Semi-real-time removal of baseline fluctuations in electrocardiogram (ECG) signals by an infinite impulse response low-pass filter (IIR-LPF)

2018 ◽  
Vol 74 (12) ◽  
pp. 6785-6793 ◽  
Author(s):  
Jeong-Hwan Kim ◽  
Kang-Hwi Lee ◽  
Jeong-Whan Lee ◽  
Kyeong-Seop Kim
Keyword(s):  
Real Time ◽  
Impulse Response ◽  
Infinite Impulse Response ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Ecg Signals ◽  
Low Pass ◽  
Electrocardiogram Ecg

Near‐real time reduction of shipboard gravity using Kalman‐filtered GPS measurements

Geophysics ◽  
1991 ◽  
Vol 56 (12) ◽  
pp. 1971-1979 ◽  
Author(s):  
J. F. Genrich ◽  
J.-B. Minster
Keyword(s):  
Real Time ◽  
Impulse Response ◽  
Finite Impulse Response ◽  
Yield Data ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Marine Gravity ◽  
Gravity Measurements ◽  
Low Pass ◽  
Stabilized Platform

We have developed a Kalman filter to estimate accurate Eötvös corrections and horizontal ship accelerations from Global Positioning System (GPS) fixes. High‐resolution shipboard gravity measurements are obtained with a newly designed, linear phase, Finite Impulse Response (FIR) low‐pass filter. Both filters are combined to yield accurate, near‐real time, Eötvös‐corrected underway gravity estimates. Error ranges that reflect uncertainty in navigation for these estimates are calculated from autocovariances of Kalman velocity estimates by means of variance propagation expressions for time‐invariant linear digital filters. Estimates of horizontal ship acceleration are combined with a simplified instrument impulse response model in an attempt to remove transient noise from the gravimeter output. We apply the technique to data collected by two shipboard gravimeters, a LaCoste & Romberg Model S Air‐Sea Gravity Meter and a Bell Aerospace BGM-3 Marine Gravity Meter System, operated side‐by‐side on the Scripps R/V Thomas Washington during Leg 1 of the Roundabout expedition. In the absence of significant horizontal accelerations due to course or speed changes, both instruments yield data with good repeatability, characterized by rms differences of less than 1 mGal. Horizontal accelerations generate transient signals that cannot be modeled at present to an accuracy of better than 5 mGal. Difficulties in removing these transients are primarily due to insufficient quantitative knowledge of the response of the instrument, including the gyro‐stabilized platform. This can be determined analytically or empirically.

scholarly journals Real-Time Filter Method Based on the Structural Frequency of FAST Cabin-Cable System

2014 ◽  
Vol 6 ◽  
pp. 129302
Author(s):  
Wenhua Xu ◽  
Hong Bao ◽  
Jianwei Mi ◽  
Guigeng Yang
Keyword(s):  
Real Time ◽  
Fundamental Frequency ◽  
Variable Structure ◽  
Measured Data ◽  
Filter Method ◽  
Great Flexibility ◽  
Cable System ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass

Due to great flexibility, low damping, and variable structure in the cabin-cable system of five hundred meter Aperture Spherical Radio Telescope (FAST), a real-time digital low-pass filter based on the analysis of frequency is presented in this paper. Firstly, by the Lomb-Scargle theorem, it can obtain the fundamental frequency of cabin-cable system. Then, using the obtained frequency, a digital low-pass filter is designed to filter the measured data. After being filtered, the measured data are used for coarse control. Finally, the results of the experiments on the FAST 5 m model show that calculating the fundamental frequency is accurate and the filter is effective.

Band-Stop Filter Algorithm Research Based on Nano-Displacement Positioning System

2013 ◽  
Vol 380-384 ◽  
pp. 697-700 ◽  
Author(s):  
Yue Zhou ◽  
Xiao Xiao Yao ◽  
Jin Xiang Pian ◽  
Yan Qiang Su
Keyword(s):  
Transfer Functions ◽  
Experimental Simulation ◽  
Infinite Impulse Response ◽  
Positioning System ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Control Precision ◽  
Band Stop Filter ◽  
Low Pass ◽  
Inherent Frequency

This paper proposed the algorithms of infinite impulse response (IIR) band-stop filter and all-pass filter to eliminate the inherent frequency for piezoelectric ceramics and improve the control precision for nanodisplacement positioning system. The IIR algorithm was composed of five steps (such as the determination of normalized frequency, filter orders and transfer functions of analog low-pass filter, analog band-stop filter and digital band-stop filter). Based on the experimental simulation results on the nanodisplacement positioning platform, the butterworth band-stop filter algorithm can achieve the requested filtering effects within 10 orders .

Pseudo-real-time low-pass filter in ECG, self-adjustable to the frequency spectra of the waves

2017 ◽  
Vol 55 (9) ◽  
pp. 1579-1588 ◽  
Author(s):  
Ivaylo Christov ◽  
Tatyana Neycheva ◽  
Ramun Schmid ◽  
Todor Stoyanov ◽  
Roger Abächerli
Keyword(s):  
Real Time ◽  
Frequency Spectra ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass ◽  
The Waves

scholarly journals Convolution Integral: How a Graphical Type of Solution Can Help Minimize Misconceptions

2021 ◽  
Vol 3 (2) ◽  
pp. 103
Author(s):  
Hendra J. Tarigan
Keyword(s):  
Impulse Response ◽  
Physical System ◽  
Convolution Integral ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Convolution Integrals ◽  
Low Pass ◽  
Simulation And Experiment ◽  
Rc Circuit ◽  
Set Up

A physical system, Low Pass Filter (LPF) RC Circuit, which serves as an impulse response and a square wave input signal are utilized to derive the continuous time convolution (convolution integrals). How to set up the limits of integration correctly and how the excitation source convolves with the impulse response are explained using a graphical type of solution. This in turn, help minimize the students’ misconceptions about the convolution integral. Further, the effect of varying the circuit elements on the shape of the convolution output plot is presented allowing students to see the connection between a convolution integral and a physical system. PSpice simulation and experiment results are incorporated and are compared with those of the analytical solution associated with the convolution integral.

scholarly journals Parking detection system using background subtraction and HSV color segmentation

2021 ◽  
Vol 10 (6) ◽  
pp. 3211-3219
Author(s):  
Awang Hendrianto Pratomo ◽  
Wilis Kaswidjanti ◽  
Alek Setiyo Nugroho ◽  
Shoffan Saifullah
Keyword(s):  
Real Time ◽  
Background Subtraction ◽  
Detection System ◽  
Threshold Value ◽  
Color Segmentation ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Detection Process ◽  
Average Accuracy ◽  
Low Pass

Manual system vehicle parking makes finding vacant parking lots difficult, so it has to check directly to the vacant space. If many people do parking, then the time needed for it is very much or requires many people to handle it. This research develops a real-time parking system to detect parking. The system is designed using the HSV color segmentation method in determining the background image. In addition, the detection process uses the background subtraction method. Applying these two methods requires image preprocessing using several methods such as grayscaling, blurring (low-pass filter). In addition, it is followed by a thresholding and filtering process to get the best image in the detection process. In the process, there is a determination of the ROI to determine the focus area of the object identified as empty parking. The parking detection process produces the best average accuracy of 95.76%. The minimum threshold value of 255 pixels is 0.4. This value is the best value from 33 test data in several criteria, such as the time of capture, composition and color of the vehicle, the shape of the shadow of the object’s environment, and the intensity of light. This parking detection system can be implemented in real-time to determine the position of an empty place.

scholarly journals Real-time digital signal recovery for a low-pass transfer function system with multiple complex poles

2018 ◽  
Author(s):  
Jhinhwan Lee
Keyword(s):  
Transfer Function ◽  
Real Time ◽  
Digital Signal ◽  
Signal Recovery ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Recovery Method ◽  
Noise Characteristics ◽  
Resonance Frequencies ◽  
Low Pass

In order to solve the problems of waveform distortion and signal delay by many physical and electrical systems with linear low-pass transfer characteristics with multiple complex poles, a general digital-signal-processing (DSP)-based method of real-time recovery of the original source waveform from the distorted output waveform is proposed. From the convolution kernel representation of a multiple-pole low-pass transfer function with an arbitrary denominator polynomial with real valued coefficients, it is shown that the source waveform can be accurately recovered in real time using a particular moving average algorithm with real-valued DSP computations only, even though some or all of the poles are complex. The proposed digital signal recovery method is DC-accurate and unaffected by initial conditions, transient signals, and resonant amplitude enhancement. The noise characteristics of the data recovery shows inverse of the low-pass filter characteristics. This method can be applied to most sensors and amplifiers operating close to their frequency response limits or around their resonance frequencies to accurately deconvolute the multiple-pole characteristics and to improve the overall performances of data acquisition systems and digital feedback control systems.

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