Mètro kai olokl rwma Lebesgue. EgqeirÐdio Qr shc. Perieqìmena. Prìlogoc. 1 Mètro Lebesgue sto R. Miqˆlhc Kolountzˆkhc

Size: px
Start display at page:

Download "Mètro kai olokl rwma Lebesgue. EgqeirÐdio Qr shc. Perieqìmena. Prìlogoc. 1 Mètro Lebesgue sto R. Miqˆlhc Kolountzˆkhc"

Transcription

1 Εκδοση 1.1 (21 Οκτ. 2010) Mètro kai olokl rwma Lebesgue. EgqeirÐdio Qr shc. Miqˆlhc Koloutzˆkhc Tm. Majhmatik, Paepist mio Kr thc, Lewfìroc KwsoÔ, Hrˆkleio, kolout T gmail.com Perieqìmea 1 Mètro Lebesgue sto R 1 2 Olokl rwma Lebesgue 3 3 Oi q roi L p () 8 Prìlogoc Sto keðmeo autì perilambˆotai merikèc basikèc g seic gia to mètro kai to olokl rwma Lebesgue. Grˆfthke ste a sumperilˆbei tic g seic pou apaitoôtai gia a mporèsou oi foithtèc pou paðrou to mˆjhma <<rmoik ˆlush>> (Pa. Kr thc, Fjiìpwro ) a parakolouj sou èa mˆjhma pou akoloujeð mia fusiologik poreða. H upìjesh ìti oi foithtèc de èqou mˆjei ta tou oloklhr matoc Lebesgue epibˆllei ìlec oi apodeðxeic a gðotai me qr sh tou oloklhr matoc Riema kai mìo. Oi strebl seic pou prokaleð sto mˆjhma autì mia tètoia prosèggish eðai pollèc. Pollˆ jewr mata thc rmoik c ˆlushc de mporoô a apodeiqjoô sth fusiologik touc geikìthta,, kai a mporoô, h apìdeixh eðai aagkastikˆ polô duskolìterh ap' ì,ti a kaeðc gwrðzei to olokl rwma Lebesgue. Mia polô ètoh tètoia perðptwsh eðai ìta pˆei kaeðc a apodeðxei tic diˆforec idiìthtec thc suèlixhc dôo oloklhrwsðmw suart sew. EÐai loipì protimìtero, omðzw, oi foithtèc a apokt sou pr ta tic g seic pou apaitoôtai gia a kˆou qr sh tou oloklhr matoc Lebesgue, akìmh kai a, lìgw thc èlleiyhc qrìou, de dou tic apodeðxeic tw basik jewrhmˆtw akìmh kai a agooô basikèc èoiec ìpwc h èoia thc metrhsimìthtac suìlw kai suart sew. 1 Mètro Lebesgue sto R E R to mètro (Lebesgue) tou E, pou to sumbolðzoume me m(e) me E eðai mia geðkeush thc èoiac tou m kouc. E = (a, b) eðai diˆsthma tìte fusikˆ to m koc tou eðai Ðso me b a. EÔkola mporeð kaeðc a orðsei to m koc miac peperasmèhc akìmh kai arijm simhc èwshc diasthmˆtw m( (a, b )) = (b a ), a fusikˆ ta diast mata eðai aˆ dôo xèa. Upˆrqou ìmwc polô pio perðploka sôola apì autˆ. O geikìc orismìc tou mètrou eìc suìlou dðdetai èmmesa. PaÐroume ìlec tic kalôyeic tou suìlou E me arijm simec oikogèeiec apì diast mata I : E (a, b ) kai paðroume to ifimum tw posot tw (b a ). (ProkÔptei eôkola ìti me to orismì autì de allˆzei to mètro tw diasthmˆtw.) Gia lìgouc pou de jèloume a perigrˆyoume se autì to keðmeo prokôptei ìti de mporeð kaeðc a orðsei to mètro se ìla ta uposôola tou R kai tautìqroa a perimèei a eðai qr simo. Gia a apokt sei to mètro Lebesgue tic kalèc tou idiìthtec (perigrˆfotai parakˆtw) eðai aparaðthto 1

2 a periorðsoume ta uposôola tou R ta opoða èqou mètro. Th oikogèeia aut tw suìlw gia tw opoðw to mètro mporoôme a milˆme th apokaloôme <<ta metr sima sôola tou R>> kai de prìkeitai a th perigrˆyoume se opoiad pote leptomèreia ektìc apì to a poôme ìti (a) ìla ta sôola ta opoða ja suat soume ja eðai metr sima kai (b) ìti qreiˆzetai arket douleiˆ (kai to legìmeo <<axðwma thc epilog c>>) gia a deðxei kaeðc ìti upˆrqou mh metr sima sôola. pì dw kai pèra ja milˆme mìo gia metr sima sôola qwrðc a to lème kˆje forˆ. Je rhma 1.1 (Idiìthtec tou mètrou Lebesgue) 1. 0 m() gia kaje R. 2. 'Ola ta diast mata (a, b) (aexart twc a ta ˆkra touc eðai mèsa) èqou mètro b a. 3. (MootoÐa) B tìte m() m(b). 4. (Prosjetikìthta) E 1, E 2,... R eðai aˆ dôo xèa tìte m( E ) = m(e ). 5. (Upoprosjetikìthta) E 1, E 2,... R (de zhtˆme a eðai aˆ dôo xèa) tìte m( E ) m(e ). 6. (Ôxousa èwsh suìlw) E E +1 tìte m( E ) = lim m(e ). 7. (FjÐousa tom suìlw) E E +1 kai gia kˆpoio 0 isqôei m(e 0 ) < tìte m( E ) = lim m(e ). 8. (Prosèggish apì pˆw me aoiqtˆ sôola) E R kai ɛ > 0 tìte upˆrqei aoiqtì sôolo G E tètoio ste m(g \ E) ɛ. 9. (Prosèggish apì mèsa me kleistˆ) E R kai ɛ > 0 tìte upˆrqei kleistì sôolo F E tètoio ste m(e \ F ) ɛ. 10. (alloðwto wc proc tic metaforèc) E R, t R kai E + t = {x + t : x E} eðai h <<metaforˆ tou E katˆ t>> tìte m(e + t) = m(e). 11. (OmoiojesÐa) E R, λ R kai tìte m(λe) = λ m(e). λe = {λx : x E} 1.1 podeðxte ìti kˆje arijm simo sôolo E = {x 1, x 2,...} R èqei m(e) = 0. 'Estw ɛ > 0 kai jewreðste th kˆluyh tou E apì ta aoiqtˆ diast mata (x ɛ2, x + ɛ2 ). 1.2 DeÐxte ìti to sôolo tw arr tw tou [0, 1] èqei mètro 1. To sôolo tw rht eðai arijm simo. 2

3 Sqedì patoô: Lème ìti mia prìtash pou exartˆtai apì to x isqôei <<sqedì gia kˆje x>> a isqôei gia ìla ta x ektìc apì èa sôolo exairèsew me mètro 0. Me ˆlla lìgia upˆrqei èa sôolo E me m(e) = 0 tètoio ste h prìtas mac isqôei a x / E. to x eoeðtai tìte lème <<sqedì patoô>>. Gia parˆdeigma, < suˆrthsh χ Q eðai sqedì patoô Ðsh me to 0>> (afoô m(q) = 0). 1.3 DeÐxte ìti to triadikì sôolo Cator èqei mètro 0. To sôolo autì C kataskeuˆzetai wc mia fjðousa tom kleist uposuìlw tou [0, 1] C = E. =0 IsqÔei kat' arq E 0 = [0, 1] kai to kˆje E ftiˆqetai apì to E 1 wc ex c: to E 1 eðai mia peperasmèh èwsh kleist diasthmˆtw. Gia a pˆroume apì to E 1 to E aplˆ afairoôme apì to kˆje èa apì ta diast matˆ tou to mesaðo èa trðto (qwrðc ta ˆkra tou). Gia parˆdeigma E 1 = [0, 1/3] [2/3, 1]. ProkÔptei ìti to sôolo C eðai mh keì, sumpagèc kai mˆlista uperarijm simo (de mporoôme dhl. a grˆyoume ìla ta stoiqeða tou wc mia akoloujða). DeÐxte ìti m(c) = 0. Gia kˆje to sôolo E eðai mia kˆluyh tou C me diast mata. Poio to mètro tou E? 1.4 podeðxte ìti sto Je rhma de mporoôme a paraleðyoume th upìjesh ìti kˆpoio apì ta E èqei peperasmèo mètro. Pˆrte th periptwsh E = (, + ). 1.5 Lème ìti èa sôolo S R eðai tôpou G δ a eðai arijm simh tom aoiqt, a upˆrqou dhl. aoiqtˆ sôola G R tètoia ste S = G. E R deðxte ìti upˆrqei G δ sôolo S E tètoio ste m(s \ E) = 0. QrhsimopoieÐste to Je rhma Olokl rwma Lebesgue To megˆlo meioèkthma tou oloklhr matoc Riema eðai ìti eðai polô euaðsjhto se mikrèc allagèc sth suˆrthsh. Prˆgmati, to olokl rwma Riema orðzetai wc to ìrio tw legìmew Riema ajroismˆtw ta opoða qrhsimopoioô tic timèc thc upì olokl rwsh suˆrthshc se shmeða tou diast matoc. MporoÔme eôkola loipì a <<katastrèyoume>> autˆ ta Riema ajroðsmata peirˆzotac th suˆrthsh sta katˆllhla shmeða, prˆgma pou sðgoura de ja èprepe a èqei epðptwsh sto embadì tou ajroðsmatoc kˆtw apì to grˆfhma thc suˆrthshc. utìc eðai kai o lìgoc pou suart seic pou eðai polô eôkolo a oristoô de èqou olokl rwma Riema. To pio aplì Ðswc parˆdeigma eðai h qarakthristik suˆrthsh tw rht (ìpwc kai aut tw arr tw) thc opoðac ìla ta kˆtw Riema ajroðsmata eðai 0 kai ìla ta ˆw Riema ajroðsmata eðai 1 (sto diˆsthma [0, 1] gia parˆdeigma), kai ˆra de eðai Riema oloklhr simh. IdoÔ ˆllh mða èdeixh tou pìso pio eôqrhsto eðai to olokl rwma Lebesgue se sqèsh me tic timèc thc suˆrthshc se memowmèa shmeða. Ja epitrèpoume apì dw kai pèra stic suart seic a paðrou kai tic timèc + kai autì de ja mac empodðsei, wc epð to pleðsto, a brðskoume to olokl rwmˆ touc. c eðai loipì R = R {, + } oi epektetamèoi pragmatikoð arijmoð kai ac eðai f : R R mia suˆrthsh. Qreiˆzetai ki ed h Ðdia proeidopoðhsh ìpwc kai gia ta metr sima sôola. De eðai duatì a orðsoume to olokl rwma Lebesgue kˆje suˆrthshc, oôte ka kˆje mh arhtik c suˆrthshc. Oi suart seic tw opoðw to olokl rwma orðzoume eðai oi legìmeec <<metr simec>> suart - seic. Kai ed ja epilèxoume a mh poôme sqedì tðpote ˆllo gi' autèc ektìc apì to ìti (a) ìsec suart seic ja suat soume ja eðai metr simec, (b) de eðai eôkolo a kataskeuasteð 3

4 mh metr simh suˆrthsh kai (g) a de up rqa mh metr sima sôola de ja up rqa oôte mh metr simec suart seic. 'Opwc kai me ta sôola ètsi kai me tic suart seic, apì dw kai pèra ìlec oi suart seic gia tic opoðec milˆme ja eðai metr simec eðte to lème autì eðte ìqi. c xeki soume me mia polô apl perðptwsh: f(x) = χ E (x) eðai h qarakthristik suˆrthsh eìc suìlou E (eðai 0 èxw apì to E, 1 mèsa se autì). De èqoume kamiˆ epilog gia to pìso prèpei a eðai to olokl rwma thc f, a fusikˆ jèloume a orðsoume mia posìthta pou a mh atifˆskei me ìsa dh xèroume gia to olokl rwma Riema: f = m(e). epðshc jèloume to olokl rwma a eðai grammikì, a isqôei dhl. (λf + µg) = λ f + µ g, λ, µ R, f, g suart seic tìte xèroume amèswc pwc a orðsoume to olokl rwma Lebesgue gia peperasmèouc grammikoôc suduasmoôc qarakthristik suart sew suìlw: N c j χ Ej = j=1 j=1 N c j m(e j ), (2.1) ìpou c j R kai E j R. O orismìc thc (2.1) de eðai pl rhc a de apodeðxei kaeðc ìti h posìthta pou orðsame wc olokl rwma thc f de allˆzei a grˆyoume th f me diaforetikì trìpo wc peperasmèo grammikì suduasmì qarakthristik suart sew. H apìdeixh aut de eðai dôskolh. Prèpei fusikˆ a eðmaste lðgo prosektikoð me th prosjafaðresh arijm tou R kai a jumìmaste ìti de prosjètoume potè to + me to. Mia ˆllh diaforˆ me th aˆlush ìpwc th xèrame wc t ra eðai ìti sto parapˆw tôpo èa giìmeo tou tôpou 0 eðai pˆta Ðso me Mia suˆrthsh pou eðai peperasmèoc grammikìc suduasmìc qarakthristik suart sew oomˆzetai <pl >> suˆrthsh. DeÐxte ìti mia suˆrthsh eðai apl a kai mìo a to sôolo tw tim pou paðrei eðai peperasmèo. QrhsimopoieÐste ta sôola E v = {x : f(x) = v} ìpou v mia tim pou paðrei h suˆrthsh. 2.2 DeÐxte ìti χ Q = 0. O orismìc tou oloklhr matoc Lebesgue gia mia opoiad pote mh arhtik suˆrthsh f : R R {+ } gðetai qrhsimopoi tac ìlec tic mh arhtikèc aplèc suart seic pou eðai kˆtw apì th f: { f = sup g : 0 g f kai g apl To olokl rwma loipì miac f 0 pˆta upˆrqei allˆ mporeð a eðai kai f g tìte 0 f g }. (2.2) Tèloc, a f : R R eðai opoiad pote suˆrthsh mporoôme a grˆyoume th f wc diaforˆ dôo mh arhtik suart sew f = f + f ìpou f + = max {0, f} kai f = mi {0, f}. (ParathreÐste ìti isqôei f = f + + f.) H grammikìthta mac epibˆllei a orðsoume to olokl rwma miac tètoiac f wc f = f + f, 4

5 kai pˆli bèbaia me th proôpìjesh ìti de èqoume th aprosdiìristh morf (se aut kai mìo th perðptwsh de orðzetai to olokl rwma). t ra h f eðai migadik suˆrthsh, f = u + iv, ìpou u, v eðai pragmatikèc suart seic, orðzoume to olokl rwma thc f (kai pˆli lìgw thc epijumht c grammikìthtac) a eðai f = u + i v. Mèqri t ra èqoume orðsei mìo to olokl rwma miac suˆrthshc pˆw se ìlh th pragmatik eujeða. P c mporoôme a orðsoume to olokl rwma miac suˆrthshc pˆw se èa uposôolo R? PolÔ aplˆ f = χ f me th proôpìjesh fusikˆ ìti to dexð mèloc orðzetai. xðzei ed a aafèroume ìti h katˆstash me to olokl rwma Riema eðai polô diaforetik : mia suˆrthsh eðai Riema oloklhr simh a kai mìo a to sôolo tw shmeðw ìpou eðai asueq c èqei mètro f 0 sto kai f = 0 deðxte ìti f = 0 sqedì patoô sto. = 1, 2,... mporeð to mètro tou suìlou ìpou f > 1/ a eðai jetikì? ParathreÐste ìti {f > 0} = {f > 1/} kai qrhsimopoieðste to Je rhma B kai f : B [0, + ] tìte f B f. Oloklhrwsimìthta. L 1 (). Mia suˆrthsh lègetai <åloklhr simh>> sto R a f <, prˆgmata pou eðai isodôamo me to a isqôei f + <, f <. Gia migadikèc suart seic èqoume to Ðdio orismì oloklhrwsimìthtac (a eðai dhl. f < ). Grˆfoume L 1 () gia to q ro ìlw tw suart sew f : C pou eðai oloklhr simec sto. 2.6 podeðxte ìti kˆje fragmèh suˆrthsh eðai oloklhr simh se kˆje sôolo R me m() <. 2.7 f : [0, + ] eðai oloklhr simh tìte h f eðai peperasmèh sqedì patoô sto. Me ˆlla lìgia m{x : f(x) = } = 0. SugkrÐete th f me th apl suˆrthsh g pou eðai 0 ekeð ìpou h f eðai peperasmèh kai ìpou kai h f. Poio to g kai poia h sqèsh tou me to f? 2.8 (isìthta Markov) 0 f L 1 () kai λ > 0 tìte m{x : f(x) λ} f/λ. E = {x : f(x) λ} tìte f E f E λ. UpologismoÐ. podeikôetai eôkola ìti a h f : [a, b] R eðai sueq c suˆrthsh (se fragmèo diˆsthma) tìte to olokl rwma Riema thc f eðai Ðdio me to olokl rwma Lebesgue. 'Etsi mporoôme a qrhsimopoioôme ìlec tic teqikèc upologismoô pou èqoume mˆjei gia to olokl rwma Riema gia a upologðzoume oloklhr mata Lebesgue sueq suart sew. EpÐshc suqˆ qrhsimopoioôme to, suhjismèo apì to olokl rwma Riema, sumbolismì a f(x) dx b a f atð gia to [a,b] f. EpÐshc isqôei o gwstìc mac tôpoc gia th allag metablht c. φ : [a, b] R eðai sueq c paragwgðsimh kai aôxousa tìte b a f(φ(x))φ (x) dx = d c f(y) dy (2.3) ìpou c = φ(a), d = φ(b). To olokl rwma Lebesgue eðai polô eôqrhsto kurðwc lìgw tw oloklhrwmˆtw sôgklishc, ta opoða mac lèe ousiastikˆ gia to pìte mporoôme a allˆxoume th seirˆ dôo oriak diadikasi. 5

6 Je rhma 2.1 (Je rhma Moìtohc SÔgklishc) f : [0, + ] eðai mia akoloujða mh arhtik suart sew pou eðai moìtoh (wc proc ) f (x) f +1 (x), (x ), kai f(x) = lim f (x) [0 + ] tìte lim f = f. 2.9 f : [0, 1] [0, + ] deðxte ìti 1 0 f(x) dx = lim 1 1/ f(x) dx. Grˆyte f = χ [1/,1] f kai qrhsimopoieðste to Je rhma 2.1. QrhsimopoieÐste to autì gia a upologðsete ta oloklhr mata 1 0 xα dx gia ìlec tic timèc tou α R. Gia poiec timèc tou α eðai h x α sto L 1 ([0, 1])? Gia poiec timèc sto L 1 ([1, ])? 2.10 f : [0, + ] kai f = f (parathreðste ìti to ìrio pˆta upˆrqei sto [0, + ]) tìte a f < èpetai ìti h f eðai sqedì patoô peperasmèh. Poio to olokl rwma thc f? QrhsimopoieÐste kai to Prìblhma c eðai x [0, 1/2] kai 0 l 1/2 tètoia ste l <. DeÐxte ìti h seirˆ χ [x,x+l ](x) sugklðei (se peperasmèo arijmì) sqedì gia ìla ta x [0, 1]. Ti sumperaðete gia th posìthta N(x) = se pìsa apì ta diast mata [x, x + l ] a kei o arijmìc x [0, 1]? QrhsimopoieÐste to Prìblhma To shmatikìtero Ðswc oriakì je rhma gia to mètro Lebesgue eðai to epìmeo. Lème ìti oi f <<kuriarqoôtai>> apì th g. Je rhma 2.2 (Je rhma Kuriarqhmèhc SÔgklishc) 'Estw f, g L 1 () tètoiec ste f (x) g(x) sqedì patoô sto. 'Estw epðshc ìti upˆrqei to ìrio f(x) = lim f (x) sqedì gia kˆje x. Tìte lim f = f Upì tic proôpojèseic tou Jewr matoc 2.2 deðxte ìti isqôei kai f f 0. f f 2 g Kataskeuˆste mia akoloujða f : [0, 1] [0, + ) tètoia ste f (x) 0 gia kˆje x [0, 1] allˆ me 1 0 f +. De ja prèpei fusikˆ a isqôou oi upojèseic tou Jewr matoc 2.2 gia a ta katafèrete. 6

7 2.14 (Suèqeia tou aorðstou oloklhr matoc) f L 1 ([a, b]) kai x 0 (a, b) tìte h suˆrthsh F (x) = x a f(t) dt (pou oomˆzetai aìristo olokl rwma thc f) eðai sueq c sto x 0. h 0 deðxte ìti h posìthta F (x 0 + h ) F (x 0 ) teðei sto 0 qrhsimopoi tac to Je rhma 2.2 gia tic suart seic f = f χ [a,x0 +h ] oi opoðec kuriarqoôtai apì th f f L 1 () kai orðsoume deðxte ìti g 0. g (x) = { f(x) 0 alli c a f(x) 2.16 f L 1 () kai eðai tètoia ste m( ) 0 deðxte ìti f 0. Grˆyte th f sa ˆjroisma tw suart sew f 1 = f χ { f >M} kai f 2 = f χ { f M}, ìpou M > 0 eðai mia parˆmetroc pou epilègetai arketˆ megˆlh. DeÐxte pr ta to zhtoômeo gia th fragmèh suˆrthsh f 2 kai qrhsimopoieðste to Prìblhma 2.15 gia th f 1. Me to olokl rwma Lebesgue aplousteôotai polô ta krit ria pou mac epitrèpou a allˆxoume th seirˆ olokl rwshc se èa diplì (epaalambaìmeo) olokl rwma. De qreiˆzetai proc to parì a aaferjoôme se olokl rwma Lebesgue suart sew pou orðzotai sto R 2. Je rhma 2.3 (Fubii) f : R 2 C kai isqôei f(x, y) dx dy < (2.4) tìte isqôei f(x, y) dx dy = f(x, y) dy dx. (2.5) f : R 2 [0, + ] tìte h (2.5) isqôei qwrðc kamða proôpìjesh (allˆ mporoô fusikˆ kai ta dôo mèlh thc a eðai + ). Ta oloklhr mata (2.4) kai (2.5) pou emfaðzotai sto Je rhma 2.3 eðai epalambaìmea oloklhr mata. Gia parˆdeigma to olokl rwma thc (2.4) eðai to olokl rwma thc suˆrthshc F (y) = f(x, y) dx wc proc y f, g L 1 (R) tìte h suèlix touc orðzetai wc h suˆrthsh f g(x) = f(y)g(x y) dy. (2.6) DeÐxte ìti h suˆrthsh f g eðai kal c orismèh sqedì gia kˆje x R, ìti dhl. sqedì gia kˆje x R h suˆrthsh tou y pou oloklhr oume, h f(y)g(x y), eðai oloklhr simh, isqôei dhl. f(y)g(x y) dy <. Gia ta upìloipa x orðzoume f g(x) = 0. QrhsimopoieÐste to Je rhma 2.3 gia mh arhtikèc suart seic kai deðxte ìti f(y)g(x y) dy dx <. 'Epeita qrhsimopoieðste to Prìblhma 2.7 gia a deðxete to zhtoômeo f, g L 1 (R) deðxte ìti f g L 1 (R) kai mˆlista f g f g. (2.7) 2.19 f L 1 (R) kai g M tìte h f g orðzetai gia kˆje x R apì th (2.6), eðai fragmèh kai mˆlista f g M f podeðxte ìti oi suart seic f g kai g f eðai sqedì patoô Ðsec a f, g L 1 (R). QrhsimopoieÐste to tôpo (2.3) gia mia katˆllhlh allag metablht c kai to Je rhma

8 3 Oi q roi L p () Mèqri stigm c èqoume dei to q ro L 1 (), ìpou R èqei m() > 0, pou apartðzetai apì ìlec tic suart seic f : C pou eðai oloklhr simec, isqôei dhl. gia autèc f <. t ra p [1, ) orðzoume to q ro L p () a apartðzetai apì ìlec tic suart seic f : C gia tic opoðec f p <. H L p ìrma thc f L p () eðai h posìthta f p = ( f p ) 1/p, gia th opoða eôkola blèpoume ìti isqôei λf p = λ f p, gia λ C. Jèloume a qrhsimopoi soume th posìthta d(f, g) = f g p gia a orðsoume mia èoia apìstashc (metrik ) aˆmesa stic suart seic tou L p (). paraðthto loipì eðai a isqôei h <<trigwik aisìthta>> d(f, g) d(f, h) + d(h, g), gia kˆje f, g, h L p (). utì eðai to perieqìmeo tou epìmeou jewr matoc. Je rhma 3.1 (isìthta Mikowski) 1 p < kai f, g L p () tìte isqôei f + g p f p + g p. O kôrioc lìgoc gia to opoðo de exetˆzoume (su jwc) tic timèc p < 1 eðai ìti gia autèc de isqôei h aisìthta tou Mikowski. Tèloc, gia a mporeð a paðxei h posìthta f g p to rìlo thc apìstashc aˆmesa stic f, g L p () prèpei opwsd pote a isqôei kai h suepagwg f g p = 0 f = g. 'Omwc autì de mporeð a isqôsei mia kai mporoôme a parallˆxoume mia tuqoôsa suˆrthsh f L p () se èa sôolo mètrou mhdè, p.q. mporoôme a allˆxoume th suˆrthsh se èa shmeðo, qwrðc a allˆxoume kajìlou ìlec ti oloklhrwtikèc posìthtec pou exart tai apì th f. Pio sugkekrimèa, a f eðai Ðdia me th f ektìc apì èa shmeðo tìte oi dôo suart seic de eðai Ðdiec, afoô oi timèc touc diafèrou se kˆpoia x, allˆ f g p = 0. H mìh mac dièxodoc ed eðai a ago soume tic epousi deic diaforèc aˆmesa se dôo suart seic, jewroôme dhl. dôo suart seic f kai g Ðdiec a diafèrou oi timèc touc mìo se èa sôolo apì x pou èqou mètro 0. ja jèlame a eðmaste lðgo pio austhroð ja orðzame mia sqèsh isoduamðac aˆmesa se suart seic, ìpou dôo suart seic jewroôtai isodôamec a upˆrqei sôolo E, me m(e) = 0, t.. gia x / E èqoume f(x) = g(x). Ta stoiqeða tou q rou L p () eðai klˆseic isoduamðac aut c thc sqèshc isoduamðac pou mìlic orðsame. 3.1 podeðxte ìti h sqèsh pou mìlic orðsame eðai ìtwc mia sqèsh isoduamðac aˆmesa se suart seic. 3.2 podeðxte ìti aut mac h sômbash eðai arket : a f kai g diafèrou stic timèc touc gia x E, me m(e) > 0, tìte f g p > 0, gia kˆje p [1, ). Exetˆste ta sôola E = {x : f(x) g(x) > 1/} kai deðxte ìti kˆpoio apì autˆ prèpei a èqei jetikì mètro. 8

9 Prèpei ed a aafèroume ìti to Je rhma 3.1 eðai suèpeia thc polô shmatik c aisìthtac tou Hölder. Je rhma 3.2 (isìthta Hölder) 1 < p, q < kai 1 p + 1 q = 1 (tètoioi arijmoð p kai q oomˆzotai <<suzugeðc ekjètec>>) tìte, a f L p () kai f L q (), isqôei fg f p g q. (3.1) Eidik perðptwsh (p = q = 2) thc aisìthtac Hölder eðai h pˆra polô shmatik aisìthta Cauchy- Schwarz. Je rhma 3.3 (isìthta Cauchy-Schwarz) f, g L 2 () tìte fg f 2 g 2. Gia a orðsoume kai to q ro L () qreiazìmaste th èoia tou ousi douc supremum miac suˆrthshc, to opoðo eðai, katˆ kˆpoio trìpo, to supremum thc suˆrthshc pou ìmwc de ephreˆzetai apì epousi deic allagèc sth suˆrthsh. Gia a orðsoume loipì to ess sup f, ìpou f mia suˆrthsh orismèh sto, orðzoume kat' arq to sôolo U f = {M R : m{x : f(x) > M} = 0}. utì eðai to sôolo ìlw tou ousiwd ˆw fragmˆtw thc f, tw arijm dhl. M pou h f touc xeperˆ mìo se èa uposôolo tou pedðou orismoô thc pou èqei mètro 0. Tèloc orðzoume ess sup f = if U f a eðai to <álˆqisto>> tètoio ˆw frˆgma. O q roc L () (me m() > 0) eðai o q roc ìlw tw suart sew f : C gia tic opoðec ess sup f <. OrÐzoume tèloc th sup-ìrma ˆpeiro-ìrma thc f f = ess sup f. 'Opwc kai stouc ˆllouc qwrouc L p () ki ed de xeqwrðzoume metaxô touc dôo suart seic pou diafèrou mìo se èa sôolo shmeðw tou pou èqei mètro f, g : C diafèrou mìo se èa sôolo E me m(e) = 0 deðxte ìti ess sup f = ess sup g, kai suep c h ˆpeiro-ìrma tw suart sew sto L () eðai kal c orismèh akìmh ki a gwrðzoume th suˆrthsh mìo sqedì patoô. 3.4 ta p = 1 kai q = jewrhjoô suzugeðc ekjètec deðxte ìti h aisìthta Hölder isqôei ìpwc eðai grammèh sto Je rhma 3.2. DeÐxte epðshc ìti h trigwik aisìthta (Je rhma 3.1) isqôei kai gia p = < m() < kai 1 p 1 < p 2 deðxte ìti L p 2 () L p 1 (). DeÐxte epðshc ìti f p1 f p2 a epiplèo m() = 1. f p 1 = f p1 1. Efarmìste th aisìthta Hölder me ekjètec p 2 /p 1 kai to suzug tou. 3.6 f L p (), me 1 p <, deðxte ìti gia λ > 0 isqôei f p { f λ} f p { f λ} λp. m{x : f(x) λ} f p p λ p. 9

10 GiatÐ èqoume epilèxei aut th oomasða gia to q ro L, èa ìoma tou Ðdiou tôpou me touc q rouc L p, me p <, pou ìmwc eðai q roi pou orðzotai etel c diaforetikˆ, me èa olokl rwma dhlad? Oi q roi L p eðai ìtwc se pollˆ prˆgmata arketˆ diaforetikoð apì to L kai akìmh ki ìta sumperifèrotai parìmoia h apìdeixh gi' autì eðai diaforetik sth perðptwsh tou peperasmèou p ap' ì,ti sth perðptwsh tou L. utì eðai fusiologikì mia kai orðzotai polô diaforetikˆ. H apˆthsh sto er thma thc oomasðac ègkeitai sto Prìblhma 3.5 kai sto Prìblhma 3.7 pou akoloujeð. 3.7 m() = 1 kai f L () deðxte ìti lim p f p = f. 'Estw ɛ > 0 kai E = {x : f(x) (1 ɛ) f }. Tìte m(e) > 0 (alli c to ess sup f ja ta mikrìtero) kai f p ( E f p ) 1/p. pì th aisìthta Mikowski prokôptei ìti oi q roi L p () eðai diausmatikoð q roi kai oi atðstoiqec ìrmec toôc kajistoô parˆllhla kai metrikoôc q rouc. EÐai polô shmatikì ìti autoð eðai pl reic q roi (q roi Baach). 'Oti kai a eðai to sôolo R a f eðai mia sueq c suˆrthsh sto pou èqei sumpag forèa (upˆrqei dhl. peperasmèoc arijmìc R > 0 tètoioc ste h f mhdeðzetai ektìc tou diast matoc ( R, R)) tìte f L p () gia kˆje p [1, + ]. To akìloujo je rhma pukìthtac eðai pˆra polô shmatikì gia tic efarmogèc. Je rhma 3.4 (Pukìthta tw sueq suart sew) R me 0 < m() tìte oi q roi L p () eðai pl reic metrikoð q roi gia 1 p. Gia 1 p < o grammikìc upìqwroc tw sueq suart sew me fragmèo forèa eðai pukìc sto q ro L p (). Dhlad, gia kˆje f L p () kai gia kˆje ɛ > 0 upˆrqei sueq c g : C me fragmèo forèa t.. f g p ɛ. 3.8 podeðxte ìti o grammikìc q roc tw katˆ tm mata stajer suart sew (suart sew pou eðai dhl. peperasmèoi grammikoð suduasmoð qarakthristik suart sew fragmèw diasthmˆtw) eðai pukìc sto q ro L p (R) gia 1 p <. QrhsimopoieÐste th pukìthta tw sueq suart sew me fragmèo forèa (Je rhma 3.4) kaj c kai to ìti kˆje sueq c suˆrthsh se fragmèo kleistì diˆsthma eðai kai omoiìmorfa sueq c. 3.9 DeÐxte ìti a 1 p < kai f L p (R) tìte f( ) f( h) p 0 gia h 0. DeÐxte to pr ta a f eðai sueq c suˆrthsh me fragmèo forèa kai èpeita qrhsimopoieðste to Je rhma (L mma Riema-Lebesgue) f L 1 (R) orðzoume th suˆrthsh (metasqhmatismìc Fourier thc f) f(ξ) = f(x)e iξx dx. (3.2) ParathreÐste ìti to olokl rwma upˆrqei epeid f L 1 (R) kai mˆlista f f 1. DeÐxte ìti lim ξ f(ξ) = 0. DeÐxte to pr ta me ap' eujeðac upologismì sth perðptwsh pou f = χ [a,b], gia < a < b <. QrhsimopoieÐste to gegoìc ìti o metasqhmatismìc Fourier eðai grammik prˆxh gia a to apodeðxete gia katˆ tm mata stajerèc suart seic me fragmèo forèa. 'Epeita qrhsimopoieðste to Prìblhma f L 1 (R) deðxte ìti o metasqhmatismìc Fourier thc f (orðsthke sto Prìblhma 3.10) eðai omoiìmorfa sueq c suˆrthsh. 10

11 f(ξ + h) f(ξ) f(x) e i(ξ+h)x e iξx dx = f(x) e ihx 1 dx. Gia h 0 o 2oc parˆgotac sto olokl rwma sugklðei sto 0 gia kˆje x R. QrhsimopoieÐste to Je rhma Kuriarqhmèhc SÔgklishc 2.2 gia a deðxete ìti to olokl rwma pˆei sto 0. H omoiomorfða wc proc ξ R prokôptei ap' to ìti to frˆgma (pou pˆei sto 0) de exartˆtai apì to ξ. 11

JewrÐa Mètrou. Prìqeirec Shmei seic. Tm ma Majhmatik n Panepist mio Ajhn n Aj na, 2005 06

JewrÐa Mètrou. Prìqeirec Shmei seic. Tm ma Majhmatik n Panepist mio Ajhn n Aj na, 2005 06 JewrÐa Mètrou Prìqeirec Shmei seic Tm ma Majhmatik n Panepist mio Ajhn n Aj na, 2005 06 Perieqìmena 1 σ ˆlgebrec 1 1.1 σ ˆlgebrec 1 1.2 Paragìmenec σ ˆlgebrec 2 1.3 Borel σ ˆlgebrec 2 1.4 'Algebrec kai

More information

Genik TopologÐa kai Efarmogèc

Genik TopologÐa kai Efarmogèc Genik TopologÐa kai Efarmogèc ii Perieqìmena iii iv PERIEQŸOMENA Kefˆlaio 1 TopologikoÐ q roi 1.1 TopologÐa Orismìc 1.1. 'Estw X mh kenì sônolo kai T mða oikogèneia uposunìlwn tou X. H T kaleðtai topologða

More information

SofÐa ZafeirÐdou. Genik TopologÐa II. Bohjhtikèc Shmei seic. Tm ma Majhmatik n Panepist mio Patr n

SofÐa ZafeirÐdou. Genik TopologÐa II. Bohjhtikèc Shmei seic. Tm ma Majhmatik n Panepist mio Patr n SofÐa ZafeirÐdou Genik TopologÐa II Bohjhtikèc Shmei seic Tm ma Majhmatik n Panepist mio Patr n Pˆtra 2010 Perieqìmena 1 Basikèc ènnoiec. 5 1.1 TopologikoÐ q roi................................. 5 1.2

More information

Metaptuqiak Anˆlush II. Prìqeirec Shmei seic

Metaptuqiak Anˆlush II. Prìqeirec Shmei seic Metaptuqiak Anˆlush II Prìqeirec Shmei seic Aj na, 2007 Perieqìmena 1 Basikèc 'Ennoiec 1 1.1 Q roi Banach 1 1.2 Fragmènoi grammikoð telestèc 14 1.3 Q roi peperasmènhc diˆstashc 20 1.4 Diaqwrisimìthta

More information

Upojèseic - Jewr mata. Majhmatikˆ Plhroforik c. Upojèseic - EikasÐec. H qrus tom

Upojèseic - Jewr mata. Majhmatikˆ Plhroforik c. Upojèseic - EikasÐec. H qrus tom Upojèseic - - Jewr mata Upojèseic - - Jewr mata Upojèseic - Jewr mata Majhmatikˆ Plhroforik c o Mˆjhma Arqikìc suggrafèac: HlÐac Koutsoupiˆc Tropopoi seic: StaÔroc Kolliìpouloc Tm ma Plhroforik c kai Thlepikoinwni

More information

JewrÐa Prosèggishc kai Efarmogèc (M 2526 M 238)

JewrÐa Prosèggishc kai Efarmogèc (M 2526 M 238) JewrÐa Prosèggishc kai Efarmogèc (M 2526 M 238) Miqˆlhc Kolountzˆkhc Tm ma Majhmatik n, Panepist mio Kr thc, Lewfìroc KnwsoÔ, 714 09 Hrˆkleio, E-mail: kolount AT gmail.com Perieqìmena 'Anoixh 2010-11 1

More information

PLH 401 - Jewria Upologismou. JewrÐa UpologismoÔ. SÔnola kai sqèseic. PLH 401 - Jewria Upologismou. Logikèc protˆseic

PLH 401 - Jewria Upologismou. JewrÐa UpologismoÔ. SÔnola kai sqèseic. PLH 401 - Jewria Upologismou. Logikèc protˆseic JewrÐa UpologismoÔ SÔnola kai sqèseic Qeimerinì exˆmhno 2005 Tmhma HMMU, Poluteqneio Krhthc SelÐda 1 apì 23 Logikèc protˆseic Protˆseic: alhjeðc yeudeðc H lèxh pepìni èqei perissìtera pi ap' ìti i ta.

More information

StoiqeÐa Basik n Majhmatik n. Shmei seic

StoiqeÐa Basik n Majhmatik n. Shmei seic StoiqeÐa Basik n Majhmatik n Shmei seic A. Karagi rghc kai E. Stefanìpouloc Tm ma Majhmatik n kai Statistik c Panepist mio KÔprou Fjinìpwro 2007 2 Perieqìmena 1 StoiqeÐa Logik c 7 1.1 Protˆseic kai logikoð

More information

Apeirostikìc Logismìc II

Apeirostikìc Logismìc II Apeirostikìc Logismìc II Prìqeirec Shmei seic Tm m Mjhmtik n Pnepist mio Ajhn n - Perieqìmen UpkoloujÐec ki bsikèc koloujðec. UpkoloujÐec. Je rhm Bolzno-Weierstrss.þ Apìdeixh me qr sh thc rq c tou kibwtismoô

More information

Majhmatikˆ gia thn Plhroforik kai tic ThlepikoinwnÐec. Mèroc G: Eisagwg sth Migadik Anˆlush. (Pr th Morf Shmei sewn)

Majhmatikˆ gia thn Plhroforik kai tic ThlepikoinwnÐec. Mèroc G: Eisagwg sth Migadik Anˆlush. (Pr th Morf Shmei sewn) Majhmatikˆ gia thn Plhroforik kai tic ThlepikoinwnÐec. Mèroc G: Eisagwg sth Migadik Anˆlush. (Pr th Morf Shmei sewn) I. G. STRATHS. Mˆioc 005 Perieqìmena Oi MigadikoÐ ArijmoÐ 5. To S ma twn Migadik n Arijm

More information

x 3 Me stoiqei deic prˆxeic mporoôme na fèroume ton epauxhmèno pðnaka [A B C] sth morf 1 0 1 3 1/5(3c

x 3 Me stoiqei deic prˆxeic mporoôme na fèroume ton epauxhmèno pðnaka [A B C] sth morf 1 0 1 3 1/5(3c Grammik 'Algebra II, IoÔnioc 2009 Jèmata prohgoômenwn exetastik n Didˆskousa: Qarˆ Qaralˆmpouc Grammikˆ Sust mata. 'Estw 2 3 6 7 8, X = 2 3 x x 2, B = 5 0, C = c c 2. 5 x 3 Me stoiqei deic prˆxeic mporoôme

More information

SHMEIWSEIS EUKLEIDEIAS GEWMETRIAS GIA THN A' TAXH LUKEIOU StoiqeÐa jewrðac kai ask seic

SHMEIWSEIS EUKLEIDEIAS GEWMETRIAS GIA THN A' TAXH LUKEIOU StoiqeÐa jewrðac kai ask seic SHMEIWSEIS EUKLEIDEIAS GEWMETRIAS GIA THN A' TAXH LUKEIOU StoiqeÐa jewrðac kai ask seic Anèsthc TsomÐdhc KaterÐnh ...GNWSESJE THN ALHJEIAN KAI H ALHJEIA ELEUJERWSEI UMAS (Katˆ Iwˆnnhn H' 3) Prìlogoc Oi

More information

ASUMPTWTIKH ANALUSH MH GRAMMIKOU SUSTHMATOS DUO SUZEUGMENWN TALANTWTWN ME QRHSH TOU ALGORIJMOU CSP (COMPUTATIONAL SINGULAR PERTURBATION)

ASUMPTWTIKH ANALUSH MH GRAMMIKOU SUSTHMATOS DUO SUZEUGMENWN TALANTWTWN ME QRHSH TOU ALGORIJMOU CSP (COMPUTATIONAL SINGULAR PERTURBATION) EJNIKO METSOBIO POLUTEQNEIO SQOLH EFARMOSMENWN MAJHMATIKWN KAI FUSIKWN EPISTHMWN ASUMPTWTIKH ANALUSH MH GRAMMIKOU SUSTHMATOS DUO SUZEUGMENWN TALANTWTWN ME QRHSH TOU ALGORIJMOU CSP (COMPUTATIONAL SINGULAR

More information

AUTOMATES APODEIXEIS GEWMETRIKWN JEWRHMATWN. Metaptuqiak ergasða

AUTOMATES APODEIXEIS GEWMETRIKWN JEWRHMATWN. Metaptuqiak ergasða Tm ma Majhmatikì Panepist mio Kr thc AUTOMATES APODEIXEIS GEWMETRIKWN JEWRHMATWN Metaptuqiak ergasða KaterÐna Lagoudˆkh Dekèmbrioc 2007 Hrˆkleio, Kr th Perieqìmena 1 EISAGWGH 3 2 H MEJODOS TWN EMBADWN

More information

METAPTUQIAKH ERGASIA ASUMPTWTIKH SUMPERIFORA LUSEWN THS EXISWSHS ROHS SE PORWDH ULIKA GIWRGOS M. KAPETANAKHS EPIBLEPWN KAJHGHTHS: STAJHS FILIPPAS

METAPTUQIAKH ERGASIA ASUMPTWTIKH SUMPERIFORA LUSEWN THS EXISWSHS ROHS SE PORWDH ULIKA GIWRGOS M. KAPETANAKHS EPIBLEPWN KAJHGHTHS: STAJHS FILIPPAS PANEPISTHMIO KRHTHS SQOLH JETIKWN KAI TEQNOLOGIKWN EPISTHMWN DIATMHMATIKO PROGRAMMA METAPTUQIAKWN SPOUDWN TWN TMHMATWN MAJHMATIKWN KAI EFARMOSMENWN MAJHMATIKWN METAPTUQIAKH

More information

Empìrio s thn Agor Sunall gmatoc me Anadromik Enisqutik M jhsh

Empìrio s thn Agor Sunall gmatoc me Anadromik Enisqutik M jhsh E JNIKO M ETSOBIO P OLUTEQNEIO TMHMA HLEKTROLOGWN MHQANIKWN KAI MHQANIKWN UPOLOGISTWN TOMEAS TEQNOLOGIAS PLHROFORIKHS KAI UPOLOGISTWN ERGASTHRIO EUFUWN UPOLOGISTIKWN SUSTHMATWN Empìrio s thn Agor Sunall

More information

Ejnikì Metsìbio PoluteqneÐo Sqol Hlektrolìgwn Mhqanik n kai Mhqanik n Upologist n Tomèas TeqnologÐas Plhroforik s kai Upologist n. Diplwmatik ErgasÐa

Ejnikì Metsìbio PoluteqneÐo Sqol Hlektrolìgwn Mhqanik n kai Mhqanik n Upologist n Tomèas TeqnologÐas Plhroforik s kai Upologist n. Diplwmatik ErgasÐa Ejnikì Metsìbio PoluteqneÐo Sqol Hlektrolìgwn Mhqanik n kai Mhqanik n Upologist n Tomèas TeqnologÐas Plhroforik s kai Upologist n Jèmata DiaqeÐrishc Dedomènwn gia Efarmogèc Bioepisthm n Diplwmatik ErgasÐa

More information

'Ena Montèllo Fìrtwshc Oqhmˆtwn Se Epibathgˆ/Oqhmatagwgˆ PloÐa. Apì ton Iwˆnnh Mpìtsh. PoluteqneÐo Kr thc. IoÔnioc 2009

'Ena Montèllo Fìrtwshc Oqhmˆtwn Se Epibathgˆ/Oqhmatagwgˆ PloÐa. Apì ton Iwˆnnh Mpìtsh. PoluteqneÐo Kr thc. IoÔnioc 2009 'Ena Montèllo Fìrtwshc Oqhmˆtwn Se Epibathgˆ/Oqhmatagwgˆ PloÐa Apì ton Iwˆnnh Mpìtsh Εργασία που υποβλήθηκε ως μερική εκπλήρωση των απαιτήσεων για την απόκτηση ΜΕΤΑΠΤΥΧΙΑΚΟΥ ΔΙΠΛΩΜΑΤΟΣ ΕΙΔΙΚΕΥΣΗΣ στο PoluteqneÐo

More information

Ejniko Metsobio Poluteqneio Sqolh Hlektrologwn Mhqanikwn kai Mhqanikwn Upologistwn Tomeac Susthmatwn Metadoshc Plhroforiac kai Teqnologiac Ulikwn

Ejniko Metsobio Poluteqneio Sqolh Hlektrologwn Mhqanikwn kai Mhqanikwn Upologistwn Tomeac Susthmatwn Metadoshc Plhroforiac kai Teqnologiac Ulikwn Ejniko Metsobio Poluteqneio Sqolh Hlektrologwn Mhqanikwn kai Mhqanikwn Upologistwn Tomeac Susthmatwn Metadoshc Plhroforiac kai Teqnologiac Ulikwn Energeiakˆ Apodotikèc AsÔrmatec EpikoinwnÐec: Teqnikèc

More information

BIOGRAFIKO SHMEIWMA. http://cft.fis.uc.pt/nicholas/home.html 00 30 22350 31292, 00 30 697 8400 322 14/8/1985 14/8/1986.

BIOGRAFIKO SHMEIWMA. http://cft.fis.uc.pt/nicholas/home.html 00 30 22350 31292, 00 30 697 8400 322 14/8/1985 14/8/1986. BIOGRAFIKO SHMEIWMA Onomatep numo: Nikìlaoc Petrìpouloc HmeromhnÐa gènnhshc: 2 AprilÐou 1959 Tìpoc gènnhshc: LamÐa Fji tidac, Ellˆc DieÔjunsh: Kwst Palamˆ 7 'Agioc KwnstantÐnoc LokrÐda 35006, Ellˆc E-mail:

More information

Didaktorikh Diatribh

Didaktorikh Diatribh Panepisthmio Patrwn Poluteqnikh Sqolh Tmhma Hlektrologwn Mhqanikwn kai Teqnologiac Upologistwn Tomeac Thlepikoinwniwn kai Teqnologiac Plhroforiac Ergasthrio Asurmathc Thlepikoinwniac Teqnikèc BeltistopoÐhshc

More information

Multichannel Audio Modeling and Coding Using a Multiscale Source/Filter Model

Multichannel Audio Modeling and Coding Using a Multiscale Source/Filter Model University of Crete Department of Computer Science FO.R.T.H. Institute of Computer Science Multichannel Audio Modeling and Coding Using a Multiscale Source/Filter Model (MSc. Thesis) Kyriaki Karadimou

More information

Panepist mio Patr n Poluteqnik Sqol Tm ma Mhqanik n Hlektronik n Upologist n kai Plhroforik c. Dr. Alèxioc K. Kapìrhc

Panepist mio Patr n Poluteqnik Sqol Tm ma Mhqanik n Hlektronik n Upologist n kai Plhroforik c. Dr. Alèxioc K. Kapìrhc Panepist mio Patr n Poluteqnik Sqol Tm ma Mhqanik n Hlektronik n Upologist n kai Plhroforik c Dr. Alèxioc K. Kapìrhc Biografikì ShmeÐwma, Upìmnhma Ergasi n & PÐnakac Anafor n IoÔlioc 2008 Perieqìmena

More information

LUSEIS PRWTOU SET ASKHSEWN TOU MAJHMATOS STATISTIKH MONTELOPOIHSH KAI ANAGNWRISH PROTUPWN. Miqahl Maragkakhc, Hliac Iwshf

LUSEIS PRWTOU SET ASKHSEWN TOU MAJHMATOS STATISTIKH MONTELOPOIHSH KAI ANAGNWRISH PROTUPWN. Miqahl Maragkakhc, Hliac Iwshf LUSEIS PRWTOU SET ASKHSEWN TOU MAJHMATOS STATISTIKH MONTELOPOIHSH KAI ANAGNWRISH PROTUPWN Miqahl Maragkakhc, Hliac Iwshf Oktwbrioc 006 Askhsh.9 Jewreiste ton parakatw kanona apofashc gia kathgoriec monodiastatou

More information

PoluteqneÐo Krăthc Genikì Tmăma

PoluteqneÐo Krăthc Genikì Tmăma PoluteqneÐo Krăthc Genikì Tmăma ΠΑΡΟΥΣΙΑΣΗ ΔΙΑΤΡΙΒΗΣ ΔΙΔΑΚΤΟΡΙΚΟΥ ΔΙΠΛΩΜΑΤΟΣ ΕΙΔΙΚΕΥΣΗΣ ΤΟΥ ΜΕΤΑΠΤΥΧΙΑΚΟΥ ΦΟΙΤΗΤΗ ΣΗΦΑΛΑΚΗ ΑΝΑΣΤΑΣΙΟΥ ΤΙΤΛΟΣ: «ΔΙΑΔΟΧΙΚΕΣ ΠΡΟΣΕΓΓΙΣΕΙΣ ΚΑΙ ΠΑΡΑΛΛΗΛΟΙ ΥΠΟΛΟΓΙΣΜΟΙ ΔΙΚΤΥΟΥ/ΠΛΕΓΜΑΤΟΣ

More information

Διπλωµατική Εργασία του φοιτητή του Τµήµατος Ηλεκτρολόγων Μηχανικών και Τεχνολογίας Υπολογιστών της Πολυτεχνικής Σχολής του Πανεπιστηµίου Πατρών

Διπλωµατική Εργασία του φοιτητή του Τµήµατος Ηλεκτρολόγων Μηχανικών και Τεχνολογίας Υπολογιστών της Πολυτεχνικής Σχολής του Πανεπιστηµίου Πατρών ΠΑΝΕΠΙΣΤΗΜΙΟ ΠΑΤΡΩΝ ΤΜΗΜΑ ΗΛΕΚΤΡΟΛΟΓΩΝ ΜΗΧΑΝΙΚΩΝ ΚΑΙ ΤΕΧΝΟΛΟΓΙΑΣ ΥΠΟΛΟΓΙΣΤΩΝ ΤΟΜΕΑΣ: ΕΡΓΑΣΤΗΡΙΟ Διπλωµατική Εργασία του φοιτητή του Τµήµατος Ηλεκτρολόγων Μηχανικών και Τεχνολογίας Υπολογιστών της Πολυτεχνικής

More information

Panepist mio Patr n Poluteqnik Sqol Tm ma Mhqanik n Hlektronik n Upologist n kai Plhroforik c. Dr. Alèxioc K. Kapìrhc

Panepist mio Patr n Poluteqnik Sqol Tm ma Mhqanik n Hlektronik n Upologist n kai Plhroforik c. Dr. Alèxioc K. Kapìrhc Panepist mio Patr n Poluteqnik Sqol Tm ma Mhqanik n Hlektronik n Upologist n kai Plhroforik c Dr. Alèxioc K. Kapìrhc Biografikì ShmeÐwma, Upìmnhma Ergasi n & PÐnakac Anafor n IoÔlioc 2008 Perieqìmena

More information

Epeunhtikì antikeðmeno: Sunarthsiak Anˆlush-Majhmatik OikonomÐa.

Epeunhtikì antikeðmeno: Sunarthsiak Anˆlush-Majhmatik OikonomÐa. BIOGRAFIKA STOIQEIA Onomatep numo: Iwˆnnhc Polurˆkhc Jèsh: Kajhght c EMP, Sqol Efarmosmènwn Majhmatik n kai Fusik n Episthm n, mèloc thc Editorial Board tou periodikoô POSITIVITY. Epeunhtikì antikeðmeno:

More information

How To Write A Book On Algebra

How To Write A Book On Algebra Biografikì ShmeÐwma SpurÐdwn A. Argurìc Proswpikˆ StoiqeÐa Tìpoc kai HmeromhnÐa Gènnhshc: Peiraiˆc, 11 SeptembrÐou 1950 DieÔjunsh: Tm ma Majhmatik n,semfe, EMP, Aj na 15780 Thlèfwno: +30-2107722967 email:sargyros@math.ntua.gr

More information

Victims Compensation Claim Status of All Pending Claims and Claims Decided Within the Last Three Years

Victims Compensation Claim Status of All Pending Claims and Claims Decided Within the Last Three Years Claim#:021914-174 Initials: J.T. Last4SSN: 6996 DOB: 5/3/1970 Crime Date: 4/30/2013 Status: Claim is currently under review. Decision expected within 7 days Claim#:041715-334 Initials: M.S. Last4SSN: 2957

More information

w ith In fla m m a to r y B o w e l D ise a se. G a s tro in te s tin a l C lin ic, 2-8 -2, K a s h iw a z a, A g e o C ity, S a ita m a 3 6 2 -

w ith In fla m m a to r y B o w e l D ise a se. G a s tro in te s tin a l C lin ic, 2-8 -2, K a s h iw a z a, A g e o C ity, S a ita m a 3 6 2 - E ffic a c y o f S e le c tiv e M y e lo id L in e a g e L e u c o c y te D e p le tio n in P y o d e r m a G a n g re n o su m a n d P so r ia sis A sso c ia te d w ith In fla m m a to r y B o w e l D

More information

Tax dia sto S mpan m sa sto Q ro kai sto Qr no. 1 Eisagwg. 2 Oi Dun meic thc F shc. Panagi ta Kant

Tax dia sto S mpan m sa sto Q ro kai sto Qr no. 1 Eisagwg. 2 Oi Dun meic thc F shc. Panagi ta Kant Tax dia sto S mpan m sa sto Q ro kai sto Qr no Panagi ta Kant Tom ac Jewrhtik c Fusik c, Tm ma Fusik c, Panepist mio Iwann nwn, Iw nnina T.K. 45110, Ell da Per lhyh Met ap mia s ntomh anafor stic up loipec

More information

1.- L a m e j o r o p c ió n e s c l o na r e l d i s co ( s e e x p li c a r á d es p u é s ).

1.- L a m e j o r o p c ió n e s c l o na r e l d i s co ( s e e x p li c a r á d es p u é s ). PROCEDIMIENTO DE RECUPERACION Y COPIAS DE SEGURIDAD DEL CORTAFUEGOS LINUX P ar a p od e r re c u p e ra r nu e s t r o c o rt a f u e go s an t e un d es a s t r e ( r ot u r a d e l di s c o o d e l a

More information

Notes on metric spaces

Notes on metric spaces Notes on metric spaces 1 Introduction The purpose of these notes is to quickly review some of the basic concepts from Real Analysis, Metric Spaces and some related results that will be used in this course.

More information

H fôsh twn kosmologik n idiwmˆtwn se isotropikˆ sômpanta kai sômpanta bran n

H fôsh twn kosmologik n idiwmˆtwn se isotropikˆ sômpanta kai sômpanta bran n H fôsh twn kosmologik n idiwmˆtwn se isotropikˆ sômpanta kai sômpanta bran n thc Ifigèneiac Klaoudˆtou H Didaktorik Diatrib parousiˆsthke en pion thc Sumbouleutik c kai Exetastik c Epitrop c Proc merik

More information

Modeling, Reaction Schemes and Kinetic Parameter Estimation in Automotive Catalytic Converters and Diesel Particulate Filters

Modeling, Reaction Schemes and Kinetic Parameter Estimation in Automotive Catalytic Converters and Diesel Particulate Filters PhD Thesis UTh/MIE No. 8 Modeling, Reaction Schemes and Kinetic Parameter Estimation in Automotive Catalytic Converters and Diesel Particulate Filters THESIS submitted in partial fulfillment of the requirements

More information

Combinación de bandas óptima para la discriminación de sabanas colombianas, usando imagen Landsat ETM+ZYXWVUTSRQPONMLKJIHGFEDCB

Combinación de bandas óptima para la discriminación de sabanas colombianas, usando imagen Landsat ETM+ZYXWVUTSRQPONMLKJIHGFEDCB Combinación de bandas óptima para la discriminación de sabanas colombianas, usando imagen Landsat ETM+ZYXWVUTSRQPONMLKJIHGFEDCB O p t i m a l L a n d s a t E T M + b a n d 's c o m b i n a t i o n f o

More information

1 Norms and Vector Spaces

1 Norms and Vector Spaces 008.10.07.01 1 Norms and Vector Spaces Suppose we have a complex vector space V. A norm is a function f : V R which satisfies (i) f(x) 0 for all x V (ii) f(x + y) f(x) + f(y) for all x,y V (iii) f(λx)

More information

EM EA. D is trib u te d D e n ia l O f S e rv ic e

EM EA. D is trib u te d D e n ia l O f S e rv ic e EM EA S e c u rity D e p lo y m e n t F o ru m D e n ia l o f S e rv ic e U p d a te P e te r P ro v a rt C o n s u ltin g S E p p ro v a rt@ c is c o.c o m 1 A g e n d a T h re a t U p d a te IO S Es

More information

PROGRAMMATISMOU SUSTHMATOS

PROGRAMMATISMOU SUSTHMATOS EJNIKO KAI KAPODISTRIAKO PANEPISTHMIO AJHNWN TMHMA PLHROFORIKHS KAI THLEPIKOINWNIWN SHMEIWSEIS PROGRAMMATISMOU SUSTHMATOS PANAGIWTHS STAMATOPOULOS AJHNA 2007 1 PROGRAMMATISMOS SUSTHMATOS Perieqìmeno tou

More information

Put the human back in Human Resources.

Put the human back in Human Resources. Put the human back in Human Resources A Co m p l et e Hu m a n Ca p i t a l Ma n a g em en t So l u t i o n t h a t em p o w er s HR p r o f essi o n a l s t o m eet t h ei r co r p o r a t e o b j ect

More information

Using a table of derivatives

Using a table of derivatives Using a table of derivatives In this unit we construct a Table of Derivatives of commonly occurring functions. This is done using the knowledge gained in previous units on differentiation from first principles.

More information

M P L S /V P N S e c u rity. 2 0 0 1, C is c o S y s te m s, In c. A ll rig h ts re s e rv e d.

M P L S /V P N S e c u rity. 2 0 0 1, C is c o S y s te m s, In c. A ll rig h ts re s e rv e d. M P L S /V P N S e c u rity M ic h a e l B e h rin g e r < m b e h rin g @ c is c o.c o m > M b e h rin g - M P L S S e c u rity 2 0 0 1, C is c o S y s te m s, In c. A ll rig h ts re s e rv e d. 1 W h

More information

SCHOOL PESTICIDE SAFETY AN D IN TEG R ATED PEST M AN AG EM EN T Statutes put into law by the Louisiana Department of Agriculture & Forestry to ensure the safety and well-being of children and school personnel

More information

The Convolution Operation

The Convolution Operation The Convolution Operation Convolution is a very natural mathematical operation which occurs in both discrete and continuous modes of various kinds. We often encounter it in the course of doing other operations

More information

Understanding, Modelling and Improving the Software Process. Ian Sommerville 1995 Software Engineering, 5th edition. Chapter 31 Slide 1

Understanding, Modelling and Improving the Software Process. Ian Sommerville 1995 Software Engineering, 5th edition. Chapter 31 Slide 1 Process Improvement Understanding, Modelling and Improving the Software Process Ian Sommerville 1995 Software Engineering, 5th edition. Chapter 31 Slide 1 Process improvement Understanding existing processes

More information

G d y n i a U s ł u g a r e j e s t r a c j i i p o m i a r u c z a s u u c z e s t n i k ó w i m p r e z s p o r t o w y c h G d y s k i e g o O r o d k a S p o r t u i R e k r e a c j i w r o k u 2 0

More information

PRESS RELEASE Sw y x e m b r a c e s En t e r p r i s e m a r k e t w i t h l a u n c h o f n e w I P t e l e p h o n y p o r t f o l i o t h r o u g h a s i n g l e c o m m o n p l a t f o r m - New offerings

More information

Metric Spaces. Lecture Notes and Exercises, Fall 2015. M.van den Berg

Metric Spaces. Lecture Notes and Exercises, Fall 2015. M.van den Berg Metric Spaces Lecture Notes and Exercises, Fall 2015 M.van den Berg School of Mathematics University of Bristol BS8 1TW Bristol, UK mamvdb@bristol.ac.uk 1 Definition of a metric space. Let X be a set,

More information

The Henstock-Kurzweil-Stieltjes type integral for real functions on a fractal subset of the real line

The Henstock-Kurzweil-Stieltjes type integral for real functions on a fractal subset of the real line The Henstock-Kurzweil-Stieltjes type integral for real functions on a fractal subset of the real line D. Bongiorno, G. Corrao Dipartimento di Ingegneria lettrica, lettronica e delle Telecomunicazioni,

More information

LECTURE NOTES IN MEASURE THEORY. Christer Borell Matematik Chalmers och Göteborgs universitet 412 96 Göteborg (Version: January 12)

LECTURE NOTES IN MEASURE THEORY. Christer Borell Matematik Chalmers och Göteborgs universitet 412 96 Göteborg (Version: January 12) 1 LECTURE NOTES IN MEASURE THEORY Christer Borell Matematik Chalmers och Göteborgs universitet 412 96 Göteborg (Version: January 12) 2 PREFACE These are lecture notes on integration theory for a eight-week

More information

Chapter 5. Banach Spaces

Chapter 5. Banach Spaces 9 Chapter 5 Banach Spaces Many linear equations may be formulated in terms of a suitable linear operator acting on a Banach space. In this chapter, we study Banach spaces and linear operators acting on

More information

Software Quality Requirements and Evaluation, the ISO 25000 Series

Software Quality Requirements and Evaluation, the ISO 25000 Series Pittsburgh, PA 15213-3890 Software Quality Requirements and Evaluation, the ISO 25000 Series PSM Technical Working Group February 2004 Dave Zubrow Sponsored by the U.S. Department of Defense Background

More information

NOTICE TO MEMBERS No. 2005 047 May 10, 2005

NOTICE TO MEMBERS No. 2005 047 May 10, 2005 NOTICE TO MEMBERS No. 2005 047 May 10, 2005 SYMBOL CONVERSION LONG-TERM EQUITY OPTIONS EXPIRING IN JANUARY Bourse de Montréal Inc. (the Bourse) and Canadian Derivatives Clearing Corporation (CDCC) hereby

More information

B a rn e y W a r f. U r b a n S tu d ie s, V o l. 3 2, N o. 2, 1 9 9 5 3 6 1 ±3 7 8

B a rn e y W a r f. U r b a n S tu d ie s, V o l. 3 2, N o. 2, 1 9 9 5 3 6 1 ±3 7 8 U r b a n S tu d ie s, V o l. 3 2, N o. 2, 1 9 9 5 3 6 1 ±3 7 8 T e le c o m m u n ic a t io n s a n d th e C h a n g in g G e o g r a p h ie s o f K n o w le d g e T r a n s m is s io n in th e L a te

More information

LINEAR ALGEBRA W W L CHEN

LINEAR ALGEBRA W W L CHEN LINEAR ALGEBRA W W L CHEN c W W L Chen, 1982, 2008. This chapter originates from material used by author at Imperial College, University of London, between 1981 and 1990. It is available free to all individuals,

More information

Definition: Suppose that two random variables, either continuous or discrete, X and Y have joint density

Definition: Suppose that two random variables, either continuous or discrete, X and Y have joint density HW MATH 461/561 Lecture Notes 15 1 Definition: Suppose that two random variables, either continuous or discrete, X and Y have joint density and marginal densities f(x, y), (x, y) Λ X,Y f X (x), x Λ X,

More information

Using Predictive Modeling to Reduce Claims Losses in Auto Physical Damage

Using Predictive Modeling to Reduce Claims Losses in Auto Physical Damage Using Predictive Modeling to Reduce Claims Losses in Auto Physical Damage CAS Loss Reserve Seminar 23 Session 3 Private Passenger Automobile Insurance Frank Cacchione Carlos Ariza September 8, 23 Today

More information

THREE DIMENSIONAL GEOMETRY

THREE DIMENSIONAL GEOMETRY Chapter 8 THREE DIMENSIONAL GEOMETRY 8.1 Introduction In this chapter we present a vector algebra approach to three dimensional geometry. The aim is to present standard properties of lines and planes,

More information

Transient Voltage Suppressor SMBJ5.0 - SMBJ440CA

Transient Voltage Suppressor SMBJ5.0 - SMBJ440CA Features: Glass passivated junction Low incremental surge resistance, excellent clamping capability 600W peak pulse power capability with a 10/1,000μs waveform, repetition rate (duty cycle): 0.01% Very

More information

Properties of BMO functions whose reciprocals are also BMO

Properties of BMO functions whose reciprocals are also BMO Properties of BMO functions whose reciprocals are also BMO R. L. Johnson and C. J. Neugebauer The main result says that a non-negative BMO-function w, whose reciprocal is also in BMO, belongs to p> A p,and

More information

Limits and Continuity

Limits and Continuity Math 20C Multivariable Calculus Lecture Limits and Continuity Slide Review of Limit. Side limits and squeeze theorem. Continuous functions of 2,3 variables. Review: Limits Slide 2 Definition Given a function

More information

THE BANACH CONTRACTION PRINCIPLE. Contents

THE BANACH CONTRACTION PRINCIPLE. Contents THE BANACH CONTRACTION PRINCIPLE ALEX PONIECKI Abstract. This paper will study contractions of metric spaces. To do this, we will mainly use tools from topology. We will give some examples of contractions,

More information

BANACH AND HILBERT SPACE REVIEW

BANACH AND HILBERT SPACE REVIEW BANACH AND HILBET SPACE EVIEW CHISTOPHE HEIL These notes will briefly review some basic concepts related to the theory of Banach and Hilbert spaces. We are not trying to give a complete development, but

More information

vector calculus 2 Learning outcomes

vector calculus 2 Learning outcomes 29 ontents vector calculus 2 1. Line integrals involving vectors 2. Surface and volume integrals 3. Integral vector theorems Learning outcomes In this Workbook you will learn how to integrate functions

More information

AN EVALUATION OF SHORT TERM TREATMENT PROGRAM FOR PERSONS DRIVING UNDER THE INFLUENCE OF ALCOHOL 1978-1981. P. A. V a le s, Ph.D.

AN EVALUATION OF SHORT TERM TREATMENT PROGRAM FOR PERSONS DRIVING UNDER THE INFLUENCE OF ALCOHOL 1978-1981. P. A. V a le s, Ph.D. AN EVALUATION OF SHORT TERM TREATMENT PROGRAM FOR PERSONS DRIVING UNDER THE INFLUENCE OF ALCOHOL 1978-1981 P. A. V a le s, Ph.D. SYNOPSIS Two in d ep en d en t tre a tm e n t g ro u p s, p a r t ic ip

More information

EXERCISES PDE 31.10.12-02.11.12. v(x)

EXERCISES PDE 31.10.12-02.11.12. v(x) EXERCISES PDE 31.1.12-2.11.12 1. Exercise Let U R N 2 be a bounded open set. We say that v C (Ū) is subharmonic iff v in U. (a) Prove that subharmonic functions enjoy the following form of the mean-value

More information

tariff guide EFFECTIVE DATE 1 April 2013

tariff guide EFFECTIVE DATE 1 April 2013 tariff guide EFFEIVE DATE 1 April 2013 Delivering peace of mind UCH Logistics is a dynamic, customer focused provider of specialist transport services to the airfreight industry. Having been established

More information

G S e r v i c i o C i s c o S m a r t C a r e u ي a d e l L a b o r a t o r i o d e D e m o s t r a c i n R ل p i d a V e r s i n d e l S e r v i c i o C i s c o S m a r t C a r e : 1 4 ع l t i m a A c

More information

SCO TT G LEA SO N D EM O Z G EB R E-

SCO TT G LEA SO N D EM O Z G EB R E- SCO TT G LEA SO N D EM O Z G EB R E- EG Z IA B H ER e d it o r s N ) LICA TIO N S A N D M ETH O D S t DVD N CLUDED C o n t e n Ls Pr e fa c e x v G l o b a l N a v i g a t i o n Sa t e llit e S y s t e

More information

JCUT-3030/6090/1212/1218/1325/1530

JCUT-3030/6090/1212/1218/1325/1530 JCUT CNC ROUTER/CNC WOODWORKING MACHINE JCUT-3030/6090/1212/1218/1325/1530 RZNC-0501 Users Guide Chapter I Characteristic 1. Totally independent from PC platform; 2. Directly read files from U Disk; 3.

More information

END-POINT ESTIMATES AND MULTI-PARAMETER PARAPRODUCTS ON HIGHER DIMENSIONAL TORI

END-POINT ESTIMATES AND MULTI-PARAMETER PARAPRODUCTS ON HIGHER DIMENSIONAL TORI END-PONT ESTMATES AND MULT-PARAMETER PARAPRODUCTS ON HGHER DMENSONAL TOR A Dissertation Presented to the Faculty of the Graduate School of Cornell University in Partial Fulfillment of the Requirements

More information

Series FOURIER SERIES. Graham S McDonald. A self-contained Tutorial Module for learning the technique of Fourier series analysis

Series FOURIER SERIES. Graham S McDonald. A self-contained Tutorial Module for learning the technique of Fourier series analysis Series FOURIER SERIES Graham S McDonald A self-contained Tutorial Module for learning the technique of Fourier series analysis Table of contents Begin Tutorial c 004 g.s.mcdonald@salford.ac.uk 1. Theory.

More information

TOPPER Sample Paper - I. Class : XI MATHEMATICS. Questions. Time Allowed : 3 Hrs Maximum Marks: 100

TOPPER Sample Paper - I. Class : XI MATHEMATICS. Questions. Time Allowed : 3 Hrs Maximum Marks: 100 TOPPER Sample Paper - I Class : XI MATHEMATICS Questions Time Allowed : 3 Hrs Maximum Marks: 100 1. All questions are compulsory.. The question paper consist of 9 questions divided into three sections

More information

Lecture 6: Discrete & Continuous Probability and Random Variables

Lecture 6: Discrete & Continuous Probability and Random Variables Lecture 6: Discrete & Continuous Probability and Random Variables D. Alex Hughes Math Camp September 17, 2015 D. Alex Hughes (Math Camp) Lecture 6: Discrete & Continuous Probability and Random September

More information

The Derivative as a Function

The Derivative as a Function Section 2.2 Te Derivative as a Function 200 Kiryl Tsiscanka Te Derivative as a Function DEFINITION: Te derivative of a function f at a number a, denoted by f (a), is if tis limit exists. f (a) f(a+) f(a)

More information

and s n (x) f(x) for all x and s.t. s n is measurable if f is. REAL ANALYSIS Measures. A (positive) measure on a measurable space

and s n (x) f(x) for all x and s.t. s n is measurable if f is. REAL ANALYSIS Measures. A (positive) measure on a measurable space RAL ANALYSIS A survey of MA 641-643, UAB 1999-2000 M. Griesemer Throughout these notes m denotes Lebesgue measure. 1. Abstract Integration σ-algebras. A σ-algebra in X is a non-empty collection of subsets

More information

Metric Spaces. Chapter 1

Metric Spaces. Chapter 1 Chapter 1 Metric Spaces Many of the arguments you have seen in several variable calculus are almost identical to the corresponding arguments in one variable calculus, especially arguments concerning convergence

More information

M Official Bologna S e m inar Joint d e gr e e s- A H allm ar k of t h e E u r op e an H igh e r E d u cat ion A r e a? R e s u l t s o f q u e s t i o n n a i r e s e n t t o B o l o g n a F o l l o w

More information

W h a t is m e tro e th e rn e t

W h a t is m e tro e th e rn e t 110 tv c h a n n e ls to 10 0 0 0 0 u s e rs U lf V in n e ra s C is c o S y s te m s 2 0 0 2, C is c o S y s te m s, In c. A ll rig h ts re s e rv e d. 1 W h a t is m e tro e th e rn e t O b je c tiv

More information

B I N G O B I N G O. Hf Cd Na Nb Lr. I Fl Fr Mo Si. Ho Bi Ce Eu Ac. Md Co P Pa Tc. Uut Rh K N. Sb At Md H. Bh Cm H Bi Es. Mo Uus Lu P F.

B I N G O B I N G O. Hf Cd Na Nb Lr. I Fl Fr Mo Si. Ho Bi Ce Eu Ac. Md Co P Pa Tc. Uut Rh K N. Sb At Md H. Bh Cm H Bi Es. Mo Uus Lu P F. Hf Cd Na Nb Lr Ho Bi Ce u Ac I Fl Fr Mo i Md Co P Pa Tc Uut Rh K N Dy Cl N Am b At Md H Y Bh Cm H Bi s Mo Uus Lu P F Cu Ar Ag Mg K Thomas Jefferson National Accelerator Facility - Office of cience ducation

More information

G ri d m on i tori n g w i th N A G I O S (*) (*) Work in collaboration with P. Lo Re, G. S av a and G. T ortone WP3-I CHEP 2000, N F N 10.02.2000 M e e t i n g, N a p l e s, 29.1 1.20 0 2 R o b e r 1

More information

2 1k 0 3k 2 0 1 4 S 5 7 P a s t w a c z ł o n k o w s k i e - Z a m ó w i e n i e p u b l i c z n e n a u s ł u g- i O g ł o s z e n i e o d o b r o w o l n e j p r z e j r z y s t o c i e x - a nnt e

More information

QUADRILATERALS CHAPTER 8. (A) Main Concepts and Results

QUADRILATERALS CHAPTER 8. (A) Main Concepts and Results CHAPTER 8 QUADRILATERALS (A) Main Concepts and Results Sides, Angles and diagonals of a quadrilateral; Different types of quadrilaterals: Trapezium, parallelogram, rectangle, rhombus and square. Sum of

More information

d e f i n i c j i p o s t a w y, z w i z a n e j e s t t o m. i n. z t y m, i p o jі c i e t o

d e f i n i c j i p o s t a w y, z w i z a n e j e s t t o m. i n. z t y m, i p o jі c i e t o P o s t a w y s p o і e c z e t s t w a w o b e c o s у b n i e p e і n o s p r a w n y c h z e s z c z e g у l n y m u w z g lb d n i e n i e m o s у b z z e s p o і e m D o w n a T h e a t t i t uodf

More information

*X100/12/02* X100/12/02. MATHEMATICS HIGHER Paper 1 (Non-calculator) MONDAY, 21 MAY 1.00 PM 2.30 PM NATIONAL QUALIFICATIONS 2012

*X100/12/02* X100/12/02. MATHEMATICS HIGHER Paper 1 (Non-calculator) MONDAY, 21 MAY 1.00 PM 2.30 PM NATIONAL QUALIFICATIONS 2012 X00//0 NTIONL QULIFITIONS 0 MONY, MY.00 PM.0 PM MTHEMTIS HIGHER Paper (Non-calculator) Read carefully alculators may NOT be used in this paper. Section Questions 0 (40 marks) Instructions for completion

More information

Excel Invoice Format. SupplierWebsite - Excel Invoice Upload. Data Element Definition UCLA Supplier website (Rev. July 9, 2013)

Excel Invoice Format. SupplierWebsite - Excel Invoice Upload. Data Element Definition UCLA Supplier website (Rev. July 9, 2013) Excel Invoice Format Excel Column Name Cell Format Notes Campus* Supplier Number* Invoice Number* Order Number* Invoice Date* Total Invoice Amount* Total Sales Tax Amount* Discount Amount Discount Percent

More information

POTENTIAL REASONS: Definition of Congruence: Definition of Midpoint: Definition of Angle Bisector:

POTENTIAL REASONS: Definition of Congruence: Definition of Midpoint: Definition of Angle Bisector: Sec 1.6 CC Geometry Triangle Proofs Name: POTENTIAL REASONS: Definition of Congruence: Having the exact same size and shape and there by having the exact same measures. Definition of Midpoint: The point

More information

y cos 3 x dx y cos 2 x cos x dx y 1 sin 2 x cos x dx

y cos 3 x dx y cos 2 x cos x dx y 1 sin 2 x cos x dx Trigonometric Integrals In this section we use trigonometric identities to integrate certain combinations of trigonometric functions. We start with powers of sine and cosine. EXAMPLE Evaluate cos 3 x dx.

More information

MULTIVARIATE PROBABILITY DISTRIBUTIONS

MULTIVARIATE PROBABILITY DISTRIBUTIONS MULTIVARIATE PROBABILITY DISTRIBUTIONS. PRELIMINARIES.. Example. Consider an experiment that consists of tossing a die and a coin at the same time. We can consider a number of random variables defined

More information

Payor Sheet for Medicare Part D/ PDP and MA-PD

Payor Sheet for Medicare Part D/ PDP and MA-PD Payor Specification Sheet for MEDICARE PART D/PDP AND MA-PD PRIME THERAPEUTICS LLC CLIENTS JANUARY 1, 2006 (Page 1 of 8) BIN: PCN: See BINs on page 2 (in bold red type) See PCNs on page 2 (in bold red

More information

Workload Management Services. Data Management Services. Networking. Information Service. Fabric Management

Workload Management Services. Data Management Services. Networking. Information Service. Fabric Management The EU D a t a G r i d I n f o r m a t i o n a n d M o n i t o r i n g S er v i c es The European D at ag ri d P roj ec t Team http://www.eu- d a ta g r i d.o r g DataGrid is a p ro j e c t f u n de d

More information

C + + a G iriş 2. K o n tro l y a p ıla rı if/e ls e b re a k co n tin u e g o to sw itc h D ö n g ü le r w h ile d o -w h ile fo r

C + + a G iriş 2. K o n tro l y a p ıla rı if/e ls e b re a k co n tin u e g o to sw itc h D ö n g ü le r w h ile d o -w h ile fo r C + + a G iriş 2 K o n tro l y a p ıla rı if/e ls e b re a k co n tin u e g o to sw itc h D ö n g ü le r w h ile d o -w h ile fo r F o n k s iy o n la r N e d ir? N a s ıl k u lla n ılır? P ro to tip v

More information

@PATilKA. ENIITH]\'ONIKO tiepioaiko IYr'fP,,L\Ii\IA I,KAiAOil4I,,N0.\TIO'I'0 OPAKIKO K[N- PO _!,I}IPI,.\ OPAKIKON I\,II.I\F.TQi\

@PATilKA. ENIITH]\'ONIKO tiepioaiko IYr'fP,,L\Ii\IA I,KAiAOil4I,,N0.\TIO'I'0 OPAKIKO K[N- PO _!,I}IPI,.\ OPAKIKON I\,II.I\F.TQi\ @PATilKA ENIITH]\'ONIKO tiepioaiko IYr'fP,,L\Ii\IA I,KAiAOil4I,,N0.\TIO'I'0 OPAKIKO K[N- PO _!,I}IPI,.\ OPAKIKON I\,II.I\F.TQi\ Bpcpru;rdvo auri rqv Axa6rlpfa A0qvdv Kcr r{v Ercnpic npoq evio;iuoq rnv

More information

Convex Rationing Solutions (Incomplete Version, Do not distribute)

Convex Rationing Solutions (Incomplete Version, Do not distribute) Convex Rationing Solutions (Incomplete Version, Do not distribute) Ruben Juarez rubenj@hawaii.edu January 2013 Abstract This paper introduces a notion of convexity on the rationing problem and characterizes

More information

Instantaneous Rate of Change:

Instantaneous Rate of Change: Instantaneous Rate of Cange: Last section we discovered tat te average rate of cange in F(x) can also be interpreted as te slope of a scant line. Te average rate of cange involves te cange in F(x) over

More information

AP Calculus BC Exam. The Calculus BC Exam. At a Glance. Section I. SECTION I: Multiple-Choice Questions. Instructions. About Guessing.

AP Calculus BC Exam. The Calculus BC Exam. At a Glance. Section I. SECTION I: Multiple-Choice Questions. Instructions. About Guessing. The Calculus BC Exam AP Calculus BC Exam SECTION I: Multiple-Choice Questions At a Glance Total Time 1 hour, 45 minutes Number of Questions 45 Percent of Total Grade 50% Writing Instrument Pencil required

More information

and other CD Roms for teaching Greek

and other CD Roms for teaching Greek Using and other CD Roms for teaching Greek To Department of Education and Training se synergasi;a me to Grafei;o Ekp/shw, orga;nvse stiw 21 Marti;oy 2003, ergasth;rio me ue;ma th xrh;sh CD ROM sth didaskali;a

More information