GR Exp 1970_07

  

Exp e rime nte r E BE   9 EBE 9 

E GENERA RA VOLUME

Expeimete

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NUMBRS



JUY/SEPTMBR

 3  8 13 13 1 1 17 18 2 21 21

Signa Anayi wih gitl TiSre Ana  y z  Measurn  rasr nct on ad Impedan 



 ono rb   Sociy 

Dvtios fro Ay Nt a Cndr Inunt! Nw S fr an Od Workho Rpor fo T Fd Out of th st gaabl ead Ristor GR

17 



Mo Vratity nd Cpaty

Ren Thial A e by G R Ponn

T Genrl Radio Expimter s led wht chrg to ngns, scnss tec ncns  dators nd hs nteested in tec the nsrmns nd cqs o ec  eltons memnts Add l coesondenc to dtor  d pim1, Gnel Rdo o  onord a

© 

17

Gen Rdio Com Co s

 

Coe   usrat srate e veal of many of many suc suceu ppto ppton n of gn gna a naysi naysi   in e    Th   ably to  to   m aton a ton wth ran, wi wi  vnt vnt  ar takin g a 1 an xellt xell t cot - rduc co uco o masu asu. . It lo  i i know  know gbl gbl exp exp al al ontrol o ntrol dun dung g a a on a cotatd c otatd   wth ndsgt nd sgt d c on fored fored upn u  u  by day d dat .  d lt lt   1 an  an  ntl1ge ntl1gent nt  onoma onomall pp oa oa to r rrh, rh, dvel dvelo oent, ent, et, et, and and p o o The Th e

ton p ob ob __

mley n  mhac retn o crcs d syss hs ben r dcd n t st ten yrs. Sl opues make ossibl ou pesendy eeron  ys rorme to ay  l sots  ateaa anlytc hqus to dsgn and tt ojts ojts tat w sav gnr n m  d lls s p of sln deh anyLslso a  tm ncns n maton of conto by hmns eby png to dc zds t man o   ato s  s ngne gnes The aorty of dgn  n tst n   ry to nsed n t aseby nd utton o bl ntntton o cet some h ng whh s ebl ebl s  een m. the om anaytc tes ss  senty sold. Ts ck of sk s no n t untadmancs of asmbly b   untad-

 

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ig of he techqs vlved. vlved. ere , ndoubtdy m ro s res h br v ey ey nted rbe Wenr's lssom t M .I T to stdy  srts o ffc ffc te omucaton and of omucon nayss I e s dto you detd frm te sinr wt  mcm o nw knowed and  lnttde o rstrton yo wl eome  nhr chnc o c ce e ps o s  gna  ss eld y th ss dclt snn n h nx pag Ater t rp you wl be edy for h  of t s ouer am n nd d ssoct ed chnqs n for h nstrns ht n coora em em 

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C

 W     

Signl Alyss wth Digta me- Seres Azers thy ave not bn d xtnsv ly bease of h Although time- rs anays technqs ar o Te tratre lck of stab l qpmt ad thrfor r not aml ar to many th tchqe gnrly ha bee wrttn or a cademc adc and ha bn otn codd n abstract mthmatcs rathr than bng rtd n  rortd trms. h s an ncessary obstac to ac bore th ottal sr, bcas the s no mor d to b a mathmatcan to   t1m·sers aayzer thn thre  to s a ectr nayer In ths artc th w b retd n nonrgoros terms as mch as osbl and w be tratd wth ml from sra ycal acto

Aalyzing letrial sgas is a fudeta obem fr egees d ietit i  al fied hether they are rking i the eeh d deve pmet laray  the prdu tio lie o in the fied. Whatever the phy al  gl, or hmcal system eig tdied, the bai hene ca usu ay e coeted to eletil sga by uabe rn d aayed  p r vide fdaetal nfomati ut the system pduig the igals. il rece tl  ly a w bsic ayss pcde were ued ae f he lmttis  the techg vae fr buildig est euipmt h bsi ignalanaysi re due ther tha d re t eatn  he sigl n a  e has ee pweetm aaysis ad will baby cont ue  be s Howv te ar many othe basi anly sis produre tat d prvide muh m aluae ino matin abut  ystem ude test ut he have  bee aailal ith the perfrmae requied  mst ratcal rlems. Ee permaaly s eupment alhough sev ig deqae   de varety  proems as sevee lmtati due t the aagiruit ehlogy aaae. In th last dead, th avaiaility of geearse m pte wed the dvaced ignalaayi tehiqu s that had e ineeed y Wiee Lee et al, t e deeed to patal computata routies and ee pled scess y l many daa-y roblems They do t hee hlp th epermter or aayt wh equies e uts rady so tat he cn interat with his syste Te ost o opte s r this typ  probem s  o very hgh espcily f ngiering tm we aatg els is whn the tkn int acout  revolution i dgitape g tehgy ht is no taking pla a bought advacd gy is tec nq int the rt. Ths advane enabes cnm pduct f isrmetaon stems whh contai al   mat we equed fr at acurate  al posing he TD  923 imeSeie nlyer   be intrdud by Time Data ad GR ae the m adaned d p line of ts ype o gcesg equent Bede their omputational bility tey tai signal cndtionng ad dislay cabity wh eie i e ntrs that alow tem  be u a easily  cve toal laorator istuent

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WHERE AND HY THEY'RE USED ery oess in nre gie rise to "igas' ht ar amenbe  y y timsrie nalss tecniqe.  hey Thrr, t list of eia aliis s cn b guped into categies aed on te type f proces ig that is usd and   infmaio t  dsred ome exam  s ro  v   egie are illstated  e fr coer ad descried be

de

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

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/SEPTEMBER 1970

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Csid fst some xples i the field f strl mechnics were he bsic quatties  e analy7d ae mechaca brations in ica auomobies bildg ec Tese vibatis may b ased by may etra fores h a d ege comb tion ad tti road roug1s . eth teor ad a T dr wld ik to deter mne uch thigs as t  urc d asiss aths o t vibrts ad h xpe  d tsses d di sp  mts a vu its by aayng th ignas rm pits n th stutre Fo itan dug th testig  a autob  ob ec t  nal ibao y e und to exist i h ssen ge mparmnt Tmeseres anayss tecnqus il deter mne if e vibratn s comn r th ene, th d o frm e d   dditn e strutura member h p de the ransiin a r te bratn a be dend ad sy odi  d to t u t  t iae te wn d  vbas T aalysis syse can even be sed  s he stucte nd e externl ocs t test he ew s  befor it i cmmitd o prodctin Viratons cmng from he enge an lso be anayed  d �   its cndi  at tion ailus may be t tpetd frm such a igntu e aayis Atr xample s e deemaion o he u ateri tis  ia in    widnne ts The esonan rq and dmpg o he as vbai  e o te    a  a mas d as  ncon of aieed l dte n  ee  y cdts tat  rod ecess r untbl b n   J te thrd eam mbg btios cused by moiles driing rs a bdg.  aaLd o    o hg r nexcte adi. Th ld o bi  di i  ha ady pdd ran aatins     no egu  a u · f sohistcaed aaysis echn q s   ner os e   huan ody aturall d e ·c   a s who e   ac tisics a icate f e cditon o iu rts  te bd. Th mst   a e s t rr aph (EKG) sig a  hch pvde iation about th nd tion f h hart :ltcphagrs G' o rain e trc r anald ri g and f  i ad on  bi h  ues w beng usd y nil pycia  anay s ignls   n  us d wdsd u   im -er es aa s tq  medicine soud ns s  beco etr undesood ter a  ict n s gopy  xo,  cho rnging lcaon tha is in t  tgy a ada n  b t  iga i tastd ad c sonar rom  lg o  al ta pri  o   eived and sgn detemn t  ela

e





j

w

 

e

eded

e





 



   





  u 

 l



e ve     v   

  d     

e

e  :  

wn h a h   , ters  an  he rcding suacs. P gn as can  nancd  u nal s c  i q u , a a p   ngn eran

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 TIME-SERES ANALYSIS

 pcding p bl ca  al b vd h aus c bin ati  h   o pu ainal utns t h h   I   s aa B a  si r gna  w p  n a sccsi n   va   u l t  g f or si m ul ai n g a pia pcss  a n b an  ia aua   cti lg  a ain  an  pscal pcss i   s a ll y h t vaia bl b t  t aways h  c n ng bu  s  crs is tat h s cces v aa as  ach f t t m sre ar a  i   wa t t r va   h t t is nd ps  vlu    t r e sr. Th e r la shp a b   i n i ti·  tcal i   udi s an a hs r a i   ps s t  pica pcss can b e ers hs r s  t b dd   iad i ahacal 

u 



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

 Heiz is  u f City Col e  w Yok  b Uvr (MS   9 ) e w o  h  pdc deoe f ne· gre ii, h-d ue d  inue  o joi T/ por  GR     98 A Ai Ma  T I M  deve uh   el-mea  e  b  1

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

 







Aalyss

 pc  t p c es i    n al ut dsd Funl, w  ut prs ng t  p r     n s s  put it in t t rm  s t suia bl   w  t fomao bi u   O p ing unctns alw t by pfo bai atic ptns, oor  s m an ,  tng 

natul a o oa   w wafs  gu I  stact n    h  at b dn a t,  a ac   v a ti tt a p i nly t   gi tde   ee, n   s al  u a  dinc    ba  a n b tat a  t sia. h i nu hn nd   n  ndpndn aunts s  an r dinc. t an tn b calultd r ru s d pacnts  n sg  al w e  e e By sp agb u can w t a fm b cculating th catin  aan unctn wc is t su  t h dnatdinat  u  t  p l   a t  n  th w wa f r1s   s ult r t tw and ins  i r I sw ht h  s  si a whn t h s  dplant th sgnal nt  mise b t th v i ri n v a tw pnt s n a   r s m   pagatin t  atns tn  tw pnt s  gn  t dsplant ndicats h dcn  ppagatio Ma ung t da n tis w a es  m<y ul ap plca ins. I ad  cacuaing th clatin nctn   t  i nal !  u an c rre  t a i  n   w   gi  at n unn  h   ws hw cc  v sam p    a n a e T autcan unctn n gu e 2  th tw nd gnas      tt ucc  a p   t r sg  Fiu I   clad  a  pnn t up n ach t thn se  t h  w gna n apl  hwn n  gu   a nw t ad  b    in ns. n u  s n wa s  s a n t gna a  uttin fun ait t dt pdc  w  p   n  ry pnnt n  s  na  an ptnt aptn   autclan ntin

 a m quny ranfons

pectrl Alyss

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EASUREMT SIMLARITY  s itan -r aal i  a  hat gv a su  e i l ar t y b  w e n  nal s  cr a  aay    t i anali and iltng p   d   nain I n ct h pd  a  asu   s  i   y asd upn h uad  i f nce    g n als bu pn   uts n  i   n  f  Alo  ta and    n g ac  aa  ms e h a  cn  ltt s  si n t  np  asun t of  ry ef wl  d i  d  t.

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2

Ateti 

mo r com mo o s h m srs  hs or m cas n  pas spcra nayss was asr o mmn wh lcronc nsrmn ha corraon anayss c any  as po h orm a ay r  sal ca n b onsdd o b h m o  odl comos whos qcs ar ra mps o h ba c ro rqcy. orransom a nay ss am ar o mos ns ad scnss nvov h cacao o  a mpds ad ps ans o h  comonn  cl h c aco s ray a cro corr la on o h JYSEEMBER

17



Q  

Aa  ne wave e

 e  g 

sa w a sso o ah o the oss harmoc cs r cvly. Th rsl s ays aohr o h am rqny wh o am ld d ha r c mon   n h  h cc,  orrrasor aclo  don   mch as mor drc way by  omol mhod h hav b n dvod  rcen y Aoscra ays novs h alao o h sqard mnd o h orr crm d s h qay odc by mos srl anays Bca   o o h owr o ommoy l owr crl sy   xa y h m  on s h aocorro co n c   h o rasorm o h orln co d  an b clcd n h ay A sr msm h s no commoy vabl rom ao crm anayr b whch s xmly s

 



 nn 

  PD

n

 

 

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   IE 

EQE 

� FOUER

EI  !





4

RSS

RER TRANS

  C JGA ML P CA IN

RIN

Tm/rquoma aa

C COSS   R O

R  Q  

f,  te crossset rl fo s fct io is nomaly ca  y he m  ipicat i o of he F o e r nsform of tw sigas ike h e au o-pectral fun t on  conta exa ly t e same fomaton aot  r  fsg as  co s orao fnctio an i can  caclte fom t c ro s  or l o   Four r: form T  b  wen co  re t o n  pe al co  i lust r  by t  me  a feqey oma i ma o Fgre 4 It sos ha t e correa io funti o ca e cacla e n t way s : di rect  y i  i a, o r r c y i  reeny oman va Forir trasforms Te fas ago r ms dvopd o cacat Fr  oms ha e   �lua o b one o qu ck  y va  h  ecy on tan in te t omn a th a e he Foier rsfo te ky o   i o  i moer ies r a y z s Th  t o sp  r a l fcion for  ea signa ri h w i F g  e 3, alg wh te aocorla oise . ton ncton T snea sga s c! dsnbl  e i he r con.







FO T 

 

 i lrng



A i lte r ca  consr  s vc ta cont  uosy omprs n inpt sgnl   ore refe  e ga a o re m  imlar. po d c  ma xi mm   hen e store referec sg i e  mpl soe o te iler ie  repos of the f  a vry naro pe ped   u cr i  i o  fo th mur of im lri y s h sam  for corrl ao an sct  aly   s   act t   l a t o  arie o  eact e se way. Fie shw a ie a was dgd o tc  p sne of a c  or s p feq y iave iga ri  ose Ts typ of sg is se otn a te  rn  e sg in ra;r onar a gohyc c-rag · e    lr a gifal w  signa i r  a h s he �me ha as t  e oco lo o o  sga T e mpl  mn t t  o f fe o m a h p    e sga s qe  cl  naog omones   ac t-

















  

CS A



caly   l f h fiter is o e ea ly va    b le Wt ga metaion e iler si bco rv ry te  s efic  o  th r e t o frey d n te ier ar ain o e aefor o  cd aerisic can  e can i mrosecons.

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





COMPLEX SU

AOR

MASUEMENT OF WAVOMS

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e  l ver g ng* s vr sf or  e  e rm g  p f a sg  th  ore y raom nose en  sg n s re   e  whe t te of oec is knon s e odn x h a s  ng md n a otrod  A xm  e lg gnl  r   c  b the f i g f a lg  a  ons y e  ons  is    l  o n (okd  pon s r de  aveag to g e  . T gl-oo ra o   c u e c , bi  ae it  ea r c   wl d r w   cm g ra m w ll p   t i alJ y ·al h oh d d a  ak p o ro o  sqr roo of t e mb of ara  E ele ra s also commonl  d o h e of core aon  sct uct  o  o v t e statstia aray of th marm Cr  au as n  p mes  n  a sort sgent  a) of a f    d am     sh o  in g e 6. Te a t o s ct  l fno   f is also  m f nco v ragn h ato s p t ra  mm f  si mnt s of  ignl s h t var ia  o s .

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EASUEMENT O STATISTCAL DS TBUIONS

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 m l d to  o tn  i Js    t a     s f rnom d Yo n i   viing e a mli  d g o  qly  c lvs a   ont n g h n    ms    lu f th a  · a ll.  gra f  o do Fiur 7. Bsd  sn th g o     ar s 

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  \            *   GNR R

p

5

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5 m/ POI N

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TME CD

ure

5. Signal

5

FRQUCY HZ

5k

ge 6 Aveed ato tum of bad-lme no

etectio by flte.

exremes of siga ampudes o be epected, he apliude disutio ve inoaon abou he naty of he a toshi ewee the gas. I he e signals  p  he iu ad ou of a syse ude   he a ha boh igas hae he sae dsibt on Gau sa  hi ase d aes that he sysem is oaly ea Fro apd hisogras he ea value s vae ad highe-order  os a e auaed SUMMARY

The bi oo qed  aalyzg sga have ee dsussed ad  f he applaons have b give he "ouer r fr i n o e a fdamea aaion eued i dii iss alyLes for doig reato ad specal anlys nd frg �1any oe cuaos f ar ird suh a oheee fcon ansf u * e e u hse  eesos of the unda ea poedes de cied and ae obaind y fu o

2

0 AL  VOL

+2

Fg 7 mpt htoam o o ose

essg I s ow al y  o odr gi hnoog o bild n tha a  om  al h is h h promance eui for reorl proems C. L 1 eia

Se

page

8.

JULY/SEPTEMBER 1970



Mesurement of Trnsfe Functon d Impednce he conpt of tanse funtion i widey employed n the anayss and of linear stems o netoks . Conventional instumentaion is e suit ed to the pobem hen a simpe noe single paiculay if the ate at whih measuements mst be mde input, single output system is low. I not moden F  (Fast o ie Tansfom) techniqu povide an effiient means fo peoming pape the best east mean- squaed tan sfeuntion thee measuements at ightnng s peed te and impedane estimato hich is shown to of the o  s   peta to he input autospeta density.

3

Gna

The trnser fncon of  liner nework i  ol d sf scror of a sysem or nwork. It  a dmen onles qnty h r h inp n op b scfyn h gn (or aon) n h  hf   freqnces hu , he rn fer fncon of h syst hwn i Figur I  I(J

wher 

 (     ( 

Y  

wh

is h re phs shf nroced by he syse s mesur y h dffrence n phs ale wen  put and h rsln o



In r of coex noon h tans f  r ncon s ( I)

I(J

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 he an o th sy  for a frquency o jz a re by the rio of the ou fco Y( nd  in rvn ncon X(J

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YU x[jU]

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(  jO(

H exp[jO(J] =Y U x I i( XU)

-

O(J)]



e ransfr f fe s  masured for pr pse  sym aays r mat. Sim is p arculrly on whn he sysm nr dy  ccsi  o nwy for xrnon Exps of  e g h d son of r onro ys or suby hat cn only  ob  o  a o  ·imultion of he reson  of ·c vcs o tranien exia   i h s of a ua dag, or nayzn d   tn h ff rnfr c of a vhil sson sy e. Know of te ra�fe fcti old p  connn no o es n a  b  f ron h nssry en has b de lo   or he y n ra ie a e-av ror oeer  a a phaage r s e s oos T amie of te rsons ad s rlt ve ae n cons e n e sa esrns A aur m   sly ba whn he freny of ecta  aie hi h n    ta Saar mearmet tqe of h y ar al dque nd r rl!} s whn the.   unr o rv�on s

 

   



Xf     

(f)  

   

  



ie 1. Lnerne en 8

 I 2) Ha a n n o



GENERAL A01

p



Fue o-wok bok daa;  le ut

 ohr words, f h sysm s as show  Fre 2 h ipu ad he ouput  arc no sim ply rla by h tv  ra r un io dsrb g h  ga ad ph a e we h p u ad Y ouput bau e o h psn o u mrous no  our propagag oward h oup u po. W id hreor :





+

  + n  

+

n - 2 H n  1 H n �2 + O    + 8  .  .



 aalogou proe m arses wh  h m pda o a oisy nwork s ma urd  hs as h ip vor o gu 3 ad uao ( 7 Y bom a vo ag or vo y vor a volag o vloy o5 vor a  th u rr o r or vor. h ua io lai g h various aor i 



6)



wh  h vor orm A= Thus  h oupu   a b ou o as  sum o wo vors



h quval  o  vor has  rodud urany  he ampliud ad pha  a rms o h oupu vor Y h ma urmn produ mus inimze hs  es



 + 

7a

Z.

whr prsns h mpda o h wor o b masurd i hes erns  m  h  rsuls a su sons ha ollow or stai h rasr o wi appy drly or  m aio o he im pda uno

(7)

T u  h sysm o ur _ an e pa by a quva ln  work a show  gr 3a  po de ha we ar oy oerd wh dng h ra  io bw e p u rmal whr is app a   oup u erminas whr  s obsved T vor rlaosh p ar hown  gr  b 





 a b ow a a sm ar inpoupu raoshp exs or any o  h " inp u pors He  a be msur   smlar masurms w l esrib  oa s o wor  ran r unons J om ases oy on rasr un io i requre ha whh app bw h pu an oupu ow h sgals dsgad hrough a b  ough o a rnal nos a soures ta may o e iry obsevable n he a  he problm s ssealy he sam  asur h  rasr u o n i  h prs o ohr spurou sgas wh i w wi onvn y a noi e

------- x  va     



Hd

1

U



970

Fgur



Noewok deaon.



Performg te iicae operatios  eqaio 9 we obtai

E   Sl oane received hs BS  deee fom Noheasten Unvesy n He was a saf ebe a MIT ncon Laboao, a sysems group fom o e join Amx Cop as manar s fo poduct pan ng , en o avaned ec ology plannng Prenly he is V Peside and hnal Do of i me/Data Copao a G sbsa, wc he eped ond r Soane s a em  o a Beta  a a Kappa N

9 95

954;

Y  +   X / { = I   X1 Y exp j(8 + <1   )  1  H X Y1 exp -( 8 + 1  1)  }

€  I L L -

959

ecase we ese o mimize t eror y coosig a appropriate e imaor* or e gai, H a te pase ale  8 we proee  ierei wi respe o e pa e ale ad settig te ervaive o ero Ts

 leastMeanSquare Etimatio

8

Becase te sprios sigals are reae as a sile eiva le oie eor we a e x pre i i erm of te rasfer fctio ipt ad otpt as fol ows  = Y  X L H � or

=



1 L

L { I=  L

(8)

  = ex p ( 2j 8)

Te measqare vale of ts error afer L * measre me s  e:



+ ¢1 - l 1) ]

 X 1 Y1

+ <1 -  1)]

=  L

X Y1 e x p [-H1 -  ) 

L

L

 I  ex p( 1 )  



or ex p{ 2j 8) =

  Y1 x p(j  1)  1 e xp(j <1 ) H exp(  )   (9) L

 =O  2a)

X1Y1 e x p [ +j <1   1)]



X1 e x p (-1) Y1 exp( +j 1)



X1 e x p(+ ) Y1 e xp ( j 1

=l

L

 2)

I= l

/= l

Te esseial e timaor or e pase agle of e rasfer fco voves te m of appropiate orms of te po ct of te p ectors X 1 e x p (  1)  ad te otpt vec tos Y1x p(+1).  a similar maer  differetiatig wt rspect o te gai ctio  we otai te folowig easteasqare estmaor

were  e x p( 1 ) is e z oise vecto Y1 ex p(   1)  te z otp vector a X1 exp(j < ) s te  p vector Te ozotal ar iicates esemle ( or saistical) averge.

  e xp(j)  X 1 e x p() Y1 e xp(j1) + ex p (-j  )  X 1 e x p(j1) Y1 e x p(+j1)   H  L 2

•    p  ovs_ • A ppxi  o s v co



1

=

L  L

X1 Y1

so tat

e x p(j�) = Y e x p()  X e x p  )  e x p8)

  l

H

( 13 )

 x/ = 

GNL 1

p

 depends on oe tha he esmae or he gain nowldge o he phse func io x p (j as wel as of prod cts of he npu and otpt vecors. qaon can b resatd as fo ows

1

ex p (j�)



 e xp(j¢ 1 ) Y1 e x p(-j 1) +  X1  xp( -j<) Y1 e x p(j ) 2

so ha sbstiion of eqaion ( l 2b ) yieds

 1 e xp(j¢) Y1 x p( +j1

 x p (j §)  

2 x/

(5)

 x/ 

For the sae of compleeness we shol meton tht thse ctor fnco may be  x pressd in t rms of ectiin ear coordinates rprsnting phas ad outofphse (co and qad ) compone s by mean of te oowing i tes e x p [ ] = cos( ± j si ( 



5)

Eqaon repres s the leasmasqared estmate of e sysem ransfer nc ion Because qation ( may be e x prssed a

5)



H" exp (j  ) =



I L

 x?

and Z(  x p  ± j]  Z() cos ( ± j   in  = ( ±

( 16)

so that

  

d

"     ( = 

( 22

17

! L

( )

!=

 '

so t th  ransct on esim or bcomes

/ SU   H  x p (j O ) =



s is smpy the ao of the cos spctrm to te uto spcrm of  pu co

  j z

( 24 )

4

gue s a potorp o  trnsr fnon o  c tromechanca netok ibton et o shak ytem )  tmat  is mnr on  Tm/at Mode 00 Rp ourr Ay r T upp rc so  nps  t ow outophas or qutr coponnts T oron al ax s is fque cy of Etate

17



3

L

he te nomnaor s the Auo S pctral nsity estma S of te inpu sigal or

JULY/SEPTEMBR





"   = S   1 e x p(j¢) 1  x p(+j1) =I

= 

( 20)

 X1 exp( -j¢1) Y 1  xp( 1)

he mrator can be recoized a th Cos  pectra D sy smae   of the inpu and outpt , or

 S  = L

(4



 

 8 

y te eo  et m t o cn ry  em rom eqution ( y substtutin quo so tt

1

 X  p  ¢ [   j  X1   pj ¢1 



or



p  / 

H pU 

5





Tho t rro n ston   ntdud b ts ro dur, s ' x 1 1   p j  ¢1  27

1  p j( 



Ts tnds to poh o s th numbr o ob on ns ndntly provdd ht h nu nd no   ns  unortd, bus he nu tor ould psnt th sum o  r nu b o vos hvn ndo phs ls (  ¢ , hl h dno ould b  rl po t dnt qun y tht nrss nrly th th nub of obston  h mnsqurd ror ; n surn t n b son to b

hs ror nvovs kno o th sptrum o  ns; tho  must stmt th ror by stn th no  sptrum . T os sptu s pro ty

     "

(

30



"

hr  x   



   s th qunt outpunos s dnsty Th msqud ror s nvsly proporton o L t num br o ndpndnt obsrvtons ntrn nto h st t

= 

/ 

nd y qutos  9  d  1 6 ) , bos



  

¢ 1  6

 x/



 p

X1 1 p     ¢ '



I

3

 =

  

2

33

T nsqurd ro n trs o msub qun tts bos

w  

(34)

I

 < w = <  =

or o

a 0

gu



FQEY  HRZ



Tr fu  vb xce m.

I

( 

L

L

[

I -! ,

(

5k

r )

!l U ) _ ( GN A

(7 



 

rom ths it is apparent t at

<

1

(38)

 

Te functon  n own as the oherence Fun ction. t  ero wen the outpu t spectru  s de ent rely o noise (S   S ) an unit y we n t e system  noisefree S   T s  opuar measure for te reaity o a tran ferfunc ton estimate.

 n O

n summay we have found th at te estimator tat minmi t he e ffecs of spur ous igna or no e sources in vo ed measurements of he magnue and pase of te nput and outp t ignas for each freency of int ere t The e meaure ments coud n pncpe be obaine by ean of votees and a pase m ter However f te n um ber of fequenc y ponts that mu be use to de cribe e transfer or ipe ance functon  arge ten more effcient estmates ca be ae by means of digitsgna proce s ors using fast oer transfor te n ique . The / odes l 23A TmeSeres Ana yze 1 93  ReaTe Fast ourerTransfor nayer or I 92 3 ast Fourer ransform Analyzer Figure irecty perfor te operat ons nd cated in quaton (  9 tiizng the me/ata 90 apd Fourer oces or Te 9 3A , for exape , wi l perfor a t ransfrfunc ton anay is for 1 28 qua pace fequencies n ess t an 1 0 miscond pr copte e iate A. Joane





Fr /D Ra Aaz.





I



Fo  oo Ba



66

$. a Po A G .  Me
dom Daa. Joh W < So

 Y W   Siical Communicaions Theory, Joh W a  So

66

Soa E A "A Ioo o TS Aai ono grap I I and I T /Daa ooaio 

The onoabe Socie harles E ite of Genera Radio While presentng te award plaque, CS Charman   L Hayes ade the folowing cit aon: M arles t as un lfisy devoted i tie and energie o foru lat ng, e tn g, and sustanng te opeation of te fo he pa t eigt and onealf year  Muc of the credit for e gowth of t  pbliity and information meia is refected upon Mr Wite wo srve as chair· man of te SL ews tte r Co t tee and  dtor of te

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. E Wi

A P G Po

n ne 1  970, te atonal onfrence of Sta nda rds a boratorie pr n ted t  frst Aw ars for tsta nin g Servce o t hree member of the organi a ti on . O ne of te rcipent wa

viatons fom curcy

 tt

tt

Te uo ngneering Soct y ha announ ced  he impendng awar of Fe low member h p to Dr . P  Peterson of ne ra Ra io Pre en a ton w take place at he annua wards anque n ew York on October 0 Dr Pet erson i wel know n n te field of acou t cs.  e eceved t e on  otts ward from te S in  96  for ou a ing acie men t n t e fiel of aud o engineerng  s, also a Feow of e  nstit ute of lectrca and Eectronc Engneer an of the Acoutca Socey of America

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e are aware of two small error tat crpt nto te arc une 1 70 s u he fr t on page oud ve tran a te te te t pessue of 1 000 microns to  m g. he second on page I  o d ave specifed t at swtch ing t e of te G  frequency syn t e a een educe to _00 µs  not 00 ms) orr

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JUL/SEPTBER

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ir o t h prig ampe  hen h rguaor  u  o ool heaviylode apaivn put rm r unpu pwr uppe  r JU n  r  n  y mhanil y t (a l of wh rpon o he av rg u of  p     fr  m th r a q    k   la   n operon nfero  ra pefone e ave f dgia irmton ha rae n  w   n   m  i u  u of he evatig  f fe of i r   p n h e  py votage F rgrng f  iru  mmn pa hen uply volage ar u n mm with itoopoung numn o  U o he  R I 59 2  a bu ff  g g upp    bo of gtl intrm  n   h   p to u ga outpu h  59   eve be dr     to hp gte bowou o w pi by m  r po  n pwr  p  e urng wr J � n. I f ou  powe ompan  f o owr    al y      rg < r  ur oinu n  c y

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N  T A C I N D E  EL LA I N S T  U M   T ! Te fate of rr, raal e wn an wieyue t men aion pren i reegaio to ge wor an o neroburty wen it ome o pubzig new mo de Th pen a of new ig inovion i ompoune by te ane i n wh te genera ui iregr on bu eta n umet We've de e   h   n ge h tt de by wrig bo te nw  5  Vari® autoa otge reor wi  it f "How a u o wihou t? I     a e a y  l reae y u ha oto er o a to  ne or m   k  of e  euipmen Convaby eah nm  we engineere   own egled pow uppy Cnun  y you foe ee n o n fr a n r mr r Conir for one momen howver ha ll yor ue m p i foril y wth an  ppl vg f  05 ol Morn volage rgr ually on o any ole a   ' ominl vo by men f a onuto r powripatg uit o op e v   ge loe to the nonal ue   ae ted aov, ne orm ru ane  fai ao un of a  aed whin    of itru men  Exan he n  m b  r of ak ad yu p h ne  fo n airon tone evironmen o kee t he mean  imbtweefa re a t o f he n ume n t fom in re ng rdy  I woul ape o b goo og herefore o eue h uppy volage o 105 vo nomn an o aiain onnuou opaon a tat e Su  e w  u e te moun of ea genrae a gr nre the efu  if f he m ntlaio of

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a

     

auomat vo  t ge r ·ch 1 592 mta t nru n  v o  a g oeraing pn v I e ego   p b l of ong     oa voage r f rm an eaorye  oeae oy to atn te numeaon upl volag    h e norm voge ealie b h ower ompany dtrbuto nr   l e  r R m T m rr wh otp          e  by  -n Vr dj  b  rnfmr of pr abilty There   ore poa pi   be onere; he  R l 5 92 do t n oue  oon i no  nut uppy voltage   f  n o  o oft by uomer of r  n  pr v  u s  r         mend  e l c t  m  n  a rgr   r  k h a  pa v f s up     whe  tg  r \ae h feur    igifane i nue  f n k fr  x  m p      pa c iivenput  owe upp w  gh  uh  un t od o pk up ply vltag  i to    o   p   frm he reguto  u a  mh  3% ang n he  r-pp ouput een though h rgua l    pf    %    t  n m a nminally rt uppy . [f     we n ntr f   m al ve wh r pond      m vaue of h  uppy volag dit e eguor out pu wou     r io of he herml t i  mnr 

he



R

  

 



 R   a

 





   x

   

Chitouras, Coc f L - V   ag Rl. Ocb

Te

R



-

t n f iru n. or r      w   h  r    m  f   p p  y fr llminan ve patg bah  im appin aialy affeed by n- riaio   he

 R  5   ava  for oad

p   0

VA Lgher  ma nb  uld be m by t V ode C   p  e      c i f       n  f  r the m  bw e valb tag A pph   deri rde of vl ag n   rr dr our r

   R   aa qut :  ito   0 Bk   1 0  7 4  n i          b sh  Mn

 

152 W A.

C  G

Descripton

59700      0     · 1  0

P

52  tot ot t $525. 1 20 V ± 0% mp 525 1 2· V  m 525  230'0-V  1 525. 30/20 V 0  2. 0V ±0 i    qy

Pes ub1

G RA1

Cod, MA US. d

p

seond to onventona  and g speed moon piur ameras wen he new GR I 5 4 1 Mash Geneaor s used The mplemening ysem use a srobosope mulas generator, and sl amera to provde rozen aion on a snge Poarod** m wtn  5 seonds of he event A sees o om pee mages, ea uniquey posoned on e m in time and spae sequene s avaable for udy and aion A Gnc  h Feare

GA

mong he nu meros aturs o te GR 1 5 4  generator are: • a h busts adjusabe n numbes and n tcvals • Vesale trigger irut desiged to aep a varey of npus in terms o sgnals and  onneors

  Mul i flas Generto

W SHOS F O R AN OLD WOKHOS For a perod of abou 38* year, he pnipe o obsing g-speed mo tion by means o eetron srobo sopcs 1 as been mpemented by po abe GR equpment of varous types. Tee has been wde aepane by n dustry o is srmenaton, apable o " opping a  moo n wt out pysa onat or quk analyss and o preseing the stoppedmoon on flm o laer study  A ehnque famlar to phoogaph er, wh has not eeved wde aen

  

 e og



for  ptil nt of th sob atic

ton in eneering and e ear atv ies, is mos sefu n topp ng he mo ton of extremely g-veloy aions wiou he se o hig-speed aeras Ths is e mul -a  o burs-as enque by whh a sine see o lm  exposed n on euve order, by a seres o robe ashes The same enque an be expad ed,  test requremens beome more omplex to provde o a pulse but om a sinle srobosope (gue 1 or a bur o ndv dual ase om mu ipe srobes gue 1 b). T provdes equvaenshter speeds of a mo



 •

ash neals ta an be alibrated Adapt aby o exng srobo sopes  Smal, ligh, and ugged onsru on • Hihest ntensy etaned t I 0µs tervas • Bust mode prodes or nital sgn o atvae onatboune and noise ret on irus

   n I Tacks any ppiaons n motionaa ss work ave bee n developed d ring e years that te srobosope a been **R td  mrk o h Pr C.

0   0  0 0  0  Cmo

F eQuncy orol

Muifosh gto

Fgre UY/EPEME

17

0 0 8 0 1 a

Fuy oo

  ash-bst techiqes  a igl  stobe i ; b

u1s oor

Mut t





P 

ir

2





h Sme c m m o n se n uc of s po r s e q   p m e n  i  a s s he ip ad i o olf r   oto o op al (Fgur l  n b o w i n  p   s . tt d wouldbe    ete e he fo i n p h o t s   tobe lht Th lty f rojcls   u e  3 nd 4 )  th  l t n   hnry    t s c     d  t  r m  n  d b th s o two or o las h .   s  a b  fatur in  u l s d t o be l gh   n g  h h m ov   obje t r bvatn g o of   w  a bw fas. If  ds o sues v e w ov  r l  and "wsh ot   oberd a Orap  s p  r m   b owe   t re  ken for d  t  r  r d  oe o n l y  h  the   pp  es cn or b e solv I olo fl s u w  d ffre  o lt ov e     h ( Fe b), old-imge roni ion issusaiall  n r 

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F 3. Instrume a m  j m  

G  i nyss  p ,    good 

Yo can or th ao o  a igy e       r ng  y  of thr hq  T  n  o nt ·h  p  a   a  k w    p r p e a l t o. Vwd  stobd ght t ig dot s  p e  s o wdfd o tre w  t h fo hich t aeleto a e otd. A prt aa o is thnqu    h   of   r  sob  plugd o h  re   5 4 1 b  t n o t a j a    t  o  e  ech o th e   By   pp  g k o c   ins th po sepaatio t ow v locty  h r b  r rs l   i o n o  he tst dt. Yo  epa s   ; h    rn t t  c h n    e  how p o  ogr a h  . Th cpa r  h G R  5  1 ,  5 3 8   d   3 9 s t o  l  m s appxe a p o n   o  ig   w a ct u   l y  a  By   f ulp  paa t o n  g  r  he    h eh  cee d i   inrv of e  o

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dir t  ra o fil o pde a ecod,   k  tat o th t      l �p  u . ch z o   i s    i  q     m  

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 (    x v pho d  be' sok wa) ad  i p i u l   l  h a    d    n a   r · o f  h  b  r    T  a  i   t h  f r  p  l  f  h  r  od    r    on  s a    p-     i a p p  i   . h o b  w   te snlerob s n F   ad h  s ro  c ys    r l  d p n e r q    d O at drg      n h q  p o  r g  d  nmer. h   h e o     gk b tm  lme L th xm   aowa a f r ah y g ig. o  x a      1  5 3 9    5 40   d p o 00  i  e     o n   R 1 5 3  p  o t    s p sond. U n f     n      ,  h  r lo i n       n: qn     p r :i   i  in r

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Experimente

Figure4 Mltple-flash shadograp quence.

dd pth of i h dr  xpur rpd  ppr xly 2-1 / ftp pr iyg tp o GR trp Wh ltip u d  y  hgh  ty  g , thu r ig a uh a o 7/2 f- tp ih uty ir  ph  id  Eah rb  igahd   tha t h g iu br f q u  y  I 00000 pr d id y by h  I 54  gr. Uig  rb gh a th h iat lu ad ra ditr p v i hghpd ara ad  t  p ov  d   ap lghtg a l  �rby :d d t  w gh a   p u pr Fr xa p fu  R   1  4 1 ga 138 o an b u  pdu 1 0000 ahspco igh a b  u  h p p pd t f  gd fla highpd aa

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h   5 4 1 Mutah Grar  prv f r urt rp o f     h  w i   pr x btw ah y adjta f 1 0 m   r    n   I 00 li. ggr it i th i    pvd f  a vat f  u tig

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a  'tri phtia ad r hail h t igg iuit i digd  j i r iga c  th taig ga �in rl ha a ba rr ia f u v i d r a i ti ga t  a x     i t  i g< tr uay  q.

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REFRNCS   was    .  Lmson, H. W "Te S  oocscobrc " Mile W  o  , who o o l   o r o  o . zos M J .  c Eyes o od us y S Se<cons o    s  cr sh   he     o s     U S  s s   es M A  l yss  s hS  c     M  Sobo  soosoe $ 154 e C  .      Vew i    e 1 B      n e s s  

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Go  il 

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GR pmtr

Holtj, o A 

G 1 92

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 ! A  GR xprm 

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GR Expm, Sm/Ocoe  69

Catalog Number

    -9 7    5  9 

Descripon

l 

Pre

M or Cab b



Prce n crd, MA SA Sjc o quy s.

Reports fom the Field A  t<g tu i  h    t  u  iludd a r f th rdui  t ral ti that a    u    d  h  R 650 Ip Bidg i 1 9    L rugh ti r th a,  pla . One    r t   u   r  v hr  Sa Dg Caliri, a pd    th ar  whh h h



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J U L Y / S  P T   B  R 

diz · hi  bidg puhd  h urplu k  r go d  h bary b  h t iid gal ga 00 400   4k a ! Ok  z)  a i  p dpay h lr u pr t th pat l  b al a   ul p  ipy ad i uy.

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O U T O F  H E PA S

The G E N E RA L  RA D I O

EXPERIMENTR VOL VI  No. 7

DECEMBER, 1932

E LECTRICAL COMMUNICAIONS  � C  N I Q   A N D I  S A P  L  C A  O N S  N A L L  E D F   L D S HE SROBOSCOP

I  E

 ro bo 

cn. is  m  en   nt a pan i an �  n  f a d <   an n P t a  n \ hie n t  r m i l t  n  mal pd \ t   t  h a  ar 1   bs •  a  1  n · , e   h   v 1  a  8r  r VCn <"k\ a  l w   h r   r pogai, f a mo u obj c  n n  n u · t  a  a b sc a a nn a     f t   r d    p f  I ig t rom r s,  h mton. h x  l Tn E quick nes f h hd T l" -otion i a   i  t y. B tb   nd,  o ri  w, p  a faa a a t a     ii mehax am p  L itera r  an    m i t i l dv  by h e s  i n g a n  p r fi  a  l  d L    t t i n  f gtons  machn l f nfmt wich th  m h� o 'jg h r  han  rkay  t l   i "  om a j  p fr b  ef t' hn ch Hnc t stb p i i    i  r   y    y f a   T i t  n a  n Egetn·tr      n t '   c  ne  r  f t o n p wih . ai  a k p a a a S l )  f srbp, p  b  ahg ra    I   T f w ·  Ed io a( d b t n lay er can l  p  t an ib. T gn  p n pg 5. slo  cham1 ngxaplC.O sl hip  ro  a m ti p' f  da  lagga    adad  f  nv   a a an   vu bao. Prap anJ  h m   n p  ara i a m       u   i a nnr"ad   bt n a    rob p  i vewe  o     h · l c m  n   a r i an o rea  a r a a a a a   a a u t at ng a  a a  h  ad nd an w   p u h a  i  n  a   h w  v r, an   e a  i a b   nu f a J µn      a  nn l a y   wh e  a    rok  t

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dena on h u an imes a  1 t in a dak ne oom ligh  ashd on hi a t a con Lant rae exatl ua o h troking ra  he wi  appea a though oton I ' nder ontinuou luation. t he a h p e   gh  y low  th an h r ing ate h  · a will e ilinae a  e fa her along in he troke ah te he ht he and a t he rea n  he iag bt \een a he  a l y okng pa  wil u , and a ine  e an  over a mi n t f de r  Hman ,  nni pla)ng o othe1 , annot repea un ir · e a an uh p an and we \·er a hne ae  igne  or u d t he ab i t to wath hei p aton n sow o n � i tho  potoraph  a b Th o op p ri t toping th moion o the a hine ( a  f e amnan a hi n or pod   a an par of it orati n  ' hi al hP me fow int th op undi ni hed d   rha a suakin dulh, a ai n ha, o a at rin  ae sprn an d e \ e n a C  an a wang pb low c \ h1 " il not wai long 1 tion tud  \ i l hO\ t he trou o t h pria ot on a  t pd and th i raing ebe ad a on puo a a  q i obru ont. oei th t an en movemen  o ra ion ake pl a e a o hgh a pee r t he ee ev n w i t h t h piary i n r pho grap  re ored o  a o lo  ing down h an in  l le on idra ton of wh at i eing done  the rob ope i  n o up t he eqi emen t of at f one An aua  ean o imn t  pomp and a uae r  ash and on o the ash ontol are  t ia oherw e ojet wil e ie\ed a ea ineal  an  rations not pr n in t he obec  ewe< wl  intr du ed ho Th fa t be of e xt e d ation herwi aeabl o

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wll pp r burr d n in< (ah pint  f the dy u   ept  l  a mou t  mti dug th nral f bPvato t s rthr dt th th p  o t of  re< p iL  of th j!t i it o my  ntd    l l ml ) h ft g t phs f th 1hono1 hr  l  h w it h rp  t th mti. Th p i c  of I rf   yhrim  wh h L fequn f moti ad of  ri r idtcl  i  ow  uda mentl  •honm f   gra r ta h oj  i ll >  r d n t vry h o mi tht th in tgt illuminaion  rd<'l  te  t nl mont " im the lum tn t fm t l ycoim Ahgh y dt f pC f  n rom w mp  t mt i L irl  wor t t nd tion is bvs f fumta hrnm. n t  r 

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st Cop O dmi t feqcy o spd f l moti. W hY  fr nzd  fdamtal lw f L  < tbop  Itin of    nchoim ithr p1ti r pt Cidr ow th  h!r t  f f mtio i liht ra( n a ingal mltip f t  fu  01•·r ti N =  + s ( SJ "h   ml mprd to R. T m' Hih) t  mon objt wil o than  ·l of moto uig l i  a " n bratio  tht th ph. t " h1 t i R'n rboopcll   il  ·ntinuy avn. h bj  t wll t  w rf  ap· p  m  t  o" li f1u•t') f R  (a i1 uni im nd L tr\ .I i l· w di o    r  i u 10\  n Cons) , if th i r•u  lhl)  tn  itgral mltl   l  u  brtion th p at hi th jt i {  rJ8   l 10ti r o  t th jt \ l ppl' o mo e t a low lic frequ    i·ti ppit  h t mti:

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h l" troboop 0 on i     obtai in ti an·r, ad  hh !n ju t t  m   ma fractin f  tru p m  trocp 'r  wthg IH c<l mo in  mahir rng  pr•J  hih  l lod with t nade  he frequnc f lJo mtn, m b mad as w  d  t otr d i f  i rad  a i lmi l orvd mt bo trtnt 1 le tfcty  rp es f  l LU 

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o  so 

PROGRAMMABLE DECADE RESISTR The GR 1 435 Pogramma bl Decad Rsistor was d igned for mximum customer-us fexibiity consistet with accuracy nd cost. Th bsic instrme n t covrs th fve-dcad spn from I 0D to 1 00k per stp with each decd a puin boad Mchic d ectric povisio has b md t o aow simp conversio to a six- or sv-dcde i strum t houd the d arise Rd switchs usd throuhout th nst mt are of th miiatur mercuy wttd typ for ow ad repatab zro rsstc as wel s bouc-fre oprtio Th hg ad ow trmias of th rsistors ar soated from ound; this prmts us whre a ot ig rsistor is requed Thre dtnct mods of opratio ae provdd for th user's conveiec: nua Mode The desird rsistnce s st o the front-pne dias just s oe woud et  coventioa decade resis tor This is usfu for exmp, whn you ar maig accuracy chck o th GR 1435 to determi how mny d cds d b remotey controd for  prticuar appictio or whn you are mnuy chcki proper system opr tion u rin iiti setup stags Mode Some of th d cades my be set to th R" position o the front-pal dis nd b remotey con tro d whi  th rmi dias e st to a paticur vu of rsstac d hd constt Ths hs th dva tae of requiri four lss contro is for evry dcade which is muy et Rmot Mod You ca seect ths mod by tui  dcade dias to th R positio by tuig th powr swtch to th REMOTE positio or by appyn oc "O  to pi V of the rar-pe conctor Resistc is st by appicaton of netiv tue 1 -2-4-8 BC sias at stndd TL or TT vs or conta ct cosure to ound  to ach dcd vi the 36-pi rear-p connctor  fatue tht desrves speci me ton is the abity to short or ope the 2

dcad sstor trmias rmoty Grounding pi 1 8 of th 6pin rar pae conctor horts the resistor ter mis wie groundig pn 1 7 opn the rsi t or termi as This is paticur y usefu if discontinuities are obec tionbe which my ex t whe rsis tac sttgs r changd (th wort cas bei th chnge betwen " 7  nd8 whr a four reeds che stat) With proper timig th restor trmns coud b eithr opened or hortd dur this switchi inte whichever is more prtit to th ap pcatio  few ypic ppictions re ius trtd i th fiurs rang from a simpe prormmabe ampifier oad to  progrmbe oscator or tme con stnt Caalog Numbr

1 4597  -97

P r ce

Dsciption

3 Programmab Decade Resisor Bench Me R el

Compete scificatons fo te GR 1 45 a  G R Caaog U ; m no r evi sios re shown low Feqe Chatristics: At igh-resstace va ues feuecy haateristcs eped manly o capacaces ad on the type o conntons usd ( 2- or 3teminal gu o urdd) . At low re s snce val es  the y depnd  ny on the  ndu ctance Ccua tions ba o vales shown ou gve approximte ries-resstane eror C)

- GOND Param

ecd sstce  = 1 O  = 100 kO 9 pF 76 pF 9  47 pF 46 F 606 pF µH

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Pres ne F O B Conod, MA, USA. Subje o qy on.

PROGAMMABLE CURRNT SRC

hs stnt was designed by Pete Ga of GR' Comoent and Netwok Tt ng Gop, who conti bed the terl for this tce

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Unrsi of 1 0-03 Standar  Aapo Kt showng sock ole a ypil - oeos.

U vesal Devce Aao

 1790-MH VSTY  CPCTY V aiiy   th nam or the  vrsa deve adap r eety made vaille to  R 1 7 0 m he ogir t anyze r ha provn itsef ace enough o hade mos ogicirit e proes preseted  m py y mans of siby adaped itefaing. GR engineers icorporated sevral im portat rnps i th desig of the n ter[ae ada pto ot ions wit h n t daptor re mad smply by wr wrapg to trminls mounted on pntd ods Aa cotol or moniorg crcuit    ods re easily onnted wihin t dpos ustomes are gvn the c hoie of a std ard adpor or of a uve a dap to I n th esa adaptos nput  otpu t onecons an be detemined by he tst pogrm hi ermts acep an  of a greater v ar y o f dvies as wel s prvdg eks of opu s ad inpu ts Use of he un vs adapo pe mi t e s o sotd p ts The dptos are esy i setd i o o rmoved fom the lye r y atio o a sige vr Provson s mde on on st dard boad o mout sokts  rows 0 ch aprt and spaed at  25inc h nenas. d pto re avaia w 24, 48 2 nd 96 i ps d 8 7 _    20, and 1 ou pts in predtermned ombiai ons omp speifico n deil o the R  790 t  avaiab on  h ea h  t th ak o h  su.

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As csomes for the standard R  7 LogiCicit A alyze eome familiar with ts opertio w a iipae he r nds wl grow o expan d ts apptio  o more comp ess. Or a eed for xpde d mmoy sorage wil vd as tes pogams engthe i ee poible th at orgiall y im t ed fudg  apial ependit u my creased s t he svin gs made po s be y he GR  90 n   brogt o mangement's atteto For ay of he  resons  R s repard to he p its cstomers epnd t   i  tadard aa lyzrs y spplyn g ad ista ln g seve  re rofi kis Ki ar av in two ba c fo mas  one o expnd memo by 0

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

$ 0,5000 0,600.00  3,500  0 230.0 23  00.00



Hecent Tehnical Aries by GR Personne

 is i  E   

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1 790 Logic-rcut Anyzer Rt s, o saa o oos   ld by G  rso  2A o Addo Mmoy 0Hz    K 2B  or Ad on Mmoy  50z li  3 K fo Poam L 50   0  z   i  A3  o  on 0-      i  2   3  fo o 50 

O

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imes (Opton 2) and th ohe to ro vid  pabilit y or th addi o of po grmmabe logic leves and rogmmab owe suppis Oton 3) oth otions r avable i 50z  wl as 6Hz version  to aommodt ovs  m Oti are aso avab pratly or ombid. eithr opt o r re mo pyical p The kits w e nstalld by GR dsc f svcedeprtm t ronnl nng rqured r ton wth pio 2 will be pvidd h cs ome; o rthr traiing is rqid for Opton 3 Comple t efaon detas fo he  R 1 7 opions re vab on t t a st at th  k of h is isse

,

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