In this thesis, a linear interpolation direct digital frequency synthesizer (DDFS) with improved structure to simplify the hardware complexity by correction block is proposed. Correction block is mainly used to compensate for the error curve of linear interpolation DDFS. From the analysis of these error curves, these error curves have similar behavior between each others. After selecting an error curve, the other error curves can be derived and multiplied by a fixed scale. From the simulation results, the correction block using the above method can improve about 12 dB spurious frequency dynamic range (SFDR). The goal of the DDFS designed in this thesis is to achieve 80 dB SFDR. Minimum required number of bits for each block in the proposed DDFS is carefully selected by simulation. In general, DDFS with piecewise linear interpolation theoretically needs 32 segments of piecewise linear interpolation to achieve 84 dB SFDR. In this thesis, 16 segments of piecewise linear interpolation with correction block can achieve the target SFDR. The chip¡¦s simulation is implemented by TSMC standard 0.13um 1P8M CMOS process with core area 78.11 x 77.49 um2.

No description available.

We propose a dynamic programming (DP) approach for pricing American options over a finite time horizon. We model uncertainty in stock price that follows geometric Brownian motion (GBM) and let interest rate and volatility be fixed. A procedure based on dynamic programming combined with piecewise linear interpolation approximation is developed to price the value of options. And we introduce the free boundary problem into our model. Numerical experiments illustrate the relation between value of option and volatility.

In oil industry, it is essential to have the knowledge of the stratified rocksâ lithology and, as consequence, where are placed the oil and the natural gases reserves, in order to efficiently drill the soil, without a major expense. In this context, the analysis of seismological data is highly relevant for the extraction of such hydrocarbons, producing predictions of profiles through reflection of mechanical waves in the soil. The image of the seismic mapping produced by wave refraction and reflection into the soil can be analysed to find geological formations of interest. In 1978, H. Sakoe et al. defined a model called Dynamic Time Warping (DTW)[23] for the local detection of similarity between two time series. We apply the Dynamic Time Warping Interpolation (DTWI) strategy to interpolate and simulate a seismic landscape formed by 129 depth-dependent sequences of length 201 using different values of known sequences m, where m = 2, 3, 5, 9, 17, 33, 65. For comparison, we done the same operation of interpolation using a Standard Linear Interpolation (SLI). Results show that the DTWI strategy works better than the SLI when m = 3, 5, 9, 17, or rather when distance between the known series has the same order size of the soil layers.

Globally increasing levels of bandwidth and capacity requirements force the optical communications industry to produce new products that are faster, more powerful, and more efficient. In particular, optical-electronic-optical (O-E-O) conversions in Wavelength Division Multiplexing (WDM) mechanisms prevent higher data transfer speeds and create a serious bottleneck for optical communications. These O-E-O transitions are mostly encountered in the Wavelength converters of WDMs, and as a result, all-optical wavelength conversion methods have become extremely important. The main discussion in this thesis will concentrate on a specific all-optical wavelength conversion mechanism. In this mechanism, photonic crystal structures are integrated with moving MEMS/NEMS structures to create a state-of-the-art all-optical wavelength converter prototype. A wavelength conversion of 20% is achieved using this structure. Since the interaction of light with moving MEMS/NEMS structures plays an important role in the proposed wavelength conversion mechanism, modeling and simulation of electromagnetic waves becomes a very crucial step in the design process. Consequently, a subsection of this thesis will focus on a proposed enhancement to the finite-difference time-domain (FDTD) to model moving structures more efficiently and more realistically. This technique is named "Linear Dielectric Interpolation" and will be applied to more realistically and efficiently model the proposed photonic crystal MEMS/NEMS wavelength conversion mechanism.

Made available in DSpace on 2017-05-12T14:47:11Z (GMT). No. of bitstreams: 1 Parte_ 1.pdf: 2323383 bytes, checksum: ed140b1811bbee3ce5c6779946931193 (MD5) Previous issue date: 2015-02-12 / The objective of this research was to regionalize the minimum seven-day flows, annual average long-term duration, maximum and permanence flows of 90 and 95% of the catchment area of the Piquiri River - PR. The peak flows were regionalized associated with a specific return period (2, 5, 10, 25, 50 and 100 years) and the minimum flow lasting seven days was associated with a 10-year return period. To represent the series of maximum and minimum flows probability Pearson type III distributions, two- and three-parameter Log-normal and type III Log-Pearson, Gumbel (for maximum flows) and Weibull (for minimum flows only) were used. Type III Log-Pearson distribution obtained in 100% of cases, the lowest standard error, presenting the best adjustment with the minimum flow data. About 70% of the data stations showed the lowest standard error when adjusted to this three- parameter log-normal distribution. Thus, the three-parameter Log-Normal distribution was adopted as default, but stations 64765000 (Porto Paiquerê), 64771500 (Porto Guarani), 64785000 (Goio Bang Bridge), which did not obtain adjustment with this distribution, used the two-parameter Log-Normal distribution. The period average flow, once it is characterized as the average of the annual average flow, was regionalized without considering the risk level. In order to obtain the permanence curve, the procedure based on obtaining the frequency classes was carried out. In the regionalization procedure the following methods were employed: the traditional method described by Eletrobrás (1985a), the linear interpolation method (ELETROBRÁS, 1985b), the method proposed by Chaves et al. (2002), the modified linear interpolation and the modified Chaves (NOVAES et al., 2007). As explanatory variables for the traditional method, the following physical characteristics were used: drainage area; the length of the main river; the basin mean land slope; the mean land slope of the main river; drainage density, and climatic characteristics: the total annual rainfall; the precipitation of the wettest quarter; the precipitation of the driest quarter. The regression models that best fit the flow data are the simple potential and the multiple potential ones. The area and the density drainage are the best explanatory variables for the estimate the minimum seven-day flow and ten-year return period (Q7,10). The length of the main river is the best explanatory variable for the estimate of flow rates of 90 and 95% of permanence (Q90 and Q95, respectively). The area and the drainage density are the best explanatory variables to estimate the minimum seven-day flow and the ten-year return period of (Q7,10), the length of the main river and the area for the flow estimate with 90 and 95% of permanence (Q90 and Q95, respectively) and the length of the main river is the best explanatory variable for the estimate of maximum flows considering all return periods studied. The method of linear interpolation produces similar estimates to the ones obtained with the Conventional method and can be used in situations, especially when there is sufficient information for adjustment of the regression models. Estimates of minimum flows (Q7,10, Q90 and Q95) and of average flow (Qmed), performed by using the Chaves method are similar to the ones obtained with the Conventional method , while the estimates of peak flows for all return periods studied, presented major errors. The modified methods did not promote significant improvement of the estimates compared to the original methods. / O objetivo deste trabalho foi regionalizar as vazões mínimas com sete dias de duração, média anual de longa duração, máxima e vazões de permanência de 90 e 95% da bacia hidrográfica do Rio Piquiri - PR. As vazões máximas foram regionalizadas associadas a um período de retorno específico (2, 5, 10, 25, 50 e 100 anos) e a mínima com duração de sete dias foi associada ao período de retorno de 10 anos. Para representar as séries de vazões máximas e mínimas foram utilizadas as distribuições de probabilidade de Pearson tipo III, Log-Normal a dois e três parâmetros e Log-Pearson tipo III, Gumbel (apenas para máximas) e Weibull (apenas para mínima). A distribuição Log-pearson tipo III obteve em 100% dos casos, o menor erro padrão, apresentando-se com o melhor ajuste aos dados de vazão mínima. Cerca de 70% dos dados das estações apresentaram o menor erro padrão quando ajustadas a esta distribuição Log-Normal a três parâmetros. Desta maneira a distribuição Log-Normal a três parâmetros, foi adotada de forma padrão para as vazões máximas, porém as estações 64765000 (Porto Paiquerê), 64771500 (Porto Guarani), 64785000 (Ponte do Goio-Bang) que não obtiveram ajuste a esta distribuição, utilizou a distribuição Log-Normal a dois parâmetros. A vazão média de longo período, por ser caracterizada como a média das vazões médias anuais, foi regionalizada sem considerar o nível de risco. Para a obtenção da curva de permanência realizou-se o procedimento baseado na obtenção de classes de frequência. No procedimento de regionalização foram empregados: o método Tradicional descrito por Eletrobrás (1985a), o método de Interpolação linear (ELETROBRÁS, 1985b), o método de Chaves et al. (2002), Interpolação linear modificado e Chaves modificado (NOVAES et al., 2007). Como variáveis explicativas, para o método Tradicional, foram utilizadas as características físicas: área de drenagem; o comprimento do rio principal; declividade média da bacia; declividade média do rio principal; densidade de drenagem, e as características climáticas: precipitação total anual; precipitação do trimestre mais chuvoso; precipitação do trimestre mais seco. Os modelos de regressão que melhor se ajustam aos dados de vazão são o potencial simples e o potencial múltiplo. A área e a densidade de drenagem são as melhores variáveis explicativas para a estimativa da vazão mínima com duração de sete dias e período de retorno de dez anos (Q7,10). O comprimento do rio principal é a melhor variável explicativa para a estimativa das vazões com 90 e 95% de permanência (Q90 e Q95, respectivamente). A área e a densidade de drenagem são as melhores variáveis explicativas para a estimativa da vazão mínima com duração de sete dias e período de retorno de dez anos (Q7,10), o comprimento do rio principal e a área para a estimativa das vazões com 90 e 95% de permanência (Q90 e Q95, respectivamente) e o comprimento do rio principal é a melhor variável explicativa para a estimativa das vazões máximas considerando todos os períodos de retorno estudados. O método da interpolação linear faz estimativas semelhantes ao método Tradicional e pode ser utilizado em situações, principalmente quando não há informações suficientes para o ajuste dos modelos de regressão. As estimativas das vazões mínimas (Q7,10, Q90 e Q95) e vazão média (Qmed), realizadas pelo método de Chaves são semelhantes ao Tradicional, enquanto que as estimativas das vazões máximas, para todos os períodos de retorno estudados, apresentaram erros muito elevados. Os métodos modificados não promoveram a melhora expressiva das estimativas em comparação com os métodos originais

This paper explores how known machine learning techniques can be applied in unique ways to simplify software and therefore dramatically increase its usability. As software has increased in popularity, its complexity has increased in lockstep, to a point where it has become burdensome. By shifting the focus from the software to the user, great advances can be achieved by way of simplification. The example problem used in this report is well known: suggest local dining choices tailored to a specific person based on known habits and those of similar people. By analyzing past choices and applying likely probabilities, assumptions can be made to reduce user interaction, allowing the user to realize the benefits of the software faster and more frequently. This is accomplished with Java Servlets, Apache Mahout machine learning libraries, and various third party resources to gather dimensions on each recommendation. / text

Submitted by Jean Medeiros (jeanletras@uepb.edu.br) on 2017-10-23T13:03:47Z No. of bitstreams: 1 PDF - José Alberto Souza Paulino.pdf: 32306126 bytes, checksum: d8a3a4c1c537e36b743590e029308f8c (MD5) / Approved for entry into archive by Secta BC (secta.csu.bc@uepb.edu.br) on 2017-10-26T16:55:17Z (GMT) No. of bitstreams: 1 PDF - José Alberto Souza Paulino.pdf: 32306126 bytes, checksum: d8a3a4c1c537e36b743590e029308f8c (MD5) / Made available in DSpace on 2017-10-26T16:55:17Z (GMT). No. of bitstreams: 1 PDF - José Alberto Souza Paulino.pdf: 32306126 bytes, checksum: d8a3a4c1c537e36b743590e029308f8c (MD5) Previous issue date: 2017-05-08 / This research proposes to evaluate and implement a solution for metal artifact reduc- tion in computed tomography, this one aiming to meet a demand from the prototyping laboratory of the Núcleo de Tecnologias Estratégicas em Saúde (Nutes) da Univer- sidade Estadual da Paraíba, where impressions of biomodels are made for surgical planning. The CT affected by metal artifacts need to be corrected prior to the printing process, this manual intervention implies excessive delay for delivery of the biomodels. The development of the proposed solution is based on the sinogram correction method which according to Mouton et al (2013) and Gjesteby (2016) is the most utilized method for reducing metal artifacts and makes uses of linear interpolation to correction the cor- rupted data. In order to validate the preference for linear interpolation in the state of the art, others interpolative techniques were implemented and evaluated; Fist through simulations and then by a form for qualitative evaluation, upon which statistical tests were applied. The results obtained confirm the use of interpolation as the best option for the reconstruction of data corrupted by metallic artifacts. / Esta pesquisa se propõe a avaliar e implementar uma solução para redução de artefatos metálicos em tomografias computadorizadas, solução esta que visa atender uma demanda do laboratório de prototipagem do Núcleo de Tecnologias Estratégicas em Saúde (Nutes) da Universidade Estadual da Paraíba, onde são realizadas impressões de biomodelos para planejamentos cirúrgicos. As tomografias afetadas por artefatos metálicos necessitam de correção antes do processo de impressão, esta intervenção realizada de forma manual implica em demora excessiva para entrega dos biomodelos. O desenvolvimento da solução proposta baseia-se no método de correção de sinograma que, de acordo com Mouton et al (2013) e Gjesteby (2016), é o método mais difundido para redução de artefatos metálicos e faz uso da técnica de interpolação linear para correção dos dados corrompidos. Objetivando validar a preferência pelo uso da interpolação linear no estado da arte, foram implementadas outras técnicas interpolativas as quais foram submetidas a avaliação; Primeiro por meio de simulações e depois via fomulário para avaliação qualitativa, na qual foram aplicados testes estatísticos. Os resultados obtidos ratificam o uso da interpolação linear como melhor opção para reconstrução de dados corrompidos pelos artefatos metálicos.

Made available in DSpace on 2017-07-10T19:23:57Z (GMT). No. of bitstreams: 1 Parte_ 1.pdf: 2323383 bytes, checksum: ed140b1811bbee3ce5c6779946931193 (MD5) Previous issue date: 2015-02-12 / The objective of this research was to regionalize the minimum seven-day flows, annual average long-term duration, maximum and permanence flows of 90 and 95% of the catchment area of the Piquiri River - PR. The peak flows were regionalized associated with a specific return period (2, 5, 10, 25, 50 and 100 years) and the minimum flow lasting seven days was associated with a 10-year return period. To represent the series of maximum and minimum flows probability Pearson type III distributions, two- and three-parameter Log-normal and type III Log-Pearson, Gumbel (for maximum flows) and Weibull (for minimum flows only) were used. Type III Log-Pearson distribution obtained in 100% of cases, the lowest standard error, presenting the best adjustment with the minimum flow data. About 70% of the data stations showed the lowest standard error when adjusted to this three- parameter log-normal distribution. Thus, the three-parameter Log-Normal distribution was adopted as default, but stations 64765000 (Porto Paiquerê), 64771500 (Porto Guarani), 64785000 (Goio Bang Bridge), which did not obtain adjustment with this distribution, used the two-parameter Log-Normal distribution. The period average flow, once it is characterized as the average of the annual average flow, was regionalized without considering the risk level. In order to obtain the permanence curve, the procedure based on obtaining the frequency classes was carried out. In the regionalization procedure the following methods were employed: the traditional method described by Eletrobrás (1985a), the linear interpolation method (ELETROBRÁS, 1985b), the method proposed by Chaves et al. (2002), the modified linear interpolation and the modified Chaves (NOVAES et al., 2007). As explanatory variables for the traditional method, the following physical characteristics were used: drainage area; the length of the main river; the basin mean land slope; the mean land slope of the main river; drainage density, and climatic characteristics: the total annual rainfall; the precipitation of the wettest quarter; the precipitation of the driest quarter. The regression models that best fit the flow data are the simple potential and the multiple potential ones. The area and the density drainage are the best explanatory variables for the estimate the minimum seven-day flow and ten-year return period (Q7,10). The length of the main river is the best explanatory variable for the estimate of flow rates of 90 and 95% of permanence (Q90 and Q95, respectively). The area and the drainage density are the best explanatory variables to estimate the minimum seven-day flow and the ten-year return period of (Q7,10), the length of the main river and the area for the flow estimate with 90 and 95% of permanence (Q90 and Q95, respectively) and the length of the main river is the best explanatory variable for the estimate of maximum flows considering all return periods studied. The method of linear interpolation produces similar estimates to the ones obtained with the Conventional method and can be used in situations, especially when there is sufficient information for adjustment of the regression models. Estimates of minimum flows (Q7,10, Q90 and Q95) and of average flow (Qmed), performed by using the Chaves method are similar to the ones obtained with the Conventional method , while the estimates of peak flows for all return periods studied, presented major errors. The modified methods did not promote significant improvement of the estimates compared to the original methods. / O objetivo deste trabalho foi regionalizar as vazões mínimas com sete dias de duração, média anual de longa duração, máxima e vazões de permanência de 90 e 95% da bacia hidrográfica do Rio Piquiri - PR. As vazões máximas foram regionalizadas associadas a um período de retorno específico (2, 5, 10, 25, 50 e 100 anos) e a mínima com duração de sete dias foi associada ao período de retorno de 10 anos. Para representar as séries de vazões máximas e mínimas foram utilizadas as distribuições de probabilidade de Pearson tipo III, Log-Normal a dois e três parâmetros e Log-Pearson tipo III, Gumbel (apenas para máximas) e Weibull (apenas para mínima). A distribuição Log-pearson tipo III obteve em 100% dos casos, o menor erro padrão, apresentando-se com o melhor ajuste aos dados de vazão mínima. Cerca de 70% dos dados das estações apresentaram o menor erro padrão quando ajustadas a esta distribuição Log-Normal a três parâmetros. Desta maneira a distribuição Log-Normal a três parâmetros, foi adotada de forma padrão para as vazões máximas, porém as estações 64765000 (Porto Paiquerê), 64771500 (Porto Guarani), 64785000 (Ponte do Goio-Bang) que não obtiveram ajuste a esta distribuição, utilizou a distribuição Log-Normal a dois parâmetros. A vazão média de longo período, por ser caracterizada como a média das vazões médias anuais, foi regionalizada sem considerar o nível de risco. Para a obtenção da curva de permanência realizou-se o procedimento baseado na obtenção de classes de frequência. No procedimento de regionalização foram empregados: o método Tradicional descrito por Eletrobrás (1985a), o método de Interpolação linear (ELETROBRÁS, 1985b), o método de Chaves et al. (2002), Interpolação linear modificado e Chaves modificado (NOVAES et al., 2007). Como variáveis explicativas, para o método Tradicional, foram utilizadas as características físicas: área de drenagem; o comprimento do rio principal; declividade média da bacia; declividade média do rio principal; densidade de drenagem, e as características climáticas: precipitação total anual; precipitação do trimestre mais chuvoso; precipitação do trimestre mais seco. Os modelos de regressão que melhor se ajustam aos dados de vazão são o potencial simples e o potencial múltiplo. A área e a densidade de drenagem são as melhores variáveis explicativas para a estimativa da vazão mínima com duração de sete dias e período de retorno de dez anos (Q7,10). O comprimento do rio principal é a melhor variável explicativa para a estimativa das vazões com 90 e 95% de permanência (Q90 e Q95, respectivamente). A área e a densidade de drenagem são as melhores variáveis explicativas para a estimativa da vazão mínima com duração de sete dias e período de retorno de dez anos (Q7,10), o comprimento do rio principal e a área para a estimativa das vazões com 90 e 95% de permanência (Q90 e Q95, respectivamente) e o comprimento do rio principal é a melhor variável explicativa para a estimativa das vazões máximas considerando todos os períodos de retorno estudados. O método da interpolação linear faz estimativas semelhantes ao método Tradicional e pode ser utilizado em situações, principalmente quando não há informações suficientes para o ajuste dos modelos de regressão. As estimativas das vazões mínimas (Q7,10, Q90 e Q95) e vazão média (Qmed), realizadas pelo método de Chaves são semelhantes ao Tradicional, enquanto que as estimativas das vazões máximas, para todos os períodos de retorno estudados, apresentaram erros muito elevados. Os métodos modificados não promoveram a melhora expressiva das estimativas em comparação com os métodos originais

In this study, universal motion command generator systems utilizing a Field Programmable Gate Array (FPGA) and an interface board for Robotics and Computer Numerical Control (CNC) applications have been developed. These command generation systems can be classified into two main groups as polynomial approximation and data compression based methods. In the former type of command generation methods, the command trajectory is firstly divided into segments according to the inflection points. Then, the segments are approximated using various polynomial techniques. The sequence originating from modeling error can be further included to the generated series. In the second type, higher-order differences of a given trajectory (i.e. position) are computed and the resulting data are compressed via lossless data compression techniques. Besides conventional approaches, a novel compression algorithm is also introduced in the study. This group of methods is capable of generating trajectory data at variable rates in forward and reverse directions. The generation of the commands is carried out according to the feed-rate (i.e. the speed along the trajectory) set by the external logic dynamically. These command generation techniques are implemented in MATLAB and then the best ones from each group are realized using FPGAs and their performances are assessed according to the resources used in the FPGA chip, the speed of command generation, and the memory size in Static Random Access Memory (SRAM) chip located on the development board.