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This paper sets out a simple and affordable method for the manufacture of a water sensor involving drawing marks on paper using a flexible pencil. The sensor indicates flexible endurance variations upon exposure to water or a drying surface. It can be employed for the detection of water substances in ethanol. In addition, an experiment suggests the usefulness of the sensor for the sensing of breath. This shows the immense potential applications of the sensor for wearable and also health-inspecting electronic gadgetry. [ABSTRACT FROM AUTHOR]

Ink element is one of the unique cultural symbols in China, which has profound cultural heritage and spiritual connotation. Animation is a form of art design, and it is a good way to convey and express information in an intuitive artistic image. The use of ink and wash elements for animation design has important value and significance in the start of Chinese culture. It can develop animation design, innovate animation creation forms, and can highlight Chinese style. In this article, we have studied the creation process, creation format, and creation method of using angular digital technology to make ink VR design. It combines art and technology and combines practical experience with wireless sensors. Based on HTML5 interactive technology, we have studied its advantages more deeply, better introduced the theme of ink element animation, solved the problem of realizing ink effect in 3D virtual environment, and better introduced ink animation in virtual reality environment. During the production process, students will learn how to simulate digital objects, render ink and wash effects, build a virtual environment in the later stage, and explore the knowledge, operation, and meaning of ink and wash elements and the animation design process. This paper studies the wireless sensor and HTML5 interactive technology and applies it to the design of ink and wash animation VR, aiming to promote its vigorous development and application. [ABSTRACT FROM AUTHOR]

H. Malon et al. in their paper entitled "Application of Low-Cost Sensors for the Development of a Methodology to Design Front-End Loaders for Tractors" developed a methodology to design tractor front-end loaders with a view of obtaining their accurate design during the bucket loading process. Sensors and applications in agricultural and environmental monitoring have become more essential to (1) farming practices, (2) crop growth estimation, (3) environmental changes, (4) global and regional observations on the earth's environment, and (5) monitoring/environmental analysis. M. Li et al. in their paper entitled "Response Mechanism of Cotton Growth to Water and Nutrients under Drip Irrigation with Plastic Mulch in Southern Xinjiang" studied the effects of water and nutrient control measures on cotton plant height, stem diameter, biomass, seed yield, and soil moisture under an irrigated plastic mulch production system. [Extracted from the article]

The combination of sensor and neural network (NN) is an important development direction for intelligent sensors. This paper discusses the application of NN in sensor signal processing, introduces the basic principles, and illustrates the application method with examples. Based on the analysis of sensor's abnormal signal processing, a diagnostic scheme based on CNN is proposed. At each current moment, NN is trained by the latest historical dataset of fixed length to complete the forecast of the next moment. The confidence interval is determined by the model's residual of NN. Moreover, this paper proposes a signal noise reduction and compression method of multisensor system using the CNN. A multisensor sequence of noisy output signal and target's true value are used as samples for network training, and the trained network is tested with test samples. The effectiveness of this method is verified by simulation of several typical function approximations. The results strongly suggest the advantages of CNN method for sensor abnormal signal processing and provide a solid foundation for the reliable use of sensors in such type of problems. [ABSTRACT FROM AUTHOR]

This paper reports the deposition of Zn on glass substrates using the physical vapor deposition (PVD) method, followed by an annealing process to grow ZnO for gas-sensing applications. Surface morphologies were characterized using scanning electron microscopy, which revealed nanowire shape. The diameter of the wire was about 35 nm. In addition, X-ray diffraction analysis demonstrated that the ZnO nanowire possessed a wurtzite structure with an orientation of (002). Three types of resistive gas sensors with a spiral-square and two-comb electrode geometries were designed, fabricated, and tested for their ethanol vapor-sensing properties. The experimental results show that the sensor with square-spiral electrode has the sensitivity of 43% for 2,000 ppm of ethanol vapor at 200°C, while the sensor with a comb electrode shows the sensitivity of 32% at the same conditions. Also, two sensors with different dimensions of comb-shaped electrodes showed the same sensitivity, as both the width and the distance between the fingers change simultaneously in the larger comb-shaped electrode. The response time for the comb electrode is shorter than the square-spiral type, and the recovery time is almost independent of the electrode geometry. Therefore, the optimal structure should be selected based on the application. [ABSTRACT FROM AUTHOR]

Due to its large network scale, open communication environment, unstable wireless network, and other characteristics, it is extremely vulnerable to attacks and causes security problems, resulting in the collapse of the Internet of Vehicles system. The application of the Internet of Vehicles is becoming more and more extensive, but there are still problems such as information security and privacy leakage in the Internet of Vehicles. Through the analysis of the security threats and privacy protection requirements faced by the Internet of Vehicles system, this paper mainly studies information security, vehicle identity privacy, and location privacy in the process of Internet of Vehicles wireless communication. Therefore, it is urgent to conduct research on the information security and privacy protection issues of the Internet of Vehicles. This paper discusses the research on the security and privacy protection of the consensus algorithm for the Internet of Vehicles based on wireless sensors, compares and analyzes the wireless sensor data privacy protection protocols based on sharding technology, Tongtai encryption technology, and perturbation technology, and selects an optimized Kalman consensus filter. The algorithm is applied to the node information exchange of the sensor network, and two filters (low pass and band pass) are used to unify the observations and covariance of the network. Estimation of the sensor network state with and without data packet loss, the effect of system estimation error under different packet loss rates, data privacy protection algorithm performance, vehicle network data communication volume, and confusion factors on algorithm efficiency and the node energy consumption was compared and analyzed. Based on the application of wireless sensors, the estimation error and inconsistency estimation error of the algorithm in this paper finally converge to about 0.5, and both can maintain good stability and have good robustness. In addition, the communication volume of the algorithm in this paper is about 30% of the SCPDA algorithm. The Kalman consensus filtering algorithm reduces the amount of confusing data sent, improves privacy protection, and also achieves lower communication overhead. [ABSTRACT FROM AUTHOR]

With the development and progress of society, science and technology have entered the field of view of scholars at home and abroad. As the science and technology with the biggest potential in recent years, wireless sensor network has been involved in many scientific fields. This paper aims to study the wireless sensor modeling optimization algorithm of artificial intelligence neural network. In this paper, a WSN data fusion algorithm (DFRMP) based on regression model prediction is proposed, and the four algorithms of SLR, PAQ, TINA, and DFRMP are compared. The experimental results of this paper show that when the mean square error and mean absolute error are not much different, the data transmission rate of DFRMP algorithm is the smallest. When the absolute error threshold is 1, the data transfer rates of these four algorithms are 0.033, 0.0327, 0.035, and 0.017, respectively. This shows that the DFRMP algorithm proposed in this paper has superior performance. [ABSTRACT FROM AUTHOR]

In order to solve the practical application of the continuously developing remote sensing technology in urban planning, this paper proposes a method of temporal and spatial evolution of urban green spatial pattern based on GIS remote sensing information. Based on the Landsat Image data of the main urban area of Xi'an from 2000 to 2012, different classification methods are used to extract the urban green space information and compare the accuracy. The classification results with high accuracy are selected to analyze the temporal and spatial evolution law of urban green space and the change of landscape pattern in the study area. In this paper, the change of vegetation coverage can be divided into five levels: significant degradation: <-0.006; slight degradation: -0.006~-0.002; stable: -0.002~0.002; slight improvement: 0.002~0.006; and significant improvement: >0.006. The results of this paper prove that this method can be used to understand and evaluate the ecological consequences of urbanization and improve our quality of life. At the same time, it can provide basic information for decision-making. [ABSTRACT FROM AUTHOR]

Optical microcavity, which is based on light-matter interaction, has the advantage of high sensitivity as a sensor. It has been widely used in the security, medicine, and environment fields these years. Detection of atoms and molecules at the nanoscale is one of the practical usages of optical microcavity. In this paper, we focus on a ultra-high-quality factor optical microcavity of whispering gallery mode (WGM) to detect the explosive of cyclotrimethylenetrinitramine (RDX). To demonstrate the high sensitivity of the method, we detect the solution at different concentrations. Different types of optical microcavities are suitable for the detection of different matters. Considering the effects of the results and the simplicity of operation, we use silica microspheres chiefly. This is the first time to detect the explosive using highly sensitive optical microcavity. The detection of pure explosives will be helpful for establishing the database of the explosives. [ABSTRACT FROM AUTHOR]

Artificial intelligence is the study of the laws of human intelligent activities, the construction of artificial systems with certain intelligence, and the study of how to make computers perform tasks that required human intelligence in the past. Basic Theories, Methods, and Techniques. With the maturity of the highly integrated hardware technology, the rapid development of sensor technology provides a material basis for the study of sports states based on smart terminals, and the mature model method theory provides a theoretical research basis for the research. The working principle of the sensor is to convert the specific measured signal into a certain "available signal" according to a certain rule through the sensitive element and the conversion element and output it to meet the requirements of information transmission, processing, recording, display, and control. In order to deeply study the application of artificial intelligence sensor theory in athlete training physical condition testing, this article uses theoretical analysis method, formula image combination method, and real person survey method, collects samples, analyzes artificial intelligence sensor, and streamlines the algorithm. In studying the accuracy of smart sensors on athlete training tests, a total of 9 athletes, 4 females, and 5 males are selected. Each experimenter performed three actions of running, jumping, and squatting 10 times, with an interval of more than 20 s. The results showed that the recognition accuracy of running in this paper was 98.51%, and the recognition accuracy of jumping and squatting was 92.59% and 93.33%; we have achieved more than 92% recognition rate for the three kinds of actions. On further study of the real-time performance of the sensor, the average response time of the algorithm is the average value obtained from 80 experimental records in this paper. The average response time of the algorithm proposed in this paper is within 1.5 s. Since the falling process occurs within 2.1 s, the recognition algorithm proposed in this paper has high real-time performance. It is basically realized that starting from the theory of artificial intelligence sensors, a high-precision sensor that can be applied to athlete training is designed. [ABSTRACT FROM AUTHOR]

The application of multisource sensors to drones requires high-quality images to ensure it. In simultaneous interpreting two or more multisensor images based on the same scene or target, the image obtained by the UAV sensor is limited by the imaging time and the shooting angle. The images obtained may not be aligned in the spatial position, thus affecting the fusion effect. Therefore, different sensor images must be registered before image fusion. During the shooting process of the drone imaging sensor, imaging angle, and environmental conditions, the obtained various sensor images will have rotation, translation, and other deformations in the spatial position so that they do not reach the spatial position. Therefore, it is impossible to directly perform image fusion directly. Therefore, before the multisensor image fusion, the image registration process must be completed to ensure that the two images are aligned in space. This paper analyzes the principles; based on the principles of the Powell search algorithm and improved walking algorithm, an algorithm combining Powell and improved walking algorithm is proposed. This paper also studies several traditional image neutrosophic fusions. The algorithm combines the fusion optimization algorithm proposed in this paper greatly reduces the calculation speed and improves the performance of the optimization algorithm and success rate. [ABSTRACT FROM AUTHOR]

Providing security to the healthcare data stored in an IoT-cloud environment is one of the most challenging and demanding tasks in recent days. Because the IoT-cloud framework is constructed with an enormous number of sensors that are used to generate a massive amount of data, however, it is more susceptible to vulnerabilities and attacks, which degrades the security level of the network by performing malicious activities. Hence, Artificial Intelligence (AI) technology is the most suitable option for healthcare applications because it provides the best solution for improving the security and reliability of data. Due to this fact, various AI-based security mechanisms are implemented in the conventional works for the IoT-cloud framework. However, it faces significant problems of increased complexity in algorithm design, inefficient data handling, not being suitable for processing the unstructured data, increased cost of IoT sensors, and more time consumption. Therefore, this paper proposed an AI-based intelligent feature learning mechanism named Probabilistic Super Learning- (PSL-) Random Hashing (RH) for improving the security of healthcare data stored in IoT-cloud. Also, this paper is aimed at reducing the cost of IoT sensors by implementing the proposed learning model. Here, the training model has been maintained for detecting the attacks at the initial stage, where the properties of the reported attack are updated for learning the characteristics of attacks. In addition to that, the random key is generated based on the hash value of the data matrix, which is incorporated with the standard Elliptic Curve Cryptography (ECC) technique for data security. Then, the enhanced ECC-RH mechanism performs the data encryption and decryption processes with the generated random hash key. During performance evaluation, the results of both existing and proposed techniques are validated and compared using different performance indicators. [ABSTRACT FROM AUTHOR]

With the development of information technology in recent years, people have integrated English reading with information technology, which is conducive to improving the ability of English reading and cultivating the humanistic spirit of college students. This paper aims to study how to play the role of English reading in the cultivation of college students' humanistic spirit based on the embedded sensor system. This paper proposes an embedded speech recognition technology algorithm and a speech recognition technology algorithm based on dynamic time warping. Speech recognition technology is conducive to the improvement of English reading efficiency, as well as improving the reading interest of college students. The experimental results of this paper show that the speed of English development has become faster and faster in recent years, from 30% in 2011 to 55% in 2020. Although it fluctuates in the middle, the overall trend is upward. People pay more and more attention to English, and the proportion of English reading is gradually increasing, from about 20% in 2011 to about 41%. Therefore, it can be known that the importance of English reading is getting higher and higher, and it is necessary to cultivate the humanistic spirit of college students through English reading. [ABSTRACT FROM AUTHOR]

The process of sports action recognition involves not only the change of motion speed but also the change of motion attitude, so it is necessary to carry out coordinate system transformation and attitude behavior recognition in combination with the actual situation, and inertial sensors play an important role in this process. Moreover, this paper combines inertial sensors to construct a sports action recognition and analysis system. In addition, based on the STA/LTA-AIC vibration wave picking method, an improved STA/LTA-AIC method based on wavelet packet decomposition is proposed to automatically identify the first arrival of vibration waves. Through the experimental research, it can be seen that the sports action recognition system based on an inertial sensor proposed in this paper has a good performance in sports action feature recognition. [ABSTRACT FROM AUTHOR]

A foot placement of inertial sensors is commonly used for heel-strike (HS) and toe-off (TO) event detection. However, in clinical practice, such sensor placement may be difficult or even impossible due to the deformity of patients' feet. The first contribution of this paper is a new algorithm for HS and TO event detection for cases when the sensors are placed on the lateral malleolus. Such sensor placement allows gait analysis in patients with foot deformities. In addition, the placement of the sensor directly on the wide bone surface of the lateral malleolus ensures secure fixation of the sensor during walking. The proposed algorithm is based on deep neural networks, which can be easily adapted (by retraining the neural networks) for analysis of various pathological gait patterns. It is especially important in clinical practice when the number of possible pathological gait patterns is very large. The algorithm proposed in this paper was implemented in a new wearable system for the clinical gait analysis. The second contribution is a validation of this new wearable system. The performance of both proposed algorithm and gait analysis system was evaluated against a reference treadmill system where a capacitance–based pressure platform was used. A total of 117 healthy volunteers participated in the comparison (62 males and 55 females, age 24–55 years, height 162–183 cm). They were asked to perform 2 min walking trials with different speed. Mean accuracy ± precision was – 0.021 ± 0.091 s for gait cycle, 0.589 ± 1.144 steps/min for cadence, – 0.051 ± 0.544 % for stance phase, – 0.37 ± 0.649 % for single support, 0.296 ± 0.711 % for double support, 0.132 ± 0.561 % for load response, and 0.106 ± 0.661 % for preswing. Limitations of the proposed algorithm and its compassion with state-of-the-art algorithms were discussed. [ABSTRACT FROM AUTHOR]

The number of new malware has been increasing year by year, and the construction of the malware sample space is also changing with time. The existing research studies on malware detection mainly focus on how to improve detection performance and how to effectively detect the evasion malware and improve the detection performance of adversarial samples, while ignoring the concept drift of malware samples over time. The concept drift of the sample will lead to the aging of the detector model, thus resulting in the reduction of the detection accuracy. Concerning this problem, we proposed a malware sample generator based on auxiliary classifier GAN, according to the malware samples generated, to train the detection model. In this paper, the API call sequence is used as a feature to train the improved generative adversarial network, and the trained generator model is used to generate samples that simulate concept drift for the purpose of training detection models. Meanwhile, using the detection results of the detector as the training set again, the generator is used to generate samples, so as to repeatedly train the detection model and improve the anticoncept drift performance of the monitoring model. In this paper, real malware samples and generated samples are used to train the detector model, and malware samples are segmented in a linear time sequence as test sets to verify the effectiveness of the proposed method. The results reveal that the framework can maintain good detection accuracy and effectively mitigate the aging of the detector in a longer time dimension. [ABSTRACT FROM AUTHOR]

In order to improve the information fusion effect of sports training intensity, this paper analyzes the information fusion process of Kinect sensor. In order to prevent the simulation platform from exceeding its working space and ensure that the sports athletes obtain a more realistic sense of motion, the adaptive washout algorithm can change the adaptive parameters online in real time according to the input athlete's acceleration and angular velocity and the current motion state of the simulation platform. Moreover, this paper uses Kinect as the input device and combines the human node model to identify the features of training intensity information. After constructing an intelligent system, the performance of the system of this paper is verified. The research results show that the sports training intensity information fusion method based on the Kinect sensor proposed in this paper has a good effect in sports training intensity information fusion. [ABSTRACT FROM AUTHOR]

A multichannel autoencoder deep learning approach is developed to address the present intrusion detection systems' detection accuracy and false alarm rate. First, two separate autoencoders are trained with average traffic and assault traffic. The original samples and the two additional feature vectors comprise a multichannel feature vector. Next, a one-dimensional convolution neural network (CNN) learns probable relationships across channels to better discriminate between ordinary and attack traffic. Unaided multichannel characteristic learning and supervised cross-channel characteristic dependency are used to develop an effective intrusion detection model. The scope of this research is that the method described in this study may significantly minimize false positives while also improving the detection accuracy of unknown attacks, which is the focus of this paper. This research was done in order to improve intrusion detection prediction performance. The autoencoder can successfully reduce the number of features while also allowing for easy integration with different neural networks; it can reduce the time it takes to train a model while also improving its detection accuracy. An evolutionary algorithm is utilized to discover the ideal topology set of the CNN model to maximize the hyperparameters and improve the network's capacity to recognize interchannel dependencies. This paper is based on the multichannel autoencoder's effectiveness; the fourth experiment is a comparative analysis, which proves the benefits of the approach in this article by correlating it to the findings of various different intrusion detection methods. This technique outperforms previous intrusion detection algorithms in several datasets and has superior forecast accuracy. [ABSTRACT FROM AUTHOR]

Uncrewed aerial systems (UASs) are becoming very popular in the domain of water resource mapping and management (WRMM). Being a cheaper and quicker option capable of providing high temporal and spatial resolution data, UAS has become a much sought-after platform for remote sensing. Still, their application in the field is in its early stage. This paper encompasses basic concepts of UAS, different payloads and sensor technologies available, various methodologies for its application in WRMM, different software available, and challenges associated with them, thus presenting a comprehensive review of multiple applications of UAS in different sub-domains of water resources. From cryosphere, rivers and lakes, and coastal areas to sub-surface water, as well as from water quality to wastewater management, the authors have discussed various applications of uncrewed aerial vehicles. At the end of the paper, the authors have identified the issues posing problems in the wider implementation of UAS in WRMM. Also, the future scope of the UAS in WRMM has been discussed. [ABSTRACT FROM AUTHOR]

This paper studies a semiconductor wireless sensor system, which is composed of a semiconductor wireless sensor sampling circuit, gas-sensitive signal alarm and wireless transmitting circuit, and wireless radio frequency signal receiving circuit. The system is suitable for wireless monitoring of hydrogen fluoride gas in chemical plants. The hydrogen fluoride gas sensor is designed, integrated, and classified according to the polarity and size of the sensor output signal. The signal processing circuit of the sensor output signal is made with an integrated design. This paper developed a simulation experimental system for the wireless monitoring network characteristics of toxic hydrogen fluoride gas and completed the monitoring system's sensor characteristic calibration and accuracy comparison simulation experiment, the communication distance test experiment of the communication system, and the research experiment on the influence of environmental humidity on the sensor characteristics of the monitoring system. In terms of software, the workflow of network nodes has been optimized. Since the structure of the wireless sensor network is not exactly the same in different application fields, the toxic gas monitoring system based on wireless sensor networks must focus on extending the network's life cycle. Without affecting the normal operation of the system, distributed compressed sensing can greatly extend the service life of the system. Therefore, this subject combines the compressed sensing technology developed in recent years with the air monitoring system for the processing of transmission data, in order to achieve the purpose of further reducing the energy consumption of the system. The simulation experiment demonstrated that the lmF neural network combined with gas sensor array technology can realize qualitative identification, quantitative analysis of single gas, and quantitative analysis of mixed combustible gas. The research work in this area also provides a new way to further combine the miniature hydrogen fluoride gas sensor unit with sensor technology, integrate the hydrogen fluoride gas sensor unit and the electronic tag, and expand the wireless application of the gas sensor. [ABSTRACT FROM AUTHOR]

Aiming at the problems of inaccurate interaction point position, interaction point drift, and interaction feedback delay in the process of LiDAR sensor signal processing interactive system, a target tracking algorithm is proposed by combining LiDAR depth image information with color images. The algorithm first fuses the gesture detection results of the LiDAR and the visual image and uses the color information fusion algorithm of the Camshift algorithm to realize the tracking of the moving target. The experimental results show that the multi-information fusion tracking algorithm based on this paper has achieved higher recognition rate and better stability and robustness than the traditional fusion tracking algorithm. [ABSTRACT FROM AUTHOR]

In this paper, passive wireless sensor network technology is used to conduct in-depth research and analysis on the monitoring of the in-transit health status of railway transportation. Safety detection sensors, train communication networks, and other onboard devices organically constitute a train sensor network with comprehensive state sensing, information aggregation, and business collaboration. First, this paper analyzes the three-layer architecture of the sensing layer, network layer, and application layer of the train operation status safety detection sensor network. Based on analyzing the feasibility of Ethernet application in an onboard communication network, ring, multiring, and ladder-type sensor network structure schemes are designed. Next, the real-time performance and reliability of various sensor network structures are analyzed. First, the characteristics of bandwidth demand, priority, and importance of information transmission of each monitoring object are analyzed; then, the system utility function is established according to the bandwidth usage efficiency and communication demand of information transmission of each monitoring object; finally, the bandwidth allocation optimization model is solved by using a particle swarm optimization algorithm, and the remaining bandwidth resources are dynamically rationed on demand while ensuring the complete transmission of basic information of each monitoring object. Rail transit technology has profoundly affected and changed the travel and lifestyle of residents in the new era and will provide strong infrastructure support for the smooth advancement of new urbanization construction and development strategies. The remaining bandwidth resources are dynamically allocated on the premise of ensuring the complete transmission of basic information of each monitoring object. In summary, this paper presents a complete study on the optimal allocation of resources for wireless sensor networks dedicated to rail transit condition monitoring, starting from the perspectives of routing protocol optimization, data fusion rate optimization, and bandwidth dynamic allocation optimization to achieve the efficient use of limited resources. The achieved research results enrich the research content in the field of wireless monitoring of rail transit system conditions at home and abroad in a certain sense and will also provide theoretical and empirical support for practical practitioners, managers, and scholars of service condition monitoring of rail transit systems. [ABSTRACT FROM AUTHOR]

YOLO-Tiny is a lightweight version of the object detection model based on the original "You only look once" (YOLO) model for simplifying network structure and reducing parameters, which makes it suitable for real-time applications. Although the YOLO-Tiny series, which includes YOLOv3-Tiny and YOLOv4-Tiny, can achieve real-time performance on a powerful GPU, it remains challenging to leverage this approach for real-time object detection on embedded computing devices, such as those in small intelligent trajectory cars. To obtain real-time and high-accuracy performance on these embedded devices, a novel object detection lightweight network called embedded YOLO is proposed in this paper. First, a new backbone network structure, ASU-SPP network, is proposed to enhance the effectiveness of low-level features. Then, we designed a simplified version of the neck network module PANet-Tiny that reduces computation complexity. Finally, in the detection head module, we use depthwise separable convolution to reduce the number of convolution stacks. In addition, the number of channels is reduced to 96 dimensions so that the module can attain the parallel acceleration of most inference frameworks. With its lightweight design, the proposed embedded YOLO model has only 3.53M parameters, and the average processing time can reach 155.1 frames per second, as verified by Baidu smart car target detection. At the same time, compared with YOLOv3-Tiny and YOLOv4-Tiny, the detection accuracy is 6% higher. [ABSTRACT FROM AUTHOR]

Vehicle trajectory data is in high demand for transportation research due to its rich detail. Lane information is an important aspect of trajectory data, which is typically obtained using sensors such as cameras or LiDAR, which are able to extract road lane features. However, some sensors for trajectory tracking (e.g., MMW radar sensors) are unable to provide lane information. Vehicle detection and trajectory tracking systems based on these sensing technologies can integrate with lane information through manual calibration during initial installation, but this process is labor-intensive and requires frequent recalibration as the sensors gradually become deviated by wind and vibration. This has posed a challenge for trajectory tracking, particularly for real-time applications. To address this challenge, this paper proposes a method for estimating lane-level road geometrics using microscopic trajectory data. The method involves segmenting the trajectory points using direction vectors and clustering them and fitting a series of cluster center points. The mean error (ME) of the distance between the estimated result and the ground truth reference is used to measure the accuracy of the lane-level road geometrics estimation in different conditions. Results show that when the average trajectory data includes at least approximately 30 points per meter in each segment, the ME is always less than 0.1 m. The method has also been tested on MMW wave radar data and found to be effective. This demonstrates the feasibility of our approach for dynamic calibration of road alignment in vehicle trajectory tracking systems. [ABSTRACT FROM AUTHOR]

With the development of sensor technology and the Internet of Things (IoT) technology, the trend of miniaturization of sensors has prompted the inclusion of more sensors in IoT, and the perceptual feedback mechanism among these sensors has become particularly important, thus promoting the development of multiple sensor data fusion technologies. This paper deeply analyzes and summarizes the characteristics of sensory data and the new problems faced by the processing of sensory data under the new trend of IoT, deeply studies the acquisition, storage, and query of sensory data from the sensors of IoT in e-commerce, and proposes a ubiquitous storage method for massive sensory data by combining the sensory feedback mechanism of sensors, which makes full use of the storage resources of IoT storage network elements and maximally meets the massive. In this paper, we propose a ubiquitous storage method for massive sensing data, which makes full use of the storage resources of IoT storage network elements to maximize the storage requirements of massive sensing data and achieve load-balanced data storage. In this paper, starting from the overall development of IoT in recent years, the weak link of intelligent information processing is reinforced based on the sensory feedback mechanism of sensor technology. [ABSTRACT FROM AUTHOR]

This article analyzes the method of reading data from inertial sensors. We introduce how to create a 3D scene and a 3D human body model and use inertial sensors to drive the 3D human body model. We capture the movement of the lower limbs of the human body when a small number of inertial sensor nodes are used. This paper introduces the idea of residual error into the deep LSTM network to solve the problem of gradient disappearance and gradient explosion. The main problem to be solved by wearable inertial sensor continuous human motion recognition is the modeling of time series. This paper chooses the LSTM network which can handle time series as well as the main frame. In order to reduce the gradient disappearance and gradient explosion problems in the deep LSTM network, the structure of the deep LSTM network is adjusted based on the residual learning idea. In this paper, a data acquisition method using a single inertial sensor fixed on the bottom of a badminton racket is proposed, and a window segmentation method based on the combination of sliding window and action window in real-time motion data stream is proposed. We performed feature extraction on the intercepted motion data and performed dimensionality reduction. An improved Deep Residual LSTM model is designed to identify six common swing movements. The first-level recognition algorithm uses the C4.5 decision tree algorithm to recognize the athlete's gripping style, and the second-level recognition algorithm uses the random forest algorithm to recognize the swing movement. Simulation experiments confirmed that the proposed improved Deep Residual LSTM algorithm has an accuracy of over 90.0% for the recognition of six common swing movements. [ABSTRACT FROM AUTHOR]

In recent years, many deep learning algorithms based on seismic signals have been proposed to solve the moving target recognition problem in unattended ground sensor systems. Despite the excellent performance of these deep networks, most of them can only be deployed on cloud-based devices and cannot be deployed on low-power hardware devices due to the large network size. Second, since seismic signals are affected by the terrain, employing only seismic signals as reconnaissance means for unattended ground sensors cannot achieve multiterrain-type adaptability. In response, this paper proposes an MFC-TinyNet method facing a multiterrain. The method adds depthwise separable convolutional layers to the network, which effectively reduces the size of the network while keeping the target recognition accuracy constant, and solves the problem that the model is difficult to deploy on low-power hardware. It also uses the Mel-frequency spectrum feature extraction method to fuse sound and seismic signals to improve the accuracy of the model's moving target recognition on a multiterrain. Experiments demonstrate that the method can combine the two advantages of the small network model and multiterrain applicability. [ABSTRACT FROM AUTHOR]

For the long delay in the communication field of the unmanned robot, which seriously affects the accurate evaluation of the operation status of the UAV, this paper introduces the multi-information fusion sensor into the field of distributed UAV nest communication. By using the multi-information fusion sensor to fuse the attitude data information of the UAV flight, the delay of the information obtained in the communication process is reduced. The multi-information fusion sensor needs to fully consider the delay problem in the communication field during the use of the UAV nest. By using the acceleration data information to make up for the shortcomings of the delay, relatively accurate and high-precision UAV motion evaluation results can be obtained. According to the simulation of the UAV using the multi-information fusion sensor during the flight, the method proposed in this paper is verified on the UAV system. After comparing the simulation and test results, it can be seen that the method used in this paper can effectively improve the application efficiency of multi-information fusion sensors in the communication field of distributed UAV nests, and has a high reference value for data information analysis. [ABSTRACT FROM AUTHOR]

Driven by the market economy, the city's infrastructure has been continuously improved, and the living standards of the people have been continuously improved. The need for the integrated development of public art and urban environmental design has become one of the important tasks in the entire urban construction work. However, in the integrated development, public art and urban environment, as two subjects, have different development histories and complex influencing factors. Combining this information for effective analysis is a computationally and processing intensive task. This paper uses the data fusion method in wireless sensors to study the fusion development of public art and urban environment design and understand the historical development and various characteristics of public art and urban environment design. And we apply it in fusion analysis experiments, use wireless sensors for information collection, use data fusion methods for analysis, and compare with traditional methods. The experimental results show that the wireless sensor data fusion method is used in the research of the fusion development of public art and urban environment design. The average value of associated fusion degree reaches 84.08% and the average value of balanced fusion degree reaches 80.60%, which has certain effectiveness. Popularizing it in the current stage of urban construction can highlight the city's personality and style and enhance the city's cultural taste and modern cultural atmosphere. [ABSTRACT FROM AUTHOR]

With the wide application of sensors, people's behavior in life has been transformed into data resources. The emergence of big data can more effectively deal with a large amount of information generated. Network big data is increasingly applied to the analysis of economic problems. By analyzing the common phenomenon of price discrimination in life, we can better solve the differences between buyers and sellers. Because both businesses care about the number of vested interests, it is necessary to find a way to balance the two psychologically. Sellers feel that they have made a profit, while buyers feel that they have not lost, and then they will feel that this is a fair deal. In fact, this is not absolutely fair, only their own ideas. The main purpose of this paper is to analyze the economic phenomenon of price discrimination. On this basis, this paper proposes to use the combination of big data that netizens pay attention to and network sensors to detect price changes under the background of big data. Through the analysis of the collected sample data and related economic models, the experimental data show that when p > v , if buyers choose to trade at this time, they will bring negative material returns to themselves. There was a significant negative correlation between second-class buyers and whether they accepted the transaction P < 0.01 . In the case of bringing negative material returns to themselves, vulnerable buyers are more likely to choose to refuse the transaction. [ABSTRACT FROM AUTHOR]

In order to solve the application problems in motor automation control, a motor automation control system based on sensor technology is proposed. Sensor technology has been widely used in the field of automation. Therefore, when studying sensor technology, we should also study its specific application in the process of motor automation control. This paper briefly introduces the sensor technology in electromechanical automation control, discusses the application of this technology in various fields, and analyzes the development trend of sensor technology in electromechanical automation control in the future. The experimental data show that in the motor starting phase, the sensorless SRM using the observer can quickly identify the given speed, quickly approach the given speed in 0.05 seconds, and make the motor run faster and reach a stable state. The application of sensor technology has greatly improved the quality and production efficiency of mechanical manufacturing. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of electric inspection robot navigation and positioning, an improved SVM algorithm was proposed to improve the accuracy of inspection. The research focuses on sensor calibration technology, lane line detection and robot positioning technology, obstacle detection and tracking technology, and substation road scene understanding technology. The results show that the radar measurement results have great fluctuation and deviation due to the existence of noise, but the results are smoother after EKF estimation. Secondly, the accuracy of the improved SVM classifier in this paper is much higher than that of the traditional method, and the improvement effect is obvious. [ABSTRACT FROM AUTHOR]

In multitarget tracking, the issue of sensor control is a challenging problem in theoretical analysis and calculation. In this paper, we study the sensor control strategies for multitask planning based on information criteria and propose two sensor control strategies according to the two different task objectives. Initially, we propose a sensor control strategy to improve the overall multitarget tracking performance within the partially observed Markov decision process (POMDP) framework, where the reward function is calculated by the Bhattacharyya distance between the prior and the posterior multitarget densities. In this strategy, we present a target-oriented multi-Bernoulli (TOMB) particle sampling method to approximate the multitarget density and then derive the solution of Bhattacharyya distance in detail. Subsequently, as another important contribution of this paper, we propose a threat-based sensor control strategy, which is still solved under the information theory where the goal is to prioritize multiple threat targets and then to track preferentially the maximum threat target. These strategies are finally used to optimize the sensor trajectory for range-bearing multitarget tracking. [ABSTRACT FROM AUTHOR]

Skin disease is the major health problem around the world. The diagnosis of skin disease remains a challenge to dermatologist profession particularly in the detection, evaluation, and management. Health data are very large and complex due to this processing of data using traditional data processing techniques is very difficult. In this paper, to ease the complexity while processing the inputs, we use multilayered perceptron with backpropagation neural networks (MLP-BPNN). The image is collected from the devices that contain nanotechnology sensors, which is the state-of-art in the proposed model. The nanotechnology sensors sense the skin for its chemical, physical, and biological conditions with better detection specificity, sensitivity, and multiplexing ability to acquire the image for optimal classification. The MLP-BPNN technique is used to envisage the future result of disease type effectively. By using the above MLP-BPNN technique, it is easy to predict the skin diseases such as melanoma, nevus, psoriasis, and seborrheic keratosis. [ABSTRACT FROM AUTHOR]

Decision making on transformer insulation condition based on the evaluated incipient faults and aging stresses has been the norm for many asset managers. Despite being the extensively applied methodology in power transformer incipient fault detection, solely dissolved gas analysis (DGA) techniques cannot quantify the detected fault severity. Fault severity is the core property in transformer maintenance rankings. This paper presents a fuzzy logic methodology in determining transformer faults and severity through use of energy of fault formation of the evolved gasses during transformer faulting event. Additionally, the energy of fault formation is a temperature-dependent factor for all the associated evolved gases. Instead of using the energy-weighted DGA, the calculated total energy of related incipient fault is used for severity determination. Severity of faults detected by fuzzy logic-based key gas method is evaluated through the use of collected data from several in-service and faulty transformers. DGA results of oil samples drawn from transformers of different specifications and age are used to validate the model. Model results show that correctly detecting fault type and its severity determination based on total energy released during faults can enhance decision-making in prioritizing maintenance of faulty transformers. [ABSTRACT FROM AUTHOR]

This paper presents a fiber optic sensor for the detection of corrosion processes that occur on metal surfaces. In the proposed sensor design, a sensing fiber with an inscribed high-order Bragg grating is attached to the observed metal surface. A broadening of high-order Bragg grating spectral characteristics is observed and used to detect small and highly localized longitudinal strain variations that occur along the fiber as a result of corrosive rust flakes' formations beneath the sensing fiber. The proposed approach provides a straightforward fabrication method, the possibility for unobtrusive mounting, including mounting of the sensor beneath the corrosion-protective layers, and consistent corrosion detection yields. [ABSTRACT FROM AUTHOR]

The effects of the Faraday rotation mirror on the all-fiber optic current sensor (AFOCS) are studied in this paper. The reflectivity degradation of FRM with the long-term operation, the misalignment of the optical axis between FRM and the sensing fiber, and the influence of temperature fluctuation on FRM are modeled and simulated in this paper. The effect of temperature is the biggest obstacle to the application of the Faraday rotation mirror, while other factors can be easy to overcome by the data processing method. The experiments are designed and realized for verification. The temperature fluctuation range is related to the measured electric current to satisfy the required accuracy for IEC 60044-8 class 0.2S. [ABSTRACT FROM AUTHOR]

In order to solve the complex environment in the process of vehicle driving and the complexity of self-vehicle structure, intelligent vehicles are prone to rear end collision, lateral collision, and other safety accidents in the presence of tall trees, mountains, and other road environments, endangering the safety of people on board. According to parameters such as the speed of the vehicle, the movement of the blind spot, and the relationship between the vehicle and the blind spot, the model is based on the safety mode of the preceding vehicle. Based on the static obstacles that may exist in the sensing blind area, a sensor sensing blind area safety distance model is established. Based on the possible dynamic obstacles, the active collision avoidance algorithm based on the sensor perceived blind area is studied and simulated. The experimental results show that the selected sensor sensing blind area active collision avoidance controller can well adapt to a variety of special and emergency working conditions, can accurately complete the accurate control of sensor sensing blind area active collision avoidance, and avoid collision accidents to the greatest extent. Compared with the control group, the system designed in this paper can avoid more than 80% of the collision scenes compared with the previous anticollision system. It provides a reference for the future research of sensor sensing blind area-related topics and sensor sensing blind area active collision avoidance system. To a certain extent, it can improve the ability of intelligent vehicle environmental perception and reduce the incidence of rear end collision accidents. [ABSTRACT FROM AUTHOR]

An IoT system often consists of many sensors to collect data in different aspects. Meanwhile, all these sensors describe the IoT system's functional status, to which it belongs. The correlations between subsystems are always emphasized for a complex system that contains several IoT subsystems. At the same time, there are still no good ways to calculate these types of correlations since that (1) multiple sensors describe an IoT system as a matrix while the correlation between matrices cannot be calculated by the traditional methods (i.e., vector ways such as Pearson correlation coefficient) and (2) AI methods such as neural networks were introduced to resolve this problem; however, these black-box approaches cannot explain the mathematical mechanisms, and lots of memory or time are consumed. This paper proposed a novel approach named the matrix-oriented correlation computing method (MOCC) to learn the correlations between IoT systems. The critical problem of this proposed method is calculating the correlation between two curved surfaces, which are modeled as matrices, since an IoT system often contains many sensors which characterize different aspects of this system and continuously generate data in time series. By our MOCC method, the correlation or interaction between any two subsystems can be accurately measured, which means that we can predict the state of a system by its most important related system. Missing data value prediction based on our MOCC method is also presented in this paper. We verified the efficiency and effect of our proposed method via a satellite, a typical IoT system consisting of massive sensors, and the experimental result was proved to outperform existing methods. [ABSTRACT FROM AUTHOR]

The main purpose of the object detection process is to determine the category of the scene object and use the display 3D and 3D frame size. At present, in the case of 3D object detection, we can extract more accurate features by learning a large number of data, and this deep learning network has good results, but there is a very big problem, including the error of input information, extraction error, and so on. Therefore, solving the above problems has become an important direction to promote the rapid development of 3D target detection technology. This paper mainly studies the deep learning wireless sensor technology and also studies the deep learning infrared and visible image fusion. At the same time, based on the introduction of wireless sensor technology and research status, this paper summarizes the existing algorithms. Texture image classification is a more important visual cue in life. Because it will be affected by light intensity, noise size, image scale, and so on. This makes the classification and feature extraction of image scale and texture image more difficult. To solve these problems has become a hot topic of computer vision research in recent years. The shape of the point cloud is completed by using the 3D target detection method to complete the algorithm research. The radar point cloud is extracted by the 3D target detection method, and the radar point group of the overall shape of the object is obtained. The principal component analysis algorithm is used to extract the principal features of the radar point cloud with the complete shape of the object, and the more accurate 3D target frame is obtained after feature adjustment. [ABSTRACT FROM AUTHOR]

The power of wireless network sensor technologies has enabled the development of large-scale in-house monitoring systems. The sensor may play a big part in landslide forecasting where the sensor linked to the WLAN protocol can usefully map, detect, analyze, and predict landslide distant areas, etc. A wireless sensor network comprises autonomous sensors geographically dispersed for monitoring physical or environmental variables, comprising temperature, sound, pressure, etc. This remote management service contains a monitoring system with more information and helps the user grasp the problem and work hard when WSN is a catastrophic event tracking prospect. This paper illustrates the effectiveness of Wireless Sensor Networks (WSN) and artificial intelligence (AI) algorithms (i.e., Logistic Regression) for landslide monitoring in real-time. The WSN system monitors landslide causative factors such as precipitation, Earth moisture, pore-water-pressure (PWP), and motion in real-time. The problems associated with land life surveillance and the context generated by data are given to address these issues. The Wireless Sensors Network (WSN) and Artificial Intelligence (AI) give the option of monitoring fast landslides in real-time conditions. A proposed system in this paper shows real-time monitoring of landslides to preternaturally inform people through an alerting system to risky situations. [ABSTRACT FROM AUTHOR]

Pipa has been widely used as a wooden musical instrument as early as the Han period. It mainly relies on human fingers to pluck the strings to make sounds. The timbre of the pipa also has a very strong national characteristic. Understanding the meaning of words, phrases, and sentences requires tone recognition. For tone identification, the fundamental frequency conveys the most unique information. This paper summarizes the influence of the pipa's characteristics, technology and skills of the player, and the player's factors on the timbre of the pipa performance. In addition, given the problems of low accuracy and high data packet loss rate in the existing pipa playing tone recognition methods, this paper proposes a pipa string playing tone recognition algorithm based on wireless sensors. According to the sensor stage and weight-corresponding characteristics, on the premise of minimizing the total mean square error, the optimal weight corresponding to the sensor measurement value is found according to the adaptive mode. The unbiased estimated value of the measured value of each node is obtained through iterative calculation. The Euclidean distance between the measured value and the estimated value of each sensor obtained by the normalization process is used as the adaptive weighted recognition weight. It completes the conversion of the adaptive weighted recognition algorithm structure and the binary recognition result. The experimental results show that the proposed method has good tonal recognition accuracy in the pipa playing environment. [ABSTRACT FROM AUTHOR]

Reinforced concrete structures are widely used in modern buildings such as buildings, bridges, and tunnels. However, the structural damage caused by reinforcement corrosion seriously affects the service life of the buildings. Because the corrosion of reinforcement in concrete is a very slow process, the continuous detection of reinforcement corrosion is very important. In view of the slow development of steel corrosion detection equipment in China, which cannot meet the application requirements of concrete structures in China, wireless monitoring technology has more advantages than traditional monitoring technology. In this paper, combined with wireless transmission technology, a concrete reinforcement corrosion detection system based on wireless sensor network is designed. Embedded sensors based on the principle of electromagnetic coupling are used to collect steel corrosion information. It is determined that the 16-bit ultralow power consumption microcontroller MSP430F149 is the main control chip, the AD9850 based on DDS technology is the signal generation chip, and the nRF905 is the detection circuit scheme of the wireless transceiver chip. The star network is determined by polling the entire network topology and MAC protocol. The embedded sensor is designed through simulation. This article discusses the principle of using embedded sensors to detect steel corrosion and determines the feasibility of its low-cost implementation. The embedded sensor and steel corrosion detection system were tested. The simulation method of the embedded sensor is verified through actual measurement. The test results show that the system network is normal and all parts work stably, which can meet the requirements of the project. [ABSTRACT FROM AUTHOR]

Remote sensor network is a multijump self-coordinating organization framework shaped by countless energy-restricted miniature sensor hubs sent in the observing region. Be that as it may, the correspondence conventions of customary organization configuration cannot be straightforwardly applied to remote sensor organizations. The improvement of committed correspondence conventions and estimation techniques has turned into a pressing exploration subject in the field of remote sensor organizations. The research purpose of this paper is to research the Internet of Things network topology discovery algorithm based on wireless sensors. This paper analyzes and mines an adaptive neighbor discovery scheduling algorithm with lower latency, acquires the information of potential neighbor nodes based on existing neighbor nodes, discovers potential neighbor nodes by proactively waking up, and studies the information recommendation mechanism between neighbor nodes and compares. The intimacy between neighbor nodes (such as common neighbor rate) is used to selectively receive recommended information from neighbor nodes, thereby filtering redundant data information, lessening hub energy utilization, and accomplishing the motivation behind broadening the organization life cycle. A geography disclosure calculation in light of versatile specialists is proposed to tackle the geography revelation issue under the various leveled geography structure in remote sensor organizations. The calculation joins the qualities of portable specialist innovation and various leveled Internet of Things network geography and simultaneously further develops the relocation methodology of versatile specialists for information assortment, intermittent undertaking assignment, and organization status observing. Research shows that in the investigation of the effect of hub obligation cycle on framework execution, the energy utilization of the DDC-Group calculation is marginally expanded contrasted and the gathering calculation, and the increase is not more than 1%, but compared to the CNR-Group increase more. [ABSTRACT FROM AUTHOR]

Sensor deployment is an important issue in wireless sensor network (WSN), which is a typical nonlinear system. Conditions of both area coverage and point coverage should be considered in research studies on sensor coverage. It is generally necessary to ensure high coverage ratio of area when controlling sensor locations, and covering specific point targets to ensure long lifetime is also important sometimes. In current studies, swarm intelligence algorithms such as particle swarm optimization (PSO) are widely used to solve the sensor deployment problem in WSN. In this paper, coverage rate and network life indicators are analyzed comprehensively with establishment of a more general K-coverage model. In related calculation examples with different coverage requirements including target coverage, area coverage, and boundary coverage, several improved algorithms based on PSO are applied to solve the problem in the paper. Simulation results show that the improved algorithms can achieve a good performance and deployment effect. [ABSTRACT FROM AUTHOR]

Large-scale mechanical equipment monitoring involves various kinds and quantities of information, and the present research on multisensor information fusion may face problems of information conflicts and modeling complexity. This paper proposes an analysis method combining correlation analysis and deep learning. According to the characteristics of monitoring data, three types of correlation coefficients between sensors in different states are obtained, and a new composite correlation analytical matrix is established to fuse the multisource heterogeneous data. The matrix represents fault feature information of different equipment states and helps further image generation. Meanwhile, a convolutional neural network-based deep learning method is developed to process the matrix and to discover the relationship between results and equipment states for fault diagnosis. To verify the method of this paper, experimental and field case studies are performed. The results show that it can accurately identify fault states and has higher diagnostic efficiency and accuracy than traditional methods. [ABSTRACT FROM AUTHOR]

Fault detection (FD) is important for health monitoring and safe operation of dynamical systems. Previous studies use model-based approaches which are sensitive to system specifics, attenuating the robustness. Data-driven methods have claimed accurate performances which scale well to different cases, but the algorithmic structures and enclosed operations are "black," jeopardizing its robustness. To address these issues, exemplifying the FD problem of aircraft air data sensors, we explore to develop a robust (accurate, scalable, explainable, and interpretable) FD scheme using a typical data-driven method, i.e., deep neural networks (DNN). To guarantee the scalability, aircraft inertial reference unit measurements are adopted as equivalent inputs to the DNN, and a database associated with 6 different aircraft/flight conditions is constructed. Convolutional neural networks (CNN) and long-short time memory (LSTM) blocks are used in the DNN scheme for accurate FD performances. To enhance robustness of the DNN, we also develop two new concepts: "large structure" which corresponds to the parameters that can be objectively optimized (e.g., CNN kernel size) via certain metrics (e.g., accuracy) and "small structure" that conveys subjective understanding of humans (e.g., class activation mapping in CNN) within a certain context (e.g., object detection). We illustrate the optimization process we adopted in devising the DNN large structure, which yields accurate (90%) and scalable (24 diverse cases) performances. We also interpret the DNN small structure via class activation mapping, which yields promising results and solidifies the robustness of DNN. Lessons and experiences we learned are also summarized in the paper, which we believe is instructive for addressing the FD problems in other similar fields. [ABSTRACT FROM AUTHOR]

Aerobics is an indispensable part of school teaching. Aerobics can exercise the human body's flexibility, muscle extension, and cardiopulmonary function. However, there are many details in the aerobics movements that need attention, so this article mainly studies the perception and recognition of the upper limb movement trajectory of aerobics. This article is mainly based on multi-intelligent sensors to perform motion capture and recognition of upper limb movements in aerobics. Therefore, in order to design a multismart sensor-based aerobics upper limb movement perception recognition system, this paper proposes to combine the VM-i three-axis magnetic sensor and MEMS smart sensor to capture the upper limb movement trajectory. Then, the aerobics upper limb movement trajectory and posture mathematical description method to obtain the model of the action perception recognition system are written. In order to verify the system, this paper also designed the actual effect of attitude calculation and A-RRM algorithm verification experiment. Finally, it is optimized with the data obtained from the experiment, and the accuracy and efficiency comparison experiment with the traditional motion perception recognition system is carried out. Experimental results show that the motion recognition accuracy of the aerobics upper limb movement trajectory recognition system based on multi-smart sensors is improved by 13%-23% compared with the traditional motion recognition system. The recognition efficiency of the action perception recognition system based on multismart sensors is 9%-15% higher than that of the traditional action recognition system. [ABSTRACT FROM AUTHOR]

In order to deeply analyze the performance of Electromagnetic Field (EMF) on wireless sensor Signal Processing (SP), this paper first examines the influencing factors of wireless sensor SP performance based on the relevant theories of EMF and wireless sensor. Then, it discusses the influence of EMF on wireless sensor information Transmission and Reception (T & R) time, Package Reception Rate (PRR), and communication throughput. Furthermore, the Packet Loss Rate (PLR) and Bit Error Rate (BER) of the sensor are described, and finally, a conclusion is drawn. The research findings are as follows. This paper makes a statistical study on whether the wireless sensor is disturbed by EMI from three aspects (wireless sensor's information T & R time, PLR, and communication throughput). In any case, EMF will prolong the wireless sensor's information T & R time and reduce the PRR and communication throughput. When the distance between the transmitter and the receiver is set to 5 m, the change of field intensity at the transmitter significantly impacts the BER but has little impact on the PLR. However, when the field intensity of the transmitter and the receiver is the same, PLR and BER show no evident difference. Therefore, this paper studies the SP performance of the wireless sensor in the EMI environment, which provides an excellent reference significance for the rapid development of wireless sensors in the future. [ABSTRACT FROM AUTHOR]

Marine ecosystem is increasingly deteriorating. In order to assess anthropogenic influence and instigate appropriate remedial actions, it is still of great significance to develop the technology of sensors applied for nutrient detection (e.g., nitrate, phosphate, and silicate) in seawater. This brief review shows an important direction for the development of nutrient detection sensors in seawater and also the limitations and challenges based on data from the ISI Web of Science database. Being different from previous review papers, in this short critical review paper (1) we unified the unit of limit of detection (LOD) for making the comparison within different researches possible; (2) only the literatures focusing on the technological development of sensors in seawater were used; and (3) not only the detection methods but also the detected analytes and publication years were discussed to supply more valuable information for the development of nutrient sensors applied in seawater. In total, 109 literatures were collected with regard to technological development. The quantity of literatures has increased most during 2011-2020. For analytes, literatures related to nitrate, phosphate, ammonium, and phosphate will continue to increase with more accurate data. For detection methods, spectrophotometry, colorimetry, fluorimetry, and electrochemistry are the most widely used sensors. LODs show thousands of orders. In general, there are lower LOD to nitrite and ammonium and fluorimetry method. Now, for analytes, nitrate 1.0983 > silicate 0.5495 > phosphate 0.4823 > ammonium 0.1324 > nitrite 0.0568 . For detection methods, microfluidics 1.7617 > electrochemistry 1.2607 > colorimetry 0.4462 > spectrophotometry 0.2941 > fluorimetry 0.0558 . This result indicated that the development level of detection methods is closer for nitrate, nitrite, phosphate, and silicate. For ammonium, spectrophotometry has significantly lower LOD than electrochemistry (p < 0.05), and fluorimetry also has significantly lower LOD than electrochemistry (p < 0.05). Our results imply that sensors with accurate LOD should be developed in the future. In addition, more detection methods should be considered by future sensors. [ABSTRACT FROM AUTHOR]