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Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The power grid is an important connection between power sources and users, responsible for supplying and distributing electric energy to users. Modern power grids are widely distributed and large in scale, and their security faces new problems and challenges. Information entropy theory is an objective weighting method that compares the information order of each evaluation index to judge the weight value. With the wide application of entropy theory in various disciplines, the subject of introducing entropy into the power system has been gradually concerned. This article aims to study the smart terminal security technology of the power grid perception layer based on information entropy data mining. This article analyzes its related methods and designs a smart terminal for the power grid. On this basis, a data analysis platform is built and a safety plan is designed. The result is that the average absolute error, root mean square error, average absolute percentage error, and mean square error of the platform's power load forecast are 1.58, 1.96, 8.2%, and 3.93, respectively. These error values are within the ideal range, and the data processing ability is strong. The packet loss rate of the adversary's eavesdropping was tested, and the average packet loss rates at locations a, b, c, and d were 1.05, 1.2, 1.81, and 2.2%, respectively. Data packets will be definitely lost, so the platform is highly secure.

Casualties caused by people trapped in cars have been common in recent years. Despite a variety of solutions, complex detection devices need to be arranged, or privacy is poor. Since device-free Wi-Fi sensing has attracted much attention due to its simplicity, low cost, and no need for additional hardware, this paper proposes a contactless wireless Wi-Fi sensing-based method for detecting people left in cars: Car-Sense. The method uses ESP32 devices in the vehicle to build a wireless Wi-Fi network for low-cost, real-time, and accurate personnel awareness. By capturing and analyzing the CSI (Channel State Information) signal, extracting features, and building a machine-learning correlation model, the number and location of occupants can be estimated and further inferred in combination with sensing data such as vehicle temperature. Even better, with the computing power of the edge-side devices to process data in collaboration with the cloud, the computing process is partially localized to reduce the computing pressure and latency in the cloud. The approach has been experimentally verified to have more than 85% accuracy. [ABSTRACT FROM AUTHOR]

Objectives: With the popularity of mobile devices and low‑cost sensors,the universal positioning capability of mobile terminals has become a strong demand. In order to build a universally available cloud positioning system, it is necessary to use various positioning sensors in smart terminals to obtain wireless signals, and search in the positioning database to complete the calculation of the position.Methods: In light of smart phone devices in location based service(LBS) scenarios, we focus on location service for massive smart phone devices and put forward the cloud computing network architecture. The breakthroughs on key engineering technologies such as hierarchical cloud positioning capabilities, low ‑cost access for developers, fault tolerance strategy and distributed positioning services are gained. Results: The testing result shows that cloud positioning system has realized the access processing capability of hundreds of billions of requests per day. Conclusions: Consequently, the system can provide location services for hundreds of millions of users and can be widely used in various location based applications. [ABSTRACT FROM AUTHOR]

Casualties caused by people trapped in cars have been common in recent years. Despite a variety of solutions, complex detection devices need to be arranged, or privacy is poor. Since device-free Wi-Fi sensing has attracted much attention due to its simplicity, low cost, and no need for additional hardware, this paper proposes a contactless wireless Wi-Fi sensing-based method for detecting people left in cars: Car-Sense. The method uses ESP32 devices in the vehicle to build a wireless Wi-Fi network for low-cost, real-time, and accurate personnel awareness. By capturing and analyzing the CSI (Channel State Information) signal, extracting features, and building a machine-learning correlation model, the number and location of occupants can be estimated and further inferred in combination with sensing data such as vehicle temperature. Even better, with the computing power of the edge-side devices to process data in collaboration with the cloud, the computing process is partially localized to reduce the computing pressure and latency in the cloud. The approach has been experimentally verified to have more than 85% accuracy.

This article proposes a novel design of the dual-band antenna pair. Starting from the single-band antenna pair composed of T-shaped monopole and T-shaped slot that are intersected with each other, orthogonal T-monopole and odd-symmetrical slot modes are excited. By adding symmetrical low-pass high-stop filters into the horizontal stub of the T-monopole and high-pass low-stop filters into the gap of the T-slot, an extra mode is introduced for each antenna, producing dual-band operation. Impedance bandwidths and radiation efficiencies of both bands can be adjusted flexibly using the filters. A detailed design guideline is given. The dual-band antenna pair operating at partial N78 (p-N78, 3.4~3.6 GHz) and partial N79 (p-N79, 4.8~4.9 GHz) is with a footprint of $15\times 6$ mm2. The measured results show that the average efficiencies are both −3.4 dB at p-N78/p-N79 for the odd-symmetrical slot and −2.8 dB/−3.7 dB at p-N78/p-N79 for the T-monopole, isolations are better than −16.8 dB at p-N78 and −11.8 dB at p-N79, and ECCs are lower than 0.1 at p-N78 and 0.2 at p-N79. The proposed dual-band antenna pair exhibits great potential to be applied in the fifth-generation (5G) multiple-input multiple-output (MIMO) terminal. [ABSTRACT FROM AUTHOR]

Cyber-physical system (CPS) can intelligently feel the interactive information between data and terminal. The combination of CPS data and intelligent terminal can make terminal devices communicate with each other, coordinate operation, and share information in a complex environment. At present, the demand for smart terminal technology in university libraries and national cultural museums is also increasing. However, the data for smart terminal display and development of shared services face the problem that the original data is difficult to collect and the incomplete images are difficult to repair. In order to display the ancient books in a smart terminal, we have established a model for image inpainting through deep learning. In this paper, we set up a smart terminal display solution to display the digital protection effect of incomplete character images. Then, we propose a model based on Conditional Generative Adversarial Network (CGAN) to solve the problem of repairing incomplete character images. We put this model into the computing module and deployed it to the cloud computing platform of the smart terminal. Moreover, we construct a handwritten Yi character image data set in the cloud. To verify the CGAN calculation model, we design and construct two quality detection tests. According to the experimental results, the image inpainting quality of the CGAN is better than the traditional image restoration technology. Based on the experimental analysis and evaluation criteria, we find that the CGAN calculation model is of great significance for ancient book inpainting.

With the vigorous promotion of the construction of smart campus by the ministry of education, the development concept of smart campus will have broad application prospects. However, colleges and universities are still at the stage of digital campus and there are many problems left. It is difficult to complete the transition from digital campus to smart campus. The main problem is that the campus data has only been digitized but not informational. The purpose of this article is to study a smart campus management system based on the Internet of Things technology. This research uses the unified data collection source of face recognition terminal hardware products based on the Internet of Things technology, unified management in the background of the system, and calculates and analyzes the data to obtain valuable campus big data. This study designed and implemented a complete smart campus management system by analyzing the system design principles and design goals. This system is mainly divided into the face recognition terminal hardware and smart campus software system based on the Internet of Things. By analyzing the data generated by students and faculty and staff, it can provide a reference for campus managers to improve management quality, and help teachers and students to formulate more efficient learning and teaching and research plans. This article tests the practicability of the system and obtains the user’s satisfaction as 8.0. [ABSTRACT FROM AUTHOR]

Featured Application: An economical and practical demand response system is implemented based on multi communication with the ability to undertake multi-time-scale electricity control tasks. Demand response (DR) is widely accepted as a feasible and potential solution to improve the operation of the power system. In this paper, an economical and practical DR system architecture based on internet and Internet of things (IoT) communication technologies is discussed to achieve wide-area DR control without using an expensive metering infrastructure. Multi agents are introduced with respective control strategies to implement multi-time-scale control in a power system. In order to support quick DR strategies, a novel smart terminal design for the proposed DR system is described with functions of local parameter detection and action. The practicality of the proposed system was validated on a developed hardware-in-loop co-simulation platform. [ABSTRACT FROM AUTHOR]

Demand response (DR) is widely accepted as a feasible and potential solution to improve the operation of the power system. In this paper, an economical and practical DR system architecture based on internet and Internet of things (IoT) communication technologies is discussed to achieve wide-area DR control without using an expensive metering infrastructure. Multi agents are introduced with respective control strategies to implement multi-time-scale control in a power system. In order to support quick DR strategies, a novel smart terminal design for the proposed DR system is described with functions of local parameter detection and action. The practicality of the proposed system was validated on a developed hardware-in-loop co-simulation platform.

The last few decades have seen the emergence of many new methods for measuring leaf area index (LAI). One attractive method is to measure LAI by using a readily available simple instrument, such as a smartphone, as an alternative to traditional complicated and thus costly commercial instruments. We designed a novel integrated LAI measurement system, LAISmart, which is implemented by using two smart terminals communicating through a wireless connection in an integrated support infrastructure. The LAI is calculated by using the Beer gap fraction model on the assumption of spherical leaf angle distribution that takes the G value (the projection of unit foliage area on the plane perpendicular to the view direction θ) as constant at 0.5. The gap fraction is calculated by segmenting the image pixel into canopy and background. For the selection of image segment features, LAISmart provides two options, greenness index and blue band, which may be suited to different vegetation types. The proposed LAISmart was validated by measurements on four types of vegetation, i.e., Evergreen Needleleaf Forest (ENF), Deciduous Broadleaf Forest (DBF), Deciduous Needleleaf Forest (DNF), and broadleaf crop, on which 114 LAI values were calculated in the eight datasets. A pairwise comparison between LAISmart and LAI-2000 showed that the LAI values derived from LAISmart correlated strongly with those of the LAI-2000, with an R 2 value of 0.97, and had high accuracy in total, with a root–mean–square error (RMSE) of 0.45 m 2 /m 2 . The validation results revealed that the overestimation on LAI caused by underestimation of the G value might complement the bias caused by misclassification. Moreover, although there is no limitation on the shooting angle of LAISmart, it is recommended that LAISmart be used at the low zenith angle mode, e.g., upward- or downward-looking mode, rather than at the large zenith angle. Further validation will focus on other vegetation types such as grass and shrubs in order to improve the performance of LAISmart for different vegetation types. [ABSTRACT FROM AUTHOR]

Nowadays operators are focusing on developing the video communication services based on smart terminals.The characteristics and key affecting factors of video communication quality were discussed.Furthermore，according to the analysis of the methods of subjective and objective video quality assessment，an assessment model of video communication service quality was proposed and verified by experimental data，which can provide test reference and evaluation methods for operators to improve the quality of video communication service.

Copyright of Technical Electrodynamics / Tekhnichna Elektrodynamika is the property of National Academy of Sciences of Ukraine, Institute of Electrodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Floor-standing air conditioners (FSACs) have been widely used in civil and office buildings due to their high cooling capacity and easy installation in recent years. Increasing attention has been given to reducing the draft sensation of FSACs. This study introduces a new form of smart terminal for air conditioners that can be automatically adjusted by identifying the positions of humans in the room. The smart terminal has certain practical value given its simple structure and relatively low cost. A target air supply evaluation index for regional thermal comfort evaluation named the air velocity target value is proposed. A computational fluid dynamics model is established, and an orthogonal experiment of 4 factors and 4 levels is designed. The optimal parameters of the terminal are further determined through a series of optimization steps. The effect of the smart terminal is validated with numerical simulation and experimental measures, and the local velocities of the occupied zone are mostly below 0.3 m/s, showing that the draft level in the room is acceptable. Compared with the case of an air conditioner only, the air velocity target value of the room with a smart terminal decreases by 21.3%–63.3%. Compared with other existing manually controlled terminals purchased online, the smart terminal satisfies the minimum air velocity target value in the occupied zone. This study can also be used to guide the positioning and setting of air supplies for different positions of humans in a room under other air distributions. ● A new smart terminal for floor-standing room air conditioners was introduced. ● A comprehensive performance evaluation index, AVTV, was proposed. ● The AVTV with the smart terminal is 21.3%–63.3% smaller than air conditioners only. ● The AVTV with the smart terminal is the minimum compared with other terminals. [ABSTRACT FROM AUTHOR]