 displaying blood vessels of a deep part of mucosa at an appropriate brightness without performing cumbersome work of administration of a medicine, wherein narrow band light near the wavelength of 600 nm and narrow band light near the wavelength of 630 nm are emitted, each narrow band signal is multiplied by a light adjustment control parameter, and a largest weight 0.6 is provided to a narrow band signal near the wavelength of 600 nm. A perennial problem in data analysis is the increasing dataset sizes. This trend dictates the need not only for more efficient compression schemes, but also for analytic operations that work directly on the compressed data. Efficient compression schemes can be designed based on exploiting inherent patterns and structures in the data. Data periodicity is one such characteristic that can significantly boost compression.
Periodic behavior is omnipresent, many types of collected measurements exhibit periodic patterns, including weblog data [1, 2, 3], network measurements [4], environmental and natural processes [5, 6], medical and physiological measurements. The aforementioned are only a few of the numerous scientific and industrial fields that handle periodic data.
When data contain inherent structure, efficient compression can be performed with minimal loss in data quality. This is achievable by encoding the data using only few high-energy coefficients in a complete orthonormal basis representation, e.g., Fourier, Wavelets, Principal Component Analysis (PCA).
In the data-mining community, searching on time-series data under the Euclidean metric has been studied extensively, as e.g., described in [8]. However, such studies have typically considered compression using only the first Fourier or wavelets. The use of diverse sets of coefficients has been studied as described in [1].
The majority of data compression techniques for sequential data use the same set of low-energy coefficients whether using Fourier [7, 8], Wavelets [9, 10] or Chebyshev polynomials [11] as the orthogonal basis for representation and compression. Using the same set of orthogonal coefficients has several advantages: First, it is immediate to compare the respective coefficients. Second, space-partitioning indexing structures, such as R-trees, may be directly used on the compressed data. Third, there is no need to store also the indices of the basis functions that the stored coefficients correspond to. The disadvantage may be that both object reconstruction and distance estimation may be far from optimal for a given fixed compression ratio.
Side-information may also be recorded, such as the energy of the discarded coefficients, to better approximate the distance between compressed sequences by exploiting the Cauchy-Sch
displaying blood vessels of a deep character of mucous membrane at an appropriate brightness without performing awkward work of government of a medicine, wherein narrow set light near the wavelength of 600 nm and narrow set light near the wavelength of 630 nm are utter, each narrow band signal is breed by a light adjustment control parameter, and a largest system of weights 0.6 is provided to a narrow band signal near the wavelength of 600 new mexico. A perennial problem in data analysis is the increasing dataset size. This trend dictates the need not only for more efficient compaction schemes, but also for analytic operations that work directly on the compressed datum. effective compaction schemes can be designed based on exploiting inherent patterns and structures in the datum. Data cyclicity is one such characteristic that can significantly promote compaction. 
 Periodic demeanor is omnipresent, many types of collected measurements exhibit periodic patterns, including weblog data [ 1, 2, 3 ], network measurement [ 4 ], environmental and natural processes [ 5, 6 ], medical and physiological measurements. The aforementioned are only a few of the numerous scientific and industrial fields that handle periodic data. 
 When data contain built-in structure, effective compaction can be perform with minimal loss in data quality. This is accomplishable by encoding the data using only few high - energy coefficients in a complete orthonormal basis theatrical performance, e.g., Fourier, Wavelets, Principal Component Analysis (PCA). 
 In the data - mining community, searching on time - serial data under the Euclidean metric has been studied extensively, as for example, described in [ 8 ]. However, such studies have typically consider compression using only the first Fourier or ripple. The use of diverse sets of coefficient has been studied as described in [ 1 ]. 
 The majority of data compression proficiency for consecutive data use the like set of low - energy coefficient whether using Fourier [ 7, 8 ], Wavelets [ 9, 10 ] or Chebyshev polynomials [ 11 ] as the orthogonal basis for representation and compression. Using the same set of orthogonal coefficients have respective advantages: First, it is immediate to compare the respective coefficients. Second, space - partitioning indexing structures, such as R - tree, may be directly used on the compressed datum. Third, there is no motivation to store also the indices of the basis function that the stored coefficients correspond to. The disadvantage may be that both object reconstruction and distance estimation may be far from optimal for a given sterilize compression ratio. 
 Side - information may also be recorded, such as the department of energy of the discard coefficients, to better estimate the distance between compressed sequence by exploiting the Cauchy - Sch
 didplaying blood vessels on a deep part of mucosa at an approprkate brightness without perflrning xumbersome work of admknistratiln of a nedirine, wherein narckw band light kear chx wavelength of 600 nm and ndrrow band ligvt nzar the wavelength of 630 nm are emitteq, each magrow band signwl ix mulfppoied by a light adjustment cohtrol perameter, and a kargest weight 0.6 is providef to a narrow band sigjal near thg wadwlength of 600 vm. A perennial problem in data analysis is the increaring bataset sizgs. Tjhs trend dirtates the need nob only xor morr efficient cokprxssiin schemes, but also fmr analytic operatyons that wkrk directly on tye comptesseg dagq. Ewfidixnt comprfssmon schemes can be desugned based on explpiepmg inherent lattergs and structures in the data. Data periodpcitg is one such characterustic that can signiflcantly bjost compression.
Periodic behavior is omnipresent, kany vyoes on couoefted measurements exhibit periodic patterns, igdlidpng weblog data [1, 2, 3], network mradutgments [4], envirovmental ahd natural processfs [5, 6], meqical and physyolobical measurements. The aforwmentioned age obly a few of the nbmerous scieutific and ondustrial fields that kandle periodic dwta.
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Ln the data-mmning comiuniry, swarchine on time-series data undvr the Euclidean metric mas bgeh studied extenwivtlt, as e.g., descrined in [8]. Joxever, vuch studies havd thlicaluy considercd zomptession using only tve fjrst Fourier or wafejets. The use of qiverse sets pf coefficients had beei studmed as defcribed in [1].
The majority of datz compresdiok techniques sor wequential dcta use the same set of low-energy coeffirients whether using Foyrier [7, 8], Wavelets [9, 10] pv Chebyshev 'olynoiials [11] as the orthogonal basiw for representatlon and compression. Usjng tha samf set of orthogonal coefficients has several advantages: First, it is immeduate to compare ths rexpectpve eoefficyentd. Second, space-parnitioning indexing structures, surh as R-treas, may be directly used on the cumpressed datx. Third, there is no need to storr also the indices of the basis functions that the storcd coeffixienta corrvapund to. The disadvamtage kay bw that voth object reconrtricbion and dlstaice estimation may be far from optinal foe w given fixed cokpression rwuio.
Side-pnformajion may also be decodfed, soch as the enetgy of the disvarded coefficients, no fetter approxinzte the distsnce benween compresxed sequencas by exploiting the Cauchy-Sch
displaying blood vessels of a deep part at appropriate brightness performing cumbersome work wherein band light near wavelength of 600 and narrow band light near the of 630 nm are emitted, each narrow band signal is multiplied by a adjustment control parameter, and a largest weight 0.6 is provided to a narrow signal the of nm. A perennial problem in data analysis is the increasing dataset sizes. This trend dictates the not only for more efficient compression schemes, but for analytic operations that directly on the compressed data. compression can be based exploiting patterns and structures the data. Data periodicity is one such characteristic that can significantly boost compression. Periodic behavior is omnipresent, types of exhibit periodic including data 2, 3], network environmental and natural processes [5, 6], measurements. The aforementioned are only a few of numerous scientific industrial fields that handle periodic data. data contain inherent structure, efficient compression can be with minimal loss in data quality. This is achievable by encoding the data using only coefficients in a complete basis representation, e.g., Wavelets, Component (PCA). the data-mining searching on time-series data under the Euclidean metric has been studied as e.g., described in [8]. However, such studies have typically using the first Fourier wavelets. The use of sets coefficients has been studied in The compression for data use the same of low-energy coefficients whether using [7, 8], Wavelets [9, as the orthogonal basis for representation and compression. the same set of orthogonal coefficients has advantages: First, it is immediate to compare the respective coefficients. Second, space-partitioning structures, such may be directly used on the compressed data. there is no need store also the indices of the basis functions that stored correspond to. disadvantage may be both object reconstruction distance estimation may from optimal a fixed may also be recorded, such as energy the discarded coefficients, to better the compressed sequences by exploiting the Cauchy-Sch
 displaying blood vessels of a Deep part of MucosA at An aPpRoprIate Brightness withOUt peRforming cumbersome work Of admInIStraTIoN of a mEdicine, WHeREIn nArRoW baNd LIgHt neaR thE waveleNgth of 600 nm anD naRrOw band light nEAr The wavelenGth Of 630 nm are emittEd, eAch narRoW baND signAl iS multIplied BY a lighT adjustmeNt COntrol PArameteR, ANd A larGest weight 0.6 is proviDEd TO a narrow band siGnal neAr THe WAVelEngTh of 600 nm. A perEnNial pROblem in DAtA ANAlySIs is the increaSing dataset SIzeS. This tReNd dICtates The neEd NOt oNly for more eFficIent comprEssion SChemes, bUT also foR analyTic OpeRatiONs ThAt wOrK DirECtLy oN The CompressEd DaTa. EffIcieNT COMpreSsiOn scHemes Can be designed BasEd on EXplOitinG inheRent PaTternS and stRuctuReS in the data. Data pErioDicity is oNe sUcH chArActerIStic thAt cAn sIgnificAntly boOSt cOmPRESsIon.
Periodic behavioR iS OMnIpresent, Many tyPEs Of COllected MeAsuRemeNTS exhiBit pERiOdic pattErns, inCLuDiNg webloG dAta [1, 2, 3], netWoRk mEasUremeNTs [4], enVironmEntal and NaturAL processes [5, 6], mediCAl and physioloGIcAL MeASureMenTs. The aforemEntiONed aRe onLY a Few OF the nUmeroUs SCiENtific and industrial FiElds thAt hanDle periodic daTa.
When data CONTain inheRent STrUCture, efficient ComprEssion can bE PerformeD with Minimal lOss in data QUAlity. ThiS is AchIevAblE BY eNcoding the datA USing OnLy few hiGh-eNergy coEffIciEntS in A cOmplete orThonormaL bAsIs RePreSentaTIon, e.g., FouRiEr, WAvEleTs, PriNCipal COmponEnt ANaLySIs (PcA).
In the DAtA-MIninG cOmMuniTy, sEaRchinG on tIMe-sEries daTa under thE EuCLideAn MeTric has Been studied exTeNsively, as e.G., dEscRibed iN [8]. hOwever, suCh studies have typically cONsidereD coMpresSion Using only The First FOurIEr or waVelets. the usE oF diVERse seTS Of CoeFfIcients has BEEn sTudieD aS desCribed iN [1].
The majority of data COmpRession techniQueS for SEQuEntIAl DAta UsE The SAMe set of low-energY coefficieNtS WhEther using fOurIeR [7, 8], WaveleTs [9, 10] or CheBysheV PolynomIals [11] as the OrthogonaL bAsis FOR rePresentatiOn and comPression. USIng thE SaMe set Of oRthogoNaL coEfficIents hAS seVeral AdvantAgEs: FirsT, it is ImMediate tO compare the respective coEfficiEnts. SEcoNd, space-paRtiTIonIng indexiNg stRuctures, suCh aS R-tRees, mAy bE DirecTly uSEd On tHE compRessED data. ThirD, ThEre IS No Need to store ALSO thE indiCes OF the baSis fUnctions that the stORed coefficientS corRESpoNd tO. the dIsAdvantage may be ThaT bOTH object rEcOnstruction And distaNcE EstimAtion mAy be faR from opTIMaL For a giVen fIxeD compressIon RaTIo.
Side-iNfOrMAtion mAy alSo Be recoRded, suCH as tHE Energy of the discaRded cOEFficiENts, To betTeR approxIMate The distancE between comPresseD seqUenceS by explOiTing thE CaUcHy-Sch
 displaying blood vesselsof a deeppartofmuc os a at  anappropriate br i ghtn ess without performing  cumb er s omew or k ofadminis t ra t i onof  a  me di c in e, wh ere in narr ow band li ght  n ear the wave l en gth of 600  nm  and narrowban d ligh tnea r  thewav eleng th of6 30 nmare emitt ed ,  eachn arrow b a n dsign al is multipliedb ya  light adjustm ent co nt r ol p ara met er, and ala rgest weight0 .6 i s  pr o vided to a na rrow band s i gna l near  t hew avelen gth o f6 00nm. A peren nial  problemin dat a  analys i s is th e incr eas ing  dat a se tsiz es .  Th i stre n d d ictatesth eneednoto n l y  for  mo re e ffici ent compressi onsche m es,  butalsoforan alyti c oper ation sthat work direc tlyon the co mpr es sed  d ata.E fficie ntcom pressio n schem e s c an b e  d esigned based on e xp l o it ing inhe rent p a tt er n s and st ru ctu resi n  thedata .  D ata peri odicit y  i sone suc hcharac te ris tic  that cansignif icantlyboost compression.
P e riodic behavi o ri s  o m nipr ese nt, many ty peso f co llec t ed  me a surem entsex h ib i t periodic patterns ,includ ing w eblog data [1 , 2, 3], n e t w ork meas urem e nt s  [4], environm ental  and natur a l proces ses [ 5, 6], m edical an d physiolo gic almea sur e m en ts. The afore m e ntio ne d are o nly  a fewofthe  nu mer ou s scienti fic andin du st ri alfield s  that ha nd lepe rio dic d a ta.
Wh en da ta c on ta i n i nherent st r u ctur e,  e ffic ien tcompr essi o n c an be p erformedwit h  min im al  loss i n data qualit y.  This is a ch iev able b y encoding  the data using only fe w  high-e ner gy co effi cients in  acomple teo rthono rmal b asisre pre s e ntati o n ,e.g .,  Fourier,W a vel ets,Pr inci pal Com ponent Analysis (P C A). 
In the data- min ingc o mm uni t y, sea rc h ing o n time-series d ata underth e  E uclidean m e tri chas bee n studi ed ex t ensivel y, as e.g ., descri be d in [ 8].  However,such stu dies have typic a ll y con sid ered c om pre ssion  using onl y the  first  F ourier  or w av elets. T he use of diverse setsof coe ffici ent s has bee n s t udi ed as des crib ed in [1]. 
Th e m ajori tyo f dat a co m pr ess i on te chni q ues for s e qu ent i a ldata use th e s ame  setofl ow-ene rgycoefficients whet h er using Fouri er [ 7 ,  8] , W a vele ts  [9, 10] or Ch eby sh e v  polynom ia ls [11] asthe orth og o nal b asis f or rep resenta t i on and co mpre ssi on. Using  th es ame set  o fo rthogo nalco effici ents h a s se v e ral advantages:First , it is imm ediat eto comp a re t he respect ive coeffic ients.  Sec ond,space-p ar tition ing  i ndexing st r uctures,suchas R-tr ee s, m aybe dir ectl y usedon t he  co mpressedd a ta .  T hi r d,ther e isno  nee d to stor e  also th e i n dices o fthe b asis f u nc t i ons that t hestore d coefficien t s co r re s pondto. Th e disa dvantag e  ma ybe that  bo t h  object r econstruc t ion  a nd d istancees tima ti onma y be far from o ptimal f or a giv e n  fixed  co m pres s ion ra ti o.
Side-info rmati on may als o berecorde d , s uch  as  the  en ergy of the discarded coeffic ient s, to bet ter a pprox im ate thed ista nce betw e en compressed  sequences by e x plo i ting  th e  C au chy-Sch
 displaying_blood vessels_of a deep part_of mucosa_at_an appropriate_brightness_without performing cumbersome_work of administration_of a medicine, wherein_narrow band light_near_the wavelength of 600 nm and narrow band light near the wavelength of 630_nm_are emitted,_each_narrow_band signal is multiplied by_a light adjustment control parameter,_and a_largest weight 0.6 is provided to a narrow_band_signal near the_wavelength of 600 nm. A perennial problem in data_analysis is the increasing dataset sizes._This trend dictates_the_need_not only for more_efficient compression schemes, but also for_analytic operations that work directly on_the compressed data. Efficient compression schemes can_be designed based on exploiting inherent_patterns and structures in the_data. Data_periodicity is one such characteristic_that can significantly_boost compression.
Periodic_behavior is omnipresent,_many types of collected measurements exhibit_periodic patterns, including_weblog data [1, 2, 3], network_measurements_[4], environmental and_natural_processes_[5, 6],_medical and physiological_measurements._The aforementioned_are_only a few of the numerous_scientific_and industrial fields that handle periodic data.
When_data contain inherent structure,_efficient_compression can be performed_with minimal loss in data_quality. This is achievable by encoding_the data_using only_few high-energy coefficients in a complete orthonormal basis representation, e.g., Fourier,_Wavelets, Principal Component Analysis (PCA).
In the_data-mining community, searching on_time-series data_under_the Euclidean metric_has_been studied_extensively, as e.g., described in [8]. However,_such studies_have typically considered compression using only_the first Fourier or_wavelets._The use of diverse sets of_coefficients has been studied as described_in [1].
The majority of data_compression_techniques_for sequential data use the_same set of low-energy coefficients whether_using Fourier [7,_8], Wavelets [9, 10] or Chebyshev polynomials_[11]_as the orthogonal basis for representation_and_compression. Using the same set of_orthogonal_coefficients_has several advantages: First, it_is immediate to compare the respective_coefficients. Second, space-partitioning indexing structures, such as R-trees, may_be directly used_on the compressed data. Third,_there_is_no need to store also the indices of the basis_functions that_the stored coefficients_correspond to. The disadvantage may be that both object reconstruction_and distance estimation may be far from_optimal for a given fixed compression ratio.
Side-information may also be recorded,_such as the energy of the discarded coefficients,_to better approximate the distance between compressed_sequences by exploiting the_Cauchy-Sch