Data Acquisition Module Research Articles

Evaluation of underwater acoustic images obtained by the early stage monotone pulse-echo Synthetic Aperture Sonar

Synthetic Aperture Sonar (SAS) is a side-looking underwater acoustic imaging sonar that can obtain higher resolution images than those by the conventional Side Scan Sonar (SSS). Due to its high resolution images, it can be applied to various fields of studies from military to civilian purposes. Republic of Korea recently has launched a research project developing the prototypes of SAS being operated in a few hundreds kHz of acoustic signals. This project adopted the towfish as the SAS platform that are to be towed under a few tens of water depth, which has a price advantage over Autonomous Underwater Vehicle (AUV), but gives unstable motion. It also adopted the monotone pulse-echo mode as transceiver method of the prototypes that gives relatively simpler implementation of the data acquisition module although it gives inferior SNR to the chirp mode. In the current study, the underwater acoustic images of the protype SAS taken both in the fresh water and offshore of Korea are to be shown. Current study also...

A Wideband Electrical Impedance Tomography System Based on Sensitive Bioimpedance Spectrum Bandwidth

A wideband electrical impedance tomography (EIT) system based on bioimpedance spectrum (BIS) analysis was proposed to image biological objects. The system integrated 16 electrodes to realize driving and measurement over 1 kHz-1.1 MHz. A modified Howland current source and voltage measurement channels were designed to obtain complex impedance. Peripheral component interconnect interface-based serial data acquisition module transferred the measurement data to a host PC. The system has the least signal-to-noise ratio of 40 dB in each channel and good consistency. The system utilized a two-step strategy, based on short-time Fourier transform and Hilbert transform, the wideband chirp excitation signal was employed to analyze the BIS first, from which the sensitive bandwidth, i.e., the frequency range in which the amplitude (and phase angle) of the object changes rapidly, is obtained. Then, the discrete frequency points among the sensitive bandwidth were selected to compose a multisinusoidal signal for impedance tomography with time-difference and frequency-difference (FD) methods. An impedance model was tested to evaluate the performance of BIS analysis. Experiments on phantoms consisting of carrot cylinder and nylon rod were designed to verify the working strategy of the system. The results indicated that the developed system can distinguish the objects and reconstruct medium distribution with high image contrast and frequency resolution. Compared with the traditional multifrequency EIT, the proposed method and system show advantages in imaging the local electrical properties with high-frequency resolution and obtaining the FD images in the sensitive BIS bandwidth.

Effective Establishment of Glide-Path to Reduce Torsional Stress during Nickel-Titanium Rotary Instrumentation.

This study compared the torque generation during canal shaping with a nickel-titanium endodontic instrument according to the extent of glide-path establishment. Seventy-five simulated S-shaped canal blocks were divided into five groups (n = 15) according to the number of repetitive insertions to the working length using a One G glide-path instrument: groups with 5, 10, 15, and 20 insertions as well as group Z without glide-path establishment. When the tip of the One G file reached the working length, the file was moved back and forth repetitively at the working length for the designated number of times for each group. The instrumentation procedure with HyFlex EDM had 15 pecking strokes. During instrumentation, the generated torque was transmitted to a customized data acquisition module and collected using customized software. Data were computed to determine the maximum torque and total stress. The maximum screw-in forces were statistically analyzed using one-way analysis of variance and Tukey’s post hoc comparison test with a significance level of 95%. While the maximum stress did not have significant differences among the five groups (p > 0.05), groups with more than 10 repetitive insertions generated lower total stress during instrumentation than did the group with 5 insertions and group Z (p < 0.05). Under the limitations of this study, repetitive insertions of glide-path establishment files at the working length reduced stress generation during the shaping using nickel-titanium instruments.

Cross Regulation Reduced Optimal Multivariable Controller Design for Single Inductor DC-DC Converters

Single Inductor (SI) converters with the advantage of using one inductor for any number of inputs/outputs find wide applications in portable electronic gadgets and electrical vehicles. SI converters can be used in Single Input Multiple Output (SIMO) and Multiple Input Multiple Output (MIMO) configurations but they need controllers to achieve good transient and steady state responses, to improve the stability against load and line disturbances and to reduce cross regulation. Cross regulation is the change in an output voltage due to change in the load current at another output and it is an added constraint in SI converters. In this paper, Single Input Dual Output (SIDO) and Dual Input Dual Output (DIDO) converters with applications capable of handling high load current working in Continuous Conduction Mode (CCM) of operation are taken under study. Conventional multivariable PID and optimal Linear Quadratic Regulator (LQR) controllers are developed and their performances are compared for the above configurations to meet the desired objectives. Generalized mathematical models for SIMO and MIMO are developed and a Genetic Algorithm (GA) is used to find the parameters of a multivariable PID controller and the weighting matrices of optimal LQR where the objective function includes cross regulation as a constraint. The simulated responses reveal that LQR controller performs well for both the systems over multivariable PID controller and they are validated by hardware prototype model with the help of DT9834® Data Acquisition Module (DAQ). The methodologies used here generate a fresh dimension for the case of such converters in practical applications.

Implementation of Sound Source Localization System Based on LabVIEW

This paper uses LabVIEW as a software platform combined with the PXIe-4497 sound and vibration acquisition card to design a sound source localization system. Using the sounding device as sound source, the data is received by the microphone array. After the processing of the sound source localization system, the sound image is added with the video to achieve the locating of the sound source. The system consists of three modules: sound pressure data acquisition module, video acquisition module and data processing module. The sound pressure data acquisition module is programmed by using the DAQmx API function, the video acquisition module is based on the IMAQdx function, and the data processing module uses the phase-shift sum beamforming algorithm. The design of this system can provide practical references for future applications such as noise detections.

Correlation between seismic wave velocity, rock porosity and maximum principal stress based on the laboratory test data

In order to determine the internal relationships among seismic wave velocity, axial pressure, and rock porosity, the rock samples taken from NRS170143 borehole of the Nickel Rim South mine are tested using a Hoek type triaxial cell equipped with axial linear variable differential transducers (LVDTs) and a data acquisition module. The empirical expression between seismic wave velocity and rock pressure is fitted based on the laboratory test data of rock samples. Then, P-V model P-φ model and φ-ε model are created to analyze the laboratory test data. The results show that: (1) the relationship between axial pressure and rock porosity can be represented by a new empirical equation φ=a*e-b*P-c. With an increase of axial pressure, the value of rock porosity gradually decreases below the straight line φ=1% and close to 0. The P-φ model can be a good judge if the pressure has reached the maximum compression pressure in the process of rock compression experiment; (2) The P wave velocity and S wave velocity exponentially increase with increasing axial pressure; Rock porosity and crack density parameter exponentially decrease with increasing axial pressure; (3) there is a linear positive correlation between the ratio of rock porosity to crack density parameter and the ratio of crack thickness to crack length. (4) the relationship between Vp and Vs in each compression test can be fitted to the linear equation Vp=a*Vs+b; for all different samples of NR170143, the ratios (M) of Vp to Vs ranges from 1.35 to 1.85. In summary, the P-V model, P-φ model, φ-ε model and Vp-Vs-φ model can intuitively reflect the relationship among seismic wave velocity, axial pressure and rock porosity.

Large dynamic range data acquisition system for time-domain electromagnetic interference (TDEMI) measurement receiver.

Electromagnetic interference (EMI) measurement is essential in wireless communications to avoid both performance degradation and serious fault failures. In this paper, we present the design of an acquisition system for a time-domain EMI measurement receiver intended for monitoring potential EMI. The platform consists of both hardware and software systems. The hardware system is constructed using a modular design method and contains a conditioning module, a data acquisition and processing module, and a clock generation module. The software system provides an interactive interface for device operation and offers an automatic sampling/processing procedure that is developed on a slot 0 controller. An additional outstanding feature of the proposed system is its high dynamic range. We propose a novel multi-stage architecture and a data reconstruction algorithm that are combined to achieve large dynamic range acquisition. The proposed acquisition architecture breaks through the dynamic range limitations of conventional analog-to-digital converters (ADCs) on a significant scale. The implemented system is able to capture an analog waveform with a 300 MHz bandwidth and a 1 GS/s sampling rate. The experimental results show that the maximum dynamic range achieved is 96.43 dB, which is far greater than the single ADC dynamic range of 52 dB.

Digital frequency domain approach for fluorescence lifetime spectroscopy of the cornea using an enhanced confocal scanning microfluorometer

We report on enhancement to a confocal scanning microfluorometer (CSMF) for depth-resolved frequency domain lifetime spectroscopy across the cornea. Fluorescence was detected by a photomultiplier tube (PMT; R928P, Hamamatsu) and coupled to computer-controlled data acquisition module (K520, Digital Frequency Domain, ISS Inc.).The digital frequency domain (DFD) module performed acquisition of PMT pulses for further calculation of phase (ϕ) and modulation (M) in the emission of the sample, relative to excitation pulses, at 30 harmonics. As per the DFD approach, fluorescence lifetime (τ) was calculated by tanϕ =ωτ, where ω= 2πf with f being the excitation harmonic in Hz. The modulation depth can be calculated byM=1/√(1+ω2τ2). The DFD module was also used to pulse the excitation laser (450 nm; ISS Model N742) with the repetition rate of up to 10 MHz (fundamental frequency). Apart from coupling to DFD module, two other modifications were incorporated to enhance the CSMF: first, a sighting optic was constructed and placed before the PMT for rapid and precise confocal alignment of the excitation and emission slits, and second, a stepper motor was interfaced to the vertical nanostage for rapid depth scanning. These enhancements have enabled the CSMF to consistently produce a depth resolution of 7 µm during transcorneal scans with a 40x water immersion objective (Zeiss; WD = 1.2 mm, NA= 0.75). Overall, the CSMF is now capable of measuring transcorneal fluorescence lifetime with a wide array of fluorophores exhibiting lifetimes in the range of µs to nanoseconds.

Managing Data, Information, and Technology in Cyber Physical Systems: Public Safety as a Service and its Systems

This paper introduces a unified representation for collaborative development of complex services in public safety to explain the infusion of digital technologies into the design of the community resilience processes with respect to the potential hazardous events. It aims at helping to understand how ICT-based knowledge may help stakeholders situated in various positions inside society to participate collaboratively to service development actions. It presents a roadmap to conceive, design, and operationalize the creation of digital artefacts to compound its supporting systems, including the digital one. This representation includes a way of describing the domain of interest to conceive complex services employing human-oriented development, a way of reasoning on the resilience processes complexity, using semantic reasoning along with the time series quality assurance (TSQA) solution ontology, and a way of developing data processing components as internal (technical) services in enterprise information systems to support the design of novel environmental monitoring digital services. A unified semantic reasoning-based approach to evaluate data quality in cyber-physical systems is described to exemplify the creation of a complex public service ecosystem that promotes collaborative knowledge sharing to formalize the domain expertise through the information intensive services. The TSQA ontology integrates knowledge from other domain-specific ontologies to define and share concepts designating observations acquired from sensors, quality issues, methods for detecting quality issues and correcting data, and tags applied to data objects to assure the data traceability. A semantic component that manages the TSQA ontology and the SWRL-encoded rules are introduced in the data acquisition module of a cyber-physical system application for environmental monitoring to solve a specific problem of data cleaning associated with the water resources management. This method is applicable to any time series of measured data.

Novel infrared differential optical absorption spectroscopy remote sensing system to measure carbon dioxide emission**Project supported by the Key Program of the National Natural Science Foundation of China (Grant No. 41530644).

A CO2 infrared remote sensing system based on the algorithm of weighting function modified differential optical absorption spectroscopy (WFM-DOAS) is developed for measuring CO2 emissions from pollution sources. The system is composed of a spectrometer with band from 900 nm to 1700 nm, a telescope with a field of view of 1.12°, a silica optical fiber, an automatic position adjuster, and the data acquisition and processing module. The performance is discussed, including the electronic noise of the charge-coupled device (CCD), the spectral shift, and detection limits. The resolution of the spectrometer is 0.4 nm, the detection limit is 8.5 × 1020 molecules·cm−2, and the relative retrieval error is < 1.5%. On May 26, 2018, a field experiment was performed to measure CO2 emissions from the Feng-tai power plant, and a two-dimensional distribution of CO2 from the plume was obtained. The retrieved differential slant column densities (dSCDs) of CO2 are around 2 × 1021 molecules·cm−2 in the unpolluted areas, 5.5 × 1021 molecules·cm−2 in the plume locations most strongly affected by local CO2 emissions, and the fitting error is less than 2 × 1020 molecules·cm−2, which proves that the infrared remote sensing system has the characteristics of fast response and high precision, suitable for measuring CO2 emission from the sources.

Developing a Modular Unmanned Aerial Vehicle (UAV) Platform for Air Pollution Profiling.

The unmanned aerial vehicle (UAV) offers great potential for collecting air quality data with high spatial and temporal resolutions. The objective of this study is to design and develop a modular UAV-based platform capable of real-time monitoring of multiple air pollutants. The system comprises five modules: the UAV, the ground station, the sensors, the data acquisition (DA) module, and the data fusion (DF) module. The hardware was constructed with off-the-shelf consumer parts and the open source software Ardupilot was used for flight control and data fusion. The prototype UAV system was tested in representative settings. Results show that this UAV platform can fly on pre-determined pathways with adequate flight time for various data collection missions. The system simultaneously collects air quality and high precision X-Y-Z data and integrates and visualizes them in a real-time manner. While the system can accommodate multiple gas sensors, UAV operations may electronically interfere with the performance of chemical-resistant sensors. Our prototype and experiments prove the feasibility of the system and show that it features a stable and high precision spatial-temporal platform for air sample collection. Future work should be focused on gas sensor development, plug-and-play interfaces, impacts of rotor wash, and all-weather designs.

Design and Experimental Analysis on Autonomous Control System of Wave Glider

In order to realize the unattended and autonomous navigation of Wave Glider in the ocean, an autonomous control system is designed, which mainly includes the control circuit part, motor driver part and control algorithm part. System integrates solar energy charging module, wireless communication module, satellite communication module, rudder motor module and data acquisition module etc. In order to improve the intelligence and autonomy, the algorithm of Gaussian earth algorithm, visual tracking algorithm and BP neural network PID control algorithm are used to ensure the navigation accuracy of Wave Glider. To verify the reliability of the system, Wave Glider is tested in the ocean in the aspects of long time running, target points tracking, velocity keeping, and data acquisition. The experiment shows that under normal ocean conditions, Wave Glider can realize the long term autonomous navigation tracking in the ocean, but the speed fluctuates significantly by the current ocean condition; The acquisition of sensor data is normal, stable and reliable, which meets the requirement of long term using in the ocean.

Design of Distributed Multi-Parameter Measurement Control System Based On CAN Bus

This paper introduces a multi-parameter distributed measurement control system based on CAN bus network and uses C8051F040 MCU as measurement control node, and corresponding hardware and software design to realize simultaneous detection of multiple measurement nodes. This paper described the overall structure of the system, introduced the hardware design and system software design of the data acquisition and control processing module based on C8051F040 microcontroller, and verified the correctness and feasibility of the system design by experimental tests.

Collaborative operation and real-time control of roller fleet for asphalt pavement compaction

Abstract Compaction is one of the most critical processes in the asphalt pavement construction. Collaborative control of multiple rollers in the construction site to enable each roller to compact the right position at the right time with the right compaction parameters to ensure compaction quality is an unaddressed challenge. There are three technical barriers, i.e. how to collect and communicate compaction data of each roller, how to aggregate the collected data to determine compaction parameters, and how to control the roller fleet based on the real-time compaction parameters. To address the technical barriers, a cyber-physical system is designed and implemented. A framework is proposed for multi-roller compaction monitoring, which consists of five modules, i.e. data acquisition module, data transmission module, remote servers, in-situ control module, and monitoring terminal. Algorithms are created to calculate compaction parameters in the scenario of multi-machine operations. A managerial mechanism involving cyber space, human intervention, and compaction machines is set up to achieve collaborative compaction control. Field experiments were conducted to validate the efficacy of the system, of which the results suggest the proposed system can effectively control the compaction process. With inappropriate compaction effort being identified, tracked, and rectified in real time, the compaction quality is ensured. The development of the cyber-physical system paves the way for deploying multiple automatic rollers in the construction site for collaborative and adaptive compaction.

Monitoring and fault diagnosis system of wind–solar hybrid power station based on ZigBee and BP neural network

ABSTRACTThe integration of real-time monitoring datas of wind-solar hybrid power stations was not high, and a design plan for monitoring system based on ZigBee and B/S architecture was proposed. The data acquisition module was based; a network centre based on B/S architecture was constructed with cloud hosting to process information uploaded and provide services for users through browser or WeChat. At the same time, the BP neural network was used to extract and analyse the fault data and fault type. It was verified by experiments that the system was highly automated with stable and reliable data transmission.

A template framework for environmental timeseries data acquisition

Environmental timeseries data variety is exploding in the Internet of Things era, making data reuse a very demanding task. Data acquisition and integration remains a laborious step of the environmental data lifecycle. Environmental data heterogeneity is a persistent issue, as data are becoming available through different protocols and stored under diverse, custom formats. In this work, we deal with syntactic heterogeneity in environmental timeseries data. Our approach is based on describing different dataset syntaxes using abstract representations, called templates. We designed and implemented EDAM (Environmental Data Acquisition Module), a template framework that facilitates timeseries data acquisition and integration. EDAM templates are written using programming language-agnostic semantics, and can be reused both for input and output, thus enabling data reuse via transformations across different formats. We demonstrate EDAM generality in seven case studies, which involve scraping online data, extracting observations from a relational database, or aggregating historical timeseries stored in local files. Case studies span different environmental sciences domains, including meteorology, agriculture, urban air quality and hydrology. We also demonstrate EDAM for data dissemination, as instructed by output templates. We identified several syntactic interoperability challenges though the case studies, that include managing with differences in formatting observables, temporal and spatial references, and metadata documentation, and addressed them with EDAM. EDAM implementation has been released under an open-source license.

Deep learning-based classification with improved time resolution for physical activities of children

BackgroundThe proportion of overweight and obese people has increased tremendously in a short period, culminating in a worldwide trend of obesity that is reaching epidemic proportions. Overweight and obesity are serious issues, especially with regard to children. This is because obese children have twice the risk of becoming obese as adults, as compared to non-obese children. Nowadays, many methods for maintaining a caloric balance exist; however, these methods are not applicable to children. In this study, a new approach for helping children monitor their activities using a convolutional neural network (CNN) is proposed, which is applicable for real-time scenarios requiring high accuracy.MethodsA total of 136 participants (86 boys and 50 girls), aged between 8.5 years and 12.5 years (mean 10.5, standard deviation 1.1), took part in this study. The participants performed various movement while wearing custom-made three-axis accelerometer modules around their waists. The data acquired by the accelerometer module was preprocessed by dividing them into small sets (128 sample points for 2.8 s). Approximately 183,600 data samples were used by the developed CNN for learning to classify ten physical activities : slow walking, fast walking, slow running, fast running, walking up the stairs, walking down the stairs, jumping rope, standing up, sitting down, and remaining still.ResultsThe developed CNN classified the ten activities with an overall accuracy of 81.2%. When similar activities were merged, leading to seven merged activities, the CNN classified activities with an overall accuracy of 91.1%. Activity merging also improved performance indicators, for the maximum case of 66.4% in recall, 48.5% in precision, and 57.4% in f1 score . The developed CNN classifier was compared to conventional machine learning algorithms such as the support vector machine, decision tree, and k-nearest neighbor algorithms, and the proposed CNN classifier performed the best: CNN (81.2%) > SVM (64.8%) > DT (63.9%) > kNN (55.4%) (for ten activities); CNN (91.1%) > SVM (74.4%) > DT (73.2%) > kNN (65.3%) (for the merged seven activities).DiscussionThe developed algorithm distinguished physical activities with improved time resolution using short-time acceleration signals from the physical activities performed by children. This study involved algorithm development, participant recruitment, IRB approval, custom-design of a data acquisition module, and data collection. The self-selected moving speeds for walking and running (slow and fast) and the structure of staircase degraded the performance of the algorithm. However, after similar activities were merged, the effects caused by the self-selection of speed were reduced. The experimental results show that the proposed algorithm performed better than conventional algorithms. Owing to its simplicity, the proposed algorithm could be applied to real-time applicaitons.

A Detection Method of Time Slot Power Based on ARM Platform

In order to effectively detect the time slot power of the repeater station and the base station which the mobile communication system of time division multiple access (TDMA) communication systems, In this paper, a signal acquisition hardware circuit based on ARM platform is designed, and a method of synthetically calculating time slot power is proposed. The signal acquisition circuit used an 8 channel ADC108 chip connected to ARM through the SPI bus, the time slot power detection method is composed of signal timing acquisition module, data acquisition module and data processing module to calculate the time slot power. And the validity of the method in detecting the time slot power under the condition of system boot and normal operation is tested, the system CPU resource consumption, the error value of the tested time slot power are tested also. The results show that the proposed method can effectively detect and control time slot power of TDMA system, and it has important significance to the repeater station and the base station system network signal coverage and optimization.

Design and Implementation of Data Acquisition System for Agricultural Meteorological IOT

A data acquisition system for agricultural meteorological IOT based on STM32 processor is proposed in this paper, including power supply module, sensor data acquisition module, GPS positioning module, GPRS module, external FLASH memory and LCD screen. Experiments and demonstrations were conducted in 56 cities and counties of Anhui Province. The results showed that the monitoring stations and software platforms were operating stably; the collected data were complete and no packet loss were found; the trend of data changing was consistent with the local agro-meteorological information, and it has a small deviation to the test results that using laboratory-standard instruments. The system can accurately monitor the agricultural meteorological conditions at the agricultural production site.

Design and Development of a Modular, Multichannel Photoplethysmography System

In this paper, we present the design, development, and validation of a ‘modular photoplethysmography (PPG) system called ZenPPG. This portable, dual-channel system has the capability to produce “raw” PPG signals at two different wavelengths using commercial and/or custom-made PPG sensors. The system consists of five modules, each consisting of circuitry required to perform specific tasks, and are all interconnected by a system bus. The ZenPPG system also facilitates the acquisition of other physiological signals on-demand including electrocardiogram (ECG), respiration, and temperature signals. This report describes the technical details and the evaluation of the ZenPPG along with results from a pilot in vivo study on healthy volunteers. The results from the technical evaluations demonstrate the superiority and flexibility of the system. Also, the systems’ compatibility with commercial pulse oximetry sensors such as the Masimo reusable sensors was demonstrated, where good quality raw PPG signals were recorded with the signal-to-noise ratio (SNR) of 50.65 dB. The estimated arterial oxygen saturation (SpO2) values from the system were also in close agreement with commercial pulse oximeters, although the accuracy of the reported SpO2 value is dependent on the calibration function used. Future work is targeted toward the development of variations of each module, including the laser driver and fiber optic module, onboard data acquisition and signal processing modules. The availability of this system will help researchers from a wide range of disciplines to customize and integrate the ZenPPG system to their research needs and will most definitely enhance research in related fields.