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Aims. Traditional long-term ECG monitoring systems have primarily been used by cardiologist. New remote and wearable easy-to-use devices have led to increased use of ECG recordings also outside cardiology clinics. The aims of this study were to assess the feasibility and diagnostic accuracy of interpretation of the one-lead ECG recordings from a patch ECG device (ECG247 Smart Heart Sensor system) by general practitioners (GP). Methods. Norwegian GPs were invited to digitally assess 10 long-term ECG recordings with different arrhythmias performed by the ECG247 Smart Heart Sensor system. For all ECG examinations, the presence/absence of different arrhythmias was registered. Results. A total of 40 GPs accepted the invitation and assessed all the 10 long-term ECG recordings. All the tests were assessed as interpretable by all the GPs. Arrhythmias (atrial fibrillation/flutter, supraventricular tachycardia, and ventricular tachycardia) were correctly identified in most cases, with sensitivity of 98% (95% CI 95-99%), specificity of 75% (95% CI 68-82%), and diagnostic accuracy of 89% (85-92%). Incorrect automatic system algorithm interpretations were rarely corrected by the GPs. Conclusion. GPs interpreted one-lead recordings by the ECG247 Smart Heart Sensor system with high diagnostic accuracy for common arrhythmias. However, in cases with rare arrhythmias, we recommend consulting a cardiologist to confirm the diagnosis before treatment is initiated. This trial is registered with NCT04700865. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) is considered as an effective technology to enhance the storage and computation capability of smart power sensors (SPSs) in smart grid networks. The MEC server is composed of multiple virtual machines (VMs) with powerful computation capability, and each VM can process multiple tasks independently, which cannot be ignored during the task computation period. In this work, we aim to minimize the energy consumption of SPSs subject to the task offloading delay by jointly optimizing the VM selection and computation resource allocation. Considering the formulated problem is nonconvex, we first utilize the linearization method to transform it into a convex optimization problem. And then, by using the branch and bound method, we propose the joint VM selection and computation resource allocation (JVMSRA) algorithm. Considering the complexity of the JVMSRA algorithm is high, we decompose the primal problem into two subproblems and solve them by utilizing the ant colony method and CVX package, respectively. Based on the solutions of the two subproblems, the resource allocation-based ant colony (RAAC) algorithm is proposed. Simulation results show that the proposed RAAC algorithm and JVMSRA algorithm decrease by 6% and 8.8% on average compared with the benchmark algorithm, respectively, when the computation resources of each VM increase from 1 to 3 GHz. [ABSTRACT FROM AUTHOR]

Sleep apnea is one of the most common sleep disorders in the world. It is a common problem for patients to suffer from sleep disturbances. In this paper, we propose a deep convolutional neural network (CNN) model based on the oxygen saturation (SpO2) signal from a smart sensor. This is the reason why we called ZleepNet a network for sleep apnea detection. The proposed model includes three convolutional layers, which include ReLu activation function, 2 dense layers, and one dropout layer for predicting sleep apnea. In this proposed model, the use of signals for detecting the sleep apnea can be reduced from 25 sensors to 1 sensor. We conducted experiments to evaluate the performance of the proposed CNN using real patient data and compared them with traditional machine learning methods such as least discriminant analysis (LDA) and support vector machine (SVM), baggy representation tree, and artificial neural network (ANN) on publicly available sleep datasets using the same parameter setting. The results show that the proposed model outperformed the other methods with the accuracy of 91.30% with the split rate of 0.2% in which the training data are 20% and testing data are 80%. The accuracy of the proposed CNN is 90.33% when compared with the LDA which achieved 86.5% accuracy with the split rate of 0.5% in which training data are 50% and testing data are 50%. It achieved 91.56% accuracy when compared with the support vector machine (SVM) in which training data are 70% and testing data are 30%. The achieved accuracy of the proposed CNN is 91.89% when compared with bagging representation tree in which training data are 90% and testing data are 10%. The accuracy of the proposed CNN is 91.30% in which training data are 83% and testing data are 17% when compared with artificial neural networks (ANN). [ABSTRACT FROM AUTHOR]

A self-aware posttensioned prestressing intelligent reinforcement system with force and sensors is proposed by combining the advantages of posttensioned prestressing reinforcement technology and intelligent structure. A static test study is then conducted with brick masonry members (wall pieces) as reinforcement objects. The experimental results demonstrate that the intelligent strand can be deployed using the same process, tensioning apparatus, and anchoring equipment as ordinary strand. Furthermore, the mechanical performance indexes with and without intelligent strands are very close, and the differences in percentages of cracking load, cracking displacement, yielding load, yielding displacement, ultimate load, and ultimate displacement are 20%, 9%, 9%, 10%, 2%, and 26%. [ABSTRACT FROM AUTHOR]

Microgrid is a power system that includes various energy sources (e.g., solar panels and wind turbines), where a number of device status and sensing data are collected and transmitted by smart sensors. Based on sensing-as-a-service in microgrid, sensor owners and sensor data consumers can effectively perform data sharing operations. However, the state-of-the-art sensor data sharing works in microgrid have the following two limitations: (i) cannot support fine-grained authorization for sensor owners and sensor data consumers and (ii) fail to simultaneously consider confidentiality and authenticity for sensor data sharing. To address the problems, in this article, we propose a lightweight privacy-preserving sensing data sharing system with fine-grained authorization in microgrid. Technically, we employed attribute-based signature methodology to design a fined-grained authorization mechanism for sensor data users. Moreover, a lightweight hyper elliptic curve-based signcryption scheme is employed to provide confidentiality and authenticity for sensor data sharing. To clarify the feasibility of our proposed system, we implement the system and evaluate the performance. The experimental results show that the system achieves small communication and time overhead, as well as highly acceptable gas consumption of smart contract. [ABSTRACT FROM AUTHOR]

In combat sports, the team members take attack and defense as the core of sports, and the technical movements are not periodic and the application process is irregular. During the competition, observe the opponent's neutral position, judge timely, and react quickly. At the same time, athletes are required not only to master skilled technical movements but also to have rapid and accurate adaptability. In order to improve athletes' rapid response ability, according to the rapid response training scheme, a combat response training tester based on a force-measuring intelligent sensor and digital circuit is designed to improve the athletes' response ability. Firstly, a comprehensive test system of combat trainer is designed based on the intelligent force-measuring sensor and MSP430, which can measure the response time, speed, and strength of boxing. Secondly, a wavelet filtering algorithm is used to filter the sensor measurement data. Finally, the striking force and striking time of each action are recorded by sensors and data acquisition devices to reflect the striking effect and reflect the "accurate" objectives of the project characteristics. Based on the noninterference and anti-interference of e-touch piezoelectric film, it can be made into universal force-measuring sensors of different specifications for fight competitions according to needs. After being connected into a module network in parallel, it can be connected with the microcontroller to determine the hit area. In addition, the team reaction training test device has laid a foundation for the scientific evaluation of improving the team members' reaction ability and attack defense conversion ability. The launch of the comprehensive ability tester for combat athletes introduces the previous fuzzy evaluation of athletes' comprehensive ability into a new way of scientific quantitative measurement and evaluation, which will provide a scientific basis for the selection of combat vibration items, the regulation of training process, the inspection of training effect, and the formulation of enrollment and examination standards for sports colleges and universities. The results show that the instrument can obtain the parameters of hitting strength, strength endurance, hitting speed, speed endurance, hitting impulse, and hitting power of sports biomechanics on the group of fighting events, which provides a reliable basis for coaches to implement teaching and training for athletes. [ABSTRACT FROM AUTHOR]

The informatization construction of the power grid is becoming increasingly popular, business application systems are constantly emerging, and power-related data is rapidly expanding. These discrete power data are scattered in various application systems, and it is not easy to directly provide advanced enterprise applications. The establishment of intelligent power statistical model is an urgent need for constructing power grid informatization. This paper proposes a model of an electric energy metering system based on intelligent sensor data and introduces the existing digital metering system. This model is the integration and promotion of business integration based on the digital metering system. It is the first time to apply new metering equipment, such as measurement and control devices with integrated metering functions, and new metering technologies, such as IEC 61850 electricity meter reading applications. It is hoped that this paper can lay a foundation for further research. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) connects a huge amount of smart sensors with the Internet for healthcare service provisioning. IoMT's privacy-preserving becomes a challenge considering the life-saving data collected and transferred through IoMT. Traditional privacy protection techniques use centralized management strategies, which lead to a single point of failure, lack of trust, state modification, information disclosure, and identity theft. Edge computing enables local computation of IoMT data, which reduces traffic to the cloud and also helps in accomplishing latency-sensitive healthcare applications and services. This paper proposes a novel framework (i.e., SecureMed) that uses blockchain-based distributed authentication implemented at the edge cloudlets to enforce privacy protection. In SecureMed, IoMT devices interact with edge cloudlets using smart contracts. It uses trusted edge nodes to implement an authentication algorithm that uses public/private key matching to authenticate IoMT. Experimental evaluation performed using the Pythereum blockchain shows that SecureMed outperforms the traditional blockchain scheme based on latency, bandwidth consumption, deployment time, scalability, and accuracy. Therefore, it can be used to protect the edge-enabled IoMT from privacy attacks and to ensure end-to-end healthcare service provisioning. [ABSTRACT FROM AUTHOR]

In the current era of data explosion, people's media have access to a large amount of information through the Internet. How to generate effective and realistic news and let interested people accept it is a hot topic in the current news media industry. The continuous development of sensor technology and the application of artificial intelligence technology enable the directional push of intelligent sensor news to be realized. Intelligent sensor media uses sensor technology to monitor and collect information and then combines artificial intelligence and big data to process the information. After obtaining effective media information, it will be pushed in a targeted way and tracked for analysis. By analyzing the current situation and difficulties of sensor news, this paper seeks for an effective combination of artificial intelligence technology and sensor news and establishes a whole process directional push model for sensor news production and thermal tracking analysis. This model is pioneering and practical in the media field. In order to further verify the effectiveness of the model, the important application field of sensor news is selected as the simulation research object, and the specific selection and layout of sensors, the data analysis obtained by sensors, the push of environmental news, and the thermal tracking analysis are simulated. The results show that through data analysis, news editing, and targeted push of water pollution environmental news, the news has gained a certain degree of attention among the main design media. The popularity index is estimated based on the number of likes and comments forwarded by the current mainstream Internet media platforms (Toutiao, Tiktok, and Weibo). The environmental pollution news thermal index of the sensor reached the highest thermal index score of 12 on the 26th day after the activity. On the Weibo client, the message reached the highest heat index of 14 points on the 28th. The simulation analysis of actual cases further illustrates the advantages of this design framework. [ABSTRACT FROM AUTHOR]

Intelligent mining provides an indispensable, strong technical support for the construction of intelligent mines. Intelligent tunneling technology is an inevitable requirement for safe and efficient production of coal and a fundamental way to solve the imbalance of mining in order to improve the deficiency of data transmission in light of intelligent coal tunneling technology. Based on intelligent sensor and 5G technology, this paper puts forward a kind of intelligent tunneling support transportation integrated technology. According to the geological conditions of the surrounding rock in different coal seams, the anchor road header and 5G continuous intelligent tunneling mode are adopted. At the same time, the data communication network coverage suitable for underground unmanned driving is expounded, and the performance indexes that need to be met for accessing WLAN network are given based on the actual engineering experience. The breakthrough of underground unmanned driving application that 5G new communication network will bring is analyzed. And the construction mode of 5G platform of underground coal mine sensor network is given. [ABSTRACT FROM AUTHOR]

The intelligent imaging sensors in IoT benefit a lot from the continuous renewal of deep neural networks (DNNs). However, the appearance of adversarial examples leads to skepticism about the trustworthiness of DNNs. Malicious perturbations, even unperceivable for humans, lead to incapacitations of a DNN, bringing about the security problem in the information integration of an IoT system. Adversarial example detection is an intuitive solution to judge if an input is malicious before acceptance. However, the existing detection approaches, more or less, have some shortcomings like (1) modifying the network structure, (2) extra training before deployment, and (3) requiring some prior knowledge about attacks. To address these problems, this paper proposes a novel framework to filter out the adversarial perturbations by superimposing the original images with the noises decorated by a new gradient-independent visualization method, namely, score class activation map (Score-CAM). We propose to trim the Gaussian noises in a way with more explicit semantic meaning and stronger explainability, which is different from the previous studies based on intuitive hypotheses or artificial denoisers. Our framework requires no extra training and gradient calculation, which is friendly to embedded devices with only inference capabilities. Extensive experiments demonstrate that the proposed framework is sufficiently general to detect a wide range of attacks and apply it to different models. [ABSTRACT FROM AUTHOR]

This paper intends to apply the Intelligent Wireless Sensor Network (IWSN) to the archaeological excavation site and make the dynamic site of archaeological excavation can be monitored intelligently in real-time. This paper firstly discusses the structure and function of Wireless Sensor Network (WSN). Secondly, according to the problem of limited dynamic monitoring of WSN, an IWSN is established. The dynamic environmental monitoring of archaeological excavation sites is achieved by using Geographic Information System (GIS) and remote sensing technology combined with IWSN. Finally, this paper analyses the environmental monitoring results of the archaeological excavation site, the dynamic environmental monitoring location of the archaeological excavation site of the IWSN, and the simulation results of the Intelligent Wireless Sensor Network-Geographic Information System (IWSN-GIS) in the dynamic environmental monitoring of the archaeological excavation site. The results show that the Inverse Distance to a Power (IDP) algorithm in temperature and humidity monitoring data on the overall downward trend, the algorithm performance is relatively stable, and the algorithm performance is better than the other two algorithms. The monitoring performance of IWSN-GIS system is better than that of IWSN system. It shows that with the increase in the communication distance of sensor nodes, the relative error of different monitoring systems for the dynamic monitoring position of the archaeological excavation site is decreasing. IWSN-GIS can monitor the environment before, during, and after the archaeological site. This paper realizes the intelligent, scientific, and technological archaeology of archaeological excavation through IWSN, GIS, and remote sensing image technology, and provides technical support for digital archaeology in archaeological research. [ABSTRACT FROM AUTHOR]

Research and optimization of the coal-blending system can greatly improve the variety diversity and quality stability of finished coal. However, the existing research on the coal-blending system has some defects, such as fuzzy ash content of coal-blending warehouse products and excessive error of the coal-blending ratio determined by artificial experience, which brings many disadvantages and difficulties to the efficient and stable production of the coal preparation plant. The genetic algorithm and intelligent sensor network are relatively advanced technologies at present. Therefore, the optimization analysis of the coal-blending model and its control system based on the intelligent sensor network and genetic algorithm has become a research hotspot. This paper first introduces the basic theory of the genetic algorithm and intelligent sensor network and their application in coal-blending research, then establishes an optimized dynamic coal-blending model based on the intelligent sensor network and genetic algorithm, and analyzes its application effect. The research results show that the coal quality prediction model can be mined from coal quality data and coal-blending data by using the genetic algorithm and ideas, and the monitoring system based on the intelligent sensor network can monitor the abnormal state anytime and anywhere. Compared with the traditional coal-blending method, the average error is 3.33%, and the accuracy is improved by 4.82%. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI), combined with the Internet of Things (IoT), plays a beneficial role in various fields, including intelligent surveillance applications. With IoT and 5G advancement, intelligent sensors, and devices in the surveillance environment collect large amounts of data in the form of videos and images. These collected data require intelligent information processing solutions, help analyze the recorded videos and images to detect and identify various objects in the scene, particularly humans. In this study, an automated human detection system is presented for a complex industrial environment, in which people are monitored/detected from a top view perspective. A top view is usually preferred because it can provide sufficient coverage and enough visibility of a scene. This study demonstrates the applications, efficiency, and effectiveness of deep learning architectures, that is, Faster Region Convolutional Neural Network (Faster R‐CNN), Single Shot MultiBox Detector (SSD), and You Only Look Once (YOLOv3), with transfer learning. Experimental results reveal that with additional training and transfer learning, the performance of all detection architectures is significantly improved. The detection results are also compared using the same data set. The deep learning architectures achieve promising results with maximum true‐positive rate of 93%, 94%, and 94% for Faster‐RCNN, SSD, and YOLOv3, respectively. Furthermore, a detailed study is performed on output results that highlight challenges and probable future trends. [ABSTRACT FROM AUTHOR]

With the continuous innovation and development of information technology and communication technology, human beings have entered a huge data center. In terms of big data, in-depth discussion on the in-depth application of big data is the current situation and development direction. Based on the intelligent sensor network, this paper conducts theoretical and empirical analysis combined with the application of individualized teaching in teaching and provides some reference opinions for the future development of individual pedagogy. On the basis of the research, the meaning of big data is explained from multiple perspectives. In the empirical research part, based on big data, based on real big data, and using relevant technologies in data mining, the relevance of each training program is analyzed. Mining was carried out using the FP-Growth method to analyze the correlation of each training scheme and optimize the FP-Growth method accordingly. The results of this experiment show that the use of big data can not only help teachers understand their students' daily life, study habits, and consumption records but also make teachers' work more dynamic, early warning, and subsidized. The research results are of great significance for guiding colleges and universities to formulate educational programs that are beneficial to students' development, keep pace with the times, and cultivate modern educational technicians with modern significance. [ABSTRACT FROM AUTHOR]

At present, intelligent sensor network technology has become a popular field with a wide range of application prospects. With the continuous development of communication protocols and component technologies, the role of intelligent sensor networks in the digital economy is growing. Intelligent sensor network technology industry is a talent-intensive, knowledge-intensive, technology-intensive, technology-intensive, and technology-intensive industry; is a strategic pillar of national development; and is an important industry to promote industrial upgrading, pulling economic development. The development of intelligent sensor network technology is an inevitable choice for China to take a new road of industrialization. In today's world, the coordinated development of the environment and the economy is being increasingly valued by countries around the world. Therefore, from a macroscopic point of view, the development of intelligent sensor network technology industry has a very important role to play in improving its international competitiveness. [ABSTRACT FROM AUTHOR]

The main purpose of smart city is to improve the quality of life, improve the efficiency of life, and save the energy resources of life, which is in line with the current global concept of low-carbon environmental protection, energy saving and emission reduction, and ecological sustainable development. Under the process of smart city, the development of urban public space is also committed to meeting the material and spiritual needs of residents' lives. For the system hardware design, the street lamp node, and the concentrator node hardware design, the sensing node is composed of multiple sensors. Its main components are temperature sensor, infrared sensor, sound sensor, and light intensity sensor, which, respectively, realize the collection of temperature, infrared, sound, and light intensity information. The street light sensing system uses multisensor information fusion technology to detect the situation of people and vehicles in real time, so that people and vehicles can turn on the street lights and turn off the street lights of unmanned vehicles. Adjust the street light switch according to the intelligent analysis of the lighting demand scene in order to enhance the accuracy of the system to identify the lighting demand scene. This enables the system to provide safe lighting conditions for urban traffic while saving the power consumption of urban lighting. The sensor system of the street light randomly selects the detection results of temperature and humidity within a certain period of time. The error rate of the final test result is 1/2 of that of the traditional test, and the accuracy is far lower than that of the traditional measurement method. Regarding the lighting system of street lamps and public art in urban public spaces, it is worthy of extensive promotion to control street lamp lighting through sensor information fusion technology and reduce energy consumption. [ABSTRACT FROM AUTHOR]

Sensor technology has been deeply into the sports industry, with the help of sensors to monitoring and collection data for the physical training in real-time. In table tennis, wearable sensors can record the amount of training, movement essentials, and the number of strokes of both hands and assist in the testing and evaluation of table tennis. This paper analyzes the application of wearable sensors in table tennis training activities and the details of signal collection and feature extraction. At the same time, machine learning technology often used to recognize and test table tennis training data, and a support vector machine (SVM) is one of the representative classifiers. Applying the processed signal data to the classification and testing of SVM can effectively identify the movement and evaluate the training effect and athletes' physical fitness. The integration of intelligent sensors and table tennis can effectively improve the evaluation efficiency and quality in the process of teaching and training. [ABSTRACT FROM AUTHOR]

The use of smart sensors and actuators to improve industrial and manufacturing processes is known as the industrial internet of things (IIoT). Innovative economic growth is one of the key elements required for the success of IIoT in contemporary industry. Innovation in IIoT is essential for fostering high-quality economic growth and achieving a competitive edge. This study aims to conduct in-depth research on the development path of an innovative economy based on data mining under the new pattern of double circulation in order to enhance industrial innovation capability, realize the modernization of the industrial chain, and accelerate the development of industrial innovation in IIoT. The first step is to use the five urban agglomerations for path analysis of innovative economic development. The five metropolitan agglomerations' pertinent facts are provided, and their industrial structure's composition and proportion as well as the input and output of innovation are all examined. We then constructed a model for analyzing the link between technological innovation and economic growth. The investigation of the innovative economic development path based on data mining is achieved using the Microsoft time series technique and the expectation-maximization algorithm to examine the data of innovative economic development. The experiment demonstrates that the method proposed in this study has strong data mining stability and ideal data clustering advantages. In IIoT, it can be used to effectively increase data mining's efficiency and innovation capacity for the growth of a knowledge-based economy and creating a new pattern in which innovation and market growth are mutually reinforcing. [ABSTRACT FROM AUTHOR]

Grass damage in the seedling corn field has always been an important factor affecting the growth and development of crops. The existence of grass not only compresses the living space of corn seedlings but also easily causes insect damage. Therefore, it is essential for weeding in the seedling corn field. The existing weeding methods usually use manual or chemical herbicide spraying, which is not only time-consuming and laborious but also inefficient. With the development of artificial intelligence and modern agricultural technology, the use of robots for field weeding has become an effective means, which has attracted more and more attention of researchers at home and abroad. Therefore, based on the full investigation of the development of relevant technologies at home and abroad, this paper carried out the research on the real-time target recognition and ranging method of field weeding robot and proposed a target recognition method of weeding corn at the seedling stage by using an intelligent sensor network and deep learning convolution neural network. Among them, the intelligent sensor is mainly used for target ranging and obstacle avoidance, and CNN is mainly used for target recognition. Taking the images of corn seedlings and weeds in the seedling stage under natural environmental conditions as samples, the migration training is carried out through the depth network model of the COCO data set, and the convolution features are shared by the CNN depth network model and Fast-CNN depth network model. VGG and ResNet feature extraction networks are compared. The experimental results show that the CNN depth network model based on this paper has obvious advantages in rape and weed target recognition. The target recognition accuracy of rape and weeds can reach 87.64%, and the recall rate can reach 80.23%. Compared with other models, it has obvious advantages, which proves the effectiveness of this model. [ABSTRACT FROM AUTHOR]

Summary: With the increasing use of composite materials in modern structures, the defect detection of carbon fiber‐reinforced plastics (CFRP) with ultrasonic waves is always a hot topic. Theoretical and experimental results demonstrate that the out‐of‐plane displacement of A0 mode of Lamb waves is dominant at low‐frequency ranges. Different types of defects including artificial defects, holes and cracks for flat plates, and artificial defects for a curved and stiffened (C&S)–CFRP plate are diagnosed. A shape compensatory matrix is introduced based on the symmetry of the structural features for C&S–CFRP plate. Then the statistical models of different types of defects are established and the probability‐based defect size estimation is carried out. For flat plates, the maximum relative errors are 16.3% for localization and 20.0% for size estimation. For the C&S–CFRP plate, the maximum relative errors are 11.7% for localization and 7.5% for size estimation. Therefore, a level three structural health monitoring system with defect localization and quantification functions is well‐established using PZT sensor layers. [ABSTRACT FROM AUTHOR]

Summary: Especially in urban contexts, the detection of damages in road infrastructures is crucial for their management and efficiency. This entails complex measurement chains. Several solutions were already developed and applied (e.g., high speed monitoring systems or sensor‐based systems), but the emerging smart city and smart road paradigms call for innovations in these fields. A possible solution could be the development of smart, non‐intrusive, and sustainable sensing systems able to perform continuous monitoring, allowing automatic and timely generation of alarms and proper maintenance strategies from Road Management Systems (RMSs). Consequently, this study aims to demonstrate the feasibility of integrating miniaturized sensing systems with high‐speed monitoring systems, to obtain innovative RMSs. In this regard, a specific web‐based platform, the core of a Smart RMS, properly defined to acquire, correlate, and exploit data from several sources was developed. To this end, high‐performance monitoring systems, an innovative sensing system (i.e., based on miniaturized devices and on feature‐ and signature‐based methods), and Micro‐electromechanical systems (i.e., smartphones accelerometers) were used. A new set of indicators has been set up and partly validated in the pursuit of setting out a new paradigm where Pavement Management Systems are supposed to become urban‐oriented and less expensive. Results show that combining traditional and innovative solutions allows providing a comprehensive overview of both the structural condition and the performance of road infrastructures. This information could be used to exponentially improve the efficiency of current RMSs in forecasting the occurrence of damages and in scheduling more effective and sustainable management interventions. [ABSTRACT FROM AUTHOR]

Objective. In order to control the vibration of the beam structure more effectively and improve the safety and availability of the beam structure, an application study of the Lagrange equation for vibration control of smart sensors for power grid monitoring is proposed. The vibration of the beam structure, the displacement of the beam structure under the excitation of seismic acceleration, the response analytical electrical formula, and the displacement response formula of the beam structure under the action of the kinematic force are deduced. The optimal parameters of the beam-TMDI system are given, and the parameter sensitivity analysis is carried out. Then, the control effect of the TMDI system is studied by numerical analysis, and the vibration reduction effect of the TMDI system and the tuned mass damper (TMD) system is compared. Experimental results show that when the mass ratio μ of the TMDI system and the TMD system are both set to a fixed value of 0.005, and the parameter β of the TMDI system is set to 0, namely β = b = 0 , at this time, the TMDI system degenerates into a TMD system. The TMD natural frequency is 14.179 rad/s and the damping ratio is 0.0432 by the DH optimization method, while the TMD natural frequency is 14.1812 rad/s, and the damping ratio is 0.0436 by the augmented Lagrangian optimization algorithm. Conclusion. The vibration displacement response spectrum of a beam structure obtained by the frequency domain method can effectively reflect electricity in the displacement response of a beam structure. The parameters that minimize the vibration response of the beam structure can be accurately obtained by using the augmented Lagrangian parameter optimization method. The sensitivity of the TMDI system is controlled by the inertial device, and the inertial device has a significant impact on its robustness. The vibration reduction performance of the TMDI system is obviously better than in the conventional TMD systems. [ABSTRACT FROM AUTHOR]

Transmission line is a key link in the transmission line, as the main equipment in the grid are located in the field; in the long-term harsh environmental conditions, the line is very easy to appear broken strands, broken, and other defects, which need to be regularly monitored and maintained. The traditional manual line inspection operation is not only harsh, labor-intensive, inefficient, and low precision but also has a great impact on the personal safety of the operators due to the complicated operating conditions. This project mainly focuses on 5G, intelligent sensor network technology, and the application of UAVs in high-voltage line inspection, detection, and maintenance. The developed inspection UAVs can effectively improve the efficiency of line inspection and quickly discover fault points, which brings convenience to the construction of power systems. In the 5G environment, the use of UAV transmission inspection technology for line fault identification can overcome the shortcomings of manual faults, but also timely and comprehensive fault detection, and improve the effectiveness and standardization of monitoring work. [ABSTRACT FROM AUTHOR]

Digital teaching has gradually banned traditional offline teaching, and its advantages are self-evident, but the current digital teaching has certain limitations. Especially in physical education, the key content of physical education is the changes in physical movements; the process is not only complex; the differences between movements are also extremely small; and ordinary digital cannot accurately teach these complex and subtle movements. In this context, this paper mainly studies the application of digitalization of fusion intelligent sensor technology in physical education teaching and combines the characteristics of high precision and recognizable actions of intelligent sensors with digitalization to improve physical education teaching. This paper mainly selects the students majoring in physical education in the second year of school A as the research object to test the effect of digitalization of fusion intelligent sensor technology in physical education teaching. Before the experiment, the research subjects were divided into groups and the T-test was performed, and it was concluded that there was no significant difference between the experimental group and the control group. On this basis, the experimental group and the control group were respectively taught table tennis, gymnastics, martial arts, football, and 100 meters and then tested and tested. The results showed that the experimental group was higher than the control group in table tennis, gymnastics, martial arts, football, and 100 meters test. In the 100 meter test, the boys in the experimental group were 7.13, 6.94, and 6.93 points higher than the control group, and the girls in the experimental group were 2, 1, and 2 points higher than the control group. Overall, after the improved digital teaching, both boys and girls in the experimental group performed better than the control group in table tennis, gymnastics, martial arts, football, and 100 meters. It shows that the effect of the improved digital physical education is remarkable, and it also provides a certain theoretical basis and guidance for the teaching improvement of other courses. [ABSTRACT FROM AUTHOR]

Since the release of the 13th Five-Year Plan, the construction plan of the smart city and Internet of Things (IoT) has once again pushed the sensors into the limelight. In the current situation, whether in China or around the world, the construction of smart communities has become an irreversible trend. In this environment, the sensor as a smart city "bridge" will certainly usher in an industrial explosion. "Smart city" is a new concept and model for urban development. Academics have also explored the concept and theoretical model of smart cities from different perspectives in different forms. With the gradual establishment of the national smart city system, the Internet of Things and other technologies are being widely used to serve other aspects of human social life, such as urban smart communities, intelligent transportation, and intelligent home security systems. The application of technology is gradually penetrating into the life of our human society. In the context of building a smart city, scholars have also brought an unprecedented broader idea. By seeking a reasonable balance between human and urban nature, the concept of "park city," which aims to create a good ecological habitat where people and nature can coexist and develop in harmony, has emerged as China's urbanization process accelerates and the conflict between people and land becomes more and more prominent. As an important factor to promote the harmonious development of humans and nature, the planning and construction of urban parks is an important means to meet the needs of people for a better life and to improve the ecological environment of cities; therefore, the planning and design of urban parks under the concept of "park city" is getting more and more attention. Qingdao, as a typical coastal city, has a mosaic of green areas and urban construction land, so urban ecological parks with the carrier of nonconstruction land exist in large numbers in the city and become a key link in the practice of the "park city" concept. However, while urban ecological parks provide ecological and social services to citizens, their special land use has also led to a series of problems. At present, there is not enough basis for the planning and management of such special parks in Qingdao, and it is not possible to guide the planning and construction of such parks with universal standards, so how to effectively protect and reasonably utilize them to promote the construction of "park city" is an urgent problem to be solved. Based on the above background, this study takes Qingdao urban ecological park as the research object and summarizes the typical problems in the process of planning, construction, and use of this kind of park through relevant data research and extensive studies. Based on this, we propose three aspects, namely, the location and layout of parks at a macro level, the construction of the park system at meso-level, and the design optimization of parks at a micro level. The specific research content includes the following aspects: the first part defines the background, purpose, and significance of the topic and the research object and summarizes and reviews the existing research results at home and abroad, and then proposes the overall idea and framework of the research. The second part analyzes the problems of Qingdao urban ecological parks with the concept of "park city." Based on the connotation and characteristics of the "park city" concept, the impact of the concept on urban parks is analyzed, and the significance of the "park city" concept on the planning and construction of urban ecological parks is clarified based on the special characteristics of urban ecological parks. Finally, we provide the basis for the proposed strategy by combining case studies at different levels. Finally, we propose a better spatial planning for the construction of a park city in Qingdao using smart detection sensors and other means of building a smart city. [ABSTRACT FROM AUTHOR]

The application of traditional feature extraction methods in subway mechanical fault diagnosis has attracted the attention of researchers. Based on the 5G intelligent sensor network signal processing theory, this paper constructs a fault diagnosis model for subway machinery and equipment and analyzes the effectiveness of the information redundancy calculation method proposed in this paper by using the on-site vibration data of subway units. The model is obtained by analyzing the entropy characteristics of the vector vibration signal in each bearing section of the subway machinery and equipment. The entropy value quantitatively reflects the vibration complexity of the rotor in the section from different angles and solves the problem of data quantitative analysis. In the simulation process, based on the 5G intelligent sensor network signal and the fuzzy mean clustering information fusion method, in the process of identifying the fault state of the subway unit, a relatively optimistic identification result was obtained. The experimental results show that, no matter whether the Gaussian kernel or the polynomial kernel is selected, the number of kernel principal components whose cumulative contribution rate is greater than 0.85 decreases with the increase of the kernel parameter, and the fault identification support rate is 85%, that is, the diagnosis results of 3 sets of sample data are nonrolling element faults, which significantly improves the performance of the bearing. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth analysis and study of traditional corridor construction and tourism system planning using intelligent sensing networks. This paper proposes a node deployment and coverage optimization method for HWSNs based on particle swarm optimization emperor butterfly algorithm and extreme learning machine, with the objectives of reducing node deployment cost, reducing coverage redundancy and voids, improving monitoring area coverage, and increasing the agility of data processing by 15.03%, efficiency by 12.15%, and stability by 11.31%. In the paper, the static parameter table of the sensor is stored inside the smart sensor or stored on the IoT cloud platform to achieve automatic registration of the static parameters of the smart sensor. According to the characteristics of dynamic parameters of smart sensors, an autonegotiation algorithm is designed to realize the dynamic parameters of smart sensors to be determined by autonegotiation and complete registration without human intervention, which includes four parts: utility function, concussion protocol, proposal generation strategy, and response mechanism. The intelligent sensing system is composed of three parts: smart sensor, IoT gateway, and IoT cloud platform; the functions of each part in the system are analyzed, and MAR and STM32F4 are selected as the core board of smart sensor and IoT gateway. And for the problems of time-consuming and tedious access to smart sensors in the system, we propose to study the adaptive sensor UART serial port parameters and automatic registration of sensor parameters. Based on the analysis of the current environment and resource characteristics of the traditional corridor, the current situation of ecotourism resources of the traditional corridor is sorted out, an evaluation index system is established to evaluate ecotourism resources, and the current development basis is analyzed from four aspects: natural environment, tourism resources, product development, and infrastructure. Finally, to promote the sustainable development of the ecotourism corridor in the Chengdu section of the southern foot of the Qinling Mountains, the planning and development strategies to match the ecotourism corridor in the Chengdu section of the southern foot of the Qinling Mountains are proposed in four aspects: transportation organization optimization, ecological landscape enhancement, support system construction, and regional tourism development. [ABSTRACT FROM AUTHOR]

With the development and application of intelligent nervous system, intelligent control system represented by machine vision has been promoted and used in various fields, including manipulator control system. Mechanical arm is the main actuator of robot and other mechanical components, but the application environment of mechanical arm is extremely complex, and with the development of industrial process, the sensitivity of technology and operating system for its arbitrary posture operation is also steadily improved. Based on the above problems, this paper designs a manipulator control system based on machine vision theory and orthogonal parallel six-dimensional force sensor to meet the working requirements of manipulator in high-precision environment. Systems hardware are consisted by machine vision structure and six dimensions of force sensor. The practical application effect of the model was verified by collision detection experiment. The relevant research can provide theoretical guidance and practical reference for the research of manipulator control field. Dance and practical application reference for the research of manipulator control field. [ABSTRACT FROM AUTHOR]

English cross-cultural communication is crucial in global trade, technology circulation, and cultural exchange, because not only is the difference between different cultures in language, but the knowledge requirements for cross-cultural communication are even higher. However, in the decades of English teaching development, is still difficult for the research on cross-cultural communication teaching to get rid of the test-taking mode, which makes it difficult for most people to apply it to real life after learning English. In order to solve the problem that English learning is not thorough enough in cross-cultural communication and lacks practicality, this paper starts with the selection of intelligent image sensors, based on CMOS image sensors, sets the sensor parameters and characteristics, and builds multimedia teaching equipment at the same time. Through these methods, it forms an English teaching system for cross-cultural communication and proposes classroom teaching procedures and methods. After four months of teaching experiments with this system, the educated students have made remarkable progress in cross-cultural communication. They have improved to varying degrees in four aspects: cross-cultural communication skills, mastery of cultural knowledge, accuracy of English expression, and ability to adapt to cultural situations. Among them, the accuracy of English expression improved the most in different cultures, and the average score increased by 16 points. In terms of comprehensive performance, the average level of students improved by 17% from preexperiment to postexperiment. Therefore, the teaching system in this paper can help students to have better performance in cross-cultural English communication. [ABSTRACT FROM AUTHOR]

Judging from the current online teaching practice of higher physical education basic theory courses, there are many problems such as lagging theory, unclear models, confusing mechanisms, outdated methods, and lack of resources, resulting in the low interest of students. Basic theory courses are studied. The phenomenon of skipping class and being late is common, which seriously affects the improvement of the comprehensive quality of physical education students. Therefore, it has become an urgent problem for the majority of higher sports workers to develop learning resources for basic physical education courses and realize the innovation and application of the teaching mode of higher sports online courses. Compared with the traditional teaching method, the students' physical education teaching performance is improved by about 30%, and their personality is fully developed. Linking teacher performance to changes in student performance parameters can effectively improve teacher motivation. This shows that under the background of intelligent sensor network, the physical education network course can effectively improve the teaching efficiency. [ABSTRACT FROM AUTHOR]

Water vapor is an important part of the atmospheric system and plays a key role in the global water cycle, energy balance, extreme weather, and long-term climate research. It is of great significance to fully grasp the spatial and temporal distribution characteristics of water vapor for the study of various climate models. The traditional water vapor detection technology mainly has the problems of low spatial-temporal resolution and poor capture of fine changes. In recent decades, the rapid development of intelligent sensor technology and Walktrap algorithm has made it a reliable means to obtain and analyze the characteristics of water vapor. However, the acquisition of atmospheric water vapor requires two key parameters: station pressure (PS) and weighted average temperature (TM). Since most traditional stations are mainly used for geodetic research, few are equipped with meteorological intelligent sensors. Therefore, based on the traditional meteorological stations, this paper improves the equipment of the traditional stations and endows the traditional stations with precise and sensitive intelligent sensor equipment to obtain the regional water vapor characteristic data of El Niño and a province in the east of China, combined with the advanced Walktrap algorithm, in order to use the above equipment and technology to realize the correlation analysis of El Niño and regional water vapor characteristics. The results show that through the water vapor information calculated by 12 coastal stations, the relationship between monthly average PWV and seawater surface temperature is studied, and the correlation degree between the relevant characteristics is as high as 94.6%. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet today, the number of various mobile communication terminals has increased rapidly, and 5G has also appeared. With the popularization of 5G technology, new development trends have emerged in more business scenarios. For example, in the 5G era, there will be a lot of room for development in traditional business applications. On this basis, we design a resource allocation that utilizes DQN technology to achieve 5G high-band services. First of all, considering the characteristics of boundary operation, the connection between the base station and the user, the transmission capacity of the base station to the user, and other factors are used as judgment factors, the total energy efficiency is maximized as the goal, and the requirements of mobile users are the constraints. Based on customer service quality assurance, QoS assurance is the object. On the basis of DQN, the convex optimization method is used to solve the given maximum transmission energy between nodes, and DQN is used for iterative iteration to obtain the optimal node and optimal power distribution. Through simulation experiments, the results show that the algorithm has high learning efficiency and convergence and can effectively optimize the allocation of network resources under the premise of ensuring the mobile terminal's requirements for service quality. [ABSTRACT FROM AUTHOR]

With the rapid development and iteration of CMOS image sensor and other related technologies, in addition to the more advanced technology, the corresponding sensor also integrates the image signal processing system, so it is a leap for the image acquisition system. Based on this, this paper will take the visual intelligent sensor produced and developed by a semiconductor company as the hardware to complete the hardware design of the image acquisition and transmission system. In addition to the core processor of the system, this paper will also use audio and video processing chips and image data processing chips for collaborative analysis, so as to ensure the processing capacity and processing quality of the image acquisition system at the hardware design level. Compared with the traditional system, at the corresponding system software level, in order to further improve the quality of video signal processing, this paper creatively proposes a switch weighted median filter denoising algorithm, which has a significant impact on the processing of random impulse noise in video and the overall image filtering. At the same time, the algorithm also protects the edge details of the video image, thus improving the authenticity of the image and video. Based on this, this paper has built a verification platform based on supporting hardware and corresponding algorithms. Through hardware debugging and system operation analysis, when the operating frequency of the visual intelligent sensor is 100 MHz, the set acquisition depth is 10 bits, and the corresponding system collects data at a rate of 251 fps per second. In terms of the corresponding performance, the iteration number of the algorithm proposed in this paper is about 40% more than that of the traditional algorithm in unit time. In terms of the corresponding algorithm performance, the algorithm proposed in this paper is about 30% higher than the traditional algorithm. In terms of the efficiency of the corresponding image and video acquisition system, the algorithm proposed in this paper is about 44% higher than the traditional algorithm. Based on this, the system proposed in this paper has obvious advantages over the traditional algorithm. [ABSTRACT FROM AUTHOR]

This paper uses intelligent sensors to build a data network, collects information about listed enterprises in all aspects, performs frequency statistics and semantic analysis based on the financial domain lexicon on information related to listed enterprises, and introduces big data variables into a nonlinear support vector machine early warning model of enterprises combined with financial indicators. This paper introduces online reviews as big data indicators based on financial early warning theories and methods. Suitable financial indicators and big data indicators are selected for the financial early warning model to filter the available indicators. The prediction results of only financial indicators are compared with the prediction results of incorporating big data indicators. Quantify the comment information related to enterprises on the platform through sentiment classification and statistics on the number of comment information posted. The cost-sensitive support vector machine is used as the base classifier of the improved AdaBoost algorithm to build a dynamic imbalance warning model. To address the problems of large complexity and computation of unbalanced big data classification, high reliance on a priori knowledge, and classification performance to be improved, the classification and detection method of the intelligent sensor data network is proposed. By introducing migration learning, the problem of knowledge acquisition and training efficiency of high-dimensional complex data feature extraction in a big data environment is effectively solved, and the network performance is optimized by a conjugate gradient descent algorithm. Through simulation experiments, the prediction accuracy of the model for positive class samples is significantly improved after the nonequilibrium processing, the recall rate of the network model reaches 84.15%, and the prediction accuracy of the network model under different time steps reaches more than 90%. The experiment proves that the model can send out financial alert information more quickly and efficiently and accurately when a financial crisis may occur, relative to the traditional financial forecasting methods. [ABSTRACT FROM AUTHOR]

This paper adopts a 5G smart sensor network to design intelligent sensing of environmental information in aquaculture waters, and in this way, we study the significance of optimizing the intelligent sensing model. Microservice architecture and big data analysis and processing will become the innovation of technology in the new era. In this regard, it is very important to study a set of aquarium monitoring and management system based on microservice architecture, so that users can understand the changes in the environment in the aquarium, manage a large amount of historical data quickly, and help users solve existing problems. The design pattern of MVVM architecture, unified management of the life cycle, and microservice architecture make the system very easy to expand and maintain; secondly, it realizes the automatic monitoring of the aquarium environment while making multilevel storage optimization for a large amount of data and realizes fast retrieval of a large amount of data; finally, the system can make intelligent regulation in the environmental control of ornamental fish and can automatically adapt to the environmental requirements of different ornamental fish. Finally, the system can make intelligent control in the environment control of ornamental fish and can automatically adapt to the needs of different ornamental fish environments. After testing its server concurrent processing capability, communication stability, and Android performance, the system runs stably. This paper analyzes the architecture module reuse rate and protocol performance; the module reuse rate analysis results show that the various types of modules in the architecture are reused many times and have strong generality, which effectively reduces the development cycle and development cost, and the architecture can quickly and efficiently integrate hardware units such as sensors, actuators, and communication modules; the protocol performance analysis shows that the designed protocol has high communication performance and meets the architecture. The protocol performance analysis shows that the designed protocol has high communication performance and meets the requirements of the architecture in terms of the transmission rate. At the same time, the edge computing module of the architecture further shifts down the edge computing tasks and lays the hardware foundation for the subsequent implementation of sensor data anomaly detection, data encryption transmission, and other functions. [ABSTRACT FROM AUTHOR]

5G intelligent sensor network technology realizes the perception, processing, and transmission of information. It forms the three pillars of information technology together with computer technology and communication technology and is an important part of the Internet of Things technology. The 5G smart sensor network is a wireless communication module added to the sensor nodes, and a wireless communication network is formed by a large number of stationary or movable sensor nodes in the form of self-organization and multihop transmission. This paper proposes a keypoint feature extraction method based on deep learning, which can extract keypoint local features for matching. This method uses the convolutional network structure, which is pretrained based on the Siamese network structure and then adjusted to the ternary network structure to continue training to improve the accuracy. This paper proposes a high-art visual communication image classification based on multifeature extraction and classification decision fusion. In the data preprocessing stage, the correlation alignment algorithm is performed on the datasets of different domains (source domain and target domain) to reduce the difference in spatial distribution, and then, a multifeature extractor is designed to extract artistic visual communication images and spatial information. In the process, the multitask learning method is introduced to jointly train the networks of multiple data sets to reduce the degree of overfitting of the model, solve the problem of insufficient labeled samples in the target domain data set, and affect the classification accuracy of high-art visual communication images. Finally, the classification results are obtained through the fusion of voting decisions. The experimental results show that the advantage of this framework is that it utilizes the artistic visual communication image and spatial structure information from the source and target scenes, which can significantly reduce the dependence on the number of labeled samples in the target domain and improve the classification performance. In this paper, a dual-channel deep residual convolutional neural network is designed. The multiple convolution layers of the residual module in the network use hard parameters to share, so that the deep feature representation on the joint spatial spectrum dimension can be automatically extracted. The features extracted by the network are transferred to maximize the auxiliary role of the labeled samples in the source domain and avoid the negative transfer problem caused by the forced transfer between irrelevant samples. [ABSTRACT FROM AUTHOR]

The traditional way of museum exhibition is physical exhibition, which is essentially restricted by the venue, time, space, and display conditions and is no longer applicable to the display form of modern museum exhibits. Therefore, with the development of a large number of modern technologies such as information technology, wireless sensor technology and image processing technology, the way of museum cultural relics display also tends to be more intelligent and intelligent. Based on this, this paper will set up corresponding gateway nodes, routing nodes, and corresponding terminal nodes for each node of the corresponding museum based on the intelligent wireless sensor network technology; transmit the audio impact information of the corresponding museum exhibits through the terminal nodes; and automatically send the node short address information of the corresponding information to the corresponding analysis end in real time for analysis and processing, and then based on the information characteristics of the corresponding exhibits, at the same time, combined with virtual fusion and human-computer interaction technology, a set of augmented reality application system for tourist mobile terminal is developed. Abandoning the disadvantages of traditional museum display methods, this paper creatively designs the corresponding museum heritage intelligent display workflow based on intelligent wireless sensor network through C/S architecture and finally realizes the enhancement effect of virtual reality on tourism terminals. In order to verify the superiority of the intelligent display mode of museum cultural relics based on intelligent wireless sensor network proposed in this paper, this paper compares it with the traditional display mode. The experimental results show that this paper has obvious advantages in the display comprehensiveness, visitor satisfaction, and display effectiveness, which further improves the effect of museum cultural relics display. [ABSTRACT FROM AUTHOR]

Smart instruments, sensors, and AI technologies are playing an important role in many fields such as medical science, Earth science, astronomy physics, and space study. This article attempts to study the role of sensors, instruments, and AI (artificial intelligence) based smart technologies in lunar missions during navigation of trajectories. Lunar landing missions usually divide the power descent phase into three to four sub-phases. Each sub-phase has its own set of initial and final constraints for the desired system state. The landing systems depend on human competencies for making the most crucial landing decisions. Trajectory planning and designing are very significant in lunar missions, and it requires inputs with precision. The manual systems may be prone to errors. In contrast, AI and smart sensor-based measurements give an accurate idea about the trajectory paths and make appropriate decisions where manual systems may turn into disasters. The manual systems are either pre-fed or have manual controls to guide the trajectory. For autonomous landing problems, trajectory design is a very crucial task. The automated trajectories play a vital role in the measurement and prediction of landing state parameters of the space rocket. Nowadays, sensors, intelligent instruments, and the latest technologies go hand in hand to devise measurement methods for accurate calculations and make appropriate decisions during landing space rockets at the designated destination. Space missions are very expensive and require huge efforts to design smart systems for navigation trajectories. This paper attempts to design all possible candidates of reference navigation trajectories for autonomous lunar descent by employing 3D non-linear system dynamics with randomly chosen initial state conditions. The generated candidates do not rely on multiple hops and thus exhibit an ability to serve autonomous missions. This research work makes use of smart sensors and AI federated techniques for smartly training the system to serve the ultimate purpose. The trajectories are simulated in an automated simulating environment to perform exhaustive analyses. The results accurately approximate the trajectories analogous to their numerical counterparts and converge to their measured final state estimates. The generation rate of feasible trajectories measures the accuracy of the algorithm. The algorithm's accuracy is near 0.87 for 100 sec flight time, which is reasonable. [ABSTRACT FROM AUTHOR]

The field bus control system based on field bus is a multidisciplinary emerging technology with intelligent sensor, automatic control, computer, communication, network, and other technologies as the main content. All have broad application prospects. In this paper, the CAN bus technology in the field bus is combined with the sensor, and the OPC technology is used to realize the acquisition of the underlying data in the application and control system. In this paper, the industry agglomeration model, the leading industry model, the urban gravity model, and its index are calculated, and the agglomeration effect, leading industry, and urban gravity of the sports industry in two provinces and one city in a certain region are judged from the micro level. Location entropy and relative density of technical elements, scale effect function, product income elasticity coefficient, market share, and other economic indicators are compared and described, and the sports leading industry and sports industry growth pole area are deduced, and the corresponding industrial development stage is judged. We analyze the strength of the polarization effect and diffusion effect of the sports industry in the above-mentioned regions. Economic factors, social factors, and traffic road factors are selected to analyze the driving force of the distribution of gymnasiums. According to the results of binary and multivariate correlation analysis, it is found that the gross domestic product is very important to the development of the sports industry and directly determines the regional demand for sports. There are sports industry forms that are clustered in areas with high economic levels; population density is selected as the representative of social factors, and the research results show that population density has a strong correlation with the number of sports industry distributions. The reason is that the development of the sports industry ultimately depends on demand. It is difficult to form a concentrated demand for related sports activities in areas with small population density, so the distribution of the sports industry is also very small; and the transportation factor is the main factor affecting economic development and population flow. Therefore, it also indirectly affects the spatial layout of the sports industry. [ABSTRACT FROM AUTHOR]

Intelligent sensor networks are a current hot topic in the field of communication and are widely used in subject education, quality education, and home security monitoring. In today's world of increasingly diverse services and growing information needs, wireless communication systems require more information to better understand and analyse the observed objects. In this context, this paper presents a study on the identification and practice of English reading modality based on intelligent sensor networks. The paper divides English reading modality into two parts: semantic modality and situational modality, and its system consists of three main modules: English reading article data collection, English article data semantic analysis, and English reading article situational picture feedback module. Finally, a practical study is carried out on the basis of this application, and it is concluded that the development and future prospects of this application are considerable. [ABSTRACT FROM AUTHOR]

In today's rapidly developing information technology, computers are becoming faster and more accurate with more and more sensors, and the birth of new technologies such as cloud computing, big data, the Internet of Things, and artificial intelligence has turned the whole world upside down. At the same time, there is a growing interest in more natural and harmonious ways of human-machine interaction, and many people are devoting themselves to research in this area. Its nodes can be distributed arbitrarily in the target environment and can obtain relevant information about the surrounding environment. In a multinode wireless sensor network, the nodes organize themselves with each other through special protocols to realize their respective functions. In this paper, the application of a multisubject collaboration model to sports training is analyzed, and its superiority is verified by means of examples. [ABSTRACT FROM AUTHOR]

Innovation and entrepreneurship is the soul of entrepreneurship, and realizing the integration of innovation and entrepreneurship education and collaborative experiment platform in colleges and universities is an important way to promote the development of entrepreneurship and facilitate higher quality employment. In this paper, the authors propose the 3D-IVF intelligent sensor overlay algorithm wisdom laboratory system structure and application mode through in-depth investigation of collaborative laboratory, build the macroenvironment and construction demand of collaborative experiment management platform of innovation and entrepreneurship education, comprehensively analyze the current situation, problems, and effectiveness of its integration of innovation and entrepreneurship education and professional education. In addition, we explore the operation mechanism of its organizational system, curriculum system, management system, and platform construction in the practice of integrating innovation and entrepreneurship education with professional education. The average coverage rate of the 3D-IVF-based 3D coverage algorithm is 92.15%, and the deployment time is 2.5857 s; the average coverage rate is improved by 0.76%, and the deployment time is reduced by 0.1712 s. Design FIR filter using MATLAB and Hilbert transform filter, generate HDL code, and implement it on FPGA. Use the designed low-pass and band-pass filters to filter the 100 KHz square wave signal sampled by the ADC. When the number of iterations of the algorithm reaches 30, the coverage rates of the four-node deployment strategies will stabilize, i.e., the forces between the nodes of the 3D-VF-based 3D coverage algorithm of this paper. The equilibrium state is reached. We use resources and real-time experimental data sharing to realize collaborative design among team members, further optimize the curriculum structure of innovation and entrepreneurship education, adopt diversified forms of curriculum implementation, and implement a diversified curriculum evaluation system, etc. [ABSTRACT FROM AUTHOR]

Building microrenovation refers to the building renewal method implemented through partial demolition, repair, preservation, function replacement, renovation, improvement, protection, decoration, and activation under the premise of maintaining the current construction pattern basically unchanged. City information modeling (CIM) technology provides a three-dimensional space floor with all elements for refined city governance and smart city construction; the data layers of smart sensor network (SSN) technology can aggregate multisource data by docking with facility layers and can classify integrated data resources. On the basis of summarizing and analyzing the previous research results, this study expounded the research status and significance of data analysis of building microrenovation, elaborated the development background, current status, and future outlooks of CIM and SSN, introduced the methods and principles of the aggregation and segmentation of information resources and the aggregation and interconnection of multisource data, performed the feature extraction and data storage of the original buildings, conducted the scheme design and information classification of microrenovation, implemented the data extraction and model construction of building microrenovation based on CIM and SSN, performed the data analysis of building appearance improvement, conducted the data analysis of building function replacement, implemented the database establishment and data analysis of building microrenovation based on CIM and SSN, and finally carried out a case application and its result analysis by taking a middle school two-story teaching building as an example. The study results show that the CIM technology can provide buildings' actual data information such as geometry, physics, structure, and rules for building microrenovation; the SSN technology greatly reduces a lot of work caused by the rewiring of control system in the construction of building microrenovation. The combination of CIM and SSN can effectively connect various entity elements in building microrenovation to form a theme library through various data spatial relationships and spatial coding, realizing the connection of spatial data and nonspatial data in multiple dimensions. The data analysis of building microrenovation based on CIM and SSN includes multiple dimensions of achievements, processes, resources, characteristics, and applications, which is more in line with CIM integration applications and is easier for realistic information exchange and sharing applications. [ABSTRACT FROM AUTHOR]

With the widespread use of UAVs, it is gradually difficult for single UAV to meet the needs of increasingly complex scenarios. At the same time, the problems of low autonomy and high dependence on control stations in central UAV cluster networks are gradually highlighted. In this paper, we analyze the theoretical conditions of network topology establishment and network connectivity, design a series of UAV distributed cluster automation control algorithm frameworks, and achieve certain research results, taking the distributed clusters of flight self-organizing networks as the background and using mathematical tools such as algebraic graph theory and random geometry to build a vibration sensor array model based on multiple intelligent sensor management. Based on this, a distributed connectivity maintenance algorithm based on the importance of nodes is designed to realize the "self-healing" of the flight's self-organizing network. This study also improves the Mavlink flight control communication protocol customization and Zigbee wireless networking mode design to solve the UAV swarm communication link collision problem. Compared with the existing distributed spectrum estimation-based node importance algorithm, the proposed algorithm further analyzes the topological changes caused by the removal of associated edges by failed nodes and the reconstruction of new associated edges between neighboring nodes, so that the theoretical results are closer to the actual topological dynamics of the flight self-organizing network. [ABSTRACT FROM AUTHOR]

Intelligent sensor technology is a professional course of communication, electronics, information, computer, and other related majors and is a course of expansion and extension of computer network (Ministry of Education of the People's Republic of China, 2001). It is connected with the Internet by using information sensing device to realize the intercommunication of time and space, people, machines, and things. Intelligent sensor technology is an advanced subject; it contains a lot of ideological elements; in teaching, it should be according to the characteristics of the subject, set the goal of ideological education, to design the content of the ideological education, improve the ideological education, to carry on the course of the construction of the ideological practice and exploration, and guide students to set up the correct world outlook, the outlook on life, and values (The State Council of the Central Committee of the Communist Party of China, 2010). Integrating the elements of thinking and politics into the teaching of intelligent sensor technology and organically combining thinking and politics teaching with professional teaching is conducive to realizing the nurturing function of professional teaching and establishing an all-round curriculum system, so that professional and thinking and politics courses can go hand in hand and produce synergy. With the concept of "civic education as the soul, general education as the root, professional education as the foundation," the strategy of integrating civic education from "point to surface (Wei, 2015), from history to the present, from inside to outside" is summarized, and a system of assessment, evaluation, and continuous improvement is established. It also establishes a system of assessment, evaluation, and continuous improvement. The strategy has proved to be effective in combining ideological and political education with moral education, enabling students to develop theoretical, institutional, and cultural confidence on the road to socialism with Chinese characteristics (Ministry of Education of the People's Republic of China, 2004). [ABSTRACT FROM AUTHOR]

In our teaching, teachers cannot understand students' learning situation deeply, they can only judge students' mastery of English through usual homework situation and test scores, but this is not an accurate way of judging. In addition, the combination of the Internet of Things and sensors and their application in the teaching field has made preliminary progress, and the phenomenon of Internet of Things smart sensors being used in teaching is becoming more and more common. For this reason, this article proposes that the brainwaves of the students' learning process can be sent to a remote server for persistent transmission through the Internet of Things. It also collects brainwave data to match the learning structure in the student management system and finally establishes a machine learning model for analyzing the student's learning process to analyze the student's learning situation. This article discusses and constructs a brain wave collection terminal based on the intelligent sensor of the Internet of Things for English listening test. Experiments have shown that the brainwave collection terminal in this article has achieved an accurate grasp of how students learn in the English listening test. Its accuracy is as high as 89.34 ols pay more and more attention to the education of their children. However, the understanding of the students' learning situation only stays on the surface, and it is impossible to have a deep understanding of the students' specific learning situation. In addition, the development of science and technology has become more and more mature. The Internet of Things and smart sensors are gradually being used in the teaching field. Although they provide convenience for daily teaching activities, their research and development in this area is not very mature, and there are still many problems. In addition, products used in the field of brainwave sensors for teaching have appeared. However, the data cannot be obtained and the computer wave model cannot be established or modified. Therefore, the technology for applying brain wave sensors to teaching is not mature. Therefore, this article needs to study the brainwave collection terminal of English listening test, and provide technical support for more accurate grasp of students' learning situation. [ABSTRACT FROM AUTHOR]

Analog-to-digital converters (ADCs) are moving toward high speed and high resolution for low-cost testing. Based on the theory of intelligent sensor network, this paper designs a low-cost test solution for high-precision ADC chips, which solves the problems related to signal integrity. It mainly includes the following: designing an appropriate circuit connection scheme, planning an appropriate PCB stack-up structure, formulating detailed layout and wiring constraints, etc., and building a high-speed ADC test platform to obtain static and dynamic performance; based on the existing instruments in the laboratory, the effects of different signal sources, different input powers, and the presence or absence of filters on the dynamic performance of high-speed ADCs are studied. In the simulation process, the HyperLynx simulation platform is used to design and simulate the signal integrity of the high-speed acquisition board. Combined with the relevant theoretical knowledge of the signal integrity of high-speed digital circuits, the signal integrity analysis and simulation of the ADC module circuit and the DDR3 high-speed memory circuit are carried out, respectively. The results show that, taking the histogram method as a reference, when the optimal 30 windows are selected, the integral nonlinearity (INL) error of the proposed method is 0.12 LSB, the highest sampling frequency is up to 5GSps, and 61440 sampling points are required. The time is reduced by about 30% compared with the excitation error identification and removal (SEIR) method, which effectively improves the low-cost test effect of the ADC chip. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth study and analysis of the localization and tracking of multiple targets in soccer training using a distributed intelligent sensor approach. An event-triggered mechanism is used to drive the acoustic array sensors in the distributed acoustic array sensor network, which solves the problem of increased communication load caused by frequent communication of microphones and effectively reduces the communication load between microphones as well as the energy consumption of the acoustic array sensor network. By designing a suitable state estimation equation for the acoustic source target and fully utilizing the measurement and state estimation information of its nodes as well as the state estimation information of neighboring nodes, the next moment state of the acoustic source target can be accurately predicted. A correlation filtering tracking algorithm based on multiscale spatial co-localization is proposed. In the proposed algorithm, the tracker contains a total of several subfilters with different sampling ranges. Then, this paper also proposes a collaborative discrimination method to judge the spatial response of the target image samples of each filter and jointly localize the target online. Based on this, this paper further explores the potential of correlation filter tracking algorithms in complex environments and proposes a robust correlation filter tracking algorithm that fuses multiscale spatial views. The cross-view geometric similarity measure based on multiframe pose information is proposed, and the matching effect is better than that based on single-frame cross-view geometric similarity; to solve the problem of player appearance similarity interference, a graph model-based cross-view appearance similarity measure learning method is further proposed, with players in each view as nodes, player appearance depth features as node attributes, and connections between cross-view players as edges to construct a cross-view player graph. The similarity obtained by the graph convolutional neural network training is better than the appearance similarity calculated based on simple cosine distance. [ABSTRACT FROM AUTHOR]

The purpose is to conform to the development of the times and study a new way of urban community network governance. First, the ways of governance, social governance, urban community governance, and urban community network governance are discussed. Next, the intelligent sensor function of Internet of things (IoT) technology is applied to realize the communication between IoT and urban community network governance. An urban community network governance system based on an intelligent sensor network is established. The system combines Kalman filtering to realize the dynamic monitoring of the mobile Application (APP) of the urban community residents and can update and process the collected information in real time. Finally, a survey is conducted on the satisfaction of residents of a community in Jiaozuo City, Henan Province, in 2019 on community network governance under the IoT. The efficiency and ability of urban community network governance under different network modes are compared and analyzed. The results show that the community has improved the needs of community residents through IoT network governance in multiple aspects, but the community service level of culture is not high. The community needs to strengthen the network publicity of community culture to improve the cultural level of the community. Meanwhile, in urban community governance, the comprehensive governance ability of the network governance model under network organization management is the highest, with an average of 50.93%. It shows that the established system is beneficial to the construction of urban community network governance. This exploration provides the direction for the IoT urban community network governance and constructs the urban community network governance system based on the intelligent sensor network. [ABSTRACT FROM AUTHOR]