This work is the result of the definition, design and evaluation of a novel method to interconnect the computational elements - commonly known as Configurable Analogue Blocks (CABs) - of a programmable analogue array. This method is proposed for total or partial replacement of the conventional methods due to serious limitations of the latter in terms of scalability. With this method, named Asynchronous Spike Event Coding (ASEC) scheme, analogue signals from CABs outputs are encoded as time instants (spike events) dependent upon those signals activity and are transmitted asynchronously by employing the Address Event Representation (AER) protocol. Power dissipation is dependent upon input signal activity and no spike events are generated when the input signal is constant. On-line, programmable computation is intrinsic to ASEC scheme and is performed without additional hardware. The ability of the communication scheme to perform computation enhances the computation power of the programmable analogue array. The design methodology and a CMOS implementation of the scheme are presented together with test results from prototype integrated circuits (ICs).

International Telemetering Conference Proceedings / October 17-20, 1994 / Town & Country Hotel and Conference Center, San Diego, California / In many signal processing and telemetry applications only a portion of the Discrete Fourier Transform (DFT) of a data sequence is of interest. This paper develops an algorithm which enables computation of the FFT only over the frequency values of interest, reducing the computational complexity. As will be shown, the algorithm is also very modular which lends to efficient parallel processing implementation. This paper will begin by developing the frequency selective FFT algorithm, and conclude with a comparative analysis of the computational complexity of the algorithm with respect to the traditional FFT.

Approved for public release; distribution is unlimited / This thesis investigates the viability of a new method for numerically computing the input impedance and the currents on simple antenna structures. This technique considers the antenna between two ground planes and uses multiregion cylindrical harmonic expansions with tangential field continuity to obtain the surface currents and input impedance. The computed results are compared to the results obtained from the Numerical Electromagnetics Code for various physical parameters to assess computational accuracy. / http://archive.org/details/computationofmon00hurl / Lieutenant, United States Navy

This paper explores the potential applications of existing spectral clustering algorithms to real life problems through experiments on existing road traffic data. The analysis begins with an overview of previous unsupervised machine learning techniques and constructs an effective spectral clustering algorithm that demonstrates the analytical power of the method. The paper focuses on the spectral embedding method’s ability to project non-linearly separable, high dimensional data into a more manageable space that allows for accurate clustering. The key step in this method involves solving a normalized eigenvector problem in order to construct an optimal representation of the original data. While this step greatly enhances our ability to analyze the relationships between data points and identify the natural clusters within the original dataset, it is difficult to comprehend the eigenvalue representation of the data in terms of the original input variables. The later sections of this paper will explore how the careful framing of questions with respect to available data can help researchers extract tangible decision driving results from real world data through spectral clustering analysis.

One of the main applications of machine learning in bioinformatics is the construction of classification models which can accurately classify new instances using information gained from previous instances. With the help of machine learning algorithms (such as supervised classification and gene selection) new meaningful knowledge can be extracted from bioinformatics datasets that can help in disease diagnosis and prognosis as well as in prescribing the right treatment for a disease. One particular challenge encountered when analyzing bioinformatics datasets is data noise, which refers to incorrect or missing values in datasets. Noise can be introduced as a result of experimental errors (e.g. faulty microarray chips, insufficient resolution, image corruption, and incorrect laboratory procedures), as well as other errors (errors during data processing, transfer, and/or mining). A special type of data noise called class noise, which occurs when an instance/example is mislabeled. Previous research showed that class noise has a detrimental impact on machine learning algorithms (e.g. worsened classification performance and unstable feature selection). In addition to data noise, gene expression datasets can suffer from the problems of high dimensionality (a very large feature space) and class imbalance (unequal distribution of instances between classes). As a result of these inherent problems, constructing accurate classification models becomes more challenging. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2015. / FAU Electronic Theses and Dissertations Collection

In response to the massive amounts of data that make up a large number of bioinformatics datasets, it has become increasingly necessary for researchers to use computers to aid them in their endeavors. With difficulties such as high dimensionality, class imbalance, noisy data, and difficult to learn class boundaries, being present within the data, bioinformatics datasets are a challenge to work with. One potential source of assistance is the domain of data mining and machine learning, a field which focuses on working with these large amounts of data and develops techniques to discover new trends and patterns that are hidden within the data and to increases the capability of researchers and practitioners to work with this data. Within this domain there are techniques designed to eliminate irrelevant or redundant features, balance the membership of the classes, handle errors found in the data, and build predictive models for future data. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2015. / FAU Electronic Theses and Dissertations Collection

Nesta tese de doutoramento nós tivemos como principal objetivo desenvolver novos métodos para proteção da informação e computação quântica. Começamos, de forma introdutória, ilustrando os conceitos básicos e fundamentais da teoria da informação e computação quântica, como os bits quânticos (qubits), o operador densidade, o emaranhamento e as operações lógicas quânticas. Na seqüência, apresentamos os formalismos utilizados para tratar sistemas abertos, ou seja, sujeitos a erros, além das principais técnicas existentes a fim de proteger a informação quântica, como os códigos de correção de erros, os subespaços livres de erros e o desacoplamento dinâmico. Finalmente, baseando-nos na técnica de desacoplamento dinâmico, introduzimos um esquema de proteção para operações lógicas quânticas e o emaranhamentos entre qubits utilizando campos de alta freqüência. Ilustramos em detalhes a proteção da operação lógica quântica de Hadamard e do emaranhamento entre dois qubits, além de apresentarmos as principais diferenças e vantagens de nosso método quando comparado às técnicas tradicionais de desacoplamento dinâmico. / The main objective of this thesis is the development of a new procedure for quantum information and computation protection. We begin by briefly illustrating the basic concepts of quantum information and computation theory, such as quantum bits (qubits), density matrix operator, entanglement, and quantum logical operations. Subsequently, we present the formalism utilized to treat quantum open systems, i.e., systems subjected to errors, and the main strategies to protect quantum information, such as quantum error correction codes, decoherence-free subspaces, and dynamical decoupling. Finally, based on the dynamical decoupling strategies, we introduce a procedure to protect quantum logical operations and entanglement utilizing high-frequency continuous fields. We illustrate, in details, the protection of a Hadamard quantum gate and of entanglement between two qubits, and present the differences and advantages of our procedure when compared with traditional techniques of dynamical decoupling.

Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2016. / S.M. !c Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 2016 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 83-88). / Cities today are strained by the exponential growth in population where they are homes to the majority of world's population. Understanding the complexities underlying the emerging behaviors of human travel patterns on the city level is essential toward making informed decision-making pertaining to urban transportation infrastructures This thesis includes several attempts towards modeling and understanding human mobility at the scales of individuals and the scale of aggregate population movement. The second chapter includes the development of a browser delivering visual insights of the aggregate behavior of populations in cities. The third chapter provides a computational framework for clustering regions in cities based on their attraction behavior and in doing so aids a predictive model in predicting inflows to newly developed regions. The fourth chapter investigates the patterns of individuals' movement at the city scale towards developing a predictive model for a persons' next visited location. The predictive accuracy is then increased by adding movement information of the population. The motivation behind the work of this thesis is derived from the demand of tools that provides fine-grained analysis of the complexity of human travel within cites. The approach takes advantage of the existing built infrastructures to sense the mobility of people eliminating the financial and temporal burdens of traditional methods. The outcomes of this work will assist both planners and the public in understanding the complexities of human mobility within their cities. / by Fahad Alhasoun. / S.M. / S.M. !c Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science

by Ngan Po Shun. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 109-115). / Abstract also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Data Mining --- p.1 / Chapter 1.2 --- Motivation --- p.4 / Chapter 1.3 --- Contributions of the research --- p.5 / Chapter 1.4 --- Organization of the thesis --- p.6 / Chapter 2 --- Related Work in Data Mining --- p.9 / Chapter 2.1 --- Decision Tree Approach --- p.9 / Chapter 2.1.1 --- ID3 --- p.10 / Chapter 2.1.2 --- C4.5 --- p.11 / Chapter 2.2 --- Classification Rule Learning --- p.13 / Chapter 2.2.1 --- AQ algorithm --- p.13 / Chapter 2.2.2 --- CN2 --- p.14 / Chapter 2.2.3 --- C4.5RULES --- p.16 / Chapter 2.3 --- Association Rule Mining --- p.16 / Chapter 2.3.1 --- Apriori --- p.17 / Chapter 2.3.2 --- Quantitative Association Rule Mining --- p.18 / Chapter 2.4 --- Statistical Approach --- p.19 / Chapter 2.4.1 --- Chi Square Test and Bayesian Classifier --- p.19 / Chapter 2.4.2 --- FORTY-NINER --- p.21 / Chapter 2.4.3 --- EXPLORA --- p.22 / Chapter 2.5 --- Bayesian Network Learning --- p.23 / Chapter 2.5.1 --- Learning Bayesian Networks using the Minimum Descrip- tion Length (MDL) Principle --- p.24 / Chapter 2.5.2 --- Discretizating Continuous Attributes while Learning Bayesian Networks --- p.26 / Chapter 3 --- Overview of Evolutionary Computation --- p.29 / Chapter 3.1 --- Evolutionary Computation --- p.29 / Chapter 3.1.1 --- Genetic Algorithm --- p.30 / Chapter 3.1.2 --- Genetic Programming --- p.32 / Chapter 3.1.3 --- Evolutionary Programming --- p.34 / Chapter 3.1.4 --- Evolution Strategy --- p.37 / Chapter 3.1.5 --- Selection Methods --- p.38 / Chapter 3.2 --- Generic Genetic Programming --- p.39 / Chapter 3.3 --- Data mining using Evolutionary Computation --- p.43 / Chapter 4 --- Applying Generic Genetic Programming for Rule Learning --- p.45 / Chapter 4.1 --- Grammar --- p.46 / Chapter 4.2 --- Population Creation --- p.49 / Chapter 4.3 --- Genetic Operators --- p.50 / Chapter 4.4 --- Evaluation of Rules --- p.52 / Chapter 5 --- Learning Multiple Rules from Data --- p.56 / Chapter 5.1 --- Previous approaches --- p.57 / Chapter 5.1.1 --- Preselection --- p.57 / Chapter 5.1.2 --- Crowding --- p.57 / Chapter 5.1.3 --- Deterministic Crowding --- p.58 / Chapter 5.1.4 --- Fitness sharing --- p.58 / Chapter 5.2 --- Token Competition --- p.59 / Chapter 5.3 --- The Complete Rule Learning Approach --- p.61 / Chapter 5.4 --- Experiments with Machine Learning Databases --- p.64 / Chapter 5.4.1 --- Experimental results on the Iris Plant Database --- p.65 / Chapter 5.4.2 --- Experimental results on the Monk Database --- p.67 / Chapter 6 --- Bayesian Network Learning --- p.72 / Chapter 6.1 --- The MDLEP Learning Approach --- p.73 / Chapter 6.2 --- Learning of Discretization Policy by Genetic Algorithm --- p.74 / Chapter 6.2.1 --- Individual Representation --- p.76 / Chapter 6.2.2 --- Genetic Operators --- p.78 / Chapter 6.3 --- Experimental Results --- p.79 / Chapter 6.3.1 --- Experiment 1 --- p.80 / Chapter 6.3.2 --- Experiment 2 --- p.82 / Chapter 6.3.3 --- Experiment 3 --- p.83 / Chapter 6.3.4 --- Comparison between the GA approach and the greedy ap- proach --- p.91 / Chapter 7 --- Medical Data Mining System --- p.93 / Chapter 7.1 --- A Case Study on the Fracture Database --- p.95 / Chapter 7.1.1 --- Results of Causality and Structure Analysis --- p.95 / Chapter 7.1.2 --- Results of Rule Learning --- p.97 / Chapter 7.2 --- A Case Study on the Scoliosis Database --- p.100 / Chapter 7.2.1 --- Results of Causality and Structure Analysis --- p.100 / Chapter 7.2.2 --- Results of Rule Learning --- p.102 / Chapter 8 --- Conclusion and Future Work --- p.106 / Bibliography --- p.109 / Chapter A --- The Rule Sets Discovered --- p.116 / Chapter A.1 --- The Best Rule Set Learned from the Iris Database --- p.116 / Chapter A.2 --- The Best Rule Set Learned from the Monk Database --- p.116 / Chapter A.2.1 --- Monkl --- p.116 / Chapter A.2.2 --- Monk2 --- p.117 / Chapter A.2.3 --- Monk3 --- p.119 / Chapter A.3 --- The Best Rule Set Learned from the Fracture Database --- p.120 / Chapter A.3.1 --- Type I Rules: About Diagnosis --- p.120 / Chapter A.3.2 --- Type II Rules : About Operation/Surgeon --- p.120 / Chapter A.3.3 --- Type III Rules : About Stay --- p.122 / Chapter A.4 --- The Best Rule Set Learned from the Scoliosis Database --- p.123 / Chapter A.4.1 --- Rules for Classification --- p.123 / Chapter A.4.2 --- Rules for Treatment --- p.126 / Chapter B --- The Grammar used for the fracture and Scoliosis databases --- p.128 / Chapter B.1 --- The grammar for the fracture database --- p.128 / Chapter B.2 --- The grammar for the Scoliosis database --- p.128

Current cryptographic algorithms pose high standards of security yet they are susceptible to side-channel analysis (SCA). When it comes to implementation, the hardness of cryptography dangles on the weak link of side-channel information leakage. The widely adopted AES encryption algorithm, and others, can be easily broken when they are implemented without any resistance to SCA. This work applies state of the art techniques, namely Secret Sharing and Secure Multiparty Computation (SMC), on AES-128 encryption as a countermeasure to those attacks. This embedded C implementation explores multiple time-memory trade-offs for the design of its fundamental components, SMC and field arithmetic, to meet a variety of execution and storage demands. The performance and leakage assessment of this implementation for an ARM based micro-controller demonstrate the capabilities of masking schemes and prove their feasibility on embedded software.