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The design of a smart water monitoring and control system in urban areas plays a pivotal role in providing efficient distribution mechanisms to reduce leakage, especially in regions facing water scarcity and limited resources. The convergence of the Internet of Things (IoT) and blockchain technology to improve the system's performance, enhance its security, and provide a decentralized and tamper-proof environment presents an excellent opportunity to evolve the system further and form a state-of-the-art Water 4.0 ecosystem. The proposed Blockchain of Things (BCoT) water system is introduced as a pilot to explore its potential in delivering and managing Water 4.0 applications. An Ethereum platform formed the heart of the BCoT system, while a Raspberry Pi 4 acted as a node to the blockchain that collected data from various sensors and microcontrollers via MQTT programmed by Node-Red. LabVIEW software also provided supervisory control and data acquisition (SCADA). The BCoT system was tested, and its functionality was verified, showing good promise to take smart water systems to a new level of innovation that may resolve the many challenges faced by countries with limited water resources and address the challenges of the 21st-century "Water 4.0" ecosystem. [ABSTRACT FROM AUTHOR]

A novel detection method, known as RGB‐tracking, was utilized to quantify the relative color changes in reactants/products during the dyeing of a solution of indigo as a model compound. The RGB‐tracking system consists of a digital camera, a light source, a data acquisition device, a LabVIEW program, and its built‐in function (Machine Vision). For the reduction of indigo, two types of reagents were employed: sodium dithionite (Na2S2O4) for the chemical redox process and yeast (Saccharomyces cerevisiae) for the biological fermentation process. As a result, in the case of chemical redox during the reduction of the indigo solution, the progress of the reaction can be described by a color change, that is, the curves of R‐ and G‐pixels, respectively. When the curve for the G‐pixels began to increase, this indicated that leuco‐indigo is being formed. A dramatic increase in the R‐curve indicates that the amount of leuco‐indigo has exceeded than that of indigo itself and that the reaction will gradually reach equilibrium. In contrast to this, additional complex behavior was observed in the case of biological fermentation. When the RGB‐tracking curves for the R‐ and G‐pixels reach their maximum values, this indicates that the concentration of leuco‐indigo was significantly higher than that of indigo itself. The flattening of the RGB‐tracking curves indicates that the reaction has reached equilibrium. [ABSTRACT FROM AUTHOR]

Visible and near-infrared (VIS-NIR) spectral technology is fast, accurate, and non-destructive, which meets the requirements of the separation of coal and gangue. However, the performance of the classification models established by the spectra collected according to the usual single-location collection methods was unsatisfactory. Simulating the production conditions of coal gangue sorting, this paper proposed a multilocation spectral feature fusion identification method for coal and gangue to improve the identification accuracy and developed an identification system. In the multilocation spectral collection method, the spectra of the sample were collected at three locations under the probe, which obtained richer information. In the classification models built based on different feature fusion methods, the one-dimensional convolutional neural network model with feature layer fusion had the strongest classification ability, with an accuracy of 97.61%, an improvement of 8.84% over the best single-location model. The regions of interest for model decision-making were visualized using the Gradient-weighted Class Activation Mapping (Grad-CAM) method to explore the reasons why the multilocation feature fusion method was more advanced. Furthermore, a coal gangue identification system was developed using LabVIEW, which can achieve fast and accurate recognition with an accuracy of 93.15% and an expenditure time of 1.241 s. [ABSTRACT FROM AUTHOR]

This research proposes an application of the image processing device for the detection of knitted fabrics. The machine rolls up knitted fabrics and then the fabrics moves through a detection area, where the camera is connected to the image processing device. Next, an image captured after a defect exists is sent to be processed and analyzed by the NI myRIO device. An area pixel is computed based on Hue, Saturation, and Lightness (HSL) system and interpreted for defect selection. This procedure is performed on region of interest (ROI) to verify the defect. Three defects, examined in this research, consist of small holes, loose threads, and crook knitting needles. These knitted fabric defects are classified from the proposed weighted voting classifier, the results were found that the defects of three knitted fabrics can be accurately predicted. The proposed method can be available to the textile industry. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Nowadays the detection of fabric defects is an active research topic to detect and resolve the difficulties faced in processing fabric in printing and knitting in textile industries. The traditional approach of visual screening of human fabrics is exceedingly time consuming and it is not reliable as it is much susceptible to human errors. There are two major issues in defect inspection like defect identification and classification in fabric. Automatic identification of defects is quite important in the current scenario. For enhancing the quality of the fabric, this paper proposes a Texture Defect Detection (TDD) algorithm. This TDD algorithm utilizes pre-processing for the extraction of luminance plane and Discrete wavelet frame decomposition for dividing the image into several subbands with same resolution as input image. Statistical features are extracted using Gray Level Co-occurrence Matrix and these features are applied to Support Vector Machine for classifying the defective images. This improves the quality of texture segmentation and classification of visible defects. The experimental setup is done with the fabric conveyor and three high resolution industrial cameras acA4600-7gc for covering the entire width of the fabric while running. This TDD algorithm is developed under LabVIEW platform. Textile Texture Database (TILDA) multi-class dataset is used for testing the proposed algorithm. This algorithm is tested for 4 different classes of fabric defects including 2800 defective and 400 non defective fabric images. The success rate of detection of fabric defect is 96.56% with the images from the database. The validation results with real time fabric images show 97% of accuracy in the detection of defects in fabric images. [ABSTRACT FROM AUTHOR]

In the present paper, an affordable innovative physical experimental equipment consisting of an upper computer, an ultrasonic sensor module, and an Arduino microcontroller has been designed. The relationship between the position of the slider fixed on two springs and time is measured by using the ultrasonic sensor module. A system for slider motion data and image acquisition is constructed by using the LabVIEW interface of Arduino UNO R3. The purpose of this experiment is to demonstrate and interpret the propagation of waves represented by harmonic motion. The spring oscillator system including a slider and two springs is measured and recorded, and the motion can be realized using curve fitting to the wave equation in Sigmaplot. The vibration periods obtained from experimental measurements and curve fitting of the wave equation are 1.130 s and 1.165 s, respectively. The experimental data are in good agreement with the theoretical model. The experimental measurement results show that the maximum kinetic energy is 0.0792 J, the maximum potential energy is 0.0795 J, and the total energy at the position of half the amplitude is 0.0791 J. The results verify the mechanical energy conservation of spring oscillator system in a short time. This self-made instrument has improved the visualization and the automation level of the corresponding experiments. [ABSTRACT FROM AUTHOR]

In the process of metal wire and additive manufacturing, due to changes in temperature, humidity, current, voltage, and other parameters, as well as the failure of machinery and equipment, a failure may occur in the manufacturing process that seriously affects the current situation of production efficiency and product quality. Based on the demand for monitoring of the key impact parameters of additive manufacturing, this paper develops a parameter monitoring and prediction system for the additive manufacturing feeding process to provide a basis for future fault diagnosis. The fault diagnosis and prediction system for metal wire supply and additive manufacturing utilizes STM 32 as its core, enabling the capture and transmission of temperature, humidity, current, and voltage data. The upper computer system, designed on the LabVIEW 2019 virtual instrument platform, incorporates an LSTM neural network model and facilitates a connection between LabVIEW and MATLAB 2019 to achieve the prediction function. The monitoring and prediction system established in this study is intended to provide basic research assistance in the field of fault diagnosis. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Smart Rail Transit is the property of Smart Rail Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In light of the heavy load applied by traditional air spring test equipment and its complex structural system design, a lever-type torque loading air spring test system is designed. It adopts the principle of Chinese scales to apply the load on the air spring in the form of proportional amplification, which can apply a simulated load on the air spring of 500–800 kg, using the vertical sliding shaft as the transverse limit to make the air spring elongate and compress by 280 mm in the longitudinal direction. The measurement and control system of the test equipment is then developed based on the LabVIEW platform, and the required sensors are selected and installed. The system can achieve real-time data acquisition of the air pressure, load, height and other parameters of the air spring and air spring charging and discharging control. Following the debugging of the function of the test equipment, the function of each subsystem is normal and able to meet the requirements of air spring characteristic and pressure tightness tests. For small spaces, such as laboratories, by reducing the installation of hydraulic and other oil source systems, avoiding the use of large mass blocks to simulate the loading of air spring loads and optimizing the complex installation and debugging process, this miniaturized design for air spring test equipment has benefits for practical applications. [ABSTRACT FROM AUTHOR]

Conventional baseband processing techniques for communication must be adapted to the specificities of terahertz channels. Currently, there is limited research on channel coding applicable to terahertz bands, particularly in terms of performing real-time tests that differentiate them from offline processing. This paper presents a test platform for a 300 GHz wireless communication system based on the PXIe platform. The experiment utilizes a high-performance, low-complexity serial scheduling approach with the Minimum Sum-Log Likelihood Ratio Belief Propagation (MS-LLR-BP) soft decision decoding algorithm to test point-to-point real-time line-of-sight communication for low-density parity-check (LDPC) code communication systems. The results indicate that the implementation of LDPC coding in QPSK terahertz wireless transmission systems results in a performance enhancement of approximately 25–55% in bit error rate (BER), demonstrating the excellent performance of LDPC codes against terahertz channel fading. This highlights their potential in future ultra-high-speed communication systems. [ABSTRACT FROM AUTHOR]

Objective To ensure the stability, reliability, compatibility, as well as the accuracy of data transmission and control in train network systems, research on the consistency testing of train networks is needed. Method The design of the train Ethernet testing platform is based on the IEC 61375 standard and the construction of the testing platform is completed. The testing platform mainly consists of hardware and software platforms. The hardware platform includes testing instruments, companion devices, fixtures, and industrial control computers. The software platform is based on LabVIEW programming language, NI TestStand software testing framework, and Tcl scripting language for system integration. Result & Conclusion The automation testing of the physical layer, protocol layer, and performance of the train Ethernet is achieved, and certified by CNAS (China National Accreditation Service for Conformity Assessment).

Utilizing an embedded system is crucial for exploring various fields of study, such as ground wave propagation, which interests earth and civil engineering scientists/students. The constructed system includes a data acquisition (DAQ) module and vibration sensors, which can be employed to study wave propagation. It is particularly beneficial for students lacking access to necessary instruments. The sensors utilized are MEMS accelerometers. Developed using LabVIEW programming software, a graphical interface controls the DAQ and visualizes the wave signals. The primary parameter of the DAQ is the sampling frequency, which is adjustable by the user from 1 kHz to 25 kHz. Consequently, when testing with distances ranging from 1 to 10 m between the sensor and the source, a sampling rate exceeding 10 kHz represents ground acceleration reliably. Another aspect addressed in subsequent studies using this system involves analyzing the results to characterize ground wave attributes. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The magnetoelectric (ME) sensor is a new and promising type of magnetic field sensor with ultrahigh sensitivity. However, there are few reports on the research of real‐time measurement system which can promote its practical application. Herein, a novel real‐time measuring approach for weak AC magnetic fields at relatively low frequency is proposed using Metglas/PZT‐5B ME sensors. The system mainly consists of an oscilloscope, a signal generator, and a program developed with LabVIEW programming. Real‐time measurement of relatively low‐frequency magnetic fields has been achieved by using frequency upconversion methods, simultaneously displaying the frequency and magnitude of the magnetic field. As a result, the real‐time measurement system is able to detect a weak AC magnetic field as low as 0.1 nT@1 Hz, which is promising to push the ME sensor to practical application. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Water quality is crucial to the health of the prawn industry in Malaysia, where unpredictable weather is common. However, many breeders still monitor the water quality manually, which limits their capacity to respond quickly to changing conditions. To address this, an automated system was developed using Internet of Things (IoT) technology to continually monitor and control the pH level and temperature of the saltwater. Sensors are connected to a LabVIEW-equipped data acquisition, and a servo motor operates the probe of the sensors. The proposed system employs ThingSpeak software to display real-time water quality data and has a system of alarms that can be activated to notify the user of any issues. An acid or alkali solution is pumped into the tank using a submersible water pump to regulate the pH level, and a fuzzy logic controller is integrated into the submersible water pump to control the valve for the solutions. From the results, the systems managed to measure the maximum value of the temperature which is 33.86°C meanwhile the minimum value is 30.6 °C. These temperature values are in the desirable range of temperature for prawn which is between 25 °C to 35 °C. Hence, this system depicts that the water is a good temperature for the prawn. Furthermore, the data transfer through wireless via ThingSpeaks' website is the same as the data transfer through mobile's app. Hence, this prove that the system is able to deliver the water quality data accurately and can be monitored remotely. The system will greatly benefit the aquaculture industry, and continued development will only improve its effectiveness. [ABSTRACT FROM AUTHOR]

This paper is devoted to the improvement existing models of electronics devices, which are used in powers electronics as switching devices, and investigate a LabVIEW-based automatic test-bench for Silicon carbide (SiC) power devices. In recent years, power electronic devices are required to be capable handle with higher voltage, leads to development of new generation of power electronic devices, such as SiC devices. However, using a simulation platform, such as Spice, to diminish the complexity of power electronic design with these new devices is hindered by the lack of precise models. The proposed test-bench enables not only measuring static characteristics of SiC power devices, but also extracting key parameters required by simulations. These extracted parameters are then employed in the existing device model, and the simulation results which are based on the model with original parameters and models with extracted parameters are compared with measured results. The comparison clearly demonstrates that parameters obtained from the proposed test-bench significantly enhance the Spice model. [ABSTRACT FROM AUTHOR]

Copyright of Railway Signalling & Communication Engineering is the property of Railway Signalling & Communication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In large-scale manufacturing, ensuring the efficient operation of rotating machines is crucial to avoid breakdowns and failures during production. This article introduces a method for detecting gearbox faults by analyzing vibration signals and employing artificial intelligence techniques, with a particular emphasis on comparing these methods. The diagnostic process consists of three stages: extracting features using Wavelet Packet Transform (WPT) and statistical analysis, selecting optimal properties through the gain ratio method, and using Support Vector Machine (SVM) and Artificial Neural Network (ANN) models to distinguish between faults and assess their performance. The diagnostic outcomes demonstrate that both SVM and ANN models accurately identify various fault patterns depending on the operating conditions. Remarkably, the study highlights the ANN model's superiority over the SVM model in classifying gearbox faults, indicating its suitability for gearbox fault diagnosis. This research yields valuable insights into machine condition monitoring, showcasing the ANN model as a robust tool for gearbox fault detection. The findings advocate for the implementation of ANN-based approaches in real-world applications to enhance the reliability of fault detection and prevention in rotating machines. Furthermore, future research directions may explore additional enhancements and optimizations for ANN models, leading to more advanced machine health monitoring systems in the manufacturing industry. [ABSTRACT FROM AUTHOR]

The current study introduces a novel technique to handle electrochemical localized corrosion in certain limited regions rather than applying comprehensive cathodic protection (CP) treatment. An impressed current cathodic protection cell (ICCPC) was fabricated and firmly installed on the middle of a steel structure surface to deter localized corrosion in fixed or mobile steel structures. The designed ICCPC comprises three essential parts: an anode, a cathode, and an artificial electrolyte. The latter was developed to mimic the function of the natural electrolyte in CP. A proportional-integrated-derivative (PID) controller was designed to stabilize this potential below the ICCPC at a cathodic potential of −850 mV, which is crucial for protection efficacy. The controller of the protection system was designed to automatically activate the ICCPC when the environment of the steel structure is exposed to humidity. At a temperature range from 27 °C to 35 °C, the protection potential, current, and power showed an increase in percentage by 411 %, 688.74 %, and 2842.3 %, respectively when the humidity level rose from 10 % to 100 %. The intended spraying of sodium chloride (NaCl) solution on the structure at different concentrations from 1.5 % to 5 % showed excellent protection against corrosion, notable improvements in electrical conductivity, and reduction in resistance between the anode and cathode. The energy dispersive spectroscopy and scanning electron microscopy (SEM-EDS) analyses confirm effective corrosion inhibition under ICCPC coverage compared to unprotected areas. These tests revealed an absence of oxidation beneath the ICCPC compared with areas outside of its coverage. The areas outside of ICCPC protection showed a reduction in iron and carbon contents from 57.1 % to 47.0 % and 41.4 %–22.4 %, respectively, and an increase in oxygen from 1.6 % to 30.6 %.

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Global Navigation Satellite System (GNSS) receivers perform many important signal-processing operations, among which signal acquisition is of great importance. The acquisition provides a coarse estimate of the code delay and Doppler frequency values of the received signal, which plays an important role in synchronizing the local code with the received signal. Depending on the application domain, GNSS signals may be affected by indoor and urban channel conditions, interference, and jamming. Since the GNSS signal has to travel thousands of kilometers to reach the receiver antenna and then the receiver circuitry, the received signal becomes weak and degraded. One way to deal with such situations is the implementation of an advanced signal-processing algorithm in the receiver capable of giving appropriate warnings or improving the receiver's performance. In this paper, we propose a method based on wavelet denoising that helps improve receiver acquisition sensitivity, thus stepping up the accuracy of the receiver. The proposed method uses wavelet transform (WT) to divide the wavelet space, with improved de-correlation capability resulting in improved signal acquisition. This method is implemented on a Navigation with Indian Constellation (NavIC) receiver built on a LabVIEW-based software platform. Real-time experiments showed that the proposed method could improve the acquisition and receiver performance by 49–52% under the degraded signal environment. [ABSTRACT FROM AUTHOR]