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Today, computer graphics and graphic image processing techniques have been widely used in daily life and industrial production. Due to the development of computers, computer graphics has brought more convenience to our daily life. In order to give full play to the value of computers, this paper takes the Hakka paper-cut art with local characteristics as the starting point, first of all its development history, artistic characteristics, compositional forms, expression techniques, cultural connotations, Hakka paper-cut patterns, and the symbolic meaning of folk customs, and then we design a visualization system for the paper-cut works of Gannan Hakka based on computer graphics. In addition, the system provides a solution for the integration of Gannan Hakka paper-cut art and Jiangxi native product packaging design and provides a reference for the theory and practice of modern native product packaging design. [ABSTRACT FROM AUTHOR]

In order to improve the effect of intelligent automatic test paper composition, this paper combines the hybrid fuzzy clustering algorithm to study the computer automatic test paper composition algorithm. In this paper, a computer automatic test paper composition system based on hybrid fuzzy clustering algorithm is constructed. Moreover, the hybrid fuzzy clustering method used in this paper is used as the basic algorithm of the system, and the algorithm is improved according to the actual needs of intelligent paper composition. In addition, this paper uses an intelligent algorithm to input the relevant constraint parameters and combines the original parameters to select the most suitable test questions from the database and combine them into test papers. Finally, this paper constructs the system structure based on the requirements of intelligent test paper composition. The experimental research shows that the computer automatic test paper composition system based on the hybrid fuzzy clustering algorithm proposed in this paper has a good test paper composition function, which can effectively promote the progress of the intelligent examination mode in colleges and universities. [ABSTRACT FROM AUTHOR]

The traditional home product design system mainly depends on relatively shallow learning network, relatively simple embedded technology and Internet of things technology. The traditional home design system mainly depends on the traditional self-encoder technology. When combined with the deep neural network, this technology has serious defects in the computer vision algorithm, resulting in the serious waste of the corresponding system storage and computing resources, the corresponding system learning efficiency is relatively poor and the learning ability is weak. Based on this, this paper will build a home product design system based on the deep neural network of self-encoder. By improving the sparsity of self-encoder in the process of learning and training, we can further improve the sparsity of the system and further optimize the structure of self-encoder in the design system, The performance of the deep learning model of the design system is further improved through the hierarchical features continuously learned by the self-encoder in the process of home case design. Based on the optimization of the home product design system in this paper, the system effectively improves and improves the accuracy and stability of the internal feature classifier of the system, and improves the overall performance of the furniture design system. In the specific system construction part, based on ZigBee technology and embedded technology as the design carrier, and adhering to the goal of simplicity, intelligence and convenience, this paper designs and constructs the home product design system. The experimental results show that the noise processing level of the proposed home product design system is lower than 4-5db compared with the traditional design system, and the corresponding image classification accuracy is about 4% higher than the traditional design system. Therefore, the experimental results show that the home design system proposed in this paper has obvious advantages. [ABSTRACT FROM AUTHOR]

Nowadays, with the development of computer science and technology, computer intelligent algorithms are more and more widely used in various industries. Every calculation formula in the computer intelligent algorithm has systematic logic and singleness, in order to expound the dynamic algorithm of football training optimized by the computer intelligent algorithm in detail. In this paper, the monitoring system using the computer intelligent algorithm can dynamically observe people or objects and systematically analyze them. This paper mainly studies the research of a football training dynamic monitoring system based on the computer intelligent algorithm and the design and optimization of the computer intelligent dynamic monitoring system in football training. Finally, the overall composition of the computer intelligent dynamic monitoring system and the application of the optimized computer intelligent dynamic monitoring system to the analysis of sample data are studied. [ABSTRACT FROM AUTHOR]

Recently, wireless sensor networks have employed the concept of mobile agent to reduce energy consumption and obtain effective data gathering. Typically, in data gathering based on mobile agent, it is an important and essential step to find out the optimal itinerary planning for the mobile agent. However, single-agent itinerary planning suffers from two primary disadvantages: task delay and large size of mobile agent as the scale of the network is expanded. Thus, using multi-agent itinerary planning overcomes the drawbacks of single-agent itinerary planning. Despite the advantages of multi-agent itinerary planning, finding the optimal number of distributed mobile agents, source nodes grouping, and optimal itinerary of each mobile agent for simultaneous data gathering are still regarded as critical issues in wireless sensor network. Therefore, in this article, the existing algorithms that have been identified in the literature to address the above issues are reviewed. The review shows that most of the algorithms used one parameter to find the optimal number of mobile agents in multi-agent itinerary planning without utilizing other parameters. More importantly, the review showed that theses algorithms did not take into account the security of the data gathered by the mobile agent. Accordingly, we indicated the limitations of each proposed algorithm and new directions are provided for future research. [ABSTRACT FROM AUTHOR]

An introduction to the journal is presented in which the editor discusses various reports within the issue on topics related to reconfigurable computing including calculation of logarithm function of data, graphical processing units (GPUs), and used of dynamic instruction merging (DIM) approach.

This paper comprehensively analyzes the characteristics of flexible job shop scheduling problem (FJSSP), takes the dynamic factors in the actual scheduling process such as the arrival and departure of jobs, the breakdown and recovery of machines into account at the same time, and establishes a new dynamic multi-objective mathematical model. Take the Squirrel Search Algorithm (SSA) as the core evolution strategy, and combine the multi-objective framework and the dynamic processing technology to solve the established mathematical model. Experimental results show that the mathematical model proposed in this paper can solve the flexible job shop scheduling problem effectively. Compared with other mathematical models, the mathematical model established in this paper can keep better balance between the efficiency and stability. [ABSTRACT FROM AUTHOR]

People's lives are undergoing tremendous changes with the development of the times. Compared with the past, people's pursuit of spiritual and cultural life also makes our education field usher in a huge development to adapt to the changes in the context of the times. But, at the same time, the development of labor education is gradually being downplayed by people, resulting in a series of problems such as people preferring comfort and not working. Aiming at this common problem, this paper will use the ant colony algorithm and particle swarm optimization algorithm in the computer intelligent algorithm to optimize the way of labor education. It includes the principle and basic process of the ant colony algorithm, the establishment of the mathematical model of the original ant colony algorithm, and the improved algorithm of the ant colony algorithm. The research results of the optimization method of labor education showed the following: when the number of ant colonies reaches 51, the number of iterations of the algorithm will be the least, and the corresponding shortest path is also the best solution; when the combination of pheromone intensity and volatility factor is 3, the optimal solution can be quickly found, and the algorithm inflection point of MMAS is 44.82. From the research results, it can be seen that the computer intelligent algorithm has a good choice for the optimization of labor education and can achieve a major breakthrough in the traditional model of labor education. [ABSTRACT FROM AUTHOR]

With the development of economy, all world countries are entering the ranks of competitive sports. The Olympic Games or large-scale gymnastics performances are of great significance to the publicity and implementation of the national health plan. This paper aims to study the computer simulation algorithm of group gymnastics formation change path under artificial intelligence and network big data, so as to find out the path change algorithm of each team member, so that members in group gymnastics can clearly see their own moving route, and finally achieve the purpose of reducing the difficulty and cost of rehearsal. In this paper, the ellipse search algorithm and the traditional D i j k s t r a algorithm, the rectangle search algorithm, and the traditional D i j k s t r a algorithm are deeply compared and analyzed, and then, the experimental comparison between the ellipse search algorithm and the rectangle search algorithm is carried out. It is concluded that when finding the shortest path and when the straight-line distance between two nodes is 4.0 km, the time used by the rectangle search algorithm is 2.1 s, while the ellipse search algorithm needs 3.98 s. This shows that the rectangle search algorithm greatly saves the operation time and has superior performance. [ABSTRACT FROM AUTHOR]

In order to improve the nursing effect of emergency general surgery, this paper combines computer algorithms to carry out the intelligent management of general surgery nursing, and realizes the standardization of nursing information, the electronic nursing file, the precision of nursing workload, and the intelligentization of nursing quality control by means of informatization. This truly and objectively reflects the nursing operation and treatment situation, prevents the occurrence of some adverse events, and effectively reduces the workload of nursing care. Moreover, this paper uses a standardized software design method to define the software concept, and then conducts a detailed demand analysis of the nursing display function through detailed investigation, class work, discussion and analysis, and comparison decision-making methods. In addition, this paper compiles the software through strict coding standards, and finally designs test cases to test and improve the software. Through actual case studies, it can be seen that the computer-assisted emergency general surgery nursing method proposed in this paper has a certain progress compared with the traditional nursing method. [ABSTRACT FROM AUTHOR]

Recently, the major challenging attack is the side channel attack for both symmetric and asymmetric algorithm. Because in both cases, the secret key has to be sent out via a safe secured channel. The advanced encryption standard (AES) is mentioned as the best standard encryption algorithm by the US government, but there is one major attack on the AES that is the brute force attack. So, how to rule out these kinds of attack and how it is implemented effectively in hardware are being discussed in this paper. The honey encryption scheme is an effective algorithm, and it is also being discussed. In this paper, the detailed description of the AES and the methods to improve its speed and how it is integrated with the honey technique have been discussed. The different kinds of attacks and the various solutions are discussed for both of the algorithms. A survey of papers which has used these algorithms for various reasons with different perspective has been given. The honey algorithm creates the fake message for the wrong try of the key puncher. Thus, this is considered as the best algorithm, but it is also has some disadvantages that is also focussed in our survey. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Computer is one of the indispensable tools in the human world, and human needs for it are increasing, so the emergence and application of more advanced computers are needed. The current computers do not respond intelligently, and it is difficult to meet people's needs for information processing in the era of big data. In order to solve these problems, this paper proposes the application of a neural network-based data classification algorithm in computers, aiming to study the practical application of the algorithm in computers. The research method of this paper is to introduce the BP neural network, select the appropriate method of classification features, and then study the data classification algorithm. The function of the research method is to compare the classification error and convergence speed of the BP network composed of different hidden layer nodes, to study whether a certain feature item of the data exists and the difference in the amount of information classification of the entire document, and to select high efficiency, accuracy, and scalability algorithm. This paper compares the forward reasoning time of the model before and after cutting through experiments based on neural network model design, algorithm design, and man-machine dialogue model design. The results show that, in terms of computing speed, the adaptive model compression method based on the accuracy and redundancy ratio compresses the model after the forward reasoning time is greatly reduced, and the reasoning time becomes 35% of the original, and in terms of calculation accuracy, the absolute error after using the SOM method in this article has not reached 0.5. [ABSTRACT FROM AUTHOR]

Wireless sensor network is a network that integrates sensor technology, computer technology, information processing technology, and communication technology. This paper aims to study how to analyze and study the routing optimization of wireless sensor network based on deep learning and describe the neural network. This paper puts forward the problem of routing optimization, which is based on the dynamic programming of wireless sensor network, and then elaborates around its concept and related algorithms and designs and analyzes the case of wireless sensor network optimization. Through the comparative analysis of the five algorithms in computer simulation, although the average network delay performance of DPER reached 0.47 s, it could effectively prolong the life cycle of the network. The DPER algorithm not only improves the network life but also improves the network energy utilization rate, shortens the average path length of the network, and reduces the standard deviation of the remaining energy of the node. [ABSTRACT FROM AUTHOR]

In the video monitoring of piglets in pig farms, study of the precise segmentation of foreground objects is the work of advanced research on target tracking and behavior recognition. In view of the noninteractive and real-time requirements of such a video monitoring system, this paper proposes a method of image segmentation based on an improved noninteractive GrabCut algorithm. The functions of preserving edges and noise reduction are realized through bilateral filtering. An adaptive threshold segmentation method is used to calculate the local threshold and to complete the extraction of the foreground target. The image is simplified by morphological processing; the background interference pixels, such as details in the grille and wall, are filtered, and the foreground target marker matrix is established. The GrabCut algorithm is used to split the pixels of multiple foreground objects. By comparing the segmentation results of various algorithms, the results show that the segmentation algorithm proposed in this paper is efficient and accurate, and the mean range of structural similarity is [0.88, 1]. The average processing time is 1606 ms, and this method satisfies the real-time requirement of an agricultural video monitoring system. Feature vectors such as edges and central moments are calculated and the database is well established for feature extraction and behavior identification. This method provides reliable foreground segmentation data for the intelligent early warning of a video monitoring system. [ABSTRACT FROM AUTHOR]

In order to improve the transient stability of frequency in a small stand-alone microgrid (SSM), this paper takes a SSM composed of a direct-drive permanent magnet synchronous generator (D-PMSG) and a micro gas turbine (MGT) as the background and uses wind turbine generator (WTG) virtual inertia (VI) to participate in the primary (short-term) system frequency regulation. First of all, this paper constructs a grid-connected model composed of a WTG and a MGT, analyzes the WTG virtual inertia frequency regulation mechanism, and explains the principle of proportional-differentiation (PD) virtual inertia control (VIC) and its shortcomings. Secondly, the paper introduces the structure principle of n-order active disturbance rejection control (ADRC) and deduces the design process of second-order ADRC-VIC. Finally, through the simulation and experimental verification, comparing the frequency perturbation of without-VIC, PD-VIC, and ADRC-VIC, it is concluded that PD-VIC and ADRC-VIC both can use the WTG virtual inertia to participate in the primary frequency regulation. The frequency regulation effect of ADRC-VIC is better than PD-VIC, ADRC-VIC can extend the rotor speed recovery time and avoid overshoot, and its frequency fluctuation amplitude and settling time are obviously improved, and ADRC-VIC can effectively avoid the overshoot phenomenon of the MGT output power. [ABSTRACT FROM AUTHOR]

With the rise in biometric-based identity authentication, facial recognition software has already stimulated interesting research. However, facial recognition has also been subjected to criticism due to security concerns. The main attack methods include photo, video, and three-dimensional model attacks. In this paper, we propose a multifeature fusion scheme that combines dynamic and static joint analysis to detect fake face attacks. Since the texture differences between the real and the fake faces can be easily detected, LBP (local binary patter) texture operators and optical flow algorithms are often merged. Basic LBP methods are also modified by considering the nearest neighbour binary computing method instead of the fixed centre pixel method; the traditional optical flow algorithm is also modified by applying the multifusion feature superposition method, which reduces the noise of the image. In the pyramid model, image processing is performed in each layer by using block calculations that form multiple block images. The features of the image are obtained via two fused algorithms (MOLF), which are then trained and tested separately by an SVM classifier. Experimental results show that this method can improve detection accuracy while also reducing computational complexity. In this paper, we use the CASIA, PRINT-ATTACK, and REPLAY-ATTACK database to compare the various LBP algorithms that incorporate optical flow and fusion algorithms. [ABSTRACT FROM AUTHOR]

In order to improve the identifiable teaching effect of physics, this paper studies the identifiable teaching technology of physics combined with computer simulation analysis technology, changes the traditional physics teaching method, and studies the identifiable teaching effect of computer simulation algorithm. The advantages are obvious; that is, it overcomes scaling, translation, and rotation and does not require a lot of time to learn samples, etc. In addition, this paper combines the computer simulation analysis technology and the actual needs of physics teaching to construct an intelligent and identifiable teaching system. The experimental research results show that the physics identifiable teaching system based on computer simulation analysis proposed in this paper can play an important role in physics teaching. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence and the rapid development of the computer industry, the practicability of computer vision programs is gradually improved. In this paper, the badminton path tracking algorithm based on computer vision analyzes the badminton trajectory and speed. This paper is aimed at analyzing the image processing technology and path tracking algorithm by using computer vision to obtain relevant data and then exploring the factors of badminton path and ball speed transformation, which provides reference significance for badminton players in future training. The path tracking algorithm is used to predict the rotation angle, the ball speed, and the athlete's body information during the badminton movement through sensors, and the position information of the moving target is captured based on the visual field tracking and target dynamic tracking. Combined with specific badminton players, we first analyze the angle of each limb and the speed of the racket in the process of movement and record the data. Determine different positioning points for different actions, such as pushing the ball, picking the ball, hooking the ball, and rubbing the hair. In this process, we aim at the connection between the highest point and the lowest point of the badminton trajectory and the ball speed. This process fully combines the theoretical knowledge of the path tracking algorithm. The experimental results show that different service skills have different effects on the trajectory and speed of badminton. In the test of relevant data by using the push and receive skills, the lowest point of the ball served by player A in the first three times is higher than that by player B. The most significant difference between the lowest points of the five times is the second time, with a difference of 0.2 m, and the third time, with a minimum difference of 0.03 m. [ABSTRACT FROM AUTHOR]

Offshore channel clearance is an essential underwater task to protect vessels and divers effectively, but current underwater target classification relies heavily on operator identification. Machine learning provides highly accurate methods for image classification as well as detection. In this paper, a new hybrid quantum-classical classification algorithm is proposed. It uses quantum devices to reduce dimension and denoise data sets, greatly reducing the difficulty of classical computer processing data. Using abundant classical classification algorithms, the classification problem of different scenarios can be processed, improving the classification efficiency. Using two kinds of underwater object data sets as examples, the numerical simulation results show that the quantum algorithm can accurately achieve dimensionality reduction. This hybrid algorithm has polynomial acceleration in dimension reduction than classical methods, even considering the classical readout of quantum data. The results also show that the classification accuracy of the training set improves from 0.772 to 0.821 compared to the original dataset. Furthermore, different classical classifiers can be selected in the case of different objects, so this hybrid algorithm has broad application prospects in different fields. [ABSTRACT FROM AUTHOR]

Quantum machine learning uses quantum mechanical concepts of superposition of states to make the decision. In this work, we used these quantum advantages to enhance deep reinforcement learning (DRL). Our primary and foremost goal is to investigate and elucidate a way of representing and solving the frozen lake problems by using PennyLane which contains Xanadu's back-end quantum processing unit. This paper specifically discusses how to enhance classical deep reinforcement learning algorithms with quantum computing technology, making quantum agents get a maximum reward after a fixed number of epochs and realizing the effect of a number of variational quantum layers on the trainability of enhanced framework. We have analyzed that, as the number of layers increases, the ability of the quantum agent to converge to the optimal state also increases. For this work, we have trained the framework agent with 2, 3, and 5 variational quantum layers. An agent with 2 layers converges to a total reward of 0.95 after the training episode of 526. The other agent with layers converges to a total reward of 0.95 after the training episode of 397 and the agent which uses 5 quantum variational layers converges to a total reward of 0.95 after the training episode of 72. From this, we can understand that the agent with a more variational layer exploits more and converges to the optimal state before the other agent. We also analyzed our work in terms of different learning rate hyperparameters. We recorded every single learning epoch to demonstrate the outcomes of enhanced DRL algorithms with selected 0.1, 0.2, 0.3, and 0.4 learning rates or alpha values. From this result, we can conclude that the greater the learning rate values in quantum deep reinforcement learning, the fewer timesteps it takes to move from the start point to the goal state. [ABSTRACT FROM AUTHOR]

In this paper, a novel bearing intelligent fault diagnosis method based on a novel krill herd algorithm (NKH) and kernel extreme learning machine (KELM) is proposed. Firstly, multiscale dispersion entropy (MDE) is used to extract fault features of bearings to obtain a set of fault feature vectors composed of dispersion entropy. Then, it is imported into the kernel extreme learning machine for fault diagnosis. But considering the kernel function parameters σ and the error penalty factor C will affect the classification accuracy of the kernel extreme learning machine, this paper uses the novel krill herd algorithm (NKH) for their optimization. The opposite populations are added to the NKH in the initialization of population to improve its speed and prevent local optimum, and during the period of looking for the optimal solution, the impulse operator is introduced to ensure it has enough impulse to rush out of the local optimal once into the local optimum. Finally, in order to verify the effectiveness of the proposed method, it was applied to the bearing fault experiment of Case Western Reserve University and XJTU-SY bearing data set. The results show that the proposed method not only has good fault diagnosis performance and generalization but also has fast convergence speed and does not easily fall into the local optimum. Therefore, this paper provides a method for fault diagnosis under different loads. Meanwhile, the new method (NKH-KELM) is compared and analyzed with other mainstream intelligent bearing fault diagnosis methods to verify the effectiveness and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we analyse and study the interdisciplinary style of stable parallel algorithms for online computer education and real problem scenarios for STEM education. Under the guidance of the STEM concept, the project theme is designed based on the principles of project design, and the interdisciplinary knowledge points related to the theme are determined. Based on the project theme, the teaching design model of the STEM-based robotics project was constructed. The analysis of the results showed that the STEM-based middle-school robotics project not only increased students' interest in robotics but also promoted their creativity, problem-solving, teamwork, and communication skills, as well as their learning of other subjects. In this paper, an adaptive overflow-aware loss amplification strategy is proposed, which effectively alleviates the training nonconvergence problem caused by gradient overflow in mixed accuracy training. Also, this paper demonstrates that data-parallel training under multiple machines and multiple cards should use local batch normalization, which is particularly effective in accelerating neural networks containing more batch normalization layers. [ABSTRACT FROM AUTHOR]

The optimal linear estimation problems are investigated in this paper for a class of discrete linear systems with fading measurements and correlated noises. Firstly, the fading measurements occur in a random way where the fading probabilities are regulated by probability mass functions in a given interval. Furthermore, time-delay exists in the system state and observation simultaneously. Additionally, the multiplicative noises are considered to describe the uncertainty of the state. Based on the projection theory, the linear minimum variance optimal linear estimators, including filter, predictor, and smoother are presented in the paper. Compared with conventional state augmentation, the new algorithm is finite-dimensionally computable and does not increase computational and storage load when the delay is large. A numerical example is provided to illustrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Camera robot is an important tool for realizing and reproducing complex camera motion path in modern special film effects. This paper proposed an inverse kinematics optimization algorithm for PRRPR-S redundant degrees of freedom (DoF) camera robot. This paper analyzed the motion characteristics, in Genetic Mix (GM) method, from the idea of movement boundary composed of part robot axis. Then proposed Simplify Mix (SM) method which can stably converge to the global optimal solution in a shorter time. [ABSTRACT FROM AUTHOR]

Mobile cloud computing (MCC) has attracted extensive attention in recent years. With the prevalence of MCC, how to select trustworthy and high quality mobile cloud services becomes one of the most urgent problems. Therefore, this paper focuses on the trustworthy service selection and recommendation in mobile cloud computing environments. We propose a novel service selection and recommendation model (SSRM), where user similarity is calculated based on user context information and interest. In addition, the relational degree among services is calculated based on PropFlow algorithm and we utilize it to improve the accuracy of ranking results. SSRM supports a personalized and trusted selection of cloud services through taking into account mobile user’s trust expectation. Simulation experiments are conducted on ns3 simulator to study the prediction performance of SSRM compared with other two traditional approaches. The experimental results show the effectiveness of SSRM. [ABSTRACT FROM AUTHOR]

With the continuous in-depth exploration of life sciences, bioinformatics based on life sciences, computer algorithms, and statistics have gradually developed. The research of bioinformatics mainly focuses on the study of genes, and the structural characteristics of genes lead to a large amount of extremely complex data in the study of bioinformatics. Analyzing data in bioinformatics research requires accurate calculation by computer algorithms. However, common computer algorithms such as the dynamic programming algorithm and the genetic algorithm have the disadvantages of large memory or inaccurate optimization. Combining the ant colony algorithm (ACA) and GA can give the advantages of the two methods that should be fully utilized to efficiently analyze the biological information data. In this paper, the ant colony fusion genetic algorithm (ACA-GA), GA, and dynamic programming algorithm are used to compare and analyze the sensitivity, convergence speed, sequence alignment accuracy, and required memory space of gene sequences. The experimental results show that compared with ACA-GA, the dynamic programming algorithm has the advantage of finding the optimal alignment of 100%, but the memory required is too large, the memory required is more than 10 times that of the ant colony fusion genetic algorithm and the sensitivity is not as good as ACA-GA. The convergence speed of ACA-GA is faster than the gene comparison speed of GA and the accuracy is 2.6% better than that of GA on average. ACA-GA has the advantages of GA and ACA, which can improve the computational efficiency of biological data in bioinformatics. [ABSTRACT FROM AUTHOR]

An introduction to the journal is presented in which the editor discusses the article on design of moving target search (MTS) algorithms for computer generated bots by Peter Kok Keong Loh, the formal framework for shortest-path decision process problem representation by Julio Clempner, and the fractal model to examine aesthetic values of a new class of obstacle prone by Coleman Ron.

With the development of computer vision technology, human action pose recognition has gradually become a popular research direction, but there are still some problems in the application research based on pose recognition in sports action assisted evaluation. In this paper, the human motion pose recognition technology based on deep learning is introduced into this field to realize the intelligence of sports-assisted training. Firstly, we analyze the advantages and limitations of the state-of-the-art human motion pose recognition algorithms in computer vision in specific fields. On this basis, a human motion space recognition method based on periscope neural network is proposed. Firstly, the classical radar signal processing method is used to preprocess the echo signal of human spatial position and generate the frequency image in the process of human spatial position. Then, the periscope neural network (CNN) is constructed, and the time-frequency image is used as the input data of CNN to train the network parameters. Finally, the method is tested by using the open dataset in the network. The experimental results show that the designed CNN can accurately identify four different types of physical motion, and the accuracy coefficient is at least 97%. [ABSTRACT FROM AUTHOR]

Nowadays, laser processing technology is being used more and more in various fields, and the requirements for laser control procedures are getting higher and higher. This paper aims to study the path generation problem of laser marking technology in order to improve the efficiency of laser marking as well as the protection of the marking material. Therefore, we creatively propose two-path generation methods, namely, sawtooth parallel and contour parallel, and design the boundary curve offset algorithm and domain partition intersection algorithm for the computer simulation of the two marking paths, respectively. Through the simulation, we discussed the efficiency and marking quality of the two path generation methods and gave the conclusion that the efficiency of the sawtooth parallel path generation method is greater than that of the contour parallel path generation method under specific parameters. [ABSTRACT FROM AUTHOR]

In lightweight cryptographic primitives, round functions with simple operations XOR, modular addition, and shift (or rotation) are widely used nowadays. Among these ciphers, TEA and XTEA are two famous lightweight block ciphers. At AFRICACRYPT 2012, Jiazhe Chen, Meiqin Wang, and Bart Preneel proposed a method to establish impossible differential distinguishers for TEA and XTEA. At FSE 2012, with similar approach, Andrey Bogdanov and Meiqin Wang identified zero correlation linear distinguishers for TEA and XTEA. We find similarities in these two kinds of distinguishers and then probe into the deeper relationship between them. In this paper, we extend the TEA and XTEA to a more general TEA family-type ciphers and study the impossible differential distinguishers and zero correlation linear distinguishers for this kind of ciphers. More specifically, with the methods proposed in these two references earlier, firstly, we prove the longest lengths for impossible differential distinguishers and zero correlation linear distinguishers on TEA family-type ciphers. Secondly, the number of longest impossible differential distinguishers and zero correlation linear distinguishers are calculated respectively. Then, the specific forms of their input and output differences (or linear masks) are given. Thirdly, a dual property is proposed to demonstrate the deeper relationship between these two kinds of distinguishers. Finally, we give some suggestions for algorithm designers on how to shorten these two kinds of distinguishers for TEA family-type ciphers. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In the past 20 years, although there are many achievements in the model analysis and research, there are still problems of low data utilization and low accuracy. This paper analyzes the mental health level of college students based on chaotic algorithm. At the same time, the application of computer monitoring algorithm to students' real life psychology is discussed. According to different types of mental health analysis models, the high-precision matching analysis of different students is realized. At the same time, according to the personality characteristics and psychological changes of different students, the model is established and analyzed. Finally, an experiment is designed to carry out practical application and data analysis of the mental health analysis model. The results show that the intelligent analysis model based on computer chaos algorithm has better classification effect. In addition, the algorithm can also make different evaluation strategies according to the different personality of students and can carry out multidimensional classification for college students of different majors. It has effectively increased the proportion of college students' mental health groups. Compared with the current mainstream algorithms, the algorithm used in this study can adaptively classify college students of different majors. The accuracy of the experimental results is improved by at least 37% compared with the traditional method, and the error is low. [ABSTRACT FROM AUTHOR]

With the continuous advancement of big data technology, there are many drawbacks in the past urban greenway design. The environment, population distribution, geographic location, spatial distribution, and other factors affect the greenway design. At the same time, a large amount of historical data is mixed. The systematic arrangement has indirectly led to the fact that urban greenway design does not know how to analyze and use past data. There is always a waste of various resources. While greenways bring many positive impacts, they also have negative impacts: it is difficult for the wild animals in the greenways to move along the predetermined passages, and most animals spread in the greenways in a disorderly manner, so that the greenways accelerate the invasion of alien species and diseases to a certain extent. Using big data technology can enable us to allocate resources more reasonably and accurately. The purpose of this article is to study the use of big data technology to rationalize our urban greenway design. This paper adopts a calculation method that closely combines quantitative and qualitative features to study its features. The local feature analysis algorithm can extract and analyze urban spatial data. Spatial remote sensing image technology can deepen our knowledge and understanding of urban spatial characteristics, conduct data mining, provide all the information of urban space, analyze the content and type, and classify it; then filter and integrate the big data in the greenway planning and design information. The information about greenway planning and design in the big data of Dao Design is digitized. This information can be composed of traditional data and dynamic data. Finally, after scientific analysis, the data is subjected to scenario assumptions, modeling, and output, we applied various analysis results to actual operations, and we obtain the final inspection. In fact, this is also going out from actual work, summing up the experience we have done, and again starting from the perspective of specific practice. Compared with our traditional field investigation before, with the guidance of big data analysis, the experimental results of this article show that under the use of big data technology, the wrong resolution of the SegNet semantic classification algorithm for buildings is 9.6%, and the others are 10.9%, 16%, 20%, etc., which are all greater than 9.6%, so the segNet model has a higher image acquisition accuracy. It can solve the problems caused by the lag of the past questionnaires and the small sample size, improve the accuracy of data extraction through algorithms, find some features and problems of greenway design, and complete the urban greening rate through computer algorithms. Calculation and analysis can better provide the most reasonable method for greenway design site selection and route selection. [ABSTRACT FROM AUTHOR]

Image compression is the most important image processing method extensively deployed in different appliances. "Discrete wavelet transform (DWT)" is one of the well‐adopted transforming methods exploited for compressing images. The extremely deployed version of DWT is convolution‐oriented. Nevertheless, the lifting‐oriented DWT scheme requires more contemplation on more proficient performance and lesser computation cost. This paper intends to propose a deep learning‐based image compression model with a lifting scheme for predicting high‐frequency subbands. Moreover, the fine‐tuning in lifting factorization is done by a new Sea Lion with Averaged Update Evaluation that includes new cosine estimation under the COordinate Rotation DIgital Computer algorithm. Similarly, this study defines a new single objective function that merges the multiconstraints, like, "Peak Signal to Noise Ratio (PSNR) and Compression Ratio (CR)". At last, the supremacy of the presented approach is proved with respect to varied measures, like, CR, PSNR and so on. [ABSTRACT FROM AUTHOR]

In this paper, we propose a lying posture discrimination algorithm to monitor the behavior pattern of a person lying in bed. Using FSR (force sensing resistor) sensors arranged in a grid structure, three major body parts such as head, shoulder, and hips are identified, and six lying positions are determined based on these body parts. Head, shoulder, and hips are relatively high in pressure and are easy to distinguish due to low movement. In addition, we have effectively limited the search space to identify the above body parts based on standard body dimensions. Experimental results show that there is a correlation between pressure distribution and lying posture. The results of this study can be applied to the monitoring of sleep posture and can also be used as an aid to prevent fall accidents and the occurrence of pressure ulcers of elderly patients who are unable to move. [ABSTRACT FROM AUTHOR]

Mobile Robot Indoor Positioning System has wide application in the industry and home automation field. Unfortunately, existing mobile robot indoor positioning methods often suffer from poor positioning accuracy, system instability, and need for extra installation efforts. In this paper, we propose a novel positioning system which applies the centralized positioning method into the mobile robot, in which real-time positioning is achieved via interactions between ARM and computer. We apply the Kernel extreme learning machine (K-ELM) algorithm as our positioning algorithm after comparing four different algorithms in simulation experiments. Real-world indoor localization experiments are conducted, and the results demonstrate that the proposed system can not only improve positioning accuracy but also greatly reduce the installation efforts since our system solely relies on Wi-Fi devices. [ABSTRACT FROM AUTHOR]

Keratoconus (KTC) is a noninflammatory disorder characterized by progressive thinning, corneal deformation, and scarring of the cornea. The pathological mechanisms of this condition have been investigated for a long time. In recent years, this disease has come to the attention of many research centers because the number of people diagnosed with keratoconus is on the rise. In this context, solutions that facilitate both the diagnostic and treatment options are quickly needed. The main contribution of this paper is the implementation of an algorithm that is able to determine whether an eye is affected or not by keratoconus. The KeratoDetect algorithm analyzes the corneal topography of the eye using a convolutional neural network (CNN) that is able to extract and learn the features of a keratoconus eye. The results show that the KeratoDetect algorithm ensures a high level of performance, obtaining an accuracy of 99.33% on the data test set. KeratoDetect can assist the ophthalmologist in rapid screening of its patients, thus reducing diagnostic errors and facilitating treatment. [ABSTRACT FROM AUTHOR]

As cloud computing becomes prevalent, many data owners outsource their large number of graph data into the cloud for cost savings. The cloud may not be fully trusted and usually likes to learn owners' data. To protect the security of graph data, sensitive data have to be stored in an encrypted form. Neighbor queries are the most essential operations, many other operations can be built based on the neighbor queries. Ranked neighbor queries can make the queries very flexible, a query user can get ranked search results. When the graph data are encrypted, the operations over graph data are very inconvenient. Ranked neighbor queries over encrypted graph data are difficult to directly achieve. In this paper, we propose a solution to perform privacy-preserving ranked neighbor query over encrypted graph data in the cloud, which is named as PRNQ. Our work utilizes ranking function and encryption mechanism to perform ranked neighbor query. The query users can obtain the most wanted search results by this solution. Our paper proposes a design scheme for the weighted directed graph and proves the security and correctness. Experimental results with a real graph data set demonstrate the validity and efficiency of our scheme. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Critical infrastructures are exposed to many natural and man-made hazards and threats. Recently, in the era of moving previously disconnected (tele)-monitoring, control and supervisory systems (e.g., information and communication technologies or supervisory control and data acquisition) towards full network connection, the risk of cyber attacks on such systems is continuously growing. One of the current trends in information and communication technology systems controlling critical infrastructures (such as energy grids, telecommunication networks, or water systems) is deployment of wireless sensor networks. Therefore, the cyber security of the mentioned networks becomes an important challenge to be solved. In this paper, we propose a discrete wavelet transform-based method of anomaly detection in wireless sensor networks that could be deployed in critical infrastructures (e.g., in energy grids, to measure some parameters). The major contribution of the paper is the implementation of the innovative SNORT-based pre-processor using the effective anomaly detection methods based on discrete wavelet transform. We apply the discrete wavelet transform to 26 network traffic parameters measured in our realistic testbed. We also discuss which parameters of the traffic are most useful for such purpose. Moreover, we implement our method as an algorithm in a dedicated SNORT preprocessor in order to be compliant with this popular state of the art intrusion detection system. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Efficient implementation of elliptic curve cryptography on resource-constrained microcontroller is considered to be one of the hot and challenging research topics because of the limited computing power and storages of target platforms and high computational costs of elliptic curve cryptography. In this paper, we focus on enhancing the performance of scalar multiplication over G F(2 m) by suggesting a new technique for speeding up the performance of multiplication, called Karatsuba-Block-Comb (KBC) multiplication. KBC method combines both the advantages of Karatsuba algorithm and Block-Comb method. This technique replaces the part of expensive Block-Comb binary field multiplications with several cheap additions by following Karatsuba rule. In case of squaring, we describe an optimized squaring algorithm with 8-bit look-up table that is significantly faster than previous works with 4-bit look-up table. Both of the proposed approaches improve the best known results by a factor of 24.6% and 16.8% (160-bit operand) over 8-bit AVR processor (Atmel Corporation, San Jose, CA, USA), respectively. Finally, we realize the scalar multiplication over G F(2163), which only requires 0.29 s for a full scalar multiplication when the processor runs at 7.37 MHz. This result outperforms the previous best implementation by a factor of 9.3%. The research results presented in this paper prove that it is also possible to achieve high performance over binary fields by combing the algorithm with sub-quadratic complexity. Furthermore, we suggest constant time KBC method. Block-Comb method does not provide constant time, and look-up table method is also vulnerable to memory address side channel attack. However, our method is establishing the scalar multiplication in 0.35 s with high security against both attacks. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

As the de facto Internet inter-domain routing protocol, BGP protocol has a number of vulnerabilities and weakness. Monitoring BGP is an effective way to improve the security of inter-domain routing. This paper presents a cooperative BGP monitoring method, which is called cooperative information sharing model (CoISM). CoISM is based on self-organization and can be used to solve some problems during BGP monitoring, such as route validation and bogus route notification delivery. CoISM provides autonomous systems with a more comprehensive information view by introducing information diffuse reflection based on initiative inquiry and making use of the relativity of monitoring information. CoISM optimizes the information transmission by leveraging the data locality caused by BGP policy and implements ISP coordination with low communication and deployment cost. More specifically, CoISM provides a self-organizing and incentive mechanism, which drives autonomous systems to coordinate independently and shares information on-demand. CoISM supports incremental deployment and can also be applied to a wide range of inter-domain cooperative management applications such as inter-domain routing failure analysis and intrusion detection. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Task allocation is an important issue in wireless sensor networks (WSNs), and the existing traditional solutions to this problem in high-performance computing cannot be directly implemented in WSNs because of limitations such as resource availability and shared communication medium. In this paper, we address the task allocation problem for a heterogeneous WSN, and a trust dynamic task allocation algorithm is proposed. Firstly, to ensure the nodes in the same coalition are mutually closer in distance, a discrete particle swarm optimization (PSO) is designed to generate a structure of the parallel coalitions. Secondly, in order to minimize the execution time of the tasks, save the energy cost of the nodes and balance the load of the network, we design task strategies and payoff functions by invoking the game theory in WSNs and propose a PSO with the redesigned fitness function to find the Nash equilibrium point for the purpose of improving the effectiveness of scheduling and the reliability of the network. In this step, the sink node will play the role of trust manager, and it will allocate tasks based on the Nash equilibrium point, which is a trust solution to make sure all tasks can be finished. Finally, the extensive experiments are conducted to compare our algorithm with two other algorithms. The experimental results show the feasibility and effectiveness of our algorithm, which can obtain a good balance between local solution and global exploration and achieve superior energy efficiency and network reliability within a short period. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In the face of rising container throughput and the tasks associated with collecting and dispatching these containers, contributions to the development of sea-rail intermodal transport are required to improve the capacity of container transportation. According to the characteristics and the requirements of the operation of sea-rail intermodal transport, this paper puts forward a design for the transport yard which facilitates container loading and unloading. Through the establishment of a mixed integer programming model, a flexible schedule for gantry crane operation can be realized, so that, during the planned period, the amount of task overflow in loading and unloading operations and the distance covered by all gantry cranes in the yard is minimized. In addition, a genetic algorithm is designed to solve this model. Finally, a specific example of the loading and unloading of a container train at the sea-rail intermodal transport yard is selected to verify the model and the algorithm. The results show that the algorithm can reasonably schedule the gantry cranes to improve their loading and unloading efficiencies in the sea-rail intermodal transport yard. [ABSTRACT FROM AUTHOR]

Abstract: Human beings represent and process imperfect information in a natural way, whereas effective approaches are still being looked for in the area of soft computing. Atanassov's intuitionistic fuzzy sets and especially Atanassov's intuitionistic fuzzy relations are are tools that make it possible to model in an effective way imperfect information. In this paper, we discuss the transitivity problem of Atanassov's intuitionistic fuzzy relations. The transitivity property reflects the consistency of a preference relation. Therefore, transitivity is important from the point of view of real problems appearing, e.g., in group decision making, choice, and utility theories widely making use of the preference procedures. We study here in detail transitivity property of Atanassov's intuitionistic fuzzy relations and propose a new algorithm for generating weakly transitivity among the alternatives (options) considered. Next, making use of the new algorithm we propose a procedure of ranking of the alternatives in a group decision‐making problem. [ABSTRACT FROM AUTHOR]

Radar and communication (RadCom) systems have received increasing attention due to their high energy efficiency and spectral efficiency. They have been identified as green communications. This paper is concerned with a joint estimation of range-Doppler-angle parameters for an orthogonal frequency division multiplexing (OFDM) based RadCom system. The key idea of the proposed method is to derive different factor matrices by the tensor decomposition method and then extract parameters of the targets from these factor matrices. Different from the classical tensor decomposition method via alternating least squares or higher-order singular value decomposition, we adopt a greedy based method with each step constituted by a rank-1 approximation subproblem. To avoid local extremum, the rank-1 approximation is solved by using a multiple random initialized tensor power method with a comparison procedure followed. A parameterized rectification method is also proposed to incorporate the inherent structures of the factor matrices. The proposed algorithm can estimate all the parameters simultaneously without parameter pairing requirement. The numerical experiments demonstrate superior performance of the proposed algorithm compared with the existing methods. [ABSTRACT FROM AUTHOR]

The explosive demand of online video watching brings huge bandwidth pressure to cellular networks. Efficient video caching is critical for providing high-quality streaming Video-on-Demand (VoD) services to satisfy the rapid increasing demands of online video watching from mobile users. Traditional caching algorithms typically treat individual video files separately and they tend to keep the most popular video files in cache. However, in reality, one video typically corresponds to multiple different files (versions) with different sizes and also different video resolutions. Thus, caching of such files for one video leads to a lot of redundancy since one version of a video can be utilized to produce other versions of the video by using certain video coding techniques. Recently, fog computing pushes computing power to edge of network to reduce distance between service provider and users. In this paper, we take advantage of fog computing and deploy cache system at network edge. Specifically, we study transcoding based video caching in cellular networks where cache servers are deployed at the edge of cellular network for providing improved quality of online VoD services to mobile users. By using transcoding, a cached video can be used to convert to different low-quality versions of the video as needed by different users in real time. We first formulate the transcoding based caching problem as integer linear programming problem. Then we propose a Transcoding based Caching Algorithm (TCA), which iteratively finds the placement leading to the maximal delay gain among all possible choices. We deduce the computational complexity of TCA. Simulation results demonstrate that TCA significantly outperforms traditional greedy caching algorithm with a decrease of up to 40% in terms of average delivery delay. [ABSTRACT FROM AUTHOR]

Vehicle license plate detection is an important step in automatic license plate recognition, which is prone to be influenced by the background interference and complex environment conditions. It is known that cartoon-texture decomposition split an image into geometric cartoon and texture component, which can remove background interference away from the vehicle image. In this paper, we introduce a fast cartoon-texture decomposition filter into the detection process. Combining the edge detection, morphological filtering and Radon transform based tilt correction method, we formulate a new license plate detection algorithm. Experiment results confirm that the proposed algorithm can remove background interference away, inhibit the emergence of fake license plates, and improve the detection accuracy. Moreover, there is no inner loop iteration in the new algorithm, so it is fast and high-efficiency. [ABSTRACT FROM AUTHOR]

Question answering (QA) system is becoming the focus of the research in medical health in terms of providing fleetly accurate answers to users. Numerous traditional QA systems are faced to simple factual questions and do not obtain accurate answers for complex questions. In order to realize the intelligent QA system for disease diagnosis and treatment in medical informationization, in this paper, we propose a depth evidence score fusion algorithm for Chinese Medical Intelligent Question Answering System, which can measure the text information in many algorithmic ways and ensure that the QA system outputs accurately the optimal candidate answer. At the semantic level, a new text semantic evidence score based on Word2vec is proposed, which can calculate the semantic similarity between texts. Experimental results on the medical text corpus show that the depth evidence score fusion algorithm has better performance in the evidence-scoring module of the intelligent QA system. [ABSTRACT FROM AUTHOR]

With the development of Internet of Things (IoT), the massive data generated by it forms big data, and the complexity of dealing with big data brings challenges to resource scheduling in edge computing. In order to solve the problem of resource scheduling and improve the satisfaction of users in edge computing environment, we propose a user-oriented improved spectral clustering scheduling algorithm (ISCM) in this paper. Based on the improved k-means algorithm, the ISCM algorithm solves the problem that the clustering result is sensitive to the initial value and realizes the reclustering, which makes the obtained clustering results more stable. Finally, the edge computing resource scheduling scheme is obtained based on the clustering results. The experimental results show that the resource scheduling scheme based on improved spectral clustering algorithm is superior to traditional spectral clustering algorithm in edge computing environment. [ABSTRACT FROM AUTHOR]