KAS FORMANDID PEEGELDAVAD EMOTSIOONE?

Size: px
Start display at page:

Download "KAS FORMANDID PEEGELDAVAD EMOTSIOONE?"

Transcription

1 do: /ery8.15 KAS FORMANDID PEEGELDAVAD EMOTSIOONE? Kr Tmur Ülevde. Formnte peetkse üheks emotsoon peegeldvks kustlseks prmeetrks kõnelej emotsonlne sesund võb põhjustd muutus häälkute formntstruktuurs. Uurmuse eesmärgks on ted sd, ks j kuds mõjutb lusung emotsoon eestkeelse etteloetud emotsonlse kõne lühkes vokle nng ettelugej rtkultsoon täpsust. Selleks mõõdet etteloetud emotsonlse nng neutrlse kõne lühkeste rõhulste voklde,, u esmest j test formnt. Uurmstulemustest selgus, et emotsoonprt (k. neutrlne kõne) on sttstlselt olulsed ernevused olems voklde j esmeste formntde (F1) keskmstes. Tene formnt (F2) emotsoonde erstmsel olulst roll e mäng. Voklde redutseerumst j rtkultsoon täpsust uurt eukledlse kuguse rvutmse bl. Tulemused nätsd, et vh- j rõõmu-lusungtes jääb kõnelej rtkultsoon täpsus võrreldes neutrlse kõneg enm-vähem smks, kurbuse-lusungtes see g lngeb.* Võtmesõnd: emotsonlne kõne, kustk, formntnlüüs, rtkultsoon täpsus, eest keel EESTI RAKENDUSLINGVISTIKA ÜHINGU AASTARAAMAT 8, Sssejuhtus Pljud emotsoonsüntees j -tuvstuseg seotud uurmused on nädnud, et gl emotsoonl on just tlle omsed kustlsed tunnused. Kuuljd suudvd emotsoond kõnes är tund nuüks hel põhjl, lm kõnelejt nägemt (nt telefonvestlustes) (Bchorowsk 1999). Kõnelõgu emotsoon ollkse võmelsed määrm seg ss, ku kõk selle lõgu sõnd on tähenduset (Goudbeek jt 2009). See, et kuuljd suudvd nult hääle järg emotsoone usldusväärselt är tund, toobk ks oletuse, et hääl knnb kogu nformtsoon kõnelej emotsonlse sesund koht 1 j et emotsoonde häälelsed väljendused on ernev kustlse mustrg (nt Peschke jt 1999, Bnse, Scherer 1996). * Artkkel on vlmnud tänu projektdele EKT1 j SF s09. 1 Sed vd ss, ku kuulj e näe kõnelejt, sest vstsel juhul muutub kustlste tunnuste domneermne emotsoon ärtundmsel küstvks. 231

2 1.1. Formndd Üheks kustlseks prmeetrks, md emotsonlse kõne puhul uurtkse, on formndd. Formntde bl sb teh järeldus selle koht, kuds emotsoond ükstesest kustlselt ernevd nng kuds mõjutvd emotsoond häälkute kvlteet j kõnelej rtkultsoon täpsust. Formndd on kustlse energ kontsentrtsoon prkonnd kõnespektrs. Kõkdel hellstel häälkutel on om formntstruktuur nng nende foneetlst kvlteet tjutkse hrlkult esmesest kolmest formndst (F1, F2 j F3). Formntde sukohd e ole fkseertud, vd sõltuvd kõnetrkt suurusest j kujust. Kõnetrkt muutub, ku vreerub häälduselundte (nt keel, lõug j huuled) send. Smut muudvd kõnetrkt suurust j kuju emotsoond. Ku kõnelej emotsonlne sesund muutub, ss muutuvd tem näolme nng häälduselundte send j lhspnge (Ververds, Kotropoulos 2006, Johnstone, Scherer 2000). Nertdes öeldun kõlb lusung kustlselt üsng ernevlt sellest, ku öeld sm sj kulmu kortsutdes (Trtter 1980). See omkord tektb kõnesgnls kustls muutus, ms peegelduvd formntmustrtes. Uurmstulemused on nädnud, et emotsoond mõjutvd kõge rohkem F1 j F2 väärtus (nt Tolkmtt, Scherer 1986) Artkultsoon täpsus j redutseerunud häälkud Põhjus, mks kuuldkse foneetls ernevus khe vokl vhel, petub selles, et vokldel on ernev sukoht khemõõtmelses voklruums, md seloomustvd vokl kõrgus ehk vertklne dmensoon j vokl eespoolsus-tgpoolsus ehk horsontlne dmensoon. Need kks mõõdet on seotud keele sukohg suus vokl moodustmsel. Jonthn Hrrngton (2010: 46) krjeldb vokle khemõõtmelse jotuseg F1 x F2. Vokl kõrguse ksv seostub F1 väärtuse vähenemseg, vokl tgpoolsuse suurenemne seostub g F2 khnemseg. Seeg: vokl F1 < vokl e F1-st j vokl F2 < vokl ä F2-st. Ku voklruum ktseneb, ss kõnelej rtkultsoon täpsus väheneb. Vokld, md hääldtkse vähem selgelt, kpuvd redutseerum, s.t nd kotvd om kvlteet. Ku lmneb kõrvleklle vokl n-ö normlsest postsoonst kustlses ruums, ss nmettkse sed vokl tsentrlseerumseks (ngl centrlzton). Tsentrlseerumse puhul moodustub vokl kõnelej voklruum keskpunkt lähedl j srnneb šv-häälkug, mllel pole selget kvlteet. (Hrrngton 2010: ) Uurmstulemustest (Tolkmtt, Scherer 1986) on selgunud, et näteks stresss võ depressoons nmene e rtkuleer hells häälkud sm jõupngutuseg ngu neutrlse kõne puhul. Sellsel juhul häälkud redutseeruvd nng rtkultsoon täpsus lngeb. Hrrngton (2010: ) krjutb, et selge kõne puhul setsevd vokld tenetesest kugeml nng kuuljl on ernevte häälkute vhel kergem vhet teh. Voklruum muutub suuremks nng rtkultsoon täpsus tõuseb. 232

3 2. Uurmsküsmus j -mterjl Formntnlüüs eesmärk on led vstused khele küsmusele: 1) ks eestkeelse etteloetud emotsonlse kõne lühkes vokle mõjutb lusung emotsoon nng 2) ks emotsoond mõjutvd ettelugej rtkultsoon täpsust. Uurmstöö kustlne bs on Eest Keele Insttuud eest emotsonlse kõne korpus ehk EEKK 2. Korpus toetub sesukohle, et emotsoond on hääle põhjl psvlt häst är tuntvd k loomulkus, mttenädeldud kõnes, j et mttenädeldud kõne on loomulku kõnesüntees eeldus (Id jt 2003). Korpuse loomsel on lähtutud prntsbst, et lõgu ssu kutsub lugejs esle mng emotsoon. Seetõttu pole lugejle ette öeldud, ms emotsoong tekst luged. Korpus ssldb ühe nshääle etteloetud jkrjnduslkke tekstlõke, ms on segmenteertud luseteks, sõndeks j häälkuteks. Korpuses sslduvte lusete emotsoond (vh, rõõm j kurbus) võ neutrlsus on määrtud tjuktseteg (vt Altrov 2008). Uurmsmterjls on EEKK lused, mlle emotsoon võ neutrlsust on tjunud üle 50% kuuljst. Formntnlüüsks vlt nest lusetest välj kolm lühkest vokl:, j u (vokle on kokku 2395, täpsemt jotust vt tbelst 1). Need on eest keeles sgedst esnevd vokld, ms moodustvd kolmnurkse voklruum ( on mdl tgvokl, kõrge eesvokl, u kõrge tgvokl), mlle bl sb häst krjeldd kõnelej rtkultsoon täpsust. Voklde juures ol olulne k nende rõhulne postsoon. Selle tngs soov led vokle, ms oleksd võmlkult vähe redutseerunud, sest on ted, et mtterõhulste slpde häälkud kpuvd om kvlteet kotm (vt nt Moon, Lndblom 1994). Kun eest keeles sub rõhk sõndes tvlselt esmesel slbl, ss pärnevd väljvltud vokld sõnlgulstest postsoondest, mlle struktuur on VC, CVC võ CCVC 3. Tbel 1. Formntnlüüs mterjl Vokl Emotsoon Voklde rv u vh 395 rõõm 279 kurbus 244 neutrlne 319 KOKKU 1237 vh 220 rõõm 146 kurbus 154 neutrlne 145 KOKKU 665 vh 138 rõõm 133 kurbus 115 neutrlne 107 KOKKU 493,, u KOKKU Vt ( ). 3 V vokl; C konsonnt. 233

4 3. Meetod Uurmsküsmustele vstuse ledmseks mõõdet esmlt etteloetud emotsonlse nng neutrlse kõne lühkeste rõhulste voklde, j u esmest j test formnt. Hrrngton (2010: ) krjutb, et otsus vokl kvlteed koht tehkse kustlste ndmete põhjl mngl kndll jhetkel j hrlkult on selleks koht, ms ühtb vokl kestuse keskpunktg (ngl vowel trget). Suurest vltkse vokl keskpunkt formntde mõõtmseks seepärst, et nberhäälkute kontekstlne mõju on ss mõõdetvle voklle kõge väksem nng selles punkts on vokl kõge stblsem, s.t formntväärtused muutuvd kõge vähem. Voklde redutseerumst j rtkultsoon täpsust mõõdet eukledlse kuguseg 4, et näh, ks vokld on võrreldes neutrlse kõneg tsentrlseerunud võ mtte nng ks voklruum on võrreldes neutrlse kõneg emotsoont suurenenud võ vähenenud. (Vt Hrrngton 2010: ) Et eukledlst kugust mõõt, määrt esmlt kõnelej voklruum keskpunkt (ngl centrod). Selleks rvutt välj neutrlne häälk x, võttes rvesse kõg korpuses leduvte j uurmsmterjl tngmustele vstvte sõnlgulste struktuurde VC, CVC võ CCVC lühkeste voklde keskmsed. Akustlste prmeetrte mõõtmsel ksutt progrmm Prt 5 nng kõne ndmebsde süsteem EMU 6 b. Formntväärtuste utomtse rvutmse ved korrgeert kästs nng pärst sed nlüüst mõõtmstulemus sttstkkeskkonns R 7. R- bl vd läb ANOVA-nlüüs 8. Klus R. Scherer (1986) on välj töötnud emotsoonkustk vldkonns mudel, ms ennustb emotsoon mõju häälelsele väljendusele. Scherer component process model e CPM võtb rvesse psühholoogls j füsoloogls mõjusd, ms ksnevd emotsoon väljendmseg, nng nätb, et on olems emotsoonspetsflsed kustlsed mustrd. Scherer krjeldb emotsoon ku kohnemsmuutuste (ngl dptve chnges) srj, ms on vststkuses seoses. Emotsoon tekkmsest mpuls snud närvsüsteem mõjutb n kõnelej hngmst ku k tem kõneorgnte lhspnget nng see toob ks ernevus kõnesgnl kustks. Nt mdg väg ebmeeldvt vldub tht neelu j kõr ptstuses, mlle tgjärjel läheb kõnetrkt pngesse nng väljtulev hääl on kõrgem sgeduseg (Thompson, Blkwll 2006). CPM-mudel ennustb muu hulgs, kuds muutuvd emotsoonde mõjul voklde F1 j F2 väärtused võrreldes neutrlse kõneg. Võrdlus ntud töö uurmstulemuste j CPM-mudel nng vrsemte uurmuste vhel esttkse 6. petüks. 4. Emotsoondeg ksnevd muutused formntväärtustes Sensed uurmused emotsoonde formntväärtuste koht pole ndnud pärs ühesed tulemus. Põhjus võb petud ernevs uurmsmterjls (nädeldud vs. esle kutsutud emotsoong vs. spontnne kõne; hel vs. hel + plt), ernevs uurtvs Eukledlse kuguse vlem: d(x,v em ) = sqrt((f1x-f1v em ) 2 + (F2x-F2V em ) 2 ), kus x on neutrlne vokl j V em vdeldv emotsoon vokl keskmne. 5 ( ). 6 ( ). 7 ( ). 8 Tbel genereermseks ksutt R skrpt (vt lähtendmed vt ( ).

5 keeles j kultuurs nng ernevs emotsoondele ntvs ssus (nt vh ll võb mõst n revu ku k lhtslt ärrtust, rõõmu ll rhulolu võ vmustust, kurbuse ll pettumust võ muret). (Burkhrdt jt 2006, McIntyre, Rolnd 2006, Wltng jt 2006, Dougls-Cowe jt 2003, Scherer 2003) Smut erneb vs, kuds emotsoone krjeldtkse. Domneervd kks lähenemst ktegorlne (ngl ctegory pproch) j dmensoonlne (ngl dmensonl pproch). Esmene lähenemne määrtleb emotsoond ktegoorten, nt kurbus, vh j rõõm. Tene lähenemne ksutb emotsoonde krjeldmseks dmensoone. Kks enm ksuttvt dmensoon on vlents (postvne vs. negtvne) (ngl vlence) j vrgumne (mdl vs. kõrge) (ngl rousl). (McIntyre, Rolnd 2006, Cowe, Cornelus 2003) Ku pnn emotsoond khemõõtmelsele teljestkule, ss on rõõm kõrge vrgumse j postvse vlentsg emotsoon, vh kõrge vrgumse j negtvse vlentsg emotsoon nng kurbus mdl vrgumse j negtvse vlentsg emotsoon. Vrsemd uurmused prntsus- (Goudbeek jt 2009), ngls- (Murry, Arnott 2008, Trtter 1980), sks- (Kenst, Sendlmeer 2000, Peschke jt 1999) j jpnkeelse (Id jt 2003) emotsonlse kõne koht on nädnud, et esnevd mngd üldsed tendentsd tetud emotsoonde j nende kustlste korreltde vhel Vh j selle võrdlus teste emotsoondeg Võrreldes teste põhemotsoonde j neutrlse kõneg on vh puhul täheldtud voklde kõrgemt F1 sgedust (Goudbeek jt 2009, Murry, Arnott 2008, Jusln, Lukk 2003) nng vokll olulselt mdlmt F2 sgedust (Goud beek jt 2009). Smut on vh-emotsoong voklde juures märgtud nende kõrgemt rtkultsoon täpsust (Murry, Arnott 2008, Jusln, Lukk 2003, Kenst, Sendlmeer 2000, Peschke jt 1999). Vh koht on sdud k testsugused tulemus. Id jt (2003) uurmusest selgus, et kõrgem helkõrguse j krem tempog emotsoondel (ngu vh) on voklruum ktsenenud nng vokld rohkem redutseerunud Rõõm j selle võrdlus teste emotsoondeg Rõõmu-lusungte vokldel on võrreldes teste põhemotsoonde j neutrlse kõneg täheldtud F1 j F2 väärtuste tõusu, kusjuures F1 on enmkul vokldel rohkem mõjuttud ku F2 (Jusln, Lukk 2003, Kenst, Sendlmeer 2000, Trtter 1980). Mrtjn Goudbeek kolleegdeg (2009) märks voklde, j u F1 keskmse tõusu kõrge vrgumseg emotsoonde (nt rõõm) puhul, kud võrreldes mdl vrgumseg (nt kurbus) emotsoondeg ol nel vokll mdlm F2 keskmne. Postvse emotsoong (nt rõõm) vokldel ol F2 keskmne kõrgem ku negtvse emotsoong (nt vh) vokldel. Rõõmu-lusungte voklde rtkultsoon täpsuse koht on sdud khesugused ndmed rtkultsoon täpsus ks tõuseb (nt Murry, Arnott 2008, Jusln, Lukk 2003, Johnstone, Scherer 2000) võ on srnne neutrlse kõne rtkultsoon täpsuseg (Kenst, Sendlmeer 2000). 235

6 4.3. Kurbus j selle võrdlus teste emotsoondeg Kurbuse-lusungte vokldele on võrreldes teste põhemotsoonde j neutrlse kõne vokldeg omne F1 keskmse lngus (Jusln, Lukk 2003). Märgtud on k sed, et negtvse vlentsg emotsoonde (nt kurbus) vokldel on F2 keskmne mdlm ku postvsete emotsoonde (nt rõõm) vokldel. (Goudbeek jt 2009) Kurbuse-lusungte vokldele on omne k mdl rtkultsoon täpsus (Murry, Arnott 2008, Jusln, Lukk 2003, Kenst, Sendlmeer 2000). Emotsoonde tekttud muutus hellste häälkute formntväärtustes on Jusln j Lukk (2003) selgtnud selleg, et vh j rõõm on kõrge vrgumseg emotsoond, ms pnevd kõnelej rääkmsel rohkem pngutm (t püüb selgemlt rtkuleerd). Lhspnge tektb kõrs g ptstuse. Pngutmne j ptstus vvd kõnetrkt lühenemsen nng toovd ks kõrgem F1 j täpsem rtkultsoon. Rõõmu puhul muudb kõnetrkt lühemks k nertmne (Trtter 1980). Mdl emotsonlse vrgumseg kurbuse puhul pole g kõrs sellst lhspnget j kõnelej räägb lõdvestunumlt. See tngb mdlm F1 j ebtäpsem rtkultsoon. 5. Uurmstulemused: voklde,, u esmese j tese formnd väärtused emotsoont Et sd ted, ks eestkeelse etteloetud emotsonlse kõne lühkeste voklde F1 j F2 väärtused on mõjuttud olulselt lusung emotsoonst võrreldes neutrlse kõneg nng ks emotsonlse kõne voklde formntväärtused ernevd üks tesest olulselt, mõõdet voklde,, u F1 j F2 väärtused nng rvutt emotsoont nende keskmsed (vt tbel 2). Sn j edspd on väärtused esttud n hertsdes ku k brkdes (psühhokustlne skl). Tbel 2. Voklde,, u esmese j tese formnd keskmsed emotsoont Vokl Emotsoon F1 (Hz) F1 (Brk) F2 (Hz) F2 (Brk) u vh 615 5, ,7 rõõm 644 6, ,8 kurbus 558 5, ,9 neutrlne ,9 vh 386 3, ,9 rõõm 382 3, ,9 kurbus 359 3, ,9 neutrlne ,9 vh 427 4, ,1 rõõm 418 4, kurbus 410 4, ,3 neutrlne 430 4,

7 Tbelst 2 selgub, et ernevused emotsonlse kõne j neutrlse kõne voklde F1 keskmste vhel on väkesed. Kõge enm erstub neutrlsest kõnest kurbuseemotsoon (vokl 635 Hz / 6 Brk vs. 558 Hz / 5,4 Brk j vokl 403 Hz / 4 Brk vs. 359 Hz / 3,6 Brk). Emotsoont ernevd F1 keskmsed kõgl kolmel vokll, kud k sn e ole ernevused lt suured. Kõge rohkem erneb testest emotsoondest kurbus voklde j puhul. Vokl juures vh vs. kurbus (615 Hz / 5,9 Brk vs. 558 Hz / 5,4 Brk) j rõõm vs. kurbus (615 Hz / 5,9 Brk vs. 558 Hz / 5,4 Brk) nng vokl juures vh vs. kurbus (386 Hz / 3,9 Brk vs. 359 Hz / 3,6 Brk). Ernevused emotsonlse kõne j neutrlse kõne voklde F2 keskmstes on smut väkesed. Kõge enm erstub neutrlsest kõnest kurbuse-emotsoon vokl u juures (1274 Hz / 10 Brk vs Hz / 10,3 Brk). Emotsoont ernevd F2 keskmsed voklde j u puhul. Vokl juures erneb kõge rohkem vh kurbusest (1412 Hz / 10,7 Brk vs Hz / 10,9 Brk) nng vokl u juures rõõm kurbusest (1272 Hz / 10 Brk vs Hz / 10,3 Brk). Et sd ted, ks emotsoonrühmde ernevused on sttstlselt olulsed, teht formntväärtustele ANOVA-nlüüs. Tulemused on esttud tbeltes 3 j 4. Tbel 3. ANOVA-nlüüs tulemused emotsonlsete lusete lühkeste voklde,, u F1 koht. (Sttstlselt olulne ernevus on märgtud hll tustg) Vokl Emotsoonpr DF F vlue u Pr(>F) Hertz skl Pr(>F) Brk skl vh vs. rõõm 672 6,43 0,0115 0,03 vh vs. neutrlne 712 4,01 0,0457 0,04 vh vs. kurbus ,17 0,0001 0,01 rõõm vs. neutrlne 596 0,65 0,4211 0,69 rõõm vs. kurbus ,04 0,0001 0,01 neutrlne vs. kurbus ,42 0,0001 0,01 vh vs. rõõm 364 0,45 0,5010 0,44 vh vs. neutrlne 363 5,70 0,0174 0,02 vh vs.kurbus ,77 0,0001 0,01 rõõm vs. neutrlne 289 6,48 0,0115 0,01 rõõm vs. kurbus 298 6,81 0,0095 0,01 neutrlne vs. kurbus ,41 0,0001 0,01 vh vs. rõõm 269 0,45 0,5013 0,39 vh vs.neutrlne 243 0,11 0,7380 0,78 vh vs. kurbus 251 2,11 0,1478 0,14 rõõm vs. neutrlne 238 0,78 0,3778 0,31 rõõm vs. kurbus 246 0,38 0,5384 0,60 neutrlne vs. kurbus 220 2,59 0,1089 0,11 237

8 Tbelst 3 nähtub, et emotsoonprt on sttstlselt olulsed ernevused (p < 0,05) voklde F1 keskmstes n ku k puhul (v. vokl vh vs. rõõm). Vokl u puhul pole ernevused F1 keskmstes emotsoont olulsed. Ku võrreld emotsoone neutrlse kõneg, ernevd sellest olulselt vokl puhul vh- j kurbuse-emotsoon nng vokl juures kõk kolm emotsoon. Tbel 4. ANOVA-nlüüs tulemused emotsonlsete lusete lühkeste voklde,, u F2 koht. (Sttstlselt olulne ernevus on märgtud hll tustg) Vokl Emotsoonpr DF F vlue u Pr(>F) Hertz skl Pr(>F) Brk skl vh vs. rõõm 672 1,77 0,1839 0,28 vh vs. neutrlne 712 3,69 0,0553 0,05 vh vs. kurbus 637 4,42 0,0358 0,07 rõõm vs. neutrlne 596 0,13 0,7195 0,49 rõõm vs. kurbus 521 0,55 0,4600 0,50 neutrlne vs. kurbus 561 0,25 0,6176 0,93 vh vs. rõõm 364 0,07 0,7926 0,84 vh vs. neutrlne 363 0,19 0,6667 0,63 vh vs. kurbus 372 0,19 0,6610 0,40 rõõm vs. neutrlne 289 0,02 0,8948 0,81 rõõm vs. kurbus 298 0,31 0,5753 0,40 neutrlne vs. kurbus 297 0,48 0,4896 0,30 vh vs. rõõm 269 0,71 0,4015 0,38 vh vs. neutrlne 243 0,51 0,4758 0,40 vh vs. kurbus 251 1,14 0,2859 0,32 rõõm vs. neutrlne 238 0,00 0,9623 0,96 rõõm vs. kurbus 246 3,27 0,0718 0,07 neutrlne vs. kurbus 220 2,55 0,1118 0,09 Tbelst 4 selgub, et emotsoont on sttstlselt olulne ernevus (p < 0,05) F2 keskmstes vd vokl hertsde puhul, kus F2 erstb vh kurbusest (p = 0,0358). Brk skll sttstlselt olulst ernevust sn e ole. Ülejäänud F2 keskmsed e erne olulselt üheg uurtud vokl puhul e emotsoont eg k võrdluses neutrlse kõneg. 6. Formntnlüüs tulemuste võrdlus CPM-mudel j vrsemte uurmusteg 238 CPM-mudel ennustb, et võrreldes neutrlse kõneg tõuseb vh-lusungtes hellste häälkute F1 j lngeb F2. Ku kõrvutd see ennustus ntud uurmuse tulemusteg, ss selgub, et neutrlse kõne vokldest ernesd vh puhul j, mlle F1 keskmsed vstupdselt CPM- ennustusele lngesd. Voklde, j u F2 keskmsed neutrlse kõne F2-st e ernenud. Vrsemtes uurmustes on vh puhul täheldtud n voklde kõrgemt F1 sgedust (Goudbeek jt 2009, Murry,

9 Arnott 2008, Jusln, Lukk 2003) ku k vokl olulselt mdlmt F2 sgedust (Goudbeek jt 2009). Ned tulemus käesolev uurmus e knntnud. Rõõmu puhul ennustb CPM-mudel F1 khnemst. Antud uurmuse tulemused nätvd, et rõõmu-lusungtes erneb neutrlse kõne voklde F1-st sttstlselt olulselt vd F1, mlle väärtus tõest lngeb. Tesed uurmused on nädnud, et rõõmu voklde F1 j F2 väärtused tõusevd (Jusln, Lukk 2003, Kenst, Sendlmeer 2000, Trtter 1980) nng nel on mdl vrgumseg emotsoondest (nt kurbus) vokl F2 keskmne mdlm. Smut on rõõmu-lusungte vokldel F2 keskmne kõrgem ku negtvse emotsoong (vh j kurbus) lusungte vokldel. (Goudbeek jt 2009) Käesolev uurmuse tulemustes polnud rõõmu puhul F2 väärtustes emotsoont eg võrdluses neutrlse kõneg sttstlselt oluls ernevus. Seeg k rõõmu puhul e ühtnud ntud uurmuse tulemused vrsemteg. Kurbuse koht ennustb CPM-mudel, et võrreldes neutrlse kõneg hellste häälkute F1 keskmne tõuseb nng F2 keskmne lngeb. Antud uurmuse tulemused nätvd, et kurbuse-emotsoon puhul ernevd voklde F1 keskmsed sttstlselt olulselt neutrlse kõne vokldest j, mlle F1 väärtused lngevd. Vrsemtes uurmustes on kurbuse-lusungte vokle seloomusttud neutrlsest kõnest mdlm F1 keskmseg (Jusln, Lukk 2003). Negtvse vlentsg kurbuse puhul on märgtud k voklde mdlmt F2 keskmst võrreldes postvsete emotsoondeg (nt rõõm) (Goudbeek jt 2009). Käesolevst uurmusest selgub, et üheg vokl F2 väärtused e erne sttstlselt olulselt emotsoont (v. vokll vh vs. kurbus Hertz skll, vt tbel 4) eg võrreldes neutrlse kõneg. Tulemustest võb järeldd, et rõõmu j vh väljendtkse eest keeles test ku keeltes, mlle koht on sen uurmus tehtud. Oslne srnsus on g kurbuse väljendmses. Sms peb slms pdm sed, et ernevuste põhjus võb petud k ernevs uurmsmterjls. 7. Artkultsoon täpsus emotsoont Selleks, et sd vstus küsmusele, ks emotsoond mõjutvd ettelugej rtkultsoon täpsust, rvutt esmlt välj lugej neutrlne häälk x (F1 = 511 Hz / 4,9 Brk j F2 = 1745 Hz / 11,7 Brk) nng seejärel eukledlne kugus emotsonlse j neutrlse kõne voklde, j u keskmste nng neutrlse häälku x vhel ksutdes vlemt d(x,v em ) = sqrt((f1x-f1v em ) 2 + (F2x-F2V em ) 2 ), kus x on neutrlne vokl j V em vdeldv emotsoon vokl keskmne. Eukledlse kuguse mõõtmstulemused on esttud tbels 5. Tbel 5. Eukledlne kugus voklde j neutrlse häälku x vhel (Hertz skl / Brk skl) Vokl x vh x rõõm x kurbus x neutrlne 348,9/1,4 332,7/1,5 288,8/0,9 321,8/1,4 915,6/3,3 922,1/3,4 909,8/3,4 921,3/3,3 u 454,8/1,7 482,1/1,8 423,2/1,6 477,9/1,8 239

10 Tbelst 5 selgub, et emotsonlse kõne voklde, j u rtkultsoon täpsus emotsoont j võrreldes neutrlse kõneg ert e erne. Erndks on kurbuseemotsoon, ms on võrreldes neutrlse kõne j teste emotsoondeg tsentrlsele häälkule x märkmsväärselt läheml. Kurbuse-emotsoon rtkultsoon täpsus on seeg muutunud nng vokl on om kvlteed kotnud. Vt joons 1. 6 F1 5 x vh rõõm neutrlne kurbus 4 u u F2 Joons 1. Emotsoond voklruums Brk skll 10 Ku võrreld ntud uurmuse rtkultsoon täpsuse tulemus vrsemte uurmusteg, sb öeld, et kurbuse puhul need oslselt kttuvd pljud uurjd on kurbuse-lusungtes märknud rtkultsoon täpsuse lngemst (Murry, Arnott 2008, Jusln, Lukk 2003, Kenst, Sendlmeer 2000). Vh koht on sdud ernevd tulemus. Os uurjd vädb, et vh puhul rtkultsoon täpsus tõuseb (Murry, Arnott 2008, Jusln, Lukk 2003, Kenst, Sendlmeer 2000, Peschke jt 1999, Scherer 1986), tesed g märgvd rtkultsoon täpsuse lngemst (Id jt 2003). Antud uurmuse tulemuste põhjl võb öeld, et vh puhul rtkultsoon täpsus võrreldes neutrlse kõneg olulselt e muutu. Rõõmu-lusungte voklde rtkultsoon täpsuse koht on sdud smut khesugused ndmed rtkultsoon täpsus on ks tõusnud (nt Murry, Arnott 2008, Jusln, Lukk 2003, Johnstone, Scherer 2000) võ on olnud srnne neutrlse kõne rtkultsoon täpsuseg (Kenst, Sendlmeer 2000). Käesolev uurmuse ndmed olulst rtkultsoon täpsuse tõusu rõõmu-lusungtes võrreldes neutrlse kõneg e nät. Kurbuse rtkultsoon täpsuse vähenemse tendents knntb sed, et kurbust väljendtkse keelet srnselt. Teste emotsoonde koht sdud tulemuste vsturääkvus võb seletd n uurmsmterjl ernevuse ku k nende emotsoonde kultuurspetsflsem väljendusldg. 240

11 8. Kokkuvõte Uurmuse eesmärgks ol ted sd, ks j kuds mõjutb lusung emotsoon eestkeelse etteloetud emotsonlse kõne lühkes vokle nng ettelugej rtkultsoon täpsust. Formntnlüüs näts, et eest emotsoond vh, rõõm j kurbus mõjutsd vdeldud lühkestest vokldest, j u vokle j. Emotsoonde mõju nele vokldele väljendus nende F1 väärtuste muutumses. Kõge selgemn erstus testest emotsoondest kurbus. Voklde, j u F2 väärtus emotsoond sttstlselt olulselt e mõjutnud. Artkultsoon täpsuse mõõtmsed nätsd, et emotsoondel on sellele väkene mõju olems. Kug vh- j rõõmu-lusungtes jä rtkultsoon täpsus võrreldes neutrlse kõneg enm-vähem smks, ss kurbuse-lusungtes see lnges. Vdtud krjndus Altrov, Rene Eest emotsonlse kõne korpus: teoreetlsed toetuspunktd. Keel j Krjndus, 4, Bchorowsk, Jo-Anne Vocl expresson nd percepton of emoton. Current Drectons n Psychologcl Scence, 8 (2), Bnse, Rner; Scherer, Klus R Acoustc profles n vocl emoton expresson. Journl of Personlty nd Socl Psychology, 70 (3), org/ / Burkhrdt, Felx; Audbert, Ncols; Mltest, Lor; Türk, Oytun; Arsln, Levent M.; Auberge, Veronque Emotonl prosody does culture mke dfference? Proceedngs of Speech Prosody. Dresden, Germny. My 2 5, Dougls-Cowe, Ellen; Cmpbell, Nck; Cowe, Roddy; Roch, Peter Emotonl speech: Towrds new generton of dtbses. Speech Communcton, 40 (1-2), Goudbeek, Mrtjn; Goldmn, Jen Phlppe; Scherer, Klus R Emoton, dmensons nd formnt poston. INTERSPEECH-2009, Hrrngton, Jonthn Phonetc Anlyss of Speech Corpor. Chchester: Wley- Blckwell. Id, Akem; Cmpbell, Nck; Hguch, Fumto; Ysumur, Mchk A corpus-bsed speech synthess system wth emoton. Speech Communcton, 40 (1-2), Johnstone, Tom; Scherer, Klus R Vocl communcton of emoton. M. Lews, J. Hvlnd (Eds.). Hndbook of Emoton, 2nd ed. New York: Gulford, Jusln, Ptrk N.; Lukk, Petr Communcton of emotons n vocl expresson nd musc performnce: Dfferent chnnels, sme code? Psychologcl Bulletn, 129 (5), Kenst, Mrm; Sendlmeer, Wlter F Acoustcl nlyss of spectrl nd temporl chnges n emotonl speech. R. Cowe, E. Dougls-Cowe, M. Schroeder (Eds.). Speech nd Emoton: Proceedngs of the ISCA workshop. Newcstle, Co. Down, McIntyre, Gordon; Rolnd, Göcke Reserchng emotons n speech. Proceedngs of the 11th Austrln Interntonl Conference on Speech Scences & Technology, Moon, Seung-Je; Lndblom, Björn Intercton between durton, context, nd spekng style n Englsh stressed vowels. Journl of the Acoustcl Socety of Amerc, 96 (1),

12 Murry, In R.; Arnott, John L Applyng n nlyss of cted vocl emotons to mprove the smulton of synthetc speech. Computer Speech nd Lnguge, 22 (2), Peschke, Astrd; Kenst, Mrm; Sendlmeer, Wlter F F0-contours n emotonl speech. Proc. ICPhS 99, Sn Frncsco, Vol. 2, Scherer, Klus R Vocl ffect expresson: A revew nd model for future reserch. Psychologcl Bulletn, 99 (2), Scherer, Klus R Vocl communcton of emoton: A revew of reserch prdgms. Speech Communcton, 40 (1-2), S (02) Trtter, V. C Hppy tlk: Perceptul nd coustc effects of smlng on speech. Percepton nd Psychophyscs, 27 (1), Thompson, Wllm F.; Blkwll, L.-L Decodng speech prosody n fve lnguges. Semotc, 158 (1/4), Tolkmtt, Frnk J.; Scherer, Klus R Effect of expermentlly nduced stress on vocl prmeters. Journl of Expermentl Psychology: Humn Percepton nd Performnce, 12 (3), Ververds, Dmtros; Kotropoulos, Constntne Emotonl speech recognton: Resources, fetures, nd methods. Speech Communcton, 48 (9), Wltng, Jnneke; Krhmer, Emel; Swerts, Mrc Rel vs. cted emotonl speech. Proceedngs of Interspeech 2006 ICSLP, Pttsburgh, PA, USA, Võrgumterjld ANOVA-nlüüs lähtendmed. ( ). ANOVA-nlüüs tbel genereermseks ksuttud sttstkkeskkonn R skrpt. peeter.ek.ee:5000/nov_u.r ( ). Boersm, Pul; Weennk, Dvd Prt: dong phonetcs by computer, Verson [Computer progrm]. ( ). Eest emotsonlse kõne korpus. ( ). Kõne ndmebsde süsteem EMU. ( ). Sttstkkeskkond R. ( ). Kr Tmur (Eest Keele Insttuut) uurmsvldkond on emotsonlse kõne kustk. kr.tmur@ek.ee 242

13 DO FORMANTS SPEAK OF EMOTIONS? Kr Tmur Insttute of the Estonn Lnguge Formnts belong to the coustc prmeters ddressed by the reserch of emotonl speech. The m of the present study ws to fnd out how, f t ll, the utternce emoton of Estonn red emotonl speech my ffect short vowels nd rtcultory precson. The coustc bss of the study ws the Estonn Emotonl Speech Corpus (EESC) of the Insttute of the Estonn Lnguge. The Corpus contns non-cted speech (journlstc pssges) red by femle voce, whch hve been segmented nto sentences, words nd speech sounds. The emotonl colourng (nger, joy, sdness) or neutrlty of the Corpus sentences hs been scertned by percepton tests. The reserch mterl conssted of such Corpus sentences whose emoton or neutrlty hd been confrmed by more thn hlf of the lsteners. Three short vowels,, nd u, were pcked from those sentences nd subjected to formnt nlyss. Those three re frequent Estonn vowels defnng trngulr vowel spce ( beng low bck vowel, hgh front vowel nd u hgh bck vowel) enblng fne descrpton of speker s rtcultory precson. To solve the reserch queston, mesurements were conducted on the frst nd second formnts (F1 nd F2) of the short stressed vowels,, u s segmented from red Estonn emotonl nd neutrl speech. The results showed tht when mesured prwse (prtners ncludng neutrl sentences) sgnfcnt dfferences re reveled between the F1 mens of nd. F2, however, plys no sgnfcnt role n emoton dfferentton. Vowel reducton nd rtcultory precson were nvestgted by usng Eucldn dstnce. Accordng to the results, n the cse of ngry nd joyous utternces rtcultory precson ws more or less the sme s n neutrl ones, whle fll sgnlled sd utternces. Keywords: emotonl speech, coustcs, formnt nlyss, rtcultory precson, Estonn 243

A Comparison of South East Asian Face Emotion Classification Based on Optimized Ellipse Data Using Clustering Technique

A Comparison of South East Asian Face Emotion Classification Based on Optimized Ellipse Data Using Clustering Technique A Comprson of South Est Asn Fce Emoton Clssfcton Bsed on Optmzed Ellpse Dt Usng Clusterng Technque K. Muthukruppn, S. Thrugnnm, R. Ngrjn, M. Rzon, S. Ycob, M. Muthukumrn3, nd T. Rmchndrn3 School of Scence

More information

Sinusoidal Steady State Analysis

Sinusoidal Steady State Analysis CHAPTER 8 Snusodl Stedy Stte Anlyss 8.1. Generl Approch In the prevous chpter, we hve lerned tht the stedy-stte response of crcut to snusodl nputs cn e otned y usng phsors. In ths chpter, we present mny

More information

Pre-distortion Linearization for 64-QAM Modulation in Ka-Band Satellite Link

Pre-distortion Linearization for 64-QAM Modulation in Ka-Band Satellite Link IJCSNS Interntonl Journl of Computer Scence nd Network Securty, VOL.8 No.8, August 008 47 Pre-dstorton Lnerzton for 64-QAM Modulton n K-Bnd Stellte Lnk P. Sojood Srdrood,, G.R. solt nd P. Prvnd Summry

More information

Knowledge Unit Relation Recognition Based on Markov Logic Networks

Knowledge Unit Relation Recognition Based on Markov Logic Networks JOURNAL OF NETWORKS, VOL. 9, NO. 9, SEPTEMBER 2014 2417 Knowledge Unt Relton Recognton Bsed on Mrkov Logc Networks We Wng 1, 2, We We 2, Je Hu 2, Juntng Ye 1, nd Qnghu Zheng 1 1. School of Electronc nd

More information

Synchronous Machine Parameter Measurement

Synchronous Machine Parameter Measurement Synchronous Mchine Prmeter Mesurement 1 Synchronous Mchine Prmeter Mesurement Introduction Wound field synchronous mchines re mostly used for power genertion but lso re well suited for motor pplictions

More information

Lecture 20. Intro to line integrals. Dan Nichols MATH 233, Spring 2018 University of Massachusetts.

Lecture 20. Intro to line integrals. Dan Nichols MATH 233, Spring 2018 University of Massachusetts. Lecture 2 Intro to line integrls Dn Nichols nichols@mth.umss.edu MATH 233, Spring 218 University of Msschusetts April 12, 218 (2) onservtive vector fields We wnt to determine if F P (x, y), Q(x, y) is

More information

To: Professor Avitabile Date: February 4, 2003 From: Mechanical Student Subject: Experiment #1 Numerical Methods Using Excel

To: Professor Avitabile Date: February 4, 2003 From: Mechanical Student Subject: Experiment #1 Numerical Methods Using Excel To: Professor Avtable Date: February 4, 3 From: Mechancal Student Subject:.3 Experment # Numercal Methods Usng Excel Introducton Mcrosoft Excel s a spreadsheet program that can be used for data analyss,

More information

Efficient Large Integers Arithmetic by Adopting Squaring and Complement Recoding Techniques

Efficient Large Integers Arithmetic by Adopting Squaring and Complement Recoding Techniques The th Worshop on Combnatoral Mathematcs and Computaton Theory Effcent Large Integers Arthmetc by Adoptng Squarng and Complement Recodng Technques Cha-Long Wu*, Der-Chyuan Lou, and Te-Jen Chang *Department

More information

Synchronous Machine Parameter Measurement

Synchronous Machine Parameter Measurement Synchronous Mchine Prmeter Mesurement 1 Synchronous Mchine Prmeter Mesurement Introduction Wound field synchronous mchines re mostly used for power genertion but lso re well suited for motor pplictions

More information

RECREATIONAL VEHICLE LISTING PROGRAM

RECREATIONAL VEHICLE LISTING PROGRAM RECREATIONAL VEHICLE LISTING PROGRAM Customer: Thor Motor Coach, Inc. Class: Recreational Vehicles Location: Elkhart, Indiana, USA 46515 Website: https://thormotorcoach.com/ Listing No. REC568 Effective

More information

International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN

International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN 48(Prnt), ISSN 97 499(Onlne) Volume 4, Issue 5, July August (2), IAEME ENGINEERING AND TECHNOLOGY (IJARET) ISSN 97-48 (Prnt) ISSN 97-499 (Onlne) Volume 4,

More information

Section 17.2: Line Integrals. 1 Objectives. 2 Assignments. 3 Maple Commands. 1. Compute line integrals in IR 2 and IR Read Section 17.

Section 17.2: Line Integrals. 1 Objectives. 2 Assignments. 3 Maple Commands. 1. Compute line integrals in IR 2 and IR Read Section 17. Section 7.: Line Integrls Objectives. ompute line integrls in IR nd IR 3. Assignments. Red Section 7.. Problems:,5,9,,3,7,,4 3. hllenge: 6,3,37 4. Red Section 7.3 3 Mple ommnds Mple cn ctully evlute line

More information

Math Circles Finite Automata Question Sheet 3 (Solutions)

Math Circles Finite Automata Question Sheet 3 (Solutions) Mth Circles Finite Automt Question Sheet 3 (Solutions) Nickols Rollick nrollick@uwterloo.c Novemer 2, 28 Note: These solutions my give you the nswers to ll the prolems, ut they usully won t tell you how

More information

ECE315 / ECE515 Lecture 5 Date:

ECE315 / ECE515 Lecture 5 Date: Lecture 5 Date: 18.08.2016 Common Source Amplfer MOSFET Amplfer Dstorton Example 1 One Realstc CS Amplfer Crcut: C c1 : Couplng Capactor serves as perfect short crcut at all sgnal frequences whle blockng

More information

Simplified Algorithm and Hardware Implementation for the (24, 12, 8) Extended Golay Soft Decoder Up to 4 Errors

Simplified Algorithm and Hardware Implementation for the (24, 12, 8) Extended Golay Soft Decoder Up to 4 Errors The Interntonl Arb Journl of Informton Technology, Vol., No., Mrch 04 Smplfed Algorthm nd Hrdwre Implementton for the (4,, 8 Extended Goly Soft Decoder Up to 4 Errors Dongfu Xe College of Mechncl nd Electrcl

More information

Multi-beam antennas in a broadband wireless access system

Multi-beam antennas in a broadband wireless access system Multi-em ntenns in rodnd wireless ccess system Ulrik Engström, Mrtin Johnsson, nders Derneryd nd jörn Johnnisson ntenn Reserch Center Ericsson Reserch Ericsson SE-4 84 Mölndl Sweden E-mil: ulrik.engstrom@ericsson.com,

More information

METHOD OF LOCATION USING SIGNALS OF UNKNOWN ORIGIN. Inventor: Brian L. Baskin

METHOD OF LOCATION USING SIGNALS OF UNKNOWN ORIGIN. Inventor: Brian L. Baskin METHOD OF LOCATION USING SIGNALS OF UNKNOWN ORIGIN Inventor: Brin L. Bskin 1 ABSTRACT The present invention encompsses method of loction comprising: using plurlity of signl trnsceivers to receive one or

More information

PRACTICAL, COMPUTATION EFFICIENT HIGH-ORDER NEURAL NETWORK FOR ROTATION AND SHIFT INVARIANT PATTERN RECOGNITION. Evgeny Artyomov and Orly Yadid-Pecht

PRACTICAL, COMPUTATION EFFICIENT HIGH-ORDER NEURAL NETWORK FOR ROTATION AND SHIFT INVARIANT PATTERN RECOGNITION. Evgeny Artyomov and Orly Yadid-Pecht 68 Internatonal Journal "Informaton Theores & Applcatons" Vol.11 PRACTICAL, COMPUTATION EFFICIENT HIGH-ORDER NEURAL NETWORK FOR ROTATION AND SHIFT INVARIANT PATTERN RECOGNITION Evgeny Artyomov and Orly

More information

mac profile Configuration Guide Adobe Photoshop CS/CC Sawgrass Virtuoso SG400/SG800 Macintosh v

mac profile Configuration Guide Adobe Photoshop CS/CC Sawgrass Virtuoso SG400/SG800 Macintosh v mc profile Mcintosh 10.5-10.10 Configurtion Guide Adoe Photoshop CS/CC Swgrss Virtuoso SG400/SG800 v20150427 Configurtion Guide - Photoshop CS/CC Swgrss SG400/800 Before proceeding, ensure the correct

More information

Product Information. Jaw quick-change system BSWS-PGZN-plus

Product Information. Jaw quick-change system BSWS-PGZN-plus Product Informaton BSWS-PGZN-plus BSWS-PGZN-plus Productve. Flexble. Cost-effectve. BSWS jaw quck-change system The BSWS jaw quck-change system allows top jaws to be changed on the grpper manually and

More information

GLONASS Inter-frequency Biases and Their Effects on RTK and PPP Carrier-phase Ambiguity Resolution

GLONASS Inter-frequency Biases and Their Effects on RTK and PPP Carrier-phase Ambiguity Resolution GLONASS Inter-frequency Bses nd Ther Effects on RTK nd PPP Crrer-phse Ambguty Resoluton Nco Reussner, Lmbert Wnnnger Geodetc Insttute, Technsche Unverstät Dresden (TU Dresden), Germny BIOGRAPHIES Nco Reussner

More information

Arduino for Model Railroaders

Arduino for Model Railroaders Steve Mssikker Arduino for Model Rilroders Ornge Book Protocol 2 Full Description November 28 Tble of contents Dontors Documenttion Kit V.4-8 Pge 2 I wnt to tke the time to sincerely thnk you for your

More information

Section 16.3 Double Integrals over General Regions

Section 16.3 Double Integrals over General Regions Section 6.3 Double Integrls over Generl egions Not ever region is rectngle In the lst two sections we considered the problem of integrting function of two vribles over rectngle. This sitution however is

More information

Design-weighted Regression Adjusted Plus-Minus

Design-weighted Regression Adjusted Plus-Minus Design-weighted Regression Adjusted Plus-Minus Schuckers, Im, Mcdonld, McNulty August 3, 208 Schuckers, Im, Mcdonld, McNulty CASSIS-Schuckers August 3, 208 / 26 A etter title Design-weighted Regression

More information

MAXIMUM FLOWS IN FUZZY NETWORKS WITH FUNNEL-SHAPED NODES

MAXIMUM FLOWS IN FUZZY NETWORKS WITH FUNNEL-SHAPED NODES MAXIMUM FLOWS IN FUZZY NETWORKS WITH FUNNEL-SHAPED NODES Romn V. Tyshchuk Informtion Systems Deprtment, AMI corportion, Donetsk, Ukrine E-mil: rt_science@hotmil.com 1 INTRODUCTION During the considertion

More information

Outcome Matrix based Phrase Selection

Outcome Matrix based Phrase Selection Outcome Mtrix bsed Phrse Selection Aln R Wgner Georgi Tech Reserch Institute 50 4 th Street NW, Atlnt GA 0-08 Abstrct This rticle presents method for using outcome mtrices for socil phrse selection. An

More information

CHAPTER 2 LITERATURE STUDY

CHAPTER 2 LITERATURE STUDY CHAPTER LITERATURE STUDY. Introduction Multipliction involves two bsic opertions: the genertion of the prtil products nd their ccumultion. Therefore, there re two possible wys to speed up the multipliction:

More information

He Is The God Of Abram (BLIND SIGHT - Scene 1 - Ben, Deborah and Chorus)

He Is The God Of Abram (BLIND SIGHT - Scene 1 - Ben, Deborah and Chorus) 4 2 4 2 s The od Of rm (BLN SHT - Scene 1 - Ben, eorh hor) 1 m # pre # od r Words Mic y - rm, love Lord, e 7 # # # m thnk od r 7 # - cre co, Lord, e # Lord ill od help # s - r - ev' - ry el dy n B n n

More information

Safety Relay Unit. Main contacts Auxiliary contact Number of input channels Rated voltage Model Category. possible 24 VAC/VDC G9SA-501.

Safety Relay Unit. Main contacts Auxiliary contact Number of input channels Rated voltage Model Category. possible 24 VAC/VDC G9SA-501. Sfety Rely Unit The Series Offers Complete Line-up of Compct Units. Four kinds of -mm wide Units re ville: A -pole model, -pole model, nd models with poles nd OFF-dely poles, s well s Two-hnd ler. Simple

More information

Wake Up Your Passion & Vitality

Wake Up Your Passion & Vitality Wake Up Your Passon & Vtalty Energy As Your Compass Most women I meet have so many thngs on ther to do lst, most of whch brng them lttle joy or exctement. They are should do s & must do tems for work,

More information

UM1430/1440. General Description. Applications. Features. Typical Application Circuit. Rev.01 Dec.

UM1430/1440. General Description. Applications. Features. Typical Application Circuit.   Rev.01 Dec. General Description 18V, 30mA, Micropower Linear Regulator UM1430S-xx SOT23-3 UM1430S5-xx SOT23-5 UM1430Y-xx SOT89-3 UM1430B-xx SOT89-3 UM1440S-xx SOT23-5 UM1440Y-xx SOT89-5 The series are high input low

More information

Student Book SERIES. Patterns and Algebra. Name

Student Book SERIES. Patterns and Algebra. Name E Student Book 3 + 7 5 + 5 Nme Contents Series E Topic Ptterns nd functions (pp. ) identifying nd creting ptterns skip counting completing nd descriing ptterns predicting repeting ptterns predicting growing

More information

Study Guide # Vectors in R 2 and R 3. (a) v = a, b, c = a i + b j + c k; vector addition and subtraction geometrically using parallelograms

Study Guide # Vectors in R 2 and R 3. (a) v = a, b, c = a i + b j + c k; vector addition and subtraction geometrically using parallelograms Study Guide # 1 MA 26100 - Fll 2018 1. Vectors in R 2 nd R 3 () v =, b, c = i + b j + c k; vector ddition nd subtrction geometriclly using prllelogrms spnned by u nd v; length or mgnitude of v =, b, c,

More information

problems palette of David Rock and Mary K. Porter 6. A local musician comes to your school to give a performance

problems palette of David Rock and Mary K. Porter 6. A local musician comes to your school to give a performance palette of problems Davd Rock and Mary K. Porter 1. If n represents an nteger, whch of the followng expressons yelds the greatest value? n,, n, n, n n. A 60-watt lghtbulb s used for 95 hours before t burns

More information

Experiment 3: The research of Thevenin theorem

Experiment 3: The research of Thevenin theorem Experiment 3: The reserch of Thevenin theorem 1. Purpose ) Vlidte Thevenin theorem; ) Mster the methods to mesure the equivlent prmeters of liner twoterminl ctive. c) Study the conditions of the mximum

More information

Michael Dorman Department of Speech and Hearing Science, Arizona State University, Tempe, Arizona 85287

Michael Dorman Department of Speech and Hearing Science, Arizona State University, Tempe, Arizona 85287 On the number of channels needed to understand speech Phlpos C. Lozou a) Department of Electrcal Engneerng, Unversty of Texas at Dallas, Rchardson, Texas 75083-0688 Mchael Dorman Department of Speech and

More information

ANNUAL OF NAVIGATION 11/2006

ANNUAL OF NAVIGATION 11/2006 ANNUAL OF NAVIGATION 11/2006 TOMASZ PRACZYK Naval Unversty of Gdyna A FEEDFORWARD LINEAR NEURAL NETWORK WITH HEBBA SELFORGANIZATION IN RADAR IMAGE COMPRESSION ABSTRACT The artcle presents the applcaton

More information

Mixed CMOS PTL Adders

Mixed CMOS PTL Adders Anis do XXVI Congresso d SBC WCOMPA l I Workshop de Computção e Aplicções 14 20 de julho de 2006 Cmpo Grnde, MS Mixed CMOS PTL Adders Déor Mott, Reginldo d N. Tvres Engenhri em Sistems Digitis Universidde

More information

Misty. Sudnow Dot Songs

Misty. Sudnow Dot Songs Sudnow Dot Songs isty T The Dot Song is nottionl system tht depicts voiced chords in wy where the non-music reder cn find these firly redily. But the Dot Song is not intended be red, not s sight reder

More information

Rough Set Approach for Categorical Data Clustering 1

Rough Set Approach for Categorical Data Clustering 1 Interntonl Journl of Dtbse Theory nd Applcton Vol., No., Mrch, Rough Set Approch for Ctegorcl Dt Clusterng Tutut Herwn*, Rozd Ghzl, Iwn Tr Ryd Ynto, nd Mustf Mt Ders Deprtment of Mthemtcs Educton nversts

More information

Challenge! 1 Warm-up. 2 Conversation. Language box. a Discuss the pictures with a partner.

Challenge! 1 Warm-up. 2 Conversation. Language box. a Discuss the pictures with a partner. Focus Grmmr Vocbulry Strtegy tlking bout wishes nd possibilities second conditionl I wish + simple pst verbs nd definitions moving to new country showing surprise 1 Wrm-up Discuss the pictures with prtner.

More information

Exercise 1-1. The Sine Wave EXERCISE OBJECTIVE DISCUSSION OUTLINE. Relationship between a rotating phasor and a sine wave DISCUSSION

Exercise 1-1. The Sine Wave EXERCISE OBJECTIVE DISCUSSION OUTLINE. Relationship between a rotating phasor and a sine wave DISCUSSION Exercise 1-1 The Sine Wve EXERCISE OBJECTIVE When you hve completed this exercise, you will be fmilir with the notion of sine wve nd how it cn be expressed s phsor rotting round the center of circle. You

More information

Content Based Color Image Retrieval via Wavelet Transforms

Content Based Color Image Retrieval via Wavelet Transforms 8 IJCSNS Interntonl Journl of Computer Scence nd Network Securty, VOL.7 No., December 7 Content Bsed Color Imge Retrevl v Wvelet Trnsforms Mrs.Y. M. Lth Dr.B.C.Jng V.S.K.Reddy, GNITS,JNTU,Ind Rector,JNTU,Ind

More information

COVERAGE HOLES RECOVERY ALGORITHM BASED ON NODES BALANCE DISTANCE OF UNDERWATER WIRELESS SENSOR NETWORK

COVERAGE HOLES RECOVERY ALGORITHM BASED ON NODES BALANCE DISTANCE OF UNDERWATER WIRELESS SENSOR NETWORK INTENATIONAL JOUNAL ON SMAT SENSING AND INTELLIGENT SYSTEMS VOL. 7, NO. 4, DECEMBE 2014 COVEAGE HOLES ECOVEY ALGOITHM BASED ON NODES BALANCE DISTANCE OF UNDEWATE WIELESS SENSO NETWOK Hengchng Jng College

More information

A Novel Back EMF Zero Crossing Detection of Brushless DC Motor Based on PWM

A Novel Back EMF Zero Crossing Detection of Brushless DC Motor Based on PWM A ovel Bck EMF Zero Crossing Detection of Brushless DC Motor Bsed on PWM Zhu Bo-peng Wei Hi-feng School of Electricl nd Informtion, Jingsu niversity of Science nd Technology, Zhenjing 1003 Chin) Abstrct:

More information

DEVELOPMENT OF AN EFFICIENT EPILEPSY CLASSIFICATION SYSTEM FROM EEG SIGNALS FOR TELEMEDICINE APPLICATION

DEVELOPMENT OF AN EFFICIENT EPILEPSY CLASSIFICATION SYSTEM FROM EEG SIGNALS FOR TELEMEDICINE APPLICATION Interntonl Journl of Cvl Engneerng nd Technology (IJCIET) Volume 8, Issue 1, December 017, pp. 38 5, Artcle ID: IJCIET_08_1_005 Avlble onlne t http://http://www.eme.com/jcet/ssues.sp?jtype=ijciet&vtype=8&itype=1

More information

..., E. Channel I,slands Boulevard Retail Center

..., E. Channel I,slands Boulevard Retail Center /""2'l41)channel slands Retal Center "---rag; 0 f3. Master Sgn program PZ 03-500-32 PC RE50 #2004-50 (Aprl 192006). GENERAL MASTER SGN PROGRAM... E. Channel slands Boulevard Retal Center l. The purpose

More information

Web-based Remote Human Pulse Monitoring System with Intelligent Data Analysis for Home Healthcare

Web-based Remote Human Pulse Monitoring System with Intelligent Data Analysis for Home Healthcare We-sed Remote Humn Pulse Montorng System wth Intellgent Dt Anlyss for Home Helthcre Chh-Mng Chen Grdute Insttute of Lrry, Informton nd Archvl Studes, Ntonl Chengch Unversty, Tpe 6, Twn, R.O.C. chencm@nccu.edu.tw

More information

PB-735 HD DP. Industrial Line. Automatic punch and bind machine for books and calendars

PB-735 HD DP. Industrial Line. Automatic punch and bind machine for books and calendars PB-735 HD DP Automtic punch nd bind mchine for books nd clendrs A further step for the utomtion of double loop binding. A clever nd flexible mchine ble to punch nd bind in line up to 9/16. Using the best

More information

SUPPLEMENTARY INFORMATION

SUPPLEMENTARY INFORMATION SUPPLEMENTARY INFORMATION doi:1.138/nture11434 Supplementry Figure 1. (),() Cross-section HRTEM imges of thermlly nneled (3 o C, 6 min) nd photonneled (12 min) IGZO films on Si wfers. (c) RBS spectr of

More information

RECREATIONAL VEHICLE LISTING PROGRAM. Recreational Vehicles including: Class A, Class B, Class C Motor Homes

RECREATIONAL VEHICLE LISTING PROGRAM. Recreational Vehicles including: Class A, Class B, Class C Motor Homes 3980 North Fraser Way Burnaby, BC V5J 5K5 (604) 527-8378 ph. (604) 527-8368 fx. www.qai.org RECREATIONAL VEHICLE LISTING PROGRAM Class: Recreational Vehicles Customer: Thor Motor Coach, Inc. Location:

More information

Learning to Unlearn and Relearn Speech Signal Processing using Neural Networks: current and future perspectives

Learning to Unlearn and Relearn Speech Signal Processing using Neural Networks: current and future perspectives Learning to Unlearn and Relearn Speech Signal Processing using Neural Networks: current and future perspectives Mathew Magimai Doss Collaborators: Vinayak Abrol, Selen Hande Kabil, Hannah Muckenhirn, Dimitri

More information

Have u Herd? Herd is the Word. join. toolkit. COOL WAYS to TALK ABOUT BOOKS WITH YOUR

Have u Herd? Herd is the Word. join. toolkit. COOL WAYS to TALK ABOUT BOOKS WITH YOUR Hve u Herd? Herd is the Word join Th to K nks Wel ir from Libr lingto comin ry L n b g brilli up with for The nt nme the Word o Herd f! toolkit COOL WAYS to TALK ABOUT BOOKS WITH YOUR your friends for

More information

Enhanced performance compression solutions

Enhanced performance compression solutions Enhnced performnce compression solutions Fittings Introduction Pegler Yorkshire hs upgrded the Kuterlite K900 rnge to Kuterlite K900-PRO As prt of our continuous improvement nd development progrmme we

More information

Old text. From Through the Looking Glass by Lewis Carroll. Where is the setting of this place? Describe in your own words.

Old text. From Through the Looking Glass by Lewis Carroll. Where is the setting of this place? Describe in your own words. Old text Read ths extract carefully, then answer, n complete sentences, the questons that follow. For some mnutes Alce stood wthout speakng, lookng out n all drectons over the country and a most curous

More information

Auditory Mood Detection for Social and Educational Robots

Auditory Mood Detection for Social and Educational Robots Audiry Mood Detection for Socil nd Eductionl Robots Pul Ruvolo, In Fsel, nd Jvier Movelln University Cliforni Sn Diego {pul, infsel, movelln}@mplb.ucsd.edu Abstrct Socil robots fce fundmentl chllenge detectg

More information

I PROPOS1V GRADE Al BASELINE

I PROPOS1V GRADE Al BASELINE S00L 81 ST S08 100 METAL POST Plans showing: WSTALL ( Water Mains and Valves, Fire Hydrants, and Road Gradients WA Y 5.-------- 1857 CAL rns PA A2EY4WM00tASTFC PER CAL 4 A AP 22 177WG Q 10 (FEET) loch

More information

Two-Factor Mixed Design

Two-Factor Mixed Design Two-ctor Mixed Design One Between-Subects ctor nd One Within-Subects ctor /7/ -ctor Mixed Design Exercise 9. Ech prticipnt in study ttempts to solve ngrms (ttunesd?), which vry in length: 5,, 7, or 8 letters

More information

PERFORMANCE COMPARISON OF THREE ALGORITHMS FOR TWO-CHANNEL SINEWAVE PARAMETER ESTIMATION: SEVEN PARAMETER SINE FIT, ELLIPSE FIT, SPECTRAL SINC FIT

PERFORMANCE COMPARISON OF THREE ALGORITHMS FOR TWO-CHANNEL SINEWAVE PARAMETER ESTIMATION: SEVEN PARAMETER SINE FIT, ELLIPSE FIT, SPECTRAL SINC FIT XIX IMEKO World Congress Fundamental and ppled Metrology September 6, 009, Lsbon, Portugal PERFORMNCE COMPRISON OF THREE LGORITHMS FOR TWO-CHNNEL SINEWVE PRMETER ESTIMTION: SEVEN PRMETER SINE FIT, ELLIPSE

More information

Application Note. Differential Amplifier

Application Note. Differential Amplifier Appliction Note AN367 Differentil Amplifier Author: Dve n Ess Associted Project: Yes Associted Prt Fmily: CY8C9x66, CY8C7x43, CY8C4x3A PSoC Designer ersion: 4. SP3 Abstrct For mny sensing pplictions, desirble

More information

Energy Efficient Session Key Establishment in Wireless Sensor Networks

Energy Efficient Session Key Establishment in Wireless Sensor Networks Energy Effcent Sesson ey Estlshment n Wreless Sensor Networks Y Cheng nd Dhrm P. Agrwl OBR Center for Dstruted nd Mole Computng, Deprtment of ECECS Unversty of Cncnnt, Cncnnt, OH 45 {chengyg, dp}@ececs.uc.edu

More information

LATEST CALIBRATION OF GLONASS P-CODE TIME RECEIVERS

LATEST CALIBRATION OF GLONASS P-CODE TIME RECEIVERS LATEST CALIBRATION OF GLONASS P-CODE TIME RECEIVERS A. Fos 1, J. Nwroci 2, nd W. Lewndowsi 3 1 Spce Reserch Centre of Polish Acdemy of Sciences, ul. Brtyc 18A, 00-716 Wrsw, Polnd; E-mil: fos@c.ww.pl; Tel.:

More information

Modified Venturini Modulation Method for Matrix Converter Under Unbalanced Input Voltage Conditions

Modified Venturini Modulation Method for Matrix Converter Under Unbalanced Input Voltage Conditions Preprnts (www.preprnts.org) NOT PEER-REVIEWED Posted: 22 y 28 do:.2944/preprnts285.28.v 2 4 5 6 7 8 9 2 4 5 6 7 8 9 2 2 22 2 24 25 26 27 28 29 2 4 5 6 7 8 9 4 4 42 Artcle odfed Venturn odulton ethod for

More information

Study on SLT calibration method of 2-port waveguide DUT

Study on SLT calibration method of 2-port waveguide DUT Interntionl Conference on Advnced Electronic cience nd Technology (AET 206) tudy on LT clibrtion method of 2-port wveguide DUT Wenqing Luo, Anyong Hu, Ki Liu nd Xi Chen chool of Electronics nd Informtion

More information

A Preliminary Study on Targets Association Algorithm of Radar and AIS Using BP Neural Network

A Preliminary Study on Targets Association Algorithm of Radar and AIS Using BP Neural Network Avalable onlne at www.scencedrect.com Proceda Engneerng 5 (2 44 445 A Prelmnary Study on Targets Assocaton Algorthm of Radar and AIS Usng BP Neural Networ Hu Xaoru a, Ln Changchuan a a Navgaton Insttute

More information

Improved corner neutron flux calculation for Start-up Range Neutron Monitor

Improved corner neutron flux calculation for Start-up Range Neutron Monitor Proceedngs of Internatonal Symposum on EcoTopa Scence 2007, ISETS07 (2007) Improved corner neutron flux calculaton for Start-up ange Neutron Montor Masato Watanabe 1, Hdetsugu Okada 1 and Yosho Kmura 2

More information

The Analysis and Simulation of Robot Kinematics and Dynamics Based on RoboAnalyzer

The Analysis and Simulation of Robot Kinematics and Dynamics Based on RoboAnalyzer nterntonl Journl of Emergng echnolog nd Advnced Engneerng Webste: www.ete.com (SSN 5-459, SO 9:8 Certfed Journl, Volume 5, ssue 4, Arl 5 he Anlss nd Smulton of Robot nemtcs nd nmcs Bsed on RoboAnlzer Q

More information

Robot Deception: Recognizing when a Robot Should Deceive

Robot Deception: Recognizing when a Robot Should Deceive Robot ecepton: Recognzng when Robot Should eceve ln R. Wgner, Student ember IEEE nd Ronld C. rkn, Fellow IEEE bstrct Ths rtcle explores the possblty of developng robot control softwre cpble of dscernng

More information

STATISTICS. is given by. i i. = total frequency, d i. = x i a ANIL TUTORIALS. = total frequency and d i. = total frequency, h = class-size

STATISTICS. is given by. i i. = total frequency, d i. = x i a ANIL TUTORIALS. = total frequency and d i. = total frequency, h = class-size STATISTICS ImPORTANT TERmS, DEFINITIONS AND RESULTS l The mean x of n values x 1, x 2, x 3,... x n s gven by x1+ x2 + x3 +... + xn x = n l mean of grouped data (wthout class-ntervals) () Drect method :

More information

IEE Electronics Letters, vol 34, no 17, August 1998, pp ESTIMATING STARTING POINT OF CONDUCTION OF CMOS GATES

IEE Electronics Letters, vol 34, no 17, August 1998, pp ESTIMATING STARTING POINT OF CONDUCTION OF CMOS GATES IEE Electroncs Letters, vol 34, no 17, August 1998, pp. 1622-1624. ESTIMATING STARTING POINT OF CONDUCTION OF CMOS GATES A. Chatzgeorgou, S. Nkolads 1 and I. Tsoukalas Computer Scence Department, 1 Department

More information

Geometric quantities for polar curves

Geometric quantities for polar curves Roerto s Notes on Integrl Clculus Chpter 5: Bsic pplictions of integrtion Section 10 Geometric quntities for polr curves Wht you need to know lredy: How to use integrls to compute res nd lengths of regions

More information

SAMPLE KYRIE. Dm (Em) Dm (Bm) (Bm) (G) (Em) (Bm) (D) Chri ste. ri e e. son. ri e e lé. Gm7 F (G) Gm7. (Bm) (Em7) (D) (Em7) (D) son. Chri ste.

SAMPLE KYRIE. Dm (Em) Dm (Bm) (Bm) (G) (Em) (Bm) (D) Chri ste. ri e e. son. ri e e lé. Gm7 F (G) Gm7. (Bm) (Em7) (D) (Em7) (D) son. Chri ste. KYRIE Capo 3: () m () m () m () m () m () () B e e (7) m7 lé () m () m lé son. Ky r e e () son. Chr ste SMPLE Text: raduale Romanum, 1974. Musc: Chant Mass; raduale Romanum, 1974; gutar acc. 1995, OCP.

More information

A Substractive Clustering Based Fuzzy Hybrid Reference Control Design for Transient Response Improvement of PID Controller

A Substractive Clustering Based Fuzzy Hybrid Reference Control Design for Transient Response Improvement of PID Controller IB J. Eng. Sc. Vol. 4, No., 009, 67-86 67 A Substrctve lusterng Bsed Fuzzy Hybrd Reference ontrol Desgn for rnsent Response Improvement of PID ontroller Endr Joelnto & Prlndungn H. Stnggng Instrumentton

More information

Pseudo Peak Suppression in Generating Range Profile with Multi-carrier Chirp

Pseudo Peak Suppression in Generating Range Profile with Multi-carrier Chirp Pseudo Peak Suppresson n Generatng Range Profle wth Mult-carrer Chrp YANG Chao, ZHENG Ln,, BAI Yunhao. Key Lab. of Cogntve Rado & Informaton Processng, the Mnstry of Educaton, Guln, P. R. Chna. Sc. and

More information

Figure 1. DC-DC Boost Converter

Figure 1. DC-DC Boost Converter EE46, Power Electroncs, DC-DC Boost Converter Verson Oct. 3, 11 Overvew Boost converters make t possble to effcently convert a DC voltage from a lower level to a hgher level. Theory of Operaton Relaton

More information

Fiber length of pulp and paper by automated optical analyzer using polarized light (Five-year review of T 271 om-12) (no changes since Draft 1)

Fiber length of pulp and paper by automated optical analyzer using polarized light (Five-year review of T 271 om-12) (no changes since Draft 1) OTICE: Ths s a DRAFT of a TAPPI Standard n ballot. Although avalable for publc vewng, t s stll under TAPPI s copyrght and may not be reproduced or dstrbuted wthout permsson of TAPPI. Ths draft s OT a currently

More information

Algorithms for Memory Hierarchies Lecture 14

Algorithms for Memory Hierarchies Lecture 14 Algorithms for emory Hierrchies Lecture 4 Lecturer: Nodri Sitchinv Scribe: ichel Hmnn Prllelism nd Cche Obliviousness The combintion of prllelism nd cche obliviousness is n ongoing topic of reserch, in

More information

CHAPTER 3 AMPLIFIER DESIGN TECHNIQUES

CHAPTER 3 AMPLIFIER DESIGN TECHNIQUES CHAPTER 3 AMPLIFIER DEIGN TECHNIQUE 3.0 Introduction olid-stte microwve mplifiers ply n importnt role in communiction where it hs different pplictions, including low noise, high gin, nd high power mplifiers.

More information

ABB STOTZ-KONTAKT. ABB i-bus EIB Current Module SM/S Intelligent Installation Systems. User Manual SM/S In = 16 A AC Un = 230 V AC

ABB STOTZ-KONTAKT. ABB i-bus EIB Current Module SM/S Intelligent Installation Systems. User Manual SM/S In = 16 A AC Un = 230 V AC User Mnul ntelligent nstlltion Systems A B 1 2 3 4 5 6 7 8 30 ma 30 ma n = AC Un = 230 V AC 30 ma 9 10 11 12 C ABB STOTZ-KONTAKT Appliction Softwre Current Vlue Threshold/1 Contents Pge 1 Device Chrcteristics...

More information

CONTENTS. 2 Mastering Ukulele

CONTENTS. 2 Mastering Ukulele ONENS bout the uthors... 4 INRODUION... 5 hpter he World hordng to Uke 6 Lesson : he Hrmonzed Mjor Scle...6 Lesson : our ypes of 7th hord Inversons...7 Lesson : Mjor nd Mnor 6th Inversons...8 Lesson 4:

More information

Topic 20: Huffman Coding

Topic 20: Huffman Coding Topic 0: Huffmn Coding The uthor should gze t Noh, nd... lern, s they did in the Ark, to crowd gret del of mtter into very smll compss. Sydney Smith, dinburgh Review Agend ncoding Compression Huffmn Coding

More information

MEASURE THE CHARACTERISTIC CURVES RELEVANT TO AN NPN TRANSISTOR

MEASURE THE CHARACTERISTIC CURVES RELEVANT TO AN NPN TRANSISTOR Electricity Electronics Bipolr Trnsistors MEASURE THE HARATERISTI URVES RELEVANT TO AN NPN TRANSISTOR Mesure the input chrcteristic, i.e. the bse current IB s function of the bse emitter voltge UBE. Mesure

More information

EFFECTIVE CURRENT CONTROL DESIGN AND ANALYSIS OF SINGLE PHASE INVERTER FOR POWER QUALITY IMPROVEMENT

EFFECTIVE CURRENT CONTROL DESIGN AND ANALYSIS OF SINGLE PHASE INVERTER FOR POWER QUALITY IMPROVEMENT VOL., NO. 7, APRIL 5 IN 89-668 ARPN Journl of Engneerng nd Appled cences 6-5 Asn Reserch Publshng Network (ARPN). All rghts reserved. EFFECTIVE CURRENT CONTROL DEIGN AND ANALYI OF INGLE PHAE INVERTER FOR

More information

Surface Mount > 200W > SMF5.0AT1G Series. Description. Features

Surface Mount > 200W > SMF5.0AT1G Series. Description. Features SMF.TG Series Pb OBSOLETE/EOL Description DTE June/3/8 PCN/ECN# LFPCN446 REPLCED BY SMF Series The SMF.TG Series is designed to protect voltage sensitive components from high voltage, high energy transients.

More information

Process. Controller. Output. Measurement. Comparison FIGURE 4.1. A closed-loop system. Dorf/Bishop Modern Control Systems 9/E

Process. Controller. Output. Measurement. Comparison FIGURE 4.1. A closed-loop system. Dorf/Bishop Modern Control Systems 9/E Controller Process Output Comparison Measurement FIGURE 4. A closed-loop system. R(s) E a (s) G(s) Y(s) R(s) E a (s) G(s) Y(s) H(s) H(s) FIGURE 4.3 A closed-loop control system (a feedback system). v in

More information

The Math Learning Center PO Box 12929, Salem, Oregon Math Learning Center

The Math Learning Center PO Box 12929, Salem, Oregon Math Learning Center Resource Overview Quntile Mesure: Skill or Concept: 300Q Model the concept of ddition for sums to 10. (QT N 36) Model the concept of sutrction using numers less thn or equl to 10. (QT N 37) Write ddition

More information

Example. Check that the Jacobian of the transformation to spherical coordinates is

Example. Check that the Jacobian of the transformation to spherical coordinates is lss, given on Feb 3, 2, for Mth 3, Winter 2 Recll tht the fctor which ppers in chnge of vrible formul when integrting is the Jcobin, which is the determinnt of mtrix of first order prtil derivtives. Exmple.

More information

Lecture 30: Audio Amplifiers

Lecture 30: Audio Amplifiers Whtes, EE 322 Lecture 30 Page 1 of 9 Lecture 30: Audo Amplfers Once the audo sgnal leaes the Product Detector, there are two more stages t passes through before beng output to the speaker (ref. Fg. 1.13):

More information

Low-Delay 16 kb/s Wideband Speech Coder with Fast Search Methods

Low-Delay 16 kb/s Wideband Speech Coder with Fast Search Methods Low-Delay 16 b/s Wdeband Speech Coder wth Fast Search Methods M. HALIMI M. BENGHERABI A. KADDAI Speech Codng eam Centre de Développement des echnologes Avancées Haouch Oul BP. 17 Baba Hassen Algers ALGERIA

More information

No.Sl. NATIONAL RADIO ASTRONOMY OBSERVATORY Charlottesville, Virginia. January 2 2, i986. Rack and Module Plan for VLBA Electronics

No.Sl. NATIONAL RADIO ASTRONOMY OBSERVATORY Charlottesville, Virginia. January 2 2, i986. Rack and Module Plan for VLBA Electronics NATONAL RADO ASTRONOMY OBSERVATORY Charlottesvlle, Vrgna January 2 2, 986 To: VLBA Electroncs Group Proms Dck Thompson Subject: Rack and Module Plan for VLBA Electroncs The VLBA electroncs subsystems that

More information

B inary classification refers to the categorization of data

B inary classification refers to the categorization of data ROBUST MODULAR ARTMAP FOR MULTI-CLASS SHAPE RECOGNITION Chue Poh Tn, Chen Chnge Loy, Weng Kin Li, Chee Peng Lim Abstrct This pper presents Fuzzy ARTMAP (FAM) bsed modulr rchitecture for multi-clss pttern

More information

Research on error compensation and measurement technology in robot flexible measurement

Research on error compensation and measurement technology in robot flexible measurement Reserch on error compenston n mesurement technology n robot flexble mesurement Yong-Je Ren, J-Gu Zhu, Xue-You Yng, Sheng-u Ye Stte Key Lbortory of Precson Mesurng Technology n Instruments Tnjn Unversty,

More information

2-7 Calibration of SAR Probe

2-7 Calibration of SAR Probe Reserch nd Development of Clrton Technology -7 Clrton of SAR Proe Lr HAMADA nd Soch WATANABE The clrton of spefc sorpton rte proe s nmely clrton of the electrc feld mesurement sensor n the phntom lqud

More information

Dataflow Language Model. DataFlow Models. Applications of Dataflow. Dataflow Languages. Kahn process networks. A Kahn Process (1)

Dataflow Language Model. DataFlow Models. Applications of Dataflow. Dataflow Languages. Kahn process networks. A Kahn Process (1) The slides contin revisited mterils from: Peter Mrwedel, TU Dortmund Lothr Thiele, ETH Zurich Frnk Vhid, University of liforni, Riverside Dtflow Lnguge Model Drsticlly different wy of looking t computtion:

More information

Side-Match Vector Quantizers Using Neural Network Based Variance Predictor for Image Coding

Side-Match Vector Quantizers Using Neural Network Based Variance Predictor for Image Coding Sde-Match Vector Quantzers Usng Neural Network Based Varance Predctor for Image Codng Shuangteng Zhang Department of Computer Scence Eastern Kentucky Unversty Rchmond, KY 40475, U.S.A. shuangteng.zhang@eku.edu

More information

Domination and Independence on Square Chessboard

Domination and Independence on Square Chessboard Engineering nd Technology Journl Vol. 5, Prt, No. 1, 017 A.A. Omrn Deprtment of Mthemtics, College of Eduction for Pure Science, University of bylon, bylon, Irq pure.hmed.omrn@uobby lon.edu.iq Domintion

More information

THE SONGWRITING CLUB SONGS

THE SONGWRITING CLUB SONGS THE SOGWRITIG LUB SOGS LYRIS AD UKE HORDS BOOK OE It s time to sing with your friends nd fmily! Tips for plying re included re on the lst pge. You cn listen to the recordings (nd the kroke trcks) nd find

More information

2E - 3E High Wind Kit Manual

2E - 3E High Wind Kit Manual Yag Dpole Vertcal (Patent# 6,677,914 E - 3E Hgh Wnd Kt Manual Antarctca at 7 mph SteppIR Antennas 11-116th Ave NE, Sute -, Bellevue, WA 984 Tel: 4-43-191 Fax: 4-46-441 Tech Support: 4-891-6134 www.steppr.com

More information

Design of Neuro-Fuzzy System Controller for DC Servomotor- Based Satellite Tracking System

Design of Neuro-Fuzzy System Controller for DC Servomotor- Based Satellite Tracking System IOSR Journl of Electrcl nd Electroncs Engneerng (IOSR-JEEE) e-issn: 78-676,p-ISSN: 3-333, Volume, Issue 4 Ver. III (Jul. Aug. 6), PP 89- www.osrjournls.org Desgn of Neuro-Fuzzy System Controller for DC

More information

Learning Ensembles of Convolutional Neural Networks

Learning Ensembles of Convolutional Neural Networks Learnng Ensembles of Convolutonal Neural Networks Lran Chen The Unversty of Chcago Faculty Mentor: Greg Shakhnarovch Toyota Technologcal Insttute at Chcago 1 Introducton Convolutonal Neural Networks (CNN)

More information