To design filter of any magnitude Function- remezb Calling Sequence an=remezb(nc,fg,ds,wt) nc:number of cosine functions fg:dense grid of frequency ds:derived magnitude values on this grid wt:error weighting vectors an:filter coeffficientsįiltering of discrete signals by flts function Function- flts Calling Sequence y,=flts(u,sl) u:the data to be filtered x0:initial state vector/matrix giving necessaty i/p-o/p.It allows for filtering of length signals x:optimal variable which gives the state sequence.K-means clustering and vector quantization ( Window based Linear Phase FIR filter Calling Sequence =wfir(ftype,forder,cfreq,wtype,fpar) Arguments ftype:’lp’,’hp’,’bp’,’sb’ wtype:’re’,’tr’,’hm’,’hn’,’kr’,’ch’ cfreq:2-vector of cutoff frequencies fpar:2-vector of window parameters wft:time domain filter coefficients wfm:frequency domain filter response on the grid fr fr:frequency gridĮquiripple FIR Filter Design Calling Sequence =eqfir(nf,bedge,des,wate) Arguments nf:number of output filter points desiredĮquiripple FIR Filter Design Calling Sequence =eqfir(nf,bedge,des,wate) Arguments nf:number of output filter points desired bedge:Mx2 matrix giving a pair of edges for each bandĮquiripple FIR Filter Design Calling Sequence =eqfir(nf,bedge,des,wate) Arguments nf:number of output filter points desired bedge:Mx2 matrix giving a pair of edges for each band des:M-vector giving desired magnitude for each bandĮquiripple FIR Filter Design Calling Sequence =eqfir(nf,bedge,des,wate) Arguments nf:number of output filter points desired bedge:Mx2 matrix giving a pair of edges for each band des:M-vector giving desired magnitude for each band wate:M-vector giving relative weight of error in each bandĮquiripple FIR Filter Design Calling Sequence =eqfir(nf,bedge,des,wate) Arguments nf:number of output filter points desired bedge:Mx2 matrix giving a pair of edges for each band des:M-vector giving desired magnitude for each band wate:M-vector giving relative weight of error in each band hn:output of linear-phase FIR filter coefficientsĬalling Sequence =iir(n,ftype,fdesign,frq,delta) Arguments n:the filter orderĬalling Sequence =iir(n,ftype,fdesign,frq,delta) Arguments n:the filter order ftype:’lp’,’hp’,’bp’,’sb’Ĭalling Sequence =iir(n,ftype,fdesign,frq,delta) Arguments n:the filter order ftype:’lp’,’hp’,’bp’,’sb’ fdesign:’butt’,’cheb1’,’cheb2’ and ’ellip’Ĭalling Sequence =iir(n,ftype,fdesign,frq,delta) Arguments n:the filter order ftype:’lp’,’hp’,’bp’,’sb’ fdesign:’butt’,’cheb1’,’cheb2’ and ’ellip’ frq:2-vector of discrete cut-off frequenciesĬalling Sequence =iir(n,ftype,fdesign,frq,delta) Arguments n:the filter order ftype:’lp’,’hp’,’bp’,’sb’ fdesign:’butt’,’cheb1’,’cheb2’ and ’ellip’ frq:2-vector of discrete cut-off frequencies delta:2-vector of error values Window based Linear Phase FIR filter Calling Sequence =wfir(ftype,forder,cfreq,wtype,fpar) Arguments ftype:’lp’,’hp’,’bp’,’sb’ wtype:’re’,’tr’,’hm’,’hn’,’kr’,’ch’ cfreq:2-vector of cutoff frequencies fpar:2-vector of window parameters wft:time domain filter coefficients wfm:frequency domain filter response on the grid fr Window based Linear Phase FIR filter Calling Sequence =wfir(ftype,forder,cfreq,wtype,fpar) Arguments ftype:’lp’,’hp’,’bp’,’sb’ wtype:’re’,’tr’,’hm’,’hn’,’kr’,’ch’ cfreq:2-vector of cutoff frequencies fpar:2-vector of window parameters wft:time domain filter coefficients Window based Linear Phase FIR filter Calling Sequence =wfir(ftype,forder,cfreq,wtype,fpar) Arguments ftype:’lp’,’hp’,’bp’,’sb’ wtype:’re’,’tr’,’hm’,’hn’,’kr’,’ch’ cfreq:2-vector of cutoff frequencies fpar:2-vector of window parameters Window based Linear Phase FIR filter Calling Sequence =wfir(ftype,forder,cfreq,wtype,fpar) Arguments ftype:’lp’,’hp’,’bp’,’sb’ wtype:’re’,’tr’,’hm’,’hn’,’kr’,’ch’ cfreq:2-vector of cutoff frequencies Window based Linear Phase FIR filter Calling Sequence =wfir(ftype,forder,cfreq,wtype,fpar) Arguments ftype:’lp’,’hp’,’bp’,’sb’ wtype:’re’,’tr’,’hm’,’hn’,’kr’,’ch’ ![]() Window based Linear Phase FIR filter Calling Sequence =wfir(ftype,forder,cfreq,wtype,fpar) Arguments ftype:’lp’,’hp’,’bp’,’sb’ win=window(’kr’,n,alpha) Chebyshev Window. In this slide i will be describing different windowing techniques.This can be performed by different window functions with window length by using the in-built command window(). Filter design by different in-built functions available in scilab. This presentation is being divided into following parts: Different windowing techniques. In this presentation i will show how differnt types of filters can be designed using scilab. ![]() What is a filter? A filter is a device or process that removes some unwanted component or feature from a signal. ![]() Manas Das Indian Institute of Technology, Bombay March 1, 2012
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