Data Acquisition System Research Articles

The safety effect of increased pedestrian protection, autonomous emergency braking for pedestrians and bicyclists on passenger cars, and speed management

This was the first retrospective study to estimate the effect of increased pedestrian protection, autonomous emergency braking, and speed management to reduce serious injuries among pedestrians and bicyclists. More specifically, the aim was to estimate the injury mitigating effects of the following interventions: AEB with pedestrian and bicyclist detection, Euro NCAP pedestrian test score, active bonnet, traffic calming at pedestrian and bicycle crossings, and additionally, the combined effect of the above-mentioned treatments. The main source of data was the Swedish traffic data acquisition system (Strada), where information of road traffic crashes between passenger cars and pedestrians or bicyclists for the period 1 January 2003–31 December 2022 was obtained. Cars with optional fitment of AEB systems were identified, and the license registration number was used to access individual car equipment lists to identify whether the vehicle was equipped with AEB with pedestrian and/or cyclist detection. Information about traffic calming at pedestrian and bicycle crossings was obtained from the Swedish Transport Administration. The injury metric used was risk of permanent medical impairment (RPMI) of at least one percent and ten percent. RPMI captures the risk of long-term medical impairment based on a diagnosed injury location and Abbreviated Injury Severity (AIS) score. The relative difference between the mean values of RPMI (mRPMI1%+ and mRPMI10%+) was calculated and tested using an independent two sample t-test which was conducted for unequal sample sizes and variance. Although many results were found to be statistically non-significant, the following results were found to be significant at least at 90% level. Pedestrian mRPMI10%+ was reduced by 44% in speed zones ≤ 50 km/h comparing the group struck by cars equipped with AEB with pedestrian detection compared to the group struck by cars without the system. For cyclists, the mRPMI10%+ was reduced by 35% in speed zones ≤ 50 km/h. For crashes within ± 20 meters from a pedestrian or bicycle crossing, the AEB system reduced 60% of pedestrians mRPMI10%+ at crossings with good safety standard compared to crossings of poor safety standard. The comparison of cars with poor performance (1–9 points) in the NCAP pedestrian test and cars with a high score (28–36 points) showed that pedestrian mRPMI10%+ was reduced by 48% across all speed limits, and by 64% including only those aged ≤ 64 years. For bicyclists, a significant reduction of cyclist mRPMI10%+ was found comparing low scoring cars to high scoring cars in ≤ 30 km/h speed limit (-73%) and across all speed limits (-36%). Including only those aged ≤ 64 years, the reduction was 49%. For the active bonnet, a significant reduction of mRPMI1%+ by 24% was observed but given that the rate of helmet wearing was higher in the group struck by cars with active bonnet, this difference cannot be attributed to an effect of an active bonnet. The STA safety rating of pedestrian and bicycle crossings showed that overall pedestrian mRPMI1%+ was reduced by 15%, while cyclists mRPMI10%+ was reduced by 32% comparing crossings of high safety level to crossings of poor safety level. The analysis of combined interventions showed that the total reduction of pedestrians and cyclists mRPMI10%+ together was 69%, from 6.4% to 2%. This paper demonstrates that a road environment with adapted infrastructure and speed, combined with passenger car technologies that improve the safety for vulnerable road users, can create significant reductions of serious (long-term) injuries among pedestrians and bicyclists.

Common criteria for security evaluation and malicious intrusion detection mechanism of dam supervisory control and data acquisition system

Common criteria for security evaluation and malicious intrusion detection mechanism of dam supervisory control and data acquisition system

HDR brachytherapy afterloader quality assurance optimization using monolithic silicon strip detectors.

There currently exists no widespread high dose-rate (HDR) brachytherapy afterloader quality assurance (QA) tool for simultaneously assessing the afterloader's positional, temporal, transit velocity and air kerma strength accuracy. The purpose of this study was to develop a precise and rigorous technique for performing daily QA of HDR brachytherapy afterloaders, incorporating QA of: dwell position accuracy, dwell time accuracy, transit velocity consistency and relative air kerma strength (AKS) of an Ir-192 source. A Sharp ProGuide 240mm catheter (Elekta Brachytherapy, Veenendaal, The Netherlands) was fixed 5mm above a 256 channel epitaxial diode array 'dose magnifying glass' (DMG256) (Centre for Medical and Radiation Physics, University of Wollongong). Three dwell positions, each of 5.0s dwell times, were spaced 13.0mm apart along the array with the Flexitron HDR afterloader (Elekta Brachytherapy, Veenendaal, The Netherlands). The DMG256 was connected to a data acquisition system (DAQ) and a computer via USB2.0 link for live readout and post-processing. The outputted data files were analyzed using a Python script to provide positional and temporal localization of the Ir-192 source by tracking the centroid of the detected response. Measurements were repeated on a weekly basis, for a period of 5weeks to determine the consistency of the measured parameters over an extended period. Using the DMG256 for relative AKS measurements resulted in measured values within 0.6%-3.0% of the expected activity over a 7-week period. The sub-millisecond temporal accuracy of the device allowed for measurements of the transit velocity with an average of (10.88±1.01) cm/s for 13mm steps. The dwell position localization for 1, 2, 3, 5, and 10mm steps had an accuracy between 0.1 and 0.3mm (3σ), with a fixed temporal accuracy of 10ms. The DMG256 silicon strip detector allows for clinics to perform rigorous daily QA of HDR afterloader dwell position and dwell time accuracy with greater precision than the current standard methodology using closed circuit television and a stopwatch. Additionally, DMG256 unlocks the ability to perform measurements of transit velocity/time and relative AKS, which are not possible using current standard techniques.

Hardware and Software Design of Programmable Medium and High-Speed Data Acquisition (DAQ) Board of Fiber Optic Signal for Partial Discharge Acquisition

The anti-electromagnetic interference capability of partial discharge (PD) acoustic signal conversion and collection circuits severely restrict the sensitivity of PD detection. The data acquisition (DAQ) systems available in the current market are costly and have limited functionality, making it difficult to satisfy the acquisition requirements for PD detection. This paper proposes a medium to high-speed fiber optic signal acquisition board with an adjustably controlled sampling rate and filter cutoff frequency. The circuit achieves a higher signal-to-noise (SNR) ratio by distributing the noise in each part of the signal acquisition chain reasonably. The temperature characteristics of the acquisition module are improved by utilizing the programmable T-type structure for transimpedance amplification of photocurrent. The DAQ card performs data acquisition and processing using STM32H743 internal ADC and caches data in bulk with an SRAM and SD card. A data uploading method based on time reference has been proposed, which enables full, effective information signal upload through a low-cost transmission interface. The research ultimately achieves a stable sampling of three channels at 1 MSps, SNR of 63 dB, and programmable gain amplification of the photocurrent with 0–60 dB. Finally, the system is used for PD acoustic signal acquisition in the frequency range of 20 Hz to 100 kHz.

Long-term performance analysis of operational efficiency of a grid-connected solar power plant under Mauritania climate

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.

Experimental investigation of multi-step airfoils in low Reynolds numbers applications

This study provides a detailed analysis of the aerodynamic performance of various airfoil configurations, focusing on lift coefficient, stall characteristics, and maximum lift-to-drag ratio. The investigation includes the NACA23012C profile and configurations with different step geometries, ranging from one-step to five-step designs. Experimental measurements were conducted using a well-equipped aerodynamic laboratory, Institute of Aviation Engineering and Technology (IAET), Giza, Egypt. The lab features a wind tunnel, propeller test rig, and data acquisition system. The experiments were conducted meticulously to ensure accuracy and reproducibility, with a standardized method employed for uncertainty analysis. The results reveal distinct aerodynamic behaviors among the different configurations, highlighting the significant impact of design variations on aerodynamic performance. Notably, the three-step configuration consistently exhibited high performance, with a competitive or superior lift coefficient across a range of Reynolds numbers, showing an improvement of up to 35.1 %. Similarly, the four-step configuration demonstrated substantial increases in lift-to-drag ratios, reaching up to 53.2 %, while the five-step configuration exhibited varying trends with a minimum drag coefficient. The study also investigated stall characteristics and sensitivity to Reynolds numbers, revealing the complex trade-offs inherent in airfoil design. The findings provide valuable insights into optimizing airfoil performance under different operational conditions. Additionally, the adoption of two and three stepped airfoils resulted in significant reductions in blade material and associated costs for turbine blades.

Miniaturized preamplifier integration in ultrasound transducer design for enhanced photoacoustic imaging.

Photoacoustic imaging (PAI) utilizes the photoacoustic effect to record both vascular and functional characteristics of a biological tissue. Photoacoustic signals have typically low amplitude that cannot be read efficiently by data acquisition systems. This necessitates the use of one or more amplifiers. These amplifiers are somewhat bulky (e.g., the ZFL-500LN+, Mini-Circuits, USA, or 351A-3-50-NI, Analog Modules Inc., USA). Here, we describe the fabrication and development process of a transducer with a built-in low-noise preamplifier that is encased within the transducer housing. This new, to the best of our knowledge, design could be advantageous for applications where a compact transducer + preamplifier is required. We demonstrate the performance of this compact detection unit in a laser scanning photoacoustic microscopy system by imaging a rat ear ex vivo and a rat brain vasculature in vivo.

A high-precision hazard ranging method for transmission lines based on laser point cloud

Abstract To ensure the safe operation of the line, it is necessary to discover the hidden danger in time and accurately. This paper proposes a high-precision hidden danger-ranging method for transmission lines based on the laser point cloud. This method uses LiDAR to form a data acquisition system, which is installed in the fixed position of the transmission line channel power tower to obtain the point cloud data of the channel field at regular intervals, according to a certain rule. Two ends of the suspension points and some of the conductor point cloud data are used to fit the whole conductor in three-dimensional space and carry out the precise hidden danger ranging through the fitted conductor combined with the point cloud data and ranging. By comparing the original conductor point cloud data with the fitted conductor data to verify the reliability of the conductor fitting, and verifying the feasibility of the conductor arc prolapse measurement method based on the measured arc prolapse of the conductor and the distance to the ground, it can provide more timely and effective early warning information for the safety of the transmission line channel.

Optimal Design of Data Acquisition System for Fault Diagnosis of Hydraulic Machinery

Abstract Fault diagnosis of hydraulic machinery has high requirements on the synchronization of monitoring data and the anti-interference of monitoring equipment. The performance and data quality of the existing operation monitoring equipment of the unit cannot meet the requirements. At the same time, due to the variety of sensors and the different wiring methods of different types of sensors, the preparation work such as wiring of data acquisition equipment in the test is complicated. This paper analyzes the application requirements of fault diagnosis data and the problems existing in the conventional data collection process. A data collection solution specified for diagnosis of hydraulic machinery, which better solves the problem of data synchronization, is designed. The new designed system has high sampling accuracy and anti-interference characteristics, as well as the advantages of strong capability and simple equipment deployment. The proposed system has been applied in multiple power stations, pumping stations and pump test platforms. The relevant functions have been effectively verified.

FRF-based modal testing of the highest timber building in the world in operation using wireless accelerometers

Abstract This paper describes practical application of a novel and oven-controlled crystal oscillator (OCXO) high-precision synchronised wireless data acquisition system for a modal testing of the tallest all-timber building in the world – the 18-storey, 88.8m tall Mjøstårnet (the Mjøsa Tower) in Brumunddal, Norway. The modal testing was challenging as it was conducted in an occupied building in normal operation and was based on measuring both dynamic excitation and response yielding a set of frequency response functions (FRFs) across four different floors of the building. To the best knowledge of the authors, this was the first time that an FRF-based modal test was attempted on such a large building structure in operation. Moreover, although the structure was in operation, the testing was completed in only 48h, including commissioning and de-commissioning of all of the instrumentation, yielding a full set of point- and transfer accelerance FRFs. The paper presents the equipment used, key logistical challenges and quality assurance steps made to assure measurement of good quality FRF data in such short period of time demonstrating feasibility of the whole exercise. A total of seven modes of vibration, including higher order modes of vibration were estimated by curve-fitting the measured FRF data. This includes the 3rd bending and 2nd torsion modes difficult to measure via the standard ambient vibration testing of tall buildings. These modes could be used to investigate a number of uncertain modelling features in tall timber buildings, such as the stiffness of timber joints.

Development of silicon-on-insulator direct electron detector with analog memories in pixels for sub-microsecond imaging.

We have developed a high-speed recordable direct electron detector based on silicon-on-insulator technology. The detector has sixteen analog memories in each pixel to record sixteen images with sub-microsecond temporal resolution. A dedicated data acquisition system has also been developed to display and record the results on a personal computer. The performance of the direct electron detector as an image sensor is evaluated under electron irradiation with an energy of 30 keV in a low-voltage transmission electron microscope equipped with a photocathode electron gun. We demonstrate that the detector can record images at an exposure time of 100 ns and an interval of 900 ns.

Lake Environmental Data Harvester (LED) for Alpine Lake Monitoring with Autonomous Surface Vehicles (ASVs)

This article introduces the Lake Environmental Data Harvester (LED) System, a robotic platform designed for the development of an innovative solution for monitoring remote alpine lakes. LED is intended as the first step in creating portable robotic tools that are lightweight, cost-effective, and highly reliable for monitoring remote water bodies. The LED system is based on the Shallow-Water Autonomous Multipurpose Platform (SWAMP), a groundbreaking Autonomous Surface Vehicle (ASV) originally designed for monitoring wetlands. The objective of LED is to achieve the comprehensive monitoring of remote lakes by outfitting the SWAMP with a suite of sensors, integrating an IoT infrastructure, and adhering to FAIR principles for structured data management. SWAMP’s modular design and open architecture facilitate the easy integration of payloads, while its compact size and construction with a reduced weight ensure portability. Equipped with four azimuth thrusters and a flexible hull structure, SWAMP offers a high degree of maneuverability and position-keeping ability for precise surveys in the shallow waters that are typical of remote lakes. In this project, SWAMP was equipped with a suite of sensors, including a single-beam dual-frequency echosounder, water-quality sensors, a winch for sensor deployment, and AirQino, a low-cost air quality analysis system, along with an RTK-GNSS (Global Navigation Satellite System) receiver for precise positioning. Utilizing commercial off-the-shelf (COTS) components, a Multipurpose Data-Acquisition System forms the basis for an Internet of Things (IoT) infrastructure, enabling data acquisition, storage, and long-range communication. This data-centric system design ensures that acquired variables from both sensors and the robotic platform are structured and managed according to the FAIR principles.

Characterization and Performance Evaluation of ESP32 for Real-time Synchronized Sensor Networks

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.

IoT-Based Monitoring, Communication, and Control of Small Wind Turbines Using IoT Cloud Service

Addressing global climate change requires a shift to sustainable and renewable energy sources. Utilizing wind power via wind turbines presents a viable remedy. However, because wind patterns are unpredictable and these turbines are located in remote areas, effectively operating wind farms continues to be a difficulty. In this work, we present an integrated system that uses IOT Cloud Service to monitor, connect with, and manage small wind turbines utilizing IoT (Internet of Things) technology. We solve the issues with data collection and control those older turbines without supervisory control and data acquisition (SCADA) systems confront by fusing cloud-enabled IoT hubs with on-site sensing units. Our method guarantees effective control of wind energy devices, two-way communication, and real-time monitoring. With an emphasis on utilizing IoT cloud services, this paper offers a thorough examination of the application of Internet of Things (IoT) technology for the monitoring, communication, and control of tiny wind turbines. In decentralized and off-grid energy applications, small wind turbines are vital, and maximizing energy output and guaranteeing long-term sustainability requires optimizing their performance and maintenance. The scalability, flexibility, and real-time capabilities required to efficiently operate small wind energy projects are sometimes absent from traditional monitoring and control systems.

Spatio-temporal features based deep learning model for depression detection using two electrodes

Deep learning has made significant contributions to the medical field and has shown great potential in various applications. Its ability to process vast amounts of data and extraction of patterns has enabled breakthroughs in medical research, diagnosis and treatment. The application of deep learning plays a vital role in depression detection. Depression is a neurological disorder characterized by persistent feelings of sadness, hopelessness and a lack of interest. The prevalence of depression is a significant factor contributing to the rise in suicide cases on a global scale. The electroencephalogram (EEG) is a non-invasive technique used to detect depression. It records brain activity using multiple electrodes. The number of EEG electrodes used for measurement directly affects the instrumentation and measurement complexity of the experiment. The present manuscript proposes a deep learning model for depression detection, focusing on two electrodes named FP1 and FP2. The purpose of employing two electrodes is to enhance the system’s portability while reducing data acquisition time and system cost. EEG is spatio-temporal data and possesses inherent spatial and temporal features. The present manuscript proposes a methodology for extracting temporal and spatial features. The temporal feature extraction module extracts temporal features in the time domain and the spatial module extracts spatial features in the spatial domain. This manuscript presents a study on the applicability of two electrodes for depression detection. This research can enhance accessibility, user-friendliness and easier data collection and analysis. The proposed deep learning model is evaluated on two benchmark datasets. It achieves 93.41% classification accuracy, 92.54% precision, 93.23% recall, 93.06% F1 score and 97.80% area under the curve (AUC) for Hospital University Sains Malaysia dataset and for Multi-modal Open Dataset for Mental-disorder Analysis dataset it achieves 79.40% accuracy, 81.18% precision, 67.73% recall, 73.80% F1 score and 85.66% AUC.

Power System State Estimation Based on Fusion of PMU and SCADA Data

This paper introduces a novel hybrid filtering algorithm that leverages the advantages of Phasor Measurement Units (PMU) to address state estimation challenges in power systems. The primary objective is to integrate the benefits of PMU measurements into the design of traditional power system dynamic estimators. It is noteworthy that PMUs and Supervisory Control and Data Acquisition (SCADA) systems typically operate at different sampling rates in power system estimation, necessitating synchronization during the filtering process. To address this issue, the paper employs a predictive interpolation method for SCADA measurements within the framework of the Extended Kalman Filter (EKF) algorithm. This approach achieves more accurate estimates, closer to real observation data, by averaging the KL distribution. The algorithm is particularly well-suited for state estimation tasks in power systems that combine traditional and PMU measurements. Extensive simulations were conducted on the IEEE-14 and IEEE-30 test systems, and the results demonstrate that the fused estimator outperforms individual estimators in terms of estimation accuracy.

Design and implementation of a beam-synchronized data acquisition system for CSNS accelerator

Design and implementation of a beam-synchronized data acquisition system for CSNS accelerator

Design and Development of Arduino-Based Four-Channel Data Acquisition System with Digital Temperature Control for Chemiresistive Sensors

We report the design and development of simple and economical Arduino-based four-channel data acquisition system with digital temperature control that is suitable for sensor parameter optimization involved in the search of appropriate chemiresistive gas sensing material for a particular gas. The developed system is capable of simultaneously capturing the data as well as controlling the desired operating temperature (up to 300 °C) of four sensing elements. This is achieved by employing sixteen analog and digital channels of the Arduino nano microcontroller. The sensing circuit is capable of recording the resistance changes from 20 kΩ to 5 MΩ at a rate of 15 kHz. The developed system has been tested rigorously, validated and demonstrated for its effectiveness towards investigating the gas sensing properties of chemiresistive NO2 sensors realized using ZnO nanowires. The present simple and low-cost alternative finds its application for developing the target specific chemiresistive sensors in particular for investigating the gas sensing properties of metal oxide semiconductors.

Design and Experiments of the Data Acquisition System for Bale Rolling Characteristic Parameters on a Large-Scale Round Bale Machine

The parameters of the roll characteristics of a large-scale round bale machine were collected in real time to investigate the bale rolling mechanism. This investigation develops a set of adaptable and highly integrated data acquisition systems for the bale rolling performance parameters of large-type round bale machines. A rolling experiment is conducted using sunflower straw as the material, and the power consumption and radial tension of the roller-round bale machine during the bale rolling process are studied. In the grass core formation stage, the round bale machine’s torque need was minimal, the radial tension of the bale remained nearly constant, and the bale chamber was primarily filled with loose sunflower straw. The motor torque and the straw bale’s radial tension both showed a tendency of gradual increase when the round bale machine was in the grass-filling stage. The motor torque and bale radial tension displayed a roughly linear trend of rapid rise as the sunflower straw continued to enter the rolling bale chamber; this was when the round bale machine was in the compressed bale rolling stage. When the power consumption of the round bale machine was measured using the data acquisition system during the test bench empty run and core-creation stage, the energy consumption comparison analysis produced a relative error of 5.8%. During the stage of bale rolling and compression, the data acquisition system monitored the power consumption of the round bale machine. The relative error was 9.5%. The data acquisition system of the round bale machine test bed has an accuracy of 90.5%–94.2% when measuring the machine’s power consumption, indicating that it is a stable and efficient system. This study provides a foundation for further research on intelligent the roller-round bale machine.

High-Speed Surface Property Recognition with a 140 GHz Frequency

In the field of integrated sensing and communication, there is a growing need for advanced environmental perception. The terahertz (THz) frequency band, significant for ultra-high-speed data connections, shows promise in environmental sensing, particularly in detecting surface textures crucial for autonomous systems’ decision-making. However, traditional numerical methods for parameter estimation in these environments struggle with accuracy, speed, and stability, especially in high-speed scenarios like vehicle-to-everything communications. This study introduces a deep learning approach for identifying surface roughness using a 140-GHz setup tailored for such conditions. A high-speed data acquisition system was developed to mimic real-world scenarios, and a diverse set of rough surface samples was prepared for realistic high-speed datasets to train the models. The model was trained and validated in three challenging scenarios: random occlusions, sparse data, and narrow-angle observations. The results demonstrate the method’s effectiveness in high-speed conditions, suggesting terahertz frequencies’ potential in future sensing and communication applications.