The LLAhclust Package
|
|
- Liliana Golden
- 5 years ago
- Views:
Transcription
1 Verion Date The LLAhclut Package September 1, 2007 Title Hierarchical clutering of variable or object baed on the likelihood linkage analyi method Author Ivan Kojadinovic, Iraël-Céar Lerman, Philippe Peter Maintainer Ivan Kojadinovic Decription The likelihood linkage analyi i a general agglomerative hierarchical clutering method developed in France by Lerman in a long erie of reearch article and book. Initially propoed in the framework of variable clutering, it ha been progreively extended to allow the clutering of very general object decription. The approach mainly conit in replacing the value of the etimated imilarity coefficient by the probability of finding a lower value under the hypothei of abence of link. The package LLAhclut contain routine for computing variou type of probablitic imilarity coefficient between variable or object decription. Once the imilarity value between variable/object are computed, a hierarchical clutering can be performed uing everal probabilitic and non-probabilitic aggregation criteria, and indice meauring the quality of the partition compatible with the reulting hierarchy can be computed. Depend R(>= 2.1.0) Encoding latin1 Licene CeCILL 2 (GNU GPL 2 compatible) URL R topic documented: LLAhclut LLAparteval LLAimobj LLAimvar a.llaim a.matrix.llaim empcopula.imulate Index 14 1
2 2 LLAhclut LLAhclut Likelihood linkage analyi hiearichal clutering Decription Uage Build a hierarchy from imilarity coefficient among object or variable a returned by LLAimvar, LLAimobj or a.llaim. The default aggregation criteria, called lla, can be regarded a a probabilitic verion of the ingle linkage. LLAhclut(, method = "lla", epilon = 1, member = NULL) Argument Value Similarity coefficient a returned by LLAimvar, LLAimobj or a.llaim. method Linkage method (i.e. aggregation criterion). Can be one of lla (default), tippett (Tippett p-value combination method), average, complete, fiher (Fiher p-value combination method), uniform (uniform p-value combination method; can be regarded a a probabilitic verion of the average linkage), normal (normal p-value combination method) or maximum (maximum p-value combination method; can be regarded a a probabilitic verion of the complete linkage). See the lat reference for more detail. epilon member Coefficient ued in the lla linkage. Should lie in [0,1]: epilon=0 correpond to the ingle linkage, epilon=1 (default) yield a probabilitic verion of the ingle linkage. "Weight" of the object to be clutered if not of equal "weight". See hclut for more detail. An object of cla hclut with the correponding attribute. See hclut for more detail. Reference I.C. Lerman (1981), Claification et analye ordinale de donné, Dunod, Pari. I.C. Lerman (1991), Foundation of the likelihood linkage analyi claification method, Applied Stochatic Model and Data Analyi, 7, page I.C. Lerman (1993), Likelihood linkage analyi claification method: An example treated by hand, Biochimie, 75, page I.C. Lerman, Ph. Peter and H. Leredde (1993), Principe et calcul de la méthode implantée dan le programme CHAVL (Claification Hiérarchique par Analye de la Vraiemblance de Lien), Modulad, 12, page I. Kojadinovic (2007), Hierarchical clutering of continuou variable baed on the empirical copula proce, ubmitted.
3 LLAparteval 3 See Alo LLAimvar, LLAimobj, a.llaim, LLAparteval, hclut. Example data(usarret) ## Compute imilaritie between variable baed on ## the LLAnumerical method: <- LLAimvar(USArret) ## Perform the hierarchical clutering of the variable ## uing the default aggregation criterion (lla): h <- LLAhclut() plot(h) ## Compute the quality of the partition compatible ## with the hierarchy in term of the tatitic defined by Lerman: LLAparteval(h,) ## Compute imilaritie between variable uing the claical ## bilateral tet of independence baed on Spearman' rho: <- LLAimvar(USArret, method = "pearman.ab") ## Perform the hierarchical clutering of the variable ## uing Fiher' p-value combination method: h <- LLAhclut(,method="fiher") plot(h) ## NB: the height in the dendrogram i a p-value ## and can be ued to identify mutually independent clae of ## variable, if any. ## Compute the quality of the partition compatible ## with the hierarchy in term of the indice defined in the ## lat reference: LLAparteval(h,) LLAparteval Evalute the quality of each partition compatible with a hierarchy in term of everal indice
4 4 LLAparteval Decription Evalute the quality of each partition compatible with the hierarchy returned by LLAhclut. If the hierarchy i obtained from imilarity coefficient computed uing LLA* method, the global and local tatitic propoed by Lerman are calculated. Otherwie, for imilarity coefficient obtained from independence tet (ee LLAimvar), for each partition, the inter-cla p-value are combined uing Tippett and Fiher rule. Furthermore, the minimum inter-cla p-value and the maximum intra-cla p-value are given. See the lat reference and the example below for more detail. Uage LLAparteval(tree,, m=null) Argument tree m An object of cla hclut a returned by LLAhclut. An object of cla LLAim a returned by LLAimvar, LLAimobj or a.llaim. Integer. If et, the quality of the m coaret partition only i evaluated. Value Return a data.frame whoe column are: global.tat and local.tat if the hierarchy i obtained from imilarity coefficient computed uing LLA* method, and tippett.inter, fiher.inter, min.inter and max.intra in cae of imilarity coefficient obtained from independence tet. Reference I.C. Lerman (1981), Claification et analye ordinale de donné, Dunod, Pari. I.C. Lerman (1991), Foundation of the likelihood linkage analyi claification method, Applied Stochatic Model and Data Analyi, 7, page I.C. Lerman (1993), Likelihood linkage analyi claification method: An example treated by hand, Biochimie, 75, page I.C. Lerman, Ph. Peter and H. Leredde (1993), Principe et calcul de la méthode implantée dan le programme CHAVL (Claification Hiérarchique par Analye de la Vraiemblance de Lien), Modulad, 12, page I. Kojadinovic (2007), Hierarchical clutering of continuou variable baed on the empirical copula proce, ubmitted. See Alo LLAimvar, LLAimobj, a.llaim, LLAhclut.
5 LLAimobj 5 Example data(usarret) ## Compute imilaritie between variable baed on ## the LLAnumerical method: <- LLAimvar(USArret) ## Perform the hierarchical clutering of the variable: h <- LLAhclut() plot(h) ## Compute the quality of the partition compatible ## with the hierarchy in term of the tatitic defined by Lerman: LLAparteval(h,) ## Compute imilaritie between variable uing the claical ## bilateral tet of independence baed on Spearman' rho: <- LLAimvar(USArret, method = "pearman.ab") ## Perform the hierarchical clutering of the variable ## uing Fiher' p-value combination method: h <- LLAhclut(,method="fiher") plot(h) ## NB: the height in the dendrogram i a p-value ## and can be ued to identify mutually independent clae of ## variable, if any. ## Compute the quality of the partition compatible ## with the hierarchy in term of the indice defined in the ## lat reference: LLAparteval(h,) LLAimobj Compute imilaritie among object Decription Compute imilaritie among object uing the likelihood linkage analyi approach propoed by Lerman. The likelihood linkage analyi method mainly conit in replacing the value of the imilarity coefficient between two object by the probability of finding a lower value under the hypothei of abence of link. See the reference below for more detail. Uage LLAimobj(x, method = "LLAeuclidean", upper = FALSE)
6 6 LLAimobj Argument x method upper a numeric matrix or data frame. Can be one of LLAeuclidean, LLAcoinu, LLAcategorical, LLAordinal, or LLAboolean. The two firt method can be ued to compute imiliarty coefficient between object decribed by numerical variable. logical value indicating whether the upper triangle of the imilarity matrix hould be printed by print.llaim. Detail The following function are alo defined for object of cla LLAim: name.llaim, format.llaim, a.matrix.llaim and print.llaim. Value Return an object of cla LLAim whoe attribute are very imilar to thoe of object of cla dit. See dit for more detail. Reference I.C. Lerman (1981), Claification et analye ordinale de donné, Dunod, Pari. I.C. Lerman (1991), Foundation of the likelihood linkage analyi claification method, Applied Stochatic Model and Data Analyi, 7, page I.C. Lerman (1993), Likelihood linkage analyi claification method: An example treated by hand, Biochimie, 75, page I.C. Lerman, Ph. Peter and H. Leredde (1993), Principe et calcul de la méthode implantée dan le programme CHAVL (Claification Hiérarchique par Analye de la Vraiemblance de Lien), Modulad, 12, page See Alo LLAimvar, a.llaim, LLAhclut, LLAparteval, dit. Example data(usarret) ## Compute imilaritie between object baed on ## a local Euclidean ditance (ee reference above): <- LLAimobj(USArret)
7 LLAimvar 7 LLAimvar Compute imilaritie among variable uing the likelihood linkage analyi approach Decription Compute imilaritie among variable uing the likelihood linkage analyi approach propoed by Lerman. The likelihood linkage analyi method mainly conit in replacing the value of the etimated imilarity coefficient between two variable by the probability of finding a lower value under the hypothei of tochatic independence, called abence of link in that context. Nine imilarity coefficient can be computed uing the LLAimvar function. Uage LLAimvar(x, method = "LLAnumerical", upper = FALSE, imulated.ditribution = NULL) Argument x method upper a numeric matrix or data frame. Can be one of LLAnumerical, LLAcategorical, LLAordinal, LLAboolean, chi.quare, pearon.ab, pearman.ab, kendall.ab or empirical.copula. The method LLA* were initially defined by Lerman (ee reference below). The four remaining method compute the imilarity between two variable a one minu the p-value obtained from a tet of independence. See the lat reference and the example ection below for more detail. logical value indicating whether the upper triangle of the imilarity matrix hould be printed by print.llaim. imulated.ditribution Object of cla empcopula.imulation. Should be et only if the method empirical.copula i elected. See function empcopula.imulate and the example ection below for more detail. Detail The following function are alo defined for object of cla LLAim: name.llaim, format.llaim, a.matrix.llaim and print.llaim. Value Return an object of cla LLAim whoe attribute are very imilar to thoe of object of cla dit. See dit for more detail.
8 8 LLAimvar Reference I.C. Lerman (1981), Claification et analye ordinale de donné, Dunod, Pari. I.C. Lerman (1991), Foundation of the likelihood linkage analyi claification method, Applied Stochatic Model and Data Analyi, 7, page I.C. Lerman (1993), Likelihood linkage analyi claification method: An example treated by hand, Biochimie, 75, page I.C. Lerman, Ph. Peter and H. Leredde (1993), Principe et calcul de la méthode implantée dan le programme CHAVL (Claification Hiérarchique par Analye de la Vraiemblance de Lien), Modulad, 12, page P. Deheuvel (1979), La fonction de dépendance empirique et e propriété: un tet non paramétrique d indépendance, Acad. Roy. Belg. Bull. Cl. Sci. 5th Ser. 65, C. Genet and B. Rémillard (2004). Tet of independence and randomne baed on the empirical copula proce. Tet, 13, I. Kojadinovic (2007), Hierarchical clutering of continuou variable baed on the empirical copula proce, ubmitted. See Alo a.llaim, empcopula.imulate, LLAimobj, LLAhclut, LLAparteval, dit. Example data(usarret) ## Compute imilaritie between variable uing the ## LLAnumerical method: <- LLAimvar(USArret) ## Compute imilaritie between variable uing the claical ## bilateral tet of independence baed on Spearman' rho: <- LLAimvar(USArret, method = "pearman.ab") ## Compute imilaritie between variable uing the claical ## bilateral tet of independence baed on Kendall' tau: <- LLAimvar(USArret, method = "kendall.ab") ## Compute imilaritie between variable uing the tet of ## independence à la Deheuvel baed on the empirical copula ## proce recently tudied by Genet and Rémillard: <- LLAimvar(USArret, method = "empirical.copula")
9 a.llaim 9 ## The previou computation could have been done in two tep: d <- empcopula.imulate(n=50,n=2000) <- LLAimvar(USArret, method = "empirical.copula", imulated.ditribution = d) a.llaim Convert the lower triangle of a quare matrix into a LLAim object Decription Uage Convert the lower triangle of a quare matrix into a LLAim object. The LLAim object contain imilarity coefficient among object or variable of interet. a.llaim(m, upper = FALSE, probabilitic = FALSE) Argument m Detail Value input quare imilarity matrix. upper logical value indicating whether the upper triangle of the imilarity matrix hould be printed by print.llaim. probabilitic logical value indicating whether the coefficient in the input imilarity matrix hould be treated a probabilitic imilarity value. If et to FALSE, the input imilarity coefficient are caled. See example below. The following function are alo defined for object of cla LLAim: name.llaim, format.llaim, a.matrix.llaim and print.llaim. Return an object of cla LLAim whoe attribute are very imilar to thoe of object of cla dit. See dit for more detail. Reference I.C. Lerman (1981), Claification et analye ordinale de donné, Dunod, Pari. I.C. Lerman (1991), Foundation of the likelihood linkage analyi claification method, Applied Stochatic Model and Data Analyi, 7, page I.C. Lerman (1993), Likelihood linkage analyi claification method: An example treated by hand, Biochimie, 75, page
10 10 a.matrix.llaim I.C. Lerman, Ph. Peter and H. Leredde (1993), Principe et calcul de la méthode implantée dan le programme CHAVL (Claification Hiérarchique par Analye de la Vraiemblance de Lien), Modulad, 12, page See Alo LLAimvar, LLAimobj, a.matrix.llaim, dit. Example ## Aume that we have at hand a probabilitic imilarity matrix ## between 5 object (lower triangle only): m <- matrix(runif(25), 5, 5) ## The correponding LLAim object i obtained a follow: <- a.llaim(m, probabilitic=true) ## Diplay the initial matrix and the LLAim object: m ## Aume now that we have at hand a non-probabilitic imiliarty ## matrix: m <- matrix(rnorm(25), 5, 5) ## The correponding LLAim object i obtained a follow: <- a.llaim(m, probabilitic=false) ## Diplay the initial matrix and the LLAim object: m ## Notice that the coefficient in are caled: mean() d() a.matrix.llaim Ueful function for dealing with LLAim object Decription Uage The function a.matrix.llaim convert a LLAim object into a quare ymmetrical matrix. The uual R function format, print and name have alo been extended to deal with LLAim object. a.matrix.llaim(x,...)
11 a.matrix.llaim 11 Argument x the LLAim object to be converted.... nothing o far. Value An object of cla matrix. Reference I.C. Lerman (1981), Claification et analye ordinale de donné, Dunod, Pari. I.C. Lerman (1991), Foundation of the likelihood linkage analyi claification method, Applied Stochatic Model and Data Analyi, 7, page I.C. Lerman (1993), Likelihood linkage analyi claification method: An example treated by hand, Biochimie, 75, page I.C. Lerman, Ph. Peter and H. Leredde (1993), Principe et calcul de la méthode implantée dan le programme CHAVL (Claification Hiérarchique par Analye de la Vraiemblance de Lien), Modulad, 12, page See Alo LLAimvar, LLAimobj, a.llaim. Example data(usarret) ## Compute imilaritie between object baed on ## a local Euclidean ditance (ee reference above): <- LLAimobj(USArret) ## Convert to a matrix object: a.matrix() ## Other ueful function: print(, upper=true) name() ## For the format function, ee the R help.
12 12 empcopula.imulate empcopula.imulate Simulation tep ued in the independence tet baed on the empirical copula proce implemented in the LLAimvar function Decription Uage Simulation tep ued in the independence tet baed on the empirical copula proce a propoed by Chritian Genet and Bruno Rémillard. To be ued in conjunction with the LLAimvar function (method="empirical.copula"). The imulation tep conit in imulating the ditribution of the tet tatitic under independence for the ample ize under conideration. More detail can be found in the article cited in the reference ection. empcopula.imulate(n, N = 2000) Argument n N Sample ize when imulating the ditribution of the tet tatitic under independence. Number of repetition when imulating under independence. Detail Value See the reference below for more detail, epecially the third one. The function empcopula.imulate return an object of cla empcop.imulation whoe attribute are: ample.ize, number.repetiton and dit.independence (a vector of length N containing the value of the tet tatitic for each each repetition). Reference P. Deheuvel (1979), La fonction de dépendance empirique et e propriété: un tet non paramétrique d indépendance, Acad. Roy. Belg. Bull. Cl. Sci. 5th Ser. 65, P. Deheuvel (1981), A non parametric tet for independence, Publ. Int. Statit. Univ. Pari 26, C. Genet and B. Rémillard (2004). Tet of independence and randomne baed on the empirical copula proce. Tet, 13, C. Genet, J.-F. Quey and B. Rémillard (2006). Local efficiency of a Cramer-von Mie tet of independence. Journal of Multivariate Analyi, 97, C. Genet, J.-F. Quey and B. Rémillard (2007). Aymptotic local efficiency of Cramer-von Mie tet for multivariate independence. The Annal of Statitic, 35, in pre. I. Kojadinovic (2007), Hierarchical clutering of continuou variable baed on the empirical copula proce, ubmitted.
13 empcopula.imulate 13 See Alo LLAimvar, LLAhclut. Example data(usarret) ## Compute imilaritie between variable uing the tet of ## independence à la Deheuvel baed on the empirical copula ## proce recently tudied by Genet and Rémillard: <- LLAimvar(USArret, method = "empirical.copula") ## The previou computation could have been done in two tep: d <- empcopula.imulate(n=50,n=2000) <- LLAimvar(USArret, method = "empirical.copula", imulated.ditribution = d)
14 Index Topic cluter a.llaim, 9 a.matrix.llaim, 10 empcopula.imulate, 12 LLAhclut, 2 LLAparteval, 3 LLAimobj, 5 LLAimvar, 7 a.llaim, 3, 4, 6, 8, 9, 11 a.matrix.llaim, 10, 10 dit, 6 10 empcopula.imulate, 7, 8, 12 format.llaim (a.matrix.llaim), 10 hclut, 2, 3 LLAhclut, 2, 4, 6, 8, 13 LLAparteval, 3, 3, 6, 8 LLAimobj, 3, 4, 5, 8, 10, 11 LLAimvar, 3, 4, 6, 7, 10, 11, 13 name.llaim (a.matrix.llaim), 10 print.llaim (a.matrix.llaim), 10 14
The norm Package. November 15, Title Analysis of multivariate normal datasets with missing values
The norm Package November 15, 2003 Verion 1.0-9 Date 2002/05/06 Title Analyi of multivariate normal dataet with miing value Author Ported to R by Alvaro A. Novo . Original by Joeph
More informationPerformance of a Robust Filter-based Approach for Contour Detection in Wireless Sensor Networks
Performance of a Robut Filter-baed Approach for Contour Detection in Wirele Senor Network Hadi Alati, William A. Armtrong, Jr., and Ai Naipuri Department of Electrical and Computer Engineering The Univerity
More informationMAT 155: Describing, Exploring, and Comparing Data Page 1 of NotesCh2-3.doc
MAT 155: Decribing, Exploring, and Comparing Data Page 1 of 8 001-oteCh-3.doc ote for Chapter Summarizing and Graphing Data Chapter 3 Decribing, Exploring, and Comparing Data Frequency Ditribution, Graphic
More informationOn combining Learning Vector Quantization and the Bayesian classifiers for natural textured images
On combining Learning Vector Quantization and the Bayeian claifier for natural textured image María Guiarro Dept. Ingeniería del Software e Inteligencia Artificial Facultad Informática Univeridad Complutene
More information/06/$ IEEE 364
006 IEEE International ympoium on ignal Proceing and Information Technology oie Variance Etimation In ignal Proceing David Makovoz IPAC, California Intitute of Technology, MC-0, Paadena, CA, 95 davidm@ipac.caltech.edu;
More information3D SMAP Algorithm. April 11, 2012
3D SMAP Algorithm April 11, 2012 Baed on the original SMAP paper [1]. Thi report extend the tructure of MSRF into 3D. The prior ditribution i modified to atify the MRF property. In addition, an iterative
More informationHassan Ghaziri AUB, OSB Beirut, Lebanon Key words Competitive self-organizing maps, Meta-heuristics, Vehicle routing problem,
COMPETITIVE PROBABIISTIC SEF-ORGANIZING MAPS FOR ROUTING PROBEMS Haan Ghaziri AUB, OSB Beirut, ebanon ghaziri@aub.edu.lb Abtract In thi paper, we have applied the concept of the elf-organizing map (SOM)
More informationANALYSIS OF THE FIRST LAYER IN WEIGHTLESS NEURAL NETWORKS FOR 3_DIMENSIONAL PATTERN RECOGNITION
ANALYSIS OF THE FIRST LAYER IN WEIGHTLESS NEURAL NETWORKS FOR 3_DIMENSIONAL PATTERN RECOGNITION A. Váque-Nava * Ecuela de Ingeniería. CENTRO UNIVERSITARIO MEXICO. DIVISION DE ESTUDIOS SUPERIORES J. Figueroa
More informationA TOPSIS based Method for Gene Selection for Cancer Classification
Volume 67 No17, April 2013 A TOPSIS baed Method for Gene Selection for Cancer Claification IMAbd-El Fattah,WIKhedr, KMSallam, 1 Department of Statitic, 3 Department of Deciion upport, 2 Department of information
More informationA note on degenerate and spectrally degenerate graphs
A note on degenerate and pectrally degenerate graph Noga Alon Abtract A graph G i called pectrally d-degenerate if the larget eigenvalue of each ubgraph of it with maximum degree D i at mot dd. We prove
More informationLoad Estimation of Social Networking Web Sites Using Clustering Technique
International Journal of Electronic and Electrical Engineering Vol. 4, No. 6, December 216 Load Etimation of Social Networking Web Site Uing Clutering Technique Deepti Bhagwani and Setu Kumar Chaturvedi
More informationPackage qad. R topics documented: December 23, Type Package Title Quantification of Asymmetric Dependence Version 0.1.0
Type Package Title Quantification of Asymmetric Dependence Version 0.1.0 Package qad December 23, 2018 Author Florian Griessenberger, Robert R. Junker, Wolfgang Trutschnig Maintainer Florian Griessenberger
More informationMarkov Random Fields in Image Segmentation
Preented at SSIP 2011, Szeged, Hungary Markov Random Field in Image Segmentation Zoltan Kato Image Proceing & Computer Graphic Dept. Univerity of Szeged Hungary Zoltan Kato: Markov Random Field in Image
More informationPerformance Evaluation of an Advanced Local Search Evolutionary Algorithm
Anne Auger and Nikolau Hanen Performance Evaluation of an Advanced Local Search Evolutionary Algorithm Proceeding of the IEEE Congre on Evolutionary Computation, CEC 2005 c IEEE Performance Evaluation
More informationTrainable Context Model for Multiscale Segmentation
Trainable Context Model for Multicale Segmentation Hui Cheng and Charle A. Bouman School of Electrical and Computer Engineering Purdue Univerity Wet Lafayette, IN 47907-1285 {hui, bouman}@ ecn.purdue.edu
More informationAnalyzing Hydra Historical Statistics Part 2
Analyzing Hydra Hitorical Statitic Part Fabio Maimo Ottaviani EPV Technologie White paper 5 hnode HSM Hitorical Record The hnode i the hierarchical data torage management node and ha to perform all the
More informationA Multi-objective Genetic Algorithm for Reliability Optimization Problem
International Journal of Performability Engineering, Vol. 5, No. 3, April 2009, pp. 227-234. RAMS Conultant Printed in India A Multi-objective Genetic Algorithm for Reliability Optimization Problem AMAR
More informationA reduced reference image quality metric based on feature fusion and neural networks
Univerity of Wollongong Reearch Online Faculty of Engineering and Information Science - Paper: Part A Faculty of Engineering and Information Science 2011 A reduced reference image quality metric baed on
More informationStochastic Search and Graph Techniques for MCM Path Planning Christine D. Piatko, Christopher P. Diehl, Paul McNamee, Cheryl Resch and I-Jeng Wang
Stochatic Search and Graph Technique for MCM Path Planning Chritine D. Piatko, Chritopher P. Diehl, Paul McNamee, Cheryl Rech and I-Jeng Wang The John Hopkin Univerity Applied Phyic Laboratory, Laurel,
More informationIMPROVED JPEG DECOMPRESSION OF DOCUMENT IMAGES BASED ON IMAGE SEGMENTATION. Tak-Shing Wong, Charles A. Bouman, and Ilya Pollak
IMPROVED DECOMPRESSION OF DOCUMENT IMAGES BASED ON IMAGE SEGMENTATION Tak-Shing Wong, Charle A. Bouman, and Ilya Pollak School of Electrical and Computer Engineering Purdue Univerity ABSTRACT We propoe
More informationA Hybrid Deployable Dynamic Traffic Assignment Framework for Robust Online Route Guidance
A Hybrid Deployable Dynamic Traffic Aignment Framework for Robut Online Route Guidance Sriniva Peeta School of Civil Engineering, Purdue Univerity Chao Zhou Sabre, Inc. Sriniva Peeta School of Civil Engineering
More informationLaboratory Exercise 6
Laboratory Exercie 6 Adder, Subtractor, and Multiplier The purpoe of thi exercie i to examine arithmetic circuit that add, ubtract, and multiply number. Each type of circuit will be implemented in two
More informationManeuverable Relays to Improve Energy Efficiency in Sensor Networks
Maneuverable Relay to Improve Energy Efficiency in Senor Network Stephan Eidenbenz, Luka Kroc, Jame P. Smith CCS-5, MS M997; Lo Alamo National Laboratory; Lo Alamo, NM 87545. Email: {eidenben, kroc, jpmith}@lanl.gov
More informationHow to Select Measurement Points in Access Point Localization
Proceeding of the International MultiConference of Engineer and Computer Scientit 205 Vol II, IMECS 205, March 8-20, 205, Hong Kong How to Select Meaurement Point in Acce Point Localization Xiaoling Yang,
More informationPARALLEL knn ON GPU ARCHITECTURE USING OpenCL
PARALLEL knn ON GPU ARCHITECTURE USING OpenCL V.B.Nikam 1, B.B.Mehram 2 1 Aociate Profeor, Department of Computer Engineering and Information Technology, Jijabai Technological Intitute, Matunga, Mumbai,
More informationDrawing Lines in 2 Dimensions
Drawing Line in 2 Dimenion Drawing a traight line (or an arc) between two end point when one i limited to dicrete pixel require a bit of thought. Conider the following line uperimpoed on a 2 dimenional
More informationPackage princurve. R topics documented: June 12, Version 2.0.2
Verion 2.0.2 Package princurve June 12, 2018 Title Fit a Principal Curve in Arbitrary Dimenion Decription Fitting a principal curve to a data matri in arbitrary dimenion. Licene GPL-2 Depend R (>= 3.0)
More information3D MODELLING WITH LINEAR APPROACHES USING GEOMETRIC PRIMITIVES
MAKARA, TEKNOLOGI, VOL. 9, NO., APRIL 5: 3-35 3D MODELLING WITH LINEAR APPROACHES USING GEOMETRIC PRIMITIVES Mochammad Zulianyah Informatic Engineering, Faculty of Engineering, ARS International Univerity,
More informationUniversität Augsburg. Institut für Informatik. Approximating Optimal Visual Sensor Placement. E. Hörster, R. Lienhart.
Univerität Augburg à ÊÇÅÍÆ ËÀǼ Approximating Optimal Viual Senor Placement E. Hörter, R. Lienhart Report 2006-01 Januar 2006 Intitut für Informatik D-86135 Augburg Copyright c E. Hörter, R. Lienhart Intitut
More informationA SIMPLE IMPERATIVE LANGUAGE THE STORE FUNCTION NON-TERMINATING COMMANDS
A SIMPLE IMPERATIVE LANGUAGE Eventually we will preent the emantic of a full-blown language, with declaration, type and looping. However, there are many complication, o we will build up lowly. Our firt
More informationDWH Performance Tuning For Better Reporting
DWH Performance Tuning For Better Sandeep Bhargava Reearch Scholar Naveen Hemrajani Aociate Profeor Dineh Goyal Aociate Profeor Subhah Gander IT Profeional ABSTRACT: The concept of data warehoue deal in
More informationTexture-Constrained Active Shape Models
107 Texture-Contrained Active Shape Model Shuicheng Yan, Ce Liu Stan Z. Li Hongjiang Zhang Heung-Yeung Shum Qianheng Cheng Microoft Reearch Aia, Beijing Sigma Center, Beijing 100080, China Dept. of Info.
More informationA Boyer-Moore Approach for. Two-Dimensional Matching. Jorma Tarhio. University of California. Berkeley, CA Abstract
A Boyer-Moore Approach for Two-Dimenional Matching Jorma Tarhio Computer Science Diviion Univerity of California Berkeley, CA 94720 Abtract An imple ublinear algorithm i preented for two-dimenional tring
More informationOn successive packing approach to multidimensional (M-D) interleaving
On ucceive packing approach to multidimenional (M-D) interleaving Xi Min Zhang Yun Q. hi ankar Bau Abtract We propoe an interleaving cheme for multidimenional (M-D) interleaving. To achieved by uing a
More informationA Novel Feature Line Segment Approach for Pattern Classification
12th International Conference on Information Fuion Seattle, WA, USA, July 6-9, 2009 A Novel Feature Line Segment Approach for Pattern Claification Yi Yang Intitute of Integrated Automation Xi an Jiaotong
More information1 The secretary problem
Thi i new material: if you ee error, pleae email jtyu at tanford dot edu 1 The ecretary problem We will tart by analyzing the expected runtime of an algorithm, a you will be expected to do on your homework.
More informationReporting Checklist for Nature Neuroscience
Correponding Author: Manucript Number: Manucript Type: Jeremy Elman NNBC48172A Brief Communication Reporting Checklit for Nature Neurocience # Figure: 2 # Figure: 5 # Table: 6 # Video: 0 Thi checklit i
More informationMulticlass Road Sign Detection using Multiplicative Kernel
Proceeding of the Croatian Computer Viion Workhop, Year 1 Multicla Road Sign Detection uing Multiplicative Kernel Valentina Zadrija Mireo d. d. Zagreb, Croatia valentina.zadrija@mireo.hr Siniša Šegvić
More informationTopics. Lecture 37: Global Optimization. Issues. A Simple Example: Copy Propagation X := 3 B > 0 Y := 0 X := 4 Y := Z + W A := 2 * 3X
Lecture 37: Global Optimization [Adapted from note by R. Bodik and G. Necula] Topic Global optimization refer to program optimization that encompa multiple baic block in a function. (I have ued the term
More informationThe Association of System Performance Professionals
The Aociation of Sytem Performance Profeional The Computer Meaurement Group, commonly called CMG, i a not for profit, worldwide organization of data proceing profeional committed to the meaurement and
More informationShortest Path Routing in Arbitrary Networks
Journal of Algorithm, Vol 31(1), 1999 Shortet Path Routing in Arbitrary Network Friedhelm Meyer auf der Heide and Berthold Vöcking Department of Mathematic and Computer Science and Heinz Nixdorf Intitute,
More informationLinkGuide: Towards a Better Collection of Hyperlinks in a Website Homepage
Proceeding of the World Congre on Engineering 2007 Vol I LinkGuide: Toward a Better Collection of Hyperlink in a Webite Homepage A. Ammari and V. Zharkova chool of Informatic, Univerity of Bradford anammari@bradford.ac.uk,
More informationDelaunay Triangulation: Incremental Construction
Chapter 6 Delaunay Triangulation: Incremental Contruction In the lat lecture, we have learned about the Lawon ip algorithm that compute a Delaunay triangulation of a given n-point et P R 2 with O(n 2 )
More informationEdits in Xylia Validity Preserving Editing of XML Documents
dit in Xylia Validity Preerving diting of XML Document Pouria Shaker, Theodore S. Norvell, and Denni K. Peter Faculty of ngineering and Applied Science, Memorial Univerity of Newfoundland, St. John, NFLD,
More informationAnalysis of the results of analytical and simulation With the network model and dynamic priority Unchecked Buffer
International Reearch Journal of Applied and Baic Science 218 Available online at www.irjab.com ISSN 2251-838X / Vol, 12 (1): 49-53 Science Explorer Publication Analyi of the reult of analytical and imulation
More informationIMPLEMENTATION OF CHORD LENGTH SAMPLING FOR TRANSPORT THROUGH A BINARY STOCHASTIC MIXTURE
Nuclear Mathematical and Computational Science: A Century in Review, A Century Anew Gatlinburg, Tenneee, April 6-, 003, on CD-ROM, American Nuclear Society, LaGrange Park, IL (003) IMPLEMENTATION OF CHORD
More informationANALYSIS OF PILE DRIVING IN VERTICAL AND HORIZONTAL DIRECTIONS USING A HYBRID MODEL
ANALYSIS OF PILE DRIVING IN VERTICAL AND HORIZONTAL DIRECTIONS USING A HYBRID MODEL TATSUNORI MATSUMOTO i) PASTSAKORN KITIYODOM ii) EIJI KOJIMA iii) HIROMICHI KUMAGAI iv) SATOSHI NISHIMOTO v) and KOUICHI
More informationGrowing Networks Through Random Walks Without Restarts
Growing Network Through Random Walk Without Retart Bernardo Amorim, Daniel Figueiredo, Giulio Iacobelli, Giovanni Neglia To cite thi verion: Bernardo Amorim, Daniel Figueiredo, Giulio Iacobelli, Giovanni
More informationUsing Mouse Feedback in Computer Assisted Transcription of Handwritten Text Images
2009 10th International Conference on Document Analyi and Recognition Uing Moue Feedback in Computer Aited Trancription of Handwritten Text Image Verónica Romero, Alejandro H. Toelli, Enrique Vidal Intituto
More informationComputer Arithmetic Homework Solutions. 1 An adder for graphics. 2 Partitioned adder. 3 HDL implementation of a partitioned adder
Computer Arithmetic Homework 3 2016 2017 Solution 1 An adder for graphic In a normal ripple carry addition of two poitive number, the carry i the ignal for a reult exceeding the maximum. We ue thi ignal
More informationLearning-Based Quality Control for Cardiac MR Images
1 Learning-Baed Quality Control for Cardiac MR Image Giacomo Tarroni, Ozan Oktay, Wenjia Bai, Andrea Schuh, Hideaki Suzuki, Jonathan Paerat-Palmbach, Antonio de Marvao, Declan P. O Regan, Stuart Cook,
More informationDistributed Partial Information Management (DPIM) Schemes for Survivable Networks - Part II
IEEE INFOCO 2002 1 Ditributed Partial Information anagement (DPI) Scheme for Survivable Network - Part II Dahai Xu Chunming Qiao Department of Computer Science and Engineering State Univerity of New York
More informationBuilding a Compact On-line MRF Recognizer for Large Character Set using Structured Dictionary Representation and Vector Quantization Technique
202 International Conference on Frontier in Handwriting Recognition Building a Compact On-line MRF Recognizer for Large Character Set uing Structured Dictionary Repreentation and Vector Quantization Technique
More informationKeywords: Defect detection, linear phased array transducer, parameter optimization, phased array ultrasonic B-mode imaging testing.
Send Order for Reprint to reprint@benthamcience.ae 488 The Open Automation and Control Sytem Journal, 2014, 6, 488-492 Open Acce Parameter Optimization of Linear Phaed Array Tranducer for Defect Detection
More informationCombining Web Usage Mining and Fuzzy Inference for Website Personalization
Combining Web Uage Mining and Fuzzy Inference for Webite Peronalization Olfa Naraoui and Chritopher Petene Dept. of Electrical and Computer Engineering 206 Engineering Science Bldg. The Univerity of Memphi
More informationComparison of Multistart Global Optimization Algorithms on the BBOB Noiseless Testbed
Comparion of Multitart Gloal Optimization Algorithm on the BBOB Noiele Teted Lázló Pál Sapientia - Hungarian Univerity of Tranylvania 00 Miercurea-Ciuc, Piata Liertatii, Nr., Romania pallazlo@apientia.iculorum.ro
More informationAalborg Universitet. Published in: Proceedings of the Working Conference on Advanced Visual Interfaces
Aalborg Univeritet Software-Baed Adjutment of Mobile Autotereocopic Graphic Uing Static Parallax Barrier Paprocki, Martin Marko; Krog, Kim Srirat; Kritofferen, Morten Bak; Krau, Martin Publihed in: Proceeding
More informationChapter 13 Non Sampling Errors
Chapter 13 Non Sampling Error It i a general aumption in the ampling theory that the true value of each unit in the population can be obtained and tabulated without any error. In practice, thi aumption
More informationAN INTELLIGENT SCHEME FOR FACIAL EXPRESSION RECOGNITION
AN INTELLIGENT SCHEME FOR FACIAL EXPRESSION RECOGNITION A. Raouzaiou, S. Ioannou, K. Karpouzi, N. Tapatouli, S. Kollia 1, R. Cowie 2 1 Department of Electrical and Computer Engineering National Technical
More informationDirectional Histogram Model for Three-Dimensional Shape Similarity
Directional Hitogram Model for Three-Dimenional Shape Similarity Xinguo Liu Robin Sun Sing Bing Kang Heung-Yeung Shum Microoft Reearch Aia Zhejiang Univerity Microoft Reearch Microoft Reearch Aia Abtract
More informationSo we find a sample mean but what can we say about the General Education Statistics
So we fid a ample mea but what ca we ay about the Geeral Educatio Statitic populatio? Cla Note Cofidece Iterval for Populatio Mea (Sectio 9.) We will be doig early the ame tuff a we did i the firt ectio
More informationDEveloping a deep insight into how traffic flows
A Model Approach to the Etimation of -to- Traffic Matrice Ke Xu, Senior Member, IEEE, Meng Shen, Yong Cui, Member, IEEE, Mingjiang Ye, and Yifeng Zhong Abtract -to- (P2P) application have witneed an increaing
More informationResearch Article Real-Time Communications in Large-Scale Wireless Networks
Hindawi Publihing Corporation International Journal of Digital Multimedia Broadcating Volume 2008, Article ID 586067, 16 page doi:10.1155/2008/586067 eearch Article eal-time Communication in Large-Scale
More informationParallel Approaches for Intervals Analysis of Variable Statistics in Large and Sparse Linear Equations with RHS Ranges
American Journal of Applied Science 4 (5): 300-306, 2007 ISSN 1546-9239 2007 Science Publication Correponding Author: Parallel Approache for Interval Analyi of Variable Statitic in Large and Spare Linear
More informationSLA Adaptation for Service Overlay Networks
SLA Adaptation for Service Overlay Network Con Tran 1, Zbigniew Dziong 1, and Michal Pióro 2 1 Department of Electrical Engineering, École de Technologie Supérieure, Univerity of Quebec, Montréal, Canada
More informationDistribution-based Microdata Anonymization
Ditribution-baed Microdata Anonymization Nick Kouda niverity of Toronto kouda@c.toronto.edu Ting Yu North Carolina State niverity yu@cc.ncu.edu Diveh Srivatava AT&T Lab Reearch diveh@reearch.att.com Qing
More informationDAROS: Distributed User-Server Assignment And Replication For Online Social Networking Applications
DAROS: Ditributed Uer-Server Aignment And Replication For Online Social Networking Application Thuan Duong-Ba School of EECS Oregon State Univerity Corvalli, OR 97330, USA Email: duongba@eec.oregontate.edu
More informationCS201: Data Structures and Algorithms. Assignment 2. Version 1d
CS201: Data Structure and Algorithm Aignment 2 Introduction Verion 1d You will compare the performance of green binary earch tree veru red-black tree by reading in a corpu of text, toring the word and
More informationComparison of Methods for Horizon Line Detection in Sea Images
Comparion of Method for Horizon Line Detection in Sea Image Tzvika Libe Evgeny Gerhikov and Samuel Koolapov Department of Electrical Engineering Braude Academic College of Engineering Karmiel 2982 Irael
More informationLecture 14: Minimum Spanning Tree I
COMPSCI 0: Deign and Analyi of Algorithm October 4, 07 Lecture 4: Minimum Spanning Tree I Lecturer: Rong Ge Scribe: Fred Zhang Overview Thi lecture we finih our dicuion of the hortet path problem and introduce
More informationNearly Constant Approximation for Data Aggregation Scheduling in Wireless Sensor Networks
Nearly Contant Approximation for Data Aggregation Scheduling in Wirele Senor Network Scott C.-H. Huang, Peng-Jun Wan, Chinh T. Vu, Yinghu Li and France Yao Computer Science Department, City Univerity of
More informationAdvanced Encryption Standard and Modes of Operation
Advanced Encryption Standard and Mode of Operation G. Bertoni L. Breveglieri Foundation of Cryptography - AES pp. 1 / 50 AES Advanced Encryption Standard (AES) i a ymmetric cryptographic algorithm AES
More informationSIMIT 7. Profinet IO Gateway. User Manual
SIMIT 7 Profinet IO Gateway Uer Manual Edition January 2013 Siemen offer imulation oftware to plan, imulate and optimize plant and machine. The imulation- and optimizationreult are only non-binding uggetion
More informationLaboratory Exercise 2
Laoratory Exercie Numer and Diplay Thi i an exercie in deigning cominational circuit that can perform inary-to-decimal numer converion and inary-coded-decimal (BCD) addition. Part I We wih to diplay on
More informationAn Algebraic Approach to Adaptive Scalable Overlay Network Monitoring
An Algebraic Approach to Adaptive Scalable Overlay Network Monitoring ABSTRACT Overlay network monitoring enable ditributed Internet application to detect and recover from path outage and period of degraded
More informationOptimal Gossip with Direct Addressing
Optimal Goip with Direct Addreing Bernhard Haeupler Microoft Reearch 1065 La Avenida, Mountain View Mountain View, CA 94043 haeupler@c.cmu.edu Dahlia Malkhi Microoft Reearch 1065 La Avenida, Mountain View
More informationPackage sound. R topics documented: November 10, Version Date Title A Sound Interface for R
Verion 1.4.5 Date 2017-11-10 Title A Sound Interface for R Package ound November 10, 2017 Author Matthia Heymann Maintainer Stefan Langenberg Depend R
More informationPlanning of scooping position and approach path for loading operation by wheel loader
22 nd International Sympoium on Automation and Robotic in Contruction ISARC 25 - September 11-14, 25, Ferrara (Italy) 1 Planning of cooping poition and approach path for loading operation by wheel loader
More information( ) subject to m. e (2) L are 2L+1. = s SEG SEG Las Vegas 2012 Annual Meeting Page 1
A new imultaneou ource eparation algorithm uing frequency-divere filtering Ying Ji*, Ed Kragh, and Phil Chritie, Schlumberger Cambridge Reearch Summary We decribe a new imultaneou ource eparation algorithm
More informationService and Network Management Interworking in Future Wireless Systems
Service and Network Management Interworking in Future Wirele Sytem V. Tountopoulo V. Stavroulaki P. Demeticha N. Mitrou and M. Theologou National Technical Univerity of Athen Department of Electrical Engineering
More informationKS3 Maths Assessment Objectives
KS3 Math Aement Objective Tranition Stage 9 Ratio & Proportion Probabilit y & Statitic Appreciate the infinite nature of the et of integer, real and rational number Can interpret fraction and percentage
More informationComputer Aided Drafting, Design and Manufacturing Volume 25, Number 3, September 2015, Page 10
Computer Aided Drafting, Deign and Manufacturing Volume 5, umber 3, September 015, Page 10 CADDM Reearch of atural Geture Recognition and Interactive Technology Compatible with YCbCr and SV Color Space
More informationParallel MATLAB at FSU: Task Computing
Parallel MATLAB at FSU: Tak John Burkardt Department of Scientific Florida State Univerity... 1:30-2:30 Thurday, 07 April 2011 499 Dirac Science Library... http://people.c.fu.edu/ jburkardt/preentation/...
More informationModeling of underwater vehicle s dynamics
Proceeding of the 11th WEA International Conference on YTEM, Agio Nikolao, Crete Iland, Greece, July 23-25, 2007 44 Modeling of underwater vehicle dynamic ANDRZEJ ZAK Department of Radiolocation and Hydrolocation
More informationThe Data Locality of Work Stealing
The Data Locality of Work Stealing Umut A. Acar School of Computer Science Carnegie Mellon Univerity umut@c.cmu.edu Guy E. Blelloch School of Computer Science Carnegie Mellon Univerity guyb@c.cmu.edu Robert
More informationSemi-Distributed Load Balancing for Massively Parallel Multicomputer Systems
Syracue Univerity SUFAC lectrical ngineering and Computer Science echnical eport College of ngineering and Computer Science 8-1991 Semi-Ditributed Load Balancing for aively Parallel ulticomputer Sytem
More informationMulti-Target Tracking In Clutter
Multi-Target Tracking In Clutter John N. Sander-Reed, Mary Jo Duncan, W.B. Boucher, W. Michael Dimmler, Shawn O Keefe ABSTRACT A high frame rate (0 Hz), multi-target, video tracker ha been developed and
More informationES205 Analysis and Design of Engineering Systems: Lab 1: An Introductory Tutorial: Getting Started with SIMULINK
ES05 Analyi and Deign of Engineering Sytem: Lab : An Introductory Tutorial: Getting Started with SIMULINK What i SIMULINK? SIMULINK i a oftware package for modeling, imulating, and analyzing dynamic ytem.
More informationA New Approach to Pipeline FFT Processor
A ew Approach to Pipeline FFT Proceor Shouheng He and Mat Torkelon Department of Applied Electronic, Lund Univerity S- Lund, SWEDE email: he@tde.lth.e; torkel@tde.lth.e Abtract A new VLSI architecture
More informationMinimum congestion spanning trees in bipartite and random graphs
Minimum congetion panning tree in bipartite and random graph M.I. Otrovkii Department of Mathematic and Computer Science St. John Univerity 8000 Utopia Parkway Queen, NY 11439, USA e-mail: otrovm@tjohn.edu
More informationApplication of Social Relation Graphs for Early Detection of Transient Spammers
Radolaw rendel and Henryk Krawczyk Application of Social Relation raph for Early Detection of Tranient Spammer RADOSLAW RENDEL and HENRYK KRAWCZYK Electronic, Telecommunication and Informatic Department
More informationA System Dynamics Model for Transient Availability Modeling of Repairable Redundant Systems
International Journal of Performability Engineering Vol., No. 3, May 05, pp. 03-. RAMS Conultant Printed in India A Sytem Dynamic Model for Tranient Availability Modeling of Repairable Redundant Sytem
More informationCOLLAGEN ORIENTATION AND WAVINESS WITHIN THE VEIN WALL
XI International Conference on Computational Platicity. Fundamental and Application COMPLAS XI E. Oñate, D.R.J. Owen, D. Peric and B. Suárez (Ed) COLLAGEN ORIENTATION AND WAVINESS WITHIN THE VEIN WALL
More informationDeep Learning-Based Feature Representation for AD/MCI Classification
Deep Learning-Baed Repreentation for AD/MCI Claification Heung-Il Suk and Dinggang Shen Department of Radiology and Biomedical Reearch Imaging Center (BRIC), Univerity of North Carolina at Chapel Hill
More informationImproved Inference in Bayesian Segmentation Using Monte Carlo Sampling: Application to Hippocampal Subfield Volumetry
Improved Inference in Bayeian Segmentation Uing Monte Carlo Sampling: Application to Hippocampal Subfield Volumetry Juan Eugenio Igleia a, Mert Rory Sabuncu a, Koen Van Leemput a,b,c, for the Alzheimer
More informationA Linear Interpolation-Based Algorithm for Path Planning and Replanning on Girds *
Advance in Linear Algebra & Matrix Theory, 2012, 2, 20-24 http://dx.doi.org/10.4236/alamt.2012.22003 Publihed Online June 2012 (http://www.scirp.org/journal/alamt) A Linear Interpolation-Baed Algorithm
More informationSecurity Analysis of the Efficient Chaos Pseudo-random Number Generator Applied to Video Encryption
Journal of Electronic Teting (2018) 34:709 715 http://doi.org/10.1007/10836-018-5767-0 Security Analyi of the Efficient Chao Peudo-random Number Generator Applied to Video Encryption Dragan Lambić 1 Alekandar
More informationA CLUSTERING-BASED HYBRID REPLICA CONTROL PROTOCOL FOR HIGH AVAILABILITY IN GRID ENVIRONMENT
Journal of Computer Science 10 (12): 2442-2449, 2014 ISSN: 1549-3636 2014 R. Latip et al., Thi open acce article i ditributed under a Creative Common Attribution (CC-BY) 3.0 licene doi:10.3844/jcp.2014.2442.2449
More informationA Practical Model for Minimizing Waiting Time in a Transit Network
A Practical Model for Minimizing Waiting Time in a Tranit Network Leila Dianat, MASc, Department of Civil Engineering, Sharif Univerity of Technology, Tehran, Iran Youef Shafahi, Ph.D. Aociate Profeor,
More information(A)ATSR RE-ANALYSIS FOR CLIMATE - CLOUD CLEARING METHODOLOGY
(A)ATSR RE-ANALYSIS FOR CLIMATE - CLOUD CLEARING METHODOLOGY Chri Old, Chri Merchant Univerity of Edinburgh, The Crew Building, Wet Main Road, Edinburgh, EH9 3JN, United Kingdom Email: cold@ed.ac.uk Email:
More information