scholarly journals A VLSI DSP DESIGN AND IMPLEMENTATION OF COMB FILTER USING UN-FOLDING METHODOLOGY

2012 ◽  
pp. 72-77
Author(s):  
PURU GUPTA ◽  
TARUN KUMAR RAWAT
Keyword(s):  
Signal Processing ◽  
Computation Time ◽  
Digital Signal ◽  
Vlsi Circuits ◽  
Comb Filter ◽  
Destructive Interference ◽  
Sample Period ◽  
Iteration Bound ◽  
Comb Filters ◽  
Digital Waveguide

In signal processing, a comb filter adds a delayed version of a signal to itself, causing constructive and destructive interference. Comb filters are used in a variety of signal processing applications that is Cascaded Integrator-Comb filters, Audio effects, including echo, flanging, and digital waveguide synthesis and various other applications. Comb filter when implemented has lower through-put as the sample period can not be achieved equal to the iteration bound because node computation time of comb filter is larger than the iteration bound. Hence throughput remains less. This paper present the comb filter using one of the methodology needed to design custom or semi custom VLSI circuits named as Un-Folding which increases the throughput of the comb filter. Un-Folding is a transformation technique that can be applied to a DSP program to create a new program describing more than one iteration of the original program. It can unravel hidden con-currency in digital signal processing systems described by DFGs. Therefore, unfolding has been used for the sample period reduction of the comb filter for its higher throughput.

scholarly journals Development of a method for synthesis the FIR filters with a cascade structure based on genetic algorithm

2021 ◽  
Vol 4 (2(60)) ◽  
pp. 6-11
Author(s):  
Ruslan Petrosian ◽  
Vladyslav Chukhov ◽  
Arsen Petrosian
Keyword(s):  
Genetic Algorithm ◽  
Signal Processing ◽  
Digital Signal Processing ◽  
Digital Filters ◽  
Computation Time ◽  
Digital Signal ◽  
Digital Filtering ◽  
Fir Filters ◽  
Filter Synthesis ◽  
Cascade Structure

The object of research is the process of digital signal processing. The subject of research is methods of synthesis of digital filters with a finite impulse response based on a genetic algorithm. Digital filtering is one of the tasks of digital signal processing. FIR filters are always stable and provide a constant group delay. There are various methods for synthesizing digital filters, but they are all aimed at synthesizing filters with a direct structure. One of the most problematic areas of a digital filter with a direct structure in digital processing is the high sensitivity of the filter characteristics to inaccuracies in setting the filter coefficients. Genetic algorithm-based filter synthesis methods use an ideal filter as the approximated filter. This approach has a number of disadvantages: it complicates the search for an optimal solution; computation time increases. The study used random search method, which is the basis of genetic algorithm (used for solving optimization problems); theory of digital filtering in filter analysis; numerical methods for modeling in a Python program. Prepared synthesis method FIR filter with the cascade structure, which is less sensitive to the effect of finite bit width. Computation time was reduced. This is due to the fact that the proposed method searches for the most suitable filter coefficients based on a genetic algorithm and has a number of features, in particular, it is proposed to use a piecewise-linear function as an approximated amplitude-frequency response. This makes it possible to reduce the number of populations of the genetic algorithm when searching for a solution. The synthesis of an FIR filter with a cascade structure based on a genetic algorithm showed that for a 24-order filter it took about 30–40 generations to get the filter parameters close to the optimal values. In comparison with classical methods of filter synthesis, the following advantages are provided: calculations of the coefficients of a filter with a cascade structure directly, the possibility of optimizing coefficients with limited bit depth.

Signal Processing

2016 ◽  
pp. 153-177
Author(s):  
Deepika Ghai ◽  
Neelu Jain
Keyword(s):  
Signal Processing ◽  
Data Flow ◽  
Digital Signal ◽  
Rate Data ◽  
Data Flow Graph ◽  
Flow Graph ◽  
Iteration Bound ◽  
Signal Processing Algorithms ◽  
Data Flow Graphs ◽  
Flow Graphs

Digital signal processing algorithms are recursive in nature. These algorithms are explained by iterative data-flow graphs where nodes represent computations and edges represent communications. For all data-flow graphs, time taken to achieve output from the applied input is referred as iteration bound. In this chapter, two algorithms are used for computing the iteration bound i.e. Longest Path Matrix (LPM) and Minimum Cycle Mean (MCM). The iteration bound of single-rate data-flow graph (SRDFG) can be determined by considering the Multi-rate data-flow graph (MRDFG) equivalent of the SRDFG. SRDFG contain more nodes and edges as compared to MRDFG. Reduction of nodes and edges in MRDFG is used for faster determination of the iteration bound.

scholarly journals DIGITAL PROCESSING OF INVESTIGATED DAC’S SIGNALS

2010 ◽  
Vol 2 (1) ◽  
pp. 45-49
Author(s):  
Tomas Ustinavičius
Keyword(s):  
Signal Processing ◽  
Digital Filters ◽  
Internal Noise ◽  
Digital Signal ◽  
Digital Processing ◽  
Periodic Sequence ◽  
Comb Filter ◽  
The Given ◽  
Very High

The article shows that in designed algorithm for determination of digital signal processing and settling time of DAC the greatest influence on the test has 1/f type internal noise of the sampling converter. It is offered to filter the preliminary digital signal and to construct pseudo-periodic sequence from n realization periods of examined signals and internal noise. It is shown, that standard digital filters because of very high the demands are not suitable. The structure of digital comb filter is proposed. Investigations have shown that the given filter can effectively be used for filtering of various signals.

Method of estimation of hidden correlation of narrowband noise signals

2019 ◽  
pp. 34-39 ◽  
Author(s):  
E.I. Chernov ◽  
N.E. Sobolev ◽  
A.A. Bondarchuk ◽  
L.E. Aristarhova
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
New Technology ◽  
Digital Signal ◽  
Measuring Instruments ◽  
Central Frequency ◽  
Pearson Criterion ◽  
Useful Signal ◽  
Band Limited ◽  
Narrowband Noise

The concept of hidden correlation of noise signals is introduced. The existence of a hidden correlation between narrowband noise signals isolated simultaneously from broadband band-limited noise is theoretically proved. A method for estimating the latent correlation of narrowband noise signals has been developed and experimentally investigated. As a result of the experiment, where a time frag ent of band-limited noise, the basis of which is shot noise, is used as the studied signal, it is established: when applying the Pearson criterion, there is practically no correlation between the signal at the Central frequency and the sum of signals at mirror frequencies; when applying the proposed method for the analysis of the same signals, a strong hidden correlation is found. The proposed method is useful for researchers, engineers and metrologists engaged in digital signal processing, as well as developers of measuring instruments using a new technology for isolating a useful signal from noise – the method of mirror noise images.

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