Locating Global System for Mobile Communication(GSM)Base Stations Using Geographic Information System(GIS)

The global system for mobile communication(GSM)is planned to meet the needs of the whole subscribers.The number of subscribers increased as the population increased due to the acceptance of GSM services by the subscribers.Thus,there should be a way to monitor base stations that will meet the increasing demand of subscribers in any area as a population surge will lead to more subscriptions.This will allow GSM network operators to serve their subscribers better and ease network congestion.This work presents a review of mobile evolution from the first generation to the fifth generation.A review of global positioning system(GPS)technology and its applications to geographic information systems(GIS)was done.The coordinates of these base stations were taken using a GPS device.These base station coordinates were then exported to QGIS for the design of the map.Thereafter,the output map was then integrated into the website.The discussions on the results followed and some useful suggestions given will go a long way to help the operators of GSM in Nigeria and in general.If the propositions given are adhered to,it will go a long way to help the operators reduce congestion on their network and thereby increase the satisfaction of the subscribers.

Spatial Interaction Between the Industrial Undertaking Capacity and Global Value Chain Position of East Asian Countries

A comprehensive understanding of the spatial interaction between the industrial undertaking capability and the position of the global value chain of the 12 East Asian countries is conducive to strengthening regional cooperation, gaining a say in foreign trade and becoming the dominant player in the global division of labor system. The article reveals the operating rules of the interaction between the industrial undertaking capacity and the global value chain position of East Asian countries by calculating the Global Moran Index(Moran’s I), coupling coordination degree and other indicators. The results show that: in time, the values of industrial undertaking capacity and the positions of global value chain in East Asian countries showed a sustained and stable growth trend, and have a consistent trend of change. Spatially, both of the two indexes had significant positive spatial correlation, with Moran’s I showing an ‘inverted U’pattern, and the spatial aggregation distribution of global value chain position lagged behind the spatial aggregation distribution of industrial undertaking capacity by one year. In terms of spatial coupling coordination, the coupling coordination values of the two indicators show a steady upward trend. Combined with the comparative advantage of each country, this paper provides suggestions for promoting the positions of Chinese and other East Asian industries in the global value chain from the perspectives of enhancing independent innovation capability and upgrading industrial structure.

Outdoor position estimation based on a combination system of GPS-INS by using UPF

This paper proposes a technique that global positioning system(GPS)combines inertial navigation system(INS)by using unscented particle filter(UPF)to estimate the exact outdoor position.This system can make up for the weak point on position estimation by the merits of GPS and INS.In general,extended Kalman filter(EKF)has been widely used in order to combine GPS with INS.However,UPF can get the position more accurately and correctly than EKF when it is applied to real-system included non-linear,irregular distribution errors.In this paper,the accuracy of UPF is proved through the simulation experiment,using the virtual-data needed for the test.

Remote positioning system based on GPS/GPRS

Considering the need of target positioning,a remote positioning system is designed based on global positioning system(GPS)and general packet radio service(GPRS);The data collection terminal is based on microcontroller unit(MCU)PIC24FV301.It uses GPRS network to create wireless link and transmits GPS source information which is collected by LEA-5H board to monitor center on the Internet.The monitor center obtains the target information through processing and analysis of the calculated data.Actual operation results indicate that the designed system has excellent performance and achieves the goal of the remote location.

Rapid Fault Analysis by Deep Learning-Based PMU for Smart Grid System

Smart Grids(SG)is a power system development concept that has received significant attention nationally.SG signifies real-time data for specific communication requirements.The best capabilities for monitoring and controlling the grid are essential to system stability.One of the most critical needs for smart-grid execution is fast,precise,and economically synchronized measurements,which are made feasible by Phasor Measurement Units(PMU).PMUs can pro-vide synchronized measurements and measure voltages as well as current phasors dynamically.PMUs utilize GPS time-stamping at Coordinated Universal Time(UTC)to capture electric phasors with great accuracy and precision.This research tends to Deep Learning(DL)advances to design a Residual Network(ResNet)model that can accurately identify and classify defects in grid-connected systems.As part of fault detection and probe,the proposed strategy uses a ResNet-50 tech-nique to evaluate real-time measurement data from geographically scattered PMUs.As a result of its excellent signal classification efficiency and ability to extract high-quality signal features,its fault diagnosis performance is excellent.Our results demonstrate that the proposed method is effective in detecting and classifying faults at sufficient time.The proposed approaches classify the fault type with a precision of 98.5%and an accuracy of 99.1%.The long-short-term memory(LSTM),Convolutional Neural Network(CNN),and CNN-LSTM algo-rithms are applied to compare the networks.Real-world data tends to evaluate these networks.

Analysis of Geo Positioning System Using RF &DA

Indoor positioning systems (IPSs) have been intended to provide position information of persons and devices. Higher user percentage of handheld devices such as tablets or mobile phones had led to the development of a number of indoor positioning systems. In this research a work on a real time portable RFID indoor positioning device such as on smartphone will be performed. The personal networks will be designed to meet the users’ needs and interconnect users’ devices equipped with different communications technologies in various places to form one network for better result. Radio frequency identification (RFID) with directional antenna has proved its potential for locating objects in indoor environment. Hence, the proposed device idea will be used to exploit various unknown locations in an indoor environment such as college campus;this interpretation will rely on Wireless LAN, Received Signal Strength values from Access Points (AP) in specific mentioned arenas;these APs will be monitored constantly by RFID with directional antenna (DA) and handheld devices. For obtaining the better results from existing devices, algorithms of Range Estimation are proposed, which can be used on various handheld devices for locating indoor objects.

THE POSITION AND ROLE OF THE BRICS IN GLOBAL GOVERNANCE

Global issues,just as the name implies,refer to the common threats and challenges facing the whole globe.Upon the backdrop of deepening globalization and increasing interdependence among all the countries,global issues feature diversification and complexity.Furthermore,without the so-called'world government'in place,all the countries should

The measurement of time spent outdoors in child myopia research： a systematic review

The purpose of this article is to summarize the methods most commonly used to measure time spent outdoors and provide a comprehensive review of time and activity recording methods with the aim of encouraging the development of new methods. Pub Med, Embase and the Cochrane Library were searched from Jan. 1 st, 1990 to Aug. 31 th, 2017. Studies including the following specific terms: "outdoor", "outside", "outdoor activity", "outside activity", "outdoor time", "outside time", and "outdoor AND measurement of time spent outdoors" were considered for this review. In total, three kinds of outdoor time measurements were discussed. Questionnaires have the longest history and are the most thoroughly revised instruments for assessing time spent outdoors, but recall bias is their most substantial drawback. Global positioning system(GPS) tracking can distinguish between indoor and outdoor locations, but its utility is limited due to several factors such as subject compatibility. Light exposure measurement devices are newly emerging, but all of these devices require good subject cooperation. Further efforts and exploration are needed to develop better methods and new tools to record exposure to the outdoors in real time. Moreover, inventing a new device by combining two or more types of devices mentioned above and using the latest technology of en ergy supplementation and autoswitching may make the best use of the advantages and bypass the disadvantages of each tool.

Influential nodes identification in complex networks based on global and local information

Identifying influential nodes in complex networks is essential for network robust and stability,such as viral marketing and information control.Various methods have been proposed to define the influence of nodes.In this paper,we comprehensively consider the global position and local structure to identify influential nodes.The number of iterations in the process of k-shell decomposition is taken into consideration,and the improved k-shell decomposition is then put forward.The improved k-shell decomposition and degree of target node are taken as the benchmark centrality,in addition,as is well known,the effect between node pairs is inversely proportional to the shortest path length between two nodes,and then we also consider the effect of neighbors on target node.To evaluate the performance of the proposed method,susceptible-infected(SI)model is adopted to simulate the spreading process in four real networks,and the experimental results show that the proposed method has obvious advantages over classical centrality measures in identifying influential nodes.

Driving Style Recognition System Using Smartphone Sensors Based on Fuzzy Logic

Every 24 seconds,someone dies on the road due to road accidents and it is the 8th leading cause of death and the first among children aged 15–29 years.1.35 million people globally die every year due to road traffic crashes.An additional 20–50 million suffer from non-fatal injuries,often resulting in longterm disabilities.This costs around 3%of Gross Domestic Product to most countries,and it is a considerable economic loss.The governments have taken various measures such as better road infrastructures and strict enforcement of motor-vehicle laws to reduce these accidents.However,there is still no remarkable reduction in the number of accidents.To ensure driver safety and achieve vision of zero accidents,there is a great need to monitor drivers’driving styles.Most of the existing driving behavior monitoring solutions are based on expensive hardware sensors.As most people are using smartphones in the modern era,a system based on mobile application is proposed,which can reduce the cost for developing intelligent transport systems(ITS)to a large extent.In this paper,we utilize the accelerometer sensor data and the global positioning system(GPS)sensor deployed in smartphones to recognize driving and speeding events.A driving style recognition system based on fuzzy logic is designed to classify different driving styles and control reckless driving by taking the longitudinal/lateral acceleration and speed as input parameters.Thus,the proposed system uses fuzzy logic rather than taking the crisp values of the sensors.Results indicate that the proposed system can classify reckless driving based on fuzzy logic and,therefore,reduce the number of accidents.

Design on a Multi-Sensor Integrated Attitude Determination System for Small UAVs

This paper discusses the design and implementation of a low cost multi-sensor integrated attitude determination system for small unmanned aerial vehicles( UAVs),which uses strapdown inertial navigation system( SINS) based on micro electromechanical system( MEMS) inertial sensors,commercial GPS receiver,and 3-axis magnetometer.MEMS-SINS initial attitude determination cannot be well performed for the reason that the MEMS inertial sensors biases are time-varying and poor repeatability,therefore in this paper the magnetometer and inclinometer are used to assist the MEMS-SINS initial attitude determination and MEMS inertial sensors field calibration.Furthermore,the attitude determination algorithms are presented to estimate the full attitude during GPS signal outage and non-accelerating situation.Additionally,the attitude information estimation results are compared with the reference of the non-magnetic marble platform and 3-axis turntable.Then the attitude estimation precision satisfies the requirement of attitude measurement for small UAVs during GPS signal outage and availability.Finally,the small UAV autonomous flight test results show that the low cost and real-time attitude determination system can yield continuous,reliable and effective attitude information for small UAVs.

Review of satellite disciplined clock system

Satellite disciplined clock system(SDCS)composed of satellite timing receiver and local frequency synthesis is widely applied for its high accuracy and low cost.This paper provides a review of SDCS.Key technologies such as phase difference measurement,pulse noise process and frequency calibration are surveyed in detail.Disciplined clock model based on PI controller is built and disciplined process is analyzed.The methods of realizing the disciplined clock circuit are classified and summarized.A prototype based on FPGA is proposed.At last development trend of SDCS is discussed.

GPS观测得的2023年2月土耳其双震前后的地壳形变

2023年2月6日土耳其发生7.8级双震,相隔仅9小时、相距近,都是近直立断层走滑型地震,是利用GPS观测研究地震前后地壳形变稀有震例。利用美国网站得到131个GPS站欧亚(板块)参考框架位移数据,分析了2次地震前后的地壳形变。同震水平位移是分析大地震前兆地壳运动的关键现象。GPS分别观测得的2次地震同震位移,特征显著:水平位移最大达4.4 m;向西、向东和向南水平位移3个分区明显,与区域内3个板块构造运动密切相关;而垂直位移较小,无明显分区。2次地震前震中附近的水平位移分别出现(第一次地震)峰值和(第二次地震)闭锁2种类型,但无明显的垂直位移积累。与其他震例一样,在震前两种类型的水平位移区域,同震水平位移是震前位移在时间或空间上的(弹性)回跳,表明大地震有前兆形变。2次地震重合的孕震区内,有对2次地震同震水平位移影响相近的区域。GPS观测明确显示,地壳水平运动是2次大地震的成因。尽管观测不足和未能预报2次地震,但仍突显GNSS可在地震监测、预警和地震预测中发挥作用的优势。

Analysis of RFID Based Positioning Technique Using Received Signal Strength and Directional Antenna

Automatic robot navigation is being utilized in many industries for the purpose of high speed work delivery. Color follower, fix path follower robots are current solution to this activities but dynamic path configuration is not possible in these robots. Hence new system proposes effective and fully dynamic path follower robots using RFID and directional antenna. Radio Frequency Identification (RFID) system permits automatic identification of objects with RFID tags using radio waves which have been widely used in mobile robot navigation, localization and mapping both in indoor and outdoor environment. This article presents a navigation strategy for autonomous mobile robot using passive RFID system. Proposed robot system is provided with RFID tag functionality which will load tag number and direction instruction. At some turning point, user will put RF tag, this tag will be read by RF reader which is placed on robot. As per direction instruction robot will change the direction and reach to the destination. Also as per the movement, robot will send its GPS location to PC (Personal Computer) which will be displayed on PC. Hence main goal is to provide more reliable and low energy consumption based indoor positioning system which will be achieved using directional antenna.

The mathematical weighting of GNSS observations based on different types of receivers/antennas and environmental conditions

Stochastic models play an important role in achieving high accuracy in positioning,the ideal estimator in the least-squares(LS)can be obtained only by using the suitable stochastic model.This study investigates the role of variance component estimation(VCE)in the LS method for Precise Point Positioning(PPP).This estimation is performed by considering the ionospheric-free(IF)functional model for code and the phase observation of Global Positioning System(GPS).The strategy for estimating the accuracy of these observations was evaluated to check the effect of the stochastic model in four modes:a)antenna type,b)receiver type,c)the tropospheric effect,and d)the ionosphere effect.The results show that using empirical variance for code and phase observations in some cases caused erroneous estimation of unknown components in the PPP model.This is because a constant empirical variance may not be suitable for various receivers and antennas under different conditions.Coordinates were compared in two cases using the stochastic model of nominal weight and weight estimated by LS-VCE.The position error difference for the east-west,north-south,and height components was 1.5 cm,4 mm,and 1.8 cm,respectively.Therefore,weight estimation with LS-VCE can provide more appropriate results.Eventually,the convergence time based on four elevation-dependent models was evaluated using nominal weight and LS-VCE weight.According to the results,the LS-VCE has a higher convergence rate than the nominal weight.The weight estimation using LS-VCE improves the convergence time in four elevation-dependent models by 11,13,12,and 9 min,respectively.

An artificial neural network model in predicting VTEC over central Anatolia in Turkey

This research investigates the capability of artificial neural networks to predict vertical total electron content(VTEC)over central Anatolia in Turkey.The VTEC dataset was derived from the 19 permanent Global Positioning System(GPS)stations belonging to the Turkish National Permanent GPS NetworkActive(TUSAGA-Aktif)and International Global Navigation Satellite System Service(IGS)networks.The study area is located at 32.6°E-37.5°E and 36.0°N-42.0°N.Considering the factors inducing VTEC variations in the ionosphere,an artificial neural network(NN)with seven input neurons in a multi-layer perceptron model is proposed.The KURU and ANMU GPS stations from the TUSAGA-Aktif network are selected to implement the proposed neural network model.Based on the root mean square error(RMSE)results from 50 simulation tests,the hidden layer in the NN model is designed with 41 neurons since the lowest RMSE is achieved in this attempt.According to the correlation coefficients,absolute and relative errors,the NN VTEC provides better predictions for hourly and quarterly GPS VTEC.In addition,this paper demonstrates that the NN VTEC model shows better performance than the global IRI2016 model.Regarding the spatial contribution of the GPS network to TEC prediction,the KURU station performs better than ANMU station in fitting with the proposed NN model in the station-based comparison.

A wireless geophone based on STM32

Seismic geophone is the key instrument for seismic data acquisition in the fields of oil and gas exploration and other relevant fields.It plays an important role in data quality,subsequent processing and interpretation.Aiming at the shortcomings of the existing geophones,such as large volume,heavy weight,unable to monitor in real-time,and heavy workload of data transmission and storage,this paper designs and implements a wireless geophone based on STM32 embedded microcontroller.It is mainly composed of STM32 microcontroller,acceleration sensor,global positioning system(GPS)module,Wi-Fi module and real-time seismic data receiving and processing module.Firstly,the acceleration of vibration in three directions is sensed by the acceleration sensor;secondly,the sampling and analog-to-digital conversion of the vibration data is controlled by the microcontroller;thirdly,the vibration and GPS data are sent to the personal computer(PC)through Wi-Fi,and the personal coomputer PC-side software module completes the reception,display,monitoring and storage of the seismic data;finally,the feasibility and effectiveness of the designed wireless geophone are verified through actual seismic data acquisition experiments.

Digital Trade Rules and the Position of Chinese Enterprises in the Global Value Chain

Digital trade rules and higher positions of enterprises in the global value chain(GVC)are both important topics in the context of high-quality economic development.This paper refers to the data of RTA digital trade rules concluded by China in the TAPED database in 2000-2014 and the matched data in the WIOD database,the China Customs Import and Export Database and the China Industrial Enterprise Database to investigate the effects of digital trade rules on Chinese enterprises as to their ascending GVC positions.It finds that signing the RTA digital trade rules can steadily and significantly promote the position ascendance;compared with the digitalization rules on trading objects,signing the digitalization rules on trade modes produces greater effects of driving up the positions,which are realized mainly via the three channels of advancing cross-border flow of R&D factors,promoting corporate digital transformation,and improving professional management.Digital trade rules that are horizontally broader and vertically deeper are in greater favor of Chinese enterprises for ascending in the global value chain,which is especially evident among those in digital industry and processing trade.During the negotiations of digital trade rules,it's imperative to pay close attention to the rules on e-commerce cooperation,awareness of e-commerce importance,intellectual property protection,attempts in big data-related trade in goods,and cross-border data flow,so as to secure the core interests of Chinese enterprises in gaining higher positions.This paper offers great policy implications as to how to sign digital trade rules by China in the future and how to select partners in greater support for Chinese enterprises to ascend to the middle and high end of the global value chain.

Absolute and relative sea-level rise in the New York City area by measurements from tide gauges and satellite global positioning system

The absolute and relative rates of rise of the sea level are computed for the New York City area by coupling global positioning system records of the position of fixed domes nearby tide gauges,with the tide gauges’records.Two tide gauges are considered,one long-term trend,more reliable,The Battery,in lower Manhattan,and one shorter,less reliable,Sandy Hook,in New Jersey.The relative rates of rise of the sea level are+2.851 and+4.076 mm/yr.The subsidence rates are-2.151 and-3.076 mm/yr.The absolute rates of rise of the sea level are+0.7 and+1.0 mm/yr.The relative sea-level acceleration,reliable only in The Battery,is about+0.008 mm/yr².This acceleration is about the same as the world average long-term trend tide gauge,as well as the average long-term trend tide gauge of the East Coast of North America.The absolute rate of rise of the sea level by 2050 in the lower Manhattan area will be likely less than 30 mm,and the absolute rate of rise of the sea level by 2100 likely less than 80 mm.The relative rate of rise of the sea level by 2050 in the Manhattan area will be likely 85 mm,and the relative rate of rise of the sea level by 2100 likely 228 mm,because of the overwhelming subsidence contribution.

Detection of the Pine Wilt Disease Tree Candidates for Drone Remote Sensing Using Artificial Intelligence Techniques

Pine wilt disease(PWD)has recently caused substantial pine tree losses in Republic of Korea.PWD is considered a severe problem due to the importance of pine trees to Korean people,so this problem must be handled appropriately.Previously,we examined the history of PWD and found that it had already spread to some regions of Republic of Korea;these became our study area.Early detection of PWD is required.We used drone remote sensing techniques to detect trees with similar symptoms to trees infected with PWD.Drone remote sensing was employed because it yields high-quality images and can easily reach the locations of pine trees.To differentiate healthy pine trees from those with PWD,we produced a land cover(LC)map from drone images collected from the villages of Anbi and Wonchang by classifying them using two classifier methods,i.e.,artificial neural network(ANN)and support vector machine(SVM).Furthermore,compared the accuracy of two types of Global Positioning System(GPS)data,collected using drone and hand-held devices,for identifying the locations of trees with PWD.We then divided the drone images into six LC classes for each study area and found that the SVM was more accurate than the ANN at classifying trees with PWD.In Anbi,the SVM had an overall accuracy of 94.13%,which is 6.7%higher than the overall accuracy of the ANN,which was 87.43%.We obtained similar results in Wonchang,for which the accuracy of the SVM and ANN was 86.59%and 79.33%,respectively.In terms of the GPS data,we used two type of hand-held GPS device.GPS device 1 is corrected by referring to the benchmarks sited on both locations,while the GPS device 2 is uncorrected device which used the default setting of the GPS only.The data collected from hand-held GPS device 1 was better than those collected using hand-held GPS device 2 in Wonchang.However,in Anbi,we obtained better results from GPS device 2 than from GPS device 1.In Anbi,the error in the data from GPS device 1 was 7.08 m,while that of the GPS device 2 data was 0.14 m.In conclusion,both classifiers can distinguish between healthy trees and those with PWD based on LC data.LC data can also be used for other types of classification.There were some differences between the hand-held and drone GPS datasets from both areas.