Made available in DSpace on 2014-06-12T15:59:01Z (GMT). No. of bitstreams: 2 arquivo4812_1.pdf: 2843132 bytes, checksum: cf053779fad5d73c77a2b107542256b3 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2003 / Ricardo de Melo Queiroz, Sérgio; de Assis Tenório Carvalho, Francisco. Group recommendation strategies based on collaborative filtering. 2003. Dissertação (Mestrado). Programa de Pós-Graduação em Ciência da Computação, Universidade Federal de Pernambuco, Recife, 2003.

Fingerprints are the oldest and most used form of biometric identification. A critical step is reliable extract minutiae from the fingerprint images. However fingerprint images are rarely of perfect quality, they may be degraded and corrupted due to natural variations in skin and sensing conditions. Thus, image enhancement techniques are necessary prior to minutiae extraction. This work includes implementation of three techniques for fingerprint image enhancement, minutiae extraction and consturction of fingerprint reading device. Experiments are realized with two sets of fingerprints to evaluate the performance of implemented techniques.

This master's thesis describes creation of recommender system that is used in real server cbdb.cz. A~fully operational recommender system was developed using collaborative and content-based filtering techniques. Thanks to many user feedback, we were able to evaluate their opinion. Many recommended books were tagged as desirable. This thesis is extending current functionality of cbdb.cz with recommender system. This system uses its extensive database of ratings, users and books.

This thesis deals with problems of recommender systems and their usage in web applications. There are three main data mining techniques summarized and individual approaches for recommendation. Main part of this thesis is a suggestion and an implementation of web applications for recommending dishes from restaurants. Algorithm for food recommending is designed and implemented in this paper. The algorithm deals with the problem of frequently changing items. The algorithm utilizes hybrid filtering technique which is based on content and knowledge. This filtering technique uses cosine vector similarity for computation.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第21091号 / 工博第4455号 / 新制||工||1692(附属図書館) / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 五十嵐 晃, 教授 八木 知己, 准教授 古川 愛子 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

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Sound pressure equalization of audio signals using digital signal processors has been a subject of ongoing study for many years. The traditional approach is to equalize sound at a point in a listening environment, but because of its specific dependence on the room frequency response between a source and receiver position, this equalization generally causes the spectral response to worsen significantly at other locations in the room. This work presents both a time-invariant and a time-varying implementation of an adaptive acoustic energy density equalization filter for a one-dimensional sound field. Energy density equalization addresses the aforementioned challenge and others that relate to sound equalization. The theory and real-time implementation of time-invariant sound pressure and energy density equalizers designed using the least-squares method are presented, and their performances are compared. An implementation of a time-varying energy density equalizer is also presented. Time-invariant equalization results based on real-time measurements in a plane-wave tube are presented. A sound pressure equalizer results in a nearly flat spectral magnitude at the point of equalization. However, it causes the frequencies corresponding to spatial nulls at that point to be undesirably boosted elsewhere in the sound field, where those nulls do not exist at the same frequencies. An energy density equalization filter identifies and compensates for all resonances and other global spectral effects of the tube and loudspeaker. It does not attempt to equalize the spatially varying frequency nulls caused by local pressure nodes at the point of equalization. An implementation of a time-varying energy density equalizer is also presented. This method uses the filtered-x filter update to adjust the filter coefficients in real-time to adapt to changes in the sound field. Convergence of the filter over time is demonstrated as the closed end of the tube is opened, then closed once again. Thus, the research results demonstrate that an acoustic energy density filter can be used to time-adaptively equalize global spectral anomalies of a loudspeaker and a one-dimensional sound field.

The study of nonlinear dynamical systems via the spectrum of the Koopman operator has emerged as a paradigm shift, from the Poincaré's geometric picture that centers the attention on the evolution of states, to the Koopman operator's picture that focuses on the evolution of observables. The Koopman operator-theoretic framework rests on the idea of lifting the states of a nonlinear dynamical system to a higher dimensional space; these lifted states are referred to as the Koopman eigenfunctions. To determine the Koopman eigenfunctions, one performs a nonlinear transformation of the states by relying on the so-called observables, that is, scalar-valued functions of the states. In other words, one executes a change of coordinates from the state space to another set of coordinates, which are denominated Koopman canonical coordinates. The variables defined on these intrinsic coordinates will evolve linearly in time, despite the underlying system being nonlinear. Since the Koopman operator is linear, it is natural to exploit its spectral properties. In fact, the theory surrounding the spectral properties of linear operators has well-known implications in electric power systems. Examples include small-signal stability analysis and direct methods for transient stability analysis based on the Lyapunov function. From the applications' standpoint, this framework based on the Koopman operator is attractive because it is capable of revealing linear and nonlinear modes by only applying well-established tools that have been developed for linear systems. With the challenges associated with the high-dimensionality and increasing uncertainties in the power systems models, researchers and practitioners are seeking alternative modeling approaches capable of incorporating information from measurements. This is fueled by an increasing amount of data made available by the wide-scale deployment of measuring devices such as phasor measurement units and smart meters. Along these lines, the Koopman operator theory is a promising framework for the integration of data analysis into our mathematical knowledge and is bringing an exciting perspective to the community. The present dissertation reports on the application of the Koopman operator for identification, estimation, and control of electric power systems. A dynamic state estimator based on the Koopman operator has been developed and compares favorably against model-based approaches, in particular for centralized dynamic state estimation. Also, a data-driven method to compute participation factors for nonlinear systems based on Koopman mode decomposition has been developed; it generalizes the original definition of participation factors under certain conditions. / PHD / Electric power systems are complex, large-scale, and given the bidirectional causality between economic growth and electricity consumption, they are constantly being expanded. In the U.S., some of the electric power grid facilities date back to the 1880s, and this aging system is operating at its capacity limits. In addition, the international pressure for sustainability is driving an unprecedented deployment of renewable energy sources into the grid. Unlike the case of other primary sources of electric energy such as coal and nuclear, the electricity generated from renewable energy sources is strongly influenced by the weather conditions, which are very challenging to forecast even for short periods of time. Within this context, the mathematical models that have aided engineers to design and operate electric power grids over the past decades are falling short when uncertainties are incorporated to the models of such high-dimensional systems. Consequently, researchers are investigating alternative data-driven approaches. This is not only motivated by the need to overcome the above challenges, but it is also fueled by the increasing amount of data produced by today’s powerful computational resources and experimental apparatus. In power systems, a massive amount of data will be available thanks to the deployment of measuring devices called phasor measurement units. Along these lines, the Koopman operator theory is a promising framework for the integration of data analysis into our mathematical knowledge, and is bringing an exciting perspective on the treatment of high-dimensional systems that lie in the forefront of science and technology. In the research work reported in this dissertation, the Koopman operator theory has been exploited to seek for solutions to some of the challenges that are threatening the safe, reliable, and efficient operation of electric power systems.

Master of Science / Statistics / Nora M. Bello / Big bluestem (Andropogon gerardii) is a wide-ranging dominant prairie grass of ecological and agricultural importance to the US Midwest while edaphic subspecies sand bluestem (A. gerardii ssp. Hallii) grows exclusively on sand dunes. Sand bluestem exhibits phenotypic divergence related to epicuticular properties and enhanced drought tolerance relative to big bluestem. Understanding the mechanisms underlying differential drought tolerance is relevant in the face of climate change. For bluestem subspecies, presence or absence of these phenotypes may be associated with RNA transcripts characterized by low number of read counts. So called low-count transcripts pose particular inferential challenges and are thus usually filtered out at early steps of data management protocols and ignored for analyses. In this study, we use a plasmode-based approach to assess the relative performance of alternative inferential strategies on RNA-seq transcripts, with special emphasis on low-count transcripts as motivated by differential bluestem phenotypes. Our dataset consists of RNA-seq read counts for 25,582 transcripts (60% of which are classified as low-count) collected from leaf tissue of 4 individual plants of big bluestem and 4 of sand bluestem. We also compare alternative ad-hoc data filtering techniques commonly used in RNA-seq pipelines and assess the performance of recently developed statistical methods for differential expression (DE) analysis, namely DESeq2 and edgeR robust. These methods attempt to overcome the inherently noisy behavior of low-count transcripts by either shrinkage or differential weighting of observations, respectively. Our results indicate that proper specification of DE methods can remove the need for ad- hoc data filtering at arbitrary expression threshold, thus allowing for inference on low-count transcripts. Practical recommendations for inference are provided when low-count RNA-seq transcripts are of interest, as is the case in the comparison of subspecies of bluestem grasses. Insights from this study may also be relevant to other applications also focused on transcripts of low expression levels.

ITC/USA 2006 Conference Proceedings / The Forty-Second Annual International Telemetering Conference and Technical Exhibition / October 23-26, 2006 / Town and Country Resort & Convention Center, San Diego, California / Recent advancements in IRIG 106 Chapter 10 recording systems allow the recording of all on board 1553 bus and PCM traffic to a single media. These advancements have also brought about the issue of extracting data with different levels of classification that was written to single location. Carrying GPS “smart” weapons further complicates this issue since the recording of GPS keys adds another level of classification to the mix. The ability to separate and/or remove higher level data from a data product is now required. This paper describes the design of a hardware device that will filter specified data from IRIG 106 Chapter 10 recorder memory modules (RMMs) to prevent the storage device or computer from becoming classified at the level of the specified data.