Acquisition Unit Research Articles

Cybersecurity risk assessment of VDR

AbstractThe voyage data recorder (VDR) is a data recording system that aims to provide all navigational, positional, communicational, sensor, control and command information for data-driven investigation of accidents onboard ships. Due to the increasing dependence on interconnected networks, cybersecurity threats are one of the most severe issues and critical problems when it comes to safeguarding sensitive information and assets. Cybersecurity issues are extremely important for the VDR, considering that modern VDRs may have internet connections for data transfer, network links to the ship's critical systems and the capacity to record potentially sensitive data. Thus, this research adopted failure modes and effects analysis (FMEA) to perform a cybersecurity risk assessment of a VDR in order to identify cyber vulnerabilities and specific cyberattacks that might be launched against the VDR. The findings of the study indicate certain cyberattacks (false information, command injection, viruses) as well as specific VDR components (data acquisition unit (DAU), remote access, playback software) that required special attention. Accordingly, preventative and control measures to improve VDR cybersecurity have been discussed in detail. This research makes a contribution significantly to the improvement of ship safety management systems, particularly in terms of cybersecurity.

Software of information-measuring system for electric impedance tomography of biological objects

This article describes the development of software for an electrical impedance tomography device that solves the problems of controlling the acquisition unit, primary processing and transmission of measurement data, their further processing on a personal computer, reconstruction, visualization and saving of results. Software modules are defined, algorithms for their functioning are developed, and their block diagrams are shown. An algorithm for the interaction of electrical impedance tomography device software modules with each other has been developed. A graphical interface for the device software has been developed. An algorithm for interaction with EIDORS software has been developed.Criteria for experimental testing of the functioning of the developed software have been developed, and possible errors in the operation of each module have been identified.

Single Camera-Based Dual-Channel Near-Infrared Fluorescence Imaging system.

In this study, we propose a single camera-based dual-channel near-infrared (NIR) fluorescence imaging system that produces color and dual-channel NIR fluorescence images in real time. To simultaneously acquire color and dual-channel NIR fluorescence images of two fluorescent agents, three cameras and additional optical parts are generally used. As a result, the volume of the image acquisition unit increases, interfering with movements during surgical procedures and increasing production costs. In the system herein proposed, instead of using three cameras, we set a single camera equipped with two image sensors that can simultaneously acquire color and single-channel NIR fluorescence images, thus reducing the volume of the image acquisition unit. The single-channel NIR fluorescence images were time-divided into two channels by synchronizing the camera and two excitation lasers, and the noise caused by the crosstalk effect between the two fluorescent agents was removed through image processing. To evaluate the performance of the system, experiments were conducted for the two fluorescent agents to measure the sensitivity, crosstalk effect, and signal-to-background ratio. The compactness of the resulting image acquisition unit alleviates the inconvenient movement obstruction of previous devices during clinical and animal surgery and reduces the complexity and costs of the manufacturing process, which may facilitate the dissemination of this type of system.

Three‐in‐One Portable Electronic Sensory System Based on Low‐Impedance Laser‐Induced Graphene On‐Skin Electrode Sensors for Electrophysiological Signal Monitoring

AbstractOn‐skin sensors can precisely perceive important electrophysiological signals, including electroencephalogram (EEG), electrocardiogram (ECG), and electromyogram (EMG). Despite significant advances in the development of soft materials as electrode sensors, data acquisition (DAQ) unit—another indispensable component of on‐skin electronic sensory systems—typically exhibits bulkiness or unimodal sensing, which is detrimental to the portability of the sensory system or the comprehensiveness of the perceived information. Here, a portable and multimodal DAQ unit to tackle these challenges is designed. By assembling the DAQ unit with low‐impedance (<100 Ω) laser‐induced graphene on‐skin electrode sensors, a wireless communication module, a power supply module, and a 3D printed protective shell, the completed sensory system can realize three‐in‐one monitoring of EEG, ECG, and EMG with a light weight of 22 g and a low cost of $25. Moreover, a mobile App is developed to display the perceived electrophysiological signals in real time. Human–machine interface and embedded machine learning are demonstrated using the designed sensory system, indicating its potential applications in artificial intelligence. The success of this inexpensive three‐in‐one portable electronic sensory system sheds light on design, fabrication, and commercialization of multifunctional wearable electronics with wide applications in fitness tracking, medical diagnostics, and human–machine interface.

An integrated anti-interference suppression strategy based on L-type and π type circuit series for integrated distribution box

With the wide application of intelligent distribution equipment, the operation and management level of the distribution network has been significantly improved. At present, domestic and foreign scholars' researches on the primary and secondary integration technology of distribution switch are still in its infancy. And it needs to be further discussed. Based on the structure theory of the distribution terminal acquisition unit, this paper analyzes the influence of temperature rise, pressure, and strong electromagnetic interference on the performance of primary and secondary fusion equipment under field operating conditions. To improve the operation reliability of the integrated equipment, an anti-interference suppression measure of the primary and secondary fusion distribution switch is proposed based on the series connection of L-type and π-type circuits and the accuracy detection method of the transformer. It solves the problem of high-frequency damping oscillation effectively at the input of the transformer under the condition of electromagnetic interference. This paper verifies the feasibility and effectiveness of the integrated fusion technology of the primary and secondary fusion equipment of distribution switches through theory and experiment.

Buckling Susceptibility of a K-File during the Initial Negotiations of Narrow and Curved Canals Using Different Manual Techniques.

(1) Background: One possible way to investigate the potential impact or susceptibility of buckling on different manual techniques is to measure compressive loads during canal negotiation. The higher their values, the easier and quicker the critical load level to buckling is reached, leading to possible instrument lateral deformation. The objective of the present study was to investigate the impacts of compressive loads on a small K-file manipulated with different techniques for canal negotiation in simulated narrow and curved canals. (2) Methods: The tooth model selected was a plastic double-curved premolar 23 mm long (DRSK Group AB, Kasernvagen 2, SE-281 35, Hassleholm, Sweden) with an extremely narrow canal lumen to mimic a very difficult anatomical scenario. An experienced endodontist performed the negotiation of 90 of these artificial teeth randomly assigned to 3 different groups of 30 blocks each, respectively, using 3 different techniques: Group A: watch winding/pull (WW) motion; Group B: balanced forces (BF) technique; Group C: envelope of motion (EOM). The measurement system was based on the use of a dynamometer, Instron, Ltd. (model 2525-818 2kN f.s.), linked to a data acquisition unit HBM MGC+ to test all the compression and tensile loads, including all the peaks. (3) Results: All data acquired were processed by the CATMAN AP HBM software. Multiple comparisons for the highest compressive loads estimated the mean difference between WW vs. BF techniques of 3.60 [95% confidence interval (CI): 2.85 to 4.35, p < 0.001], WW vs. EOM of -1.76 (95% CI: -2.11 to 1.40, p < 0.001), and BF vs. EOM -5.36 (95% CI: -6.04 to -4.67, p < 0.001). (4) Conclusions: In conclusion, among the tested manual motions, the BF technique (Group B) was the most susceptible to buckling with the highest compressive load. WW motion (Group A) and EOM (Group C) were less susceptible to buckling than the BF technique. Therefore, a pressure-free manipulation of manual files, such as WW motion or EOM, can help reduce the susceptibility to buckling during the negotiation of narrow-curved canals.

A flexible adhesive surface electrode array capable of cervical electroneurography during a sequential autonomic stress challenge

This study introduces a flexible, adhesive-integrated electrode array that was developed to enable non-invasive monitoring of cervical nerve activity. The device uses silver-silver chloride as the electrode material of choice and combines it with an electrode array consisting of a customized biopotential data acquisition unit and integrated graphical user interface (GUI) for visualization of real-time monitoring. Preliminary testing demonstrated this electrode design can achieve a high signal to noise ratio during cervical neural recordings. To demonstrate the capability of the surface electrodes to detect changes in cervical neuronal activity, the cold-pressor test (CPT) and a timed respiratory challenge were employed as stressors to the autonomic nervous system. This sensor system recording, a new technique, was termed Cervical Electroneurography (CEN). By applying a custom spike sorting algorithm to the electrode measurements, neural activity was classified in two ways: (1) pre-to-post CPT, and (2) during a timed respiratory challenge. Unique to this work: (1) rostral to caudal channel position-specific (cephalad to caudal) firing patterns and (2) cross challenge biotype-specific change in average CEN firing, were observed with both CPT and the timed respiratory challenge. Future work is planned to develop an ambulatory CEN recording device that could provide immediate notification of autonomic nervous system activity changes that might indicate autonomic dysregulation in healthy subjects and clinical disease states.

Power system abnormal pattern detection for new energy big data

Abstract The energy crisis is a problem that countries all over the world pay more and more attention to, and a series of ecological problems caused by it have become increasingly prominent. It is difficult for traditional fossil fuels to maintain a healthy and coordinated sustainable development of society and economy. The establishment of a sustainable energy system has become the development trend of various countries to solve energy problems. Electric energy is a secondary energy that all primary energy can be converted into, and an irreplaceable consumable for all industrial technologies and people’s lives. Electric power data has the characteristics of large rate span, numerous data sources, complicated interaction methods, and various types of data. The existence of abnormal data in the power system will greatly reduce the accuracy of the system state estimation and the state estimation convergence rate. This paper introduces the power grid industrial control system, combines the data flow of power big data, and analyzes the abnormal information detection process in detail. It takes the data stream acquired by the acquisition unit PMU of the wide area measurement system as the research object. The rapid development of the Hadoop big data platform provides important technical support for the research of power grid big data. Based on the Hadoop platform, the clustering algorithm is used to complete the anomaly detection of real-time data. The LOF algorithm has poor performance when dealing with a large amount of high-dimensional data, and has high time and space complexity. In order to make up for the shortcomings of the LOF algorithm, this paper uses the K-means clustering algorithm to propose an improved algorithm K-LOF of the density-based local abnormal factor detection algorithm LOF, and optimizes the neighborhood query process. It is verified by experiments that the K-LOF algorithm can effectively reduce the time complexity of the anomaly detection algorithm and improve the detection accuracy by 2–4.2%.

Real-time digital signal processing implementation for in-beam PET of radiotherapy imaging in HIMM

The in-beam positron emission tomography (ibPET) is dedicated to radiotherapy imaging of the heavy-ion medical machine (HIMM), which requires precise online measurement, as well as real-time signal processing capability of the electronics. In this paper, we present the implementation of real-time digital signal processing in the data acquisition unit (DAQU) for an ibPET system based on the photomultiplier tube (PMT) readout of lutetium-yttrium oxyorthosilicate (LYSO) scintillator crystals. We have designed 10-channel customized signal processing circuit on a high-performance FPGA, which supports 3 working modes (single event processing mode, self-calibration mode and raw ADC data mode), position calculation, crystal locating, event sorting, and etc. Moreover, the implementation of real-time corrections is capable of dealing with photon peak correction to 511 keV and crystal ID-based time correction. The implemented real-time ibPET system can achieve 46% data compression and beyond 1,000,000 events/sec/channel signal processing capabilities. A series of initial tests is conducted, and the results indicate that this design meets the application requirement on online processing.

Micro vision-based measurement of concentricity for both TO base and active area of a APD chip in optical component packaging.

The avalanche photodiode (APD) chip is the core component of the transistor outline (TO). The concentricity between the inner circle (IC) of the APD active area and the outer circle (OC) of the TO base will directly affect a component's key performance indicators, such as external quantum efficiency, receiving sensitivity and responsivity, thereby impacting quality assurance, performance improvement, and stable operation. Nevertheless, as the surge in demand for components increases, the traditional visual inspection relying on manual and microscope has been unable to meet the requirements of mass manufacturing for real-time quality and efficiency. Thus, a Concentricity Microscopic Vision Measurement System (CMVMS) mainly composed of a microscopic vision acquisition unit and an intelligent concentricity measurement unit has been proposed, designed, and implemented. On the basis of analyzing the 3D complex environment of TO components, a coaxial illumination image acquisition scheme that can take into account the characteristics of the OC and IC has been proposed. Additionally, a concentricity image measurement method based on dynamic threshold segmentation has been designed to reduce the interference of complex industrial environment changes on measurement accuracy. The experiment results show that the measurement accuracy of the CMVMS system is over 97%, and with a single measurement time of less than 0.2s, it can better meet the real-time and accuracy requirements. To the best of our knowledge, this is the first report on the realization of real-time concentricity measurement in optical component packaging, and this technology can be extended to other fields of concentricity measurement.

관상동맥 CT 혈관조영술에서 ECG Window Acquisition 단위 변환을 통한 선량 및 화질 평가에 관한 연구

The purpose of this study was to evaluate radiation dose and image quality compared to those of the relative delay scan when using an absolute delay scan after converting the ECG window acquisition unit in coronary CT angiography. We used chest phantom and ECG simulator through the phantom study. Patient study was conducted on 90 sinus rhythm patients and 76 atrial fibrillation patients. The absolute delay scan was performed after unit conversion based on the average heart rate. After setting the region of interest, quantitative evaluation of image quality was made, qualitative evaluation was made by two observers. The dose was evaluated using the CTDIvol and DLP. There were no significant differences in quantitative values in terms of the phantom study. At an irregular heart rate 79, the CTDIvol of the experimental group decreased by 18% compared to the control group, the DLP decreased by 17.4% compared to the control group.(P<0.05) As a result of patient study, there were no significant differences in quantitative values in terms of the patient study. The CTDIvol of the artrial fibrillation experimental group decreased by 33% compared to the control group, the DLP decreased by 30% compared to the control group.(P<0.05) As a result, when examining coronary CT angiography of patients with atrial fibrillation, it will help reduce the patient’s dose without affecting the image quality if the scan range is set after converting the ECG window acquisition unit to milliseconds based on the average heart rate.

Experimental imaging and algorithm optimization based on deep neural network for electrical capacitance tomography for LN2-VN2 flow

The deep neural network (DNN) method is applied to better map the phase distribution compared with the conventical reconstruction algorithm for nonlinear problems of electrical capacitance tomography (ECT). Three DNNs are trained and evaluated in the numerical experiment for cryogenic two-phase flow. A capacitance acquisition unit is designed. And a cryogenic experiment with liquid nitrogen and nitrogen vapor (LN2-VN2) as the working fluids is conducted to verify the imaging solution for practical application. The DNN with the input of the measured capacitance data (DNN-C) has shown its generalization ability for the practical imaging application. Further, the DNN optimal method is coupled to the conventional LBP and Landweber iteration algorithm by evaluating the nonlinearity between the error capacitance vector and the real phase distribution vector (DNN-EC). The comparison shows that the DNN modification method can significantly improve the phase distribution closed to the pattern in the training input samples with an elimination of the artifacts and sharpening interface, but it fails to recognize the flow pattern which is not included in the samples. The result shows that compared with the DNN-EC, DNN-C is a better choice for the image reconstruction for the cryogenic application.

Mobile Electrocardiogram Monitoring System with Cloud-Based Approach

Cardiovascular disease (CVD) is a heart related disease and is the top silent killer in worldwide. Frequent electrocardiogram (ECG) monitoring for patients with CVD is vital to check if arrhythmia occurs. Though there are lots of commercialized portable ECG monitoring device available, many of them are dedicated to professional clinical staff with complex user interface, or only targeted to specific arrhythmia for home monitoring with lack of data management system. In this study, an in-house developed ECG monitoring device was integrated with an Android-based mobile application through Bluetooth wireless communication and implements Google Cloud Technology. The Android-based mobile app supports main features of real-time ECG graph display, automated location detector, data management system and simple arrhythmia detection. The ECG graph displays acquired ECG signal in real-time by ­in-house ECG acquisition unit through Bluetooth wireless communication and stored as text files in phone’s local memory. Firebase Authentication and Firebase Storage based on Google Cloud technology are implemented for data management system development. This allows users and administrators to upload or access data securely through online Google Cloud Platform. Three types of heart rhythm, namely normal sinus rhythm, bradycardia and tachycardia can be classified based on heart rate analysis. Smartphone’s location service is enabled to allow user shares their location with caretaker in emergency case. The final solution utilizes the Internet-of-Things (IoT) technology to facilitate heart disease management towards telemedicine applications.

Design and Development of Electrogastrograph Wireless Electrode Module

Electrogastrography is a pain free diagnosing method for evaluating stomach myoelectric activity. People are drawn to EGG because it is painless diagnostic procedure. However, given to its non-invasive characteristics, there has been significant debate about the validity and applicability of EGG. The major goal of this project is to decouple the electrode-equipment probe connection. This enhances the patient’s mobility (infant, athlete, handicapped) while lowering probe-to-electrode misconnection. This portable electrode system allows physicians and nurses to examine data of a patient from a distance, decreasing the need for them to be physically present behind the subject for examination. The signal capture, data acquisition unit and communication interface with the Wireless Fidelity module are utilized to quantify stomach activity. Arduino is used to take an analogue signal and convert it to a digital value. The Arduino programming language is used to build the programmers, which are subsequently embedded in the board. As a consequence, the value retrieved from the Electrogastrograph module is transferred over Wireless Fidelity module to the thing speak online platform. Arduino accepts an analogue signal and transforms it to a digital value. The Arduino programming language is used to build the applications, which are subsequently incorporated into the board. As a result, the value retrieved from the EGG module is transferred through Wireless Fidelity module to thing speak online platform.

Remote consultation image stitching method based on wireless sensor technology and mathematical morphology

INTRODUCTION: In order to obtain seamless and high-precision remote consultation image mosaic results, a remote consultation image mosaic method based on wireless sensor technology and mathematical morphology is studied. A consultation image acquisition unit based on wireless sensor technology is designed, and the remote consultation image signal is collected by sensor; In the process of signal conditioning, a filter based on mathematical morphology is used to reduce the influence of noise on the accuracy of remote consultation image acquisition. OBJECTIVES: Compressed sensing technology is used to realize the compression, transmission and recovery of consultation image sampling data. METHODS: After preprocessing the image through shadow correction, surf algorithm is used to construct the scale space to determine the main direction of feature points in the image; The extended surf descriptor is constructed based on feature points for consultation image registration. RESULTS: Based on the spatial transformation relationship between images, the improved gradual in and gradual out stitching method is used to complete the remote consultation image stitching. Experimental results show that this method can accurately collect consultation image signals, and the corresponding rate of the feature point extraction results reaches nearly 99%, which is relatively robust. CONCLUSION: The RMSE error of the image registration results is less than 2.692, which improves the accuracy of the remote consultation image stitching results, well solves the problem of image visual field reduction, and there is no seam in the stitching area.

Entity Relationship Modeling for IoT Data Fusion Driven by Dynamic Detecting Probe

To solve the problem of integrating and fusing scattered and heterogeneous data in the process of data space construction, we propose a novel entity association relationship modeling approach driven by dynamic detecting probes. By deploying acquisition units between the business logic layer and data access layer of different applications and dynamically collecting key information such as global data structure, related data, and access logs, the entity association model for enterprise data space is constructed from three levels: schema, instance, and log. At the schema association level, a multidimensional similarity discrimination algorithm combined with semantic analysis is used to achieve the rapid fusion of similar entities; at the instance association level, a combination of feature vector-based similarity analysis and deep learning is used to complete the association matching of different entities for structured data such as numeric and character data and unstructured data such as long text data; at the log association level, the association between different entities and attributes is established by analyzing the equivalence relationships in the data access logs. In addition, to address the uncertainty problem in the association construction process, a fuzzy logic-based inference model is applied to obtain the final entity association construction scheme.

Data acquisition system for a neutron spectrometer with a remote diamond detector

The article presents the design and first test results of the neutron spectrometer with count rate up to 7 × 106 events per second with remote detector based on artificial diamond, intended for use on burning plasma experiments. The spectrometer consists of the broadband low-noise amplifier and the data acquisition and processing unit. The design of the amplifier with characteristic impedance matching of the cable line by the dynamic resistance of the transistor base-emitter junction is considered. The data acquisition and processing unit is realized using a real-time event-processing algorithm based on harmonic data analysis. This algorithm includes effective techniques for detecting and excluding overlaps and suppression of fluctuations in the baseline level corresponding to zero signal.

Benchmarking a New Digital Data Acquisition System for Fast Neutron Metrology

The application of a CAEN DT5730 digitizer unit for fast neutron metrology is explored. Measurements were made at the Accelerator for Metrology and Neutron Applications in External Dosimetry (AMANDE) monoenergetic neutron facility of the IRSN, France, over a neutron energy range from 1.2 to 7.0 MeV using a BC501A scintillator. The DT5730 digitizer and an analog system based on NIM electronics and an MPA-3 multichannel analyzer acquisition unit were used, under identical beam conditions, for data acquisition. Several observables were compared between the two systems including the quality of pulse shape discrimination (PSD), neutron energy distribution derived from unfolding analyses, and deadtime effects over a range of beam currents from 0.1 to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.1~\mu \text{A}$ </tex-math></inline-formula> . The suitability of the digital system for fast neutron metrology measurements both in the laboratory and in workplace fields is discussed.

Basis pursuit sparse decomposition using tunable-Q wavelet transform (BPSD-TQWT) for denoising of electrocardiograms.

The electrocardiogram (ECG) is an essential diagnostic tool to identify cardiac abnormalities. So, the primary issue in an ECG acquisition unit is noise interference. Essentially, the prominent ECG noise sources are power line interference (PLI) and Baseline drift (BD). Therefore, in the study, a new technique called the basis pursuit sparse decomposition (BPSD) using tunable-Q wavelet transform (TQWT) is proposed to remove the PLI and BD present in the ECG recordings. Chiefly, the TQWT method is a wavelet transform with distinct Quality factors (Q) which can adjust the signal to the natural non-stationary behaviour in time and space. Further, the method decomposes the signal into high-Quality factor and low-Quality factor components of wavelet coefficients to eliminate PLI and BD by choosing appropriate redundancy (r) and decomposition levels (J2). The 'r' and 'J' values are chosen based on the trial-and-error method concerning signal-to-noise ratio (SNR). It has been found that the PLI noise has been suppressed significantly with the redundancy of 3 and decomposition levels of 10; more so, the BD has been removed with the redundancy of 4 and decomposition levels of 19. The proposed method BPSD-TQWT was evaluated using the open-source MIT-BIH Arrhythmia database and the real-time ECG recordings collected through a wearable Silver Plated Nylon Woven (Ag-NyW) textile-based ECG monitoring system. The performance was then evaluated using fidelity metrics such as SNR, maximum absolute error (MAX), and normalized cross-correlation coefficient (NCC). The results were compared with IIR filter, stationary wavelet transform (SWT), non-local means (NLM) and local means (LM) methods. Using the proposed method on MIT-BIH Arrhythmia Database, performance evaluation parameters such as SNR, MAX, and NCC were improved by 4.3dB and 6.8dB, 0.37 and 0.78, 0.2 and 0.46 compared to IIR and SWT methods respectively. On the other hand, using the proposed method on the real-time datasets, values of SNR, MAX, and NCC were improved by 0.3dB and 0.6dB, 0.009 and 0.74 and 0.3 and 0.35 compared to IIR and SWT methods respectively. Finally, it can be concluded that the proposed method shows improved performance over IIR, SWT, NLM and LM methods for PLI and BD removal.

Smart Parking System Mobile Application using Ultrasonic Detector

Finding an empty parking lot is a big challenge as it can take lots of time and resources. This paper describes the design and development of a prototype of a smart parking system for assisting the vehicle drivers who always experiencing problem in finding indoor parking lots. The objectives of the proposed system are to develop a IoT parking lot system utilizing ultrasonic sensor to detect the presence of a vehicle and to develop a mobile application to display the parking lot layout for assisting car drivers to locate an empty parking lot.For the controller unit, it consists of the Arduino UNO microcontroller which is programmed using JavaScript language. Arduino is used to control the sensor, build a connection with the firebase server and update any changes in the hardware to the firebase. This enables the ultrasonic transmitter and collects the sensing data to be passed to the data transmission and acquisition unit. Once the ultrasonic sensor detects a car, the signal is read and processed by the microcontroller before it is sent to the server to update the status of the parking availability. The accuracy of vehicle detection is 100% with a standard deviation for sensor A0 and sensor A1 are 0.5 and 0.32 respectively. The display result of the parking lot availability is automatically updated in the mobile phone application in real-time.