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A miniaturized and wideband four-port multiple-input multiple-output (MIMO) antenna pair for Wi-Fi mobile terminals application is proposed. The proposed antenna pair consists of four multi-branch antenna elements arranged orthogonally, with an overall size of 40 × 40 × 3.5 mm3 and each antenna element size of 15.2 × 3.5 mm × 0.8 mm3. The performance of the proposed antenna shows the advantages of a wide frequency band, low mutual coupling, high efficiency, and a compact structure. The wideband characteristics of the antenna elements are achieved through multi-mode resonance. The suppression of coupling is accomplished by strategically positioning the four compact antenna elements to ensure their maximum radiation directions are orthogonal, thus eliminating the need for an additional decoupling structure. In this paper, the proposed antenna is optimized in terms of the parameters then simulated and measured. The simulated results illustrate that an impedance bandwidth of the antenna is about 15% (5.06~5.88 GHz) with S11 < -10 dB, excellent port isolation exceeds 20 dB between all ports, a high radiation efficiency ranges from 51.2% to 89.9%, the maximum gain is 4.5 dBi, and the ECCs are less than 0.04. The measured results show that the -10 dB impedance bandwidth of the antenna is about 13% (5.13~5.80 GHz), the isolation between the antenna elements is better than 21 dB, the radiation efficiency ranges from 51.8% to 92.3%, the maximum gain is 5.3 dBi, and the ECCs are less than 0.05. The proposed four-port MIMO antenna works on the 5G LTE band 46 and Wi-Fi 6E operating bands. As a mobile terminal antenna, the proposed design scheme demonstrates excellent performance and applicability, fulfilling the requirements for 5G mobile terminal applications.

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The accurate identification of sheep is crucial for breeding, behavioral research, food quality tracking, and disease prevention on modern farms. As a result of the time-consuming, expensive, and unreliable problems of traditional sheep-identification methods, relevant studies have built sheep face recognition models to recognize sheep through facial images. However, the existing sheep face recognition models face problems such as high computational costs, large model sizes, and weak practicality. In response to the above issues, this study proposes a lightweight sheep face recognition model named LSR-YOLO. Specifically, the ShuffleNetv2 module and Ghost module were used to replace the feature extraction module in the backbone and neck of YOLOv5s to reduce floating-point operations per second (FLOPs) and parameters. In addition, the coordinated attention (CA) module was introduced into the backbone to suppress non-critical information and improve the feature extraction ability of the recognition model. We collected facial images of 63 small-tailed Han sheep to construct a sheep face dataset and further evaluate the proposed method. Compared to YOLOv5s, the FLOPs and parameters of LSR-YOLO decreased by 25.5% and 33.4%, respectively. LSR-YOLO achieved the best performance on the sheep face dataset, and the mAP@0.5 reached 97.8% when the model size was only 9.5 MB. The experimental results show that LSR-YOLO has significant advantages in recognition accuracy and model size. Finally, we integrated LSR-YOLO into mobile devices and further developed a recognition system to achieve real-time recognition. The results show that LSR-YOLO is an effective method for identifying sheep. The method has high recognition accuracy and fast recognition speed, which gives it a high application value in mobile recognition and welfare breeding.

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Drosophila is a crucial biological experimental teaching material extensively utilized in experimental teaching. In this experimental teaching, each student typically needs to manually identify hundreds of fruit flies and record multiple of each fly. This task involves substantial workload, and the classification standards can be inconsistent. To address this issue, we introduce a deep convolutional neural network that classifies the traits of every fruit fly, using a two-stage consisting of an object detector and a trait classifier. We propose a keypoint-assisted classification model with tailored training session for the trait classification task and significantly enhanced the model interpretability. Additionally, we've enhanced the RandAugment method to better fit the features of our task. The model is trained with progressive learning and adaptive regularization under limited computational resources. The final classification model, which utilizes MobileNetV3 as backbone, achieves an accuracy of 97.5%, 97.5% and 98% for the eyes, wings, gender tasks, respectively. After optimization, the model is highly lightweight, classifying 600 fruit fly traits from raw images in 10 seconds and having a size less than 5 MB. It can be easily deployed on any android device. The development of this system is conducive to promoting the experimental teaching, such as verifying genetic laws with Drosophila as the research object. It can also be used for scientific research involving a large number of Drosophila classifications, statistics and analyses.

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To solve the demand for road damage object detection under the resource-constrained conditions of mobile terminal devices, in this paper, we propose the YOLO-LWNet, an efficient lightweight road damage detection algorithm for mobile terminal devices. First, a novel lightweight module, the LWC, is designed and the attention mechanism and activation function are optimized. Then, a lightweight backbone network and an efficient feature fusion network are further proposed with the LWC as the basic building units. Finally, the backbone and feature fusion network in the YOLOv5 is replaced. In this paper, two versions of the YOLO-LWNet, small and tiny, are introduced. The YOLO-LWNet was compared with the YOLOv6 and the YOLOv5 on the RDD-2020 public dataset in various performance aspects. The experimental results show that the YOLO-LWNet outperforms state-of-the-art real-time detectors in terms of balancing detection accuracy, model scale, and computational complexity in the road damage object detection task. It can better achieve the lightweight and accuracy requirements for object detection for mobile terminal devices.

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To quickly and accurately identify the pathological features of the tongue, we developed an intelligent tongue diagnosis system that uses deep learning on a mobile terminal. We also propose an efficient and accurate tongue image processing algorithm framework to infer the category of the tongue. First, a software system integrating registration, login, account management, tongue image recognition, and doctor-patient dialogue was developed based on the Android platform. Then, the deep learning models, based on the official benchmark models, were trained by using the tongue image datasets. The tongue diagnosis algorithm framework includes the YOLOv5s6, U-Net, and MobileNetV3 networks, which are employed for tongue recognition, tongue region segmentation, and tongue feature classification (tooth marks, spots, and fissures), respectively. The experimental results demonstrate that the performance of the tongue diagnosis model was satisfying, and the accuracy of the final classification of tooth marks, spots, and fissures was 93.33%, 89.60%, and 97.67%, respectively. The construction of this system has a certain reference value for the objectification and intelligence of tongue diagnosis.

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We studied the application of a mobile terminal application program in endotracheal tube (ETT) cuff pressure measurement to improve the implementation rate of scientific ETT cuff pressure measurement and to ensure that the pressure falls within the recommended range. A pre-post controlled study lasting for 18 months was undertaken in a 40-bed general intensive care unit (GICU). This included a 6-month baseline period (baseline group) and a 6-month intervention period (intervention group). The mobile terminal application program was applied to monitor the cuff pressure of endotracheal intubation as an intervention measure during the intervention period. ETT pressure was the main outcome measure, while gender, age, causes for ICU admission, sedation score, duration of prior intubation, size of ETT, and number of VAP patients were secondary outcomes. ETT cuff pressure was monitored 742 times in both the baseline group and the intervention group. A total of 56.9% of the cuff pressure measurements in the baseline group were within the recommended range, while 78.4% of measurements in the intervention group were within the recommended range, reflecting a statistically significant difference (P < 0.05). The application of the mobile terminal application program used for ETT cuff pressure measurement could improve the percentage of ETT cuff pressure measurements falling within the recommended range.

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Objective:To develop an intelligent vision examination and management system based on mobile terminal, and evaluate its practical application.Methods:A diagnostic test study was adopted.Fifty children and adolescents (100 eyes) aged from 3 to 14 years, with an average age of (8.16±4.58) years, were enrolled in Zhongshan Ophthalmic Center from March to May, 2020.An intelligent vision examination and management system for Android and IOS devices, which applied technologies of real-time range measurement, speech recognition, and gesture recognition to achieve the real-time interaction in the vision examination, was developed using JAVA language and xcode11.The uncorrected visual acuity of subjects was tested with the intelligent vision system and the standard eye chart in hospital.The difference in the number of cooperative eyes between the two methods was analyzed by χ2 test.The LogMAR acuity of the two methods were compared by Wilcoxon rank sum test and the consistency between the two methods was analyzed by Kappa test.The study adhered to the Declaration of Helsinki, and the study protocol was approved by an Ethics Committee of Zhongshan Ophthalmic Center (No.2020KYPJ095). Written informed consent was obtained from guardians of each subject prior to any examination. Results:The intelligent vision examination and management system was successfully developed for Android and IOS devices, which was loaded with precise vision examination at 3 meters, intelligent interaction, result output and automatic filing functionalities.The inspection range of visual acuity was from 0.1 to 1.5.The size and resolution of eye chart met the national standard.There was no significant difference in the number of cooperative eyes during examination between traditional eye chart (99/100) and intelligent vision examination system (98/100) ( χ2=0.338, P=0.561). There was no significant difference in examination results between traditional eye chart 0.3 (0.2, 0.5) LogMAR and intelligent vision examination system 0.3 (0.2, 0.5) LogMAR ( Z=-1.602, P=0.109), and there was a high consistency between the two methods (Kappa coefficient=0.885). Conclusions:The intelligent vision examination and management system based on the mobile terminal is of high accuracy and practicability, which can effectively meet the public demand for at-home vision testing and long-term tracking.The system can be promoted and applied as a new mode for personalized eye health management.

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The mobile terminals used in the logistics industry can be exposed to wildly varying environments, which may hinder effective operation. In particular, those used in cold storages can be subject to frosting in the scanner window when they are carried out of the warehouses to a room-temperature space outside. To prevent this, they usually employ a film heater on the scanner window. However, the temperature and humidity conditions of the surrounding environment and the temperature of the terminal itself that cause frosting vary widely. Due to the complicated frost-forming conditions, existing industrial mobile terminals choose to implement rather simple rules that operate the film heater well above the freezing point, which inevitably leads to inefficient energy use. This paper demonstrates that to avoid such waste, on-device artificial intelligence (AI) a.k.a. edge AI can be readily employed to industrial mobile terminals and can improve their energy efficiency. We propose an artificial-intelligence-based approach that utilizes deep learning technology to avoid the energy-wasting defrosting operations. By combining the traditional temperature-sensing logic with a convolutional neural network (CNN) classifier that visually checks for frost, we can more precisely control the defrosting operation. We embed the CNN classifier in the device and demonstrate that the approach significantly reduces the energy consumption. On our test terminal, the net ratio of the energy consumption by the existing system to that of the edge AI for the heating film is almost 14:1. Even with the common current-dissipation accounted for, our edge AI system would increase the operating hours by 86%, or by more than 6 h compared with the system without the edge AI.

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An eight-element multiple-input multiple-output (MIMO) frame antenna array in the 3.5 GHz band (3400-3600 MHz) for 5G mobile terminal systems was presented. By using the adjacent grounding and electromagnetic coupling feeding technology, the loop antenna element could generate two resonant frequencies, thus effectively expanding its bandwidth. By adopting double-sided parallel strip line (DSPSL) technology, the electromagnetic coupling inside the loop antenna could be adjusted, and the size of the loop antenna could be effectively reduced so that the MIMO antenna array could obtain a low-profile structure. The total size of the MIMO array was 150 mm × 75 mm × 5.3 mm. Without additional isolation measures, the measured -6 dB impedance bandwidth (BW) was 3400-3660 MHz, and the minimum isolation between antenna elements was better than -20 dB. The proposed antenna was expected to be applied to 5G mobile terminals based on its low-profile, high-isolated characteristics, and good MIMO performance.

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The recent development of the IoT (Internet of Things), which has enabled new types of sensors that can be easily interconnected to the Internet, will also have a significant impact in the near future on the management of natural disasters (mainly earthquakes and floods) with the aim of improving effectiveness in research, identification, and recovery of missing persons, and therefore increasing the possibility of saving lives. In this paper, more specifically, an innovative technique is proposed for the search and identification of missing persons in natural disaster scenarios by employing a drone-femtocell system and devising an algorithm capable of locating any mobile terminal in a given monitoring area. In particular, through a series of power measurements based on the reference signal received power (RSRP), the algorithm allows for the classification of the terminal inside or outside the monitoring area and subsequently identify the position with an accuracy of about 1 m, even in the presence of obstacles that act in such a way as to make the propagation of the radio signal non-isotropic.

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In this study, we propose a design of a multi-band slot antenna array applicable for fourth-generation (4G) and fifth-generation (5G) smartphones. The design is composed of double-element square-ring slot radiators fed by microstrip-line structures for easy integration with radio frequency (RF)/microwave circuitry. The slot radiators are located on the corners of the smartphone printed circuit board (PCB) with an overall dimension of 75 × 150 mm². The proposed multiple-input multiple-output (MIMO) antenna is designed to meet the requirements of 4G and 5G mobile terminals with essential bandwidth for higher data rate applications. For -10 dB impedance bandwidth, each single-element of the proposed MIMO design can cover the frequency ranges of 2.5⁻2.7 GHz (long-term evolution (LTE) 2600), 3.45⁻3.8 GHz (LTE bands 42/43), and 5.00⁻5.45 GHz (LTE band 46). However, for -6 dB impedance bandwidth, the radiation elements cover the frequency ranges of 2.45⁻2.82 GHz, 3.35⁻4.00 GHz, and 4.93⁻5.73 GHz. By employing the microstrip feed lines at the four different sides of smartphone PCB, the isolation of the radiators has been enhanced and shows better than 17 dB isolation levels over all operational bands. The MIMO antenna is implemented on an FR-4 dielectric and provides good properties including S-parameters, efficiency, and radiation pattern coverage. The performance of the antenna is validated by measurements of the prototype. The simulation results for user-hand/user-head impacts and specific absorption rate (SAR) levels of the antenna are discussed, and good results are achieved. In addition, the antenna elements have the potential to be used as 8-element/dual-polarized resonators.

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Objective To sort out the functional composition of the official App of a tertiary general hospital, and its usage. Methods Taking the tertiary general hospitals identified from the national hospital quality monitoring system as the research objects, statistically analyzing their functional composition, and comparing the App and WeChat public platform of two hospitals as examples.In addition, collecting relevant data from the official website of the hospital, and analyzing the influencing factors of its download and usage with multiple linear regression methods. Results There were 469 tertiary general hospitals nationwide, of which 137 had official Apps, with an accessibility rate of 29.21%.The accessibility rated in the eastern, central and western regions were 27.85% , 13.48% and 17.43% respectively.Hospital App prioritized in turn appointment registration (94.9% ), test report ( 76.8% ), and hospital navigation ( 71.7% ). The development of the hospital App in the eastern region taked the lead compared to the middle and western ones.The App′s score and the total outpatient visits of hospital had a positive impact on the download and use of the hospital App.The App′s scoring had a noteworthy impact. Conclusions Hospitals should enable to innovate their services, and highlight their characteristics.App should optimize its interface, improve its functions, enhance the interconnection of App functions, strengthen data security management, strengthen its publicity and expand its influence.

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Objective To understand the health literacy status,influencing factors and demand carrying out the health education based on the mobile terminal among college students to provide a new idea for formulating and implementing the health literacy promotion plan for university students.Methods The multi-stage stratified cluster random sampling method was adopted to extract 1 949 students from partial universities in Nanjing City and conduct the investigation by adopting the Rapid Estimate System of Health Literacy and Health Education Demand Based on the Mobile Terminal Questionnaire of College Students.The influence factors were analyzed by using the univariate analysis and ordinal multi-classification Logistic regression analy sis.Results The university students with high,marginal and low health literacy level accounted for 31.3％,48.9％ and 19.8％ respectively.The proportions of the students with high health literacy in the four dimensions,including health knowledge,health behaviors,health skill and health status,were 57.9％,18.3％,61.0％ and 44.5％ respectively.The ordinal multi-classification Logistic regression analysis showed that the major influence factors affecting the health literacy of university students were gender,education background of mothers,majors,grades,electively taking the health education course.81.2 ％ of university students wanted to or urgently wanted to understand the health literacy knowledge.The main route getting the health literacy knowledge by the university students was network(83.8％).91.8％ of students were willing to learn the health literacy knowledge through mobile terminal.87.1％ of students believed that the WeChat was suitable to learn the health knowledge,81.3％ of students hoped to use the WeChat platform for learning the health knowledge,73.8％ of students hoped the Wechat to push the health knowledge with the modes of words combined with images and video.It was recommended that the health knowledge pushing for 1-3 times per week was suitable.Conclusion The health literacy of university students is at the marginal level.The health behaviors literacy is most lacking and the demand for the health education based on the mobile terminal is stronger.The health literacy intervention of university students should sufficiently apply the new media platform superiority and the emphasis should be placed on the populations of males,low education background mothers,non-medicine major,low grade and without electively taking the health education course.

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In this paper, a data acquisition system based on mobile terminal combining GPS, offset correction, automatic speech recognition and database networking technology was designed implemented with the function of locating the latitude and elevation information fast, taking conveniently various types of Chinese herbal plant photos, photos, samples habitat photos and so on. The mobile system realizes automatic association with Chinese medicine source information, through the voice recognition function it records the information of plant characteristics and environmental characteristics, and record relevant plant specimen information. The data processing platform based on Chinese medicine resources survey data reporting client can effectively assists in indoor data processing, derives the mobile terminal data to computer terminal. The established data acquisition system provides strong technical support for the fourth national survey of the Chinese materia medica resources (CMMR).

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Mobile learning behaviors are the greatly concerned hotspot in educational field.However, the specific mobile learning behaviors of university undergraduates are not subdivided in the current researches.The mobile learning behavior data of university undergraduates in different learning circumstances were recorded by literature survey, interview and questionnaire investigation, the characteristics of mobile learning behaviors were subdivided and analyzed in order to help them work out their strategy for mobile learning in different learning circumstances, which showed that the frequency of intelligent mobile learning was higher than that of traditional mobile learning in specific academic activities (academic exchange, self learning, academic sharing, transmission and search), and that the new generation of digital young people showed their preference to mobile learning due to its advantage of convenience while the disadvantage of its function and operation limited its persistent use.It is thus the hope of university undergraduates, especially medical undergraduates, to develop more devices and tools that are convenient for their mobile learning by making use of the advantages of intelligent mobile terminal and traditional terminals.

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In this paper, a data acquisition system based on mobile terminal combining GPS, offset correction, automatic speech recognition and database networking technology was designed implemented with the function of locating the latitude and elevation information fast, taking conveniently various types of Chinese herbal plant photos, photos, samples habitat photos and so on. The mobile system realizes automatic association with Chinese medicine source information, through the voice recognition function it records the information of plant characteristics and environmental characteristics, and record relevant plant specimen information. The data processing platform based on Chinese medicine resources survey data reporting client can effectively assists in indoor data processing, derives the mobile terminal data to computer terminal. The established data acquisition system provides strong technical support for the fourth national survey of the Chinese materia medica resources (CMMR).

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OBJECTIVE: To develop a mobile terminal data acquisition for data collection of malaria prevention and control in the field. METHODS: Based on 3G, Bluetooth, GPS and/or Bei Dou Navigation Satellite System (BDS) and wireless network communication technology (WIFI), a mobile terminal data acquisition for data collection of malaria prevention and control was developed. RESULTS: The developed system included 2 functional modules：a terminal and iCloud, and had examination, modification and deletion functions of malaria epidemiological data. By using GPS and GIS, the target population or institution could be accurately positioned to determine whether the distribution of malaria cases had a spatial aggregation. CONCLUSIONS: A mobile terminal data acquisition in malaria prevention and control is successfully developed, which is very convenient for data collection and submission of malaria prevention and control.

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Objective To develop a mobile terminal data acquisition for data collection of malaria prevention and control in the field. Methods Based on 3G,Bluetooth,GPS and/or Bei Dou Navigation Satellite System(BDS)and wireless network communication technology(WIFI),a mobile terminal data acquisition for data collection of malaria prevention and control was developed. Results The developed system included 2 functional modules:a terminal and iCloud,and had examination,modi?fication and deletion functions of malaria epidemiological data. By using GPS and GIS,the target population or institution could be accurately positioned to determine whether the distribution of malaria cases had a spatial aggregation. Conclusion A mobile terminal data acquisition in malaria prevention and control is successfully developed,which is very convenient for data collec?tion and submission of malaria prevention and control.

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Objective To develop a mobile medical information system based on informatized monitoring to enhance staff efficiency and patient safety.Methods The acquired information on vital signs was integrated into mobile terminal, which could be called, analyzed and processed along with the clinical data of the patient.Results The doctor might write and edit the prescription based on the clinical data. The nurse could read the prescription at real time, and could treat the patient timely in case of alarming.Conclusion The system lays a foundation for patient-centered medical service.

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Human anatomy system is an interdisciplinary study project of medical, information and computer technologies. A mobile platform intelligent terminal-based human anatomy system was designed and implemented according to an analysis of mobile learning and the background of human anatomy system, which has a good prospect in teaching of human anatomy and in education of popular medical knowledge.