Mobile Intelligent Terminals Research Articles

Thermal calibration for triaxial gyroscope of MEMS-IMU based on segmented systematic method

With the progress of micro electromechanical system (MEMS) technology, the performance of MEMS inertial measurement unit (IMU) composed of gyroscopes and accelerometers has been improved. Among the inertial sensors, MEMS triaxial gyroscope plays an important role in attitude estimation, navigation and positioning of intelligent mobile terminals such as unmanned aerial vehicles and unmanned vehicles. However, the measured values of low and medium cost MEMS triaxial gyroscopes are mainly affected by temperature (or thermal effect) and random errors. As results, the drift errors correlated with temperature will reduce application accuracy of MEMS triaxial gyroscope. The traditional calibration method for thermal drift errors relies on the expensive equipment, such as turntable and the temperature control system, which increases the cost of calibration. Therefore, an effective thermal calibration method that is available when using low- or high-cost tools for MEMS triaxial gyroscope will be meaningful for majority users. Hence, this paper analyzed and established the thermal drift model of MEMS triaxial gyroscope, and proposed a segmented systematic calibration method based on 24 states according to this model. Simulation shows that the proposed method can obtain the results approaching the real parameter thermal drift curves. Calibration experiments and parameters test show that the max errors of attitude calculation are reduced to 10% of the results using the original parameters, which indicates that the effectiveness of the proposed method.

Application of Improved HF-DBSCAN Algorithm in Analyzing Complex Trajectory Data of Wi-Fi Users in Smart Campus

ABSTRACT With the development of wireless network technology and the increasingly widespread application of mobile intelligent terminals, a large amount of wireless network data has been generated in smart campuses. By analyzing the trajectory data through clustering algorithms, more valuable user data information can be obtained. However, current clustering algorithms have high computational difficulty, long running time, and poor clustering accuracy for complex trajectories. Based on this background, the study proposes a spatial clustering of Application with Noise by Density-Based Spatial Clustering of Application with Noise combining Hausdorff and Frechet. The method is denoted as (Hausdorff and Frechet-Density Based Spatial Clustering of Application with Noise, HF-DBSCAN). DBSCAN denotes the Density Based Spatial Clustering of Application with Noise. In the experimental results, compared to the AH-DBSCAN, DBSCAN, and FD-DBSCAN algorithms, the running time of HF-DBSCAN algorithm in complex trajectory cluster analysis is reduced by 59.33%, 39.82%, and 35.12%, respectively, and the contour coefficient is closer to 1; the Davies-Boldin Index (DBI) values decreased by 59.24%, 64.42%, and 68.24%, respectively. The experiment shows that the optimized HF-DBSCAN algorithm has lower computational difficulty, better clustering performance, and higher clustering accuracy, which verifies the effectiveness of this study.

A Model for Detection and Prevention of AndroRat in Mobile Devices

Android operating systems has gained increasing popularity in smart phones and other mobile intelligent terminals in recent years. Unpleasantly, the accumulation development and open nature of the platform has also attracted a vast number of malware developers. This dissertation presents a model for detecting and preventing AndroRat, a notorious Remote Administration Tool (RAT), on android devices. The research systematically pursued predefined objectives, leading to the development of a robust detection framework that utilizes the Outlier Technique. This framework significantly enhances the accuracy of AndroRat identification. Moreover, the dissertation introduces a preventive mechanism based on Recurrent Neural Network (RNN), fortifying Android phones against potential threats posed by RATs. By achieving the outlined objectives, this research makes a noteworthy contribution to the field of mobile device security. It demonstrates a practical application of machine learning techniques in mitigating evolving cybersecurity threats. A dataset was gathered for constructing the model. Object Oriented Analysis and Design (OOAD) was used as the research methodology and python was used as the programming language. The implementation of the Python Programming Language in this system not only ensured efficient execution but also established a versatile and accessible platform, laying the groundwork for future enhancements and adaptations. The comparative analysis conducted against existing systems highlights the effectiveness and innovation embedded in the proposed model, affirming its potential as a valuable addition to the realm of AndroRat detection and prevention with an accuracy of 99.99%. This dissertation not only addresses current security challenges but also establishes a foundation for continued advancements in mobile device security through the integration of cutting-edge technologies.

Research on the Innovation of Teaching Mode of Orienteering Sports Courses in Colleges and Universities Based on Mobile Intelligent Terminals

Abstract This paper studied motion state recognition algorithms using acceleration signals (ACC). At the same time, research is also being conducted on ECG motion artifact suppression algorithms and respiration rate detection algorithms. Based on this, we designed a mobile intelligent terminal sports app that allows users to view physiological signals and sports status in real time at any time. In college and university orienteering classes, the mobile intelligent terminal movement app is used to teach the sport. The results of comparing the experimental group and the control group in the orienteering course show that the mobile intelligent terminal orienteering teaching makes a significant difference in the men’s physical quality, except for the seated forward body flexion (P<0.05). There were significant differences in the women’s group in terms of 50 m running (P = 0.013) and 800 m (P = 0.042). The experiment improved the dimensions of the Orienteering Campus Short Distance Test, Learning Motivation Indicator, and Emotional Management.

Research on weighted energy consumption and delay optimization algorithm based on dual‐queue model

AbstractThis article investigates a mobile edge computing (MEC) network assisted by multiple unmanned aerial vehicles (UAVs) to address the computational and offloading requirements for mobile intelligent terminals (MITs) within crowded venues. The objective is to tackle intricate task processing and diminish MITs' waiting times. Considering the randomness of task arrival at the MITs and the imbalance between the amount of data and computation for complex tasks, a dual‐queue model with data cache queue and computation queue is proposed, with minimizing the weighted system total energy consumption and average delay as the optimization objectives. Lyapunov optimization theory is employed to convert the stochastic optimization problem into a deterministic one, and the initial deployment quantity and hovering position of the UAVs are determined by the density‐based spatial clustering of applications with noise (DBSCAN) method with noise. Then PPO algorithm for MIT task, resource allocation, and UAV trajectory optimization. Numerical results display the proposed scheme can efficaciously diminish energy consumption and delay by 10% and 33% respectively, compared with the baseline scheme. This paper proposes a practical and feasible solution for stochastic computing offloading in UAV‐assisted MEC, which fills the gap in existing research on regarding the consideration of complex task imbalances.

LDPORR: A localized location privacy protection method based on optimized random response

The broad use of mobile intelligent terminals with locating functions encourages the rapid development of location-based services (LBS), which are widely used in a variety of industries such as social networking, transportation, finance, and entertainment. While enjoying the convenience brought by LBS, the threat of location privacy leakage should not be overlooked. In view of the complex encoding mechanism and low availability of the current location privacy protection methods, a local differential privacy location protection method based on optimized random response is proposed in this paper. A spatial decomposition and Hilbert encoding mechanism are designed based on the Hilbert curve, which can reduce the two-dimensional location data into one-dimensional Hilbert encoding results. A local differential privacy location perturbation method based on optimized random response is proposed to improve the availability of perturbed locations and the accuracy of data aggregation. Experiments on actual location datasets prove that the proposed method can provide better location data availability and operational efficiency based on realizing local differential privacy protection of users’ location.

Analysis of Narrative Strategies of the Popular Science Short Video "wuqiongxiaoliang"

With the rapid development of information technology, intelligent mobile terminals constantly updated, and people's increasingly fragmented spiritual needs, the short video has always been vigorously developed, and the era of short video has arrived. As the top streamer of science popularization short videos in China, "wuqiongxiaoliang" has become an important form and power of science popularization. In this study, we take the Douyin account "wuqiongxiaoliang" as a case study and analyze its communication narrative strategy in terms of narrative content, narrative form, narrative structure, narrative perspective and so on, taking into account theories related to communication and video narrative. The study concludes that "wuqiongxiaoliang" has formed a relatively stable narrative strategy, and the long-term development of science popularization short videos also requires continuous innovation in the midst of stability.

A strategy for building a smart sports platform based on machine learning models

With the rapid development of big data technology, it has greatly changed the way people get information, and also improved the speed and quality of information. In this context, smart sports has become a new trend in sports development. This paper creates an intelligent learning environment and builds a smart sports platform through advanced concepts and technical means, which can effectively optimize the integration and sharing of sports resources. Starting from the overall architecture design of smart sports, the key technologies of machine learning model to realize smart sports are sorted out. Through the five basic linking stages with machine learning model as the core, the value innovation path of platform construction structure is analyzed. The current status of sports resources application is studied, and the data mining algorithm is used to calculate the user usage data of the smart sports platform and improve the construction of the smart platform. Through the construction of the smart sports platform, people shift from traditional reading books and watching TV programs to getting information through intelligent mobile terminals, and the proportion of attention to sports information is as high as 58.6%. This shows that by building a smart sports platform, it can provide support and guarantee for the sustainable development of sports.

Online facial expression recognition based on graph convolution and long short memory networks

Video‐based facial expression recognition (FER) models have achieved higher accuracy with more computation, which is not suitable for online deployment in mobile intelligent terminals. Facial landmarks can model facial expression changes with their spatial location information instead of texture features. But classical convolution operation cannot make full use of landmark information. To this end, in this paper, we propose a novel long short memory network (LSTM) by embedding graph convolution named GELSTM for online video‐based FER in mobile intelligent terminals. Specifically, we construct landmark‐based face graph data from the client. On the server side, we introduce graph convolution which can effectively mine spatial dependencies information in a landmark‐based facial graph. Moreover, the extracted landmark's features are fed to LSTM for temporal feature aggregation. We conduct experiments on the facial expression dataset and the results show our proposed method shows superior performance compared to other deep models.

Design and Implementation of Home Video Surveillance Systems Based on IoT Location Service

Due to its simplicity, directness, and rich content, home video surveillance has gradually become the core content of the smart home security system. The rapid development of computer technology, the internet, video image processing, and transmission technology, as well as the emergence of “wireless cities,” have continuously expanded the coverage of wireless internet such as 5G and WiFi and greatly changed data processing methods and transmission speeds. Video surveillance has also gradually developed from traditional wired networking to mobile video surveillance of wireless networks. Therefore, this paper will focus on the design of the home video monitoring system based on the IoT location service. The system combines traditional network video surveillance with intelligent mobile terminals, which can meet the needs of users for video surveillance of home conditions at any location and at any time. And it compared the sensitivity, accuracy, capture time interval, and energy consumption of the two by comparing with traditional internet video surveillance.

Differential Privacy Preservation for Location Semantics.

With the rapid development of intelligent mobile terminals and communication technologies, location-based services (LBSs) have become an essential part of users' lives. LBS providers upload and share the collected users' location data. The more commonly used methods for location privacy protection are differential privacy and its extensions. However, the semantic information about location, which is an integral part of the location data, often contains sensitive user information. Most existing research methods have failed to pay enough attention to protecting the semantic information in the location data. To remedy this problem, two different scenarios for location semantic privacy protection methods are proposed in this paper to address single-point and continuous location queries. Simulation experiments on real social location check-in datasets, and comparison of three different privacy protection mechanisms, show that our solution demonstrates good service quality and privacy protection considering location semantics.

Innovation of the Development Model of Ideological Education Based on Mobile Device Information Sharing Algorithm in the Era of Big Data

Based on the mobile device information sharing algorithm, this paper proposes an ideological education model of real-time video sharing in the Ad Hoc network self-organized by mobile intelligent terminals. First, real-time video sharing and switching applications can be realized in the Ad Hoc network. APP with video sharing function. On the mobile APP, while students are receiving education, a large amount of knowledge and culture floods the campus, and various values and moral indicators collide with each other, which directly affects the future development of students. To make people realize that how to carry out ideological and political education and ideological purification of college students from the perspective of new media requires innovative development and efficient use of new media for communication.

Comparative Study on the Teaching of Japanese Listening and Speaking in the Language Room of Teachers' Intelligent Multimedia Systems in Chinese and Foreign Universities

With the popularization of computers and various mobile intelligent terminals, intelligent multimedia teaching systems with learners as the main body are more and more favored by learners, which affects and changes the methods and modes of Japanese teaching to a large extent. Native language teachers are considered the authority of the language. Fluent oral expression and profound knowledge of language and culture make them regarded as ideal foreign language teachers, especially in oral teaching, they are regarded as having incomparable advantages. This research aims to explore the differences between Chinese and foreign teachers in listening and speaking teaching under the teaching conditions of the intelligent multimedia language room, and to try to analyze the reasons for the differences. This paper interviewed 180 sophomores of Japanese majors, 4 Chinese Japanese teachers and 2 Japanese foreign teachers at foreign language universities through a questionnaire survey, and analyzed the statistical data through SPSS 21.0. The 180 students were divided into groups of 30, with 4 groups from non-Japanese teachers' classes and 2 from Japanese teachers' classes. For a long time, they have used the same Japanese listening and speaking textbooks in the intelligent multimedia language room. In order to better improve the quality of teaching, Chinese and foreign teachers should strengthen teaching cooperation and exchanges, rationally improve teaching content, optimize the use of teaching software, and achieve win-win cooperation. In the NJST class, 29.2% of students believe that they spend more time practicing listening in class than speaking. In the NNJST class, close to 39.1% of students believe that teachers spend more time on listening. Fully consider the needs of learners in learning a second language other than Chinese, and through research on Japanese learning strategies and teaching theories, to design and develop an intelligent Japanese teaching system with comprehensive functions, strong applicability, and support for individualized, autonomous and collaborative learning.

Lightweight mobile network for real-time violence recognition.

Most existing violence recognition methods have complex network structures and high cost of computation and cannot meet the requirements of large-scale deployment. The purpose of this paper is to reduce the complexity of the model to realize the application of violence recognition on mobile intelligent terminals. To solve this problem, we propose MobileNet-TSM, a lightweight network, which uses MobileNet-V2 as main structure. By incorporating temporal shift modules (TSM), which can exchange information between frames, the capability of extracting dynamic characteristics between consecutive frames is strengthened. Extensive experiments are conducted to prove the validity of this method. Our proposed model has only 8.49MB parameters and 175.86MB estimated total size. Compared with the existing methods, this method greatly reduced the model size, at the cost of an accuracy gap of about 3%. The proposed model has achieved accuracy of 97.959%, 97.5% and 87.75% on three public datasets (Crowd Violence, Hockey Fights, and RWF-2000), respectively. Based on this, we also build a real-time violence recognition application on the Android terminal. The source code and trained models are available on https://github.com/1840210289/MobileNet-TSM.git.

Comparative Analysis of the Influence of Mobile Intelligent Terminal on Human Cardiopulmonary Function in Anaerobic and Aerobic Exercise Training

In order to understand the impact of mobile smart terminals on human cardiopulmonary function in anaerobic and aerobic exercise training, we study the effects of moderate-intensity aerobic exercise and anaerobic exercise on improving human physical fitness, shape, etc. Measurement and analysis of body composition and cardiorespiratory fitness in humans during moderate-intensity aerobic and anaerobic exercise studies. The characteristics of diversified, emerging, and humanized mobile intelligent terminals have attracted the attention of college students. Mobile smart terminals have brought a lot of convenience to college students’ aerobic and anaerobic fitness. The results showed that people who participated in aerobic exercise and anaerobic exercise were more fit, and their BMI, WHR, and body fat rate were significantly better than those who did not participate in aerobic exercise and anaerobic exercise; at the same time, the lung capacity of people who participated in exercise was significantly higher. Stroke volume, maximum oxygen uptake, ejection fraction, etc., were significantly improved, and myocardial contractility was significantly improved, all better than those who did not participate in the exercise. Therefore, aerobic and anaerobic exercise can help improve a person’s body composition and improve cardiopulmonary function. Therefore, how to give full play to the value of mobile smart terminals in aerobic and anaerobic fitness has become a hot research topic.

An Intelligent Teaching Strategy of Cross-Cultural Business Communication Course Based on Big Data

The Internet's growth has opened up new avenues for educational reform, and educational informatization has shifted the traditional classroom-teaching model. Teachers' teaching techniques have changed dramatically because of the introduction of a variety of new mobile intelligent terminals to the campus and classroom. This paper examines the design content of intelligent teaching methods against the backdrop of big data, using a cross-cultural business communication course as an example to reform the classroom-teaching mode, with the goal of resolving the current classroom-teaching dilemma and improving teaching effectiveness through the new intelligent teaching mode.

A robust packet‐dropping covert channel for mobile intelligent terminals

Covert communication in this regard has been widely used for protecting the secrecy of communication. Voice over Long Term Evolution (VoLTE) is a packet-switched core network solution for high-speed and high-quality end-to-end services which usually applied to the communication between intelligent systems. However, covert channels using inter-packet delays and packet order in VoLTE services are limited by specific rules. Since minor modifications to overt traffic can be detected, existing covert channel solutions cannot be directly applied to VoLTE. Therefore, this study presents a robust packet loss covert timing channel by cascade hash coding with intelligent system. To ensure robustness and undetectability, we design hash-based inter-codeword verification, codeword self-verification based on cyclic redundancy check, and adaptive mapping matrix. The sender modulates the covert messages according to the sequence number of the actively dropped packets, and the receiver can retrieve the covert messages using a specialized verification method. To evaluate undetectability, robustness, throughput, and construction costs, a large number of experiments in mobile intelligent terminals have been conducted. The experimental results prove that the proposed scheme is feasible for VoLTE communication, as the covert message is shown to be transmitted secretly, and the bit error and throughput are within acceptable ranges.

Construction of A Mobile Learning Environment Based on Complex Network

Fragmentation Learning based on intelligent mobile terminals can generate a large amount of mobile learning trajectory data, and the network formed by these mobile terminals is a very complex network. This paper researched the construction of a mobile learning environment based on complex network, in the hopes of providing a new idea for solving problems in the learning information dissemination of mobile terminals and the integration of mobile learning resources. In the beginning, this paper introduced the concept and features of complex network for mobile learning in the context of an English fragmentation learning environment, built a learning information dissemination model, and proposed an immune strategy under the conditions that terminal nodes won’t perform similar repeated learning after completing mobile learning. Then, the paper elaborated on the construction process of a mobile learning environment based on keyword co-occurrence, and established the keyword co-occurrence adjacency matrix and weighted matrix. In the end, experimental results proved the accuracy and usability of the proposed strategy.

Trust-based federated learning for network anomaly detection

With the rapid development of social networks and the massive popularity of intelligent mobile terminals, network anomaly detection is becoming increasingly important. In daily work and life, edge nodes store a large number of network local connection data and audit data, which can be used to analyze network abnormal behavior. With the increasingly close network communication, the amount of network connection and other related data collected by each network terminal is increasing. Machine learning has become a classification method to analyze the features of big data in the network. Face to the problems of excessive data and long response time for network anomaly detection, we propose a trust-based Federated learning anomaly detection algorithm. We use the edge nodes to train the local data model, and upload the machine learning parameters to the central node. Meanwhile, according to the performance of edge nodes training, we set different weights to match the processing capacity of each terminal which will obtain faster convergence speed and better attack classification accuracy. The user’s private information will only be processed locally and will not be uploaded to the central server, which can reduce the risk of information disclosure. Finally, we compare the basic federated learning model and TFCNN algorithm on KDD Cup 99 dataset and MNIST dataset. The experimental results show that the TFCNN algorithm can improve accuracy and communication efficiency.

Design and Implementation of Adaptive Speech Control Intelligent Conference Room Based on Intelligent Speech Technology

With the development of multimedia and network technology, more and more enterprises use network conference room system. This paper implements a multi-functional intelligent conference room system based on streaming media server. This system is developed on the basis of the prototype conference system. In addition to the functions of the traditional conference system, it can also access the video monitoring system and allow intelligent mobile terminals to join the conference room. The intelligent conference room sends management commands to the user through the interface to realize the management function of the voice conference room. At the same time, the video information can be exchanged through the video processing module. Finally, the mobile terminal video access and conference room video extrapolation function are realized. The experimental data show that the intelligent conference room can improve the recognition of conference information. This provides a reference for the design and implementation of adaptive speech control intelligent conference room.