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Column-supported embankments are proven efficient in enhancing the bearing capacity of soft ground. The effect of geosynthetic encasement depends on the length and stiffness of the encasing material. With a basal geocell, the stress transferred to the subgrade is lowered, and the optimum encasement length can be reduced. Strain-controlled model tests were performed on soft clay reinforced with an encased stone column with basal geocell. Three different geotextiles were used to fabricate the encasement and geocell. Two dimensionless factors were introduced—improvement factor (IF) and successive improvement index (SII), to evaluate the independent effect of geocell and the effect of encasement length, respectively. The critical length of encasement lcr is evaluated at SII of 10% for the bearing capacity of clay and stress on the column. The test results indicate that basal geocell reduces a stone column's critical encasement length regardless of the material stiffness and stress-settlement levels. Geocell enhances bearing pressure and lowers stress on soft ground and stone columns. The lateral spreading enables the load to be distributed evenly on the stone column-reinforced ground, thus reducing the stress concentration ratio. The reduction in lcr arises from the fact that geocell dominates the action of the stone column, making the strain mobilized in the lower half of the column unutilized. Hence, the impact of geocell is unaffected by the increase in encasement length beyond 2/3rd of the column depth. For stiffer materials, the performance is enhanced at higher stress levels upon mobilization of adequate strain on the encasing sleeve and geocell walls. The numerical simulations carried out were validated with experimental observations. [ABSTRACT FROM AUTHOR]

Metallic magnetic calorimeters are a class of low-temperature particle detectors based on calorimetry，utilizing metallic paramagnetic temperature sensors to convert the temperature rise of an absorber upon the absorption of incident particle energy into a change in magnetic flux，which detected by a superconducting quantum interference device (SQUID). This process enables high-resolution measurements of radiation with exceptional precision. However，metallic magnetic calorimeter signals are inherently weak and highly susceptible to noise，making the extraction of pulse amplitude a significant challenge. Digital signal processing (DSP) plays a crucial role in the data processing of metallic magnetic calorimeter by employing digital filtering techniques to shape the signals and enhance energy resolution. This paper introduces a data acquisition system for metallic magnetic calorimeter based on a field-programmable gate array (FPGA)，designed for high-speed signal acquisition，real-time control，signal processing，and display of spectra through an upper computer. Testing of the system yielded successful acquisition of both Co-60 spectra data and noise spectrum of metallic magnetic calorimeter. The linear correlation coefficient of R2 reached 0.9781，demonstrates the system’s effective signal acquisition capability. In offline mode，the shaping effects and energy resolution enhancement of traditional nuclear detector signals，simulated，and actual metallic magnetic calorimeter signals were compared using three filtering methods: trapezoidal，cusp，and gaussian filtering. The cusp filter proved to be the most effective，significantly improving the energy resolution of high-purity germanium spectra from 0.598 keV to 0.536 keV. This enhancement validates the correctness of digital signal processing and provides a reliable basis for the selection of digital filtering methods for metallic magnetic calorimeter. In summary，our research presents a comprehensive approach to metallic magnetic calorimeter signal acquisition and processing，highlighting the potential of digital signal processing to overcome the problem of the extraction of weak signals submerged in strong noise. The FPGA-based data acquisition system developed in this study offers an efficient solution for high-resolution radiation measurements.

The application of machine learning techniques for monitoring and diagnosing faults in photovoltaic (PV) systems has been shown to enhance the reliability of PV power generation. This research introduced a novel machine learning classifier for fault diagnosis in PV systems, utilizing an ensemble algorithm known as extra trees (ETC). The study initially proposed a system with two PV modules and developed a low-cost Arduino-based data logger to gather data from the PV system in free-fault and faulty conditions. Subsequently, the study evaluated six other advanced classifiers for fault diagnosis in PV systems, namely logistic regression (LR), k-nearest neighbor (kNN), support vector machine (SVM), decision tree (DT), AdaBoost, and random forest (RF) models using the collected data from the proposed PV system. The assessment of the various models' performance indicated that the extra trees model exhibits superior classification capabilities for partial shading (PS), open circuit (OCF), partial shading with bypass diode disconnected (PSBD), and combined partial shading with bypass diode disconnected plus open circuit (PSBDOC) faults. The results demonstrated that the new ETC classifier achieves an accuracy of 92%, surpassing the 91%, 87%, 7%, and 59% accuracy of the RF, DT, kNN, and LR classifiers, respectively. This highlights the effectiveness of the extra trees model in enhancing fault detection and classification by distinguishing between open circuits and twin faults. Consequently, these results can be utilized to develop advanced diagnostic tools for photovoltaic systems, thereby improving the reliability of solar technology and accelerating the rate of installation.

Evaluating remote sensing techniques (RST)-based water quality monitoring systems in hydrological applications is a complex multi-attribute decision-making (MADM) problem due to several integrated issues. These issues include the need to consider 15 evaluation criteria, the importance of these criteria in relation to their application and network infrastructure, data variation, and trade-offs and conflicts between them. Therefore, this study proposes an MADM integrated modelling approach to weight these criteria and rank the selected RSTs using the "Fuzzy Weighted with Zero Inconsistency" (FWZIC) method coupled with the "Vlse-kriterijumska Optimizcija I Kaompromisno Resenje" (VIKOR) method. First, a decision matrix was developed for the evaluation criteria and their intersection with various RSTs in different network categories. Next, the assessment criteria were weighted using FWZIC, followed by ranking the RSTs for each category using VIKOR. The findings reveal that the criteria weighting varied across categories, as assigned by specialists-based FWZIC. For example, the "data acquisition system complexity" criterion received the maximum weight (w = 0.081) for mesh-based sensing and the lowest (w = 0.057) for cellular-based sensing. Using the obtained weights and based on the ascending order of the performance score value (Q), various RSTs were benchmarked using VIKOR. This includes (n = 22) RSTs in fixed star, (n = 8) RSTs in mesh sensing, (n = 6) RSTs in cellular sensing, (n = 3) RSTs in fixed cable sensing, and (n = 2) RSTs in movable sensing. Finally, the evaluation process for the benchmarked RSTs involved systematic ranking, sensitivity analysis, and comparison analysis, which confirmed the robustness of the proposed approach across all RST categories. [ABSTRACT FROM AUTHOR]

Data acquisition systems that receive analog signals, convert them to digital, and perform signal processing are used in a variety of systems that use acoustics, radar, sonar, indoor localization, and navigation. The previous systems, such as NI USRP-RIO, are expensive to build, and the number of signals a single device can receive is limited to between two and four. In order to receive more channels of signals, multi-channel data acquisition systems using ADCs operating at tens of MSPS have been proposed. However, these systems require additional processing time because data acquisition and signal processing are performed on different devices. In this paper, we propose a multi-channel data acquisition system using a 16-channel ADC that can support up to 100 MSPS. In particular, to reduce unnecessary signal transmission time, we propose a one-chip structure where all processes are performed on a single chip. Also, we propose a data acquisition system that applies pipelining techniques to enable real-time processing. To verify the proposed system, we used TI ADS52J90 and a Kintex UltraScale KCU105 evaluation board, and confirmed that it is possible to receive and process all channels simultaneously. Furthermore, it is possible to configure a real-time system by adjusting the speed of the signal-processing operation and the speed of the communication interface. Therefore, the proposed system is expected to reduce the cost of system construction by performing signal reception and processing with a single chip, and to reduce the time required for overall signal processing. [ABSTRACT FROM AUTHOR]

The application of machine learning techniques for monitoring and diagnosing faults in photovoltaic (PV) systems has been shown to enhance the reliability of PV power generation. This research introduced a novel machine learning classifier for fault diagnosis in PV systems, utilizing an ensemble algorithm known as extra trees (ETC). The study initially proposed a system with two PV modules and developed a low-cost Arduino-based data logger to gather data from the PV system in free-fault and faulty conditions. Subsequently, the study evaluated six other advanced classifiers for fault diagnosis in PV systems, namely logistic regression (LR), k-nearest neighbor (kNN), support vector machine (SVM), decision tree (DT), AdaBoost, and random forest (RF) models using the collected data from the proposed PV system. The assessment of the various models' performance indicated that the extra trees model exhibits superior classification capabilities for partial shading (PS), open circuit (OCF), partial shading with bypass diode disconnected (PSBD), and combined partial shading with bypass diode disconnected plus open circuit (PSBDOC) faults. The results demonstrated that the new ETC classifier achieves an accuracy of 92%, surpassing the 91%, 87%, 7%, and 59% accuracy of the RF, DT, kNN, and LR classifiers, respectively. This highlights the effectiveness of the extra trees model in enhancing fault detection and classification by distinguishing between open circuits and twin faults. Consequently, these results can be utilized to develop advanced diagnostic tools for photovoltaic systems, thereby improving the reliability of solar technology and accelerating the rate of installation. [ABSTRACT FROM AUTHOR]

Introduction: Indoor agriculture, especially plant factories, becomes essential because of the advantages of cultivating crops yearly to address global food shortages. Plant factories have been growing in scale as commercialized. Developing an on-site system that estimates the fresh weight of crops nondestructively for decision-making on harvest time is necessary to maximize yield and profits. However, a multi-layer growing environment with on-site workers is too confined and crowded to develop a high-performance system. This research developed a machine vision-based fresh weight estimation system to monitor crops from the transplant stage to harvest with less physical labor in an on-site industrial plant factory. Methods: A linear motion guide with a camera rail moving in both the x-axis and y-axis directions was produced and mounted on a cultivating rack with a height under 35 cm to get consistent images of crops from the top view. Raspberry Pi4 controlled its operation to capture images automatically every hour. The fresh weight was manually measured eleven times for four months to use as the ground-truth weight of the models. The attained images were preprocessed and used to develop weight prediction models based on manual and automatic feature extraction. Results and discussion: The performance of models was compared, and the best performance among them was the automatic feature extraction-based model using convolutional neural networks (CNN; ResNet18). The CNN-based model on automatic feature extraction from images performed much better than any other manual feature extraction-based models with 0.95 of the coefficients of determination (R2) and 8.06 g of root mean square error (RMSE). However, another multiplayer perceptron model (MLP_2) was more appropriate to be adopted on-site since it showed around nine times faster inference time than CNN with a little less R2 (0.93). Through this study, field workers in a confined indoor farming environment can measure the fresh weight of crops nondestructively and easily. In addition, it would help to decide when to harvest on the spot. [ABSTRACT FROM AUTHOR]

This work examines a solar power plant connected to the Nouakchott electricity grid in Mauritania. Operating since 2013, the 15 MWp plant's reliability and energy yield have been evaluated using a performance index. The assessment involves three phases. First, the plant's meteorological environment and technical indicators are presented. In the second phase, mathematical performance models specified by the International Energy Agency (IEA) are applied to calculate performance indices using data from the data acquisition system (SCADA). The third phase compares actual production data for 2015, 2017, and 2020 with results simulated for PVsyst for the same years. The obtained results are thoroughly analyzed to highlight relevant physical phenomena. The analysis focuses on the plant's 7-year operating period and its impact on performance indicators for electricity production fed into the grid. This study provides insights into the solar power plant's reliability and energy yield, aiding future operational enhancements. It underscores the importance of performance monitoring and assessment in optimizing solar power generation systems. [ABSTRACT FROM AUTHOR]

Owing to the continuous usage of centrifugal pump machines (CPM), health-monitoring systems are important for improving the hydraulic machine industry. While operating the CPM, supervision is carried out by either the operator or the online health-monitoring system. The Health Monitoring System (HMS) helps to understand the conditions of the pump and maintenance prospectus. For breakdown and accidents in the CPM industry, the prime reasons are impeller failure, bearing failure, cavitation, and shaft misalignment; therefore, it is necessary to supervise the parameters to provide a better life to the plant and enhance its efficiency using Data Acquisition (DAQ). The independent features and dependent features are selected and experimentally investigated to examine the characteristics of the CPM plant. The feature selection and extraction process should be compatible with the DAQ system which will help to reduce the curse of data biases and data preparation. Based on that three different DAQ systems based on Arduino, Raspberry Pi, and Dewsoft FFT are used in the experiments and compared based on 5-Point summary, Standard Deviation, cost analysis, flexibility, and maintenance. This paper presents the various techniques and procedures for acquiring the data from CPM using all three DAQ systems and selecting one of them through a statistical approach.

This study aims to achieve intelligent monitoring of clothing comfort in motion, providing data and technical assistance for the research of comfort in motion and an efficient design foundation for sportswear comfort optimization. In view of the present situation of smart wear used in clothing comfort research, this paper primarily presents the construction of an acquisition system in motion, including temperature, humidity, and pressure data acquisition device, data pre-processing, data storage, and so on, which was called the ATHPD system for short. With the help of this acquisition system, tights in motion may now be tested for pressure, heat, and humidity for the first time. It can also transmit data wirelessly. Meanwhile, in order to verify the effectiveness of the acquisition system, by comparing the measurement data of the acquisition system (ATHPD system) with the measurement data of existing acquisition equipment (AMI3037 pressure measurement system, DS1923 button temperature and humidity recorder). The findings demonstrate that there is no significant difference between the data collected by the two methods, which proves the reliability of the acquisition system in this paper. The system guarantees the same quantity of temperature, humidity, and pressure data to be gathered at the same time and the consistency of the corresponding time points and also provides relevant data support for the evaluation and prediction model construction of human comfort in motion. [ABSTRACT FROM AUTHOR]

The object of research is a bracelet that uses the electromyography (EMG) method to control a bionic prosthesis. In the conditions of the development of modern biomedical technologies and robotics, such a system becomes key to improving the quality of life of people with disabilities, providing efficient and accurate control of prostheses. The problem addressed in the research is the development and analysis of a bionic prosthesis control system using a bracelet using the EMG method. The main focus is on the optimization of data collection and processing processes, as well as the development of machine learning algorithms for gesture recognition in order to improve the accuracy and efficiency of prosthetic control. The essence of the obtained results is the development and testing of a new bionic prosthesis control system that uses EMG signals obtained with the help of a bracelet. The study showed that the classifier based on the support vector method outperforms other algorithms such as neural networks and decision trees, achieving an average accuracy of 90 %. The obtained data were successfully filtered and subjected to feature extraction, which allowed to create effective gesture recognition algorithms. The system was tested in real time, which confirmed its high accuracy and efficiency. The proposed system includes an innovative bracelet for collecting EMG data, which are then processed and analyzed using modern machine learning algorithms. The innovativeness of the proposed approach lies not only in the high accuracy of gesture recognition, but also in the possibility of adapting the system to different types of bionic prostheses and operating conditions. This is achieved by using a classifier based on the support vector method, which demonstrates significantly higher accuracy compared to other algorithms such as neural networks and decision trees. The test results show an average accuracy of 92.5 %, which confirms the high efficiency of the system. The use of this system involves the intensive use of EMG sensors, which allows more accurate determination of the user’s intentions regarding the control of the prosthesis. This, in turn, contributes to the improvement of the quality of life of users, providing them with greater functionality and convenience in the use of bionic prostheses. [ABSTRACT FROM AUTHOR]

In recent years, technological advancement characterized by heightened connectivity and digitization has revolutionized our interaction with the world. This evolution has been exemplified by the emergence of the Internet of Things, which has introduced a new era of digitization and automation, primarily propelled by devices based on the ESP32 microcontroller. This study seeks to comprehensively evaluate and characterize a fully functional sensor network built upon the ESP32 microcontroller. The evaluation covers three key aspects: network performance, the ESP32's built-in ADC peripheral, and the data acquisition system. The ESP32 device has demonstrated its capability to consistently sample sensors at a high rate with remarkable stability. The ESP32's built-in ADC exhibited undesirable behavior for analog devices, attributed to noise and non-linearity. As a result, in applications requiring precision with analog sensors, the utilization of a cost-effective external ADC, as employed in this study, is recommended. Finally, during the network analysis of the sensor network, no issues were identified. The data acquisition system exhibited no bottlenecks, and packet losses remained minimal, regardless of the sampling period. [ABSTRACT FROM AUTHOR]

Tubular structures such as blood vessels, intestines, and the trachea are common in various life forms. This paper describes a measurement system to test the mechanical compliance of tubular structures. The novelty of the system lies in its hardware and software. Here we use vascular graft as an example to demonstrate the utility of the system. A fully synthetic vascular graft would ideally mimic the mechanical and architectural properties of a native blood vessel. Therefore, mechanical testing of the graft material under physiological pressure is crucial to characterizing its potential in vivo performance. The device operates through a low-cost Arduino-based control system that simulates and measures cyclic fluid pressure changes over time and a laser micrometer that measures diameter changes with pressure. This system is low-cost, assuming one already has access to a laser micrometer. In contrast to previous methods, this system offers a simple, low-cost, and customizable option to measure compliance and is equipped with data acquisition/analysis programs. These programs include a MATLAB application that processes and synchronizes Arduino Uno pressure signals and LabChart Pro diameter readings. Lastly, this paper explains the hardware and software of the measurement system. The system is beneficial for testing the pressure-diameter relationship of tubular structures of varying sizes and materials. [ABSTRACT FROM AUTHOR]

A novel data acquisition system for condition monitoring and predictive maintenance of mechanical parts, machinery, and industrial plants is presented. Current commercial solutions rely on an analog architecture and a star topology, in which all transducers are connected to a centralized acquisition unit. Usually this requires long shielded cables, which are sensitive to electromagnetic disturbances, always present in industrial environments. The proposed solution makes use of a digital bus implemented on an Unshielded Twisted Pair to connect one or more Acquisition Nodes to a data storage system (e.g., a laptop or an industrial computer). The wiring is simplified, cabling cost is reduced, high disturbance rejection is obtained, at the same time ensuring synchronization between all signals, mandatory for the computation of the most advanced diagnostic metrics. The performance and effectiveness of the developed system are proved in comparison with a top-quality, laboratory-grade commercial solution. A 10-days experiment was performed on a radial bearing mounted on a bearing test bench, by employing both systems side-by-side. Early-stage damage identification will be demonstrated with the described solution, despite costing a fraction and offering numerous advantages for industrial applications with respect to products currently available on the market.

High-performance three-phase motor drives require accurate and robust acquisition of angular position and stator currents. A resolver sensor is widely used for angle measurement in harsh environmental conditions. This sensor receives a sinusoidal excitation signal and produces two amplitude-modulated outputs. Frequency division multiplexing is a useful alternative to simplify the data acquisition system (DAQ) to acquire the signals from stator current sensors and the resolver angular position sensor: a current signal and a resolver output are combined to produce a signal sent to the DAQ. In most cases, the sinusoidal excitation signal of the resolver is less than the switching frequency of the motor drive inverter. As a result, current signals may produce a significant interference in the angle estimation when the frequency of the resolver excitation signal is low. This paper describes an improved multiplexing approach where the resolver excitation frequency is 1.5 times the inverter switching frequency. Thus, the spectra of the resolver outputs and the stator currents are located further from each other, reducing the angle estimation interference. Synchronous sampling allows for the recovery of the current signals. A notch filter is added to an angle tracking observer to reduce even further this interference. Three different tests considering different operation conditions were performed. The proposed angle estimation method has less root mean square error in the angle estimation than other approaches (e.g., without using a notch filter or using a low resolver excitation frequency). Besides, the proposed multiplexing strategy allows recovering the fundamental component of the current signals. [ABSTRACT FROM AUTHOR]

Soil moisture monitoring and control are essential aspects for precision agriculture. The paper presents the designing and calibration of a low-cost soil moisture monitoring system. The system includes 28 capacitive sensors, connected to an Arduino based data acquisition system, allowing simultaneous multi-point measurements. The calibration process was conducted on six reference points within the moisture content range of 0-25%. The calibration results indicate a non-linear variation and reveal a significant deviation between the sensors leading to the determination of individual variation curves for each sensor. [ABSTRACT FROM AUTHOR]

When operating solar–wind power plants (SWPPs) located in populated areas, cases of premature failure of expensive batteries and other power equipment often occur. The purpose of this study is to develop a wireless data acquisition system (DAS) for the operation of an SWPP with a feedback function to prevent material damage from the failure of power equipment and to increase the efficiency of natural energy use. The principles of constructing a DAS, free from some of the disadvantages of analogues, are described in this paper. Intelligent wireless current and voltage sensors and a device for receiving and recording data with an additional feedback function have been developed, providing real-time feedback when the measured parameters go outside the norm. Measurement data are displayed on the laptop screen and alphanumeric display and stored on the hard drive along with timestamps and current event messages. An example of using a reverse communication channel to implement the functions of backup battery protection and to switch SWPP loads is described. The principles and methods proposed in this article are suitable for constructing systems for remote measurements of any physical quantities; therefore, the scope of application of the described system can be significantly expanded.

Detecting and isolating power grid faults quickly and efficiently ensures a constant power flow. The lack of speed and accuracy in conventional approaches to fault management highlights the importance of creating a smart decision model. The suggested approach uses topology analysis to better localize and isolate faults, reducing downtime and boosting grid performance. Real-time fault detection, precise fault location identification, and choosing the best isolation schemes are all difficulties in power grid fault management. Furthermore, significant issues need innovative solutions, such as managing the grid's complexity, responding to changing load conditions and guaranteeing cyber security. The present research unveils a new approach combining the Intelligent Decision Model integrated with the Supervisory Control and Data Acquisition system (SC-DAS). Here, Real-time decision-making, control, and monitoring are made possible by the SC-DAS system, which mediates between the intelligent decision model and the physical grid. In SC-DAS, Phasor Measurement Units (PMUs) are used for the real-time monitoring of the grid's electrical properties, which are collected using SCADA (Supervisory Control and Data Acquisition) systems. Further, SC-DAS uses decision-making models for instantaneous modifications and remedial measures of the grid's functioning. The grid's reliability uses Topological DAS for fault detection, localization, and isolation. Topological DAS represents a revolutionary approach that could significantly improve power grid reliability, reduce interruption frequency, and boost overall system performance in microgrids and renewable energy sources. The grid layouts, load circumstances, and fault scenarios used in the simulations help to improve the decision-making strategies in a controlled environment.

The paper presents the results of developing and creating wireless sensor networks for agriculture and medicine, which the authors have obtained during the past five years. The paper considers wireless sensor networks for estimating the state of plants by the method of chlorophyll fluorescence induction on large areas of agricultural land. The above networks include original smart sensors, built according to flexible architecture and containing elements of artificial intelligence based on neural networks. This construction scheme increases their performance in determining the effect of stress factors of different origins. The authors have constructed a smart remote medical monitoring system based on the wireless sensor network, which employs several wireless data exchange protocols. They also have created a diagnostic system to assess the quality of life and the symptoms associated with the diseases and (or) treatment. It determines the patient's health state using international and national questionnaires. The operation of proposed smart measuring devices and systems is based on the knowledge-based principles of processing and analyzing the measuring data for making managerial decisions and providing recommendations. Most of the obtained results have been put into mass production. [ABSTRACT FROM AUTHOR]

The article presents the results of the development and creation of wireless sensor networks for agriculture and medicine, obtained by the team of authors during the past five years. Wireless sensor networks for estimating the state of plants by the method of chlorophyll fluorescence induction on large areas of agricultural lands are considered. These networks include original smart sensors, built according to flexible architecture and which contain elements of artificial intelligence based on neural networks, which increases their effectiveness in determining the effect of stress factors of different origins. On the basis of the wireless sensor network, a smart remote medical monitoring system has been built, which uses several wireless data exchange protocols. To assess the quality of life and the spectrum of symptoms, associated with the diseases and/or treatment, a diagnostic system was created, which determines the patient’s state using international and national questionnaires. The basis of the operation of proposed smart measuring devices and systems is the knowledge-based principles of processing and analyzing the measuring data for making managerial decisions and giving recommendations to users. Most of the obtained results have been brought to serial production. [ABSTRACT FROM AUTHOR]

Mechanical action can damage physical objects, such as building structures. The transfer of energy to the ground through the falling mass, forming a crater, can cause kinematic forces in the soil medium. An effect of these forces, connected with the technology of geotechnical machines, is vibrations of the ground and building structures. When finding the energy impact on the object is difficult or even impossible, vibration monitoring and indirect estimation of the negative energy impact effects is possible. Commercial off-the-shelf solutions exist for vibration monitoring and recording, but they are dedicated to one application type and are relatively expensive. This paper presents a proprietary system for monitoring and recording vibrations of building objects transmitted through the ground. This allows for local registration and remote administration and presentation of data via a computer network. Vibrations can be analysed according to the requirements of developers and clients. The developed system shows that creating an effective approach for collecting data on building vibrations using components from different suppliers is possible. The waveforms collected using the system confirm the effectiveness and flexibility of the presented solution, providing a possibility of using the results to decide whether the vibrations are within the standard limits. [ABSTRACT FROM AUTHOR]

There are various tools for real-time data acquisition and analysis of high-frequency signals. Usually, the tools for data acquisition and analysis are incompatible with each other, expensive, or require the utilization of various technologies. Designing a unique toolset for real-time data acquisition and analysis of high-frequency signals presents a large challenge, both in terms of hardware and software design. Some of the main challenges include fulfilling strict real-time performance requirements, using cost-effective hardware components, and designing a software architecture with minimal software latency for data sampling on an embedded platform and data analysis on a PC machine. This paper presents a software architecture design methodology for embedded platforms, including an RTOS and an Ethernet interface, along with the application architecture on the host side. To demonstrate the advantages of the proposed methodology, a prototype consisting of the STM32H747 microcontroller, FreeRTOS operating system, and a GUI application based on the Qt framework has been created. It has been shown that using the proposed architecture design methodology makes it possible to preserve the maximal hardware sampling rate utilizing a 100 Mbps Ethernet link. In the experiments, sampling rates up to 4.57 MSps for 10-bit resolution and real-time transfer of data over Ethernet link and visualization on host PC have been achieved with this specific MCU. [ABSTRACT FROM AUTHOR]

This paper presents a summary of system identification flight testing and results for a variety of large and small fixed-wing and multirotor unmanned air systems at Texas A&M University from 1999 to 2023. The six different types of vehicles range from a large powered parafoil, to a fixed-wing vehicle with synthetic jet-actuated roll control effectors, to a radially asymmetric multirotor, to large and small fixed-wing vehicles, and to a Steppe eagle. The observer/Kalman filter identification algorithm is used to generate linear time-invariant state-space models, and the results for both near-real-time online model generation and postflight offline model generation are presented. The use and efficacy of a variety of test input types and their sensitivity to exogenous inputs such as turbulence, in addition to identified model evaluation and selection criteria, are discussed. Several generations of low size, weight, power, and cost flight-test instrumentation including the Developmental Flight-Test Instrumentation data acquisition package are also presented. Challenges that arose from the flight-testing campaigns along with solutions are highlighted in the paper. [ABSTRACT FROM AUTHOR]

This study proposed a novel methodology of data acquisition systems (DASs) benchmarking based on fuzzy-weighted zero-inconsistency (FWZIC II) and fuzzy decision by opinion score method (FDOSM II), which are applied in an intuitionistic fuzzy set (IFS) context and account for hesitation when benchmarking DASs, to support industrial community characteristics in the design and implementation of advanced driver assistance systems in vehicles. The proposed methodology comprises two consecutive phases. The first phase involves constructing a decision matrix based on the intersection of the DAS alternatives and criteria. The second phase (development phase) proposes the formulation of a novel FWZIC II to weight the criteria and the formulation of a novel FDOSM II to benchmark DASs. Fourteen DASs were benchmarked based on the 15 DAS criteria, which included seven sub-criteria for 'comprehensive complexity assessment' and eight sub-criteria for 'design and implementation', which had a significant effect on the DAS design when implemented by industrial communities. A systematic ranking and sensitivity analysis were conducted to demonstrate that the benchmarking results were subject to systematic ranking, as indicated by the high correlations across all described scenarios of changing criteria weight values. [ABSTRACT FROM AUTHOR]

Electrical impedance tomography (EIT) is an advanced visualization detection technique with non-invasive, radiationless, and fast-response characteristics. As an important means in dredging engineering, EIT-based measurement can realize the estimate and computation of key flow parameters such as the flow velocity and solid phase fraction of solid-liquid two-phase flow in a pipe. Despite progress, both the data acquisition rate and the signal-to-noise ratio (SNR) of the existing EIT system are too low to meet many practical requirements. In this study, efforts are made concerning the EIT acquisition system by optimizing the sensor array, data acquisition system, and data communication module. Experimental results show that (1) the optimized system has an SNR of 73 dB, which is about 40% higher than the original system, and (2) the optimization of data transmission methods can achieve a maximum allowable transmission rate of 316.4 Mbps, sufficient to support data transmission over 1000 frames per second. Consequently, the key problems of the existing EIT acquisition system are substantially overcome. [ABSTRACT FROM AUTHOR]

The suspension system is of paramount importance in any automobile. Thanks to the suspension system, every journey benefits from pleasant rides, stable driving and precise handling. However, the suspension system is prone to faults that can significantly impact the driving quality of the vehicle. This makes it essential to find and diagnose any faults in the suspension system and rectify them immediately. Numerous techniques have been used to identify and diagnose suspension faults, each with drawbacks. This paper's proposed suspension fault detection system aims to detect these faults using deep transfer learning techniques instead of the time-consuming and expensive conventional methods. This paper used pre-trained networks such as Alex Net, ResNet-50, Google Net and VGG16 to identify the faults using radar plots of the vibration signals generated by the suspension system in eight cases. The vibration data were acquired using an accelerometer and data acquisition system placed on a test rig for eight different test conditions (seven faulty, one good). The deep learning model with the highest accuracy in identifying and detecting faults among the four models was chosen and adopted to find defects. The results state that VGG16 produced the highest classification accuracy of 96.70%. [ABSTRACT FROM AUTHOR]

The popularity of bicycles as a mode of transportation has been steadily increasing. However, concerns about cyclist safety persist due to a need for comprehensive data. This data scarcity hinders accurate assessment of bicycle safety and identification of factors that contribute to the occurrence and severity of bicycle collisions in urban environments. This paper presents the development of the BSafe-360, a novel multi-sensor device designed as a data acquisition system (DAS) for collecting naturalistic cycling data, which provides a high granularity of cyclist behavior and interactions with other road users. For the hardware component, the BSafe-360 utilizes a Raspberry Pi microcomputer, a Global Positioning System (GPS) antenna and receiver, two ultrasonic sensors, an inertial measurement unit (IMU), and a real-time clock (RTC), which are all housed within a customized bicycle phone case. To handle the software aspect, BSafe-360 has two Python scripts that manage data processing and storage in both local and online databases. To demonstrate the capabilities of the device, we conducted a proof of concept experiment, collecting data for seven hours. In addition to utilizing the BSafe-360, we included data from CCTV and weather information in the data analysis step for verifying the occurrence of critical events, ensuring comprehensive coverage of all relevant information. The combination of sensors within a single device enables the collection of crucial data for bicycle safety studies, including bicycle trajectory, lateral passing distance (LPD), and cyclist behavior. Our findings show that the BSafe-360 is a promising tool for collecting naturalistic cycling data, facilitating a deeper understanding of bicycle safety and improving it. By effectively improving bicycle safety, numerous benefits can be realized, including the potential to reduce bicycle injuries and fatalities to zero in the near future. [ABSTRACT FROM AUTHOR]

This work presents the instrumentation, modeling, and parameterization of an electric vehicle used for public transport. The aim is to characterize the mathematical model for its application in control systems design. A system identification technique based on a gray box approach is used to estimate specific parameters of the longitudinal model that cannot be measured directly. For this purpose, a data acquisition system was designed using high-amperage current sensors and an Odroid XU4 embedded system that records the vehicle’s input current, displacement, and velocity. Additionally, a semi-automatic acceleration system was developed to introduce a pseudo-random binary-type acceleration signal to excite all possible vehicle frequencies to perform the parameter identification.

Featured Application: This system is characterized by multi-channel simultaneous acquisition capability, high signal-to-noise ratio and high universality. On one hand, the system is capable of being extended to other beamlines efficiently and easily. On the other hand, it improves the experimental efficiency and reduces experimental error. The Shanghai Synchrotron Radiation Facility Phase-II Project is now under construction and it consists of 16 newly built advanced beamlines. The experiments carried out at these new beamlines tend to be characterized by high flux, multi-mode, ultra-fast frequency, in situ and dynamic loading. To meet these emerging experimental demands of SSRF Phase-II beamlines, the authors have developed a unified and extensible data acquisition system based on Bluesky suite. It has been applied at the BL07U Spatial-resolved and Spin-resolved ARPES and magnetism beamline. The experiment results show that with up to 5 channel signals, the system is capable of collecting total electron yield signals of experimental sample and standard sample, and incident photon flux signals simultaneously. It improves the user experimental efficiency and quality. In addition, thanks to Bluesky suite, all devices can be abstracted as motors or detectors and then represented by a Python object. Therefore, with the architecture, modules and interface developed for BL07U, the system can be extended to more beamlines efficiently and easily. [ABSTRACT FROM AUTHOR]

One of the modern solutions to reduce pollution is the production of electricity from renewable sources such as solar energy using photovoltaic panels. Photovoltaic panels convert sunlight into electricity. To characterize a photovoltaic system, its real characteristics must be known. It is also desirable to test the photovoltaic panels after a period of time after installation in order to determine the evolution of the characteristics of the photovoltaic panel over time. This paper presents a system for the automatic determination of the power characteristics of a photovoltaic panel, thus being able to determine the maximum power point. [ABSTRACT FROM AUTHOR]

The objective of this study was to assess the effectiveness and practicability of an activity index combining acceleration and location data for automated estrus detection in dairy cows. By using a wearable neck tag, measurements of acceleration and location were gathered from 22 multiparous cows monitored incessantly for 6 days to derive activity records of each cow. The maximum-minimum distance clustering (MMDC) method was used to divide hourly activity data into low, medium, high, and intensity level groups. The weighted sum of the proportions of the low, medium, high, and intensity activities in an hour constituted the activity level. The activity index was defined as the ratio of the variation in hourly activity level compared to the same time period during the previous three days. Furthermore, whether the cow was in estrus was judged above a set threshold. The study showed that the power consumption and communication effects of the neck tags were acceptable for indoor-housing conditions. For the two consecutive time periods, the activity-index-based detection algorithm achieved 90.91% for accuracy, 100% for precision, 100% for specificity, 83.33% for recall, 90.91% for F1 score, and 0.82 for Kappa coefficient. On the basis of these results, it can be concluded that the combination of acceleration and location in the activity index can promote estrus detection in dairy cows.

In recent years, remote data collection and remote monitoring technologies based on artificial neural networks have been increasingly used in various industries. In order to in-depth study whether the data collection system based on artificial neural network theory can analyze the characteristics of different strokes in swimming events, this paper uses simulation comparison method, data integration method, and step-by-step construction method to collect samples, analyze the data collection system, and streamline the algorithm. And integrate and create a data collection system that can analyze the characteristics of swimming styles. After constructing the system, select the image frequency 450 ms once, and set the signal frequency to 2.5 KHZ. Set the waveforms to sawtooth wave and sine wave, respectively; the voltage range of sine wave is − 6 to 8 V, and the sampling frequency is 250. The voltage range of the sawtooth wave is − 8 to 6 V, and the sampling frequency is 45 KHZ. Experiments show that the system basically works normally during the sampling process. To further study the stability of the system, this test is in a swimming pool with a one-story building with a relative humidity of 85%. It is set to send 110 data frames from the coordinator segment to the normal segment every 2.5 s. The acquisition success rate is 88% when there is interference and 96% when there is no interference, which is much higher than that when there is interference. Therefore, a retransmission mechanism must be used when designing the software for common segment points to ensure reliable data transmission. In general, the data acquisition system we designed basically meets the design standards. It is basically realized that starting from the artificial neural network, a data acquisition system that can analyze swimming styles is designed. [ABSTRACT FROM AUTHOR]

The National Renewable Energy Laboratory manufactured, instrumented, and deployed thermoplastic composite blades and a data acquisition system (NDAQ) on one of Verdant Power's Gen5d 5 m diameter tidal turbines in New York's East River. The thermoplastic blades had internal strain gages, and the NDAQ was a stand-alone system for monitoring and recording the strain and angular position of the blades. The turbine with thermoplastic blades operated and produced power successfully for 3 months, contributing energy to the New York City electric grid. The NDAQ hardware, instrumentation, and structure all survived the deployment and were still functional upon retrieval of the system, but no data were collected. Even though the data retrieval was not a success, data acquisition for deployed subsea marine renewable structures is a new undertaking, and it is critical to share lessons learned from national laboratory experiences. The successful deployment of thermoplastic composite blades marks a significant advancement toward improved materials for subsea components, as well as an advancement in recyclable composite materials. This article outlines the methodology and lessons learned for the instrumentation and data acquisition system. [ABSTRACT FROM AUTHOR]

Distribution transformers are a necessity to ensure a reliable power supply to consumers and their inability to function properly or even breakdown should be avoided due to the high cost of replacing them. Distribution transformers are large in numbers and randomly distributed in cities and there is a need to accurately monitor their daily/hourly performance. To achieve this, real-time monitoring of the transformer's health status is proposed rather than the use of the traditional approach involving physical inspection and testing which is slow, tedious and time-consuming. This paper presents a cloud-based monitoring scheme applied to a prototype distribution transformer. A 10kVA, 0.415 kV prototype distribution transformer has been acquired and connected to three residences for data acquisition. A data acquisition system has been developed to monitor and record the parameters of the prototype transformer for 14 days. The parameters, monitored in real-time include load current, phase voltage, transformer oil level, ambient temperature and oil temperature. The acquired real-time data of the transformer is validated with the standard measuring instrument. An algorithm was developed to transmit and log the data to ThinkSpeak cloud server via node MCU (ESP 8266). Results obtained in this study, which can be visualized via the graphical user interface of ThinkSpeak, indicate that the proposed scheme can acquire vital data from the distribution transformers and transmit the information to the monitoring centre. [ABSTRACT FROM AUTHOR]

Mechanical action can damage physical objects, such as building structures. The transfer of energy to the ground through the falling mass, forming a crater, can cause kinematic forces in the soil medium. An effect of these forces, connected with the technology of geotechnical machines, is vibrations of the ground and building structures. When finding the energy impact on the object is difficult or even impossible, vibration monitoring and indirect estimation of the negative energy impact effects is possible. Commercial off-the-shelf solutions exist for vibration monitoring and recording, but they are dedicated to one application type and are relatively expensive. This paper presents a proprietary system for monitoring and recording vibrations of building objects transmitted through the ground. This allows for local registration and remote administration and presentation of data via a computer network. Vibrations can be analysed according to the requirements of developers and clients. The developed system shows that creating an effective approach for collecting data on building vibrations using components from different suppliers is possible. The waveforms collected using the system confirm the effectiveness and flexibility of the presented solution, providing a possibility of using the results to decide whether the vibrations are within the standard limits.