Novel Quantization Strategies for Linear Prediction with Guarantees
|
|
- Pauline Lester
- 5 years ago
- Views:
Transcription
1 Smon S. Du* Ychong Xu* Yuan L Hongyang Zhang Aart Sngh Pulkt Grover Carnege Mellon Unversty, Pttsburgh, PA 15213, USA *: Contrbute equally. SSDU@CS.CMU.EDU YICHONGX@CS.CMU.EDU LIYUANCHRISTY@GMAIL.COM HONGYANZ@CS.CMU.EDU AARTI@CS.CMU.EDU PGROVER@ANDREW.CMU.EDU Abstract Quantzed data s the norm n many energy constraned problems, a concrete example beng bran sgnals recorded by dstrbuted sensors placed around the head n Bran-Computer Interface BCI) applcatons. However, machne learnng algorthms typcally gnore the quantzed nature of such data. In ths paper, we undertake a prncpled study of effcent quantzaton methods for lnear classfcaton. We propose and analyze a customzed quantzaton scheme for dagonal lnear dscrmnant analyss classfer ncludng both learnng and predcton steps. Experments on synthetc and real dataset show the effectveness of our proposed strateges. 1. Introducton In ths work, we nvestgate the problem of dong centralzed predcton usng quantzed data obtaned from dstrbuted sensors. As an example, n Bran-Computer Interface BCI) applcatons, hundreds or even thousands of electrodes placed around or nsde the head are used to sense bran sgnals Lebedev & Ncolels, 2006). These quantzed sgnals are then used for a specfc predcton task such as classfcaton. For example, a neuroprosthetc goal mght nvolve predctng whether an ndvdual s tryng to move hs hand towards left or rght purely based on the quantzed bran data to decode the patent s desred movement. Other applcatons nclude wreless sensors networks for the Internet of Thngs Zhou et al., 2013) and electrc power grd Nabaee & Labeau, 2012). In these settngs, sensors need to communcate data at hgh rates, and consequently consume large amounts of energy Won et al., Proceedngs of the 33 rd Internatonal Conference on Machne Learnng, New York, NY, USA, JMLR: W&CP volume 48. Copyrght 2016 by the authors). 2014). To avod large energy consumpton, data s quantzed for both tranng and predcton. One key observaton for predcton task s that all features readngs of dfferent sensors) do not have the same relevance to the predcton goal. Thus, f we compress each feature n accordance to ts relevance, we can reduce communcaton cost and keep predcton error low smultaneously. Formally, gven communcaton constrants or equvalently, energy constrants), our am s to devse a data quantzaton technque that supports our prespecfed task. Tradtonal nformaton-theoretc quantzaton technques as those proposed by Berger, 1979; Slepan & Wolf, 1973; Cover, 1975; Wyner & Zv, 1976) are dffcult to apply to these problems because these methods requre ether movng unquantzed data to a central node pror to compresson, whch s not applcable n aforementoned settngs, or storng and estmatng parameters at each sensor, whch needs complex hardware that already consumes hgh energy. Recently, Mahzoon et al. Mahzoon et al., 2014) proposed rate allocaton and determnstc quantzaton strateges for quantzng sgnals from m sensors and then drectly used these quantzed data for lnear regresson and lnear classfcaton, but ther method needs already traned model. In ths work, we propose a two-stage actve quantzaton strategy for tranng Dagonal Lnear Dscrmnant Analyss DLDA) classfer. We frst use ntal codes based on our pror knowledge about the underlyng dstrbuton. Then after the frst round samplng, we change our codes based on these data and sample agan. Our fnal estmaton of parameters of the DLDA classfer s based on the second round quantzed data. Once the classfer s traned, we use a randomzed dtherng-nose based quantzaton for the testng data on whch predcton s desred. Fnally, Theorem 2 n Sec. 3 reveals how the number of tranng samples and total bts used for quantzaton affect the predcton accuracy. To the best of our knowledge, our pro-
2 posed strategy s the frst one that quantzes features n both learnng and predcton steps wth provable bounds. Experments on smulated and real data demonstrate the effectveness of our method Related Works The study of quantzaton starts from tradtonal nformaton theory, where one needs to estmate the jont dstrbuton across all the sensors Wyner & Zv, 1976; Cover, 1975; Slepan & Wolf, 1973; Berger, 1979), whch s hard to realze Mahzoon et al., 2014). Recently, Zhu et al. Zhu & Lafferty, 2014) focused on quantzed estmaton of Gaussan sequence models n Eucldean balls. However, there are sgnfcant dfferences between our work and exstng ones: we search for the optmal allocated bts for the quantzed data, rather than for quantzng the predctors, and conduct sold theoretcal analyss for the behavour of the quantzed data as the nput to the lnear predctor. 2. Notaton and Problem Statement In the dstrbuted sensor network settng, suppose we have m sensors and a sum rate of R bts that needs to be allocated across dfferent sensors for quantzaton. We use bold X to represent a sample and X s the feature from - th sensor. If there are n samples, we denote these samples by {X j)} n j=1. R s the number of bts we assgn to -th sensor. Thus, m =1 R R. For each feature X, ts quantzed representaton usng R bts s denoted by X. More precsely, the -th sensor uses an encoder functon E : R {0,, 2 R 1} and sends E X ) to the fuson center. We assume that the communcaton channel s noseless. Also, we do not use vector quantzaton snce despte the smplcty afforded by asymptotc vector quantzaton analyss, we use scalar quantzaton strateges because we am for desgnng technques that are applcable to sensors wth very small memory. The decson center uses a correspondng decodng functon D : {0, 1,, 2 R 1} R. Snce both tranng and predcton are done at the fuson center, we can only use quantzed data for both tasks. Our goal s to mnmze predcton error. In ths paper, we consder the problem of tranng a lnear classfer from quantzed samples then dong predcton based on quantzed features. We focus on a smple but wdely used lnear classfer, Dagonal Lnear Dscrmnant Analyss DLDA). DLDA s a classcal classfcaton method for contnuous valued features and has been wdely used n varous domans Venables & Rpley, 2013). We assume each sample X belongs to one of the two exstng classes wth equal probablty,.e., Pr [classx) = 1] = Pr [classx) = 2] = 1/2. DLDA makes the assumptons that gven the class, class X) = c, each feature s dstrbuted ndependently accordng to a Gaussan dstrbuton: X N µ c, σc) 2, and σ1 = σ 2 = σ for = 1,, m. Wthout loss of generalty, we also assume µ 1 = µ 2 = µ. Under these assumptons, DLDA s a lnear classfer wth w = µ /σ 2, for = 1,, m. Under quantzaton constrants, we can only use the quantzed observatons for both tranng and predcton. Here we want to desgn a tranng algorthm together wth quantzaton strateges ) that mnmze ] the classfcaton error: Pr [Ĉ X class X), where Ĉ ), denotes the lnear classfer traned by quantzed samples. Whle the problem of fndng the optmal strateges that mnmze classfcaton error s hard, we nstead relax the problem to estmatng the decson varable w X, and use t to obtan upper bounds on classfcaton error. 3. Actve Learnng for Quantzed DLDA 3.1. Quantzed Tranng for DLDA For DLDA, n the tranng phase, we need to estmate {µ } m =1 and {σ } m =1 usng quantzed features and labeled data. Notce that snce the two classes have symmetrc means around 0 and same varance, whenever we have a sample wth label 1, we can negate t and obtan a sample from class 2. Thus, equvalently, n the tranng phase, we are just estmatng parameters of a Gaussan dstrbuton. Our technque has two rounds. In the frst round, we use our pror knowledge about the underlyng parameters to construct ntal quantzers. Then we use these quantzed observatons to obtan a rough estmate of the underlyng dstrbutons. Based on estmated parameters from the frst round, we construct new codes to quantze data from the next round. Fnally, we use the quantzed samples from the second round to learn parameters of underlyng dstrbuton and weght vector for DLDA classfcaton. Formally, we assgn R nt bts to the -th sensor and use the followng code n the frst round: E nt X ) 1) = arg mn µ nt c nt σ nt kd nt X k=0,,2 Rnt 1 where µ nt and σ nt are our ntal guess on mean and varance. c nt = 2 max log ) σ nt /µ nt R nt, 1 ) controls the range of quantzaton regon and d nt = 2µ nt c nt σ nt )/2 Rnt 1) s the quantzaton unt. The correspondng decoder s, for k = 0,, 2 Rnt 1 D nt k) = µ nt c nt σ nt kd nt. 2) Let n 1 be the number of samples n the frst round. We estmate mean and varance by µ = n1 j=1 X j) n 1, σ 2 = n1 j=1 X j) µ ) 2 n 1.
3 5 Actve10x Actve30x Actve50x Optmal 5 Actve10x Actve30x Actve50x Optmal 5 Actve10x Actve30x Actve50x Optmal Fgure 1. Classfcaton accuracy of proposed quantzaton scheme on syntheszed data. Optmal s the optmal Bayes classfcaton rule appled to unquantzed samples. where X j) = D nt E nt X j)) ) s the quantzed representaton of X j). X X γ X In the second round, we assgn R b b 0 b b 3 3 bts to the -th sensor and we sample another set of data ponts usng unform Fgure 2. An llustraton of dtherng based quantzaton strategy. quantzaton scheme nformed by the frst round estmaton We use R on mean and varance: = 2 bts for quantzng X, so d = 2b /2 2R 1 ) = b /3. In ths scenaro, feature X s quantzed to 1 b because after addng dtherng nose, the nearest quantzaton pont s 1 3 Ẽ X ) = arg mn µ c σ k d X b. 3, 3) k=0,,2 R 1 D k) = µ c σ k d. 4) where c = 2 log ) ) m ɛ max log σ µ, 1 and d = 2 µ c σ ) /2 R 1). Let n 2 be the number of observatons from the second round we use to estmate the mean, the varance and the weght vector for DLDA: ˆµ = n2 X ) 2 n2 j=1 j), ˆσ 2 j=1 X j) ˆµ =, ŵ = ˆµ n 2 n 2 ˆσ 2, where X j) = D ) Ẽ X j)) s the quantzed representaton of X j) n the second round Quantzed Predcton for DLDA In the prevous secton, we have good estmatons on underlyng dstrbuton of features {ˆµ } m =1 and {ˆσ } m =1 ) and weght vector ŵ for DLDA. In ths secton, we dscuss how to use these estmatons for predcton. Frst, we assgn bts to each sensor accordng to 9). As the frst step of our quantzaton, we pck b for each sensor such that X b holds wth hgh probablty. Then for the -th sensor, we place 2 R quantzaton ponts unformly n the regon [ b, b ],.e., the quantzaton ponts are { b kd k = 0,, 2 R 1} where d = 2b /2 R 1) s a unt quantzaton regon. For the feature from the -th sensor, X, we frst add dtherng nose γ unformly dstrbuted wthn [ d /2, d /2], then we map ths value to the nearest quantzaton pont. Formally, our encodng and decodng functons are E x) = arg mn b kd x γ, 5) k {0,,2 R 1} D k) = b kd. 6) Fg. 2 provdes an example of such a quantzaton strategy. By addng dtherng nose, we now show that the correlaton between quantzaton error from dfferent sensors s removed consstent wth Schuchman, 1964)). Specfcally, wth we derved the followng result: Theorem 1. Suppose for = 1,, m, X b, wth dtherng nose quantzaton strategy, we have [ ) ] 2 E w X w X 4 w 2 b 2 2 2R. 7) =1 Now we can optmze bts assgnment for the test data to mnmze Eqn. 7): mn R,=1,,m s.t w 2 b 2 2 2R 8) =1 R = R, R 1 for = 1,, m =1 Routne algebra shows that the optmal bts assgnment for the -th sensor s: [ 1 R = 2 log 8 ln 2 ] w2 b2 1 1, 9) λ
4 where [x] = max{x, 0} and λ s selected such that R = R. The rates of each source are then rounded to the nearest nteger to ensure feasblty of quantzaton. The next theorem reveals how the number of tranng samples and the number of bts for quantzaton affect the predcton accuracy: Theorem 2. Assume for = 1,, m, µ µ nt, σ σ nt, n the frst stage 1 R nt = Ω )) µ nt log µ σnt µ, and 2 n 1 = Ω log ) [ ) m µ nt 2 ) σ nt 2 ) δ µ µ nt 4 ) ]) µ σ nt 4 of order 5. Then varance s calculated for each channel σ σ and n the second stage R = Ω 1 log µ ɛ σ n 2 = Ω 1 ɛ log 2 ) m 2 ɛ log m ) )) µ 4 δ σ4 σ 4 µ 4 probablty at least 1 δ, for all = 1,, m, ) ) Pr Ĉ X class X) = opt O ɛ), σ µ ))), then wth where opt denotes the classfcaton error of the best possble classfer. Theorem 2 shows the predcton error comes from two sources: one from quantzaton, the other from the statstcal nference. For a gven target accuracy parameter ɛ, the number of bts requred for each sensor R depends logarthmcally on 1/ɛ. Therefor, totally we need O m log 1/ɛ)) bts to make error nduced by quantzaton be at the order of ɛ. The number of samples requred depends quadratcally on 1/ɛ up to logarthm factor. Thus, f we have nfnte bts no quantzaton error), we recover the same sample complexty for parametrc model for nference and predcton Wasserman, 2013). 4. Experments 4.1. Smulated Data We frst test our quantzaton strateges on syntheszed data. Data s generated accordng to DLDA assumptons: for = 1,, m, µ s set to 1 and σ s set to be, 1.2 and 2 respectvely for left, mddle and rght plots of Fg. 1. We use m = 100 sensors and number of total bts R vares from 100 to 200. We use n 1 = 1000 samples n the frst round and n 2 = samples n the second round for tranng and samples for testng. The ntal guesses of parameters are set to be 10 to 50 tmes of the true values. Fg. 1 shows that the more accurate ntal guesses are, the fewer bts needed to acheve certan classfcaton accuracy. Also notce that f sgnal-to-nose rato µ /σ ) of some sensors are much larger than that of others, we need fewer bts to reach optmal classfcaton accuracy. 1 We omt loglog )) terms. 2 We omt log ) dependences on µ and σ Real Data In ths secton, we test our quantzaton scheme on EEG data. We use the bran sgnals of the frst subject n experment of data set 1 from BCI Competton IV Blankertz et al., 2007). In the experment, there are total 200 trals. Each tral corresponds to a motor magery of ether left hand or foot and lasts 8s. There total m = 59 sensors and sgnals were sampled at 100Hz. See Blankertz et al., 2007) for the detals. For each tral, raw EEG tme seres are band-pass fltered wth a butterworth IIR flter band power) and the logathm s appled to the normalzed varance to yeld a feature vector for that tral. Thus, we generate 200 nstances each wth 59 features. Then we randomly select 40 samples for testng and the remanng for tranng. For tranng, 40 samples are used n the frst round and 120 samples are used n the second round. We use 10 tmes of true mean and varance of tranng samples as ntal guesses. Fg. 3 shows the classfcaton accuracy on testng samples wth dfferent bts used. The unquantzed classfer s traned drectly usng 160 tranng samples wthout quantzaton and then s appled to unquantzed testng samples. Notce that even wth just an average of 3 bts per sensor, full nfnte number of bts) quantzaton accuracy can be acheved. Another observaton s that as we ncrease total bts, the result becomes more stable Unquantzed Actve Fgure 3. Classfcaton accuracy of proposed quantzaton scheme on EEG data. 5. Concluson In ths paper, we propose and analyze an actve learnng based quantzaton algorthm together wth a predcton algorthm that only requre quantzed samples for dagonal lnear dscrmnant analyss. Experments on synthetc and real world data show that wth a few bts, we can acheve near optmal accuracy as usng un-quantzed samples. In ths work, we only consder DLDA classfer. How to effcently assgn bts among sensors and quantze features for nonlnear classfers s an mportant problem that has both theoretcal and practcal mplcatons.
5 References Berger, T. Decentralzed estmaton and decson theory. In IEEE Seven Sprngs Workshop on Informaton Theory, Mt. Ksco, NY, Blankertz, Benjamn, Dornhege, Gudo, Krauledat, Matthas, Müller, Klaus-Robert, and Curo, Gabrel. The non-nvasve berln bran computer nterface: fast acquston of effectve performance n untraned subjects. NeuroImage, 372): , Cover, Thomas M. A proof of the data compresson theorem of slepan and wolf for ergodc sources corresp.). Informaton Theory, IEEE Transactons on, 212): , Gamburd, Alex, Lafferty, John, and Rockmore, Dan. Egenvalue spacngs for quantzed cat maps. Journal of Physcs A: Mathematcal and General, 3612):3487, th Annual Internatonal Conference of the IEEE, pp IEEE, Wyner, Aaron D and Zv, Jacob. The rate-dstorton functon for source codng wth sde nformaton at the decoder. Informaton Theory, IEEE Transactons on, 22 1):1 10, Zhou, Yang, Huang, Chuan, Jang, Tao, and Cu, Shuguang. Wreless sensor networks and the nternet of thngs: Optmal estmaton wth nonunform quantzaton and bandwdth allocaton. Sensors Journal, IEEE, 1310): , Zhu, Yuancheng and Lafferty, John. Quantzed estmaton of gaussan sequence models n eucldean balls. In Advances n Neural Informaton Processng Systems, pp , Lebedev, Mkhal A and Ncolels, Mguel AL. Bran machne nterfaces: past, present and future. TRENDS n Neuroscences, 299): , Mahzoon, Majd, Albalaw, Hassan, L, Xn, and Grover, Pulkt. Usng relatve-relevance of data peces for effcent communcaton, wth an applcaton to neural data acquston. In Communcaton, Control, and Computng Allerton), nd Annual Allerton Conference on, pp IEEE, Nabaee, Mahdy and Labeau, Fabrce. Quantzed network codng for sparse messages. In Statstcal Sgnal Processng Workshop SSP), 2012 IEEE, pp IEEE, Schuchman, Leonard. Dther sgnals and ther effect on quantzaton nose. Communcaton Technology, IEEE Transactons on, 124): , Slepan, Davd and Wolf, Jack K. Noseless codng of correlated nformaton sources. Informaton theory, IEEE Transactons on, 194): , Venables, Wllam N and Rpley, Bran D. Modern appled statstcs wth S-PLUS. Sprnger Scence & Busness Meda, Wasserman, Larry. All of statstcs: a concse course n statstcal nference. Sprnger Scence & Busness Meda, Won, Mnho, Albalaw, Hassan, L, Xn, and Thomas, Donald E. Low-power hardware mplementaton of movement decodng for bran computer nterface wth reduced-resoluton dscrete cosne transform. In Engneerng n Medcne and Bology Socety EMBC), 2014
LOW-COMPLEXITY VIDEO ENCODER FOR SMART EYES BASED ON UNDERDETERMINED BLIND SIGNAL SEPARATION
LOW-COMPLEXITY VIDEO ENCODER FOR SMART EYES BASED ON UNDERDETERMINED BLIND SIGNAL SEPARATION Jng Lu, Fe Qao *, Zhjan Ou and Huazhong Yang Department of Electronc Engneerng, Tsnghua Unversty ABSTRACT Ths
More informationError Concealment Aware Rate Shaping for Wireless Video Transport 1
Error Concealment Aware Rate Shapng for Wreless Vdeo Transport 1 Trsta Pe-chun Chen and Tsuhan Chen 2 Abstract Streamng of vdeo, whch s both source- and channel- coded, over wreless networks faces the
More informationCost-Aware Fronthaul Rate Allocation to Maximize Benefit of Multi-User Reception in C-RAN
Cost-Aware Fronthaul Rate Allocaton to Maxmze Beneft of Mult-User Recepton n C-RAN Dora Bovz, Chung Shue Chen, Sheng Yang To cte ths verson: Dora Bovz, Chung Shue Chen, Sheng Yang. Cost-Aware Fronthaul
More informationInstructions for Contributors to the International Journal of Microwave and Wireless Technologies
Instructons for Contrbutors to the Internatonal Journal of Mcrowave and Wreless Technologes Frst A. Author 1, Second Author 1,2, Thrd Author 2 1 Cambrdge Unversty Press, Ednburgh Buldng, Shaftesbury Road,
More informationSimple Solution for Designing the Piecewise Linear Scalar Companding Quantizer for Gaussian Source
94 J. NIKOIĆ, Z. PERIĆ,. VEIMIROVIĆ, SIMPE SOUTION FOR DESIGNING THE PIECEWISE INEAR SCAAR Smle Soluton for Desgnng the Pecewse near Scalar Comandng Quantzer for Gaussan Source Jelena NIKOIĆ, Zoran PERIĆ,
More informationThe UCD community has made this article openly available. Please share how this access benefits you. Your story matters!
Provded by the author(s) and Unversty College Dubln Lbrary n accordance wth publsher polces., Please cte the publshed verson when avalable. tle Dynamc Complexty Scalng for Real-me H.264/AVC Vdeo Encodng
More informationFollowing a musical performance from a partially specified score.
Followng a muscal performance from a partally specfed score. Bryan Pardo and Wllam P. Brmngham Artfcal Intellgence Laboratory Electrcal Engneerng and Computer Scence Dept. and School of Musc The Unversty
More informationStatistics AGAIN? Descriptives
Cal State Northrdge Ψ427 Andrew Answorth PhD Statstcs AGAIN? What do we want to do wth statstcs? Organze and Descrbe patterns n data Takng ncomprehensble data and convertng t to: Tables that summarze the
More informationSmall Area Co-Modeling of Point Estimates and Their Variances for Domains in the Current Employment Statistics Survey
Small Area Co-Modelng of Pont Estmates and Ther Varances for Domans n the Current Employment Statstcs Survey Jule Gershunskaya, Terrance D. Savtsky U.S. Bureau of Labor Statstcs FCSM, March 2018 Dsclamer:
More informationtj tj D... '4,... ::=~--lj c;;j _ ASPA: Automatic speech-pause analyzer* t> ,. "",. : : :::: :1'NTmAC' I
ASPA: Automatc speech-pause analyzer* D. GERVERt and G. DNELEY Unversty of Durham, Durham, England ASPA: The Programs Snce the actual detals of nterface samplng, dsk storage routnes, etc., wll depend upon
More informationHybrid Transcoding for QoS Adaptive Video-on-Demand Services
732 IEEE Transactons on Consumer Electroncs, Vol. 50, No. 2, MAY 2004 Hybrd Transcodng for QoS Adaptve Vdeo-on-Demand Servces Ilhoon Shn and Kern Koh Abstract Transcodng s a core technque that s used n
More informationA Quantization-Friendly Separable Convolution for MobileNets
arxv:1803.08607v1 [cs.cv] 22 Mar 2018 A Quantzaton-Frendly Separable for MobleNets Abstract Tao Sheng tsheng@qt.qualcomm.com Xaopeng Zhang parker.zhang@gmal.com As deep learnng (DL) s beng rapdly pushed
More informationSystem of Automatic Chinese Webpage Summarization Based on The Random Walk Algorithm of Dynamic Programming
Send Orders for Reprnts to reprnts@benthamscence.ae The Open Cybernetcs & Systemcs Journal, 205, 9, 35-322 35 Open Access System of Automatc Chnese Webpage Summarzaton Based on The Random Walk Algorthm
More informationQuantization of Three-Bit Logic for LDPC Decoding
Proceedngs of the World Congress on Engneerng and Computer Scence 2011 Vol II, October 19-21, 2011, San Francsco, USA Quantzaton of Three-Bt Logc for LDPC Decodng Raymond Moberly and Mchael E. O'Sullvan
More informationOptimized PMU placement by combining topological approach and system dynamics aspects
Optmzed PU placement by combnng topologcal approach and system dynamcs aspects Jonas Prommetta, Jakob Schndler, Johann Jaeger Insttute of Electrcal Energy Systems Fredrch-Alexander-Unversty Erlangen-Nuremberg
More informationAnchor Box Optimization for Object Detection
Anchor Box Optmzaton for Object Detecton Yuany Zhong 1, Janfeng Wang 2, Jan Peng 1, and Le Zhang 2 1 Unversty of Illnos at Urbana-Champagn 2 Mcrosoft Research 1 {yuanyz2, janpeng}@llnos.edu, 2 {janfw,
More informationDecision Support by Interval SMART/SWING Incorporating. Imprecision into SMART and SWING Methods
Decson Support by Interval SMART/SWING Incorporatng Imprecson nto SMART and SWING Methods Abstract: Interval judgments are a way of handlng preferental and nformatonal mprecson n multcrtera decson analyss.
More informationA Comparative Analysis of Disk Scheduling Policies
A Comparatve Analyss of Dsk Schedulng Polces Toby J. Teorey and Tad B. Pnkerton Unversty of Wsconsn* Fve well-known schedulng polces for movable head dsks are compared usng the performance crtera of expected
More informationA STUDY OF TRUMPET ENVELOPES
A STUDY OF TRUMPET ENVELOPES Roger B. Dannenberg, Hank Pellern, and Istvan Dereny School of Computer Scence, Carnege Mellon Unversty Pttsburgh, PA 15213 USA rbd@cs.cmu.edu, hank.pellern@andrew.cmu.edu,
More informationTRADE-OFF ANALYSIS TOOL FOR INTERACTIVE NONLINEAR MULTIOBJECTIVE OPTIMIZATION Petri Eskelinen 1, Kaisa Miettinen 2
Internatonal Conference 20th EURO Mn Conference Contnuous Optmaton and Knowledge-Based Technologes (EurOPT-2008) May 20 23, 2008, Nernga, LITHUANIA ISBN 978-9955-28-283-9 L. Saalausas, G.W. Weber and E.
More informationSimon Sheu Computer Science National Tsing Hua Universtity Taiwan, ROC
Mounr A. Tantaou School of Electrcal Engneerng and Computer Scence Unversty of Central Florda Orlando, FL 3286-407-823-393 tantaou@cs.ucf.edu Interacton wth Broadcast Vdeo Ken A. Hua School of Electrcal
More informationWhy Take Notes? Use the Whiteboard Capture System
Why Take Notes? Use the Whteboard Capture System L-we He Zhengyou Zhang and Zcheng Lu September, 2002 Techncal Report MSR-TR-2002-89 Mcrosoft Research Mcrosoft Corporaton One Mcrosoft Way Redmond, WA 98052
More informationSimple VBR Harmonic Broadcasting (SVHB)
mple VBR Harmonc Broadcastng (VHB) Hsang-Fu Yu ab, Hung-hang Yang a, Y-Mng hen c, -Mng Tseng a, and hen-y Kuo a a Dep. of omputer cence & Informaton Engneerng, atonal entral Unversty, Tawan b omputer enter,
More informationAN INTERACTIVE APPROACH FOR MULTI-CRITERIA SORTING PROBLEMS
AN INTERACTIVE APPROACH FOR MULTI-CRITERIA SORTING PROBLEMS A THESIS SUBMITTED TO THE GRADUATE SCHOOL OF NATURAL AND APPLIED SCIENCES OF MIDDLE EAST TECHNICAL UNIVERSITY BY BURAK KESER IN PARTIAL FULFILLMENT
More informationAccepted Manuscript. An improved artificial bee colony algorithm for flexible job-shop scheduling problem with fuzzy processing time
Accepted Manuscrpt An mproved artfcal bee colony algorthm for flexble ob-shop schedulng problem wth fuzzy processng tme Ka Zhou Gao, Ponnuthura Nagaratnam Suganthan, Quan Ke Pan, Tay Jn Chua, Chn Soon
More informationAnalysis of Subscription Demand for Pay-TV
Analyss of Subscrpton Demand for Pay-TV Manabu Shshkura Researcher Insttute for Informaton and Communcatons Polcy 2-1-2 Kasumgasek, Chyoda-ku Tokyo 110-8926 Japan m-shshkura@soumu.go.jp Tel: 03-5253-5496
More informationReduce Distillation Column Cost by Hybrid Particle Swarm and Ant
From the SelectedWorks of Dr. Sandp Kumar Lahr Summer July 20, 2016 Reduce Dstllaton Column Cost by Hybrd Partcle Swarm and Ant Dr. Sandp k lahr chnmaya lenka Avalable at: https://works.bepress.com/sandp_lahr/33/
More informationClock Synchronization in Satellite, Terrestrial and IP Set-top Box for Digital Television
Clock Synchronzaton n Satellte, Terrestral and IP Set-top Box for Dgtal Televson THESIS Submtted n partal fulflment of the requrements for the degree of DOCTOR OF PHILOSOPHY by MONIKA JAIN Under the Supervson
More informationA Scalable HDD Video Recording Solution Using A Real-time File System
H. L et al.: A Scalable HDD Vdeo Recordng Soluton Usng A Real-tme Fle System A Scalable HDD Vdeo Recordng Soluton Usng A Real-tme Fle System Hong L, Stephen R. Cumpson Member, IEEE, Robert Jochemsen, Jan
More informationImproving Reliability and Energy Efficiency of Disk Systems via Utilization Control
Ths paper appeared n the Proceedngs of the 2th IEEE Symposum on Computers and Communcatons (ISCC'08, Marrakech, Morocco, July 2008. Improvng Relablty and Energy Effcency of Dsk Systems va Utlzaton Control
More informationFailure Rate Analysis of Power Circuit Breaker in High Voltage Substation
T. Suwanasr, M. T. Hlang and C. Suwanasr / GMSAR Internatonal Journal 8 (2014) 1-6 Falure Rate Analyss of Power Crcut Breaker n Hgh Voltage Substaton Thanapong Suwanasr, May Thandar Hlang and Cattareeya
More informationMODELING AND ANALYZING THE VOCAL TRACT UNDER NORMAL AND STRESSFUL TALKING CONDITIONS
MODELING AND ANALYZING THE VOCAL TRACT UNDER NORMAL AND STRESSFUL TALING CONDITIONS Ismal Shahn and Naeh Botros 2 Electrcal/Electroncs and Comuter Engneerng Deartment Unversty of Sharjah, P. O. Box 27272,
More informationarxiv: v1 [cs.cl] 12 Sep 2018
Powered by TCPDF (www.tcpdf.org) Neural Melody Composton from Lyrcs Hangbo Bao, Shaohan Huang 2, Furu We 2, Le Cu 2, Yu Wu 3, Chuanq Tan 3, Songhao Pao, Mng Zhou 2 School of Computer Scence, Harbn Insttute
More informationScalable QoS-Aware Disk-Scheduling
Scalable QoS-Aware Dsk-Schedulng Wald G. Aref Khaled El-Bassyoun Ibrahm Kamel Mohamed F. Mokbel Department of Computer Scences, urdue Unversty, West Lafayette, IN 47907-1398 anasonc Informaton and Networkng
More informationQUICK START GUIDE v0.98
QUICK START GUIDE v0.98 QUICK HELP Q A 1 STEP BY STEP 3 GLOSSARY 2 A B C 1 INSTALLATION 1. Make sure that the hardware nstallaton s performed by a certfed vendor 2. Install OTOTRAK app from Apple s App
More informationRIAM Local Centre Woodwind, Brass & Percussion Syllabus
8 RIAM Local Centre Woodwnd, Brass & Percusson Syllabus 2015-2018 AURAL REQUIREMENTS AND THEORETICAL QUESTIONS REVISED FOR ALL PRACTICAL SUBJECTS AURAL TESTS From Elementary to Grade V ths area s worth
More informationTechnical Information
CHEMCUT Techncal Informaton CORPORATION Introducton The Chemcut CC8000 etcher has many new features desgned to reduce the cost of manufacturng and, just as mportantly, the cost of ownershp. Keepng the
More informationSONG STRUCTURE IDENTIFICATION OF JAVANESE GAMELAN MUSIC BASED ON ANALYSIS OF PERIODICITY DISTRIBUTION
SOG STRUCTURE IDETIFICATIO OF JAVAESE GAMELA MUSIC BASED O AALYSIS OF PERIODICITY DISTRIBUTIO D. P. WULADARI, Y. K. SUPRAPTO, 3 M. H. PUROMO,,3 Insttut Teknolog Sepuluh opember, Department of Electrcal
More informationMulti-Line Acquisition With Minimum Variance Beamforming in Medical Ultrasound Imaging
IEEE Transactons on Ultrasoncs, Ferroelectrcs, and Frequency Control, vol. 60, no. 12, Decemer 2013 2521 Mult-Lne Acquston Wth Mnmum Varance Beamformng n Medcal Ultrasound Imagng Ad Ranovch, Zv Fredman,
More informationCraig Webre, Sheriff Personnel Division/Law Enforcement Complex 1300 Lynn Street Thibodaux, Louisiana 70301
DATE OF APPLCATON: Craig Webre, Sheriff Personnel Division/Law Enforcement Complex 1300 Lynn Street Thibodaux, Louisiana 70301 N GENERAL EMAL ADDRESS: For Local Calls - (985) 532-4380 (985) 446-2255 (985)
More informationThe Traffic Image Is Dehazed Based on the Multi Scale Retinex Algorithm and Implementation in FPGA Cui Zhe1, a, Chao Li2, b *, Jiaqi Meng3, c
3rd Internatonal Conference on Mechatroncs and Industral Informatcs (ICMII 2015) The Traffc Image Is Dehazed Based on the Mult Scale Retnex Algorthm and Implementaton n FPGA Cu Zhe1, a, Chao L2, b *, Jaq
More informationIntegration of Internet of Thing Technology in Digital Energy Network with Dispersed Generation
Amercan Scentfc Research Journal for Engneerng, Technology, and Scences (ASRJETS) ISS (Prnt) 2313-4410, ISS (Onlne) 2313-4402 Global Socety of Scentfc Research and Researchers http://asrjetsjournal.org/
More informationLost on the Web: Does Web Distribution Stimulate or Depress Television Viewing?
Lost on the Web: Does Web Dstrbuton Stmulate or Depress Televson Vewng? Joel Waldfogel The Wharton School Unversty of Pennsylvana August 10, 2007 Prelmnary comments welcome Abstract In the past few years,
More informationAutomated composer recognition for multi-voice piano compositions using rhythmic features, n-grams and modified cortical algorithms
Complex Intell. Syst. (2018) 4:55 65 https://do.org/10.1007/s40747-017-0052-x ORIGINAL ARTICLE Automated composer recognton for mult-voce pano compostons usng rhythmc features, n-grams and modfed cortcal
More informationcurrent activity shows on the top right corner in green. The steps appear in yellow
Browzwear Tutorals Tutoral ntroducton Ths tutoral leads you through the best practces of color ways operatons usng an llustrated step by step approach. Each slde shows the actual applcaton at the stage
More informationAIAA Optimal Sampling Techniques for Zone- Based Probabilistic Fatigue Life Prediction
AIAA 2002-383 Optmal Samplng Technques or Zone- Based Probablstc Fatgue Le Predcton M. P. Enrght Southwest Research Insttute San Antono, TX H. R. Mllwater Unversty o Texas at San Antono San Antono, TX
More informationModeling Form for On-line Following of Musical Performances
Modelng Form for On-lne Followng of Muscal Performances Bryan Pardo 1 and Wllam Brmngham 2 1 Computer Scence Department, Northwestern Unversty, 1890 Maple Ave, Evanston, IL 60201 2 Department of Math and
More informationFPGA Implementation of Cellular Automata Based Stream Cipher: YUGAM-128
ISSN (Prnt) : 2320 3765 ISSN (Onlne): 2278 8875 Internatonal Journal of Advanced Research n Electrcal, Electroncs and Instrumentaton Engneerng An ISO 3297: 2007 Certfed Organzaton Vol. 3, Specal Issue
More informationEnvironmental Reviews. Cause-effect analysis for sustainable development policy
Envronmental Revews Cause-effect analyss for sustanable development polcy Journal: Envronmental Revews Manuscrpt ID er-2016-0109.r2 Manuscrpt Type: Revew Date Submtted by the Author: 24-Feb-2017 Complete
More information3 Part differentiation, 20 parameters, 3 histograms Up to patient results (including histograms) can be stored
st Techncal Specfcatons Desgned n France Wth a rch past and a professonal experence bult-up over 35 years, SFRI s a French nvatve company commtted to developng preon In Vtro Dst solutons. SFRI has bult
More informationStudy on the location of building evacuation indicators based on eye tracking
Study on the locaton of buldng evacuaton ndcators based on eye trackng Yue L Tsnghua Unversty yue-l5@malstsnghuaeducn Png hang Tsnghua Unversty zhangp@malstsnghuaeducn Hu hang Tsnghua Unversty, zhhu@tsnghuaeducn
More informationTHE IMPORTANCE OF ARM-SWING DURING FORWARD DIVE AND REVERSE DIVE ON SPRINGBOARD
THE MPORTANCE OF ARM-SWNG DURNG FORWARD DVE AND REVERSE DVE ON SPRNGBOARD Ken Yokoyama Laboratory of Bomechancs Faculty ofeducaton Kanazawa Unversty Kanazawa, Japan J unjro Nagano Department of Physcal
More informationProduction of Natural Penicillins by Strains of Penicillium chrysogenutn
Producton of Natural Pencllns by Strans of Pencllum chrysogenutn a J. FUSK and ЬЕ. WELWRDOVÁ ^Department of Mcrobology and Bochemstry, Slovak Techncal Unversty, Bratslava b Botka, Slovenská Ľupča Receved
More informationCritical Path Reduction of Distributed Arithmetic Based FIR Filter
Crtcal Path Reducton of strbuted rthmetc Based FIR Flter Sunta Badave epartment of Electrcal and Electroncs Engneerng.I.T, urangabad aharashtra, Inda njal Bhalchandra epartment of Electroncs and Telecommuncaton
More informationAMP-LATCH* Ultra Novo mm [.025 in.] Ribbon Cable 02 MAR 12 Rev C
AMP-LATCH* Ultra Novo Applcaton Specfcaton Receptacle Connectors for 114-40056 0.64 mm [.025 n.] Rbbon Cable 02 MAR 12 All numercal values are n metrc unts [wth U.S. customary unts n brackets]. Dmensons
More informationCorrecting Image Placement Errors Using Registration Control (RegC ) Technology In The Photomask Periphery
Correctng Image Placement Errors Usng Regstraton Control (RegC ) Technology In The Photomask Perphery Av Cohen 1, Falk Lange 2 Guy Ben-Zv 1, Erez Gratzer 1, Dmtrev Vladmr 1 1. Carl Zess SMS Ltd., Karmel
More informationFast Intra-Prediction Mode Decision in H.264/AVC Based on Macroblock Properties
Fast Intra-Predcton Mode Decson n H.264/AVC Based on Macroblock Propertes Abstract Intra-predcton s a wdely used tecnque n ntra codng. H.264/AVC adopts rate-dstorton optmzaton (RDO) tecnque to obtan te
More informationT541 Flat Panel Monitor User Guide ENGLISH
T541 Flat Panel Montor User Gude ENGLISH Frst Edton (June / 2002) Note : For mportant nformaton, refer to the Montor Safety and Warranty manual that comes wth ths montor. Ths publcaton could contan techncal
More informationColor Monitor. L200p. English. User s Guide
Color Montor L200p User s Gude Englsh Frst Edton (February / 2003) Note : For mportant nformaton, refer to the Montor Safety and Warranty manual that comes wth ths montor. Contents ENGLISH Safety (Read
More informationDetecting Errors in Blood-Gas Measurement by Analysiswith Two Instruments
CLIN. CHEM. 33/4, 512-517 (1987) Detectng Errors n Blood-Gas Measurement by Analysswth Two Instruments LouIs F. Metzger, Wllam B. Stauffer, Ann V. Kruplnskl, Rchard P. MIIlman,3 George S. Cembrowskl,2
More informationSKEW DETECTION AND COMPENSATION FOR INTERNET AUDIO APPLICATIONS. Orion Hodson, Colin Perkins, and Vicky Hardman
SKEW DETECTION AND COMPENSATION FOR INTERNET AUDIO APPLICATIONS Oron Hodson, Coln Perkns, and Vcky Hardman Department of Computer Scence Unversty College London Gower Street, London, WC1E 6BT, UK. ABSTRACT
More informationProduct Information. Manual change system HWS
Product Informaton HWS HWS Flexble. Compact. Productve. HWS manual change system Manual tool change system wth ntegrated ar feed-through and optonal electrc feed-through Feld of applcaton Excellently sutable
More informationProduct Information. Manual change system HWS
Product Informaton HWS HWS Flexble. Compact. Productve. HWS manual change system Manual tool change system wth ntegrated ar feed-through and optonal electrc feed-through Feld of applcaton Excellently sutable
More informationProduct Information. Universal swivel units SRU-plus
Product Informaton Unversal swvel unts SRU-plus SRU-plus Unversal swvel unts Robust. Fast. Hgh Performance. SRU-plus unversal rotary actuator Unversal unt for pneumatc swvel and turnng movements. Feld
More informationUser s manual. Digital control relay SVA
User s manual Dgtal control relay DISIBEINT ELECTRONIC S.L, has been present n the feld of the manufacture of components for the ndustral automaton for more than years, and mantans n constant evoluton
More informationSealed Circular LC Connector System Plug
Sealed Crcular LC Connector System Plug Instructon Sheet Kt 1828618- [ ], Receptacle Kt 1828619- [ ], 408-10079 and EMI Receptacle Kt 1985193- [ ] 07 APR 11 Plug Kt 1828618 -[ ] Cable Fttng Receptacle
More informationConettix D6600/D6100IPv6 Communications Receiver/Gateway Quick Start
Conettx / Communcatons Recever/Gateway Quck Start.0 Parts Lst able : Conettx System Components Qty. Descrpton Conettx Communcatons Recever/Gateway AC power cord Battery cable P660 I/O cable P660 Rack mount
More informationModular Plug Connectors (Standard and Small Conductor)
Modular Plug Connectors (Standard and Small Conductor) Applcaton Specfcaton 114-6016 04 APR 11 All numercal values are n metrc unts [wth U.S. customary unts n brackets]. Dmensons are n mllmeters [and nches].
More informationCASH TRANSFER PROGRAMS WITH INCOME MULTIPLIERS: PROCAMPO IN MEXICO
FCND DP No. 99 FCND DISCUSSION PAPER NO. 99 CASH TRANSFER PROGRAMS WITH INCOME MULTIPLIERS: PROCAMPO IN MEXICO Elsabeth Sadoulet, Alan de Janvry, and Benjamn Davs Food Consumpton and Nutrton Dvson Internatonal
More informationExpressive Musical Timing
Axel Berndt, Tlo Hähnel Department of Smulaton and Graphcs Otto-von-Guercke Unversty of Magdeburg {aberndt tlo}@sg.cs.un-magdeburg.de Abstract. Tmng s crucal for the qualty of expressve musc performances.
More informationImage Restoration using Multilayer Neural Networks with Minimization of Total Variation Approach
IJCSI Internatonal Jornal of Compter Scence Isses, Vol., Isse, No, Janar 4 ISSN (Prnt): 694-84 ISSN (Onlne): 694-784 www.ijcsi.org 6 Image Restoraton sng Mltlaer Neral Networs wth Mnmzaton of Total Varaton
More informationProduct Information. Miniature rotary unit ERD
Product Informaton ERD ERD Fast. Compact. Flexble. ERD torque motor Powerful torque motor wth absolute encoder and electrc and pneumatc rotary feed-through Feld of applcaton For all applcatons wth exceptonal
More informationCONNECTIONS GUIDE. To Find Your Hook.up Turn To Page 1
CONNECTIONS GUIDE To Fnd Your Hook.up Turn To Page 1 Connectng TV to Antenna (or Cable Wthout Cable Box) and No VCR (Hook-up 1A)... 2 Monaural VCR (Hook-up 1B)... 3 StereoVCR (Hook-up 1C)... 4 Cable Wth
More informationINSTRUCTION MANUAL FOR THE INSTALLATION, USE AND MAINTENANCE OF THE REGULATOR GENIUS POWER COMBI
NSTRUCTON MANUAL FOR THE NSTALLATON, USE AND MANTENANCE OF THE REGULATOR GENUS POWER COMB (TRANSLATON OF THE ORGNAL NSTRUCTON MANUAL N TALAN) PRELMNARY VERSON WARRANTY The devce s guaranteed 24 months
More informationQ. YOU SAY IN PARAGRAPH 3 OF THlf PAPER THAT YOU'VE
"t... _. ------- -~---------.--~-.-...-------.."-.-"---.~,-~.-".--.---..-..-.~-.--~.~-------"..---+-...---" --_... l... l.... BY MR. MURRY: 0. Q. BUT YOU DON'T REMEMBER THE ST~TSTCAL DFFERENCE STTNG HERE
More informationProduct Information. Universal swivel units SRU-plus 25
Product Informaton SRU-plus Robust. Fast. Hgh Performance. SRU-plus unversal rotary actuator Unversal unt for pneumatc swvel and turnng movements. Feld of applcaton Can be used n ether clean or contamnated
More informationS Micro--Strip Tool in. S Combination Strip Tool ( ) S Cable Holder Assembly (Used only
Instructon Sheet LghtCrmp* Plus LC 408-10103 (for Jacketed Cable) Connectors 18 AUG 09 Rear Protectve Cap Termnaton CoverG Boot Connector Assembly Crmp Eyelet Duplex Clp G Connector kt s shpped wth these
More informationUser Manual. AV Router. High quality VGA RGBHV matrix that distributes signals directly. Controlled via computer.
User Manual AV Router Hgh qualty VGA RGBHV matrx that dstrbutes sgnals drectly. Controlled va computer. Notce: : The nmaton contaned n ths document s subject to change wthout notce. SmartAVI makes no warranty
More informationDiscussion Paper Series
Doshsha Unversty Center for the Study of the Creatve Economy Dscusson Paper Seres No. 2013-04 Nonlnear Effects of Superstar Collaboraton: Why the Beatles Succeeded but Broke Up Tadash Yag Dscusson Paper
More informationCONNECTIONS GUIDE. To Find Your Hook.up Turn To Page 1
CONNECTIONS GUIDE To Fnd Your Hook.up Turn To Page 1 Connectng TV to Antenna (or Cable Wthout Cable Box) and No VCR (Hook-up 1A)...2 Monaural VCR (Hook-up 1B)...3 Stereo VCR (Hook-up 1C)... 4 Cable Wth
More informationINTERCOM SMART VIDEO DOORBELL. Installation & Configuration Guide
INTERCOM SMART VIDEO DOORBELL Installaton & Confguraton Gude ! Important safety nformaton Read ths manual before attemptng to nstall the devce! Falure to observe recommendatons ncluded n ths manual may
More informationIN DESCRIBING the tape transport of
Apparatus For Magnetc Storage on Three-Inch Wde Tapes R. B. LAWRANCE R. E. WILKINS R. A. PENDLETON IN DESCRIBING the tape transport of the DATAmatc 1, t s perhaps well to begn by revewng the nfluental
More informationEmotional Metaphors for Emotion Recognition in Chinese Text
Emotonal Metaphors for Emoton Recognton n Chnese Text Xaox Huang 1, Yun Yang 2 and Changle Zhou 1,2 1 College of Computer Scence, Zhejang Unversty, 310027, P.R. Chna tshere@zju.edu.cn 2 Insttute of Artfcal
More informationStep 3: Select a Class
R Step 1: Roll Ablty Scores a. ndcate dce-rollng method (p. 13):. Roll 3d6 sx tmes, n order. 11. Roll 3d6 twce per ablty, select ether. 111. Roll 3d6 sx tmes and assgn to abltes as desred. V. Roll 3d6
More informationUser Manual ANALOG/DIGITAL, POSTIONER RECEIVER WITH EMBEDDED VIACCESS AND COMMON INTERFACE
User Manual ANALOG/DIGITAL, POSTIONER RECEIVER WITH EMBEDDED VIACCESS AND COMMON INTERACE CONTENTS. Safety nstructons -------------------------------------------------------------------. eatures -------------------------------------------------------------------------------.
More informationTurn it on. Your guide to getting the best out of BT Vision
Avalable n Bralle, large prnt and audo CD. Please call FREE on 8 8 15 for your copy. Turn t on Your gude to gettng the best out of www.bt.com/btvson V.2 28656 Enchantng flms to entertan all the famly Flms
More informationLoewe bild 7.65 OLED. Set-up options. Loewe bild 7 cover Incl. Back cover. Loewe bild 7 cover kit Incl. Back cover and Speaker cover
Product nformaton Loewe bld 7.65 Page of March 07 Loewe bld 7.65 OLED EU energy effcency class: B Screen dagonal (n cm) / Screen dagonal (n nch): 64 / 65 Power consumpton ON (n W): 80 Annual energy consumpton
More informationJTAG / Boundary Scan. Multidimensional JTAG / Boundary Scan Instrumentation. Get the total Coverage!
JTAG / Boundary Scan Multdmensonal JTAG / Boundary Scan Instrumentaton IEEE 1149.6 IEEE 1149.1 IEEE 1149.7 Multdmensonal JTAG / Boundary Scan Instrumentaton IEEE 1149.4 IEEE 1532 Get the total Coverage!
More informationUser guide. Receiver-In-The-Ear hearing aids, rechargeable Hearing aid charger. resound.com
User gude Recever-In-The-Ear hearng ads, rechargeable Hearng ad charger resound.com Seral number Model number Recever type Left Hearng Ad Low Power Medum Power Hgh Power Ultra Power Seral number Model
More information,~ COUNTY OF C 0'0 K ) 2 FATHER EDWARD SCHMIDT 1 IN THE CIRCUIT COURT OF COOK COUNTY, ILLINOIS. 8 The discovery deposition of FATHER EDWARD 9 EXHIBITS
. STATE OF LLNOS ) NDEX ) SS: WTNESS EXAMNATON COUNTY OF C 0'0 K ) FATHER EDWARD SCHMDT N THE CRCUT COURT OF COOK COUNTY LLNOS BY MR. PEARLMAN t' COUNTY OEPARTMENT. LAW DVSON 'j JOHN DOE # ) ' Plantff
More informationzenith Installation and Operating Guide HodelNumber I Z42PQ20 [ PLASHATV
Installaton and Operatng Gude HodelNumber I Z42PQ20 PLASHATV To vew the extended verson of owner's manual that contans the advanced features of ths TV set, vst our webste at http://www.enthservce.com Ths
More informationUser guide. Receiver-In-Ear hearing aids. resound.com
User gude Recever-In-Ear hearng ads resound.com 400786011US-17.07-Rev.A.ndd 1 20-07-2017 12:52:40 Left Hearng Ad Rght Hearng Ad Seral number Seral number Model number Model number Recever type Recever
More informationPrinter Specifications
: Characterfonts:! Fort Pont 7P 0.5 pl Ptch 5cpl, Ocpl, 2cpl Proptlonel Epson Draf!o 0 lo j Epson Cower 0 0 0 Epson Roman O O O 0 Epson San6 Sent 0 O O O Epson Presllge j0 0 ~Epson Scnpt O 0 Epson Sormt
More informationJTAG / Boundary Scan. Multidimensional JTAG / Boundary Scan Instrumentation
JTAG / Boundary Scan Multdmensonal JTAG / Boundary Scan Instrumentaton 2 GOEPEL electronc & JTAG / Boundary Scan COMPANY GOEPEL electronc GmbH GOEPEL electronc s a global company that has been developng
More informationA question of character. Loewe Connect ID.
A queston of character. Loewe Connect ID. Modern. Etquette you can learn, character s nnate. You make a clear dstncton between good manners and genune style. Your s s lke yourself: unfussy, busnesslke
More informationSocial Interactions and Stigmatized Behavior: Donating Blood Plasma in Rural China
Socal Interactons and Stgmatzed Behavor: Donatng Blood Plasma n Rural Chna X Chen Yale Unversty and IZA Davd E. Sahn Cornell Unversty and IZA Xaobo Zhang Pekng Unversty and IFPRI March 2018 Abstract Despte
More information(12) Ulllted States Patent (10) Patent N0.: US 8,269,970 B2 P0lid0r et a]. (45) Date of Patent: Sep. 18, 2012
US008269970B2 (12) Ulllted States Patent (10) Patent N0.: P0ld0r et a]. (45) Date of Patent: Sep. 18, 12 (54) OPTICAL COMPARATOR WITH DIGITAL 6,945,652 B2 9/05 sakqta et a1 GAGE 7,058,109 B2* 6/06 Davs.....
More informationProduct Bulletin 40C 40C-10R 40C-20R 40C-114R. Product Description For Solvent, Eco-Solvent, UV and Latex Inkjet and Screen Printing 3-mil vinyl films
Product Bulletn 40C Revson D, Effectve February 2016 (Replaces C, Apr. 15) 40C-10R 40C-20R 40C-114R Product Descrpton For Solvent, Eco-Solvent, UV and Latex Inkjet and Screen Prntng 3-ml vnyl flms Quck
More informationHandout #5. Introduction to the Design of Experiments (DOX) (Reading: FCDAE, Chapter 1~3)
Hadout #5 Ttle: FAE Course: Eco 368/01 Spr/015 Istructor: Dr. I-M Chu Itroducto to the Des of Expermets (DOX) (Read: FCDAE, Chapter 1~3) I hadout oe, we leared that data ca be ether observatoal or expermetal.
More informationUser guide. Receiver-In-The-Ear hearing aids, rechargeable Hearing aid charger. resound.com
User gude Recever-In-The-Ear hearng ads, rechargeable Hearng ad charger resound.com 400973011US-18.08-Rev.A.ndd 1 01-08-2018 14:18:12 Left Hearng Ad Rght Hearng Ad Seral number Seral number Model number
More information