a lower cost, highlighting its efficiency and cost-effectiveness in the given context. [Conclusions] In this study, by comparing different preprocessing methods for spectral data, the optimal data optimization choices for corresponding algorithms were identified. As a result, the low-resolution spectrometer VNIAPD was able to achieve performance on par with the high-resolution spectrometer SINO2040 in detecting moldy wheat, providing a new option for low-cost, non-destructive detection of wheat mold and the degree of moldiness based on Visible-NIR spectroscopy. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Wildlife / Yesheng Dongwu Xuebao is the property of Chinese Journal of Wildlife Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Zhengzhou University (Natural Science Edition) is the property of Journal of Zhengzhou University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

ASCON was the most promising algorithm to become an international standard in the 2021 NIST lightweight authenticated encryption call for proposals.The algorithm was designed to achieve the best performance in IoT resource-constrained environments, and there was no hardware IP core implementation based on this algorithm in the open literature.A software-hardware collaborative implementation method of ASCON was proposed, which improved the speed and reduced the memory footprint of ASCON in IoT security authentication applications through hardware means such as S-box optimization, prior calculation and advanced pipeline design.As a comparison, ASCON has been transplanted on the common IoT embedded processor platform.The results showed that the described method was more than 7.9 times faster, while the memory footprint was reduced by at least 90%.The schemes can be used for the design and implementation of IoT security application-specific integrated circuits or SoCs.

At present, most of the gait-based smartphone authentication researches focus on a single controlled scenario without considering the impact of multi-scenario changes on the authentication accuracy. The movement direction of the smartphone and the user changes in different scenarios, and the user's gait data collected by the orientation-sensitive sensor will be biased accordingly. Therefore, it has become an urgent problem to provide a multi-scenario high-accuracy gait authentication method for smartphones. In addition, the selection of the model training algorithm determines the accuracy and efficiency of gait authentication. The current popular authentication model based on long short-term memory (LSTM) network can achieve high authentication accuracy, but it has many training parameters, large memory footprint, and the training efficiency needs to be improved. In order to solve the above problems a multi-scenario gait authentication scheme for smartphones based on Gate Recurrent Unit (GRU) was proposed. The gait signals were preliminarily denoised by wavelet transform, and the looped gait signals were segmented by an adaptive gait cycle segmentation algorithm. In order to meet the authentication requirements of multi-scenario, the coordinate system transformation method was used to perform direction-independent processing on the gait signals, so as to eliminate the influence of the orientation of the smartphone and the movement of the user on the authentication result. Besides, in order to achieve high-accuracy authentication and efficient model training, GRUs with different architectures and various optimization methods were used to train the gait model. The proposed scheme was experimentally analyzed on publicly available datasets PSR and ZJU-GaitAcc. Compared with the related schemes, the proposed scheme improves the authentication accuracy. Compared with the LSTM-based gait authentication model, the training efficiency of the proposed model is improved by about 20%. [ABSTRACT FROM AUTHOR]

When traditional convolutional neural networks are used in mineral species identification, due to their large parameters and the limitations of fixed input image resolution, sufficient computing resources and image preprocessing are required, which are difficult to deploy in actual exploration. For this reason, based on the depth separable convolution,combined with the attention mechanism, this paper constructs a mineral intelligent recognition model through dense connection, and this model can train multi-resolution mineral images. Ex perimental results show that the model's memory usage is only 20 Mb, the verification accuracy and the test accuracy are both higher than 90 %, the classification effect is better than the classic convolutional neural network, and it shows excellent ability to distinguish positive and negative samples. These results show that the proposed model h as fewer parameters and low memory footprint which is suitable for portable devices. This model can successfully identify mineral images with different resolutions, which has good generalization and potential application. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Guangdong University of Technology is the property of Journal of Guangdong University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

ObjectiveTraditional methods for detecting mold are time-consuming, labor-intensive, and vulnerable to environmental influences, highlighting the need for a swift, precise, and dependable detection approach. Researchers have utilized visible-near infrared (NIR) spectroscopy for the non-destructive, rapid assessment of wheat moisture content, crude protein content, concealed pests, starch content, dry matter, weight, hardness, origin, and other attributes. However, most of these studies rely on research-grade Visible-NIR spectrometers typically found in laboratories. While these spectrometers offer superior detection accuracy and stability, their bulky size, lack of portability, and high cost hinder their widespread use and adoption across various agricultural product distribution channels.MethodsA low-resolution Visible-NIR spectrometer (VNIAPD, with a resolution of 1.6 nm) was utilized to gather wheat data. The aim was to enhance the accuracy of moldy wheat detection by identifying suitable spectral data preprocessing methods using corresponding algorithms. A high-resolution Visible-NIR spectrometer (SINO2040, with a resolution of 0.19 nm) served as a control to validate the instrument and method's effectiveness. The Zhoumai (No. 22) wheat variety was adopted, with a total of 100 samples prepared. The spectra of fresh wheat were scanned and then placed in a constant temperature chamber at 35 °C to replicate the appropriate conditions for mold growth, thereby accelerating the reproduction of naturally occurring mold in the wheat. The degree of mold was categorized based on the cultivation time in the constant temperature chamber, with wheat classified as mildly, moderately, or severely moldy after 3, 6, and 9 days of cultivation, respectively. A total of 400 wheat spectral data points were collected, including 100 samples each of fresh wheat, wheat cultured for 3 days, wheat cultured for 6 days, and wheat cultured for 9 days. Preprocessing methods such as standard deviation normalization (SDN), standard normal variation (SNV), mean centrality (MC), first-order derivatives (1ST), Savitzky-Golay smoothing (SG), and multiple scattering correction (MSC) were applied to the spectral data. Outliers were identified and eliminated using the local outlier factor (LOF) method. Following this, the sequential projection algorithm (SPA) and Least absolute shrinkage and selection operator (LASSO) were used to extract characteristic wavelengths from the preprocessed spectra. Subsequently, six algorithms, including k-nearest neighbors (KNN), support vector machines (SVM), random forests (RF), Naïve-Bayes, back propagation neural networks (BPNN), and deep neural networks (DNN), were employed to model and analyze the feature wavelength spectra, differentiating moldy wheat and classifying the degree of mold. Evaluation criteria encompassed accuracy, modeling time, and model size to aid in selecting the most suitable model for specific application scenarios.Results and discussionsRegarding accuracy, even when utilizing the computationally slower and more memory-demanding neural network models BPNN and DNN, both the VNIAPD and SINO2040 achieved a perfect 100% accuracy in the binary classification task of distinguishing between fresh and moldy wheat. They also maintained a faultless 100% accuracy in the ternary classification task that differentiates three varying levels of mold growth. Adopting faster and more memory-efficient shallow models such as KNN, SVM, RF, and Naïve-Bayes, the VNIAPD yielded a top test set accuracy of 97.72% when combined with RF for binary classification. Conversely, SINO2040 achieved 100% accuracy using Naïve-Bayes. In the ternary classification scenario, the VNIAPD hit the mark at 100% accuracy with both KNN and RF, while SINO2040 demonstrated 97.72% accuracy with KNN and SVM. Regarding modeling speed, the shallow machine learning algorithms, including KNN, SVM, RF, and Naïve-Bayes, exhibited quicker training times, with Naïve-Bayes being the swiftest at just 3 ms. In contrast, the neural network algorithms BPNN and DNN required more time for training, taking 3 293 and 18 614 ms, respectively. Regarding memory footprint, BPNN had the largest model size, occupying 4 028 kb, whereas SVM was the most memory-efficient, with a size of only 4 kb. Overall, the VNIAPD matched the SINO2040 in detection accuracy despite having lower optical parameters: A slightly lesser optical resolution of 1.6 nm compared to the SINO2040's 0.19 nm—and a lower cost, highlighting its efficiency and cost-effectiveness in the given context.ConclusionsIn this study, by comparing different preprocessing methods for spectral data, the optimal data optimization choices for corresponding algorithms were identified. As a result, the low-resolution spectrometer VNIAPD was able to achieve performance on par with the high-resolution spectrometer SINO2040 in detecting moldy wheat, providing a new option for low-cost, non-destructive detection of wheat mold and the degree of moldiness based on Visible-NIR spectroscopy.

For the problems such as mining low accuracy of algorithm, low speed and large memory footprint when digging the complex and large-scale data sets in the biological information network, this paper proposed a biological information network multi-dimensional data mining algorithm that based on association rules mapping. The algorithm combined association mapping relationship between the network dataset to determine the association rules of network dataset, and introduced the mining factor and relative error to improve mining accuracy of the algorithm. According to the multi-dimensional subspace degree of association between the data sets to distinguish the subspace and subspace datasets in order to achieve effective excavation of different data sets. The experiment al results on the memory usage of the algorithm on the number of different sets of data, the accuracy of mining algorithm, the simulation of algorithm running time, show the association rule mining algorithm can effectively improve the mining map accuracy, reduce the memory footprint and enhance the computing speed. [ABSTRACT FROM AUTHOR]

Iterative quantization hashing algorithm does not consider that most of high-dimensional visual descriptors for images exhibit a natural matrix structure. When high-dimensional feature vectors represented the visual descriptors and assigned long binary codes,a random projection matrix requires expensive complexities in both space and time. To address the above issues, this paper proposed a hashing image retrieval method based on bilinear iterative quantization, which mapped the high-dimensional into two smaller projection matrices using compact bilinear projections instead of a single large projection matrix. Then it minimized the quantization error in an iterative quantization way and generate effective hash code. Experiments on two datasets, CIFAR-10 and Caltech256. This method achieved comparahle retrieval accuracy to the state-of-the-art 8 hashing methods while having orders of magnitude faster linear scanning time and smaller memory footprint. The results show that the proposed method can reduce the impact of the high dimensionality of data and improve the performance of ITQ, the algorithm can widely serve the hashing image retrieval application of high-dimensional data and long coded bits. [ABSTRACT FROM AUTHOR]

Copyright of Science Technology & Engineering is the property of Chinese Society of Technology Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to overcome the shortcomings of traditional EJB, CORBA, and publish/subscribe middleware software architecture, an efficient software component framework suitable for embedded distributed computing environment is proposed. The software architecture is based on the open service gateway initiative ( OSGi) standard, and can be used in multi-communications protocol environment by adding a transmission abstraction layer. It is characterized by versatility, high stability and less memory footprint. The experimental results show that the software is superior to the traditional software architecture in memory footprint, startup time and service interface response time. It can meet the harsh conditions of the embedded environment. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Image & Graphics is the property of Editorial Office of Journal of Image & Graphics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Redundant code is common in commercial and open source software,and its presence can increase memory footprint,affect code maintainability,and increase maintenance costs.Rapid type analysis algorithm is a common static analysis method in Java redundant code detection,but it still has some shortcomings in virtual method analysis.XTA is a call graph construction algorithm with high precision and efficiency in handling virtual method calls.A method based on XTA call graph construction algorithm is proposed to detect redundant code in Java code.This method is implemented in a prototype tool called redundant code Detection(RCD),and the knowledge graph is constructed to assist manual review to improve the efficiency of manual review and the reliability of redundant code detection.RCD is compared with three other redundant code detection tools by experiments on four open source Java applications.Experimental results show that RCD improves the accuracy of detecting redundant codes by 1%~30% compared with other tools,and improves the integrity of detecting redundant virtual methods by about 4%.

At present, most of the gait-based smartphone authentication researches focus on a single controlled scenario without considering the impact of multi-scenario changes on the authentication accuracy.The movement direction of the smartphone and the user changes in different scenarios, and the user’s gait data collected by the orientation-sensitive sensor will be biased accordingly.Therefore, it has become an urgent problem to provide a multi-scenario high-accuracy gait authentication method for smartphones.In addition, the selection of the model training algorithm determines the accuracy and efficiency of gait authentication.The current popular authentication model based on long short-term memory (LSTM) network can achieve high authentication accuracy, but it has many training parameters, large memory footprint, and the training efficiency needs to be improved.In order to solve the above problems a multi-scenario gait authentication scheme for smartphones based on Gate Recurrent Unit (GRU) was proposed.The gait signals were preliminarily denoised by wavelet transform, and the looped gait signals were segmented by an adaptive gait cycle segmentation algorithm.In order to meet the authentication requirements of multi-scenario, the coordinate system transformation method was used to perform direction-independent processing on the gait signals, so as to eliminate the influence of the orientation of the smartphone and the movement of the user on the authentication result.Besides, in order to achieve high-accuracy authentication and efficient model training, GRUs with different architectures and various optimization methods were used to train the gait model.The proposed scheme was experimentally analyzed on publicly available datasets PSR and ZJU-GaitAcc.Compared with the related schemes, the proposed scheme improves the authentication accuracy.Compared with the LSTM-based gait authentication model, the training efficiency of the proposed model is improved by about 20%.

This paper proposed an organization method of laser point cloud data for the efficient management and rapid visualization of the massive point cloud data of vehicle-mounted laser scanner. In this paper, both the original point cloud data and its trajectory are sectioned for the fast indexing firstly, then the LOD(levels of details) index of each section is build based on octree structure. With a tile type and multiresolution storage mode based on folder system, the depth of octree structure is represented by the level of folder directory, and in every node folder, the corresponding point cloud data file and its node properties file are both include. The storage method of this paper decreases the preprocessing time greatly and can support concurrent access owing to mutually independence of each node. Moreover, with the application of view frustum culling technology and multi-thread dynamic dispatch technology, the rendering and roaming of massive point cloud can realize real time updating according to viewpoint change, which significantly improves the dispatch efficiency of vehicle-mounted laser point cloud data. In the experiment, our method was compared with several popular software of point cloud data processing (e.g. XGRT, Quick Terrain Reader) on the data dispatch. The results show our method has obvious advantage on storage capacity, data access time, memory footprint, rendering frame rate and so on, it indicates our method is effective for the management of vehicle-mounted laser point cloud data and can improve the production efficiency for indoor-work of vehicle-mounted laser point cloud data processing. [ABSTRACT FROM AUTHOR]

In order lo solve the difficulty lhal ihe existing algorithms can't give attention lo hoih the capability of lime and space when constructing Delaunay triangulalion with massive LiDAK points cloud, a culling block Delaunay iriangulalion algorilhm using streaming compulation is presented. DeWall (Delaunay wall) is constructed firstly lo cut an independent point cloud data block with specific size and shape, which is suitable for the divide-and-conquer algorilhm and can avoid deep recursion and memory overflow. Then the culling block is iriangulaled with a divide-and-conquer algorilhm. and an algorilhm is proposed to delete the wrong triangles on the boundary of ihe culling block iriangulalion. The process described above is repealed to construct all the sub-lriangulalions. which can be merged directly according to ihe properly of ihe decoupled domain decomposition mode finally. Meanwhile, ihe streaming compulation is introduced so lhal ihe algorilhm has a more excellent capability of space. The analysis and experimenls show lhal ihe algorilhm has a low memory footprint and is efficient with a lime complexity close to is ihe number of poinls in a culling block and S

In order to solve the problems of A* algorithm in raster map path planning, such as large memory consump-tion and slow computing speed due to the traversal of many redundant nodes, an improved strategy for A* algorithm is proposed. Firstly, the specific calculation method of the heuristic function is improved. The Chebyshev distance is used to replace the Euclidean distance so that the heuristic function is exactly equal to the actual optimal path and the number of A* node extension is reduced. Secondly, jump-point-search (JPS) strategy is used to screen out a large number of unnecessary neighboring nodes added to OpenList and ClosedList instead of A* algorithm. Long distance jumps are achieved through the hops, thereby reducing the memory footprint and evaluation of nodes until the final path is generated. In order to verify the improved effect of the A* algorithm, the simulation test is carried out in the two-dimensional raster map with five sizes. The results show that the improved A* algorithm reduces a large number of nodes in pathfinding process evaluation and improves pathfinding speed. Moreover, with the increase of map size, the improved A* algorithm can increase pathfinding speed by more than one order of magnitude. Finally, the improved algorithm can be applied to experiment on mobile robot path planning. Under the same planning tasks, the improved A* algorithm under JPS strategy than traditional A* algorithm, path search consuming time is decreased by 92.2%, and expanded nodes are decreased by 97.37%, which can satisfy the mobile robot fast path planning requirements in large scenarios.

Copyright of Journal of South China Agricultural University is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As an important way of information transmission and storage, small file has been widely used in many fields. Meanwhile, its reliability and speed requirements need to be improved. For the inefficiency of small file storage, combining the advantage of big file storage of distributed storage system HDFS and the Redis cache technology, we propose a fast small file merging scheme. Small files are merged to Sequence File, which is then stored in HDFS. Loads are balanced by load coefficients that are determined by multiple linear regression analysis , and the efficiency of file access is guaranteed by cache. In experiments, the corresponding file platform is constructed to analyze and compare upload, access, delete, and memory footprint with the traditional direct upload. We can see that, compared with the traditional way of uploading files to HDFS, the improved small files treatment can ensure the reliability of files and enables users operations on small files faster. [ABSTRACT FROM AUTHOR]