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A New Vlsi Architecture for Adaptive Filter

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  National Conference on “Smart Instrumentation, Measurement and Applications” A NEW VLSI ARCHIEC!RE #R A$A%IVE ILER   R.Aarthi 11 aarthisekaren@gmail.com 1  PG Scholars, Department of Applied Electronics Engineering,SNS College of Engineering, Coimbatore– 641 !  Abstract -  In this paper the architecture is  proposed for Sign Sign LMS algorithms which is obtained from the conventional LMS recursion by replacing the tap-input vector x(n) and error vector e(n) with the vector sgnx(n)! and sgne(n)! in the weight updation e uation#  Implementation of this function may be cheaper than the conventional LMS recursion$ especially in high speed applications such as biotelemetry these types of recursions may be necessary#  %eywords &   Adoptive filters$ LMS$A'  I. I  NTRODUCTION  Signal denoising is a field of engineering that studies methods used to recoer an srcinal signal from nois! signals corru ted #! different t! es of noises. Noise ma! #e in the form of $hite noise%   ink noise% #a##le noise and man! other t! es of noise resent in the enironment. Oer the last decades% noise remoal from signals is an area of interest of researchers during signal rocessing. Ada tie algorithms are mostl! used for signal denoising. The a lication areas of are 1D or &D  #iomedical signal anal!sis% roducing ' anal!(ing irregular signals. Ada tie algorithms can decom ose a signal into seeral scales that re resent different fre)uenc! #ands and at each scale% the osition of signal*s instantaneous structures can #e determined a ro+imatel!. Such a ro ert! can #e used for denoising. Although $ithout information on the signal to #e anal!sed% filters select information #! strongl! reducing its )uantit!. The method is #ased on error alue in the signal that each alue of the desired signal is com ared to the filter out ut. Using ada tie filters to remoe noise from a signal re)uires identif!ing $hich com onents contain the noise% and then reconstructing the signal. The signal used here is s eech or ,C-. S eech signal is the #asic tool for diagnosis. During ac)uisition seeral noises contaminate the signal arameters. One of the dominant noise that contaminate the s eech signal is due to atmos here distur#ances. The noises that affect the s eech signal are $hite noise% ink noise%  #a##le noise and man! other t! es of noise   resent in the enironment. east mean s)uare /0S algorithm is one of the algorithm used in ada tie noise canceller to denoise the s eech signals. The algorithm used to u date the filter coefficient is the east 0ean S)uare /0S algorithm $hich is kno$n for its sim lification% lo$ com utational com le+it!% and #etter   erformance in different running enironments. II.  IT,RATUR, R,2I,3  A #PGA based ANC   0ohammed 4ahoura 5 6assan ,((aidi /&711 im lemented an efficient 89-A:#ased ra id   rotot! ing of an ada tie noise canceller /ANC using east 0ean S)uare /0S in ;U9 2irte+:II 9ro deelo ment #oard and ;ilin+ S!stem -enerator . Ne$ arallel and se)uential architectures of the ANC are ro osed and successfull! a lied to remoe noise from electrocardiogram and s eech signals. It is o#sered that #! roiding com ara#le filtering   erformances that of the arallel architectures% the   ro osed se)uential s!stem re)uired fe$er material resources.  $ %&S based adapti'e filters  0d. <ia Ur Rahman% Rafi Ahamed Shaik% D 2 Rama =oti Redd!% /&711 discuss im lementation seeral signed 0S #ased ada tie filters% $hich are com utationall! su erior haing multi lier free $eight u date loo s are ro osed for noise cancellation in the ,C- signal. Different filter structures are resented to eliminate the dierse forms of noise like >76( o$er line interference%  #aseline $ander% muscle noise and the motion artifact. 8inall!% these algorithms $ere a lied on real ,C- signals o#tained from the 0IT:4I6 data #ase and com ared their erformance $ith the conentional 0S algorithm. The results sho$ that the erformance of the signed regressor 0S algorithm is su erior than conentional 0S algorithm% the erformance of signed 0S and sign:sign 0S #ased reali(ations are com ara#le to that of the 0S #ased filtering techni)ues in terms of signal to noise ratio and com utational com le+it!. C (ptim)m Step Si*e %east &ean S+)are (SS%&S- algorithm So$m!a.I% Ch Sathi Ra?u% 0d. <ia Ur Rahman and D 2 Rama =oti Redd! /&711 resented a noel ada tie filtering techni)ue to remoe artifacts from cardiac signal #ased on o timum ste si(e strateg!.This resultant ada tie algorithm is called as o timum ste si(e least mean s)uare /OSS0S algorithm. The ada tie filters essentiall! minimi(es the mean:s)uared error #et$een a rimar! in ut% $hich is the nois! &'SRC(EIE )  SIMA*&+  National Conference on “Smart Instrumentation, Measurement and Applications” ,C-% and a reference in ut% $hich is either noise that is correlated in some $a! $ith the noise in the rimar! in ut or a signal that is correlated onl! $ith ,C- in the rimar! in ut. 8inall!% this algorithm $as a lied on real ,C- signal o#tained from the 0IT:4I6 data #ase and com ared the erformance $ith the conentional 0S algorithm. The results clears that the   erformance of the OSS 0S algorithm is su erior to that of the 0S #ased algorithm in noise reduction III.  SCO9, O8 T6, 9RO,CT The 8IR filter com utations la! an im ortant role in the DS9 s!stems as the ada tie t! e. The 8IR filtering is a conolution o eration $hich can  #e ie$ed as the $eighted summation of the in ut se)uences. The large amount of multi l!:and accumulate o erations attracts man! researchers to stud! this issue. The 8IR 8ilter consists of shifters% adders% and multi liers. These constitutes can #e chosen on the demand of the designer. One of the most o ular ada tie algorithms aaila#le in the literature is the stochastic gradient algorithm also called least:mean:s)uare /0S. Its o ularit! comes from the fact that it is er! sim le to #e im lemented. As a conse)uence% the 0S algorithm is $idel! used in man! a lications. east 0ean : S)uare /0S ada tie filter is the main com onent of man! communication s!stemsB traditionall!% such ada tie filters are im lemented in Digital Signal 9rocessors /DS9s. Sometimes% the! are im lemented in ASICs% $here erformance is the ke! re)uirement. 6o$eer% man! high:  erformance DS9 s!stems% including 0S ada tie filters% ma! #e im lemented using 8ield 9rogramma#le -ate Arra!s /89-As due to some of their attractie adantages. Such adantages include fle+i#ilit! and   rogramma#ilit!% #ut most of all% aaila#ilit! of tens to hundreds of hard$are multi liers aaila#le on a chi . The 0S ada tie filter en?o!s a num#er of adantages oer other ada tie algorithms% such as ro#ust #ehaior $hen im lemented in finite: recision hard$are% $ell understood conergence #ehaiour and com utational sim licit! for most situations as com ared to least s)uare methods. The #elo$ figures e+ lains the t! es of 8IR  I2. T9,S O8 8IR 8IT,RSThe figure 1 sho$s that direct form 8IR it limited #! the critical ath corres onding to the longest com utation time among all aths that contain (ero dela!s.   8ig.1 Direct form 8IR The figure & sho$s the #inar! tree direct form sho$s the critical ath is reduced $hereas figure 1. sho$s that Trans osed form sho$s that it oercomes the critical ath limitation #! retiming dela!s into the adder chain.   8ig.& 4inar! tree direct form 8IR 8ig. Trans osed form 8IR  'SRC(EIE )  SIMA*&+  National Conference on “Smart Instrumentation, Measurement and Applications” 2.  ,AST 0,AN SEUAR, A-ORIT60 The e+traction of high:resolution signals from recordings contaminated $ith #ack ground noise is an im ortant issue to inestigate. The goal for signals enhancement is to se arate the alid signal com onents from the undesired artefacts% so as to   resent an signal that facilitates. ,as! and accurate inter retation. 0an! a roaches hae #een re orted in the literature to address signal enhancement. In recent !ears% ada tie filtering has #ecome one of the effectie and o ular a roaches for the rocessing and anal!sis of the signal and other #iomedical signals. Ada tie filters ermit to detect time ar!ing otentials and to track the d!namic ariations of the signals. 4esides% the! modif! their #ehaiour according to the in ut signal. Therefore% the! can detect sha e ariations in the ensem#le and thus the! can o#tain a #etter signal estimation. Ada tie solution #ased on the 0S algorithm is suggested for noise cancellation in signal. The reference in uts to the 0S algorithm are deterministic functions and are defined #! a eriodicall! e+tended% truncated set of orthonormal #asis functions. The 0S algorithm o erates on an FinstantaneousF #asis such that the estimate. In a recent stud!% ho$eer% a stead! state conergence anal!sis for the 0S algorithm $ith deterministic reference in uts sho$ed that the stead!:state $eight ector is #iased% and thus% the ada tie estimate does not a roach the 3iener solution. To handle this dra$#ack another strateg! $as considered for estimating the coefficients of the linear e+ ansion% namel!% the #lock 0S /40S algorithm% in $hich the coefficient ector is u dated onl! once eer! occurrence #ased on a  #lock gradient  estimation. A ma?or adantage of the #lock% or the transform domain 0S algorithm is that the in ut signals are a ro+imatel! uncorrelated. 2I. S/GN S/GN A%G(0/2&  The sign sign algorithm is o#tained from the conentional 0S recursion #! re lacing the ta :in ut ector +/n and error ector e/n $ith the ector sgn G+/nH and sgn Ge/nH. Com le+it! reduction of the noise cancellation s!stem%   articularl! in a lications such as $ireless  #iotelemetr! s!stem has remained a to ic of intense research. This is #ecause of the fact that $ith increase in the s eech signal data transmission rate% the channel im ulse res onse length increases and thus the order of the filter increases. These algorithms reduce the com utational com le+it! of the ada tie algorithm $ithout affecting the signal )ualit!. In order to achiee this% the sign #ased ada tie algorithm is considered. This algorithm en?o!s less com utational com le+it! #ecause of the sign   resent in the algorithm. This algorithm re)uires no multi lications as in the 0S algorithm% thus making them attractie from ractical im lementation oint of ie$. 2II. ,;A09, 8OR NOIS, CANC,ATIONAda tie filters are re)uired $hen it is necessar! for a digital filters characteristics to #e aria#le% ada ting to changing signal characteristics. Such filters can #e classed as self designing in that the! rel! u on a recursie mathematical algorithm% that allo$ them to   erform satisfactoril! in an enironment $here com lete kno$ledge of the signal of interest /desired signal and noise is not kno$n. As a conse)uence% ada tie filters% such as the 0S /least mean s)uared algorithm hae #een used in man! real $orld a lications such as #iomedical signal enhancement% s!stem identification and noise cancellation. The follo$ing e+am le demonstrates the enhancement of a 1776( signal  #uried in #and limited $hite noise% #! irtue of a 7th order 8IR 0S filter. 2III. ADA9TI2, IN, ,N6ANC,0,NT /A,Ada tie line enhancement /A, refers to the case $here a nois! signal% +/n consisting of a sinusoidal com onent% s/n is aaila#le and the re)uirement is to remoe the noise art of the signal% n/n. This ma! #e achieed #! the arrangement sho$n #elo$. It consists of a de:correlation stage% s!m#oli(ed #! and an ada tie   redictor. The de:correlation stage attem ts to remoe an! correlation that ma! e+ist #et$een the sam les of noise% #! shifting those sam les a art. As a result% the redictor can onl! make a   rediction a#out the sinusoidal com onent of u/n% and $hen ada ted to minimi(e the instantaneous s)uared error out ut% e/n% the line enhancer $ill #e a filter o timi(ed /the 3iener solution or tuned to the sinusoidal com onent. -'SRC(EIE )  SIMA*&+  National Conference on “Smart Instrumentation, Measurement and Applications” 8ig.J Ada tie noise cancellationThe 8igure J sho$s a little further% notice ho$ the in ut to the ada tie filter is actuall! sam les  #ehind the rimar! in ut signal% +/n% since the de:correlation stage has introduced a hase shift. Therefore% in order to time align the enhanced signal% K/n $ith the in ut signal% +/n the ada tie filter must #e a#le to  redict ahead in time /formall! referred as for$ard linear rediction%  #! o timi(ing its filter coefficients in a least s)uares sense% such that the instantaneous s)uared error is minimi(ed. I;. ADA9TI2, NOIS, CANC,,R /ANCANC sho$n in figure L is an interesting a lication of ada tie filter that has #een used in a $ide range of signal rocessing s!stems. The! include o$er line interference /L7M>76( elimination from electrocardiogram /,C- signals % fetal ,C- e+traction % echo cancelation in long:distance tele hone and satellite communications % ada tie noise cancelation for hearing aids %am#ient noise reduction from #reath sound measurements % etc. The ada tie noise canceller is #ased on a finite im ulse res onse /8IR filter $hose coefficients are automaticall! ad?usted using a gradient descendant algorithm. The least mean s)uare /0S algorithm is commonl! used to minimi(e the coast function /error.8ig.L 4lock diagram of Ada tie noise canceller  ;. ADA9TI2, NOIS, CANC,ATIONThe #lock diagram of an ada tie noise canceller that consists of t$o in ut signals and an ada tie filter. The rimar! in ut contains the $anted signal sk lus the uncorrelated noise n7k . The reference in ut is the noise n1k $hich is uncorrelated $ith the signal sk #ut correlated in some unkno$n $a! $ith the corru ting noise n7k. The reference in ut is rocessed $ith the ada tie filter that iteratiel! ad?usts its im ulse res onse to minimi(e the mean s)uare error /0S, #et$een its out ut and the rimar! in ut. The ada tie filter is o timi(ed to roduce an out ut !k that must #e as close as ossi#le to n7k . This out ut is su#tracted from the rimar! in ut to roduce the s!stem out ut ek  sk n7k  !k . Assume that signal and noise are uncorrelated% the mean s)uare error /0S, is gien #!  E[e k 2   ] =E[ s k   ]  2  +E[(n ok   –y  k   ) 2   ]  ;I. T9,S O8 0S 4AS,D ADA9TI2, 8IR 8IT,RSP.Ne$ i elined arallel of the 0S:#ased ada tie 8IR filter. PSe)uential architectures of the 0S: #ased ada tie 8IR filter.Com ared to e+isting high:s eed s!stems the   ro osed i elined architectures ensure the same filtering erformances #ut the! resents adantage to re)uire less hard$are resources  #ecause the! need less dela! and shifter elements. In addition% the se)uential i elined structure needs onl! t$o multi liers regardless of the filter si(e. The arallel architectures is $ell suited for small:si(e filters% $hile the se)uential one is more a ro riate for large:si(e filters. The arallel architecture is $ell suited for a high sam ling rate re)uirement and a small num#er of coefficients. +'SRC(EIE )  SIMA*&+

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Aug 3, 2017
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