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Paper-cutting is one of the valuable intangible cultural heritages of China, with distinctive features such as “round as the autumn moon, sharp as the wheat mane, square as the green brick, missing as the serrated teeth, and thread as the beard,” and is a widely spread folk art. The art of paper-cutting brings new inspiration to the design of structures, and by changing the topology of the raw material, the material/structure can undergo significant changes in physical properties, such as optical, thermal, acoustic, and mechanical aspects. The protection of intangible cultural heritage is a process of cultural self-awareness and a kind of cultural reflection and enlightenment. In the history of promoting socialist cultural development, the protection of “intangible heritage” paper-cutting is undoubtedly of great significance. The purpose of digital image repair is to restore the integrity of the broken image, and the process is to fill in the specified area of the digital image with information, requiring a natural transition between the filled area and the original area of the image and minimizing artificial traces, so that the image looks as if it has never been broken. In this paper, the paper-cutting pattern design method based on image restoration technology is investigated, starting from the preprocessing of paper-cutting images by acquiring the edges of the images and using the Criminisi image restoration algorithm with image broken edge reconstruction to realize the image broken edge reconstruction design of paper-cutting graphics. Therefore, the paper-cut graphics generated by the method in this paper are concise and coherent as a whole, which reduces the difficulty of paper-cut design while satisfying individual design requirements.

This paper reviews the application of intelligent vision enhancement technology in the battlefield environment and explores new research directions. This paper mainly introduces three parts. First, we introduce a solution for enhancing the battlefield situation and using head-mounted displays for soldier combat. Second, we summarize three core technologies supporting intelligent vision enhancement technology: 3D environment reconstruction, tracking and registration, and situational awareness. Third, we summarize three application directions of intelligent vision enhancement technology on the battlefield. Finally, the problems and challenges in the research work are proposed, including current issues such as accurate battlefield situational awareness by augmented reality technology, multiperson collaborative management and data flow, as well as challenges in future battlefield situation enhancement and perception. The future development trend of intelligent vision enhancement technology on the battlefield has been prospected. Two meanings of this paper are as follows: the first is to review the research status of intelligent vision enhancement technology from the technical level and identify the key technical points that may restrict development in the future; the second is to analyze the advantages and disadvantages of intelligent vision enhancement technology from the level of battlefield application and the roles of users. In addition, this paper proposes how to take the lead and take initiative in future wars.

At present, the development of deep forgery technology has brought new challenges to media content forensics, and the use of deep forgery identification methods to identify forged audio and video has become a significant focus of research and difficulty. Deep forgery technology and forensic technology play a mutual game and promote each other’s development. This paper proposes a spatiotemporal local feature abstraction (STLFA) framework for facial forgery identification to solve the media industry challenges of deep forgery technology. To adequately utilize local facial features, we combine facial key points, key point movement, and facial corner points to detect forgery content. This paper establishes a spatiotemporal relation, which realizes face forgery detection by identifying abnormalities of facial keypoints and corner points for interframe judgments. Meanwhile, we utilize RNNs to predict the sequences from facial key point movement abnormalities and corner points for interframe. Experimental results show that our method achieves better performance than some existing methods and good anticompression forgery face detection performance on FF++.

Navigation and positioning is a new growth point of wireless services. And seamless positioning is the current development trend of navigation and positioning services. In view of the problems that dynamic handover cannot be performed during site movement, STA only selects the optimal AP according to the indication of received signal strength, and the handover delay is too long. This paper proposes a research on indoor and outdoor seamless positioning technology based on artificial intelligence and intelligent switching algorithm. The purpose is to study the influence of reducing the positioning data with too large error on the final positioning result. The method of this paper is to propose an intelligent switching algorithm and then discuss the research status and development direction of indoor positioning technology and outdoor positioning technology, respectively, and point out the switching methods between them. The role of these methods is to analyze the current situation of indoor and outdoor navigation technology, especially the development route of seamless positioning technology. It determines the strategy of integrating heterogeneous positioning systems to build a seamless positioning system to maximize the use of existing positioning system resources. This paper studies the design and implementation of the seamless positioning system through the simulation experiment of the intelligent switching algorithm and finally tests the system through the experimental vehicle. The online experiment results show that the system can achieve high positioning accuracy in indoor and outdoor environments, especially at its junction, and the positioning errors in all directions are within 0.2 m.

As social media use increases, the number of users has risen also. This has increased the volume of data carried over the network, making it more important to secure users’ data and privacy from threats. As users are unaware of hackers, social media’s security flaws and new forms of attack will persist. Intrusion detection systems, therefore, are vital to identifying intrusion risks. This paper examines a variety of intrusion detection techniques used to detect cyberattacks on social media networks. The paper provides a summary of the prevalent attacks on social media networks, such as phishing, fake profiles, account compromise, and cyberbullying. Then, the most prevalent techniques for classifying network traffic, including statistical and artificial intelligence (AI) techniques, are addressed. The literature also demonstrates that because AI can manage vast, scalable networks, AI-based IDSs are more effective at classifying network traffic and detecting intrusions in complex social media networks. However, AI-based IDSs exhibit high computational and space complexities; therefore, despite their remarkable performance, they are more suitable for high computing power systems. Hybrid IDSs, utilizing statistical feature selection and shallow neural networks, may provide a compromise between computational requirements and efficiency. This investigation shows that accuracies of statistical techniques range from 90% to 97.5%. In contrast, AI and ML technique detection accuracy ranges from 78% to 99.95%. Similarly, swarm and evolutionary techniques achieved from 84% to 99.95% and deep learning-based detection techniques achieved from 45% to more than 99% detection rates. Convolutional neural network deep learning systems outperformed other methods due to their ability to automatically craft the features that would classify the network traffic with high accuracy.

The increasingly large employment group in colleges and universities has brought huge employment pressure to the society. Therefore, improving the level of professional ability of college students, providing students with personalized career development direction planning, and helping students to establish a correct concept of career selection are feasible ways to solve the structural contradiction of college students’ employment. The arrival of big data era provides new development opportunities for employment work in colleges and universities. Big data drives the high-quality development of precise employment in colleges and universities, which is inevitably required to reach the precise requirements of work development, information mastering, platform docking, information pushing, and help work. The traditional employment guidance work is backward in means and poor in timeliness, which leads to poor intervention effect on students’ employment expectation. With the deepening of digital campus construction and the development of big data technology, massive educational data has been accumulated but not reasonably utilized. Therefore, in this paper, we take the massive student data accumulated in digital campus as the research object, based on the research method of convolutional neural network, to explore the hidden personalized information of students behind it, predict their future career development direction, and provide scientific and technological support for the work of university education and teaching. Thus, the architecture classical DenseNet was improvised to avoid gradient disappearance and guarantee the classification accuracy, thereby targeting to reduce the number of connections. The study also proposed a 2-DenseNet model, wherein the attention module was embedded into 2-DenseNet followed by the conduction of training and validation on the university employment dataset. The experimental results show that the method proposed in this paper could predict the appropriate career development direction timely and effectively based on multiple perspectives of students’ comprehensive quality. This would enable the students to plan and adjust their employment expectations leading to disseminating of efficient employment guidance in colleges and universities.

The advent of the Internet and portable devices, including smartphones and watches, has brought unprecedented opportunities for embedded application systems developments. Along with these developments, there is an increasing need for embedded devices to handle important services, such as the ability to pay bills or manage bank accounts remotely via mobile phones. Such applications and developments have also highlighted the issues of cyberattacks and computing network security--these developments have made mobile phones a potential target for malware, trojans, and viruses, so it is critical to design a set of security technologies for embedded devices. In fact, security has become an essential requirement in the process of embedded system design. Thus, ARM has proposed system-level security solutions based on TrustZone technology. TrustZone technology is tightly integrated with Cortex™-A processors and extends the system through the AMBA® AXI bus and specific TrustZone system IP blocks to protect peripherals such as secure memory, encryption blocks, keyboards, and screens from software attacks. It divides the system into TEE (Trusted Execution Environment) and REE (Rich Execution Environment) by hardware and provides intrinsic software security services and interfaces. More precisely, it has built system security by combining hardware and software. It is worth noting that it does not influence performance, power consumption, and area as much as possible. Owing to such characteristics, the technology has gained the wide attention of researchers worldwide. There is lack of systematic documentation of the technology. Therefore, this paper documents the significant progress achieved in the field. In particular, this article mainly analyses the primary mechanism implementation, and how to build the Trusted Execution Environment in different environments. Then, this paper discusses the related research works in the academic field and business applications of the technology. Furthermore, the advantages and weaknesses of the TrustZone technology as well as the proposed possible solutions aiming at the deficiency are outlined. Finally, a comparison of TrustZone technology with another mainstream commercial SGX, and future directions are presented.

In recent years, unmanned aerial vehicle (UAV) has gained a lot of attention, mostly due to its low cost, flexile deployment, and broad applications in many fields such as military, agriculture, and environment. In this paper, an UAV-based cognitive radio (CR) network with a wireless powered primary user (PU) is investigated, where the UAVs act as the secondary users (SUs). We assume that one transmission time slot is divided into two phases. In the first phase, UAVs transmit information to base station (BS), while the PU harvests energy from the radio frequency (RF) signals, and the second phase is exclusively occupied by PUs for primary transmission. It is assumed that the PU prices the interference energy incurred from the UAVs for the reason that UAVs need to access PU’s licensed spectrum for their transmission. In this paper, we aim at maximizing utilities of UAVs and utility of PU simultaneously. To analyze the interaction between the UAVs and PU, Stackelberg game was adopted where the UAVs act as followers and the PU acts as a leader. An alternating iterative algorithm is proposed to achieve the Stackelberg equilibrium (SE), i.e., transmission power of PU and UAVs, time allocation, and price that PU charge UAVs. According to the simulation results, the proposed scheme can achieve optimal utility in the view of power saving for UAVs while meeting the requirements of the PU which demonstrate the effectiveness of the proposed scheme.

The low deployment cost and high mobility of unmanned aerial vehicles (UAV) have given UAVs widespread attention in mobile edge computing (MEC). Aiming at the characteristics of dynamic changes in computing tasks generated by the urban transportation network, this paper proposes an UAV-assisted edge computing offloading model; i.e., when the number of tasks is within the capacity of edge servers (ES), most of the tasks are processed by ESs, but when the amount exceeds the capacity, some tasks will be offloaded to UAV and processed by on-board servers on UAVs. To minimize the task delay and the cost of computing tasks, this paper formulates a mathematical model according to the task amount generated by the urban transportation network, in which the task delay is computed by using queueing theory. A deep reinforcement learning algorithm with double deep neural networks (DNN) is used to optimize the model and obtains the resource allocation strategy of UAVs and on-board servers and the offloading strategy of edge computing. Simulation experiments verify the effectiveness of these strategies.

In this paper, we mainly research on the QoS (Quality of Service) routing algorithms for MEO/LEO (medium Earth orbit/low Earth orbit) double-layered satellite networks. In this type of networks, the rapidly changing network topology due to relative motion of satellites is one of the main challenges when designing an efficient routing algorithm. Specifically, the issues of high rerouting overhead and traffic routing with diverse QoS requirements remain to be resolved. This paper proposed a M-BMDP (modified bandwidth constrained minimum delay path) routing algorithm based on swarm and location for MEO/LEO double-layered satellite networks. This algorithm forms a set of LEO groups according to the footprint of MEO satellites and chooses the relative MEO satellites as its group manager. For delay sensitive traffic, the algorithm can improve the QoS as the cost of packet loss based on hop limit. And for users located in reversed crevice zone, the traffic can route through one MEO satellite to reduce the time delay. The simulation results show that the M-BMDP algorithm performs better in rerouting delay, overhead and pack loss rate compared with existing solutions.

The wireless body area network (WBAN) is a branch of the wireless sensor network (WSN) intended for tracking essential patients’ physiological signals and transferring this knowledge to the coordinator. During the routing of data, WBANs encounter critical routing problems like WSNs. Moreover, the particular constraints of WBANs make it more challenging that they need to be addressed. This survey article categorizes and briefly analyzes a range of current routing methods utilized in WBANs. The routing protocol is essential to the creation of any efficient and reliable wireless body area network due to a limited size of battery in the body sensor nodes. A comparison study of numerous routing protocols has been made in this paper, which is helpful in selecting the optimal routing protocol for the application being targeted. The article describes the WBAN architecture and addresses numerous challenges in the context of successful WBAN routing. In this paper, several existing WBAN routing methods are presented which are influenced by network structure, energy, quality of service (QoS), node temperature, human position, node transmission range, and other factors. The protocols, including cross-layered, cluster-based, QoS-aware, postural movement-based, temperature-aware, postural movement-based, and routing protocols, are given in an expressive taxonomy. Different routing protocols that have already been developed for WBANs are compared with more advanced protocols. The pros and cons of each protocol are looked at based on factors like delay, packet delivery ratio, and energy use. The researchers can utilize this survey paper as a reference for studying various routing protocols particularly in the medical and healthcare systems.

With the wide application of information technologies such as big data, the Internet of Things, and cloud computing, college students have accumulated a large amount of personal information and daily behavior data in their daily studies and life. How to dynamically integrate multidimensional information of students to build accurate student portraits, using multi-indicator data of student behavior and comment texts, and finding out students with abnormal behavior from among many students has become an important problem to be solved. This paper proposes an abnormal behavior prediction method integrating multiple indicators of student behavior and text information (ABPM-IMISBTI) for the problem of abnormal behavior prediction of college students in the big data environment. First, given the problems of multidimensionality, timeliness, and dynamics of student behavior information fusion in the construction of student behavior portraits, by integrating students’ objective tags and subjective tags, an optimized K-means algorithm based on a cloud platform environment is proposed. Second, aiming at the problem of insufficient text information analysis in the analysis of students’ abnormal behavior, the ABPM-IMISBTI method is proposed to solve the prediction of students’ abnormal behavior through long and short-term memory networks (LSTM) combined with student behavior multi-index data and text information. Finally, this paper takes student achievement prediction as an example for verification. The experimental results show that, compared with other prediction methods, the ABPM-IMISBTI method proposed in this paper can improve the accuracy of student behavior prediction, and then quickly determine the abnormal behavior of students, to improve the level of education management in universities and promote the development of safe campuses, smart campuses, and smart education.

In order to solve the problem of online inspection of students’ theoretical knowledge of Japanese, this paper further optimizes and adjusts the design of the online Japanese examination system and presents an online Japanese examination system based on genetic algorithm. Taking the Japanese test as the research object, on the basis of comprehensively analyzing the problems of slow test paper composition, low success rate, and low quality of traditional online test systems, an intelligent test composition model based on genetic algorithm is proposed, and the implementation process of genetic algorithm and the key steps are described in detail. The results show that the online Japanese examination system based on genetic algorithm can meet the needs of test paper generation in more complex situations. After a long time of operation and continuous improvement, the online Japanese examination system has obtained the adaptability of the best solution. The value of the fitness is 99.666667; when the fitness is at this value, the error of the question type score of the test paper is 0, the average difficulty error on the test paper is 0, and the error of the section test point distribution is 0.666667. This fully illustrates the stability and effectiveness of the Japanese online examination system, which can meet the needs of daily Japanese majors and improve the efficiency of Japanese teaching.

To effectively protect the communication content and communication behavior of social networks, anonymous communication technologies are widely used. However, the anonymous communication networks represented by Tor and I2P lack the covertness of the control plane design, which leads to important user behavioral characteristics in the process of accessing anonymous communication networks. Therefore, network monitors can analyze users' communication behavior by tracking these characteristics. In this paper, the concept of covert measurement is proposed. On this basis, a software-defined anonymous communication network architecture is presented, which also considers the covertness of the anonymous communication network control plane and the data plane. According to the theoretical analysis and experimental results, the anonymous communication network architecture proposed in this paper has better anonymity and usability than traditional anonymous communication networks, such as Tor.

With the development of modern browsing, the convenience brought by rich browser features has also produced a large number of features, which are called browser fingerprints. This article surveys the latest research results on browser fingerprinting, hoping to provide a convenient navigation for newcomers to research or apply this technology in the future. This paper first briefly introduces the browser fingerprinting technology itself, then classifies the related research on browsers, and analyzes the development of different research directions of browser fingerprinting in detail. And through the analysis of the existing results, the problems faced by different research directions are pointed out. After that, this paper introduces the application of browser fingerprint technology in detail and discusses the application achievements and technical challenges of this technology. Next, this paper introduces the theoretical tools related to the research of browser fingerprinting technology and introduces the application of different theoretical tools and practical significance. Finally, the research achievements of browser fingerprint recognition are summarized, and the future development trend is pointed out.

Conservation of energy has been a major concern for Wireless Sensor Networks (WSNs) and IoT applications. Several strategies were devised, aimed at optimizing energy consumption in these applications, based on: (a) use of low-powered hardware devices, (b) deploying mobile/relay agents for data collection, (c) clustering, and (d) data aggregation. Amongst these, data aggregation is widely acknowledged as an important tool to conserve energy in WSN and IoTs. The paper provides a comprehensive survey of various data aggregation strategies, discusses the efficacy of these strategies in handling issues that are typical to WSN and IoT applications. These issues severely impact the performance metrics such as: energy efficiency, latency, fault-tolerance, network throughput, and network lifetime. Therefore, to optimize the data aggregation approach, an application developer needs to arrive at optimal tradeoffs between these parameters. A major contribution of the paper is to present a holistic review of data aggregation approaches emphasizing the effect of topology, security, mobility, interference, and fault-tolerance in WSN and IoTs. Based on gap areas in literature, we throw open few challenges and present them as “posers”, and put-forth suggestions for further research.

Outsourcing data to cloud services is a good solution for users with limited computing resources. Privacy and confidentiality of data is jeopardized when data is transferred and shared in the cloud. The development of searchable cryptography offers the possibility to solve these problems. Symmetric searchable encryption (SSE) is popular among researchers because it is efficient and secure. SSE often requires the data sender and data receiver to use the same key to generate key ciphertext and trapdoor, which will obviously cause the problem of key management. Searchable encryption based on public key can simplify the key management problem. A public key encryption scheme with keyword search (PEKS) allows multiple senders to encrypt keywords under the receiver’s public key. It is vulnerable to keyword guessing attacks (KGA) due to the small size of the keywords. The proposal of public key authenticated encryption with keyword search (PAEKS) is mainly to resist inside keyword guessing attacks. The previous security models do not involve the indistinguishability of the same keywords ( w 0 × × = w 1 ), which brings the user’s search pattern easy to leak. The essential reason is that the trapdoor generation algorithm is deterministic. At the same time, most of the existing schemes use bilinear pair design, which greatly reduces the efficiency of the scheme. To address these problems, the paper introduces an improved PAEKS model. We design a lightweight public key authentication encryption scheme based on the Diffie-Hellman protocol. Then, we prove the ciphertext indistinguishability security and trapdoor indistinguishability security of the scheme in the improved security model. Finally, the paper demonstrates its comparable security and computational efficiency by comparing it with previous PAEKS schemes. Meanwhile, we conduct an experimental evaluation based on the cryptographic library. Experimental results show that the computational overhead of our scheme compared with the ciphertext generation algorithm, trapdoor generation algorithm and test algorithm of other schemes Our scheme reduces 274, 158 and 60 times, respectively.

Aiming at the defect that the click-through rate of marketing advertisements cannot provide accurate prediction results for the company in time in the marketing strategy of Internet companies, this paper uses a deep learning algorithm to establish a prediction model for the click-through rate of marketing advertisements. The suggested model is called high-order cross-feature network (HCN). Furthermore, this paper also introduces the combination of feature vectors into the graph structure and as the nodes in the graph; therefore, the graph neural network (GNN) is used to obtain the high-level representation ability of structured data more fully. Through numerical simulations, we observed that HCN has the capability to provide Internet companies with more accurate advertising business information, user information, and advertising content. Moreover, HCN model is more reasonable to adjust the advertising strategy and can provide better user experience. The simulation outcomes indicate that the suggested HCN approach has noble adaptability and high correctness in forecasting the click-through rate of marketing advertisements. We observed that this improvement, in terms of predictions precisions and accuracies, can be as high as 17.52% higher than the deep neural network (DNN) method and 10.45% higher than the factorization network (FM) approach.

The ultrareliable and low-latency communication (URLLC) is one of the key scenarios of the current 5G new radio (NR). The performance of the duplex technologies in URLLC to assist in meeting the needs of low-latency services is of great significance. However, time division duplex (TDD) has poor delay performance due to the extra data waiting delay caused by the frequent uplink/downlink switching. Besides, TDD systems cannot dynamically adapt to the real-time changes of user requirements due to the problem of frame structure configuration, while frequency division duplex (FDD) has more pilot overhead due to channel estimation and worse spectrum utilization which are great drawbacks in 5G NR enabled URLLC. In this paper, we further proposed the strengths of a novel duplex technology named network-assisted full-duplex (NAFD) in terms of URLLC latency performance compared with TDD and FDD. NAFD scheme exploits the resources of the time and spatial domains to form a special duplex mechanism where the central processing unit (CPU) can dynamically assign the modes of different access points (APs) as downlink transmission or uplink receiving in the same coherent time block. Some improvement of the existing NAFD scheme for better fitting for URLLC is also proposed in this paper. The simulation results and analysis showed that the NAFD scheme outperforms other duplex technologies under URLLC scenarios.

Although Internet technology brings invaluable benefits to all walks of life, the security of network and information is becoming more and more prominent. The leakage of personnel information caused by Internet security incidents causes irreparable harm and loss to individuals or enterprises. E-commerce is a virtual transaction mode based on Internet technology. Its security requirements for transactions are more stringent than those for traditional transaction modes. Traditional identity authentication technology can no longer meet its security needs. People urgently need a reliable identity authentication system, meaning to ensure the security of e-commerce. According to the actual application scenarios of the algorithm in e-commerce, this paper, based on the research on face recognition technology, focuses on the in-depth research on face detection technology under the background of big data in order to introduce face recognition technology in this paper. This paper proposes a feature-based face matching algorithm. The face image is preprocessed to improve the accuracy of real-time face detection and reduce the false detection rate. Based on the research of traditional facial feature extraction technology, a virtual sample set that can effectively support a traditional facial feature extraction algorithm is constructed to solve the problem of insufficient training samples in practical applications. The experimental results showed that the accuracy of the method in this paper can reach up to 79.5% and that the minimum time consumption is only 0.142 s. Compared with the traditional method, the accuracy rate is higher and the time consumption is shorter.

In the Chinese teaching for foreign students, Chinese teachers do not participate in the assessment of their institutions, which is not conducive to the sustainable development of Chinese language education. The potential value of the teaching evaluation is not floating on the surface, so the data mining is used to discover implicit correlations between the data. The application of data mining in the evaluation of Chinese teaching for foreign students helps to ensure safety of the teaching evaluation system for foreign students. In this paper, a Chinese teaching evaluation system that is based on the data mining is built, in which the DA is applied to the evaluation of Chinese teaching. Finally, the performance of the algorithm is tested, the results show that the DA greatly improves the classification accuracy of the model, the improved model can largely alleviate overfitting, and the feature dimension data extracted by DA are more representative. The system proposed in this paper has good performance, which can greatly improve the security of the Chinese teaching evaluation system.

In recent years, with the development of Internet, Internet technology has swept the world and become a new round of scientific and technological revolution in the world economy. Network marketing has become the “darling” of marketing, with a multidimensional and multilevel marketing layout, which provides a carrier platform for the promotion of enterprise marketing strategy. The traditional network marketing mode is mainly in the form of advertising push, with pop-ups, screen overlays, top search rankings, loading videos, texts, and pictures. Nowadays, Internet technology and social media have promoted the development of community e-commerce models. Live delivery is a new sales model, which has promoted economic development. It has many advantages, such as interactivity, real-time, and transparency. Compared with the traditional e-commerce model, it is more suitable for the development requirements of the new era. Live delivery is a new sales model, which promotes economic development. This paper starts with the AdaBoost algorithm and marketing mode of STP and 4P marketing theory, in order to reduce the execution cost of marketing. In this way, it proves that using online marketing can not only reduce the cost but also improve the marketing effect. Through the new network marketing promotion mode and algorithm described in this paper, the cost can be reduced by 17%, and the promotion effect is remarkable.

Internet of Vehicles (IoV) is a multinode network that exchanges information in an open, wireless environment. Various communication activities exist between IoV entities to share important information such as (ID, location, speed, messages, and traffic information), necessary for network operation. As part of intelligent transportation, IoV is considered a hot subject for researchers, because it is still facing many unresolved challenges, especially those concerning security and privacy. The variation of security-privacy threats that can menace the safety, privacy, and lives of vehicle occupants makes security the leading point of interest. The development of communication protocols for autonomous vehicles opens to us new issues to study and enhance the performance of IoV networks in terms of security and privacy. Several works have been reported, proposing many solutions for practical security challenges including a considerable number of survey-review papers published in respectable channels. The main motive of this review paper is to present the latest developments related to IoV security, as well as to address existing limitations. The high frequency of publication on IoV architecture, security, and new solutions leads us to write a compact, comprehensive, and up-to-date review. Inclusion criteria for selected papers include recent publications, number of citations, and impact of the research. In the present survey paper, the IoV architecture model is defined with all related communication types, and security and privacy issues are analyzed and presented with recently proposed solutions in a clear method. Clear classifications of threats, attacks, protocols, and solutions are presented. Moreover, the use of blockchain-based IoV to improve system security is discussed highlighting the most important trends and taxonomies. The paper was written to be a candidate as the first to read on the topic of the IoV security challenge, presenting problems and solutions in a clear, smooth, complete, and integrated manner.

Android and Windows are the predominant operating systems used in mobile environment and personal computers and it is expected that their use will rise during the next decade. Malware is one of the main threats faced by these platforms as well as Internet of Things (IoT) environment and the web. With time, these threats are becoming more and more sophisticated and detecting them using traditional machine learning techniques is a hard task. Several research studies have shown that deep learning methods achieve better accuracy comparatively and can learn to efficiently detect and classify new malware samples. In this paper, we present a systematic literature review of the recent studies that focused on intrusion and malware detection and their classification in various environments using deep learning techniques. We searched five well-known digital libraries and collected a total of 107 papers that were published in scholarly journals or preprints. We carefully read the selected literature and critically analyze it to find out which types of threats and what platform the researchers are targeting and how accurately the deep learning-based systems can detect new security threats. This survey will have a positive impact on the learning capabilities of beginners who are interested in starting their research in the area of malware detection using deep learning methods. From the detailed critical analysis, it is identified that CNN, LSTM, DBN, and autoencoders are the most frequently used deep learning methods that have effectively been used in various application scenarios.

The research of Internet of Things (IOT) network and deep learning has gradually become a hot research topic for academic researchers. As a typical form of IoT networks and deep learning, it is of great significance to study the IOT and ideological and political education. In view of the drawbacks of the ideological and political course, such as boring teaching content and rigid teaching methods, strengthening teaching construction with the help of intelligent teaching assistant system has become the focus of attention in the field of education. The continuous improvement of IOT technology has brought certain changes to people’s society and students’ lifestyle. At present, the teaching aids and systems on the market focus on one aspect of the teaching process, not all aspects of the teaching process. According to the problem that the ideological and political course is a required course but its teaching efficiency is not high, this paper will combine the basic characteristics of the Internet of Things and deep learning to design the ideological and political teaching system, improve the drawbacks of the traditional system with an intelligent system, which will involve the method and calculation method of the edge matching algorithm, so as to improve the efficiency of ideological and political teaching and student learning. In addition, the image feature algorithm (ORB algorithm) is used to process images, and the improved surf feature processing algorithm is combined to explore how to improve the teaching quality of the course with the help of intelligent algorithms. The studies in this paper provide an important guidance to the application of both IoT networks and deep learning.

High-speed product appearance inspection is crucial for modern automated industrial production such as bottle caps. The main detection method is to capture the image of bottle caps on the high-speed conveyor belt by industrial cameras and send them to the server through edge devices for various analyses. In order to improve production efficiency, it is necessary to increase the inspection rate of bottle caps. However, the transmission rate and huge data throughput of traditional inspection methods limit the inspection rate. Because of the application requirements for improving bottle cap detection efficiency, this paper proposes a hardware acceleration design based on edge devices to improve the bottle cap detection rate significantly. In this paper, an image processing module based on FPGA is designed as the edge device, improving algorithm execution speed through pipeline processing. It realizes the edge detection of the bottle cap and the fast detection of the front or back states and can send the instructions to the actuator to correct it in time when the back bottle cap is detected. It also realizes the positioning of the bottle cap area and cuts the image. Thereby, the amount of data sent to the server is significantly reduced. We have done both functional simulation and hardware implementation. Comparing with the pure software solution, the proposed design reduces the execution time of the algorithm from 16 ms to 3.07 ms, which achieves a more than four times rate increase. The amount of data that needs to be transmitted to the server per second is reduced from 7200 Kb is reduced to 25 Kb, which reduces the transmission capacity and server cache space by more than 280 times compared to the original image. In this paper, the hardware acceleration design of edge devices and the positioning and cropping of original images can greatly reduce the transmission pressure, data calculation pressure, and buffer space requirements of the central server and improve the detection rate. This design can not only be used for bottle cap inspection but also for various machine vision fields, such as defect detection of other workpieces.

Channel capacity is the rate limit of information transmission; many researchers strive to find the maximum channel transmission rate while balancing the performance of the whole communication system and making it close to the channel capacity. We know in the discrete channel that, when the BER of the system is small enough, the transmission rate of the channel will be extremely close to the channel capacity. According to Shannon theory, the common way to increase the channel capacity is to increase the system bandwidth and the input signal power. However, the bandwidth resources are extremely limited, so it is impractical to expand the system bandwidth, and at the same time, increasing the input signal power is impractical as well, because it will reduce the economy of the communication system. In this paper, we proposed a normalized tangent function (NorTF) which can optimize the discrete channel probability distribution, reduce the BER of the channel, and improve the channel capacity of the discrete channel, which need not expand bandwidth and increase the signal input power of the system. Through theoretical analysis and experimental verification of this method, we found that the channel capacity of the improved system was indeed much higher than that of the preimproved system, so the method proposed in this paper has some theoretical guiding significance and is feasible.

With the rapid development of information technology and network technology, all kinds of data increase exponentially, and the traditional relational databases cannot handle these data well, which is also reflected in the sharp increase in the number of college students. The amount of students' information is increasing exponentially and the simple management method in the past can no longer meet the needs of the new situation. This paper adopts B/S mode and three-tier architecture and relies on network technology and ASP.NET technology to design and implement the student information management system. The system is divided into eight functional modules: comprehensive information inquiry for students, scholarship evaluation, evaluation and award, economic recognition, student loan management, student violation, communication and feedback, and system management. Different operation interfaces are designed according to four users with different permissions: students, class tutors, college administrators, and student affairs offices. This paper describes the system analysis, design, and implementation in detail. The practice results show that the initial use of the student information database management reflects well and achieves the expected goal. The system is easy to maintain and extensible and has good application value. The operation of the system has greatly improved the management efficiency of student affairs and enabled the student affairs administrators to complete various student affairs more efficiently. Campus information construction is becoming an important part of the overall construction of colleges and universities, and it is also a basic and continuous work. Accelerating the establishment of a set of comprehensive, perfect, and efficient student comprehensive management information systems is an urgent need to promote the modernization of college student management. The modernization, informatization, and standardization of college student management are a complex systematic project. Only by relying on modern information technology can we cope with the base pressure brought by the continuous enrollment expansion of college students, promote the flat and intensive utilization of college management information, and improve the efficiency and quality of service.

Information acquisition is an important branch of information science. It is the product of the development and cross-integration of traditional sensing technology and other multidisciplines. It is characterized by the high precision, high speed, integration, and intelligence of information acquisition. An important part of information acquisition, it studies how to acquire the geometric structure and scale information of objects in three-dimensional space. With the continuous development of the level, the acquisition of 3D information has become more and more important in scientific research and industrial production. This paper takes 3D information acquisition technology as the main line. From the perspectives of fusion, effectiveness, physical limit, and measurement efficiency, high-resolution 3D information acquisition techniques are studied. In this paper, the upper and lower limb movements of each behavioral segment are segmented according to the difference. An omnidirectional stereo vision sensor composed of a single camera and two secondary conical mirrors is designed, which solves the problems of large size, narrow field of view, and asynchronous image acquisition of traditional binocular vision sensors, effectively ensuring image perspective. The invariance of projection avoids image distortion caused by curved mirror imaging and reduces the difficulty of subsequent work. The three-dimensional Delaunay triangulation algorithm is used to replace the traditional Poisson reconstruction algorithm to generate the triangular mesh model. The triangular mesh model obtained by the multiview stereo reconstruction algorithm is relatively rough. This article uses Zbrush’s ZRemesher and geometric-divide functions to smooth and simplify the model. The results show that the accuracy of the algorithm proposed in this paper is as high as 89.25% in the motion recognition of body elements. The dynamic mapping method is used to map the texture of the triangular mesh model, so that the realism of the model reaches 91.23%.

With the continuous development of technologies such as sensors, computers, and artificial intelligence, intelligent mobile robots with thinking, perception, and dynamics functions are widely used in military, political, and scientific research. Its development has had a significant impact on national defense, society, economy, science, and technology and has become a strategic research goal in the high-tech field of various countries. Robot positioning technology is one of the key research technologies for portable robots, and reliable posture is the key prerequisite for completing various tasks. This article aims to study the robot walking route driven by big data and the intelligent determination of real-time positioning based on cloud computing. This paper proposes an active general positioning algorithm based on real-time positioning function, which can improve the convergence speed and robustness of general positioning when different map scenes do not have clear geometric features and contain map noise. The most basic requirement for robots to perform autonomous operations is to have reliable positioning performance. The experimental results in this paper show that dynamic global positioning and adaptive behavior tracking are effective. Compared with the traditional algorithm, the improved algorithm increases the convergence speed of the global layout by 41.59%.

Digital teaching has gradually banned traditional offline teaching, and its advantages are self-evident, but the current digital teaching has certain limitations. Especially in physical education, the key content of physical education is the changes in physical movements; the process is not only complex; the differences between movements are also extremely small; and ordinary digital cannot accurately teach these complex and subtle movements. In this context, this paper mainly studies the application of digitalization of fusion intelligent sensor technology in physical education teaching and combines the characteristics of high precision and recognizable actions of intelligent sensors with digitalization to improve physical education teaching. This paper mainly selects the students majoring in physical education in the second year of school A as the research object to test the effect of digitalization of fusion intelligent sensor technology in physical education teaching. Before the experiment, the research subjects were divided into groups and the T-test was performed, and it was concluded that there was no significant difference between the experimental group and the control group. On this basis, the experimental group and the control group were respectively taught table tennis, gymnastics, martial arts, football, and 100 meters and then tested and tested. The results showed that the experimental group was higher than the control group in table tennis, gymnastics, martial arts, football, and 100 meters test. In the 100 meter test, the boys in the experimental group were 7.13, 6.94, and 6.93 points higher than the control group, and the girls in the experimental group were 2, 1, and 2 points higher than the control group. Overall, after the improved digital teaching, both boys and girls in the experimental group performed better than the control group in table tennis, gymnastics, martial arts, football, and 100 meters. It shows that the effect of the improved digital physical education is remarkable, and it also provides a certain theoretical basis and guidance for the teaching improvement of other courses.

Cognitive communication behavior is becoming a research hotspot in the field of communication confrontation. In theory, the behavioral intention of noncooperating parties can be obtained by analyzing communication signals. Considering the complexity of the actual electromagnetic environment, even when the signal-to-noise ratio (SNR) is low, a certain accuracy still needs to be guaranteed. In this paper, according to five types of physical burst waveforms defined by the shortwave radio interoperability standard, a signal feature extraction method based on autocorrelation spectrogram features is proposed, and a two-input convolutional neural network (CNN) for classification is designed to improve the identification ability of shortwave communication behavior. The experimental results illustrate that the five kinds of shortwave radio communication behaviors can be accurately identified even when the noise is large. The research in this paper can directly analyze the communication behavior through physical layer signal without demodulation, which has the ability to grasp the communication behavior of the shortwave radio station in real time.

How to start from the fitness needs of people and effectively improve the precision of the supply of public fitness services everyone is an important issue that needs to be solved first at the current stage. This requires us to proceed from the reality, conduct accurate research, and find a method that can match the current problem. In this paper, taking basketball projects in national fitness as an example, by introducing a proposition about the development of small basketball events, the corresponding big data modeling and analysis methods are studied. The research methods and research objectives involved in this paper are based on the relevant parameters of the multivariate statistical model. First, the article introduces the calculation principle of the multiple linear regression model. We introduce the concept of variance inflation factor involved in this principle and carry out the modeling and analysis of big data based on this variable. In order to illustrate the application effect of big data in this kind of research, this paper introduces three different big data technologies, including immune selection optimization algorithm, particle swarm optimization algorithm, and Elman neural network, to predict and analyze the variance inflation factor (VIF) corresponding to the small basketball project. The analysis results show that the Elman network exhibits certain advantages in terms of computing convergence time. And, as the number of calculation steps increases, the superiority of the Elman network is more obvious. As far as the prediction performance is concerned, the square of the correlation coefficient corresponding to the immune selection optimization algorithm is the largest and the sum of the squares of the residuals is the smallest, showing superior prediction performance.

Because our country is constantly updating training standards, the first mechanism for assessing physical fitness is far from current needs. A good assessment process can help to stimulate student learning, identify strengths and weaknesses, and better improve sports knowledge. In this paper, based on the multisensor perception of physical education and teaching practice data collection and analysis, it is found that the original sensor data often have some defects, but the Kalman filter can be processed, which can make the data more accurate. After comparing the data, it can be found that each group of data basically has an error of 0.02–0.9. After processing, the data better reflect the changes in the measurement. With the reform of the professional evolution of boxers, higher requirements have been placed on athletes. The sensor can be continuously tested. According to the experiment, the basic probability of different sensors on the test paper can be found that the fused sensor data are 1/2, while the single sensor data are 1/6, and the data of a single sensor are much lower than the confidence of fused sensors, effectively improving the comprehensive ability of boxing.

In modern times, with the development of industrialized society and the invention of clocks and watches, people have become more and more concerned about institutionalization, and higher education time and space have also shown the characteristics of institutionalization. In modern times, educational informatization has brought challenges and opportunities for educational reform and development. The vigorous development of education informatization has also produced changes in higher education spatiotemporally. For example, the rise of catechism and online learning, the importance of fragmented time, the development of space toward virtualization, and the expansion of interactive space for interpersonal interactions. In addition, the enrichment and sharing of educational materials, the informatization, and big data of educational management also contribute to the improvement of time efficiency and optimization of time structure in higher education. With the background of education under the new normal, this paper briefly analyzes the current situation of education informatization construction in colleges and universities from the perspective of education managers, and addresses the current situation and problems of teaching management informatization construction, such as organizational structure dominated by pyramidal functional departments, inefficiency, inability to connect work, difficulty in work gap, difficulty in horizontal coordination, and closed information. A work breakdown structure (WBS) based project management theory and work structure decomposition method are proposed. This paper finds that the improved association rule mining algorithm can improve the efficiency of teaching management practice in colleges and universities by more than 14.58%, which is an important reference value for college management informatization.

Chinese culture is broad and profound, and successive dynasties have left many cultural treasures. Ancient architecture is a significant treasure, and it is also the core content of the inheritance of Chinese culture. Every Chinese ancient building has its own characteristics, and the creative components of each ancient building are an important part of ancient buildings. As a new learning mode of current scientific inquiry, the deep learning model includes high-level and high-stage cognitive processing ability and innovative thinking ability. Under the background of the above modeling and optimization analysis of ancient building construction rule components and the development of deep learning mode, this paper proposes the modeling and optimization analysis of ancient building construction rule components about deep studying. The results of the experiment are as follows: (1) about the concept of the deep learning technology model and the vacancy problems existing in the current situation of the design framework and optimization of ancient building construction rule components, the research direction of the experiment is determined, and through the investigation and analysis of the modeling and optimization of ancient building construction rule components based on the deep learning model, the technical guarantee is provided for the research of this paper; (2) the convolution neural network algorithm, inversion model algorithm, loss function algorithm, and optimization algorithm are used to calculate, evaluate, and analyze the research problems, and the investigation contents are identified and analyzed through experimental research. It can not only analyze the root of the research problems but also improve the specific modeling optimization problems of ancient buildings, to reduce the unnecessary loss of time and resources.

With the rapid deployment of 5G networks in recent years, the characteristics of high bandwidth, low latency, and low energy consumption of 5G networks have enabled the rapid development of IoT (Internet of things) technology. However, 5G networks cannot provide high-quality wireless coverage for many IoT devices in border areas and hotspots with a high signal density that lack fixed infrastructure. Therefore, this paper uses the UAV (unmanned aerial vehicle) to carry the communication platform to build the NTN (nonterrestrial network) to provide wireless coverage for terrestrial fixed and mobile IoT devices. Meanwhile, since the NTN needs to provide wireless coverage for many IoT devices, we use deep reinforcement learning to provide path planning for the UAV communication platform to improve the efficiency of wireless coverage. We build a simulation environment to evaluate the performance of the NTN network for wireless coverage of IoT devices in urban hotspot areas. Experimental results show that the method proposed in this paper can provide higher downlink rates for more IoT devices than NB-IoT (narrowband Internet of things).

A contactless system became necessary for smart mobility during the COVID-19 pandemic. There are many touchpoints in private and public areas where contact is essential, such as intelligent transportation systems for vaccine carriers, patient ambulances, elevators, metros, buses, hospitals, and banks. A secured contactless device reduces the chances of COVID-19 infection spread. Several devices use smart cards, fingerprint identification, or code-based access. Most of these devices require some form of touch. The cost of such devices varies, depending on their capability and intended use. Sensors developed by using artificial intelligence (AI) to provide secured access are an emerging area. This paper presents an AI-powered contactless face recognition system. The solution has the Internet of Things (IoT) enabled access system. To identify a person, it uses AI assistance for face recognition with the help of Python Dlib’s facial recognition network. Dlib offers a wide range of functionality across several machine learning sectors and is open-source. The Arduino Uno (ATmega328P) and STK500 protocol has been used for communication to testify and validate the performance of the proposed technique. The objective is to detect and recognize faces by the proposed contactless approach. The obtained result shows 92% accuracy, 94% sensitivity, 96% precision and FRR 6% for face detection. There is a significant improvement in FRR in our work compared to the published 27.27%. The implemented solution in this paper provides accurate and secure contactless access to conventional, readily available techniques in public health safety.

Recently, deep learning has been employed in automatic feature extraction and has made remarkable achievements in the fields of computer vision, speech recognition, natural language processing, and artificial intelligence. Compared with the traditional shallow model, deep learning can automatically extract more complex features from simple features, which reduces the intervention of artificial feature engineering to a certain extent. With the development of the Internet and e-commerce, picture advertising, as an important form of display advertising, has the characteristics of high visibility, strong readability, and easy-to-obtain user recognition. An increasing number of Internet companies are paying attention to what kind of advertising pictures can attract more clicks. Based on deep learning technology, this paper studies the prediction model of click-through rate (CTR) for advertising and proposes an end-to-end CTR prediction depth model for display advertising, which integrates the feature extraction of display advertising and CTR prediction to directly predict the probability of an advertisement image being clicked by users. This paper studies the deep-seated nonlinear characteristics through the multilayer network structure of the deep network and carries out several groups of experiments on the private display advertising data set of a commercial advertising platform. The results show that the model proposed in this paper can effectively improve the prediction accuracy of CTR compared with other benchmark models and predict whether an advertisement is clicked or not by given advertisement information and user information. By establishing a reasonable advertising click-through rate prediction model, it can help the platform estimate future revenue so as to make cooperative decisions with advertisers. For advertisers, it is necessary to evaluate the price by predicting the click-through rate and estimate the bidding price of their own advertisements.

In order to improve the adaptive control effect of the multiemergency power supply networking system connected to new energy (NE), this paper studies the adaptive control strategy (ACS) of the multiemergency power supply networking system connected to NE by combining intelligent algorithms and determines the appropriate installation node of the distributed power supply according to the voltage stability index. Moreover, this paper proposes a method to optimize the distribution network feeder reorganization and distributed power configuration problems based on the fireworks algorithm to reduce the network loss and improve the voltage distribution. In addition, distribution network reorganization and distributed power configuration optimization can more effectively minimize network losses and improve voltage distribution. Through the experimental research, it can be seen that the ACS of the multiemergency power supply network system connected to NE proposed in this paper has the established effect.

In order to improve the efficiency of information management of open laboratories in colleges and universities and reduce the cost of information management of open laboratories in colleges and universities, this paper proposes the open sharing management of artificial intelligence laboratories based on the deep integration of big data. The infrastructure layer of the system transmits the information management data of the university open laboratory to the cloud platform layer. The cloud platform layer completes the data management, cloud computing, and access control and transmits the processed data to the application layer. The application layer realizes the information management of the university open laboratory according to the data fed back by the cloud platform layer. Among them, in the process of data transfer in open laboratories of colleges and universities, pyramid technology is used to realize data transfer, and the optimization calculation of particle swarm optimization algorithm is used to complete the transfer of multidimensional and complex data transmission information, so as to improve the efficiency of data transfer. The experimental results show that the system has high accuracy in transferring multidimensional and complex data in university open laboratories, the average resource utilization rate of the system is as high as 92%, the average response of the system is as fast as 8.5 ms, and the management cost is effectively reduced. Conclusion. The system designed in this paper can reduce the information management cost of open laboratories in colleges and universities.

Virtual reality technology has grown in popularity as the economy and society have changed dramatically, and it is now being used in a wide range of applications. Interactive virtual technology’s deployment in the field of art has a wide range of possibilities due to its interaction, immersion, and real-time capabilities. The advantages of this technology, extremely in interiors and design, are unrivalled by other technologies. Virtual reality interior design can help designers and consumers understand the influence of “what you see is what you get,” while also expecting people to feel the visual concept in the 3D content of this model. The research and studies of interior decoration are conducted in this paper using visually stunning virtual reality technology, with the viewing public housing developments as the research object, creating a conceptual framework for successful research and innovation of interior design assisted by visually stunning Virtual reality (VR) technology. In this paper we applied the reliability to check the accuracy of questionnaire and descriptive statistics which summarises all the data.

It is known that, given an RSA modulus, n = p q , the public key e and the corresponding private key d satisfy the modulo congruence e d ≡ 1 mod ϕ n , where ϕ n = p − 1 q − 1 . Usually, the private key d can be computed efficiently using the extended Euclidean algorithm, and it is common knowledge that the private key is unique in the sense of modular ϕ n . This paper shows that there exist multiple private keys d ; they all satisfy that d < ϕ n . This paper also presents the exact relationship between an RSA public key and a corresponding private key.

The fastest development of technologies encourages computer multimedia-oriented education. Nowadays, information technologies create human basic abilities. College English teaching mode reform based on computer multimedia is the aim of the paper. Generally, computer multimedia technologies oriented college English education merging pictures, videos, audio, vocabulary, and animation. We described the development of multimedia in English teaching mode, applications of college English teaching, a novel model of foreign language teaching in the classroom and a computer, computerized English language teaching method TPACK design, and the latest architecture of college English language teaching based on computers in this paper. We are discussing face-to-face teaching, language lab autonomous learning room, network teaching area in multimedia, and network teaching circumstances in this paper. This paper evaluated wireless communication’s range, challenges facing college English teaching, comparative analysis of attitude, comparative analysis of curriculum, and comparative analysis of technology. We express that, from the year 2000 to the present, the technologies in wireless communications with computer multimedia are developing in the range of wireless communication.

Enterprise management and internal control are used to prevent and control risks and promote enterprises to achieve development strategy. This paper adopts comprehensive multidisciplinary research method to study the basic theory of prediction of major defects in enterprise internal control. Firstly, this paper proposes the prediction index system and sample selection standard of internal control major defects. Totally, 630 listed company and 12 indicators are collected and then use the random forest classification method based on principal component analysis. The parameters of random forest are optimized by genetic algorithm. Finally, the prediction model of major internal control defects of listed companies is established. The experimental results show that the average score of PCA-RF model in TPR value reaches 85%, which is nearly 20% higher than the 65% of RF model, proving that the PCA and GA can significantly improve the classification accuracy of ST Company and has important practical significance. Therefore, the proposed method system can reasonably solve the prediction problem of major defects in internal control.

In wireless sensor networks (WSNs), radio frequency identification (RFID) plays an important role due to its data characteristics which are data simplicity, low cost, simple deployment, and less energy consumption. It consists of a series of tags and readers which collect a huge number of redundant data. It increases system overhead and decreases overall network lifetime. Existing solutions like Time-Distance Bloom Filter (TDBF) algorithm are inapplicable to the large-scale environment. Received Signal Strength (RSS) used in this algorithm is highly dependent on quality of tag and application environment. In this paper, we propose an approach for data redundancy minimization for RFID surveillance data which is a modified version of TDBF. The proposed algorithm is formulated by using the observed time and calculated distance of RFID tags. To overcome these problems, we design our approach to relevantly reduce the spatiotemporal data redundancy in the source level by adding the Received Signal Strength Indicator (RSSI) concept for energy-efficient RFID data communication in wireless sensor network scenario. We introduce in this paper the new improved idea of an existing algorithm which efficiently reduces the rate of data redundancy spatially and temporally. The implemented results overcome the limitations of existing algorithm for data redundancy reduction. Nevertheless, the performance evaluation shows the efficiency of proposed algorithm in terms of time and data accuracy. Furthermore, this algorithm supports multidimensional and large-scale environment suitable for sensor network nowadays.

The traditional Chinese language and literature distance teaching system has the problem of incomplete instruction recognition function, which leads to the slow connection speed of the system. This paper designs a computer multimedia Chinese language and literature distance teaching system. For the hardware part, strain gauge and sensor are combined, connected to VCC through 10 K ohm pull-up resistance, and connected to other components. The software part obtains the expression form of Chinese language and literature; formulates the training target, according to the antimonotone characteristics of the support degree; adopts the association rule algorithm to build the remote teaching system database, combined with the client to parse the signaling message; and designs the software instruction recognition function by using the computer multimedia technology. Experimental Results. The average connection speeds of the Chinese language and literature distance teaching system in this paper and the other two kinds of Chinese language and literature distance teaching system are 8.737 s, 13.140 s, and 13.156 s, respectively, which proves that the performance of the Chinese language and literature distance teaching system combined with computer multimedia technology is more perfect.

With the popularity of 5G mobile communication services, the number of users has increased dramatically, as well as the nonuniformity of user density distribution; many users are in nonideal channel conditions, so unmanned aerial vehicle (UAV) as a passive relay equipment platform has gradually entered people’s vision. Intelligent reflective surfaces (IRSs) capable of reconfiguring electromagnetic absorption and reflection properties in real-time are offering unprecedented opportunities to enhance wireless communication experience in challenging environments. In this paper, we start from the point of minimizing the energy consumption and nodes of UAV passive relay; the paper proposes to install intelligent reflecting surface (IRS) on UAV as a new passive relay and establish the communication coverage model of UAV-IRS. Then, the main relationship between the coverage radius and hover height of UAV-IRS is verified. In view of different distribution densities of target users, a CK++ (cyclic k -means++) is proposed to solve the spatial deployment problem of UAV-IRS, where the optimal solution of the system is obtained through cyclic clustering. And the algorithm is verified to effectively improve the performance of urban mobile communication and user communication service quality through numerical stimulation.

In order to further improve the safety production management capacity of coal enterprises and minimize the rate of coal mine safety accidents, based on the purpose of improving safety production efficiency, this paper constructs a coal mine safety detection system based on GIS technology. At the same time, on the basis of fixed-point positioning and remote monitoring at the PC end, this paper implements the whole process detection and monitoring of coal mine safety production with the help of mobile terminal intelligent system. The system software uses Java language to code it. In the first round of test, one function omission, three serious system errors, 14 general errors, and 21 minor errors are found. The accident passing rate of the second test is greater than 99.88%, and all performance indexes of the system meet the performance requirements, which proves the feasibility of the system.

From a temporal perspective, this paper examines the scale, current development, business versatility, and overall work effects of regional the movement business work, taking into account the three-level proportions of the degree of the movement business in the context of the common Internet of Things. From a spatial standpoint, we recognize the adjacent city as the primary investigative unit, take on various markers, and lead an extensive assessment of the territorial improvement of the provincial travel industry work through even and vertical correlations. This paper utilizes numerous direct relapse examinations to set up the connection between the advancement level of the district of the travel industry economy and the impacting factors; arranged by size of impact, they are the travel industry asset gift, area traffic conditions, and monetary turn of events. Utilizing a blend of a solitary pointer and various markers, the area of the travel industry economy is assessed and dissected for contrasts in existence. We select the absolute travel industry income as a pointer and use strategies like reach. To assess the time difference of the movement of the business economy in each location, standard deviation, coefficient of assortment, and coefficient were used. To examine the data, we use the Granger causality test and several methodology components influencing the travel industry work nearby and the development mode. Through the foundation of a primary model of the travel industry work development element framework, causality test and different strategies are taken on to examine the provincial travel industry business affecting variables and development techniques, and the outcomes show that the local travel industry business development mode is a speculation driven the travel industry work development mode.