Impulse response

Abstract With improvements in capacity and performance and a decrease in cost, FPGAs have become a viable solution for making custom chips and programmable DSP devices. This paper presents an efficient implementation of Finite Impulse Response Filter (FIR) using Distributed Arithmetic (DA) architecture based on FPGA with the help of xilinx system generator software. Here, the multipliers in FIR filter are replaced with multiplierless DA based technique to remove high frequency Electrocardiogram(EMG)

identification, three techniques are considered for the identification of linear system. Different techniques are frequency chirp, coherence function and pseudoinverse. In chirp method, wideband excitation such as frequency chirp is used. Frequency response is

5.1 Introduction This chapter discusses the model specification, variable selection and the estimation methods used in the study. Thereafter, the results from the data collected for this study are presented and discussed. 5.2 Data and methodology 5.2.1 Data Sources This study is based on secondary data, relying mainly on desk search and observations. The required data will be collected from various sources, though, such as Vhembe Annual Reports, World Investment Reports, African Development Bank’s

operation of the digital code of the input discrete signal in order to achieve the purpose of changing the spectrum of the signal. Digital filter has a lot of practical forms, can be roughly divided into two kinds of type: finite impulse response (FIR) and infinite impulse response (IIR). It can be used in both hardware and software platform. In the hardware implementation method, it is composed of an adder, a multiplier, which is completely separate from the analog filter which is composed of resistors

DESIGN OF LOW PASS FIR FILTER THROUGH PARTICLE SWARM OPTIMIZATION Abstract: In the common case, the impulse response is finite because there is no feedback in the FIR. A lack of feedback guarantees that the impulse response will be finite. Therefore, the term "finite impulse response" is nearly synonymous with "no feedback". However, if feedback is employed yet the impulse response is finite, the filter still is a FIR. In this paper FIR filter design through particle swarm optimization. Key

Introduction In signal processing, the purpose of a filter is to remove unwanted portions of the signal, such as random noise, or to extract useful parts of the signal, such as the constituents lying inside a precise frequency range. Figure 1.1 A block diagram of a basic filter There are two main kinds of filter, analog and digital. An analog filter uses analog electronic routes made up from constituents such as resistors, capacitors and op amps to produce the needed filtering effect. Such filter

casual Analog and digital Passive a ndactive filter Infinite impulse response (IIR) or finite impulse response (FIR) filter. IIR(Infinite impulse response IIR filters are digital filters with infinite impulse response. They have the feedback (a recursive part of a filter) and are known as recursive digital filters. They are the time invariant systems. IIR filter has better frequency response than FIR filters. FIR(Finite impulse response) The transfer function of FIR filter approaches the ideal

Wavelets, based on time-scale representations, provide an alternative to time-frequency representation based signal processing. Wavelets are then represented by dilation equations, as opposed to difference or differential equations. Wavelets maintain orthogonality with respect to their dilations and translations. Orthogonality of wavelets with respect to dilations leads to multigrid representation. Wavelets decompose the signal at one level of approximation into approximation and detail signals at

Frequency Adaptive Repetitive Control of Grid-connected Inverters Rabia Nazira,b, Keliang Zhoub, Neville R. Watsonb, Alan Woodb a Electrical Engineering Department, Lahore College for Women University Lahore, Pakistan. bElectrical and Computer Engineering Department University of Canterbury, Christchurch, New Zealand. Abstract—Grid-connected inverters (GCI) are widely used to feed power from renewable energy distributed generators into smarter grids. Repetitive control (RC) enables such inverters

magnitude and phase response. [4] ⎛π ⎞ ⎛π ⎞ (ii) Find its response to the input x(n) = cos⎜ n ⎟ + cos⎜ n ⎟, − ∞ < n < ∞. ⎝2 ⎠ ⎝4 ⎠ [4] Solution : (i) To find Magnitude and Phase Response Given (i) By ZT, y (n) = x (n) + x (n – 4) Y (z) = x (z) + z -4 x (z = x (z) (1 + z -4) H (z) = 1 + z -4 z = e jw H (e jw) = 1 + e –j4w Put = e − j2 w e j2 w + e − j2 w H (e jw ) = e − j2 w [2 cos (2w )] (i) (ii) (iii) w 0 0.1 π 0.2 π 0.3 π 0.4 π 0.5 π 0.6 π 0.7 π 0.8 π 0.9 π π [ ] Magnitude Response M (w) = | Hr(w)