Smart Mobile Terminals Research Articles

Mobile Terminal System of Intelligent College English Teaching and Training Mode

With the increasing maturity and wide application of wireless mobile communication technology and 4G networks, the use of mobile smart terminals has been popularized and has become an indispensable thing for people to live, study, and entertain. Taking college English course as an example, this paper aims to study the mobile terminal system of intelligent college English teaching and training mode. Based on the introduction of the concept, architecture, characteristics of WAP technology, and the development tools of WAP application software that need to be used in the teaching mode of the mobile terminal system, this article tentatively constructs a teaching mode of the mobile terminal system. The model is described, the collaborative filtering algorithm is proposed, and how to construct the matrix and calculate the similarity with the collaborative filtering algorithm is discussed. In order to verify the effectiveness of the system model, a test level comparison and a questionnaire survey of the teaching model were carried out. The experimental results of this article show that 91.8% of students said that their comprehensive English level has been improved in the process of applying the mobile terminal system teaching mode. In addition, 87.5% of the students said that it is necessary or basically necessary to use English for professional teaching and 76.2% of the students are very satisfied and basically satisfied with this model.

Development Analysis of Trusted Computing Technology of Smart Mobile Terminal

With the continuous development of mobile communication technology, the market scale of smart mobile terminal industry is expanding rapidly. At the same time, due to the open characteristics of the software ecosystem and usage scenarios of smart mobile terminal, its security is facing great risks. Based on this, this paper first summarizes and classifies the security risks faced by smart mobile terminals according to the risk sources, then studies the current development status of trusted computing technology, and finally analyses the feasibility of introducing trusted computing technology into smart mobile terminals.

Smart Terminal Malware Detection Technology Based on Abnormal Network Behavior

In recent years, the use of smart mobile terminals has increased rapidly, especially mobile phones on the Android platform, which account for almost half of the mobile phone market. Many attackers regard smart terminals as the target of their attacks. They can easily collect user privacy information, thus posing a huge threat to users. In this regard, researching a malware detection technology for smart terminals is of great significance to the safe use of mobile phones in my country. The purpose of this article is to study the smart terminal malware detection technology based on abnormal network behavior. In this paper, the method of reducing feature data is used to preprocess the captured original network data to improve the applicability of the naive Bayes algorithm in the network behavior data processing environment. The preprocessing part first cleans up the network data and removes useless information; then uses methods such as establishing a static address library and field query to divide the data, normalize the complex data, and finally construct the feature vector. The main part of the anomaly recognition module in this paper is the naive Bayes classifier, and the preprocessed data is introduced into the naive Bayes classifier as a feature vector for classification detection. This article introduces the privacy data monitoring technology to track the data flow of the malware detected by the anomaly recognition module, and determine the leakage path of its privacy data, so as to clarify the magnitude of its harmfulness for further processing. Research shows that this article has tested the performance of the system, and the time-consuming items all use the stopwatch timer that comes with the mobile phone to assist in timing, which can be accurate to 0.01 second.

Dynamic Resource Allocation Strategy in Mobile Edge Cloud Computing Environment

With the development of the mobile Internet, smart mobile terminals have become an indispensable tool for people's lives and mobile applications are becoming more and more powerful. This research mainly discusses the dynamic resource allocation strategy of the mobile edge cloud computing environment. The physical resource layer in the network model is responsible for providing specific resources that are actually available, such as hardware resources, computing resources, storage resources, mainly including base stations, mobile edge computing servers, spectrum, power, and other communications of different infrastructure vendor basic components of the system. The functions of the virtual machine monitor include resource virtualization and resource management. As an important component of wireless network virtualization, virtual machine monitors are usually deployed in physical base stations to provide physical resources and to consider the connection between the virtual machine stations. The business of the business cache model is an application that is requested by users running on the mobile edge computing server or cloud at the base station. The computing task scheduling in the mobile edge environment can be classified as a wireless interaction model. This model captures the user throughput in cellular network interaction. The physical layer channel access strategy (CDMA) allows all mobile users to efficiently share the same spectrum resources at the same time. When the preference coefficient for task energy consumption varies between 0.35–0.55 and 0.65–1, the superior range of maximum system efficiency achieved by RAOM accounts for 55% of the entire range. This research contributes to the reasonable allocation of resources, and the mobile edge computing model improves the fairness of users with a lower transmission cost.

Tennis Online Teaching Information Platform Based on Android Mobile Intelligent Terminal

With the development of the information age, there is almost one mobile smart device based on android, which is inseparable from mobile smart devices in both learning and life. In order to explore the effects of mobile smart devices on tennis teaching, this article mainly introduces the research on the tennis online teaching information platform based on android mobile smart terminals. This article first uses the android system framework and software architecture to design the technical aspects of the tennis online teaching information platform and then analyzes and improves the functional and nonfunctional requirements of the tennis online teaching information platform and students’ needs for mobile learning. For the design of aspects other than the technology of the online teaching information platform, the control group and the experimental group were designed to carry out teaching experiments, comparing the traditional teaching methods and the teaching methods of the online teaching information platform to bring out different teaching effects to tennis teaching. The results show that online teaching has a better learning effect on tennis skills and tennis theory knowledge, and the academic performance of the traditional teaching method is improved by 20%. In terms of increasing interest in tennis courses, the online teaching information platform has improved 70% of students’ interest.

RETRACTED ARTICLE: UI design of visual communication of coastal city landscape based on embedded network system and remote sensing data

Now the development speed of cities is getting faster and faster, and many cities have reached the stage of urban landscape design. At this stage, the requirements for the urban landscape are mainly in the transmission of information, which can be summarized as the identification of information, the requirements for ideology and readability, which clearly tells urban planners that they need to receive information from the perspective of vision To design the urban landscape. After investigation, it is found that under the current technological development trend, the application of geographic information based on smart mobile terminals has become a development trend and has become one of the research hotspots in the field of geographic information science. With more and more methods of acquiring geographic information, such as smart phones, drones, and remote sensing satellites, a large amount of spatial data is continuously presented at an extremely fast speed. In recent years, the rapid development of the computer hardware manufacturing industry has made great contributions to the performance of embedded devices in improving computing speed and expanding memory. In this high-speed development environment, the fittest will survive, and the inadequate will be eliminated. Under the background of the information age, any new field will face the problem of short life cycle. Although UI also has it, the development of UI still has a long way to go. Therefore, the UI design industry still has great development prospects.

An Exploration on Using Mobile Smart Terminals to Promote the Reform of College English Teaching and Learning

College English is a compulsory basic course for higher education in China. At the same time, handling a secondary language is a must-have under the globalization. However, the students’ English proficiency has always been unsatisfactory after so many years of teaching and learning. Meanwhile, in the era of information and data, Internet has not only changed our country’s economic model, but also brought a revolution in education. Nowadays, Every college student has a mobile smart terminal at least, and we can make full use of this device to improve the teaching methods and even implement it into classrooms. Based on this opportunity, this article explores how to use mobile smart devices to improve the traditional “teaching” and “learning” for non-English majors in universities.

Identity Authentication with Association Behavior Sequence in Machine-to-Machine Mobile Terminals

With the rapid development of machine-to-machine (M2M) mobile smart terminals, M2M services can be used in a wide range of industries, including such as tele-medicine, remote meter reading and public security. Since different industries and enterprise users have different requirements for M2M specific applications, the security identity authentication of M2M mobile terminals is particularly worthy of attention. Existing methods can effectively solve the unsustainable problem of one-time verification, however they cannot address the dynamic relevance characteristics of user behavior sufficiently. Thus, the accuracy of user identity authentication needs to be further improved. In this paper, we propose a terminal identity authentication technology based on user association behavior analysis. In order to identify the abnormal login during each behavior process of authenticated user, we take largest coincident part of the user behavior sequence and short coincide into consideration. In addition, we propose Behavior Common Subsequence Sequence Similarity Algorithm based on the traditional sequence pattern of Behavior Common Subsequence(BCS). The experimental results demonstrate that the proposed method can effectively improve the accuracy of user’s behavioral sequence, and prove the uniqueness of different sequences on the other hand.

Research on Test Methods for AI Industrial Application Capabilities of Smart Mobile Terminals

Research on Test Methods for AI Industrial Application Capabilities of Smart Mobile Terminals

Guest Editorial: Learning-Based Edge Computing Services

The recent revival of artificial intelligence (AI) is revolutionizing almost every branch of science and technology. Given the ubiquitous smart mobile terminals and Internet of Things (IoT) devices, it is expected that a majority of intelligent applications will be deployed at the edge networks. Therefore, providing intelligent and sustainable edge computing services will be a hot topic in IoT and 5G services as sustainability of edge systems becomes a necessity with the rapid constant growth of edge devices/sensors.

Detection of Head Raising Rate of Students in Classroom Based on Head Posture Recognition

The proliferation of smart mobile terminals has weakened the attention and reduced the learning efficiency of students, making them more likely to lower their heads. To quantify the classroom participation, it is helpful to detect the head raising rate (HRR) of students in classroom. To this end, this paper puts forward a novel method to recognize the HRR of students in classroom. Based on the map of predicted facial features, an extraction method was developed for the salient facial features of students, and used to realize model matching between facial contour and facial organ. Next, the face orientation of each student was determined by soft label coding. After that, a multi-task convolutional neural network (CNN) was constructed to detect the HRR of students. The authors also explained the regularization of the loss function, and the steps of target detection. The proposed method was proved effective through experiments. The research results provide a reference for the application of head posture recognition in other fields.

An Evaluation Strategy for Commercial Precision Marketing Based on Artificial Neural Network

With the proliferation of the Internet and smart mobile terminals, great progress has been made in the precision placement and benefit-sharing mechanism of commercial advertisements. Meanwhile, media marketing has become increasingly in-depth and precise. So far, mature theories have been proposed on consumer value and precision marketing. But further research is needed to mine the value from the big data on commercial precision marketing. To improve the accuracy of commercial precision marketing, this paper presents an evaluation index system (EIS) for commercial precision marketing based on improved attention-interest-desire-memory-action (ADIMA) model, and determines the principal evaluation indices through principal component analysis (PCA). Next, an artificial neural network (ANN) was established to evaluate commercial precision marketing, and optimized through k-means clustering (KMC). Finally, the optimized model was realized on MATLAB. The proposed EIS and ANN were proved scientific and effectiveness through simulations. The research results provide a reference for the application of the ANN in other fields of marketing.

Discussion on Teaching Strategies of Teaching English App Applied to Comprehensive English Course

At this stage, the application of network technology and mobile information technology in social production and life continues to expand, which has promoted the development of education information. In the current curriculum teaching reform and practice, it is necessary to accelerate the integration of information technology and curriculum teaching to effectively promote the continuous innovation of teaching concepts and methods. As the current users of smart mobile terminals continue to expand, the deployment rate of smart mobile devices among college students is getting higher and higher, and they are more proficient in the application capabilities of smart terminals. In this case, various teaching apps have begun to emerge, as have related English apps, which provide rich and diverse resources for English teaching and greatly enhance students' interest in English learning. In terms of the application of English apps in the teaching of integrated English courses, there are still some problems to be solved urgently. This article introduces the importance of pedagogical English App in the teaching of integrated English courses; analyzes its application status in the teaching of integrated English courses; and explores the countermeasures and suggestions for applying this type of App in the teaching of integrated English courses.

Increasing Teacher Professional Competence in Developing Procedural Abilities Using the Application of Assessment of Integrated Science Using Mobile Learning on Android Platform Gadgets

The development of smart mobile phone terminals, mobile learning has become an effective and efficient way for teachers to learn, so that the continuous development of teacher competency development can take place in a fun way. From the aspect of integrated science learning material, it is very clear that science learning in the 2013 curriculum is carried out on an integrated basis. Integrative science has the meaning of combining various aspects, namely the domains of attitudes, knowledge and skills. Therefore, the assessment system developed in science learning must also be able to develop four dimensions of knowledge (factual, conceptual, procedural). In this study, procedural knowledge was specifically developed using the assessment of integrated science application using mobile learning on gadgets with the android platform. The methodology developed in this study is a Research & Development spiral model as referenced by Cennamo and Kalk (2005: 6). In this spiral model, there are 5 (five) development phases, namely: (1) definition, (2) design, (3) demonstration, (4) develop, and (5) presentation (deliver). The research results that have been achieved are; (1) A model for enhancing the professional competence of teachers in the development of procedural ability tests through the design of an assessment of integrated science using mobile learning on gadgets with a proper android platform based on expert judgemnet, (2) Practicality tests of the model have been carried out based on the results of training involving junior high school teachers in the MGMP area of science teachers, Mlati District, Sleman Regency, (3) The model is effective in improving students' procedural abilities

Mobile neural intelligent information system based on edge computing with interactive data

With the increasing popularity of smart mobile terminals, smartphones, tablet computers and other mobile devices will gradually replace personal computers and become the most important computing platform for consumers. Mobile edge computing (MEC), as a key technology for the evolution of communication network architecture, can meet the requirements of the system for throughput, delay, network scalability and intelligence. Based on the MEC, the content and services provided by information system are closer to users to increase mobile network speed, reduce latency and improve connection reliability. In this paper, we propose an interactive data information system based on mobile edge detection, which is divided into client and server. The server mainly provides services such as content and user login and rights management for the client. The client needs to log into the server to run normally. Among them, the interactive data server is a favorable support and guarantee for the client. The experimental results show that the proposed method has higher efficiency and fault tolerance.

Survey on user location prediction based on geo-social networking data

With the popularity of smart mobile terminals and advances in wireless communication and positioning technologies, Geo-Social Networks (GSNs), which combine location awareness and social service functions, have become increasingly prevalent. The increasing amount of user and location information in GSNs makes the information overload phenomenon more and more serious. Although massive user-generated data brings convenience to users’ social and travel activities, it also causes certain trouble for their daily life. In this context, users are expecting smarter mobile applications, so that the location information can be employed to perceive their surrounding environment intelligently and further mine their behavior patterns in GSNs, which ultimately provides personalized location-based services for users. Therefore, research on user location prediction comes into existence and has received extensive and in-depth attention from researchers. Through systematically analyzing the location data carried by user check-ins and comments, user location prediction can mine various user behavior patterns and personal preferences, thus determining the visiting location of users in the future. Research on user location prediction is still in the ascendant and it has become an important topic of common concern in both academia and industry. This survey takes Geo-social networking data as the focal point to elaborate the recent progress in user location prediction from multiple aspects such as problem categories, data sources, feature extraction, mathematical models and evaluation metrics. Besides, the difficulties to be studied and the future developmental trends of user location prediction are discussed.

A Data-Aware Remote Procedure Call Method for Big Data Systems

In recent years, big data has been one of the hottest development directions in the information field. With the development of artificial intelligence technology, mobile smart terminals and high-bandwidth wireless Internet, various types of data are increasing exponentially. Huge amounts of data contain a lot of potential value, therefore how to effectively store and process data efficiently becomes very important. Hadoop Distributed File System (HDFS) has emerged as a typical representative of dataintensive distributed big data file systems, and it has features such as high fault tolerance, high throughput, and can be deployed on low-cost hardwares. HDFS nodes communicate with each other to make the big data systems work properly, using the Remote Procedure Call (RPC) mechanism. However, the RPC in HDFS is still not good enough to work better in terms of network throughput and abnormal response. This paper presents an optimization method to improve the performance of HDFS. The proposed method dynamically adjusts the RPC configurations between NameNode and DataNodes by sensing the data characters that stored in DataNodes. This method can effectively reduce the NameNode processing pressure, and improve the network throughput generated by the information transmission between NameNode and DataNodes. It can also reduce the abnormal response time of the whole system. Finally, the extensive experiments show the effectiveness and efficiency of our proposed method.

An Efficient Public Key Searchable Encryption Scheme for Mobile Smart Terminal

With the wide application of the mobile smart terminals, the data privacy protection of the mobile smart terminals stored in the cloud is more and more important. Public key encryption with keyword search (PEKS) and secure channel free PEKS (SCF-PEKS) have been proposed for public key searchable encryption previously. However, the security of keyword search is far from enough. In addition, these schemes are mostly based on bilinear pairing and the computational efficiency is relatively low. In this paper, we propose a novel non-bilinear pairs SCF-PEKS schemes for mobile smart terminal that offer a high computational efficiency along with better security assurances than that of the existing alternatives. Without random oracle model, we prove the security and privacy of the scheme’s keyword ciphertext and keyword trapdoor through the game hopping method. Therefore, the scheme is capable of resisting outside online keyword guessing attack and inside offline keyword guessing attack. Based on the comparison and experimental results, the scheme turns out to be secure and practicable.

An improved method for indoor positioning based on ZigBee technique

With the popular use of smart mobile terminals, it is possible to locate the mobile terminals. The traditional indoor positioning methods often bring about large errors under the circumstance of interference. In this paper, an improved K-nearest neighbourhood method based on ZigBee wireless sensor network is proposed, it selects K nearest database vectors from the minimum distance, and calculate the average of these coordinates as the final location result. The accuracy of indoor positioning is effectively improved, meanwhile, there is no significant increase of the computing time. The effectiveness of the method has been verified in the end.

LCBPA: two-stage task allocation algorithm for high-dimension data collecting in mobile crowd sensing network

Mobile crowd sensing (MCS) is a novel emerging paradigm that leverages sensor-equipped smart mobile terminals (e.g., smartphones, tablets, and intelligent wearable devices) to collect information. Compared with traditional data collection methods, such as construct wireless sensor network infrastructures, MCS has advantages of lower data collection costs, easier system maintenance, and better scalability. However, the limited capabilities make a mobile crowd terminal only support limited data types, which may result in a failure of supporting high-dimension data collection tasks. This paper proposed a task allocation algorithm to solve the problem of high-dimensional data collection in mobile crowd sensing network. The low-cost and balance-participating algorithm (LCBPA) aims to reduce the data collection cost and improve the equality of node participation by trading-off between them. The LCBPA performs in two stages: in the first stage, it divides the high-dimensional data into fine-grained and smaller dimensional data, that is, dividing an m-dimension data collection task into k sub-task by K-means, where (k < m). In the second stage, it assigns different nodes with different sensing capability to perform sub-tasks. Simulation results show that the proposed method can improve the task completion ratio, minimizing the cost of data collection.