A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z  

  Subjects -> ELECTRONICS (Total: 207 journals)
The end of the list has been reached or no journals were found for your choice.
Similar Journals
Journal Cover
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]
  • Continuous Human Motion Recognition Based on FMCW Radar and Transformer

    • Abstract: Radar-based human motion recognition has received extensive attention in recent years. Most current recognition methods generate a heat map of features through simple signal processing and then feed into a classification-based neural network for recognition. Such an approach can only identify a single action. When a set of data contains information about multiple movements, it can also only be recognized as a single movement. Another point that cannot be overlooked is that continuous action recognition methods are able to recognize continuously changing actions but ignore the issue of whether continuous actions are legitimate or not (continuous actions obtained by stitching together multiple current actions do not conform to real time). In this paper, we propose a continuous action recognition method based on micro-Doppler features and transformer, which translates the micro-Doppler features of continuous actions into machine translation tasks and uses the idea of natural language processing (NLP) to identify continuous action. In order to judge whether the continuous action is legal or not, we also design the action state transition diagram as a constraint condition to strictly control the forward and backward actions. The experimental results show that the method proposed in this paper achieves good recognition accuracy for the recognition of a single action and can also effectively segment and recognize continuous actions.
      PubDate: Tue, 24 Jan 2023 01:35:01 +000
       
  • The Impact of Digital Finance on the Commercial Circulation Industry Based
           on IoT and Big Data

    • Abstract: Digital finance represented by the Internet of Things (IoT) has strong penetration and integration. The real economy has always been an important cornerstone of social and economic development. As a bridge between producers and consumers, the commercial circulation industry also plays an irreplaceable role in the healthy development of social economy. Therefore, this study is based on the Capability Maturity Model (CMM), the integration maturity is divided into four categories, and the results are qualitatively analyzed. The research results show that under the Internet of Things mode, the integration development maturity level and the integration development maturity are concentrated at a lower stage (defined level).
      PubDate: Fri, 20 Jan 2023 13:35:02 +000
       
  • Application of a Multiple Regression Model for the Simultaneous
           Measurement of Refractive Index and Temperature Based on an
           Interferometric Optical System

    • Abstract: Interferometric optical systems have been proposed for implementing dual-parameter optical sensors. For this type of sensors, the sensitivity matrix equation is generally used to determine the parameters to be measured based on the sensitivity of each parameter to one particular feature of the output reflective spectrum of the interferometric system. One of the disadvantages of this method is that the measurement ranges will be very short if the sensitivities are not linear or if these present cross-sensitivity. In this work, a multiple regression model for the simultaneous detection of refractive index and temperature based on an interferometric optical sensor is proposed. Here, the mathematical model is a weighted sum of features used to estimate the values of two response variables. These features are functions of an initial set of 27 explanatory variables whose values were extracted of the output reflective spectrum of the interferometric system. Besides, in order to sustenance the model application, the sensor was modeled and experimentally carried out. Three cases were studied: the estimation of temperature at different refractive indices, the estimation of temperature when refractive index is equal to one, and the estimation of refractive index at different temperatures. For each one of these cases, an optimal basis of functions was founded with the algorithm proposed and used to estimate the values of the response variables. Besides, a technique to reduce the initial set of variables was implemented. Finally, for the experimental data, For each one of these cases, an optimal basis of functions was founded with the algorithm 1 proposed and used to estimate the values of the response variables.
      PubDate: Thu, 19 Jan 2023 15:20:02 +000
       
  • Does Electrode Sensor Positioning over Motor Points Affect Different
           Portions of Quadriceps Muscle Architecture during Submaximal Evoked
           Torque'

    • Abstract: Background and Objectives. Few studies have evaluated differences in muscle architecture in quadriceps femoris constituents with sensor electrodes positioned over vastus lateralis (VL) and vastus medialis (VM) motor points during a neuromuscular electrical stimulation (NMES) session. We aimed to investigate the changes in muscle architecture of the rectus femoris (RF), VL, VM, and vastus intermedius (VI) portions during evoked contractions with sensor electrodes placed over VL and VM motor points. Materials and Methods. The study is a crossover, repeated-measure design, conducted with healthy males aged years. Ultrasonography at rest and evoked contraction at 40% of maximum voluntary contraction (MVC) were used to assess the pennation angle () and fascicle length (Lf) of RF, VL, VM, and VI portions. Results. The mean torque observed was  N.m during MVC and at 40% of MVC was  N.m. There was no difference for comparing four components of the quadriceps femoris (). There was a significant () muscle evoked contraction interaction for Lf without relevant clinical importance to the study. Conclusions. There is no difference in the changes in the muscle architecture of quadriceps femoris constituents during stimulation with the electrodes placed on the VL and VM motor points. Therefore, clinicians can choose either VL or VM motor points for sensor electrode positioning and expect similar muscle architecture adaptation for a given evoked torque. Future clinical studies should be conducted to establish the optimal electrode positioning over different portions of the quadriceps muscle to optimize more rational NMES clinical settings.
      PubDate: Wed, 18 Jan 2023 15:20:00 +000
       
  • Real-Time Strain Detection Technology for Steel Structures Based on Eddy
           Current Effect

    • Abstract: To avoid strains in steel structures in special equipment caused by excessive alternating loads, which cause stress concentrations in local areas and reduce the strength and pressure-bearing capacity of the steel structures, a method for evaluating the strain state of the steel structures using the eddy current effect is proposed, and the relevant testing device is developed. The RMS voltage and tension values were tested and fitted linearly with sampling time, and their linear correlations after fitting were 0.9978560 and 0.9967905, respectively. To investigate the method’s practical application, the effect of strain on the impedance of the eddy current probe was first studied theoretically, followed by the design and fabrication of a strain detection device comprised of an eddy current probe and a signal processing system. Finally, tensile strain experiments were carried out on 5 mm thick standard Q235 steel tensile specimens using a universal tensile machine and the linear equation of RMS voltage versus strain was obtained analytically. Theoretical and experimental tests have shown that the device can detect each strain stage and quantify the strain within the elastic stage by fitting a linear equation.
      PubDate: Fri, 13 Jan 2023 04:05:01 +000
       
  • A Novel Microstructure of 2-Bit Optical Analog to Digital Converter Based
           on Kerr Effect Nonlinear Nanocavities in 2D Photonic Crystal

    • Abstract: In this paper, an all-optical analog-to-digital converter based on nonlinear with silicon materials is designed and simulated. The proposed structure consists of three nonlinear nanocavity that control the optical signal power intensity. The nonlinear material used is aluminum gallium arsenide (AlGaAs). Aluminum gallium arsenide (AlGaAs) with a linear refractive index of and a nonlinear refractive index of . Due to the small path length of the waveguides, the optical signals move a short distance and as a result, the power optical losses along the path are reduced and on the other hand, the speed of the structure is increased. The transmission percentage is between 90% and 100%. The overall dimensions of the structure are 324 μm2. The plane wave expansion (PWE) method is used to calculate the band structure. The two-dimensional finite difference time domain (2D-FDTD) method is used to calculate the transmission power spectrum and the simulation results.
      PubDate: Fri, 06 Jan 2023 12:05:00 +000
       
  • Breast Cancer Classification from Mammogram Images Using Extreme Learning
           Machine-Based DenseNet121 Model

    • Abstract: Breast cancer is characterized by abnormal discontinuities in the lining cells of a woman’s milk duct. Large numbers of women die from breast cancer as a result of developing symptoms in the milk ducts. If the diagnosis is made early, the death rates can be decreased. For radiologists and physicians, manually analyzing mammography images for breast cancer become time-consuming. To prevent manual analysis and simplify the work of classification, this paper introduces a novel hybrid DenseNet121-based Extreme Learning Machine Model (ELM) for classifying breast cancer from mammogram images. The mammogram images were processed through preprocessing and data augmentation phase. The features were collected separately after the pooling and flatten layer at the first stage of the classification. Further, the features are fed as input to the proposed DenseNet121-ELM model’s fully connected layer as input. An extreme learning machine model has replaced the fully connected layer. The weights of the extreme learning machine have been updated by the AdaGrad optimization algorithm to increase the model’s robustness and performance. Due to its faster convergence speed than other optimization techniques, the AdaGrad algorithm optimization was chosen. In this research, the Digital Database for Screening Mammography (DDSM) dataset mammogram images were utilized, and the results are presented. We have considered the batch size of 32, 64, and 128 for the performance measure, accuracy, sensitivity, specificity, and computational time. The proposed DenseNet121+ELM model achieves 99.47% and 99.14% as training accuracy and testing accuracy for batch size 128. Also, it achieves specificity, sensitivity, and computational time of 99.37%, 99.94%, and 159.7731 minutes, respectively. Further, the comparison result of performance measures is presented for batch sizes 32, 64, and 128 to show the robustness of the proposed DenseNet121+ELM model. The automatic classification performance of the DenseNet121+ELM model has much potential to be applied to the clinical diagnosis of breast cancer.
      PubDate: Sat, 31 Dec 2022 10:50:00 +000
       
  • Microwave-Based Electrochemical Sensor Design by SRR Approach for ISM
           Sensing Applications

    • Abstract: This paper presents a transmission line and a split ring resonator (SRR) based sensor structure with a high sensitivity capacity to discriminate various methanol mixtures. The height of the dielectric layer and thickness of copper are assigned as 1.6 mm and 0.035 mm, respectively. The overall dimensions of the sensor structure are defined as . The operating frequency is selected for the ISM bands, especially around 2.45 GHz. Different methanol-water mixtures are prepared at various ratios, and then, the complex permittivity values are measured. Different sensor structures are modelled and investigated using a two-port transmission line approach. Various types of SRR based sensors are designed, and an optimum design is proposed for methanol mixture detection applications. The observed quality factor of the proposed sensor is 16.5. The resonance shifts of the transmission value () are used for sensing capability around 2.45 GHz at -45 dB and 90 MHz resonance shifts. The sensitivity of the sensor has been evaluated as 1 MHz. Finally, the electric field distributions of the proposed SRR integrated transmission line are investigated. The novelty of the proposed design is to exactly sense the ratio of methanol in water with a very simple design. The proposed sensor structure can be used for methanol detection applications in medical, military, and chemical research.
      PubDate: Sat, 31 Dec 2022 09:35:01 +000
       
  • Experimental Study on Overlying Strata Movement Characteristics and
           Distributed Optical Fiber Characterization of Stope

    • Abstract: In order to study the migration characteristics and strata behavior law of mining-induced rocks in fully mechanized caving face of coal mine, taking the actual geological mining of Longwanggou Coal Mine as the background, using computer software (KSPB) to identify the location of key strata. The physical similar material simulation test was used to monitor the movement characteristics of mining-induced rocks by using the internal displacement sensor (IDS) and distributed optical fiber sensor (DOFS). A three-dimensional physical model of was built; the internal displacement of rock was measured by IDS. BOTDA distributed optical fiber was used to monitor the dynamic movement and deformation law of coated rock. Finally, the two monitoring results were compared and analyzed. The results show that: (1) the displacement curve of the fractured rock mass in the key strata shows a “stepped” increase; (2) based on the analysis of displacement continuous monitoring results, the strata behavior law of working face is obtained. The first weighting interval of 61601 working face is 90 cm, and the periodic weighting steps are 105 cm, 115 cm, 135 cm, 150 cm, 165 cm, 180 cm, 215 cm, and 240 cm; (3) the average strain of fiber is proposed. The first weighting and periodic weighting laws of the working face are represented by the average strain, which are consistent with the experimental phenomena. The first weighting in the average strain curve shows the first mutation peak, and the periodic weighting shows the periodic mutation peak change of the average strain curve.
      PubDate: Wed, 28 Dec 2022 02:05:00 +000
       
  • Prediction of Cardiovascular Disease on Self-Augmented Datasets of Heart
           Patients Using Multiple Machine Learning Models

    • Abstract: About 26 million people worldwide experience its effects each year. Both cardiologists and surgeons have a tough time determining when heart failure will occur. Classification and prediction models applied to medical data allow for enhanced insight. Improved heart failure projection is a major goal of the research team using the heart disease dataset. The probability of heart failure is predicted using data mined from a medical database and processed by machine learning methods. It has been shown, through the use of this study and a comparative analysis, that heart disease may be predicted with high precision. In this study, researchers developed a machine learning model to improve the accuracy with which diseases like heart failure (HF) may be predicted. To rank the accuracy of linear models, we find that logistic regression (82.76 percent), SVM (67.24 percent), KNN (60.34 percent), GNB (79.31 percent), and MNB (72.41) perform best. These models are all examples of ensemble learning, with the most accurate being ET (70.31%), RF (87.03%), and GBC (86.21%). DT (ensemble learning models) achieves the highest degree of precision. CatBoost outperforms LGBM, HGBC, and XGB, all of which achieve 84.48% accuracy or better, while XGB achieves 84.48% accuracy using a gradient-based gradient method (GBG). LGBM has the highest accuracy rate (86.21 percent) (hypertuned ensemble learning models). A statistical analysis of all available algorithms found that CatBoost, random forests, and gradient boosting provided the most reliable results for predicting future heart attacks.
      PubDate: Fri, 23 Dec 2022 13:50:01 +000
       
  • Research on Calibration Methods of Long-Wave Infrared Camera and Visible
           Camera

    • Abstract: Long-wave infrared (LWIR) and visible (VIS) cameras can image information at different dimensions, but the way to calibrate these two types of cameras while registering and fusing the acquired images is difficult. We propose a calibration plate and a calibration method for thermal imaging and visible imaging to solve three problems: (1) the inability of the existing calibration plates to address LWIR and VIS cameras simultaneously; (2) severe heat interference in the calibration images of LWIR cameras; (3) difficulty in finding feature points for registration due to the different imaging spectra between thermal imaging and visible imaging. Simulation tests and error analysis show the error of outline central point computation is less than 0.1 pixel. Average errors of Euclidean distances from the margin outline scattered point sets of the closed circle and closed ellipse to the outline central points decrease by 10% and 9.9%, respectively. The Mean Reprojection Error in the calibration of LWIR and VIS cameras are 0.1 and 0.227 pixels, respectively. Through image registration design and fusion experiments, the FMIdct, MS-SSIM, Qabf, SCD, and SSIM of the images fused after distortion correction are all higher than those of the images fused before distortion correction, with the highest increases being 4.6%, 0.3%, 3.1%, 7.2%, and 1.4%. These results prove the effectiveness and feasibility of our method.
      PubDate: Thu, 22 Dec 2022 10:35:01 +000
       
  • Computer-Aided Diagnosis of Muscle Mass through Antenna as a Sensor

    • Abstract: Wireless body area network (WBAN) incorporates a wireless sensor network and wearable devices in miniature size. In this paper, a dual-band microstrip patch (DBMSP) antenna as a sensor with a modified split ring resonator (SRR) and defective ground structure (DGS) is proposed for muscle mass measurement and prediction. Modified SRR on the ground plane forms a defected ground structure (DGS) for back radiation reduction and suits muscle mass measurement. The proposed dual-band microstrip patch antenna resonates at 5.2 GHz and 8.4 GHz, with impedance bandwidth of about 0.9 GHz and 1.89 GHz, input reflection coefficient is about -21.12 dB and -14.5 dB, respectively. This DBMSP antenna has an efficiency of 99.9%, with a negligible amount of specific absorption rate (SAR). From the proposed DBMSP antenna sensor, muscle mass is predicted from human muscle. The proposed antenna is fixed on the ventral surface of the forearm and biceps. DBMSP antenna sensor detects electromagnetic energy from muscle tissues under radiating near-field conditions. The muscle tissue signal is acquired through the proposed DBMSP antenna. The acquired antenna process with nondecimated wavelet transform (NDWT) and discrete wavelet transform (DWT) algorithms for noise reduction. Further, early prediction of muscle mass prevents humans from lack of protein and oxygen levels in the blood and avoids major issues in human health. The proposed DBMSP antenna-based muscle mass measurement achieves 89% accuracy when compared with laboratory measurement.
      PubDate: Thu, 22 Dec 2022 10:35:01 +000
       
  • Predictive Model Techniques with Energy Efficiency for IoT-Based Data
           Transmission in Wireless Sensor Networks

    • Abstract: Wireless sensor networks are limited by the vast majority of goods with limited resources. Power consumption, network longevity, throughput, routing, and network security are only a few of the research issues that have not yet been addressed in sensor networks based on the Internet of Things. Prior to becoming widely deployed, sensor networks built on the Internet of Things must overcome a variety of technological obstacles as well as general and specific hazards. In order to address the aforementioned problems, this research sought to improve rogue node detection, reduce packet latency/packet loss, increase throughput, and lengthen network lifetime. Wireless energy harvesting is suggested in the proposed three-layer cluster-based wireless sensor network routing protocol to extend the energy lifespan of the network. For the purpose of recognising and blacklisting risky sensor node behaviour, a three-tier clustering architecture with an integrated security mechanism is suggested. This clustering approach is cost-based, and the sink node selects the cluster and grid heads based on the cost function’s value. With its seemingly endless potential across a wide range of industries, including intelligent transportation, the Internet of Things (IoT) has gained prominence recently. To analyse the nodes and clustering strategies in IoT, the suggested method PSO is applied. A plethora of new services, programmes, electrical devices with integrated sensors, and protocols have been produced as a result of the Internet of Things’ explosive growth in popularity.
      PubDate: Tue, 20 Dec 2022 15:35:01 +000
       
  • Online Superficial Gas Velocity, Holdup, and Froth Depth Sensor for
           Flotation Cells

    • Abstract: In flotation process, the efficiency and selectivity depend on mineralogy, particle size distribution and liberation, reagents added, mixing, and particle coverage. However, the kinetics of particle recovery is highly dependent on cell hydrodynamic and circuit configuration and operational strategy. Controlling froth depth and gas flow rate, measured as superficial gas velocity, is a straightforward alternative related to kinetics in the froth and collection zones. However, these parameters are not measured accurately. Froth depth measurement is based on a floating device coupled with a sonic sensor; this configuration presents hysteresis and deviation due to variation in the gas holdup and pulp density. In self-aspirated machines, there is no technology to measure gas velocity. To address this problem, the intelligent online gas dispersion sensor based on two concentric HDPE cylindres is proposed. The intelligent online gas dispersion sensor is based on two concentric HDPE cylinders. The methodology improves the accuracy of gas velocity calculation with a new algorithm. Froth depth measurement is based on two pressure transducers, reducing the uncertainty of the floating sonic sensor to 1 cm. Pulp bulk density is directly measured, and gas holdup can be estimated. Experimental results and industrial device validation indicate that the new intelligent system can measure superficial gas velocity (Jg) online and self-calibrate, with a 2% error, the froth depth error being ±1 cm. Therefore, a multiparameter sensor for measuring gas dispersion in industrial flotation cells was experimentally designed and validated in an industrial environment (TRL 8). In this context, the proposed online gas dispersion sensor emerges as a robust technology to improve the operation of the flotation process.
      PubDate: Mon, 19 Dec 2022 15:35:00 +000
       
  • Numerical Investigation on ELF Electromagnetic Field Distribution of
           Pipeline Robot Tracking and Positioning System Using UAV

    • Abstract: Pipeline robot, as a new type of equipment for pipeline operations such as pigging and detection, will play an increasingly important role in the operation and maintenance of oil and gas pipeline networks. The tracking and positioning technology during its operation process is one of the essential topics to improve the operating performance of pipeline robots and eliminate potential pipeline accidents. This paper presents the overall design of the wireless tracking and positioning system for pipeline robots based on extremely low frequency (ELF) electromagnetic method using the unmanned aerial vehicle (UAV). Starting from the classical electromagnetic theory, a mathematical model for the distribution of ELF electromagnetic field in buried metal pipeline environment is established. According to the characteristics of ELF electromagnetic wave transceiver and the model based on classical theory, the equivalent magnetic dipole transmission model is deduced. Based on the equivalent model, the attenuation characteristics of the ELF electromagnetic wave in the external space of the pipeline are analyzed by numerical simulation. The influence of the geometrical dimensions, environmental and working parameters, and other factors on the electromagnetic field intensity outside the pipeline is given at the same time. Finally, the distribution of ELF electromagnetic field in pipeline environment is discussed for horizontal-laying pipeline and inclined-laying pipeline, and the tracking method of pipeline robot is proposed on this basis. The proposed scheme is practical and effective, and it is suitable for real-time tracking and positioning of robot working in the pipelines whose slope is no more than 60 degrees with known or unknown distribution.
      PubDate: Mon, 19 Dec 2022 13:20:00 +000
       
  • Monitoring Worker Exposure to COVID-19 and Other Occupational Risks Using
           BLE Beacons

    • Abstract: The COVID-19 pandemic has become a public health priority during 2020. Social safety distance is one of the most effective strategies to stop the spreading of the virus, as it reduces the dose of infectious particles that a person can receive. Real-time location systems (RTLS) based on ultrawideband (UWB), radio frequency identification (RFID), Global Position System (GPS), or Bluetooth Low Energy (BLE) can help keep workers safe at the workplace. The aim of the current paper is to develop a dosimeter proposal to monitor and control the distance and exposure time between workers based on BLE beacon technology considering viral load. Our proposal is based on a set of BLE beacons and safety distance estimation by filtering RSSI measurements with a Gaussian extended Kalman filter. According to the estimated proximity values and the exposure time, a finite state machine will alarm when the worker receives the maximum dose defined by health authorities. The proposed system can be applied to prevent any risk that can be eliminated or reduced controlling distances and/or exposition time of the worker to the occupational risk. The proposal is robust, is inexpensive, and respects the privacy of workers, and its accuracy is higher than that of existing smartphone applications. In future pandemic situations, the system can be easily updated to the safety distance and viral particle dose related with the new risk agent. The system can protect from additional risk incorporating beacons on the extra risk identified such as thermal, noise, or radiation.
      PubDate: Thu, 15 Dec 2022 05:35:01 +000
       
  • Image Super-Resolution Network Based on Feature Fusion Attention

    • Abstract: The residual structure may learn the entire input region indiscriminately because the residual connection can still learn well as the network depth grows. To a certain extent, the attention mechanism can focus the network’s attention to the interesting area, enhancing the learning performance of essential areas while decreasing the computational load for the system. As a result, the combination of these two advantages could have substantial research significance, for both improve the efficiency and reduce the computational load. A dense residual connection network that combine feature fusion attention approach in image super resolution process is proposed. The dense residual block is enhanced with pixel and channel attention blocks, and a dual-channel path design incorporating global maximum pooling and global average pooling is utilized. A hybrid loss function is also proposed in order to increase the network’s sensitivity to the maximum error between individual pixels. The PSNR/SSIM/ performance metrics increased after applying the hybrid loss function and our attention techniques. The experimental results demonstrated that our novel approach has several advantages over some recent approaches, as well as showing good outcomes on many testing datasets.
      PubDate: Wed, 14 Dec 2022 02:50:01 +000
       
  • OAB-YOLOv5: One-Anchor-Based YOLOv5 for Rotated Object Detection in Remote
           Sensing Images

    • Abstract: Remote sensing images are widely distributed, small in object size, and complex in background, resulting in low accuracy and slow speed of remote sensing image detection. Existing remote sensing object detection is generally based on the detector with anchors. With the proposal of a feature pyramid network (FPN) and focal loss, an anchorless detector emerges, however, the accuracy of anchorless detection is often low. First, this study analyzes the differences and characteristics of the intersection of union (IoU) and shape matchings based on anchors in mainstream algorithms and indicates that in dense or complex scenes, some labels are not easily assigned to positive samples, which leads to detection failure. Subsequently, we proposean one-anchor-based (OAB) object detection algorithm based on the idea of central point sampling in the anchor-free detector. The positive samples and negative samples are defined according to the central point sampling and distance constraint, and an anchor box is preset for each positive sample to accelerate its convergence. It reduces the complexity of the anchor-based detector, improves the inference speed, and reduces the setting of hyperparameters in the traditional matching strategy, rendering the model more flexible. Finally, in order to suppress background noise in remote sensing images, the vision transformer (ViT) is adopted to connect the neck and head, making it easier for the network to pay attention to key information. Thus, it is not easy to lose in the training process. Experiments on challenging public dataset—DOTA dataset- verified the effectiveness of the proposed algorithm. The experimental results show that the mAP of the optimized OAB-YOLOv5 method is improved by 2.79%, the number of parameters is reduced by 13.2%, and the inference time is reduced by 11% compared with the YOLOv5 baseline.
      PubDate: Tue, 13 Dec 2022 04:35:01 +000
       
  • Intelligent Management of Hydroponic Systems Based on IoT for Agrifood
           Processes

    • Abstract: There are a wide variety of new microprocessors that are easy to program and configure to perform complex tasks, and with the right features, sensors, and additional mechanisms, we can prepare them to monitor and take care of the crops with automated processes. Soil moisture, air temperature, humidity, CO2, and water level are some of the most basic parameters to monitor with sensors, but any type of sensor can be added if the signal is adapted so that the microprocessor can read it. The data read from the sensors allow us to control and automate processes using relays connected to a variety of external components like illumination, refrigeration, and irrigation systems. We present a solution to the environmental monitoring hydroponic system based on IoT. The developed device is a low powered, and the data obtained is transmitted via Zigbee to a central system where we can configure and control all the devices paired, so it is relatively easy to implement and escalate.
      PubDate: Mon, 12 Dec 2022 08:05:01 +000
       
  • Retracted: Interpolation Parameters in Inverse Distance-Weighted
           Interpolation Algorithm on DEM Interpolation Error

    • PubDate: Wed, 07 Dec 2022 15:05:00 +000
       
  • Performance Evaluation of Deep Learning Algorithm Using High-End Media
           Processing Board in Real-Time Environment

    • Abstract: Image processing-based artificial intelligence algorithm is a critical task, and the implementation requires a careful examination for the selection of the algorithm and the processing unit. With the advancement of technology, researchers have developed many algorithms to achieve high accuracy at minimum processing requirements. On the other hand, cost-effective high-end graphical processing units (GPUs) are now available to handle complex processing tasks. However, the optimum configurations of the various deep learning algorithms implemented on GPUs are yet to be investigated. In this proposed work, we have tested a Convolution Neural Network (CNN) based on You Only Look Once (YOLO) variants on NVIDIA Jetson Xavier to identify compatibility between the GPU and the YOLO models. Furthermore, the performance of the YOLOv3, YOLOv3-tiny, YOLOv4, and YOLOv5s models is evaluated during the training using our PowerEdge Dell R740 Server. We have successfully demonstrated that YOLOV5s is a good benchmark for object detection, classification, and traffic congestion using the Jetson Xavier GPU board. The YOLOv5s achieved an average precision of 95.9% among all YOLO variants and the highest success rate achieved is 98.89.
      PubDate: Wed, 07 Dec 2022 09:05:00 +000
       
  • The Application Effect of Remote Sensing Technology in Hydrogeological
           Investigation under Big Data Environment

    • Abstract: The hydrogeological investigation is a work carried out by comprehensive utilization of various exploration methods to identify hydrogeological conditions in the target area, and develop and utilize groundwater resources. There are great differences in hydrogeological conditions in different regions. Hence, it is necessary to take exploration technology according to local conditions to master hydrogeological information as much as possible. Among them, the remote sensing (RS) technology can reflect the ground surveying and mapping results with high efficiency and precision through the analysis of satellite or aerial photographs, which is a commonly used method in the current hydrogeological investigation. According to satellite RS data, this work evaluates the distribution of groundwater levels in the study area and explores the geological and hydrogeological conditions of the groundwater system in the affected area. Firstly, the human-computer interactive interpretation method is used to analyze the topography and geomorphology conditions. Secondly, the spectral characteristic curve analysis method is used to extract the spectral characteristics of regional stratum lithology, and analyze and determine the lithology composition and structure of the aquifer. Thirdly, the single-band and multiband models of soil moisture RS estimation of groundwater level are implemented. Finally, the measured data are employed to verify and analyze the estimated value of the model. The results are in line with the actual value, and good results have been achieved.
      PubDate: Wed, 07 Dec 2022 08:50:01 +000
       
  • Detection of Marine Oil Spills Based on HOG Feature and SVM Classifier

    • Abstract: Oil spill accidents have gradually increased due to the continuous development of marine transportation and petroleum processing industries. Monitoring and managing marine oil spills present important economic, social, and practical implications in preventing offshore oil pollution and maintaining ecological balance. Unmanned aerial vehicle (UAV) has become a suitable carrier for low-altitude oil spill detection because of their fast deployment and low cost. Thermal infrared remote sensing images are used as the research object in this study. A method around histogram of gradient (HOG) features combined with a support vector machine (SVM) is proposed for identifying oil spills at sea to improve the accuracy of offshore low-altitude oil spill recognition and realize all-weather monitoring of offshore oil spills in offshore waters. Steps for extracting HOG features and basic principles of the SVM classification are first investigated. Image preprocessing is then performed on collected thermal infrared image data to produce samples. HOG features of samples are extracted, and the radial basis function is selected as the kernel function for training the SVM classifier. HOG features of the infrared image to be tested are calculated and then sent to the classifier for identifying the oil spills. In addition, the proposed method is compared with the back propagation(BP) neural network method and local binary pattern (LBP) combined with the SVM classification method for analysis. The results show that the oil film recognition method based on the HOG feature and SVM has a recognition accuracy of 91.3% in the environment of small infrared oil film samples, which is significantly better than the BP and LBP-SVM recognition methods, and obtains a shorter training time. The method proposed in this study has obvious advantages in terms of small sample size and processing efficiency, can meet the requirements of all-weather inspection of oil film pollutants by UAV in offshore port areas, and has great application potential in the field of maritime supervision informatization in the future.
      PubDate: Tue, 06 Dec 2022 15:35:01 +000
       
  • 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
       
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


Your IP address: 3.236.70.233
 
Home (Search)
API
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-