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.

The design of double ridge waveguide filter using conventional stepped impedance low-pass filter method

2013 ◽  
Vol 56 (1) ◽  
pp. 120-124 ◽  
Author(s):  
Mohsen Yazdani ◽  
Luke Murphy ◽  
Alireza Mallahzadeh ◽  
Ercument Arvas ◽  
Joseph Mautz
Keyword(s):  
Ridge Waveguide ◽  
Filter Method ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass ◽  
Waveguide Filter ◽  
Double Ridge

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.

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 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 Enhanced neural tracking of the fundamental frequency of the voice

2020 ◽  
Author(s):  
Jana Van Canneyt ◽  
Jan Wouters ◽  
Tom Francart
Keyword(s):  
Fundamental Frequency ◽  
Auditory Nerve ◽  
Neural Response ◽  
Nerve Fibers ◽  
Center Frequency ◽  
Neural Processing ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass ◽  
The Voice

AbstractObjective‘F0 tracking’ is a novel method that investigates the neural processing of the fundamental frequency of the voice (f0) in continuous speech. Through linear modelling, a feature that reflects the stimulus f0 is predicted from the EEG data. Then, the neural response strength is evaluated through the correlation between the predicted and actual f0 feature. The aim of this study was to improve upon this ‘f0 tracking’ method by optimizing the f0 feature.ApproachSpecifically, we aimed to design a feature that approximates the expected EEG responses to the f0. We hypothesized that this would improve neural tracking results, because the more similar the feature and the neural response are, the easier it will be to reconstruct the one from the other. Two techniques were explored: a phenomenological model to simulate neural processing in the auditory periphery and a low-pass filter to approximate the effect of more central processing on the f0 response. Since these optimizations target different aspects of the auditory system, they were also applied in a cumulative fashion.ResultsResults obtained from EEG evoked by a Flemish story in 34 subjects indicated that both the use of the auditory model and the addition of the low-pass filter significantly improved the correlations between the actual and reconstructed feature. The combination of both strategies almost doubled the mean correlation over subjects, from 0.78 to 0.13. Moreover, canonical correlation analysis with the modelled feature revealed two distinct processes contributing to the f0 response: one driven by the compound activity of auditory nerve fibers with center frequency up to 8 kHz and one driven predominantly by the auditory nerve fibers with center frequency below 1 kHz.SignificanceThe optimized f0 features developed in this study enhance the analysis of f0-tracking responses and facilitate future research and applications.

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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