Mobile Terminals Research Articles

Antenna Pattern Measurement for Mobile Terminals via Magnetic Infinitesimal Dipole Based Source Reconstruction

Antenna Pattern Measurement for Mobile Terminals via Magnetic Infinitesimal Dipole Based Source Reconstruction

전자결제 서비스의 특성이 중국 사용자 행동에 미치는 영향에 관한 연구

In recent years, mobile payment has become widespread and has become a major payment method for many consumers in China. Products purchased mainly through mobile terminals are settled and settled through mobile payment platforms. The number of mobile payment users in China continues to expand. The volume of mobile payment transactions in China continue to increase in recent years. The scale of mobile payments in China is 432.2 trillion yuan in 2020. Technological innovation has revolutionized business models, and mobile commerce is increasingly accepted and recognized by the public. Mobile payment is an important process and key link in mobile commerce. Mobile payments have great potential and a wide market. As the number of users and frequency of use increases, the volume of mobile payment transactions will continue to grow in the future. In this context, the purpose of this study is to examine the effect of China's electronic payment service characteristics on user behavior. The purpose of this study is to explore the relationship between the electronic payment service and user behavior through an empirical analysis of the electronic payment service characteristics based on the theoretical background of the electronic payment service characteristics. This is to ensure that the electronic payment service continuously adapts to the needs of society along with economic development and finds defects in the electronic payment service in a timely manner. In this paper, the factors of electronic payment service characteristics were selected by analyzing the existing research results. In this study, SPSS 23.0 was used for basic statistics. SmartPLS 4.0 statistical package was used. for hypothesis testing. A survey was conducted with 270 people using an online survey in China. There are convenience, security, reliability, and responsiveness, which are characteristics of electronic payment service. They had a significant effect on user attitude. User attitude and user behavior had a significant effect.

Research and development of indoor positioning technology based on visible light communication

Indoor and underground space positioning and navigation systems are important infrastructure for urban lifeline construction. With the development of 5G communication technology, artificial intelligence, intelligent construction, and other technologies, autonomous mobile terminals have become the main application subjects, and the requirements for accuracy and ubiquity of indoor positioning technology are also increasing. Two main indoor positioning technologies, sensor based and RF signal based are introduced, basic concepts of new visible light communication positioning technology are discussed, and in-depth results on non-imaging and imaging positioning methods are presented in this article. We propose a pose-assisted imaging positioning method applied in the 11 m × 8 m × 3.5 m room, which is based on visible light communication. This test shows that this method can achieve the positioning results with a plane positioning error less than 5 cm and the height error less than 6 cm by using low-cost sensors. Visible light positioning technology provides a cost-effective and convenient new solution for indoor positioning, simultaneously solving the integration problem of communication and positioning. It can provide positioning technology supporting the construction of urban lifelines in enclosed spaces.

Research on Malware Detection Technology for Mobile Terminals Based on API Call Sequence

With the development of the Internet, the types and quantities of malware have grown rapidly, and how to identify unknown malware is becoming a new challenge. The traditional malware detection method based on fixed features is becoming more and more difficult. In order to improve detection accuracy and efficiency for mobile terminals, this paper proposed a malware detection method for mobile terminals based on application programming interface (API) call sequence, which was characterized by the API call sequence and used a series of feature preprocessing techniques to remove redundant processing of the API call sequence. Finally, the recurrent neural network method (RNN) was used to build the model and perform detection and verification. Furthermore, this paper constructed a malware detection model based on a two-way recurrent neural network and used the two-way long short-term memory network model (LSTM) to train the data set containing 5986 malware samples and 5065 benign software samples to obtain the final detection model and its parameters. Finally, the feature vector of the APK file to be detected was passed into the model and obtained the detection results. The experimental results indicated that the detection accuracy of this method can reach 93.68%.

Monte Carlo‐based service migration under multiple constraints in mobile edge computing

AbstractMobile edge computing as an emerging technique can provide services for mobile terminals, and meanwhile the mobility of users brings new challenges. When a user moves across different areas, the system needs to determine whether to migrate service so as to guarantee quality of experience for the user. However, it is difficult to obtain the optimal migration policy in real time due to the huge state space. Considering delay‐sensitive data‐intensive applications run by mobile terminals with limited battery power, an efficient service migration policy should be able to make a good tradeoff among service cost, service delay and terminal energy consumption. Here, an online Monte Carlo‐based service migration (MCSM) policy is proposed to minimize service cost under constraints of deadline and terminal energy consumption. A penalty mechanism is designed to update reward when partial or all constraints are not meet. State‐action value estimation and policy improvement are triggered only on the completion of each episode. Each episode is traversed reversely to calculate the average cumulative reward so as to improve policy. Experimental results show that the proposed approach can improve service success ratio and reduce average service cost compared to the existing service migration policies.

Cache‐aided multiuser UAV‐MEC networks for smart grid networks: A DDPG approach

AbstractMobile edge computing (MEC) is an important research topic in the field of wireless communication and mobile computing, as it can effectively decrease the latency and energy consumption due to the trade‐off between the communication and computing, where some intensive computing tasks can be offloaded to computational access points (CAPs), especially when the wireless transmission channel is in good condition. This article studies how to intelligently allocate the computing capability and wireless bandwidth among users for a cache‐aided multi‐terminal multi‐CAP MEC network with non‐ideal channel estimation, where there are mobile terminals and CAPs in the network. Each terminal has some tasks that need to be computed in a fast and efficient way. For such a system, we first design the system by jointly considering the computing capability and wireless bandwidth allocation, where the computing and communication delay is used as the performance of metric. To optimize the system performance, we then employ deep deterministic policy gradient to learn an effective strategy on the allocation of computing capability and wireless bandwidth, in order to decrease the system delay as much as possible. Simulations are finally conducted to show the superiority of the proposed studies in this article, especially about the advantages from cache.

A K-Anonymous Location Privacy-Preserving Scheme for Mobile Terminals

Mobile terminals boost the prosperity of location-based service (LBS) which have already involved in every aspect of People's daily life and are increasingly used in various industries. Aimed at solving the security and efficiency problem in the existing location privacy protection schemes, a K-anonymity location privacy preservation scheme based on mobile terminal is proposed. Firstly, number of rational dummy locations is selected from the cloaking region, from which more favorable locations are further filtered according to location entropy, so a better anonymity effect can be achieved. Secondly, the secure and efficient m-out-of-n oblivious transfer protocol is adopted, which not only avoids the dependency on the trusted anonymity center in existing schemes to improve the efficiency, but also meets the requirements for querying multiple interest points at one time. Security analyses demonstrate that this scheme satisfies such security properties as anonymity, non-forgeability and resistance to replay attack, and simulation results show that this scheme has higher execution efficiency and privacy level, while is low in communications costs.

A Novel Routing Control Method Using Federated Learning in Large-Scale Wireless Mesh Networks

Currently, the volume of communication by mobile terminals are increasing owing to 5G and other technologies. A robust network and appropriate routing control methods are requied to transmit information in unstable wireless communication environments and avoid congestion. Therefore, in recent years, numerous studies have been conducted on wireless mesh networks (WMNs), which provide a fault-tolerant communication environment by securing multiple communication paths and whose topology can be freely configured and extended. Additionally, machine learning routing is attracting attention as a new routing method for wireless communication environments. However, when performing machine learning on a large WMN, the learning time increases and rapid routing control may be impossible. In this study, we apply federated learning to machine learning and propose a machine-learning-based routing method that can be applied to large-scale WMNs. Furthermore, experimental results demonstrate the effectiveness of the proposed method in various environments: congestion avoidance is achieved in a large-scale WMN by machine-learning routing using federated learning. This study is expected to serve as a basis for significant progress in the realization of large-scale WMNs as wireless communication infrastructure.

Research on the Impact of Sports Short Videos Product Placement on Consumer Purchase Intention

With the fast growth of mobile Internet technology, people are spending more and more time watching short videos on their phones. In response to the fragmentation of users' use of mobile terminals, product placement in short videos is also gradually gaining favor. This study aimed to explore the impact of sports-related short video product placement on consumers purchase intentions. In this paper, the SOR model was used, different influencing factors were added to the model, and SPSS 26 was used for significant impact analysis. The conclusion of this study was that consumers pay more attention to the attitude and relevance of product placement and that good advertising attitude and highly relevant advertising can have a positive impact on their purchase intention. Therefore, video creators should pay more attention to advertising content, improve advertising quality, and increase highlights, which will have a certain production direction for video creators or advertisers, and strive to produce excellent videos.

Navigating The Complexity Overcoming Mobility Obstacles in Massive MIMO Systems with MIMO Antennas.

This research investigates the synergy between Massive Multiple Input, Multiple Output (MIMO) systems and mobility challenges, with a focus on the pivotal role played by Multiple Antenna Systems (MIMO antennas). Employing a sophisticated experimental setup featuring a 3.5 GHz Massive MIMO base station equipped with 64 MIMO antennas, mobile terminals on motorized platforms, and advanced RF measurement tools, the study explores adaptive beamforming, spatial multiplexing, and diversity techniques. Results from 100 experiments, each lasting 10 minutes, reveal that adaptive beamforming dynamically improves user tracking in response to mobility-induced channel variations. Spatial multiplexing and diversity techniques enhance throughput and reliability, especially in high user mobility scenarios. The quantitative findings align with existing literature, providing practical insights for the deployment of MIMO antennas. The study addresses limitations and proposes avenues for future research, enhancing our understanding of MIMO antenna applications and contributing to the evolution of wireless communication technologies in dynamic environments.

Blended English Teaching Model in Higher Education School Environment AR Constructive Technology

China's education modernization requirements clearly suggest exploring new teaching methods to improve teaching effectiveness. Based on this, this article investigates AR construction technology as a blended English teaching model in the university school environment. Mobile terminals are used to build the blended English teaching model, modeling tools are used to achieve split modeling, and the mobile terminals themselves are used to achieve the management and network synchronization work for the cloud classroom. For the gesture recognition model construction, 3D convolutional neural networks are used to separate and optimize the parameters. Finally, experiments are designed to simulate and analyze the AR construct of the blended English teaching model to determine accuracy. The simulation results show that the model can realize the simultaneous display of teacher explanation, and the improved algorithm of gesture recognition is improved in recognition rate, which can improve the effectiveness of blended English teaching.

Joint tensor completion anchor selection and coordinate transformation for high precision cooperative positioning in device-to-device systems

The synergistic amalgamation of device-to-device (D2D) communication between mobile terminals (MT) and ultra-dense networks in 5G systems enables cooperative positioning. In this paper, we propose a joint tensor completion, anchor selection, and coordinate transformation (JTCASCT) for high precision cooperative positioning in D2D systems. The positioning is achieved by mitigating the challenging issue of recovering the missing values from the corrupted distance between nodes and determining the global positions of unknown stationary nodes. Initially, a hankelized distance tensor structure called Euclidean distance tensor (EDT) is constructed from incomplete EDM. The missing values are recovered as tensor completion by exploiting its latent low rank structure. The optimization problem is solved using simple low rank tensor completion (SiLRTC) and high accuracy low rank tensor completion (HaLRTC) algorithms. The relative position of nodes is determined after applying multidimensionality scaling (MDS). To reduce the data dimension for coordinate transformation and to select the optimal combination of anchor nodes, we propose a low complex Kruskal–Wallis (KW) test. Finally, the global position of unknown nodes is computed using Helmert transformation (HT). Numerical evaluations demonstrate that the cooperative positioning accuracy is significantly improved with the proposed JTCASCT approach compared to the state-of-the-art techniques.

Multi-Input Multi-Output Antenna Array Design for 5G N78 Applications in Smartphones

This article presents a compact, high isolation Multi Input Multi Output (MIMO) Antenna system operating on (3408-3760) MHz for the fifth generation N78 Applications in mobile phone terminals. The suggested MIMO antenna system is built from four antenna elements. These antennas are placed over two slim side borders of a mobile device's frame to adhere to the current trend of slim and full-screen smartphone devices. The antenna element is composed of two parts: the front part and the back part. The front part consists of the second iteration of Moore curve fractal geometry and an I-shaped feeding line. The back part is of an L-shaped shorted to the system’s ground plane. The desirable antenna diminishment is accomplished based on Moore curve fractal geometry. Low mutual coupling among antennas is attained based on self-isolated and spatial diversity methods, which are less than -16.6 dB. The scattering parameters, antenna gains, antenna efficiencies, and radiation pattern characteristics have all been examined to gauge the performance of the suggested antenna element. In addition, the MEGs and ECCs are examined to understand the MIMO performance of the suggested system. The proposed four-element MIMO antenna system achieves the desired antenna and MIMO characteristics, making it a strong contender for the upcoming 5G mobile devices.

A New Remote Sensing Service Mode for Agricultural Production and Management Based on Satellite–Air–Ground Spatiotemporal Monitoring

Remote sensing, the Internet, the Internet of Things (IoT), artificial intelligence, and other technologies have become the core elements of modern agriculture and smart farming. Agricultural production and management modes guided by data and services have become a cutting-edge carrier of agricultural information monitoring, which promotes the transformation of the intelligent computing of remote sensing big data and agricultural intensive management from theory to practical applications. In this paper, the main research objective is to construct a new high-frequency agricultural production monitoring and intensive sharing service and management mode, based on the three dimensions of space, time, and attributes, that includes crop recognition, growth monitoring, yield estimation, crop disease or pest monitoring, variable-rate prescription, agricultural machinery operation, and other automatic agricultural intelligent computing applications. The platforms supported by this mode include a data management and agricultural information production subsystem, an agricultural monitoring and macro-management subsystem (province and county scales), and two mobile terminal applications (APPs). Taking Shandong as the study area of the application case, the technical framework of the system and its mobile terminals were systematically elaborated at the province and county levels, which represented macro-management and precise control of agricultural production, respectively. The automatic intelligent computing mode of satellite–air–ground spatiotemporal collaboration that we proposed fully couples data obtained from satellites, unmanned aerial vehicles (UAVs), and IoT technologies, which can provide the accurate and timely monitoring of agricultural conditions and real-time guidance for agricultural machinery scheduling throughout the entire process of agricultural cultivation, planting, management, and harvest; the area accuracy of all obtained agricultural information products is above 90%. This paper demonstrates the necessity of customizable product and service research in agricultural intelligent computing, and the proposed practical mode can provide support for governments to participate in agricultural macro-management and decision making, which is of great significance for smart farming development and food security.

Location based Access Privileges and Controlling the Clustering in Sustainable 5G Challenges

Considering the new gathering of enlisting and communicated interchanges propels with the immense advancement of the Internet, the Web, and Adaptable Correspondences, the accompanying stage is supposed to be the Convenient Web. The central responsibility of the Compact Web is to satisfy client needs for wherever, at whatever point induction to information and organizations, including Region Based Organizations (LBS). An original LBS management that is relevant to the Flexible Promoting sector is presented in this paper. Numerous Web-enabled terminals will be transported, making the Versatile Web a reality for the vast majority of users. Mobile terminals and/or mobile networks can now pinpoint the terminal's location on Earth with increasing precision. The paper presents a model for collaborating on area scopes with services, an engineering to facilitate the Web-based disclosure of area scopes with services, a map of object handles to one or more contact addresses, and the possibility for a mobile client to select different types of information results for yielding in accordance with their momentum. These key research challenges are essential for advancing the development of LBS and establishing an examination plan for LBS to positively shape the future of our portable data society. These research challenges include issues related to the center of LBS development (such as positioning, displaying, and correspondence), evaluation, and analysis of LBS-produced information, as well as friendly, moral, and behavioural issues that arise as LBS become a part of people's daily lives.

Enhancing intelligent IoT services development by integrated multi-token code completion

The Internet of Things (IoT) is a revolutionary network of interconnected devices embedded with sensors and software that enables seamless communication, data sharing, and intelligent decision-making in the form of IoT services. To facilitate the efficient development of IoT services, code completion technique provides a promising solution by providing suggestions for missing code snippets. The development trend of IoT services is to support more mobile device terminals. Mobile devices are portable and easy to use, allowing IoT device operation and management anytime and anywhere. However, the current multi-token completion methods struggle to guarantee code generation quality under the constraints of low resources and low latency, making it difficult to fully support IoT service development. We propose a multi-token code completion framework, S2RCC, which completes code from skeleton to refinement with dual encoder and dual decoder. The framework consists of two phases: first, the code skeleton, which is the simplification of code containing structure-sensitive tokens, is predicted based on the semantics of the code context; second, the broken context is repaired with the predicted skeleton, and then parsed into the code structure so that the specific tokens can be generated combining the semantics and structure of context. Furthermore, we then provide an implementation of the framework, representing the repaired code as an improved Heterogeneous code graph and fusing the semantics and structure of code context by the three-layer stacked attention. We conducted experiments on multi-token completion datasets, showing that our model has achieved the state-of-the-art with the smallest possible scale and the fastest generation speed.

Research on optimization technology based on mobile terminals convolutional neural networks

Convolutional neural networks play a very important role in computer vision, such as image classification, image segmentation, and handwriting recognition have been widely used. In daily life, this technology is used in the photo recognition of e-commerce platforms. However, the timing of the identification process grows into a major problem. Therefore, it is particularly important to reduce the recognition time by optimizing the deep learning model. To solve this problem, two experimental methods are proposed to optimize the volume of the convolutional neural network model. The first is to reduce the size of the model by scaling down the convolutional kernel. The second is to prune the model with L-1 norm to reduce the size of the model and improve the running speed. According to the experimental results, the two experimental methods have achieved remarkable optimization effects. In the first experiment, the method of scaling down convolutional kernel has an important optimization effect for training the deep learning model of small data sets. In another experiment using L-1 pruning algorithm greatly improves the running speed of models by reducing the size of models. To sum up, the optimization method proposed above for the convolutional neural network model on the mobile end can be applied in the field that requires a large amount of image classification, such as delivery package sorting. At the same time, to better improve the performance of the model, it will become feasible to use a variety of optimization methods to tune it.

An intelligent signal processing method against impulsive noise interference in AIoT

In complex industrial environments such as the Internet of Things in coal mines, large mechanical and electrical equipment can generate powerful impulsive noise, which can cause sudden errors. Because it is difficult to establish an accurate channel model, the performance of current error control techniques is limited. To enhance the reliability of information recovery in the Internet of Things in coal mines, the traditional method of shortening the communication distance between sensors is often utilized, but this can be costly. Therefore, this article proposes an intelligent signal processing method against impulsive noise interference that draws on the concept of the Artificial Intelligence of Things (AIoT) and incorporates deep learning technology. This method replaces the traditional sensor signal processing module with a Convolutional Neural Network (CNN), which learns the intricate mapping relationship between transmitted information and sensor signals in impulsive noise environments. Simulation results demonstrate that the proposed method outperforms the traditional sensor signal processing method in three impulsive noise environments by achieving a lower Bit Error Rate (BER). Moreover, this method adopts an improved lightweight neural network, which is more conducive to the deployment of mobile terminals in the Internet of Things.

An edge server deployment method based on optimal benefit and genetic algorithm

With the speedy advancement and accelerated popularization of 5G networks, the provision and request of services through mobile smart terminals have become a hot topic in the development of mobile service computing. In this scenario, an efficient and reasonable edge server deployment solution can effectively reduce the deployment cost and communication latency of mobile smart terminals, while significantly improving investment efficiency and resource utilization. Focusing on the issue of edge server placement in mobile service computing environment, this paper proposes an edge server deployment method based on optimal benefit quantity and genetic algorithm. This method is firstly, based on a channel selection strategy for optimal communication impact benefits, it calculates the quantity of edge servers which can achieve optimal benefit. Then, the issue of edge server deployment is converted to a dual-objective optimization problem under three constraints to find the best locations to deploy edge servers, according to balancing the workload of edge servers and minimizing the communication delay among clients and edge servers. Finally, the genetic algorithm is utilized to iteratively optimize for finding the optimal resolution of edge server deployment. A series of experiments are performed on the Mobile Communication Base Station Data Set of Shanghai Telecom, and the experimental results verify that beneath the limit of the optimal benefit quantity of edge servers, the proposed method outperforms MIP, K-means, ESPHA, Top-K, and Random in terms of effectively reducing communication delays and balancing workloads.

A dual‐band tightly arranged multiple‐input multiple‐output antenna pair for 5G mobile terminals

AbstractThis paper presents a dual‐band multiple‐input multiple‐output (MIMO) antenna pair for 5G cell phones. The antenna pair consists of two planar concave antenna elements that are tightly arranged on a vertical substrate. By adding capacitors to form a resonant network, an additional operating mode is excited, resulting in dual‐band performance. The resonant frequency of the additional mode can be adjusted independently by changing the capacitance value. Moreover, a neutralization line is used to improve the isolation of the high band without affecting the low band. The antenna pair occupies a small area of 13.2 × 6.3 mm2 and operates at both 3.5 and 4.85 GHz bands. The isolations are better than 17 dB at the 3.5 GHz band and better than 19 dB at the 4.85 GHz band. The proposed dual‐band antenna pair might be a good choice for 5G terminals.