Hybrid two-point modulation with 1b high-pass modulation and embedded FIR filtering

2020 ◽  
pp. 403-423
Keyword(s):  
Fir Filtering ◽  
High Pass

A V-Band Two Pole High-Pass Filter for Frequency Quadrupler Design

2016 ◽  
Vol 6 (1) ◽  
pp. 56
Author(s):  
Maryam Abata ◽  
Mahmoud Mehdi ◽  
Said Mazer ◽  
Moulhime El Bekkali ◽  
Catherine Algani
Keyword(s):  
Pass Filter ◽  
High Pass Filter ◽  
V Band ◽  
High Pass

Trombone Transfer Functions: Comparison Between Frequency-Swept Sine Wave and Human Performer Input

2012 ◽  
Vol 37 (4) ◽  
pp. 447-454
Author(s):  
James W. Beauchamp
Keyword(s):  
Transfer Function ◽  
Transfer Functions ◽  
Cutoff Frequency ◽  
Sine Wave ◽  
Nonlinear Propagation ◽  
Linear Filter ◽  
Wave System ◽  
General Effect ◽  
Filter Characteristic ◽  
High Pass

Abstract Source/filter models have frequently been used to model sound production of the vocal apparatus and musical instruments. Beginning in 1968, in an effort to measure the transfer function (i.e., transmission response or filter characteristic) of a trombone while being played by expert musicians, sound pressure signals from the mouthpiece and the trombone bell output were recorded in an anechoic room and then subjected to harmonic spectrum analysis. Output/input ratios of the signals’ harmonic amplitudes plotted vs. harmonic frequency then became points on the trombone’s transfer function. The first such recordings were made on analog 1/4 inch stereo magnetic tape. In 2000 digital recordings of trombone mouthpiece and anechoic output signals were made that provide a more accurate measurement of the trombone filter characteristic. Results show that the filter is a high-pass type with a cutoff frequency around 1000 Hz. Whereas the characteristic below cutoff is quite stable, above cutoff it is extremely variable, depending on level. In addition, measurements made using a swept-sine-wave system in 1972 verified the high-pass behavior, but they also showed a series of resonances whose minima correspond to the harmonic frequencies which occur under performance conditions. For frequencies below cutoff the two types of measurements corresponded well, but above cutoff there was a considerable difference. The general effect is that output harmonics above cutoff are greater than would be expected from linear filter theory, and this effect becomes stronger as input pressure increases. In the 1990s and early 2000s this nonlinear effect was verified by theory and measurements which showed that nonlinear propagation takes place in the trombone, causing a wave steepening effect at high amplitudes, thus increasing the relative strengths of the upper harmonics.

APLIKASI PENAJAMAN CITRAA STREET VIEW MENGGUNAKAN METODE GRAMD SCHMIDT SPECTRAL SHARPENING DAN HIGH PASS FILTERING

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Erna Verawati ◽  
Surya Darma Nasution ◽  
Imam Saputra
Keyword(s):  
Image Processing ◽  
Visual Information ◽  
Intensity Difference ◽  
Original Image ◽  
Filtering Method ◽  
The Road ◽  
Street View ◽  
Result Show ◽  
Filter Process ◽  
High Pass

Sharpening the image of the road display requies a degree of brightness in the process of sharpening the image from the original image result of the improved image. One of the sharpening of the street view image is image processing. Image processing is one of the multimedia components that plays an important role as a form of visual information. There are many image processing methods that are used in sharpening the image of street views, one of them is the gram schmidt spectral sharpening method and high pass filtering. Gram schmidt spectral sharpening method is method that has another name for intensity modulation based on a refinement fillter. While the high pass filtering method is a filter process that btakes image with high intensity gradients and low intensity difference that will be reduced or discarded. Researce result show that the gram schmidt spectral sharpening method and high pass filtering can be implemented properly so that the sharpening of the street view image can be guaranteed sharpening by making changes frome the original image to the image using the gram schmidt spectral sharpening method and high pass filtering.Keywords: Image processing, gram schmidt spectral sharpening and high pass filtering.

Baseline-free sensor fault detection for piezoelectric array

2021 ◽  
pp. 1045389X2110188
Author(s):  
Qibo Mao ◽  
Yuande Wang ◽  
Shizuo Huang
Keyword(s):  
Prior Knowledge ◽  
Frequency Response Function ◽  
Sensor Array ◽  
Piezoelectric Element ◽  
Piezoelectric Sensor ◽  
Steel Beam ◽  
Sensor Fault ◽  
Pass Filter ◽  
Sensor Fault Detection ◽  
High Pass

In this study, a new methodology is presented to detect the sensor fault for piezoelectric array based on the filtered frequency response function (FRF) shapes. The proposed method does not require prior knowledge about healthy piezoelectric array. First, the imaginary parts of FRFs from the piezoelectric array during vibration are measured and normalized to obtain the FRF shapes in different frequencies. Then the irregularities in these FRF shapes are extracted by using high-pass filter with properly chosen cut-off frequency. These abnormal irregularities on the filtered FRF shape curves indicate the location of the faulty sensor, due to the irregularity of FRF shapes introduced by the faulty piezoelectric element. The proposed sensor fault method is experimentally demonstrated on a clamped-clamped steel beam mounted with piezoelectric buzzer array. Two common piezoelectric sensor fault types including sensor breakage and debonding are evaluated. The experimental results indicate that the proposed method has great potential in the detection of the sensor fault for piezoelectric array as it is simple and does not require the FRF data of the healthy sensor array as a baseline.

Sign in / Sign up

Export Citation Format

Share Document