Digital Signal Filtering, Analysis and Restoration

2000 ◽  
Author(s):  
Jiří Jan
Keyword(s):  
Digital Signal ◽  
Signal Filtering

Hydrodynamic Coefficients of Cylinders With Strakes in Oscillatory Flows

2006 ◽  
Author(s):  
Renjeev Gopalakrishnakurup ◽  
David Clelland ◽  
Shan Huang
Keyword(s):  
Reynolds Number ◽  
Digital Signal ◽  
Water Tank ◽  
Hydrodynamic Coefficients ◽  
Oscillatory Flows ◽  
Signal Filtering ◽  
Test Matrix ◽  
Smooth Cylinder ◽  
Calm Water ◽  
Pitch Ratio

Hydrodynamic coefficients of cylinders fitted with strakes in oscillatory flows have been investigated. Three different pitch ratios have been tested, i.e. pitch ratios of infinity, 8 and 4. The cylinders are forced to oscillate in otherwise calm water in a water tank. To validate as well as to compare the experiment results, a smooth cylinder is included in the test matrix. Digital signal filtering has been found to influence the results obtained. Hence sine-fitted signals are used for obtaining the coefficients. For cylinders with strakes, it has been found that the coefficients vary little with Reynolds number. It is also concluded that the pitch ratio has a significant impact on the hydrodynamic coefficients.

A Document Retrieval Model Based on Digital Signal Filtering

2015 ◽  
Vol 34 (1) ◽  
pp. 1-37 ◽  
Author(s):  
Alberto Costa ◽  
Emanuele Di Buccio ◽  
Massimo Melucci
Keyword(s):  
Digital Signal ◽  
Document Retrieval ◽  
Retrieval Model ◽  
Signal Filtering ◽  
Model Based

scholarly journals Performance of Improved FIR filer using FPGA

2019 ◽  
Vol 8 (4) ◽  
pp. 6022-6024
Keyword(s):  
Digital Signal ◽  
Fir Filter ◽  
Digital System ◽  
Signal Filtering ◽  
Window Functions ◽  
Crucial Part

A crucial part of the digital system is the FIR filter where its framework is robust and simple to connect. In this paper, a regular FIR filter and an optimized FIR filter were modeled using window functions. The FIR filter was designed and contrived by FPGA for digital signal filtering. Also in this paper, a regular FIR filter and an optimized FIR filter were built with window usability. The main characteristic of the application is the Xillinx ISE development suite program to build the FPGA data filter accelerator. The paper also simulates the hardware and software co-designed FIR filter and provides simulation findings about hardware assets and variations in quality compared to the standard and improved FIR filter

scholarly journals Effect of Sampling Rate Reduction and Signal Filtering for Gunfire Sound Classification with Spectral Vector using ANN

2020 ◽  
Vol 18 (1) ◽  
pp. 17-27
Author(s):  
Settha Tangkawanit ◽  
Surachet Kanprachar
Keyword(s):  
Classification Accuracy ◽  
Sampling Rate ◽  
Digital Signal ◽  
Sampling Frequency ◽  
Computational Resource ◽  
Ann Model ◽  
Signal Filtering ◽  
Sound Classification ◽  
Different Types ◽  
Frequency Components

To identify the type of the shooting gun, there are many important parameters to be considered. One key feature is the gunfire sound. It has been shown [1, 2] that applying the Artificial Neural Network or ANN to the gunfire sound database, the gunfire sound classification can be obtained. The input data to the ANN model was the frequency components in the frequency range at which occupying by the gunfire sound. It is seen that if the number of input frequency components is large, a significant computational resource is required. To reduce such resource so that the whole classification process can be done in a small device; for example, in a mobile phone, the sampling frequency is considered. In this research, the sampling frequency for transforming the input gunfire sound into a digital signal is varied from 44.1 kHz down to 4.41 kHz, so that the effect of the sampling frequency on the gunfire sound classification can be studied. 6 different types of gunfire sound are considered. In order to determine the effectiveness of the classification process, noise is also added to the gunfire sound samples; thus, different values of signal-to-noise ratio are considered.  Additionally, the effect of applying different types of signal filtering on the gunfire sound classification is taken into account. It is found that only reducing the sampling frequency on the input gunfire sound signal does not deliver a good performance in terms of gunfire sound classification. To obtained a good classification accuracy. signal filtering has to also be applied to the process. With Chebyshev Type II filter and 4.41 kHz sampling frequency, the obtained classification accuracy is all 100% for the practical range of SNR; that is, between 20 dB down to 5 dB. This impressive classification accuracy comes with a huge reduction on the computational resource; that is, 10 times reduction; since the sampling frequency is reduced from 44.1 kHz to 4.41 kHz. The findings from this work can be certainly applied to the gunfire sound classification system with limited computational resource in order to obtain high classification accuracy.

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