scholarly journals Semiparametric Curve Alignment and Shift Density Estimation: ECG Data Processing Revisited

10.5772/8526 ◽  
2010 ◽  
Author(s):  
T. Trigano ◽  
U. Isserles ◽  
T. Montagu ◽  
Y. Ritov
Keyword(s):  
Data Processing ◽  
Density Estimation ◽  
Ecg Data

ECG Data Processing with the Hanover Program

2015 ◽  
pp. 127-131
Author(s):  
H. Abel ◽  
C. Zywietz ◽  
W. Schiemann ◽  
W. Alraun
Keyword(s):  
Data Processing ◽  
Ecg Data

scholarly journals Numerical Analysis of Ultrasonic Multiple Scattering for Fine Dust Number Density Estimation

2021 ◽  
Vol 11 (2) ◽  
pp. 555
Author(s):  
Homin Song ◽  
Ukyong Woo ◽  
Hajin Choi
Keyword(s):  
Data Processing ◽  
Numerical Simulations ◽  
Multiple Scattering ◽  
Density Estimation ◽  
Volume Fraction ◽  
Ultrasonic Waves ◽  
Dust Particles ◽  
Average Error ◽  
Fine Dust ◽  
Number Density

In this study, a method is presented for estimating the number density of fine dust particles (the number of particles per unit area) through numerical simulations of multiply scattered ultrasonic wavefields. The theoretical background of the multiple scattering of ultrasonic waves under different regimes is introduced. A series of numerical simulations were performed to generate multiply scattered ultrasonic wavefield data. The generated datasets are subsequently processed using an ultrasound data processing approach to estimate the number density of fine dust particles in the air based on the independent scattering approximation theory. The data processing results demonstrate that the proposed approach can estimate the number density of fine dust particles with an average error of 43.4% in the frequency band 1–10 MHz (wavenumber × particle radius ≤ 1) at a particle volume fraction of 1%. Several other factors that affect the accuracy of the number density estimation are also presented.

scholarly journals USAGE OF WAVELET TRANSFORM FOR ECG DATA PROCESSING AND ANALYSIS IN INSTRUMENTAL CARDIOLOGY

2018 ◽  
Vol 22 (6) ◽  
pp. 116-122
Author(s):  
O.M. Poleshchuk ◽  
◽  
N.G. Poyarkov ◽  
N.A. Yashin ◽  
◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Data Processing ◽  
Ecg Data

scholarly journals Bayesian Inference and Maximum Entropy Methods in Science and Engineering—MaxEnt 2019†

Proceedings ◽  
2019 ◽  
Vol 33 (1) ◽  
pp. 8
Author(s):  
Udo von Toussaint ◽  
Roland Preuss
Keyword(s):  
Parameter Estimation ◽  
Bayesian Inference ◽  
Data Processing ◽  
Maximum Entropy ◽  
Density Estimation ◽  
Model Comparison ◽  
Building Blocks ◽  
Research Area ◽  
Science And Engineering ◽  
Entropy Methods

As key building blocks for modern data processing and analysis methods—ranging from AI, ML and UQ to model comparison, density estimation and parameter estimation—Bayesian inference and entropic concepts are in the center of this rapidly growing research area. [...]

Serial Communication Based on LabVIEW for the Development of an ECG Monitor

2013 ◽  
Vol 734-737 ◽  
pp. 3003-3006
Author(s):  
Bing Yang ◽  
Yu Guo ◽  
Zhi Dong Wang ◽  
Yan Gao
Keyword(s):  
Data Processing ◽  
Program Development ◽  
Virtual Instrument ◽  
Serial Communication ◽  
High Efficient ◽  
Processing And Storage ◽  
Opening And Closing ◽  
Electrocardiogram Ecg ◽  
And Storage ◽  
Ecg Data

The techniques of serial communication utilized in a developed electrocardiogram (ECG) monitor programmed by the LabVIEW are introduced in the paper. By utilized the VISA serial communication functions included in the LabVIEW, some functions of serial ports such as opening and closing, commands sending and responses receiving, etc. can be easily called. Then, the communication between the ECG sensor and the ECG virtual instrument is established. By this way, the opening and the closing controls of the ECG sensor, the ECG data acquisition, the data processing and storage, the real-time drawing of the ECG waveform and so on can performed simply. As a result, serial communication by the LabVIEW make the program development of the ECG monitor becoming much quicker, more convenient and high efficient.

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