A Study on Developing Cardiac Signals Recording Framework (CARDIF) Using a Reconfigurable Real-Time Embedded Processor

Due to recent developments in technology, there is a significant growth in healthcare monitoring systems. The most widely monitored human physiological parameters is electrocardiogram (ECG) which is useful for inferring the physiological state of humans. Most of the existing multi-channel ECG acquisition systems were not accessible in resource-constrained settings. This research study proposes a cardiac signal recording framework (CARDIF) using a reconfigurable input-output real-time embedded processor by employing a virtual instrumentation platform. The signal acquisition was configured using Lab VIEW virtual instrumentation block sets. A graphical user interface (GUI) was developed for real-time data acquisition and visualization. The time domain heart rate variability (HRV) statistics were calculated using CARDIF, and the same were compared with a clinical grade 12-channel ECG system. The quality of the acquired signals obtained from the proposed and standard systems was measured and compared by calculating signal-to-noise ratio (SNR). The proposed CARDIF was evaluated qualitatively by visual inspection by a clinician and quantitatively by fidelity measures and vital parameters estimation. The results are quite promising and can be extended for clinical validations.

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Deep Learning Based Inversion of Locally Anisotropic Weld Properties from Ultrasonic Array Data

Applied Sciences ◽

10.3390/app12020532 ◽

2022 ◽

Vol 12 (2) ◽

pp. 532

Author(s):

Jonathan Singh ◽

Katherine Tant ◽

Anthony Mulholland ◽

Charles MacLeod

Keyword(s):

Real Time ◽

Signal To Noise Ratio ◽

Computational Cost ◽

Scattered Wave ◽

Training Data ◽

Grain Structure ◽

Time Data ◽

Grain Orientations ◽

Weld Properties ◽

Crystallographic Orientations

The ability to reliably detect and characterise defects embedded in austenitic steel welds depends on prior knowledge of microstructural descriptors, such as the orientations of the weld’s locally anisotropic grain structure. These orientations are usually unknown but it has been shown recently that they can be estimated from ultrasonic scattered wave data. However, conventional algorithms used for solving this inverse problem incur a significant computational cost. In this paper, we propose a framework which uses deep neural networks (DNNs) to reconstruct crystallographic orientations in a welded material from ultrasonic travel time data, in real-time. Acquiring the large amount of training data required for DNNs experimentally is practically infeasible for this problem, therefore a model based training approach is investigated instead, where a simple and efficient analytical method for modelling ultrasonic wave travel times through given weld geometries is implemented. The proposed method is validated by testing the trained networks on data arising from sophisticated finite element simulations of wave propagation through weld microstructures. The trained deep neural network predicts grain orientations to within 3° and in near real-time (0.04 s), presenting a significant step towards realising real-time, accurate characterisation of weld microstructures from ultrasonic non-destructive measurements. The subsequent improvement in defect imaging is then demonstrated via use of the DNN predicted crystallographic orientations to correct the delay laws on which the total focusing method imaging algorithm is based. An improvement of up to 5.3 dB in the signal-to-noise ratio is achieved.

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Numerical Control Lathe Cutting Force Signal On-Line Monitoring Design

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.711.329 ◽

2014 ◽

Vol 711 ◽

pp. 329-332

Author(s):

Lin Zhao

Keyword(s):

Cutting Force ◽

Real Time ◽

Piezoelectric Sensor ◽

Numerical Control ◽

Signal Acquisition ◽

Time Data ◽

Main Research ◽

Monitoring Design ◽

Force Signal ◽

On Line

The main research direction of Numerical control lathe cutting force signal on-line monitoring is to process real-time monitoring, using the sensor, charge amplifier, video acquisition card and computer to collect data and signal. Signal acquisition makes use of the piezoelectric sensor signals and send them to the computer in order to acquire the real-time data and display the dynamic signal so that monitor the process. Signal processing is the course that data will be collected for subsequent processing and analyzing. It includes display, filtering, correlation analysis, spectral analysis, etc. We can conclude the signal’s characteristics after the time domain and frequency domain analysis of signals.

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Detecting changes in real-time data: a user’s guide to optimal detection

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2016.0298 ◽

2017 ◽

Vol 375 (2100) ◽

pp. 20160298 ◽

Cited By ~ 3

Author(s):

P. Johnson ◽

J. Moriarty ◽

G. Peskir

Keyword(s):

Power Systems ◽

Real Time ◽

Systems Engineering ◽

Sequential Analysis ◽

Signal To Noise Ratio ◽

Detection Theory ◽

Change Point Detection ◽

Optimal Detection ◽

Time Data ◽

Detection Delay

The real-time detection of changes in a noisily observed signal is an important problem in applied science and engineering. The study of parametric optimal detection theory began in the 1930s, motivated by applications in production and defence. Today this theory, which aims to minimize a given measure of detection delay under accuracy constraints, finds applications in domains including radar, sonar, seismic activity, global positioning, psychological testing, quality control, communications and power systems engineering. This paper reviews developments in optimal detection theory and sequential analysis, including sequential hypothesis testing and change-point detection, in both Bayesian and classical (non-Bayesian) settings. For clarity of exposition, we work in discrete time and provide a brief discussion of the continuous time setting, including recent developments using stochastic calculus. Different measures of detection delay are presented, together with the corresponding optimal solutions. We emphasize the important role of the signal-to-noise ratio and discuss both the underlying assumptions and some typical applications for each formulation. This article is part of the themed issue ‘Energy management: flexibility, risk and optimization’.

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A Biofeedback Based Auto-Controlled Neurostimulator Design for Proper NCS Signal Acquisition and Measurement

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.c5157.029320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 16-25

Keyword(s):

Real Time ◽

Compound Muscle Action Potential ◽

Skin Resistance ◽

Buck Converter ◽

Signal Acquisition ◽

Time Data ◽

Feedback Parameter ◽

Custom Made ◽

Specific Duty ◽

Electrical Stimulator

This paper presents the design of a biofeedback based auto-controlled neurostimulator for acquiring nerve response. Nerve conduction study (NCS) employs an electrical stimulator that generates a stimulus to be applied over the skin of an underlying nerve. Conventional neurostimulator uses manual control of voltage or current to generate the nerve responses. It is observed that the stimulation for supramaximal response varies with subjects due to different skin resistances of the subjects. Such measurement needs repeated trials which is time consuming, irritating to subjects and often suffers difficulties in real-time applications. This study proposes a portable neurostimulator based on the skin resistance as bio-feedback parameter to control the stimulus. A custom made NCS setup is developed for experimental recording of real-time nerve signals and identified the best compound muscle action potential signal for generating optimal stimulus i.e., supramaximal stimulus (SS) manually. Then, mathematical models are investigated using real-time data and models are implemented in a microcontroller (µC) based stimulator. The µC triggers a pulse train of specific duty cycle to a buck converter for producing the required optimal voltage which is used as a SS across the electrodes. Online experimental results with new subjects show that the proposed design is efficacious and adaptable with safety.

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Frequency Comb Optoacoustic Tomography

10.1101/2021.05.12.443808 ◽

2021 ◽

Author(s):

Antonios Stylogiannis ◽

Ludwig Prade ◽

Sarah Glasl ◽

Qutaiba Mustafa ◽

Christian Zakian ◽

...

Keyword(s):

Frequency Comb ◽

Signal To Noise Ratio ◽

High Signal ◽

Signal Acquisition ◽

Competitive Advantages ◽

Optoacoustic Tomography ◽

Multiple Wavelengths ◽

Td Methods ◽

The Time Domain

Optoacoustics (OA) is overwhelmingly implemented in the Time Domain (TD) to achieve a high Signal-to-Noise-Ratio (SNR). Implementations in the Frequency Domain (FD) have been proposed, but have not offered competitive advantages over TD methods to reach high dissemination. It is therefore commonly believed that the TD represents the optimal way of performing optoacoustics. Here, we introduce a novel optoacoustic concept based on frequency comb and theoretically demonstrate its superiority to the TD. Then, using recent advances in laser diode illumination, we launch Frequency Comb Optoacoustic Tomography (FCOT), at multiple wavelengths, and experimentally demonstrate its advantages over TD methods in phantoms and in-vivo. We demonstrate that FCOT optimizes the SNR of spectral measurements over TD methods by benefiting from signal acquisition in the TD and processing in the FD, and that it reaches the fastest multi-spectral operation ever demonstrated in optoacoustics while reducing performance compromises present in TD systems.

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Development of an FPGA-based realtime DAQ system for axion haloscope experiments

Journal of Instrumentation ◽

10.1088/1748-0221/16/11/t11008 ◽

2021 ◽

Vol 16 (11) ◽

pp. T11008

Author(s):

M.J. Lee ◽

B.R. Ko ◽

S. Ahn

Keyword(s):

Real Time ◽

Data Transfer ◽

Signal To Noise Ratio ◽

Frequency Resolution ◽

Fast Fourier Transformation ◽

Time Data ◽

Real Time Data ◽

Field Programmable ◽

Analysis Window ◽

Pseudo Data

Abstract A real-time Data Acquisition (DAQ) system for the CULTASK axion haloscope experiment was constructed and tested. The CULTASK is an experiment to search for cosmic axions using resonant cavities, to detect photons from axion conversion through the inverse Primakoff effect in a few GHz frequency range in a very high magnetic field and at an ultra low temperature. The constructed DAQ system utilizes a Field Programmable Gate Array (FPGA) for data processing and Fast Fourier Transformation. This design along with a custom Ethernet packet designed for real-time data transfer enables 100% DAQ efficiency, which is the key feature compared with a commercial spectrum analyzer. This DAQ system is optimally designed for RF signal detection in the axion experiment, with 100 Hz frequency resolution and 500 kHz analysis window. The noise level of the DAQ system averaged over 100,000 measurements is around -111.7 dBm. From a pseudo-data analysis, an improvement of the signal-to-noise ratio due to repeating and averaging the measurements using this real-time DAQ system was confirmed.

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Real-time data processing algorithm for phase-demodulation distributed fiber-optic vibration sensor with signal-to-noise ratio over 30 dB

2017 16th International Conference on Optical Communications and Networks (ICOCN) ◽

10.1109/icocn.2017.8121286 ◽

2017 ◽

Cited By ~ 1

Author(s):

Xiancheng Xia ◽

Qingwen Liu ◽

Dian Chen ◽

Zuyuan He

Keyword(s):

Data Processing ◽

Real Time ◽

Fiber Optic ◽

Signal To Noise Ratio ◽

Vibration Sensor ◽

Processing Algorithm ◽

Time Data ◽

Signal To Noise ◽

Real Time Data ◽

Real Time Data Processing

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Ambulatory Assessment

European Psychologist ◽

10.1027/1016-9040.14.2.109 ◽

2009 ◽

Vol 14 (2) ◽

pp. 109-119 ◽

Cited By ~ 66

Author(s):

Ulrich W. Ebner-Priemer ◽

Timothy J. Trull

Keyword(s):

Real Time ◽

Clinical Psychology ◽

Real Life ◽

Ambulatory Assessment ◽

Computer Assisted ◽

Affective Valence ◽

Time Data ◽

Physiological Processes ◽

Context Specific ◽

Questionnaire Studies

Convergent experimental data, autobiographical studies, and investigations on daily life have all demonstrated that gathering information retrospectively is a highly dubious methodology. Retrospection is subject to multiple systematic distortions (i.e., affective valence effect, mood congruent memory effect, duration neglect; peak end rule) as it is based on (often biased) storage and recollection of memories of the original experience or the behavior that are of interest. The method of choice to circumvent these biases is the use of electronic diaries to collect self-reported symptoms, behaviors, or physiological processes in real time. Different terms have been used for this kind of methodology: ambulatory assessment, ecological momentary assessment, experience sampling method, and real-time data capture. Even though the terms differ, they have in common the use of computer-assisted methodology to assess self-reported symptoms, behaviors, or physiological processes, while the participant undergoes normal daily activities. In this review we discuss the main features and advantages of ambulatory assessment regarding clinical psychology and psychiatry: (a) the use of realtime assessment to circumvent biased recollection, (b) assessment in real life to enhance generalizability, (c) repeated assessment to investigate within person processes, (d) multimodal assessment, including psychological, physiological and behavioral data, (e) the opportunity to assess and investigate context-specific relationships, and (f) the possibility of giving feedback in real time. Using prototypic examples from the literature of clinical psychology and psychiatry, we demonstrate that ambulatory assessment can answer specific research questions better than laboratory or questionnaire studies.

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