Acquisition System Research Articles

Wind Turbine Static Errors Related to Yaw, Pitch or Anemometer Apparatus: Guidelines for the Diagnosis and Related Performance Assessment

The optimization of the efficiency of wind turbine systems is a fundamental task, from the perspective of a growing share of electricity produced from wind. Despite this, and given the complex multivariate dependence of the power of wind turbines on environmental conditions and working parameters, the literature is lacking studies specifically devoted to a careful characterization of wind farm performance. In particular, in the literature, it is overlooked that there are several types of faults which have similar manifestations and that can be defined as static errors. This kind of error manifests as a static bias occurring from a certain time onward, which can affect the anemometer, the absolute or relative pitch of the blades, or the yaw system. Static or systematic errors typically do not cause the functional failure of the wind turbine system, but they deserve attention due to the fact that they cause power production loss throughout the operation time. Based on this, the first objective of the present study is a critical review of the recent papers devoted to three types of wind turbine static errors: anemometer bias, static yaw error, and pitch misalignment. As a result, a comprehensive viewpoint, enhancing the state of the art in the literature, is developed in this study. Given that the use of data collected by Supervisory Control And Data Acquisition (SCADA) systems has, up to now, been prevailing for the diagnosis of systematic errors compared to the use of further specific sensors, particular attention in the present study is thus devoted to the discussion of the phenomena which can be observable through SCADA data analysis. Based on this, finally, a rigorous work flow is formulated for detecting static errors and discriminating among them through SCADA data analysis. Nevertheless, methods based on additional information sources (like further sensors or meteorological data) are also discussed. An important aspect of this study is that, for each considered type of systematic error, some previously unpublished results based on real-world SCADA data are reported in order to corroborate the proposed framework. Summarizing, then, the present is the first paper which considers and discusses several types of wind turbine static errors in a unified viewpoint, correctly interprets apparently controversial results collected in the literature, and finally provides guidelines for the diagnosis of this kind of error and for the quantification of the performance drop associated with their presence.

STABLE trial of spectacle provision and driving safety among myopic motorcycle users in Vietnam: study protocol for a stepped-wedge, cluster randomised trial

BackgroundTraffic crashes are the leading cause of death globally for people aged 5–29 years, with 90% of mortality occurring in low- and middle-income countries (LMICs). The STABLE (Slashing Two-wheeled Accidents by Leveraging Eyecare) trial was designed to determine whether providing spectacles could reduce risk among young myopic motorcycle users in Vietnam.MethodsThis investigator-masked, stepped-wedge, cluster randomised naturalistic driving trial will recruit 625 students aged 18–23 years, driving ≥ 50 km/week, with ≥ 1-year driving experience and using motorcycles as their primary means of transport, in 25 clusters of 25 students in Ho Chi Minh City, Vietnam. Motorcycles of consenting students who have failed self-testing on the WHOeyes app will be fitted with Data Acquisition Systems (DAS) with video cameras and accelerometers. Video clips (± 30 s) of events flagged by the accelerometer will be reviewed for crash and near-crash events per 1000 km driven (main outcome). Five clusters of 25 students will be randomly selected every 12 weeks to undergo ocular examination and an estimated 40% of these will have bilateral spherical equivalent < − 0.5 D, and better-eye presenting distance visual acuity < 6/12, correctable bilaterally to ≥ 6/7.5. They will be given free distance spectacles and their driving data before receiving spectacles will be analysed as the control condition and subsequent data as the intervention condition. Secondary outcomes include visual function, cost-effectiveness and self-reported crash events.DiscussionSTABLE will be the first randomised trial of vision interventions and driving safety in a LMIC.Trial registrationClinicalTrials.gov, NCT05466955. Initial registration: 20 July 2022, most recent update: 9 July 2024.

Thermal Optimalization with CFD Analysis and Real-Time Performance Identification in Briquette Ovens Using Modbus-Based Communication

Briquette ovens that utilize biomass energy frequently face obstacles in achieving consistent heat distribution, resulting in ineffective energy usage and varying product quality. This study tackles these challenges by merging Computational Fluid Dynamics (CFD) analysis, performed with Cradle software, alongside a Modbus-based temperature data acquisition system to significantly boost energy efficiency and temperature regulation. A total of 32 K-type thermocouples were meticulously positioned within the oven to gather real-time temperature data, which was processed through LabVIEW. CFD simulations revealed irregular heat distribution patterns, especially at the rear of the oven. The temperature data obtained via Modbus corroborated these observations, allowing for strategic modifications to enhance heat distribution and minimize energy loss. The system achieved an impressive accuracy rate of 98.89%, with a mere error margin of 1.11%, substantially elevating the oven’s energy efficiency and briquette drying capabilities. This research clearly illustrates that the integration of CFD modeling with real-time data acquisition constitutes a powerful method for optimizing the functionality of industrial heating systems.

Data Acquisition System for a Hydroelectric Turbine with Deformable Blades

The scientific paper proposes a remote data transmission and acquisition system, for the analysis of the electrical parameters of a portable hydroelectric turbine with deformable blades placed on the stream. The major objective proposed by the authors is to monitor the transformation of the kinetic energy of water into electricity. Due to the impossibility of directly measuring the turbine elements as a whole, remote data transmission was chosen. This information is collected from the generator of the hydroelectric turbine through transducer elements into digital signals, is encoded and transmitted wirelessly to the receiver located on the shore, decoded, and then processed in real time and displayed on the monitor screen of a computer system in order to make decisions when the situation requires it. Data transmission is carried out in both directions (half duplex) through an efficient request/response communication protocol. Considering that the turbine is located in a hard-to-reach place, on the river, it was imposed to supply with reserve electric energy, autonomous from batteries, for the electronic part that is responsible for acquiring data from the hydroelectric generator. In this way, the monitoring circuit works permanently regardless of the turbine’s operating mode: normal or fault. The authors also present in detail the research methodology, the research results, the final conclusions resulting from the experimental data as well as the original contributions made through this applied research.

A Newly Developed Compressed Air Cannon Test Bench Designed for Multi-Impact Analysis of Composite Structures

Understanding the response and damage evolution of structures subjected to multiple impact events is essential for designing resilient structures capable of withstanding complex loading scenarios, such as impacts from hail, gravel or foreign object debris. This article presents the development and characterization of a novel test bench, the “Compressed air multi-cannon”, designed specifically for studying the multi-impact behavior of composite materials. This test bench offers advantages over traditional impact testing methods by enabling controlled and adjustable impact parameters, including number of impacts, spatial and temporal lag, energy, angle and impactor dimensions. The primary objective of this work is to provide a detailed description of the test bench design, construction, and validation procedures. Key components such as the pressurized air system, projectile launch mechanism, target mounting arrangement, and data acquisition system are discussed. Experimental methodologies for assessing multi-impact response, specimen preparation, instrumentation, and data analysis techniques are outlined. Through a series of single-impact and multi-impact tests, distinctive damage mechanisms and energy absorption characteristics were observed in composite structures, revealing significant differences in how composites respond under single- and multi-impact conditions. It was found that the single-impact configuration remains particularly critical compared to multi-impact configurations with a high number of impacts. However, further testing is required to determine whether this result holds true under varying impact parameters, highlighting the unique value of this machine for exploring new, realistic questions in the literature.

Analysis of laser spot centroiding errors for laser acquisition system in gravitational wave detection missions

Analysis of laser spot centroiding errors for laser acquisition system in gravitational wave detection missions

Design and implementation of a scalable and high-throughput EEG acquisition and analysis system

Recent advances in neuroscience, neuromorphic intelligence, and brain–computer interface (BCI) technologies have created a need for fast, efficient, and convenient electroencephalogram (EEG) data acquisition systems. However, the existing equipment was limited in its flexibility, restricting non-invasive studies to research or medical settings. To address this issue, low-cost, compact EEG acquisition devices have been developed, allowing for frequent and flexible brain data acquisition in various scenarios. This paper introduces a scalable and high-throughput EEG signal acquisition and analysis system based on field-programmable gate array (FPGA) technology. The proposed system offers electrode scalability, on-chip computing, and optional wireless functionality extension. These features are achieved through the design of a highly scalable underlying EEG acquisition module and an FPGA central module that enables software-defined high-throughput expansion and high-speed data exchange between software and hardware. The paper presents two implementation cases that demonstrate the potential of the proposed system. The first case introduces a wearable wireless EEG system, enabling the deployment of effective and user-friendly steady-state visual evoked potential (SSVEP)-BCI applications in consumer-grade scenarios. The second case integrates an FPGA central module with multiple basic EEG acquisition modules to construct a high-throughput BCI system for cost-effective and real-time EEG data acquisition and processing. This configuration allows for flexible deployment in research and clinical applications, including attention index, SSVEP, motor imagery (MI), and emotion recognition. This combination further demonstrates the potential of scalable EEG systems and emphasizes the need for further integration or chipization. These implementations validate the feasibility of compact and efficient EEG devices and highlight the promising applications of scalable BCI system in various fields.

Velocity-space analysis of fast-ion losses measured in MAST-U using a high-speed camera in the FILD detector

Abstract A Fast-Ion Loss Detector (FILD) was installed for the first time at the MAST-U spherical tokamak during its upgrade in 2021. A new CMOS camera was installed in the MAST-U FILD acquisition system to provide high spatial resolution (1.1 MPx) with an acquisition frequency of up to 3.5 kHz. This camera has enabled the systematic analysis of the velocity-space of the fast-ion losses measured in MAST-U presented in this manuscript. The main parameters that determine the FILD measurement have been analysed to maximise the signal in the detector: The orbit-following code ASCOT predicts an inverse relation between the FILD signal and the probe's relative distance to the separatrix. This prediction has been validated experimentally, enabling the measurement of fast-ion losses in the flat-top phase of the discharge; furthermore, ASCOT simulations show a big impact of the edge safety factor (q95) on the toroidal deposition of the first-orbit losses, indicating that the signal in the MAST-U FILD can be maximised by running scenarios with q95&lt;6. This prediction was validated experimentally by a scan in the toroidal magnetic field. The experimental resolution of the MAST-U FILD has been evaluated for a typical MAST-U scenario with up to 750 kA plasma current. The results show that the diagnostic resolution is in the order of 0.5 to 1 degree in pitch angle, and of 1 to 3 cm in gyroradius in current scenarios. A systematic analysis of the velocity-space of the losses shows that the measured gyroradii of the prompt-losses match those of the NBI injection energies within the resolution of the diagnostic. The experimentally measured pitch angles have been compared with ASCOT simulations, and it has been found that the agreement is better for scenarios heated with the on-axis beam, since this beam enables measurements of the magnetic field pitch angle. This analysis has been applied to a discharge where type-III ELM-induced fast-ion losses were measured, showing that the ELMs result in an increase in the FILD signal, and that the losses are coming from passing orbits.

Improving Racing Drones Flight Analysis: A Data-Driven Approach Using Motion Capture Systems

The publication of the previous study, titled "Experimental Study on the Dynamic Behaviour of Drones Designed for Racing Competitions", highlighted the increasing interest in employing scientific methods for their design and analysis. That study examined the flight data of 15 racing drones within a large flight area, using Doppler-type sensors for data collection. Building on these findings and seeking to enhance them, the current work introduces an upgraded data acquisition system utilising optical sensors, thereby improving measurement accuracy. These enhanced flight data facilitate the development of updated quality indices and conclusions, offering a more precise and definitive analysis than was previously possible.

Lower Limb Joint Angle Prediction Based on Multistream Signaling and Quantile Regression, Temporal Convolution Network–Bidirectional Long Short-Term Memory Network Neural Network

In recent years, the increasing number of patients with spinal cord injuries, strokes, and lower limb disabilities has led to the gradual development of rehabilitation-assisted exoskeleton robots. A critical aspect of these robots is their ability to accurately sense human movement intentions to achieve smooth and natural control. This paper describes research carried out on predicting the motion angles of human lower limb joints. Based on the design of a signal acquisition system for physiological muscle signals and inertial measurement unit (IMU) data, a hybrid neural network prediction model (QRTCN-BiLSTM) and a single neural network prediction model (QRBiLSTM) were constructed using quantile regression, temporal convolution network (TCN) and bidirectional long short-term memory network (BiLSTM), respectively. At the same time, 7-channel surface electromyographic signals (sEMG) and 12-channel IMU data from hip and knee joints were collected and input into the QRBiLSTM and QRTCN-BiLSTM models to unfold the training and analyze the comparison. The results show that the QRTCN-BiLSTM model can more accurately infer human movement intention and provide a more reliable and accurate prediction tool for human–robot interaction research in rehabilitation robotics.

CONUS+ Experiment

The CONUS+ experiment aims to detect coherent elastic neutrino-nucleus scattering (CEνNS) of reactor antineutrinos on germanium nuclei in the fully coherent regime, continuing on this way the CONUS physics program started at the Brokdorf nuclear power plant, Germany. The CONUS+ setup is installed in the nuclear power plant in Leibstadt, Switzerland, at a distance of 20.7 m from the 3.6 GW thermal power reactor core. The CEνNS signature will be measured with the same four point-contact high-purity germanium (HPGe) detectors produced for the former experiment, however refurbished and with optimized low energy thresholds of about 160 eVee. To suppress the background in the CONUS+ detectors, the passive and active layers of the original CONUS shield were modified such to fit better to the significantly changed background conditions at the new experimental location. New data acquisition and monitoring systems were developed. A direct network connection between the experiment and the Max-Planck-Institut für Kernphysik (MPIK) makes it possible to control and monitor data acquisition in real time. The impact of all these modifications is discussed with particular emphasis on the resulting CEνNS signal prediction for the first data collection phase of CONUS+. Prospects of the planned upgrade in a second phase integrating new larger HPGe detectors are also discussed.

A brief history of the development of transcranial tissue Doppler ultrasound.

This article documents the early development of the first transcranial Doppler (TCD)-based ultrasound system for continuous monitoring of brain tissue pulsations (BTPs). Transcranial tissue Doppler (TCTD) uses a lightweight, wearable single-element ultrasound probe to track tissue motion perpendicular to the skin's surface, providing tissue displacement estimates along a single beam line. Feasibility tests using an adapted TCD system confirmed that brain tissue motion data can be obtained from existing TCD hardware. Brain Tissue Velocimetry (Brain TV), a TCTD data acquisition system, was then developed to provide a lightweight and portable means of continuously recording TCTD data in real-time. Brain TV measurements are synchronized to a 3-lead electrocardiogram and can be recorded alongside other physiological measurements, such as blood pressure, heart rate and end-tidal carbon dioxide. We have shown that Brain TV is able to record BTPs from sample depths ranging from 22 to 80 mm below the probe's surface and from multiple positions on the head. Studies in healthy volunteers, stroke patients and ultrasound phantom brain models demonstrate how TCTD might provide insights into the relationships between physiological measurements and brain tissue motion and show promise for rapid clinical assessment and continuous monitoring of BTPs.

Advanced battery management system enhancement using IoT and ML for predicting remaining useful life in Li-ion batteries

This study highlights the increasing demand for battery-operated applications, particularly electric vehicles (EVs), necessitating the development of more efficient Battery Management Systems (BMS), particularly lithium-ion (Li-ion) batteries used in energy storage systems (ESS). This research addresses some of the key limitations of current BMS technologies, with a focus on accurately predicting the remaining useful life (RUL) of batteries, which is a critical factor for ensuring operational efficiency and sustainability. Real-time data are collected from sensors via an Internet of Things (IoT) device and processed using Arduino Nano, which extracts values for input into a Long Short-Term Memory (LSTM) model. This model employs the National Aeronautics and Space Administration (NASA) Li-battery dataset and current, voltage temperature, and cycle values to predict the battery RUL. The proposed model demonstrates significant forecasting precision, attaining a root mean square error (RMSE) of 0.01173, outperforming all comparative models. This improvement facilitates more effective decision-making in BMS, particularly in resource allocation and adaptability to transient conditions. However, the practical implementation of real-time data acquisition systems at a scale and across diverse environments remains challenging. Future research will focus on enhancing the generalizability of the model, expanding its applicability to broader datasets, and automating data ingestion to minimize integration challenges. These advancements are aimed at improving energy efficiency in both industrial and residential applications in accordance with the Sustainable Development Goals (SDGs) of the UN.

Open-Source Data Logger System for Real-Time Monitoring and Fault Detection in Bench Testing

This paper presents the design and development of a proof of concept (PoC) open-source data logger system for wireless data acquisition via Wi-Fi aimed at bench testing and fault detection in combustion and electric engines. The system integrates multiple sensors, including accelerometers, microphones, thermocouples, and gas sensors, to monitor critical parameters, such as vibration, sound, temperature, and CO2 levels. These measurements are crucial for detecting anomalies in engine performance, such as ignition and combustion faults. For combustion engines, temperature sensors detect operational anomalies, including diesel engines operating beyond the normal range of 80 °C to 95 °C and gasoline engines between 90 °C and 110 °C. These readings help identify failures in cooling systems, thermostat valves, or potential coolant leaks. Acoustic sensors identify abnormal noises indicative of issues such as belt misalignment, valve knocking, timing irregularities, or loose parts. Vibration sensors detect displacement issues caused by engine mount failures, cracks in the engine block, or defects in pistons and valves. These sensors can work synergistically with acoustic sensors to enhance fault detection. Additionally, CO2 and organic compound sensors monitor fuel combustion efficiency and detect failures in the exhaust system. For electric motors, temperature sensors help identify anomalies, such as overloads, bearing problems, or excessive shaft load. Acoustic sensors diagnose coil issues, phase imbalances, bearing defects, and faults in chain or belt systems. Vibration sensors detect shaft and bearing problems, inadequate motor mounting, or overload conditions. The collected data are processed and analyzed to improve engine performance, contributing to reduced greenhouse gas (GHG) emissions and enhanced energy efficiency. This PoC system leverages open-source technology to provide a cost-effective and versatile solution for both research and practical applications. Initial laboratory tests validate its feasibility for real-time data acquisition and highlight its potential for creating datasets to support advanced diagnostic algorithms. Future work will focus on enhancing telemetry capabilities, improving Wi-Fi and cloud integration, and developing machine learning-based diagnostic methodologies for combustion and electric engines.

Computer vision: applications in Biomedical Engineering

The focus of this study is the presentation of two systems, which use computer vision techniques. In the first, the authors applied these techniques to the acquisition and processing system of ovitrap images. However, the second system is a physiotherapeutic evaluation platform for dynamic monitoring of body balance. By using a depth image of the patient (based on a 3D camera), the algorithm can predict the exact positions of the joints of an individual, without temporal information. The two studies described presented showed promising results, which demonstrated that computer vision techniques are of great importance. The experiments with the Aedes Aegypti mosquito egg has allowed accurate detection and counting of them. The experiments with body balance analysis has allowed the tracking of the center of mass of an individual during physiotherapeutic exercises. Software has detected the movement of the center of mass, satisfying a physiotherapeutic need. The techniques developed for computer vision have many possible applications in different areas and different populations, not only in the areas presented in this study.

Objective Assessment of Active Display Screen Fixation Among Office Workers Using an Innovative Nonwearable Acquisition System: A Pilot Study

Background: Occupational risk assessments of VDT users are usually hindered by the variability of tasks that office workers perform. Digital eye strain is related to the amount of work time dedicated to screen fixation. Purpose: This study aimed to improve the risk assessment of VDT workers by introducing an advanced version of software developed at the University of Brescia. Methods: The prototype enables the recording of the times in front of the screen and those in which the operator actively fixes. It was tested on 30 employees from different offices. The system includes a webcam placed over the workers’ screens and connected with a laptop running specifically developed monitoring software. This experiment required worker-to-worker calibration of the system by the investigators. Results: The obtained data allowed us to distinguish between the period of screen fixation and the presence in front of the monitor. The visual activity varied greatly on a daily basis because of the differences between tasks. The mean facial detection time was approximately 48%, whereas the mean eye fixation time was 29%. Conclusions: The results suggest that our prototype is a promising tool for investigating the relative contributions of screen fixation to the development of digital occupational eye strain.

The Energy Consumption Comparison of Temperature Difference-based Defrosting Exit Method and Time-based Defrosting Exit Method with Various Static Pressure Differences

The time-based defrosting exit method(TDEM) has been widely used in cold storage, but the issue of energy consumption has been heavily criticized. In this paper, a temperature difference-based defrosting exit method(TDDEM) is proposed to compare with TDEM. Firstly, a parameter acquisition system of cold storage is constructed, and the experimental setup is conducted to determine the experimental parameters. Subsequentially, the range of defrosting static pressure difference is determined based on pre-frosting experiment, and the defrosting experiment of TDEM and TDDEM are carried out at five various static pressure difference conditions, 16Pa, 14Pa, 12Pa, 10Pa and 8Pa, respectively. The results show that the temperature fluctuations per defrosting events of TDEM are more severe compared to TDDEM, with with average increases of 102.2%, 91.6%, 59.3%, and 46.5%, respectively. Energy consumptions of TDDEM are markedly lower than TDEM, with reductions of 21.4%, 15.7%, 14.09% and 14.59%, respectively. Whether TDEM or TDDEM, the lowest energy consumption is occurred at 10Pa, and highest is 8Pa.

Extracting Bridge Modal Frequencies Using Stationary Versus Drive-By Modes of Smartphone Measurements

In this research, we harmonize the two mobility approaches, stationary and mobile measurements, within the same framework to generate comparison opportunities, particularly in terms of identified bridge modal frequencies. Vibration tests were conducted to determine the natural frequency of a pedestrian bridge located in University College Dublin using smartphones. Both stationary and mobile smartphone measurements were collected, a novel use of two levels of mobility. Stationary measurements involved leaving the smartphone on the bridge deck at different positions along the bridge for a period of time, and mobile measurements were carried out using an electric scooter to ride across the bridge with the smartphone attached to the scooter deck. Single-output identification results were then compared to visualize the differences at two mobility levels. The tests showed that it is possible to extract the first natural frequency of the bridge using both stationary and mobile smartphone measurement techniques, although operational uncertainties seemed to alter the latter one. A first natural frequency of 5.45 Hz from a reference data acquisition system confirmed the accuracy of stationary smartphone data. On the other hand, the mobile data require consideration of the driving frequency, a function of the speed of the test vehicle and length of the bridge. These results show that smartphone sensors can be regarded as an alternative to industrial accelerometers with certain barriers to account for the multi-modality of the mobile sensing and identification process.

Active Remote Focus Stabilization in Oblique Plane Microscopy.

Light-sheet fluorescence microscopy (LSFM) has demonstrated great potential in the life sciences owing to its efficient volumetric imaging capabilities. For long term imaging, the light-sheet typically needs to be stabilized to the detection focal plane for the best imaging results. Current light-sheet stabilization methods rely on fluorescence emission from the sample, which may interrupt the scientific imaging and add to sample photobleaching. Here we show that for oblique plane microscopes (OPM), a subset of LSFM where a single primary objective is used for illumination and detection, light-sheet stabilization can be achieved without expending sample fluorescence. Our method achieves ~43nm axial precision and maintains the light-sheet well within the depth of focus of the detection system for hour-long acquisition runs in a lab environment that would otherwise detune the system. We demonstrate subcellular imaging of the actin skeleton in melanoma cancer cells with a stabilized OPM.

Structure-guided inhibitor design targeting CntL provides the first chemical validation of the staphylopine metallophore system in bacterial metal acquisition

To survive in the metal-scarce environment within the host, pathogens synthesize various small molecular metallophores to facilitate the acquisition of transition metals. The cobalt and nickel transporter (Cnt) system synthesizes and transports staphylopine, a nicotianamine-like metallophore, and serves as a primary transition metal uptake system in Gram-positive bacteria including the human pathogen Staphylococcus aureus. In this study, we report the design of the first inhibitor of the Cnt system by targeting the key aminobutanoyltransferase CntL which is involved in the biosynthesis of staphylopine. Through structure-guided fragment linking and optimization, a class of acceptor-adenosine dual-site inhibitors against S. aureus CntL (SaCntL) were designed and synthesized. The most potent inhibitor, compound 9, demonstrated a ΔTm value of 9.4 °C, a Kd value of 0.021 ± 0.004 μM, and an IC50 value of 0.06 μM against SaCntL. The detailed mechanism by which compound 9 inhibits SaCntL has been elucidated through a high-resolution co-crystal structure. Treatment with compound 9 resulted in a moderate downregulation of intracellular concentrations of iron, nickel, and cobalt ions in the S. aureus cells cultured in the metal-scarce medium, providing the first chemical validation of the important role of Cnt system in bacterial metal acquisition. Our findings pave the way for the development of CntL-based antibacterial agents in future.