DOCSLIB.ORG

Orr Hunter K 202104 Msc.Pdf (5.035Mb)

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Orr Hunter K 202104 Msc.Pdf (5.035Mb) Modelling Human Behaviour Based on Similarity Measurements Between Event Sequences by Hunter Orr A thesis submitted to the School of Computing in conformity with the requirements for the degree of Master of Science Queen's University Kingston, Ontario, Canada May 2021 Copyright c Hunter Orr, 2021 Abstract From a set of sequences, individual's behavioural patterns can be identified. Using these sequences of events, the metadata available can be processed into a weighted format to improve the meaningfulness of the sequence comparisons. The usefulness of this process, identifying users' behavioural patterns, is important in a number of areas such as cybersecurity. This work examines the properties a cybersecurity dataset might contain and demonstrates its effectiveness on a dataset with those properties. Building on the existing sequence comparison method, Damerau-levenshtein dis- tance, this work develops a pipeline of steps that can be used to transform the meta- data and integrate this weighted format into the sequence comparison calculation. In this pipeline, one of the most significant transformations that is applied to the meta- data is based on previous work by Brand. This transformation reduces the impact of high popularity pairwise relationships. This pipeline is shown to incorporate the metadata information into the resulting distance values. Thus, producing meaningful changes which demonstrate the benefit of these extra steps. i Acknowledgments I would like to thank Dr. David Skillicorn for his guidance and thoughtful discussions throughout this entire work. The discussions we had were invaluable and continue to encourage me to improve my thinking process. I appreciate the enthusiasm and encouragement you have provided throughout this research. I would like to acknowledge Queen's University for the educational and profes- sional opportunities it has provided. The experiences I have gained will serve me well in the future. Additionally, this thesis would not have been possible without funding from the NSERC CREATE program. ii Contents Abstract i Acknowledgments ii Contents iii List of Tables v List of Figures vi Chapter 1: Introduction 1 1.1 The problem . 1 1.2 Significance . 3 1.3 Previous work . 4 1.4 This work . 6 1.5 Organization of Thesis . 7 Chapter 2: Background 8 2.1 Behavioural Modelling . 9 2.2 Surveys of Similarity Metrics . 14 2.3 Sequence Comparison . 15 2.3.1 Edit Distance . 15 2.3.2 Jaccard Similarity . 15 2.3.3 Weighted methods . 16 2.3.4 Discrete Cosine Transform (DCT) . 17 2.4 Tools . 18 2.4.1 CountVectorizer . 18 2.4.2 FastText . 18 2.5 Correlation Matrices . 19 2.6 Covariance Matrices . 19 2.7 Brand Method . 20 2.8 Clustering . 21 iii 2.9 Visualization Techniques . 22 Chapter 3: Experiment 25 3.1 Research Objectives . 25 3.2 Tag-by-Tag . 28 3.2.1 Restaurant ID by Tag Frequency Matrix . 28 3.2.2 Problems in the Frequency Matrix . 29 3.2.3 Tag-by-Tag Correlation Matrix . 30 3.2.4 Tag-by-Tag Brand Matrix . 32 3.3 Comment-by-Comment . 32 3.4 Restaurant-by-Restaurant . 35 3.4.1 Weighted Sequence Comparison . 36 3.5 Restaurant-by-Restaurant (With Metadata) . 39 3.6 Restaurant-by-Restaurant (With Tag and Comment) . 41 3.7 User Clustering . 42 3.8 Summary . 42 Chapter 4: Results 44 4.1 Introduction . 44 4.1.1 Validation . 45 4.2 Tag-by-Tag . 46 4.3 Comment-by-Comment . 53 4.4 Restaurant-by-Restaurant Correlation Matrix . 60 4.4.1 Restaurant-by-Restaurant - From Comments . 70 4.5 Restaurant-by-Restaurant (With Tag) . 74 4.6 Restaurant-by-Restaurant (With Comments) . 77 4.7 Restaurant-by-Restaurant (With Tag and Comment) . 79 4.8 Clustering the distance matrices . 82 4.9 Summary . 87 Chapter 5: Conclusion 89 Bibliography 93 Appendix A: Nearest Neighbour Results 96 Appendix B: Cluster Contents 100 B.1 Cluster 0 . 100 B.2 Cluster 37 . 110 B.3 Cluster 45 . 110 iv List of Tables 3.1 An example of the tags data file . 29 3.2 An example of the comments data file . 33 4.1 cookies Nearest Neighbour . 51 4.2 Nearest Neighbours First 10 Most Frequent Tags . 54 4.3 Cluster 1 `Good' Examples . 56 4.4 Cluster 1 Inappropriate Examples . 57 4.5 Cluster 10 Examples . 57 4.6 Cluster 30 Examples . 57 4.7 DBSCAN results with varying parameters . 87 v List of Figures 3.1 Brand Connections Diagram . 40 4.1 Tag Frequency Plot Before Truncation . 47 4.2 Tag Frequency Plot After Upper and Lower Truncation . 48 4.3 Tag-by-Tag Correlation Matrix - Before Truncation . 49 4.4 Tag-by-Tag Correlation Matrix - After Truncation . 50 4.5 Tag-by-Tag Brand Matrix . 51 4.6 Comment Vectors visualized using SVD . 55 4.7 Dendrogram - 50 Clusters . 55 4.8 Comment-by-Comment Correlation Matrix . 58 4.9 Comment-by-Comment Brand Matrix . 60 4.10 Restaurant-by-Restaurant (From Tag) Correlation Matrix . 61 4.11 Restaurant-by-Restaurant (From Tag) Brand Matrix . 62 4.12 Nonweighted DamerauLevenshtein Distance Similarity Matrix . 64 4.13 Difference in Weighted Edit Distance using Restaurant-by-Restaurant (From Tag) Correlation Matrix . 65 4.14 Difference in Weighted Edit Distance using Restaurant-by-Restaurant (From Tag) Brand Matrix . 66 4.15 Restaurant-by-Restaurant (From Comments) Correlation Matrix . 71 vi 4.16 Restaurant-by-Restaurant (From Comments) Brand Matrix . 72 4.17 Difference in Weighted Edit Distance using Restaurant-by-Restaurant (From Comments) Correlation Matrix . 73 4.18 Difference in Weighted Edit Distance using Restaurant-by-Restaurant (From Comments) Brand Matrix . 74 4.19 Restaurant-by-Restaurant (With Tag) Correlation Matrix . 75 4.20 Restaurant-by-Restaurant (With Tag) Brand Matrix . 76 4.21 Difference in Weighted Edit Distance using Restaurant-by-Restaurant (With Tag) Correlation Matrix . 77 4.22 Difference in Weighted Edit Distance using Restaurant-by-Restaurant (With Tag) Brand Matrix . 78 4.23 Restaurant-by-Restaurant (With Comments) Correlation Matrix . 79 4.24 Restaurant-by-Restaurant (With Comments) Brand Matrix . 80 4.25 Difference in Weighted Edit Distance using Restaurant-by-Restaurant (With Comments) Correlation Matrix . 81 4.26 Difference in Weighted Edit Distance using Restaurant-by-Restaurant (With Comments) Brand Matrix . 82 4.27 Restaurant-by-Restaurant (With Tag and Comments) Correlation Ma- trix . 83 4.28 Restaurant-by-Restaurant (With Tag and Comments) Brand Matrix . 84 4.29 Difference in Weighted Edit Distance using Restaurant-by-Restaurant (With Tag and Comments) Correlation Matrix . 85 4.30 Difference in Weighted Edit Distance using Restaurant-by-Restaurant (With Tag and Comments) Brand Matrix . 86 vii 1 Chapter 1 Introduction 1.1 The problem Every day humans make decisions about what to wear, what to eat, and what to do. These decisions are not based entirely on whim or will but instead based on a large amount of data that is being considered. These decisions are choices based on the behavioural patterns of each individual. These decisions form a sequence of events. These sequences are compared to each other in order to identify patterns, or similarities, and find clusters of individuals who are similar in their behaviour. Typical decisions may include analyzing groups of people in proximity, identifying choices between different destination locations, or choosing an optimal outcome in a hypothetical situation. This concept of similarity comparison, that humans can successfully analyze even as children, is the main element of this research. Event sequences are a list of events that occur in a particular order. This list of events may share a common theme, such as the list of events to prepare a cup of tea: [boil water, add tea bag to cup, pour boiled water into the cup, add sugar(if required), add milk (if required), stir]. Event sequences are characterized by having a 1.1. THE PROBLEM 2 sequential order and may have a branching structure based on optional steps. Another component to these sequences is the extra data known about the situation. Extra data, or metadata, is the additional information that can be collected related to the principal data. In this behavioural work, the metadata is used to inform the sequence comparison computation about underlying similarities between behaviours. This is key for the prime function of this research which is to develop informed similarity matrices which relate behaviours together. Through this metadata, similar behaviours will have similar metadata relations and those behaviours with the most metadata similarities will be the most similar. This data is generally only tangentially related to the topic area or field that is within the sequence. An example of this can simply be demonstrated by considering the sequence of preparing tea above. An important piece of metadata will inform the observer that replacing milk with cream is a minor change compared to a drastic change such as replacing milk with juice. An example directly related to this work, where the main topic is comparing sequences of restaurant choices, than the metadata is information about the similarities and differences of these restaurants. Such information might be the price range or the type of food served. In this work, the metadata will be the tags and comments associated with the restaurants. To understand why this metadata is required, let us look at a simplified example of human intuition versus a standard algorithm's perception. For this example, consider three event sequences which represent the restaurants three individuals visited: • Event sequence A consists of [PizzaHut, McDonalds] • Event sequence B consists of [PizzaHut, Kelseys] • Event sequence C consists of [PizzaHut, Harveys] 1.2. SIGNIFICANCE 3 The commonality of PizzaHut is obvious. However, to identify similarities in the second restaurant choice an analysis of the metadata associated with the restaurants, needs to be conducted.
Load more

Recommended publications
  • Matrix Algebra and Control
    Appendix A Matrix Algebra and Control Boldface lower case letters, e.g., a or b, denote vectors, boldface capital letters, e.g., A, M, denote matrices. A vector is a column matrix. Containing m elements (entries) it is referred to as an m-vector. The number of rows and columns of a matrix A is nand m, respectively. Then, A is an (n, m)-matrix or n x m-matrix (dimension n x m). The matrix A is called positive or non-negative if A>, 0 or A :2:, 0 , respectively, i.e., if the elements are real, positive and non-negative, respectively. A.1 Matrix Multiplication Two matrices A and B may only be multiplied, C = AB , if they are conformable. A has size n x m, B m x r, C n x r. Two matrices are conformable for multiplication if the number m of columns of the first matrix A equals the number m of rows of the second matrix B. Kronecker matrix products do not require conformable multiplicands. The elements or entries of the matrices are related as follows Cij = 2::;;'=1 AivBvj 'Vi = 1 ... n, j = 1 ... r . The jth column vector C,j of the matrix C as denoted in Eq.(A.I3) can be calculated from the columns Av and the entries BVj by the following relation; the jth row C j ' from rows Bv. and Ajv: column C j = L A,vBvj , row Cj ' = (CT),j = LAjvBv, (A. 1) /1=1 11=1 A matrix product, e.g., AB = (c: c:) (~b ~b) = 0 , may be zero although neither multipli­ cand A nor multiplicator B is zero.
    [Show full text]
  • LCD Codes from Weighing Matrices
    LCD codes from weighing matrices Dean Crnkovi´c, Ronan Egan, B. G. Rodrigues and Andrea Svobˇ Abstract Linear codes with complementary duals are linear codes whose intersection with their duals are trivial, shortly named LCD codes. In this paper we outline a construction for LCD codes over finite fields of order q using weighing matrices and their orbit matrices. The LCD codes constructed can be of any length dimension according to the choice of matrices used in their construction. As a special case, LCD codes of length 2n and dimension n are constructed which also have the property of being formally self-dual. Alternatively, under a condition depending on q that the codes are not LCD, this method constructs self-dual codes. To illustrate the method we construct LCD codes from weighing matrices, including the Paley conference matrices and Hadamard matrices. We also extend the construction to Hermitian LCD codes over the finite field of order 4. In addition, we propose a decoding algorithm that can be feasible for the LCD codes obtained from some of the given methods. 2010 Mathematics Subject Classification: 05B20, 05B30, 94B05, 12E20. Keywords: Weighing matrix, orbit matrix, LCD code. 1 Introduction In this paper we introduce a method for constructing linear codes with complementary duals, or LCD codes. LCD codes are linear codes whose intersection with their duals are trivial; they were introduced by Massey in [23] and have been widely applied in information protection, arXiv:1812.00368v2 [math.CO] 28 Nov 2019 electronics and cryptography. They provide an optimum linear coding solution for the two user binary adder channel.
    [Show full text]
  • Circulant Weighing Matrices
    Circulant Weighing Matrices by Goldwyn Millar A Thesis submitted to the Faculty of Graduate Studies In Partial Fulfillment of the Requirements for the Degree of MASTER OF SCItrNCtr Department of Mathematics University of Manitoba Winnipeg, Manitoba Copyright @ 2009 by Goldwyn Millar THE UNIVBRSTTY OF MANTTOBA FACULTY OF GRADUATB STUDIBS COPYRIGHT PBRMISSION Circulant Weighing Matrices B--v Goldwyn Millar A Thesis/Practicum submitted to thc Faculty of Graduate Studies of The Univer.sity of Mnnitob¿r in ¡rartitl fulfillure nt of thc requirernent of the degree of Master of Science Goldrvvn MillarO2009 Permission h¿rs been grirntetl to the Univcl'sit],of M¿rnitoba Libraries to le¡rd a copy of this thesis/¡rracticttm, to Librar¡' antl Arctrives C¿rn¿rtla (LAC) to lencl a copy of this thesiii¡rracticum, and to LAC's âgent (UMIiProQuest) to microfilm, sellcopies and to pi t lirtr an abstr.act of this thesis/prncticum. This reproductiotl or copy of this tllesis h¿rs been rn:rde available by authority of the copyright owner solely for the pur¡rose of ¡rrivate study and reseârch, *nd may onl¡, 1¡" reprottucert aria copiea as perrnitted b¡' co¡rvright larvs or rvith ext)rcss n'l'itten ¿ruthoriz¿rtion frour thà copyrig¡t on,nér. Abstract A ci,rculant matrin is a matrix such that each of its rows, after the first, can be obtained from the row above it by a right cyclic shift. A ci,rculant wei,gh'ing matri,r of order n and weight w (a CW(n,w)) is a n x n circuiant (0, t1)-matrix trZ such that WWr : wI.
    [Show full text]
  • Trace, Symmetry and Orthogonality
    Canad. Math. Bull. Vol. 37 (4), 1994 pp. 461-467 TRACE, SYMMETRY AND ORTHOGONALITY R. CRAIGEN ABSTRACT. Does there exist a circulant conference matrix of order > 2? When is there a symmetric Hadamard matrix with constant diagonal? How many pairwise disjoint, amicable weighing matrices of order n can there be? These are questions concerning which the trace function gives a great deal of insight. We offer easy proofs of the known solutions to the first two, the first being new, and develop new results regarding the latter question. It is shown that there are 2* disjoint amicable weighing matrices of order 2*p, where p is odd, and that this is an upper bound for t < 1. An even stronger bound is obtained for certain cases. 1. Introduction and preliminaries. Weighing matrices A, B are said to be amicable if ABl = BAl (and antiamicable if ABf + BAl = 0). Clearly A and B are amicable (antiamicable) if and only if At and Bl are as well. We denote by A C\B the Hadamard, or entry-wise product of matrices A, B having the same size. These are disjoint if A HB = 0. One of the most powerful means by which Hadamard matrices are constructed is with orthogonal designs (see [7]). An orthogonal design OD{n\s\,... ,ty) corresponds to k pairwise disjoint, antiamicable weighing matrices of order n, with weights si,... ,^. Similar constructions use disjoint, amicable weighing matrices (see [3, Theorem 4.2], [5]). Even without the disjointness property, amicable weighing matrices are of consid­ erable interest (see pp. 217-219 of [7]).
    [Show full text]
  • Discrete Mathematics Generalized Weighing Matrices and Self
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Discrete Mathematics 309 (2009) 4697–4699 Contents lists available at ScienceDirect Discrete Mathematics journal homepage: www.elsevier.com/locate/disc Note Generalized weighing matrices and self-orthogonal codes Vladimir D. Tonchev Department of Mathematical Sciences, Michigan Technological University, Houghton, MI 49931, USA article info a b s t r a c t Article history: It is shown that the row spaces of certain generalized weighing matrices, Bhaskar Rao Received 11 October 2006 designs, and generalized Hadamard matrices over a finite field of order q are hermitian Accepted 28 May 2008 self-orthogonal codes. Certain matrices of minimum rank yield optimal codes. In the special Available online 1 July 2008 case when q D 4, the codes are linked to quantum error-correcting codes, including some codes with optimal parameters. Keywords: ' 2008 Elsevier B.V. All rights reserved. Generalized weighing matrix Generalized Hadamard matrix Bhaskar Rao design Self-orthogonal code Quantum code 1. Introduction A generalized weighing matrix over a multiplicative group G of order g is a v × b matrix M D .mij/ with entries from G [ f0g such that for every two rows .mi1;:::; mib/; .mj1;:::; mjb/, i 6D j, the multi-set f −1 j ≤ ≤ 6D g mismjs 1 s b; mjs 0 (1) contains every element of G the same number of times. A generalized weighing matrix with the additional properties that every row contains precisely r nonzero entries, each column contains exactly k nonzero entries, and for every two distinct rows the multi-set (1) contains every group element exactly λ/g times is known as a generalized Bhaskar Rao design GBRD.v; b; r; k; λI G/ [10].
    [Show full text]
  • Download Download
    Applying balanced generalized weighing matrices to construct block designs Yury J. Ionin Department of Mathematics Central Michigan University Mt. Pleasant, MI 48858, USA [email protected] Submitted: October 10, 2000; Accepted: January 27, 2001. MR Subject Classifications: 05B05 Abstract Balanced generalized weighing matrices are applied for constructing a family of symmetric designs with parameters (1 + qr(rm+1 − 1)/(r − 1),rm,rm−1(r − 1)/q), where m is any positive integer and q and r =(qd − 1)/(q − 1) are prime powers, and a family of non-embeddable quasi-residual 2−((r +1)(rm+1 −1)/(r −1),rm(r + 1)/2,rm(r − 1)/2) designs, where m is any positive integer and r =2d − 1, 3 · 2d − 1 or 5 · 2d − 1 is a prime power, r ≥ 11. 1 Introduction A balanced incomplete block design (BIBD) with parameters (v, b, r,k, λ)ora2-(v, k, λ) design is a pair D =(V,B), where V is a set (of points) of cardinality v and B is a collection of bk-subsets of V (blocks) such that each point is contained in exactly r blocks and each 2-subset of V is contained in exactly λ blocks. If V = {x1,x2,... ,xv} and B = {B1,B2,... ,Bb}, then the v ×b matrix, whose (i, j)-entry is equal to 1 if xi ∈ Bj and is equal to 0 otherwise, is the incidence matrix of the design. A (0, 1) matrix X of size t v × b is the incidence matrix of a (v, b, r,k, λ) BIBD if and only if XX =(r − λ)Iv + λJv and JvX = kJv×b,whereIv, Jv,andJv×b are the identity matrix of order v,thev × v all-one matrix, and the v × b all-one matrix, respectively.
    [Show full text]
  • Some Non-Existence and Asymptotic Existence Results for Weighing
    Some non-existence and asymptotic existence results for weighing matrices Ebrahim Ghaderpour 1 Lassonde School of Engineering York University, Canada Abstract Orthogonal designs and weighing matrices have many applications in areas such as coding theory, cryptography, wireless networking and communication. In this paper, we first show that if positive integer k cannot be written as the sum of three integer squares, then there does not exist any skew-symmetric weighing matrix of order 4n and weight k, where n is an odd positive integer. Then we show that for any square k, there is an integer N(k) such that for each n N(k), there is a symmetric weighing matrix of order n and weight k.≥ Moreover, we improve some of the asymptotic existence results for weighing matrices obtained by Eades, Geramita and Seberry. Keywords: Asymptotic existence; Orthogonal design; Skew-symmetric weighing matrix; Symmetric weighing matrix; Weighing matrix. 1 Introduction An orthogonal design (OD) [3, Chapter 1] of order n and type (s1,...,sℓ), arXiv:1602.07794v1 [math.CO] 23 Feb 2016 denoted OD(n; s1,...,sℓ), is a square matrix X of order n with entries from 0, x ,..., xℓ , where the xj’s are commuting variables, that satisfies { ± 1 ± } ℓ T 2 XX = sjxj In, j ! X=1 T where X is the transpose of X, and In is the identity matrix of order n. An OD with no zero entry is called a full OD. Equating all variables to 1 1E-mail: [email protected]. 1 in any full OD results in a Hadamard matrix. Equating all variables to 1 in any OD of order n results a weighing matrix, denoted W (n, k), where k is the weight that is the number of nonzero entries in each row (column) of the weighing matrix.
    [Show full text]
  • On Cocyclic Weighing Matrices and the Regular Group Actions of Certain
    Discrete Applied Mathematics 102 (2000) 63{101 View metadata, citation and similar papers at core.ac.uk brought to you by CORE On cocyclic weighing matrices and the regular group provided by Elsevier - Publisher Connector actions of certain paley matrices Warwick de Launey∗, Richard M. Sta ord Center for Communications Research, 4320 Westerra Court, San Diego, California, CA92121, USA Received 1 September 1998; revised 7 June 1999; accepted 2 August 1999 Abstract In this paper we consider cocyclic weighing matrices. Cocyclic development of a weighing matrix is shown to be related to regular group actions on the points of the associated group divisible design. We show that a cocyclic weighing matrix is equivalent to a relative di erence set with central forbidden subgroup of order two. We then set out an agenda for studying a known cocyclic weighing matrix and carry it out for the Paley conference matrix and for the type I Paley Hadamard matrix. Using a connection with certain near ÿelds, we determine all the regular group actions on the group divisible design associated to such a Paley matrix. It happens that all the regular actions of the Paley type I Hadamard matrix have already been described in the literature, however, new regular actions are identiÿed for the Paley conference matrix. This allows us to determine all the extension groups and indexing groups for the cocycles of the aforementioned Paley matrices, and gives new families of normal and non-normal relative di erence sets with forbidden subgroup of size two. ? 2000 Elsevier Science B.V. All rights reserved.
    [Show full text]
  • Optimization Problems with Orthogonal Matrix Constraints
    NUMERICAL ALGEBRA, doi:10.3934/naco.2018026 CONTROL AND OPTIMIZATION Volume 8, Number 4, December 2018 pp. 413–440 OPTIMIZATION PROBLEMS WITH ORTHOGONAL MATRIX CONSTRAINTS K. T. Arasu Department of Mathematics and Statistics Wright State University 3640 Colonel Glenn Highway Dayton, OH 45435, U.S.A. Manil T. Mohan∗† Department of Mathematics and Statistics Air Force Institute of Technology, 2950 Hobson Way Wright Patterson Air Force Base, OH 45433, USA (Communicated by Zhong Wan) Abstract. The optimization problems involving orthogonal matrices have been formulated in this work. A lower bound for the number of stationary points of such optimization problems is found and its connection to the num- ber of possible partitions of natural numbers is also established. We obtained local and global optima of such problems for different orders and showed their connection with the Hadamard, conference and weighing matrices. The appli- cation of general theory to some concrete examples including maximization of Shannon, Rény, Tsallis and Sharma-Mittal entropies for orthogonal matrices, minimum distance orthostochastic matrices to uniform van der Waerden matri- ces, Cressie-Read and K-divergence functions for orthogonal matrices, etc are also discussed. Global optima for all orders has been found for the optimization problems involving unitary matrix constraints. 1. Introduction. Optimization problems involving constraints like orthogonal ma- trices and unitary matrices play an important role in several applications of science, engineering and technology. Some examples include linear and nonlinear eigenvalue problems, electronic structures computations, low-rank matrix optimization, poly- nomial optimization, subspace tracking, combinatorial optimization, sparse princi- pal component analysis, etc (see [8,1, 18, 24, 25] and references therein).
    [Show full text]
  • Signed Groups, Sequences, and the Asymptotic Existence of Hadamard Matrices
    JOURNAL OF COMBINATORIALTHEORY, Series A 71, 241-254 (1995) Signed Groups, Sequences, and the Asymptotic Existence of Hadamard Matrices R. CRAIGEN * Department of Mathematics and Computer Science, University of Lethbridge, Lethbridge, Alberta, Canada T1K 3M4 Communicated by Vera Pless Received December 15, 1993 We use the newly developed theory of signed groups and some known sequences with zero autocorrelation to derive new results on the asymptotic existence of Hadamard matrices. New values of t are obtained such that, for any odd number p, there exists an Hadamard matrix of order 2tp. These include: t = 2N, where N is the number of nonzero digits in the binary expansion of p, and t = 4[-~ log2((p- 1)/2)7 + 2. Both numbers improve on all previous general results, but neither uses the full power of our method. We also discuss some of the implica- tions of our method in terms of signed group Hadarnard matrices and signed group weighing matrices: There exists a circulant signed group Hadamard matrix of every even order n, using a suitable signed group. This result stands in striking contrast to the known results for Hadamard matrices and complex Hadamard matrices, and the circulant Hadamard matrix conjecture. Signed group weighing matrices of even order n always exist, with any specified weight w ~< n. © 1995 AcademicPress, Inc. 1. INTRODUCTION Let us begin by recalling two well-known conjectures regarding orthogonal matrices, which are known respectively as the Hadamard matrix [8] and complex Hadamard matrix [12] conjectures. CONJECTURE 1. There is an Hadamard matrix of order n > 2 if and only if n-~ 0 rood 4.
    [Show full text]
  • Introduction to Graph Signal Processing
    Noname manuscript No. (will be inserted by the editor) Introduction to Graph Signal Processing LjubiˇsaStankovi´c,MiloˇsDakovi´c,and Ervin Sejdi´c Received: date / Accepted: date Abstract Graph signal processing deals with signals whose domain, defined by a graph, is irregular. An overview of basic graph forms and definitions is presented first. Spectral analysis of graphs is discussed next. Some simple forms of processing signal on graphs, like filtering in the vertex and spectral do- main, subsampling and interpolation, are given. Graph topologies are reviewed and analyzed as well. Theory is illustrated through examples, including few applications at the end of the chapter. 1 Introduction Graph signal processing is an active research area in recent years resulting in many advanced solutions in various applications. In numerous practical cases the signal domain is not a set of equidistant instants in time or a set of points in space on a regular grid. The data sensing domain could be irregular and, in some cases, not related to the time or space. The data sensing domain is then related to other properties of the considered system/network. For example, in many social or web related networks, the sensing points and their connectiv- ity are related to specific objects and their links. In some physical processes other properties than the space or time coordinates define the relation between points where the signal is sensed. Even for the data sensed in the well defined time and space domain, the introduction of new relations between the sensing points may produce new insights in the analysis and result in more advanced data processing techniques.
    [Show full text]
  • Minimum-Gain Pole Placement with Sparse Static Feedback
    1 Minimum-gain Pole Placement with Sparse Static Feedback Vaibhav Katewa and Fabio Pasqualetti Abstract—The minimum-gain eigenvalue assignment/pole system to perturbations in the system parameters, thereby placement problem (MGEAP) is a classical problem in LTI making the system robust. This is known as Robust Eigenvalue systems with static state feedback. In this paper, we study Assignment Problem (REAP). Alternatively, one can choose the MGEAP when the state feedback has arbitrary sparsity constraints. We formulate the sparse MGEAP problem as an the feedback matrix with minimum gain, thereby reducing equality-constrained optimization problem and present an an- the overall control effort. This is known as Minimum Gain alytical characterization of its solution in terms of eigenvector Eigenvalue Assignment Problem (MGEAP). Both of these matrices of the closed loop system. This result is used to problems have been studied extensively [2]- [3]. provide a geometric interpretation of the solution of the non- Recently, considerable attention has been given to the study sparse MGEAP, thereby providing additional insights for this classical problem. Further, we develop an iterative projected and design of sparse feedback control systems, where certain gradient descent algorithm to solve the sparse MGEAP using entries of the matrix F are required to be zero. Feedback a parametrization based on the Sylvester equation. We present sparsity typically arises in large scale systems in which the a heuristic algorithm to compute the projections, which also controllers do not have access to all the states of the system. provides a novel method to solve the sparse EAP. Also, a relaxed Sparsity may also be a result of the special structure of the version of the sparse MGEAP is presented and an algorithm is developed to obtain approximately sparse solutions to the control system which prohibits feedback from some states to MGEAP.
    [Show full text]