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Journal of Sensors
Journal Prestige (SJR): 0.288
Citation Impact (citeScore): 1
Number of Followers: 23  

  This is an Open Access Journal Open Access journal
ISSN (Print) 1687-725X - ISSN (Online) 1687-7268
Published by Hindawi Homepage  [339 journals]
  • Smart City: Recent Advances in Intelligent Street Lighting Systems Based
           on IoT

    • Abstract: Based on the importance of energy saving in terms of reducing the carbon impact and global warming problems, smart street lighting systems have been proposed in the past few years with different specifications. These systems include sensors for controlling the light intensity and connectivity for recording weather conditions and diagnosing lamp failure remotely. This paper discusses many published research studies regarding smart street lighting systems, providing a comparison between these systems which emphasizes the limitations of each one of them. Current and future trends are highlighted.
      PubDate: Wed, 30 Nov 2022 06:35:01 +000
       
  • Development of a Technique for Classifying Photovoltaic Panels Using
           Sentinel-1 and Machine Learning

    • Abstract: With the increasing interest in effective renewable alternative energy sources resulting from the Paris Agreement on Climate Change in 2015, photovoltaic (PV) power generation is attracting attention as a practical measure. In this study, we develop procedures for efficiently monitoring PV panels in a large area and increasing their classification accuracy to enable efficient management of PV panels, an important component of renewable energy generation. To accomplish this, first, the persistent scatterer characteristics (e.g., polarization, imaging module, and topography) of PV panels in SAR images were utilized. Then, we developed a technique for classifying panels over a certain size using the polarization and pulse-scattering characteristics of Sentinel-1. Next, by stacking Sentinel-1 ground range Doppler (GRD) images and comparing them with the surroundings of the same area, the morphological features of PV panels were derived and built as learning data for machine learning. Then, a more precise classification of PV panels was performed by applying these learning data in AI algorithms. When SAR-based AI training data for the same PV panels were used in the YOLOv3 and YOLOv5 algorithms, both algorithms showed high accuracy of over 90%, but there were differences in precision and recall. These findings will enable more efficient monitoring of PV panels, the use of which is expected to increase in the future. In addition, they can serve as a proactive response tool to address environmental problems such as PV panel waste and panels washed away during natural disasters.
      PubDate: Wed, 30 Nov 2022 03:50:00 +000
       
  • YOLOv5-PD: A Model for Common Asphalt Pavement Defects Detection

    • Abstract: In asphalt pavement detection, the defect scale changes greatly, mainly including mesh cracks, patches, and potholes. In the case of large scale, the texture feature is not clear, and the information is easily lost in the feature extraction process. Correspondingly, the number of small-scale holes is often very large, which also puts forward higher requirements for the detection model. In view of the above problems, this paper proposed a model for common asphalt pavement defects detection called YOLOv5-PD. In order to reduce the loss of information and expand the receptive field of the model, Big Kernel convolution was used to replace a part of the convolution in the original CSPDarknet. The texture feature information of the cracks is retained to the greatest extent. In order to enhance the detection performance of small defects, convolution channel attention mechanism was added after each feature fusion layer, and performs attention processing on the feature map after concat to find the defect location. This study used a public pavement defect dataset from Brazil. In this work, ablation experiments were carried out according to the task scenario, and the improved effects were compared and analyzed. The proposed model is compared with other versions of models and advanced models, which proves the superiority of the proposed model. The mAP of proposed model reached 73.3% and the model inference speed reached 41FPS, which can meet real time engineering application requirements.
      PubDate: Tue, 29 Nov 2022 02:50:01 +000
       
  • Sinc Interpolation for Further Improvement in Frequency Estimation Using
           Three DFT Samples

    • Abstract: This study presents three different sinc estimators and a method to estimate complex-valued exponential tone signal frequency using three DFT samples. The proposed method suggests using sinc interpolation together with the well-known Jacobsen estimator. According to simulation results, the root mean square error (RMSE) of the proposed algorithm is lower than those of the Jacobsen estimator and its improved version suggested by Candan. The price paid for improvements in the RMSE is a slight increase in computation time.
      PubDate: Mon, 28 Nov 2022 03:50:01 +000
       
  • Monitoring Stress State of H-Shape Steel Using Ceramic Piezoelectric
           Sensor: A Feasibility Study

    • Abstract: To monitor the stress state and yield capacity of H-beams across their entire service process, a real-time monitoring method based on the energy signal response of ceramic piezoelectric sensors is proposed in this paper. The method is applied to conduct loading experiments on H-beams under different load values and web heights. Then, the amplitude and energy of the piezoelectric signals under the two working conditions are compared and analyzed, and the finite element analysis results are verified. The experimental results show that the time-domain waveform energy index increases under an increase in web height or load. Taking the H-section steel member with a web height of 10 cm as an example, when the load value is less than 500 kN/m, the energy index increases (on average) by ~10.5% for every 100 kN/m load increase; when the load value exceeds 500 kN/m and is below 675 kN/m (yield load), the same load increases the energy index by ~13.4%. Meanwhile, a 1 cm average increase in web height increases the energy index by ~14.6%. The finite element simulation results indicate that the ceramic piezoelectric sensor load increases under external load increases up to the yielding load. Because the stress state at the sensor location directly determines the stress wave propagation, the critical buckling loads of H-beams can be predicted using the energy index.
      PubDate: Thu, 24 Nov 2022 10:35:01 +000
       
  • A Joint Data Association Method for Laser-SLAM of Unmanned Delivery
           Vehicle Based on Heuristic Search Algorithm

    • Abstract: In Laser-SLAM system of unmanned delivery vehicle, there are two kinds of association methods applied to solve the data association problem. Compared with the method of independent association for a single measurement and a single feature, the methods of batch association of measurements and features can provide more accurate association results in the state estimation stage of SLAM. In order to obtain a better association solution, a joint data association method based on heuristic search algorithm (HSA-JDA) is proposed to improve the robustness and accuracy of data association. In HSA-JDA, according to the joint maximum likelihood criterion, the data association problem is evolved into a combinatorial optimization problem of how to determine the optimal association set. A heuristic search algorithm that is an optimized artificial fish swarm algorithm by using adaptive step size and adding fish swarm jumping behavior is applied to search the optimal association solution. Experimental results show that HSA-JDA method ensures high association accuracy and then improves the robustness and accuracy of the whole state estimation results of SLAM. It can be used in the Laser-SLAM system based on Kalman filter to provide reliable association results for improving the accuracy of SLAM estimation results for unmanned delivery vehicle.
      PubDate: Wed, 23 Nov 2022 10:20:03 +000
       
  • Autonomous Braking System Using Linear Actuator

    • Abstract: The most frequent cause of vehicle accidents (car, bike, truck, etc.) is the unexpected existence of barriers while driving. An automated braking system will assist and minimize such collisions and save the driver and other people’s lives and have a substantial influence on driver safety and comfort. An autonomous braking system is a complicated mechatronic system that incorporates a front-mounted ultrasonic wave emitter capable of creating and transmitting ultrasonic waves. In addition, a front-mounted ultrasonic receiver is attached to gather ultrasonic wave signals that are reflected. The distance between the impediment and the vehicle is determined by the reflected wave. Then, a microprocessor is utilized to control the vehicle’s speed depending on the detected pulse information, which pushes the brake pedal and applies the vehicle’s brakes extremely hard for safety. For work-energy at surprise condition for velocity 20 km/hr, the braking distance is 17.69 m, and for velocity 50 km/hr, the braking distance is 73.14.
      PubDate: Tue, 22 Nov 2022 13:35:03 +000
       
  • An Improved Object Detection Method for Underwater Sonar Image Based on
           PP-YOLOv2

    • Abstract: Forward-looking sonar is widely used in underwater obstacles and objects detection for navigational safety. Automatic sonar images recognition plays an important role to reduce the workload of staff and subjective errors caused by visual fatigue. However, the application of automatic object classification in forward-looking sonar is still lacking, which is due to small effective samples and low signal-to-noise ratios (SNR). This paper proposed an improved PP-YOLOv2 algorithm for real-time detection, called as PPYOLO-T. Specifically, the proposed method first resegments the sonar image according to different aspect ratio and filters the acoustic noise in various ways. Then, attention mechanism is introduced to improve the ability of network feature extraction. Finally, the decoupled head is used to optimize the multiobjective classification. Experimental results show that the proposed method can effectively improve the accuracy of multitarget detection task, which can meet the requirement of robust real-time detection for both raw and noised sonar targets.
      PubDate: Mon, 21 Nov 2022 15:35:01 +000
       
  • Superresolution Reconstruction Algorithm of Ultrasonic Logging Images
           Based on High-Frequency Enhancement

    • Abstract: High-resolution logging images with glaring detail information are useful for analysing geological features in the field of ultrasonic logging. The resolution of logging images is, however, severely constrained by the complexity of the borehole and the frequency restriction of the ultrasonic transducer. In order to improve the image superresolution reconstruction algorithm, this paper proposes a type of ultrasonic logging based on high-frequency characteristics, with multiscale dilated convolution to feature as the basis of network-learning blocks, training in the fusion of different scale texture feature. The outcomes of other superresolution reconstruction algorithms are then compared to the outcomes of the two-, four-, and eightfold reconstruction. The proposed algorithm enhances subjective vision while also enhancing PSNR and SSIM evaluation indexes, according to a large number of experiments.
      PubDate: Mon, 21 Nov 2022 11:50:01 +000
       
  • An Intelligent Smart Parking System Using Convolutional Neural Network

    • Abstract: Saudi Arabia has started building smart cities and communities as part of the Saudi 2030 vision, which aims to digitalize all services. Smart cities use different types of technologies and data to improve the quality of life for citizens, manage resources, and make operations more efficient. In big cities such as Riyadh and Jeddah, the number of vehicles on the road has dramatically increased. Hence, parking has become a problem since there are limited spaces available. In this article, a novel, intelligent, and automated method for vehicle parking and management is proposed. This approach employs a convolutional neural network (CNN) tool to train the algorithm deeply. Image segmentation and preprocessing techniques are employed as well. All operations are automated and cost-effective since the proposed smart parking management system utilizes only a single camera to provide real-time views of the status of a parking lot. Furthermore, there is no need for human interference, and it is easy to maintain. Several simulation scenarios were conducted on MATLAB to validate this approach and prove its efficiency. A comparative evaluation between the proposed system and some works of literature is provided, and it indicates that the developed system outperforms the works from the preexisting literature.
      PubDate: Mon, 21 Nov 2022 10:05:01 +000
       
  • Delphi Collaboration Strategy for Multiagent Optical Fiber Intelligent
           Health Monitoring System

    • Abstract: Optical fiber sensors are very attractive in mechanical structure intelligent health monitoring system due to some unique characteristics, such as immunity to electromagnetic interference and to aggressive environments, high sensitive and fast response, small physical dimension, excellent resolution and range, and so on. For improving the accuracy and reliability of the optical fiber intelligent health monitoring system in practical engineering application, the collaboration and decision-making strategy based on Delphi method for multiagent optical fiber intelligent health monitoring system is studied in this paper. The proposed system is mainly composed of optical fiber sensing agent, intelligent evaluation agent, and system collaborative decision-making agent. The intelligent evaluation agent is used to evaluate the health status of the monitored mechanical structures. Delphi method is used by the system collaborative decision-making agent to consult each intelligent evaluation agent. Meanwhile, the collaborative partner selection algorithm is used to select the intelligent evaluation agent participating in the collaboration, and the intelligent evaluation agent that does not participate in the decision-making is dynamically modified by the decision result. The experiment for an aircraft wing box as the typical engineering structure is carried out and the verification system is designed, the decision result is compared with that without dynamic correction of the evaluation result. The comparative results indicate that the evaluation accuracy and reliability of the monitored mechanical structural damage are improved significantly after multiple rounds of collaboration and decision making.
      PubDate: Mon, 21 Nov 2022 10:05:01 +000
       
  • RFID Scheme for IoT Devices Based on LSTM-CNN

    • Abstract: As an essential branch of physical layer authentication research, radio frequency identification (RFID) has advantages in achieving lightweight and highly reliable authentication. However, in the Internet of Things (IoT) environment, where a large scale of devices are connected to the network, there is an issue that the difference of the RF fingerprints is less distinct among the same type of devices. To this end, in this paper, we propose an RFID scheme for IoT devices based on long-short term memory and convolutional neural network (LSTM-CNN). This scheme combines the excellent learning ability of LSTM and CNN to perceive the context information and extract the local feature of RF data. Specifically, RF data is first fed into LSTM to obtain long-term dependency features containing temporal information. Then, CNN is designed for secondary feature extraction to enlarge RF differences and further used for device classification. The experiment results on the open RF data set ORACLE indicate that the identification accuracy of the proposed scheme can reach over 99%. Compared with other schemes, the performance is improved by 6%-30%.
      PubDate: Fri, 18 Nov 2022 08:05:07 +000
       
  • Data Transmission in Backscatter IoT Networks for Smart City Applications

    • Abstract: Backscatter communication is a battery-less data transmission method for massive IoT devices. These backscatter devices receive an incident signal from an RF-source gateway to harvest energy. These devices can be operated to transmit data after harvesting energy. This technology is widely applied to smart city applications. In general, IoT devices in the smart city applications have insufficient resources. They use narrowband communication to transmit small sizes of data. Thus, a simple channel access approach should be considered for data transmission. In addition, network scalability is also important in the backscatter network for smart city applications. According to energy harvesting and data generation, devices participating in data transmission can change frequently. Providing the network scalability by the changing devices can improve the transmission efficiency in the backscatter network. Therefore, we propose a novel media access scheme for the backscatter network in the smart city applications. The proposed scheme is designed by the contention-based approach to support the network scalability. It controls backscattering signal for energy harvesting and distributes contention in multiple access. It allows additional data transmission in backscattering period for harvesting energy to provide fairness of devices. For performance evaluation, extensive computer simulations are carried out and the proposed method is compared to TDMA that is a typical media access scheme in the backscatter network.
      PubDate: Fri, 18 Nov 2022 02:50:00 +000
       
  • Multiradar Joint Tracking of Cluster Targets Based on Graph-LSTMs

    • Abstract: The cluster target brings a serious challenge to the traditional multisensor multitarget tracking algorithm because of its large number of members and the cooperative interaction between members. Using multiradar joint tracking cluster target is an alternative method to solve the problem of cluster target tracking, but it inevitably brings the problem of radar-target assignment and tracking information fusion. Aiming at the problem of radar-target assignment and tracking information fusion, a joint tracking method based on graph-long short-term memory neural nets (Graph-LSTMs) is proposed. Firstly, we use multivariable stochastic differential equations (SDE) to model the cooperative interaction of cluster members and transform the derived state space model of cluster members into the same form as the constant velocity (CV) motion model, and the target state equation of cluster which can be used for Bayesian filtering iteration is established. Secondly, based on the detection relationship between radars and cluster members, we introduce the detection confirmation matrix and propose a radar-target assignment method to achieve multiple measurements of single member and detection coverage of all cluster members. Then, each radar uses δ-GLMB filter to estimate the motion state of the assigned targets. Finally, on the basis of spatial discretization, the labels of multiple estimates of cluster member states are obtained. We use the designed Graph-LSTMs to learn the cooperative relationship between target states to fuse the labels and obtain better tracking effect. The experimental results show that the proposed method effectively simulates the cluster motion and realizes the joint estimation of cluster target motion state by multiradar. Our method makes up for the defect that a single radar cannot stably track adjacent multiple targets and achieves better estimation fusion effect than the expectation-maximization (EM) algorithm and mean method.
      PubDate: Mon, 14 Nov 2022 03:05:01 +000
       
  • Feature Selection and Training Multilayer Perceptron Neural Networks Using
           Grasshopper Optimization Algorithm for Design Optimal Classifier of Big
           Data Sonar

    • Abstract: The complexity and high dimensions of big data sonar, as well as the unavoidable presence of unwanted signals such as noise, clutter, and reverberation in the environment of sonar propagation, have made the classification of big data sonar one of the most interesting and applicable topics for active researchers in this field. This paper proposes the use of the Grasshopper Optimization Algorithm (GOA) to train Multilayer Perceptron Artificial Neural Network (MLP-NN) and also to select optimal features in big data sonar (called GMLP-GOA). GMLP-GOA hybrid classifier first extracts the features of experimental sonar data using MFCC. Then, the most optimal features are selected using GOA. In the last step, MLP-NN trained with GOA is used to classify big data sonar. To evaluate the performance of GMLP-GOA, this classifier is compared with MLP-GOA, MLP-GWO, MLP-PSO, MLP-ACO, and MLP-GSA classifiers in terms of classification rate, convergence rate, local optimization avoidance power, and processing time. The results indicated that GMLP-GOA achieved a classification rate of 98.12% in a processing time of 3.14 s.
      PubDate: Mon, 14 Nov 2022 03:05:01 +000
       
  • Agriculture Field Automation and Digitization Using Internet of Things and
           Machine Learning

    • Abstract: The real-time smart monitoring with intelligence highly gained significant attention for enhancing the productivity of the crop. Currently, IoT generates a lot of real-time data from the sensors, actuators, and identification technologies. However, extracting the meaningful insights from the data is necessary for realizing the intelligent ecosystem in agriculture. Based upon the previous studies, it is also identified that the limited studies have merely implemented machine learning (ML) on real-time data obtained through customized hardware with dedicated server. In this study, we have proposed a customized hand-held device that enables to deliver recommendations to the farmer on the basis of real-time data obtained through IoT hardware and ML. A three-layer structure is proposed in the study for realizing custom hardware with 2.4 GHz ZigBee and IoT sensors for the data acquisition, communication, and recommendation. As a part of real-time implementation, the calibration of the sensors is processed to form a real-time dataset with precision. The study evaluated four ML models and concluded that XGBoost has shown a better accuracy on the proposed dataset. The XGBoost recommended the crop based on selected parameters. The developed hand-held device can be customized with advance features with crop recommendations.
      PubDate: Sat, 12 Nov 2022 08:35:00 +000
       
  • A SLAM Algorithm Based on Edge-Cloud Collaborative Computing

    • Abstract: Simultaneous localization and mapping (SLAM) is a typical computing-intensive task. Based on its own computing power, a mobile robot has difficult meeting the real-time performance and accuracy requirements for the SLAM process at the same time. Benefiting from the rapid growth of the network data transmission rate, cloud computing technology begins to be applied in the robotics. There is the reliability problem caused by solely relying on cloud computing. To compensate for the insufficient airborne capacity, ensure the real-time performance and reliability, and improve the accuracy, a SLAM algorithm based on edge-cloud collaborative computing is proposed. The edge estimates the mobile robot pose and the local map using a square root unscented Kalman filter (SR-UKF). The cloud estimates the mobile robot pose and the global map using a distributed square root unscented particle filter (DSR-UPF). By using sufficient particles in the cloud, DSR-UPF can improve the SLAM accuracy. The cloud returns the particle with the largest posteriori probability to the edge, and the edge performs edge-cloud data fusion based on probability. Both the simulation and the experimental results show that the proposed algorithm can improve the estimation accuracy and reduce the execution time at the same time. By transferring the heavy computation from robots to the cloud, it can enhance the environmental adaptability of mobile robots.
      PubDate: Sat, 12 Nov 2022 05:50:02 +000
       
  • SF-LAP: Secure M2M Communication in IIoT with a Single-Factor Lightweight
           Authentication Protocol

    • Abstract: Machine-to-machine communication allows smart devices like sensors, actuators, networks, gateways, and other controllers to communicate with one another. The industrial Internet of things (IIoT) has become a vital component. Many industrial devices are connected to perform a task automatically in machine-to-machine communication, but they are not properly secured, allowing an adversary to compromise them against a variety of attacks due to communication system vulnerabilities. Recently, a secure lightweight authentication protocol (SLAP) was proposed by Panda et al. They asserted that every known attack that could happen in the IIoT is deterred by their suggested protocol. In this study, we prove that the SLAP protocol is vulnerable to desynchronization, impersonation, replay, and eavesdropping attacks. To prevent these attacks and enhance that protocol, we need to implement a secure authentication mechanism that ensures the security of communication. This paper proposed a secure M2M Communication in IIoT with a single-factor lightweight authentication protocol (SF-LAP). Single-factor authentication is a simple and secure way to communicate. It uses less power and communication overhead while providing a secure mechanism for conversation. In the machine-to-machine (M2M) scenario, the proposed protocol uses an exclusive-OR operation and a hashing function to ensure secure communication between the sensor and the controller. The proposed mechanism uses a secure preshared key and timestamp technique to protect and safeguard this connection against desynchronization attacks and eavesdropping attacks. We used Burrows Abadi Needham (BAN) Gong, Needham, and Yahalom (GNY) logic, and the automated validation of Internet security protocols applications (AVISPA) tool for formal verification and perform a security analysis as an informal verification to make sure the suggested protocol is secure. Analysis that shows the SF-LAP consumes the least computing and communication overhead and is more secure because it prevents desynchronization and eavesdropping attacks to all of the known attacks that are modification attacks, tracing attacks, impersonation, man-in-the-middle, and replay attacks.
      PubDate: Thu, 10 Nov 2022 12:05:00 +000
       
  • Tiny Machine Learning for Resource-Constrained Microcontrollers

    • Abstract: We use 250 billion microcontrollers daily in electronic devices that are capable of running machine learning models inside them. Unfortunately, most of these microcontrollers are highly constrained in terms of computational resources, such as memory usage or clock speed. These are exactly the same resources that play a key role in teaching and running a machine learning model with a basic computer. However, in a microcontroller environment, constrained resources make a critical difference. Therefore, a new paradigm known as tiny machine learning had to be created to meet the constrained requirements of the embedded devices. In this review, we discuss the resource optimization challenges of tiny machine learning and different methods, such as quantization, pruning, and clustering, that can be used to overcome these resource difficulties. Furthermore, we summarize the present state of tiny machine learning frameworks, libraries, development environments, and tools. The benchmarking of tiny machine learning devices is another thing to be concerned about; these same constraints of the microcontrollers and diversity of hardware and software turn to benchmark challenges that must be resolved before it is possible to measure performance differences reliably between embedded devices. We also discuss emerging techniques and approaches to boost and expand the tiny machine learning process and improve data privacy and security. In the end, we form a conclusion about tiny machine learning and its future development.
      PubDate: Thu, 10 Nov 2022 10:50:03 +000
       
  • Bibliometric Analysis of Interferometric Synthetic Aperture Radar (InSAR)
           Application in Land Subsidence from 2000 to 2021

    • Abstract: Land subsidence is one of the serious natural disasters which can cause heavy casualties and economic losses. As a vital method, Interferometric Synthetic Aperture Radar (InSAR) can provide quick and efficient solutions for analysis. Currently, most reviews on InSAR application in land subsidence only focused on single types of land areas, such as the land around groundwater and land of the mining area. There is a lack of discussion on all types of land areas. This study thus aims at conducting a bibliometric literature analysis of the existing literature from 2000 to 2021 to fill this gap. The authors used scientific mapping methods to analyze the InSAR applications in land subsidence so that researchers and practitioners can comprehend the procedure. Then, the authors identified the major research areas, development milestones, evolutionary stages, and the transaction dynamics of evolutionary stages. Knowledge maps of five aspects were applied and analyzed in this research, including temporal development analysis, countries and institutions, major research disciplines, high-frequency terms, and cocitation of high-citation papers. The results reveal that the research of land subsidence monitoring with InSAR is in the stage of diffusion from developing many tools and techniques to integrating with other research areas. Overall, the bibliometric results combined with evolutionary stages provide a holistic picture of the status quo and future trends in InSAR application in land subsidence.
      PubDate: Thu, 10 Nov 2022 10:20:02 +000
       
  • Development of Verification Device for Multitarget Radar Velocimeter Based
           on Echo Signal Simulation Technology

    • Abstract: Speeding is one of the leading causes of traffic crashes worldwide. Radar velocimeter is widely used in the capture monitoring of road overspeed violations, which can effectively reduce the probability of traffic accidents and protect people’s life and property safety to the greatest extent. As a new type of radar velocimeter, multitarget radar velocimeter (MTRV) can monitor the speed of more than two vehicles at the same time. However, the verification method and device of MTRV’s performance need to be studied. In order to solve the problem of performance verification for MTRV, a verification device based on echo signal simulation technology is developed in this paper. The measurement mechanism of MTRV with different performance including velocity, distance, and angle is first introduced. Then, a verification method based on the echo signal simulation technology is proposed. The verification device can receive the emission signal of MTRV and process the signal by echo simulation technology, including target generation, Doppler frequency shift, time delay, and angel control, and targets are simulated with nominal velocity, distance, and angle value. The processed echo signal with simulated nominal parameter values is reflected to the MTRV. After the echo signal is received and processed by MTRV, the measurement values of simulated velocity, distance, and angle for targets are obtained. Comparing the measured values of the MTRV with the simulated nominal values of the verification device, the measurement error of MTRV is obtained. The verification device of MTRV is realized to verify the accuracy and reliability of the MTRV measurement results. The simulated velocity range of the verification device is up to (-300~300) km/h, and the simulated distance range of the verification device is up to (10~45) m when the simulated incident angle range was within the range of (-60~60)°. The simulation target generation for the two targets of the device is also verified. And the maximum permissible error (MPE) of the simulated velocity was ±0.05 km/h, the MPE of simulated distance is ±0.3 m, and the MPE of simulated angle is ±0.2°. Finally, the verification and uncertainty evaluation results of the MTRV sample validated the effectiveness and feasibility of the proposed verification method and the developed verification device of MTRV.
      PubDate: Thu, 10 Nov 2022 08:50:01 +000
       
  • Adaptive Fast Independent Component Analysis Methods for Mitigating
           Multipath Effects in GNSS Deformation Monitoring

    • Abstract: Carrier-phase multipath is the main problem of GNSS deformation monitoring. Traditional methods usually adopt sidereal day filtering to mitigate the multipath. However, the necessity of presetting the session duration of the static baseline solution reduces the timeliness of the methods in real engineering. Moreover, these methods are not suitable for the systems that contain different types of GNSS satellites (e.g., BDS). To address the problems, this paper proposes an Adaptive Fast Independent Component Analysis (AF-ICA) method for mitigating multipath effects in GNSS deformation monitoring, which can effectively process the multi-GNSS data and separate several multipath signals. In the experimental study, compared with Sidereal Filtering in Observation Domain (SF-OD) method, AF-ICA method can improve both the positioning accuracy and peak-to-peak value. In GNSS deformation monitoring positioning accuracy, AF-ICA method can achieve the root-mean-square (RMS) of 1 mm horizon-tally and 2 mm vertically. Compared with the MSF method, the positioning accuracy of the AF-ICA method in the direction of ENU is improved by 44%, 14%, and 31%, respectively, and the corresponding peak-to-peak values increased by 36%, 17%, and 29%, respectively. Our proposed method can automatically get the monitoring information without estimating the orbit period in advance to realize automatic deformation monitoring. Through the automatic monitoring solution, the AF-ICA method in this paper can be applied to the natural disaster monitoring in the Internet of Things and provides real-time data monitoring information for disaster early warning.
      PubDate: Wed, 09 Nov 2022 11:05:01 +000
       
  • Monitoring of Single-Phase Induction Motor through IoT Using ESP32 Module

    • Abstract: The condition monitoring of rotating machines for critical applications plays an important role in reducing downtime. With Industry 4.0, the role of IoT in online condition monitoring of electrical machines has gained considerable significance. The main aim of the paper is the use of IoT for online monitoring of motor parameters like current, temperature, vibration, and humidity and observing its online trending using a web server. Data can be accessed in form of graphs and widgets by visiting the web page. The advantage of this project is the real-time monitoring of the motor from any remote area and in case of any abnormality operating personnel can take necessary steps for preventing complete breakdown. The proposed work can help industry people in online monitoring of motors and in the future work can be extended for fault prediction and classification.
      PubDate: Wed, 09 Nov 2022 10:20:02 +000
       
  • A Comparative Analysis of Fraudulent Recruitment Advertisement Detection
           Methods in the IoT Environment

    • Abstract: The growth of the Internet of Things has changed the way of job hunting. Online recruitment has gradually replaced the traditional offline recruitment mode. Some unscrupulous people use online recruitment platforms to publish fraudulent recruitment advertisements, which not only bring financial and reputational losses to job seekers but also harm the sustainable development of society. However, previous studies have not used unified evaluation metrics and datasets, and detecting fraudulent recruitment advertisements lacks systematic research. To resolve this problem, this paper selects four representative traditional learning methods (i.e., random forest, support vector machine (SVM), logistic regression, and Naïve Bayes) and three deep learning methods (i.e., TextCNN, gate recurrent unit (GRU), and bidirectional long-short-term memory (Bi-LSTM)), which perform good in natural language processing (NLP) and use the same evaluation metrics and datasets conducting comparative experiments on balanced and unbalanced datasets, respectively. The experimental results show that the TextCNN method achieves the best detection performance with relatively low energy consumption on the balanced dataset. All the metrics values are more significant than 0.93. On unbalanced datasets, the TextCNN method still performs best with increasing imbalanced proportion.
      PubDate: Tue, 08 Nov 2022 10:05:01 +000
       
  • An Efficient Multilevel Thresholding Scheme for Heart Image Segmentation
           Using a Hybrid Generalized Adversarial Network

    • Abstract: Most people worldwide, irrespective of their age, are suffering from massive cardiac arrest. To detect heart attacks early, many researchers worked on the clinical datasets collected from different open-source datasets like PubMed and UCI repository. However, most of these datasets have collected nearly 13 to 147 raw attributes in textual format and implemented traditional data mining approaches. Traditional machine learning approaches just analyze the data extracted from the images, but the extraction mechanism is inefficient and it requires more number of resources. The authors of this research article proposed a system that is aimed at predicting heart attacks by integrating the techniques of computer vision and deep learning approaches on the heart images collected from the clinical labs, which are publicly available in the KAGGLE repository. The authors collected live images of the heart by scanning the images through IoT sensors. The primary focus is to enhance the quality and quantity of the heart images by passing through two popular components of GAN. GAN introduces noise in the images and tries to replicate the real-time scenarios. Subsequently, the available and newly created images are segmented by applying a multilevel threshold operation to find the region of interest. This step helps the system to predict the accurate attack rate by considering various factors. Earlier researchers have obtained sound accuracy by generating similar heart images and found the ROI parts of the 2D echo images. The proposed methodology has achieved an accuracy of 97.33% and a 90.97% true-positive rate. The reason for selecting the computed tomography (CT-SCAN) images is due to the gray scale images giving more reliable information at a low computational cost.
      PubDate: Tue, 08 Nov 2022 09:05:00 +000
       
  • Automatic Epileptic Seizure Detection Using PSO-Based Feature Selection
           and Multilevel Spectral Analysis for EEG Signals

    • Abstract: Automatic epileptic seizure detection technologies for clinical diagnosis mainly rely on electroencephalogram (EEG) recordings, which are immensely useful tools for epileptic location and identification. Currently, traditional seizure detection methods based only on single-view features have great limitations for the typical dynamic and nonlinear EEG signals. An objective of this paper is to investigate the effect of multiview feature selection and multilevel spectral analysis methods on the identification of the EEG signals for seizure detection. Here, multiview features are extracted from time domain, frequency domain, and information theory to collect adequate information of EEG signals. And a feature selection algorithm based on particle swarm optimization (PSO) is proposed for automatic seizure detection. Moreover, due to the different frequency components of the EEG signals, they are divided into four kinds of brain waves for multilevel spectral analysis. The effect of these four rhythm waves on seizure detection is compared. Three well-known classifiers are employed to classify EEG signals concerning seizure or nonseizure events. The result shows that the average accuracy, specificity, and sensitivity of classification with the CHB-MIT database are 98.14%, 98.64%, and 96.79%, respectively. The application of the PSO-based feature selection method for automatic seizure detection improves accuracy by 5.99% with the SVM classifier. Compared with the state-of-the-art methods, the proposed method has superior competence with high performance for automatic seizure detection. It is further shown that the feature selection method is an indispensable step in seizure detection. With PSO-based feature selection and multilevel spectral analysis, the wave in the frequency range of 4-7 Hz shows better performance in the identification of EEG signals and is more suitable for the proposed method. The PSO-based feature selection algorithm for automatic seizure detection can be a useful assistant tool for clinical diagnosis.
      PubDate: Fri, 28 Oct 2022 15:50:01 +000
       
  • Improved Distributed Multisensor Fusion Method Based on Generalized
           Covariance Intersection

    • Abstract: In response to the multitarget tracking problem of distributed sensors with a limited detection range, a distributed sensor measurement complementary Gaussian component correlation GCI fusion tracking method is proposed on the basis of the probabilistic hypothesis density filtering tracking theory. First, the sensor sensing range is extended by complementing the measurements. In this case, the multitarget density product is used to classify whether the measurements belong to the intersection region of the detection range. The local intersection region is complemented only once to reduce the computational cost. Secondly, each sensor runs a probabilistic hypothesis density filter separately and floods the filtering posterior with the neighboring sensors so that each sensor obtains the posterior information of the neighboring sensors. Subsequently, Gaussian components are correlated by distance division, and Gaussian components corresponding to the same target are correlated into the same subset. GCI fusion is performed on each correlated subset to complete the fusion state estimation. Simulation experiments show that the proposed method can effectively perform multitarget tracking in a distributed sensor network with a limited sensing range.
      PubDate: Fri, 28 Oct 2022 06:20:01 +000
       
  • Solving the Multisensor Resource Scheduling Problem for Missile Early
           Warning by a Hybrid Discrete Artificial Bee Colony Algorithm

    • Abstract: Aiming at the problem of multisensor resource scheduling in missile early warning operation, a scheduling decomposition strategy for missile early warning tasks under cooperative detection is proposed. Taking the detection benefit factor, target threat factor, and handover factor as the fitness function, we establish a sensor-subtask assignment (SSA) model and propose a hybrid discrete artificial bee colony (HDABC) algorithm to solve the optimal solution of the SSA model. The HDABC algorithm has the following improvements: in the initialization stage, a sensor-subtask-based coding method is designed to reduce the solution dimension, and the heuristic rules are used to obtain excellent populations to improve the convergence speed; in the employed bee and onlooker bee stage, a food source update strategy based on discrete differential mutation (DDM) operation is proposed to improve the searchability of the algorithm, and a sorting-based adaptive probability (SAP) selection method is applied to enhance the global search and local optimization capacities. Simulation experiments were carried out in operation scenarios of different scales. Experimental results showed that the proposed HDABC algorithm can obtain the optimal scheduling schemes and had a better solving performance when solving the SSA model, especially in the medium-scale and large-scale operation scenarios.
      PubDate: Tue, 25 Oct 2022 06:50:01 +000
       
  • A Novel Ensemble Earthquake Prediction Method (EEPM) by Combining
           Parameters and Precursors

    • Abstract: A leading cause of death from natural disasters over the last 50years is witnessed by none other than earthquake occurrences which have a negative economic impact on the world and claimed thousands of lives over the years, causing devastation to properties. In this paper, a novel Ensemble Earthquake Prediction Method (EEPM) is proposed and implemented to produce a strong learner (ensemble method) having better accuracy in prediction, less variance, and less errors. Data (parameters) which is continuous in nature is collected from two countries, India and Nepal, for five years, and surveyor’s data (precursor) which is categorical in nature is collected from three countries India, Nepal, and Kenya for five years on the specific earthquake-prone regions. The preprocessed data is generated by combining parameters and precursor data. EEPM focuses on detecting the accurate and better early signs of an earthquake and finding the probability of occurrence of an earthquake in the specified region, i.e., better prediction and robustness. The results of EEPM produced better and less variance and less error in comparison to individual machine learning methods as well as better accuracy 87.8%, compared to state-of-the-art ensemble methods. The prediction of earthquake will alarm not only the people of the society but also the different organizations to explain the appropriate range of magnitude and dynamics of occurrence of earthquake.
      PubDate: Tue, 25 Oct 2022 06:35:01 +000
       
  • GloVe-CNN-BiLSTM Model for Sentiment Analysis on Text Reviews

    • Abstract: Nowadays, social media networks generate a tremendous amount of social information from their users. To understand people’s views and sentimental tendencies on a commodity or an event timely, it is necessary to conduct text sentiment analysis on the views expressed by users. For the microblog comment data, it is always mixed with long and short texts, which is relatively complex. Especially for long text data, it contains a lot of content, and the correlation between words is more complex than that in short text. To study the sentiment classification of these mixed texts composed of long-text and short-text, this research proposes an optimized GloVe-CNN-BiLSTM-based sentiment analysis model. In this model, GloVe is used to vectorize words, and CNN is given to represent part space character. BiLSTM is used to build temporal relationship. Twitter’s comment data on COVID-19 is used as an experimental dataset. The results of the experiments suggest that this method can effectually identify the sentimental tendency of users’ online comments, and the accuracy of sentiment classification on complete-text, long-text, and short-text can achieve to 0.9565, 0.9509, and 0.9560, respectively, which is obviously higher than other deep learning models. At the same time, experiments show that this method has good field expansion.
      PubDate: Sat, 22 Oct 2022 05:50:00 +000
       
 
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