Commercial Data Acquisition Research Articles

Study on the Identification of Unfair Competition in the Acquisition of Business Data

In the era of the digital economy, competition among operators has expanded to include competition for business data, and Unfair competition cases have sprung up around the acquisition of business data by major internet operators. In current judicial practice, referees often cite existing provisions such as the “General provisions” and “Internet specific provisions” of the anti-unfair competition law to determine the illegality and legitimacy of commercial data Unfair competition. And in the determination of such acts, there are some problems, such as the conflict between “Rights and interests protection” and “Behavior legitimacy”, the definition of data rights and interests tends to protect the interests of operators, and the judgment of legality boundary of data acquisition behavior is fuzzy. Therefore, it is suggested to correct the way of identifying the commercial data acquisition behavior, return to the nature of competition, and establish the behavior regulation paradigm. Changing the division of data ownership, confirming the portability of personal data, expanding the reasonable limit of data portability while playing the function of public-private synergy. To refine the judgment standard of justifiability of behavior, it is necessary to establish the behavior paradigm of “Reasonable legal interest test”, and to explore the rules of fact identification and cognizance of Data Acquisition Technology, in order to achieve the existing rules under the commercial data acquisition pattern of behavior identification optimization.

Multiplexed neurochemical sensing with sub-nM sensitivity across 2.25 mm2 area

Multichannel arrays capable of real-time sensing of neuromodulators in the brain are crucial for gaining insights into new aspects of neural communication. However, measuring neurochemicals, such as dopamine, at low concentrations over large areas has proven challenging. In this research, we demonstrate a novel approach that leverages the scalability and processing power offered by microelectrode array devices integrated with a functionalized, high-density microwire bundle, enabling electrochemical sensing at an unprecedented scale and spatial resolution. The sensors demonstrate outstanding selective molecular recognition by incorporating a selective polymeric membrane. By combining cutting-edge commercial multiplexing, digitization, and data acquisition hardware with a bio-compatible and highly sensitive neurochemical interface array, we establish a powerful platform for neurochemical analysis. This multichannel array has been successfully utilized in vitro and ex vivo systems. Notably, our results show a sensing area of 2.25 mm2 with an impressive detection limit of 820 pM for dopamine. This new approach paves the way for investigating complex neurochemical processes and holds promise for advancing our understanding of brain function and neurological disorders.

Controllogger: A remote monitoring system for decentralized renewable energy sources

The development of a remote monitoring and control system for decentralized renewable energy plants is described in the present paper. The system has a microcontroller-based unit for local storing and data transmission to internet by a GSM modem and a database server to store the data. The proposed architecture is easily extended for controlling a decentralized renewable energy plant by a remote operator. Correlations and performance tests are used to compare the data collected by the system prototype with data collected by a commercial data acquisition system. The results indicate that the Root Mean Square Error (RMSE) is 0.103 m/s for the wind speed measurement and the correlation factor is greater than 0.9996. Overall, the low errors rates indicate that the data have been stored in a similar form to what would be expected with the use of a commercial data logger.

Designing an Automatic Frequency Stabilization System for an External Cavity Diode Laser Using a Data Acquisition Card in the LabVIEW Platform

The frequency stability of free-running lasers is susceptible to the influence of environmental factors, which cannot meet the long-term frequency stabilization requirements for atom interferometry precision measurements. To obtain a frequency-stabilized 780 nm laser beam, an automatic frequency stabilization system for an external cavity diode laser (ECDL) based on rubidium (Rb) atomic saturated absorption spectrum was designed using a commercial data acquisition (DAQ) card. The signals acquired by the A/D terminal are processed and analyzed by LabVIEW, which can automatically identify all the locking points and output the piezoelectric ceramic transducer (PZT) scan and digital feedback through the D/A terminal. The experimental results show that the system can lock to six different frequencies separately and realize automatic relocking within 3.5 s after unlocking. The system has a stability of 1.68 × 10−10@1 s and 4.77 × 10−12@1000 s, which meets the laboratory’s requirements for atomic interference experiments.

PyDapsys: an open-source library for accessing electrophysiology data recorded with DAPSYS.

In the field of neuroscience, a considerable number of commercial data acquisition and processing solutions rely on proprietary formats for data storage. This often leads to data being locked up in formats that are only accessible by using the original software, which may lead to interoperability problems. In fact, even the loss of data access is possible if the software becomes unsupported, changed, or otherwise unavailable. To ensure FAIR data management, strategies should be established to enable long-term, independent, and unified access to data in proprietary formats. In this work, we demonstrate PyDapsys, a solution to gain open access to data that was acquired using the proprietary recording system DAPSYS. PyDapsys enables us to open the recorded files directly in Python and saves them as NIX files, commonly used for open research in the electrophysiology domain. Thus, PyDapsys secures efficient and open access to existing and prospective data. The manuscript demonstrates the complete process of reverse engineering a proprietary electrophysiological format on the example of microneurography data collected for studies on pain and itch signaling in peripheral neural fibers.

GloveSense: A Hand Gesture Recognition System Based on Inductive Sensing

Gesture recognition, in particular, hand gesture recognition, has received great attention in the field of human-computer interaction. Although several hand gesture recognition designs have been proposed in the past, they still suffer many limitations in practice. Hence, we present a wearable device, called GloveSense, which is made on a regular glove using conductive threads. The system consists of ten coils, with each pair of coils sewn on both sides of each finger and connected in series. To achieve high sensitivity, a data acquisition system is employed in which a tank circuit made of each sewn coil is measured. Since the utilized commercial data acquisition board has limited channels, a switching mechanism is employed to measure some of the extra coils. The utilized switching mechanism reduces the cross-talk effect between the readings. The GloveSense system is tested for recognition of numbers 1 to 10 in American Sign Language. A dataset of 1400 responses is employed along with a machine learning algorithm for gesture recognition. The proposed system demonstrates a high accuracy of 99% and a short recognition time of less than 1 second.

E-Nose System Based on Fourier Series for Gases Identification and Concentration Estimation From Food Spoilage

This work presents an electronic nose (EN)-based gas identification and concentration estimation method for the detection of food spoilage. The response data of sensors were acquired through a commercial gas sensor array and data acquisition circuit board and transformed into pictures with the form of the Fourier series. A convolutional neural network (CNN) model was used to identify the pictures from the conversion of sensor data, thus achieving the purpose of identifying the gases (C2H5OH, NH3, and H2S). In order to solve the problem of sample imbalance and to improve the generalization performance of classification models, the synthetic minority oversampling technique (SMOTE) and dropout technique were employed. Fivefold cross-validation was used to evaluate the performance of the model, of which the gas identification accuracy rate reached 96.67%. Moreover, a gas concentration regression model with the advantages of simplicity and strong interpretability was further proposed to estimate the concentrations of C2H5OH, NH3, and H2S. The mean absolute errors and coefficients of determination for the concentration estimation of C2H5OH, NH3, and H2S are (3.71 ppm, 0.968), (0.50 ppm, 0.968), and (0.13 ppm, 0.99), respectively. Furthermore, we used our model to evaluate the freshness of kiwifruit, pork, and beef, and it showed satisfactory predictive performance. The method proposed in this work realizes high-precision detection of gases from food spoilage and has a good application prospect in the rapid judgment of food freshness on the EN system.

Stabilizing a strongly nonlinear structure through shaker dynamics in fixed frequency voltage control tests

Bifurcations are commonly encountered during force controlled swept and stepped sine testing of nonlinear structures, which generally leads to the so-called jump-down or jump-up phenomena between stable solutions. There are various experimental closed-loop control algorithms, such as control-based continuation and phase-locked loop, to stabilize dynamical systems through these bifurcations, but they generally rely on specialized control algorithms that are not readily available with many commercial data acquisition software packages. A recent method was developed to experimentally apply sequential continuation using the shaker voltage that can be readily deployed using commercially available software. By utilizing the stabilizing effects of electrodynamic shakers and the force dropout phenomena in fixed frequency voltage control sine tests, this approach has been demonstrated to stabilize the unstable branch of a nonlinear system with three branches, allowing for three multivalued solutions to be identified within a specific frequency bandwidth near resonance. Recent testing on a strongly nonlinear system with vibro-impact nonlinearity has revealed jumping behavior when performing sequential continuation along the voltage parameter, like the jump phenomena seen during more traditional force controlled swept and stepped sine testing. This paper investigates the stabilizing effects of an electrodynamic shaker on strongly nonlinear structures in fixed frequency voltage control tests using both numerical and experimental methods. The harmonic balance method is applied to the coupled shaker-structure system with an electromechanical model to simulate the fixed voltage control tests and predict the stabilization for different parameters of the model. The simulated results are leveraged to inform the design of a set of experiments to demonstrate the stabilization characteristics on a fixture-pylon assembly with a vibro-impact nonlinearity. Through numerical simulation and experimental testing on two different strongly nonlinear systems, the various parameters that influence the stability of the coupled shaker-structure are revealed to better understand the performance of fixed frequency voltage control tests.

Development and Testing of a 5G Multichannel Intelligent Seismograph Based on Raspberry Pi.

A seismograph was designed based on Raspberry Pi. Although comprising 8 channels, the seismograph can be expanded to 16, 24, or 32 channels by using a USB interfacing with a microcontroller. In addition, by clustering more than one Raspberry Pi, the number of possible channels can be extended beyond 32. In this study, we also explored the computational intelligence of Raspberry Pi for running real-time systems and multithreaded algorithms to process raw seismic data. Also integrated into the seismograph is a Huawei MH5000-31 5G module, which provided high-speed internet real-time operations. Other hardware peripherals included a 24 bit ADS1251 analog-to-digital converter (ADC) and a STM32F407 microcontroller. Real-time data were acquired in the field for ambient noise tomography. An analysis tool called spatial autocorrelation (SPAC) was used to analyze the data, followed by inversion, which revealed the subsurface velocity of the site location. The proposed seismograph is prospective for small, medium, or commercial data acquisition. In accordance with the processing power and stability of Raspberry Pi, which were confirmed in this study, the proposed seismograph is also recommended as a template for developing high-performance computing applications, such as artificial intelligence (AI) in seismology and other related disciplines.

SENTINEL-1 SAR FOR OPERATIONAL MONITORING OF SOUTHERN AFRICA OCEANS

Abstract. The introduction of Synthetic Aperture Radar (SAR) through the Oceans and Coasts Monitoring Information system (OCIMS) has therefore become a critical tool for operational coastal and ocean monitoring in South Africa. Natural/man-made ocean phenomena can be observed using satellite Synthetic Aperture Radar (SAR) techniques, making it an integral part of large ocean area monitoring for maritime domain awareness. SAR is an active instrument on-board a satellite platform that transmits electromagnetic waves and measures the returning signal to infer what is happening on the ocean surface. The objective of this paper was to provide information and present options to the decision-makers around sustainability of commercial SAR data acquisition in support of OCIMS IVT. The OCIMS IVT tool identifies ships from multiple types of SAR data, which can provide locations of vessels that have switched off their AIS and oil slicks caused by vessels. Although open-data offerings, such as Sentinel-1, can be considered alternatives to commercial sensors, they present severe limitations for an operational service. To meet OCIMS requirements, and address the imperative to reduce costs, a hybrid of commercial acquisition being used in conjunction with an open data source such as Sentinel-1 is recommended. This would ensure that an operational service with the desired latency as well as increasing the revisit time over the EEZ.

Leveraging audio hardware for underwater acoustics

Both active and passive underwater acoustic systems frequently use purpose built hardware that is designed for a particular application. Transducers, electronics, and data acquisition systems for field experiments, therefore, are often expensive, tailored to limited frequency bands, or packaged for integration with a specific platform. Reliance on custom hardware, however, can make initial experimental investigations of new sensing paradigms cost prohibitive. The expense also creates an entry barrier to experimental work for students and researchers outside the underwater acoustics community. Audio hardware, however, is widely available, easily integrated using commercial data acquisition tools, and is often of relatively low cost. This presentation will describe two efforts to use audio hardware for inexpensive experimental investigations of underwater acoustics topics. The first, AirSAS, uses audio transducers and electronics to investigate synthetic aperture sonar problems in air that are analogous to those underwater. The second, the Citizen Scientist Hydrophone, integrates underwater transducers with consumer audio electronics to make an inexpensive, multipurpose passive sensing device. Challenges and limitations of using commercial audio hardware for underwater acoustics, especially as they relate to accuracy and data quality, will be discussed.

Stochastic Memristive Interface for Neural Signal Processing.

We propose a memristive interface consisting of two FitzHugh–Nagumo electronic neurons connected via a metal–oxide (Au/Zr/ZrO2(Y)/TiN/Ti) memristive synaptic device. We create a hardware–software complex based on a commercial data acquisition system, which records a signal generated by a presynaptic electronic neuron and transmits it to a postsynaptic neuron through the memristive device. We demonstrate, numerically and experimentally, complex dynamics, including chaos and different types of neural synchronization. The main advantages of our system over similar devices are its simplicity and real-time performance. A change in the amplitude of the presynaptic neurogenerator leads to the potentiation of the memristive device due to the self-tuning of its parameters. This provides an adaptive modulation of the postsynaptic neuron output. The developed memristive interface, due to its stochastic nature, simulates a real synaptic connection, which is very promising for neuroprosthetic applications.

Development of a low-cost efficient wireless intelligent sensor for strain measurements (LEWIS-S)

Generally, measuring strain can be a very difficult and expensive task due to the complex demands of data acquisition (DAQ) systems and pasting strain gauges (SGs) which can disincentivize taking strain measurements overall. However, measuring strain can quantify stresses and mechanical properties which can aid in determining safe operating conditions for materials. The purpose of this research is to introduce an alternative resource to efficiently and accurately measure strain in a cost-effective and easy-to-use platform. In order to accomplish this goal, a new low-cost efficient wireless intelligent sensor for strain (LEWIS-S) was developed and experimentally tested. The LEWIS-S functions on a platform of various Arduino hardware components and free integrated development environment software. The sensor is approximately 95% cheaper than standard commercial equipment and the compact design reduces the footprint of the equipment by approximately 75%. Additionally, the LEWIS-S has an inherently simple design such that limited knowledge is required to manually assemble and use the sensor. Furthermore, the versatility of the sensor allows for the compatibility of different SG attachments which can be useful for sensing optimization during testing. As such, a uniaxial friction-magnetic strain checker and a traditional pasted uniaxial SG were used in the validation testing in this research. Static and dynamic validation tests were conducted on a small cantilever beam where the LEWIS-S was compared to a commercial DAQ system to verify the accuracy and dependability of the sensor. Results from the experimental testing demonstrated good agreement between the LEWIS-S and commercial equipment.

Crosstalk Effects in the Uncertainty Estimation of Multiplexed Data Acquisition Systems

This article deals with the analysis of multichannel data-acquisition systems with the aim of identifying and combining the main uncertainty contributions according to the guide to the expression of uncertainty in measurement (GUM) framework. Particular attention has been paid toward crosstalk effect, which could be an important uncertainty contribution in multiplexed data-acquisition systems. The uncertainty analysis is described for three commercial data acquisition devices highlighting that crosstalk specifications are often not suitable for reliable uncertainty estimation in operating conditions. For this reason, an experimental setup has been arranged to fully characterize the interchannel effects of the investigated devices. The obtained results have highlighted that proper characterization of a data-acquisition system is effective in estimating the actual performance at the frequency of interest and in the operating conditions for the source resistance and the input-channel configuration. Eventually, a customized procedure has been proposed, which is effective in correcting the crosstalk effects also in very severe conditions of interchannel disturbance.

Electromagnetic Induction Imaging at Multiple Depths With a Single Coil

Electromagnetic induction imaging (EII) is an attractive imaging modality due to its contactless operation, low cost, and good penetration depth. To implement real-time imaging, in applications such as object tracking or functional biomedical imaging, fast data acquisition is required. To proceed toward this goal, in this article, we propose the use of a single coil for 3-D EII along with a fast inversion approach. To collect sufficient data that allow 3-D imaging, we propose two approaches: collecting data at multiple coil’s drive current levels and collecting data with multiple tank circuit’s capacitor values. We show that the use of such data when using a single coil allows for 3-D EII. For proof-of-concept experiments, we use a commercial data acquisition board, that is, LDC1614EVM module, and multilayer planar coils made by Texas Instruments.

A geophone-based and low-cost data acquisition and analysis system designed for microtremor measurements

Abstract. The commercial data acquisition instruments designed for three-component microtremor measurements are usually very expensive devices. In this paper, a low-cost, computer-aided, and geophone-based system designed to record, monitor, and analyze three-component microtremor data is presented. This proposed system is not a simple data acquisition system. It is also an integrated system developed to interpret the microtremor data using the horizontal-to-vertical spectral ratio (H ∕ V) method without any external software. Therefore, the H ∕ V peak frequency and amplitude can be easily estimated using this system. The proposed system has several features such as a 200 Hz sampling frequency, approximately 72 dB dynamic range, text data format, and data analysis tools. This system consists of a graphical user interface developed by using the .NET Framework 4.5.2 and external hardware that includes signal conditioning circuits, voltage converter circuit, external analog-to-digital converter, and Arduino Uno board. The proposed system uses low-cost vertical and horizontal geophones with a 4.5 Hz natural frequency to measure three-component microtremor data. The developed software undertakes many tasks such as communication between the external hardware and computer, transferring, monitoring, and recording the seismic data to the computer, and interpretation of the recorded data using the Nakamura method. Channel consistency and internal noise measurement tests were performed to demonstrate the accuracy and precision of the proposed system. The proposed system was compared to a commercial triaxial digital seismograph, and satisfactory results were obtained. The developed system is a completely open-source and open-hardware system and can be easily used in academic studies conducted by researchers and university students who are interested in seismic ambient noise analysis.

Problems of the advanced encryption standard in protecting Internet of Things sensor networks

Abstract This paper deals with a new field for electronic measurements in very-poor signal-to-noise-ratio conditions, related to experimental analysis of transducer networks cybersecurity. The security vulnerability of the most used cryptographic algorithm in Internet of Things sensor networks, the Advanced Encryption Standard (AES), is proved experimentally. To this aim, an improved version of state-of-the-art signature analysis of sensor power consumption (scatter attack) is exploited to discover the encryption key by a malicious smart soft micro-transducer. Attacking measurements are carried out by a commercial data acquisition micro-system, straightforward digital pre-processing, and suitable statistical analysis. An experimental case study, related to a successful attack to an 8-bit IoT microcontroller protected by AES-128 algorithm, is reported. The results highlight the need for the creation of an international standard of experimental characterization for IoT security.

Massively parallel microwire arrays integrated with CMOS chips for neural recording.

Multi-channel electrical recordings of neural activity in the brain is an increasingly powerful method revealing new aspects of neural communication, computation, and prosthetics. However, while planar silicon-based CMOS devices in conventional electronics scale rapidly, neural interface devices have not kept pace. Here, we present a new strategy to interface silicon-based chips with three-dimensional microwire arrays, providing the link between rapidly-developing electronics and high density neural interfaces. The system consists of a bundle of microwires mated to large-scale microelectrode arrays, such as camera chips. This system has excellent recording performance, demonstrated via single unit and local-field potential recordings in isolated retina and in the motor cortex or striatum of awake moving mice. The modular design enables a variety of microwire types and sizes to be integrated with different types of pixel arrays, connecting the rapid progress of commercial multiplexing, digitisation and data acquisition hardware together with a three-dimensional neural interface.

An open source motorized swivel for in vivo neural and behavioral recordings

In this work we propose an open source, cost-effective motorized swivel for behavioral and neural recordings in small rodents, offering a flexible solution for managing cable twisting and tangling in a variety of experimental settings with minimal human supervision.•The device operates independently of the data acquisition system, and it can be controlled through any popular platform such as Arduino or Raspberry Pi.•All mechanical parts are 3D-printed, allowing to customize the design to fit specific experimental needs, and electromechanical components can be sourced from all major distributors, keeping the cost for the entire system under $500.•The proposed commutator is compatible with commercial or custom data acquisition systems supporting up to 10 data lines (2 for LVDS signals) and 2 power lines.

Characterization of PXI-Based Generators for Impedance Measurement Setups

Multichannel digital sinewave generators play a key role in digital automated impedance bridges. Along with the technological progress in the development of commercial data acquisition (DAQ) boards, which brings with it improvements in their metrological and functional properties, it is possible to realize the DAQ-based high-precision multichannel generators. This paper describes the evaluation of properties of three commercial DAQs, available in the form of PCI eXtensions for Instrumentation modules and working as two-channel generators supervised by software developed by the authors. Due to the fact that the main application area of these generators is digital impedance bridges, this paper presents research results the most relevant for the impedance bridges, i.e., the stability of amplitude ratio and phase difference as well as total harmonic distortion and spurious-free dynamic range. The results and conclusion presented in this paper may be useful when choosing DAQs intended to be used in the multichannel generators.