Developed Data Acquisition Research Articles

A measurement system for enteric CH4 emissions monitoring from ruminants in livestock farming

In this paper, a proposal for an Internet-of-Things (IoT) based measurement system dealing with the enteric methane (CH4) emission monitoring from ruminants is presented. Herein, a brief overview of the recent advances in sensors technologies and their IoT integration for realizing measurement systems able to monitor the CH4 emissions in ruminants is also presented. Nowadays, it is confirmed that CH4 emissions, which are mainly produced during normal fermentation of feeds by the rumen microorganisms, are part of the Green-House Gas (GHG) emissions. Therefore, a classification of the existing measurement methods, sensing technologies and their impact on the animal’s welfare is presented. The proposed measurement system, together with its sensing elements and the developed data acquisition system are also reported in this paper. A preliminary disposal and field trials of the developed system in a farm facility is given.

Modular Open-Source Design of Pyrolysis Reactor Monitoring and Control Electronics

Industrial pilot projects often rely on proprietary and expensive electronic hardware to control and monitor experiments. This raises costs and retards innovation. Open-source hardware tools exist for implementing these processes individually; however, they are not easily integrated with other designs. The Broadly Reconfigurable and Expandable Automation Device (BREAD) is a framework that provides many open-source devices which can be connected to create more complex data acquisition and control systems. This article explores the feasibility of using BREAD plug-and-play open hardware to quickly design and test monitoring and control electronics for an industrial materials processing prototype pyrolysis reactor. Generally, pilot-scale pyrolysis plants are expensive custom designed systems. The plug-and-play prototype approach was first tested by connecting it to the pyrolysis reactor and ensuring that it can measure temperature and actuate heaters and a stirring motor. Next, a single circuit board system was created and tested using the designs from the BREAD prototype to reduce the number of microcontrollers required. Both open-source control systems were capable of reliably running the pyrolysis reactor continuously, achieving equivalent performance to a state-of-the-art commercial controller with a ten-fold reduction in the overall cost of control. Open-source, plug-and-play hardware provides a reliable avenue for researchers to quickly develop data acquisition and control electronics for industrial-scale experiments.

Modernized Resonant Column and Torsional Shearing Apparatus With Multipoint Contactless Displacement Detection System

Abstract In this study, a modification of resonant column/torsional shearing (RC/TS) apparatus was proposed to perform a qualitative analysis of a noncohesive soil specimen vibration during RC tests. An additional multipoint displacement detection system was installed in the RC/TS WF8500 device. In the new measuring system, 48 mini-magnets are attached to the side surface of a cylindrical soil specimen, creating a regular grid of measuring points. Around 48 Hall sensors (Honeywell SS495A1) are used to measure changes in the magnetic field strength due to the movement of the corresponding magnets on the surface of the specimen subjected to dynamic torque. The Hall sensor generates an analog signal that is proportional to the change in the magnetic field. The measurements are collected with a newly developed data acquisition system that consists of a set of analog-to-digital converters and a set of ARM (Advanced RISC (Reduced Instruction Sets Computing) Machine) microcontrollers. The measurement system is controlled with a dedicated software, ControlRec, developed by the authors. The measurements are taken synchronically with and independently from the standard RC test procedure. The new measuring technique allows to observe displacements of the 48 points on the specimens’ surface with over 4 times higher sampling rate than in the original measuring system. As a result, additional effects related to the mechanical wave propagation through soil specimen were observed (local disturbances in distribution of vibration amplitudes or significant displacements near the bottom end of the specimen, which is assumed to be fixed in the standard RC/TS results analysis), that could not be identified using the standard equipment of the device.

Data‐Dependent Acquisition with Precursor Coisolation Improves Proteome Coverage and Measurement Throughput for Label‐Free Single‐Cell Proteomics**

AbstractWe combined efficient sample preparation and ultra‐low‐flow liquid chromatography with a newly developed data acquisition and analysis scheme termed wide window acquisition (WWA) to quantify >3,000 proteins from single cells in rapid label‐free analyses. WWA employs large isolation windows to intentionally co‐isolate and co‐fragment adjacent precursors along with the selected precursor. Optimized WWA increased the number of MS2‐identified proteins by ≈40 % relative to standard data‐dependent acquisition. For a 40‐min LC gradient operated at ≈15 nL/min, we identified an average of 3,524 proteins per single‐cell‐sized aliquot of protein digest. Reducing the active gradient to 20 min resulted in a modest 10 % decrease in proteome coverage. Using this platform, we compared protein expression between single HeLa cells having an essential autophagy gene, atg9a, knocked out, with their isogenic WT parental line. Similar proteome coverage was observed, and 268 proteins were significantly up‐ or downregulated. Protein upregulation primarily related to innate immunity, vesicle trafficking and protein degradation.

Data-Dependent Acquisition with Precursor Coisolation Improves Proteome Coverage and Measurement Throughput for Label-Free Single-Cell Proteomics.

We combined efficient sample preparation and ultra-low-flow liquid chromatography with a newly developed data acquisition and analysis scheme termed wide window acquisition (WWA) to quantify >3,000 proteins from single cells in rapid label-free analyses. WWA employs large isolation windows to intentionally co-isolate and co-fragment adjacent precursors along with the selected precursor. Optimized WWA increased the number of MS2-identified proteins by ≈40 % relative to standard data-dependent acquisition. For a 40-min LC gradient operated at ≈15 nL/min, we identified an average of 3,524 proteins per single-cell-sized aliquot of protein digest. Reducing the active gradient to 20 min resulted in a modest 10 % decrease in proteome coverage. Using this platform, we compared protein expression between single HeLa cells having an essential autophagy gene, atg9a, knocked out, with their isogenic WT parental line. Similar proteome coverage was observed, and 268 proteins were significantly up- or downregulated. Protein upregulation primarily related to innate immunity, vesicle trafficking and protein degradation.

Development of a list-mode data acquisition system for prompt X-ray imaging during irradiation with carbon-ions

Prompt X-ray imaging using a low-energy X-ray camera is a promising method for observing the beam shape from outside the subject. However, such imaging has so far been conducted only on static images with relatively long acquisition times without any energy information. Consequently, we performed list-mode prompt X-ray imaging using a newly developed data acquisition system combined with a pinhole YAP(Ce) camera during irradiation of a water phantom with carbon ions. Prompt X-ray imaging was conducted in list mode with a 1-ms time stamp and 128-channel energy bins during irradiation of a water phantom with 241.5 MeV/n carbon ions. After the imaging, list-mode data were sorted to obtain the time-sequential prompt X-ray images and those with different energies. From the images with different energies, we found the energy spectra were different depending on the areas in the images, and the reduction of the background fraction was possible. From the short time-sequential prompt X-ray images, we could even observe the differences in the images depending on the acquisition times, as well as the spill and ripple shapes of the carbon ion beam.

Development of a Test Bench for Biogas-fueled Internal Combustion Engines Working in Cogeneration Mode for Residential Applications

The aim of this paper is the presentation of a test bench developed and installed at the Laboratory of the University of Bologna: the test bench has been developed for internal combustion engines, working as cogeneration units and fueled with biogas. In this perspective, the test bench purpose is to experimentally test and optimize the operation of cogenerative internal combustion engines on varying the boundary conditions, in terms of both biogas composition and electric and thermal load. Other main components are the anaerobic digester, a multi-fuel internal combustion engine, the electric load and a heat exchanger for the heat recovery from the exhaust gases and connected to a water circuit representing the thermal user. The test bench has been equipped with the opportune measurement devices, as well as with a specifically developed data acquisition system. First experimental tests, carried out on varying the methane content within the CH4-CO2 blend and the electric load allowed to optimize the test bench in terms of heat recovery section and air supply to the engine.

Temperature based rapid SAW humidity sensor

This paper investigates the effect of temperature on the sensitivity of a thin-film Polyvinyl alcohol (PVA) based SAW humidity sensor. A PVA coated 433.92 MHz SAW resonator-based humidity sensor was fabricated and tested with different levels of humidity (0.5 to 95% RH) at different operating temperatures (10°C to 70°C). The sensor response was recorded through in-house developed data acquisition software and it was observed that PVA thin film coated SAW sensor shows the maximum sensitivity for trace level moisture detection at a lower temperature (≤10 °C). The sensor sensitivity has been recorded >400Hz/% RH for trace level detection (0.5–30% RH). It has been observed that sensor sensitivity deteriorates when temperature increased to 40 °C from 10 °C. The sensor has a fast response (~1s) and recovery time (<3s) for trace level humidity detection. The proposed sensor can be used in many applications, including fabrication of electronic devices, IC fabrication, pharmaceutical, textile industries, food processing, semiconductor device fabrication, and packaging.

Beyond Hydrography - Marine Geomatics at the University of Florida

Florida depends on the oceans, yet its waters have not been extensively mapped to the highest standards. While there is a need for marine spatial data for a wide range of applications and issues, there is also a need to develop data acquisition, processing, and analytical workflows and to integrate different surveying instruments that can capture the complex and extensive coastal environment – both above and below the waterline. This note provides an overview of the research performed by scientists at the School of Forest, Fisheries, and Geomatics Sciences, University of Florida, in the field of hydrography and marine geomatics.

Design, inverted vat photopolymerization 3D printing, and initial characterization of a miniature force sensor for localized in vivo tissue measurements

BackgroundTissue healthiness could be assessed by evaluating its viscoelastic properties through localized contact reaction force measurements to obtain quantitative time history information. To evaluate these properties for hard to reach and confined areas of the human body, miniature force sensors with size constraints and appropriate load capabilities are needed. This research article reports on the design, fabrication, integration, characterization, and in vivo experimentation of a uniaxial miniature force sensor on a human forearm.MethodsThe strain gauge based sensor components were designed to meet dimensional constraints (diameter ≤3.5mm), safety factor (≥3) and performance specifications (maximum applied load, resolution, sensitivity, and accuracy). The sensing element was fabricated using traditional machining. Inverted vat photopolymerization technology was used to prototype complex components on a Form3 printer; micro-component orientation for fabrication challenges were overcome through experimentation. The sensor performance was characterized using dead weights and a LabVIEW based custom developed data acquisition system. The operational performance was evaluated by in vivo measurements on a human forearm; the relaxation data were used to calculate the Voigt model viscoelastic coefficient.ResultsThe three dimensional (3D) printed components exhibited good dimensional accuracy (maximum deviation of 183μm). The assembled sensor exhibited linear behavior (regression coefficient of R2=0.999) and met desired performance specifications of 3.4 safety factor, 1.2N load capacity, 18mN resolution, and 3.13% accuracy. The in vivo experimentally obtained relaxation data were analyzed using the Voigt model yielding a viscoelastic coefficient τ=12.38sec and a curve-fit regression coefficient of R2=0.992.ConclusionsThis research presented the successful design, use of 3D printing for component fabrication, integration, characterization, and analysis of initial in vivo collected measurements with excellent performance for a miniature force sensor for the assessment of tissue viscoelastic properties. Through this research certain limitations were identified, however the initial sensor performance was promising and encouraging to continue the work to improve the sensor. This micro-force sensor could be used to obtain tissue quantitative data to assess tissue healthiness for medical care over extended time periods.

Sustenance of Indian Moored Buoy Network During COVID-19 Pandemic – A Saga of Perseverance

The moored buoy network in the Indian Ocean revolutionized the observational programs with systematic time-series measurement of in situ data sets from remote marine locations. The real-time meteorological and oceanographic data sets significantly improved the weather forecast and warning services particularly during extreme events since its inception in 1997. The sustenance of the network requires persistent efforts to overcome the multitude of challenges such as vandalism, biofouling, rough weather, corrosion, ship time availability, and telemetry issues, among others. Besides these, the COVID-19 pandemic constrained the normal functioning of activities, mainly by delaying the maintenance of the network that resulted in losing a few expensive buoy system components and precious data sets. However, the improvements in the buoy system, in-house developed data acquisition system, and efforts in ensuring the quality of measurements together with “best practice methods” enabled 73% of the buoy network to be functional even when the cruises were reduced to 33% during the COVID-19 lockdown in 2020. The moored buoys equipped with an Indian buoy data acquisition system triggered high-frequency transmission during the Super cyclone Amphan in May 2020, which greatly helped the cyclone early warning services during the COVID-19 pandemic. The COVID-19 lockdown points toward the reliability and enhanced utility of moored buoy observations particularly when other modes of measurements are limited and necessitates more such platforms to better predict the weather systems. The present study analyzed the enhancement of the buoy program and improvisation of the buoy system that extended the life beyond the stipulated duration and enabled the high-frequency data transmission during cyclones amid the COVID-19 lockdown. The recommendations to better manage the remote platforms specifically in the event of a pandemic based on the operational experience of more than two decades were also presented.

A Bayesian Optimized Discriminant Analysis Model for Condition Monitoring of Face Milling Cutter Using Vibration Datasets

Abstract With the advent of industry 4.0, which conceptualizes self-monitoring of rotating machine parts by adopting techniques like data analytics, cloud computing, Internet of things, machine learning (ML), and artificial intelligence. The significant research area in predictive maintenance is tool condition monitoring (TCM) as the tool condition affects the overall machining process and its economics. Lately, machine learning techniques are being used to classify the tool’s condition in operation. These techniques are cost saving and help industries with adopting future-proof solutions for their operations. One such technique called discriminant analysis (DA) must be examined particularly for TCM. Owing to its less-expensive computation and shorter run times, using them in TCM will ensure the effective use of the cutting tool and reduce maintenance times. This article presents a Bayesian optimized discriminant analysis model to classify and monitor the tool condition into three user-defined classes. The data are collected using an in-house designed and developed data acquisition (DAQ) module setup on a Vertical Machining Center (VMC). The hyperparameter tuning has been incorporated using Bayesian optimization search, and the parameter that gives the best model was found out to be “linear,” achieving an accuracy of 93.3%. This study confirms the feasibility of machine learning techniques like DA in the field of TCM and using Bayesian optimization algorithms to fine-tune the model, making it industry ready.

Quantitative Evaluation of Task-Induced Neurological Outcome after Stroke.

Electroencephalography (EEG) can access ischemic stroke-derived cortical impairment and is believed to be a prospective predictive method for acute stroke prognostics, neurological outcome, and post-stroke rehabilitation management. This study aims to quantify EEG features to understand task-induced neurological declines due to stroke and evaluate the biomarkers to distinguish the ischemic stroke group and the healthy adult group. We investigated forty-eight stroke patients (average age 72.2 years, 62% male) admitted to the rehabilitation center and seventy-five healthy adults (average age 77 years, 31% male) with no history of known neurological diseases. EEG was recorded through frontal, central, temporal, and occipital cortical electrodes (Fz, C1, C2, T7, T8, Oz) using wireless EEG devices and a newly developed data acquisition platform within three months after the appearance of symptoms of ischemic stroke (clinically confirmed). Continuous EEG data were recorded during the consecutive resting, motor (walking and working activities), and cognitive reading tasks. The statistical results showed that alpha, theta, and delta activities are biomarkers classifying the stroke patients and the healthy adults in the motor and cognitive states. DAR and DTR of the stroke group differed significantly from those of the healthy control group during the resting, motor, and cognitive tasks. Using the machine-learning approach, the C5.0 model showed 78% accuracy for the resting state, 89% accuracy in the functional motor walking condition, 84% accuracy in the working condition, and 85% accuracy in the cognitive reading state for classification the stroke group and the control group. This study is expected to be helpful for post-stroke treatment and post-stroke recovery.

Design and development of the PLC based sensor and instrumentation system for self-propelled pruning residue mulcher prototype

The main idea of this paper is to present a programmable logic controller (PLC) based data acquisition, telemetry, and control system for self-propelled pruning residue mulcher prototype. The main power source of the mulcher prototype is the diesel engine. Hydraulic systems were used to transmit the motion received from the motor to the wheels, pick up and mulching unit, and steering systems. The operation and control of all systems were carried out using a PLC-based electromechanical system. The objective of this paper is to develop a PLC-based measuring and controlling system in order to control, collect, and monitor the sensor-based data such as: number of cycles of the fingers in the pick-up unit, the speed of the mulcher blades, machine forward speed, fuel consumption, and pump pressures. In the system, an embedded industrial computer and differential global positioning system (DGPS) were used to assist in real-time measuring, recording, controlling, and displaying all the sensor data during the field operation. The control of the machine is built using industry-standard off-the-shelf PLCs. The data acquisition software was written using VB.NET programming language. The PLC-based sensor and instrumentation system was tested and validated on different orchards. Test results show that the developed control and data acquisition system is well qualified with high control precision and high reaction speed. The developed machine is the first self-propelled agricultural mulcher machine developed in Turkey. And there is no other example in the world in terms of using PLC-based control and data acquisition system in self-propelled pruning residue mulchers.

A proposed data acquisition system and algorithm for signal processing of moving-coil geophone\u2019s output

The study of different types of vibrational and seismic movements is important for exploration in strata monitoring, machine health monitoring, earthquake detection, etc. In order to study these vibrational movements, it needs to be acquired first for analysis. Data acquisition using the seismic sensors is a challenging task. This paper presents a data acquisition system developed to acquire seismic signals from a moving-coil geophone. The paper also discusses a signal interpretation algorithm that is devised to perform automatic detection of a seismic event occurrence by separating through the waveform and non-waveform components in the sensor’s output using Gaussian naive Bayes classifier and Kernel density estimation technique. The proposed method is effective in the identification of a useful signal and identification of its nature of origin. Accuracy of the algorithm was 99% for the waveform classification. Sensitivity of the data acquisition system for the seismic sensors was 1.589 µm s–1. Further, the developed data acquisition system and the algorithm can be used in mines for seismological studies aimed at separating the vibration signal generated due to explosion and the one caused due to Earth’s tectonic and seismic activities.

Development of a method for studying the impact of the time reserve value on the reliability of the train schedule based on the epidemiological SIR model

It is important to find rational values of time reserves to compensate for delays during train movement to ensure train schedule reliability. This study proposes to investigate the impact of different categories of trains and their time reserve on schedule reliability. The main purpose of the study is to develop a method for studying the extent of the impact of time reserve on train schedule reliability based on the epidemiological model. Mathematical train delay propagation at the section model has been developed on the basis of the Susceptible – Infected – Removed (SIR) epidemiological model of adaptation type (taking into account the effect of using different train categories. It is suggested to use a binary genetic algorithm to search for parameters of delay propagation rate in the SIR model. According to a specially developed data acquisition algorithm, empirical data on the propagation of train delays at the station were obtained. Experimental studies have been carried out to search for train delay propagation velocity coefficients in the SIR model. The train movement restoration results during a delay at the station are given to study the impact of the time reserve value on train schedule reliability, taking into account the speed recovery delay set in the SIR model. The simulation results confirm the developed mathematical model adequacy.

Thermal comfort monitoring in aviaries by a real-time data acquisition system

ABSTRACT Thermal comfort inside broiler husbandry facilities is essential in obtaining good results in the production activity. Assessment of adequate thermodynamic conditions requires measurement and control, usually implying costs and specialized maintenance. The objective of this research was to monitor the temperature, relative humidity and air speed distributions by a developed low-cost, open-source and easy-to-use measurement system, using Arduino (hardware) and Scilab (software) for real-time data acquisition. Sensors were installed in a real facility (Cianorte, PR), with measurements for internal ambient (20 sensors for temperature/relative humidity, and two sensors for differential pressure, respectively 0.5 and 1.5 m high) and external ambient (pressure and wind speed, 1.5 m high). Data acquisition system has enabled communication with sensors which are easily read by the computer and stored in a data file. The developed data acquisition system proved to be efficient when applied in a commercial broiler husbandry facility, enabling real-time monitoring for thermal comfort parameters.

The Velociprobe: An ultrafast hard X-ray nanoprobe for high-resolution ptychographic imaging.

Motivated by the advanced photon source upgrade, a new hard X-ray microscope called "Velociprobe" has been recently designed and built for fast ptychographic imaging with high spatial resolution. We are addressing the challenges of high-resolution and fast scanning with novel hardware designs, advanced motion controls, and new data acquisition strategies, including the use of high-bandwidth interferometric measurements. The use of granite, air-bearing-supported stages provides the necessary long travel ranges for coarse motion to accommodate real samples and variable energy operation while remaining highly stable during fine scanning. Scanning the low-mass zone plate enables high-speed and high-precision motion of the probe over the sample. With an advanced control algorithm implemented in a closed-loop feedback system, the setup achieves a position resolution (3σ) of 2 nm. The instrument performance is evaluated by 2D fly-scan ptychography with our developed data acquisition strategies. A spatial resolution of 8.8 nm has been demonstrated on a Au test sample with a detector continuous frame rate of 200 Hz. Using a higher flux X-ray source provided by double-multilayer monochromator, we achieve 10 nm resolution for an integrated circuit sample in an ultrafast scan with a detector's full continuous frame rate of 3000 Hz (0.33 ms per exposure), resulting in an outstanding imaging rate of 9 × 104 resolution elements per second.

Data Acquisition System of Multi-Port Charge-Coupled Device for PAL-XFEL

After first lasing on 14 June 2016 of X-ray Free Electron Laser facility at Pohang Accelerator Laboratory (PAL-XFEL), various experiments using hard X-ray have been done using Multi-Port Charge-Coupled Device detector (MPCCD). To achieve time-resolved analysis, either the timestamp or pulse-ID tagging to the data generated from detectors is a key ingredient. For this we established the hardware interfaces including MPCCD software to synchronize PAL-XFEL event timing system. In this paper, the developed data acquisition system (DAQ) of the MPCCD is introduced including the event timing system and timestamp or pulse-ID, and the data frameworks of the PAL-XFEL.

Characterization of a Type-N Coaxial Microcalorimeter for Use as Microwave Power Standard at CENAM

This paper describes the metrological characterization of a new type-N coaxial microcalorimeter and its associated thermistor-type transfer standard, which together are used as the primary standard for microwave power from 0.05 to 18 GHz at the Centro Nacional de Metrologia, Queretaro, Mexico. The design characteristics, evaluation method, and measurement uncertainties of the primary standard are given and compared to recent realizations found in the open literature. The metrological characterization of the microcalorimeter is based on a comparison method to indirectly obtain its correction factor. The effective efficiency of the transfer standard, directly calibrated in the microcalorimeter by means of calorimetric measurements, is shown and compared against measurements performed by National Institute of Standards and Technology, showing good agreement within the estimated expanded uncertainties (0.21%–0.47%). The developed data acquisition system to automate the effective efficiency measurements and the proposed software leveling loop to keep the microwave power level constant are also discussed. This approach allows to keep an RF power stability of better than 100 ppm over several hours and is a straightforward solution to replace analog leveling systems which require obsolete hardware. Finally, the analysis followed to evaluate the uncertainty for the effective efficiency measurements is thoroughly described.