Ao contrário da recomendação tradicional do treinamento de força, a proposta do método de pré-exaustão é iniciar a sessão de treino com exercícios monoarticulares e terminar com exercícios multiarticulares. O objetivo deste estudo foi, por meio da EMG, investigar parâmetros temporais e de intensidade da ativação dos músculos peitoral maior (PM), deltóide (DA) e tríceps braquial (TB) que possam fundamentar a aplicação do método de pré-exaustão em 10RM dos exercícios supino e crucifixo. Foram comparados dois protocolos experimentais: P1) método da préexaustão; P2) recomendações tradicionais. A intensidade de ativação baseada no valor RMS, bem como a relação desta com a duração da contração muscular, estabelecida em faixas de intensidade, não obteve diferenças estatisticamente significativas para PM. Para DA, não houve diferenças estatisticamente significativas entre os protocolos na intensidade de ativação quando as repetições foram analisadas em conjunto. Entretanto, quando analisado cada repetição, este músculo apresentou aumento estatisticamente significativo de intensidade de ativação em P1, assim como maior solicitação da faixa de intensidade 80 a 100% CIVM. Para TB, a intensidade de ativação foi significativamente maior em P1 que em P2 para todas as formas de análise. Os resultados mostraram que o aparelho locomotor aumentou a dependência de TB como estratégia alternativa para tentar atingir 10RM do supino em P1. Assim, é possível afirmar que o método de pré-exaustão pode ser eficiente para impor maior estímulo neural sobre pequenos grupos acessórios na execução de um movimento e não sobre o grupo principal o qual se deseja. Entretanto, estes achados suportam que os efeitos do método de pré-exaustão ainda não podem ser afirmados categoricamente. Pois, ao longo da série em P1 não houve aumento significativo na intensidade de ativação de um mesmo músculo, bem como das faixas de intensidade, como houve em P2. Desse modo, é possível afirmar que os músculos, em P1, iniciaram a série em um nível de intensidade mais alto que em P2, pois foram estimulados previamente / Contrariwise the strength training traditional recommendation, the preexhaustion method purposes to begin a training session with monoarticular exercises and to finish it with multiarticular exercises. The aim of this study was, through EMG, to inquire into temporal and activation intensity parameters of pectoralis major (PM), deltoid (DA) and triceps brachii (TB) muscles, which can be used as a basis to bench press and flying 10RM pre-exhaustion method application. It was compared two experimental protocols: P1) pre-exhaustion method; P2) traditional recommendation. The activation intensity, as well its relationship with the muscular contraction duration, established on intensity levels, did not attain significant differences to PM. To DA, there were not differences between the protocols respecting the activation intensity when whole the repetitions were analyzed. However, when each repetition was analyzed, this muscle exhibited significant increasing in activation intensity in P1; as well it showed a more intense solicitation of 80 to 100% MVIC level. To TB, the activation intensity was significant greater in P1 than P2 respecting whole manners to data analysis. The results exhibited that the locomotor apparatus increased the TB dependence as an alternative strategy to try to attain bench press 10RM in P1. Therefore, it is possible to assert that pre-exhaustion method may be efficient to impose largest neural stimuli on small synergists muscular groups during movement execution, but not on the main target muscular group. However, these findings sustain that the pre-exhaustion method effects cannot receive a categorical affirmation, yet. Because, contrariwise the P2, during the P1 bench press set there was not significant increasing in the same muscle activation intensity, as well in the intensity levels. This way, it is possible to assert that, in P1 the muscles began the set in a highest intensity levels than in P2, because they were stimulated previously

Ao contrário da recomendação tradicional do treinamento de força, a proposta do método de pré-exaustão é iniciar a sessão de treino com exercícios monoarticulares e terminar com exercícios multiarticulares. O objetivo deste estudo foi, por meio da EMG, investigar parâmetros temporais e de intensidade da ativação dos músculos peitoral maior (PM), deltóide (DA) e tríceps braquial (TB) que possam fundamentar a aplicação do método de pré-exaustão em 10RM dos exercícios supino e crucifixo. Foram comparados dois protocolos experimentais: P1) método da préexaustão; P2) recomendações tradicionais. A intensidade de ativação baseada no valor RMS, bem como a relação desta com a duração da contração muscular, estabelecida em faixas de intensidade, não obteve diferenças estatisticamente significativas para PM. Para DA, não houve diferenças estatisticamente significativas entre os protocolos na intensidade de ativação quando as repetições foram analisadas em conjunto. Entretanto, quando analisado cada repetição, este músculo apresentou aumento estatisticamente significativo de intensidade de ativação em P1, assim como maior solicitação da faixa de intensidade 80 a 100% CIVM. Para TB, a intensidade de ativação foi significativamente maior em P1 que em P2 para todas as formas de análise. Os resultados mostraram que o aparelho locomotor aumentou a dependência de TB como estratégia alternativa para tentar atingir 10RM do supino em P1. Assim, é possível afirmar que o método de pré-exaustão pode ser eficiente para impor maior estímulo neural sobre pequenos grupos acessórios na execução de um movimento e não sobre o grupo principal o qual se deseja. Entretanto, estes achados suportam que os efeitos do método de pré-exaustão ainda não podem ser afirmados categoricamente. Pois, ao longo da série em P1 não houve aumento significativo na intensidade de ativação de um mesmo músculo, bem como das faixas de intensidade, como houve em P2. Desse modo, é possível afirmar que os músculos, em P1, iniciaram a série em um nível de intensidade mais alto que em P2, pois foram estimulados previamente / Contrariwise the strength training traditional recommendation, the preexhaustion method purposes to begin a training session with monoarticular exercises and to finish it with multiarticular exercises. The aim of this study was, through EMG, to inquire into temporal and activation intensity parameters of pectoralis major (PM), deltoid (DA) and triceps brachii (TB) muscles, which can be used as a basis to bench press and flying 10RM pre-exhaustion method application. It was compared two experimental protocols: P1) pre-exhaustion method; P2) traditional recommendation. The activation intensity, as well its relationship with the muscular contraction duration, established on intensity levels, did not attain significant differences to PM. To DA, there were not differences between the protocols respecting the activation intensity when whole the repetitions were analyzed. However, when each repetition was analyzed, this muscle exhibited significant increasing in activation intensity in P1; as well it showed a more intense solicitation of 80 to 100% MVIC level. To TB, the activation intensity was significant greater in P1 than P2 respecting whole manners to data analysis. The results exhibited that the locomotor apparatus increased the TB dependence as an alternative strategy to try to attain bench press 10RM in P1. Therefore, it is possible to assert that pre-exhaustion method may be efficient to impose largest neural stimuli on small synergists muscular groups during movement execution, but not on the main target muscular group. However, these findings sustain that the pre-exhaustion method effects cannot receive a categorical affirmation, yet. Because, contrariwise the P2, during the P1 bench press set there was not significant increasing in the same muscle activation intensity, as well in the intensity levels. This way, it is possible to assert that, in P1 the muscles began the set in a highest intensity levels than in P2, because they were stimulated previously

ITC/USA 2010 Conference Proceedings / The Forty-Sixth Annual International Telemetering Conference and Technical Exhibition / October 25-28, 2010 / Town and Country Resort & Convention Center, San Diego, California / This work explores aeronautical telemetry communication performance with the SOQPSK- TG ARTM waveforms when frequency-selective multipath corrupts received information symbols. A multi-antenna equalization scheme is presented where each antenna's unique multipath channel is equalized using a pilot-aided optimal linear minimum mean-square error filter. Following independent channel equalization, a maximal ratio combining technique is used to generate a single receiver output for detection. This multi-antenna equalization process is shown to improve detection performance over maximal ratio combining alone.

Approved for public release; distribution is unlimited / Computer simulation studies of a frequency domain adaptive beamforming algorithm are presented. These simulation studies were conducted to determine the multiple broadband target localization capability and the full angular coverage capability of the algorithm. The algorithm was evaluated at several signal-to-noise ratios with varying sampling rates. The number of iterations that the adaptive algorithm took to reach a minimum estimation error was determined. Results of the simulation studies indicate that the algorithm can localize multiple broadband targets and has full angular coverage capability. / http://archive.org/details/computersimulati00behr / Lieutenant, United States Navy

This thesis explores forecasting for intermittent demand requirements. Intermittent demand occurs at random, with some time periods showing no demand. In addition, demand, when it occurs, may not be for a single unit or a constant size. Consequently, intermittent demand creates significant problems in the supply and manufacturing environment as far as forecasting and inventory control are concerned. A certain confusion is shared amongst academics and practitioners about how intermittent demand (or indeed any other demand pattern that cannot be reasonably represented by the normal distribution) is defined. As such, we first construct a framework that aims at facilitating the conceptual categorisation of what is termed, for the purposes of this research, “non-normal” demand patterns. Croston (1972) proposed a method according to which intermittent demand estimates can be built from constituent elements, namely the demand size and inter-demand interval. The method has been claimed to provide unbiased estimates and it is regarded as the “standard” approach to dealing with intermittence. In this thesis we show that Croston’s method is biased. The bias is quantified and two new estimation procedures are developed based on Croston’s concept of considering both demand sizes and inter-demand intervals. Consequently the issue of variability of the intermittent demand estimates is explored and finally Mean Square Error (MSE) expressions are derived for all the methods discussed in the thesis. The issue of categorisation of the demand patterns has not received sufficient academic attention thus far, even though, from the practitioner’s standpoint it is appealing to switch from one estimator to the other according to the characteristics of the demand series under concern. Algebraic comparisons of MSE expressions result in universally applicable (and theoretically coherent) categorisation rules, based on which, “non-normal” demand patterns can be defined and estimators be selected. All theoretical findings are checked via simulation on theoretically generated demand data. The data is generated upon the same assumptions considered in the theoretical part of the thesis. Finally, results are generated using a large sample of empirical data. Appropriate accuracy measures are selected to assess the forecasting accuracy performance of the estimation procedures discussed in the thesis. Moreover, it is recognised that improvements in forecasting accuracy are of little practical value unless they are translated to an increased customer service level and/or reduced inventory cost. In consequence, an inventory control system is specified and the inventory control performance of the estimators is also assessed on the real data. The system is of the periodic order-up-to-level nature. The empirical results confirm the practical validity and utility of all our theoretical claims and demonstrate the benefits gained when Croston’s method is replaced by an estimator developed during this research, the Approximation method.

This thesis describes the use of scattering functions to characterize time-varying multipath radio channels. Channel Impulse responses were measured at Edwards Air Force Base (EAFB) and scattering functions generated from the impulse response data. From the scattering functions we compute the corresponding Doppler power spectrum and multipath intensity profile. These functions completely characterize the signal delay and the time varying nature of the channel in question and are used by systems engineers to design reliable communications links. We observe from our results that flight paths with ample reflectors exhibit significant multipath events. We also examine the bit error rate (BER) performance of a reduced-complexity equalizer for a truncated version of the pulse amplitude modulation (PAM) representation of SOQPSK-TG in a multipath channel. Since this reduced-complexity equalizer is based on the maximum likelihood (ML) principle, we expect it to perform optimally than any of the filter-based equalizers used in estimating received SOQPSK-TG symbols. As such we present a comparison between this ML detector and a minimum mean square error (MMSE) equalizer for the same example channel. The example channel used was motivated by the statistical channel characterizations described in thisthesis. Our analysis shows that the ML equalizer outperforms the MMSE equalizer in estimating received SOQPSK-TG symbols.

Abstract This thesis considers some linear and nonlinear time series models. In the linear case, the analysis of a large number of short time series generated by a first order autoregressive type model is considered. The conditional and exact maximum likelihood procedures are developed to estimate parameters. Simulation results are presented and compare the bias and the mean square errors of the parameter estimates. In Chapter 3, five important nonlinear models are considered and their time series properties are discussed. The estimating function approach for nonlinear models is developed in detail in Chapter 4 and examples are added to illustrate the theory. A simulation study is carried out to examine the finite sample behavior of these proposed estimates based on the estimating functions.

<p>Linear time-invariant approximations of nonlinear systems are used in many applications and can be obtained in several ways. For example, using system identification and the prediction-error method, it is always possible to estimate a linear model without considering the fact that the input and output measurements in many cases come from a nonlinear system. One of the main objectives of this thesis is to explain some properties of such approximate models.</p><p>More specifically, linear time-invariant models that are optimal approximations in the sense that they minimize a mean-square error criterion are considered. Linear models, both with and without a noise description, are studied. Some interesting, but in applications usually undesirable, properties of such optimal models are pointed out. It is shown that the optimal linear model can be very sensitive to small nonlinearities. Hence, the linear approximation of an almost linear system can be useless for some applications, such as robust control design. Furthermore, it is shown that standard validation methods, designed for identification of linear systems, cannot always be used to validate an optimal linear approximation of a nonlinear system.</p><p>In order to improve the models, conditions on the input signal that imply various useful properties of the linear approximations are given. It is shown, for instance, that minimum phase filtered white noise in many senses is a good choice of input signal. Furthermore, the class of separable signals is studied in detail. This class contains Gaussian signals and it turns out that these signals are especially useful for obtaining approximations of generalized Wiener-Hammerstein systems. It is also shown that some random multisine signals are separable. In addition, some theoretical results about almost linear systems are presented.</p><p>In standard methods for robust control design, the size of the model error is assumed to be known for all input signals. However, in many situations, this is not a realistic assumption when a nonlinear system is approximated with a linear model. In this thesis, it is described how robust control design of some nonlinear systems can be performed based on a discrete-time linear model and a model error model valid only for bounded inputs.</p><p>It is sometimes undesirable that small nonlinearities in a system influence the linear approximation of it. In some cases, this influence can be reduced if a small nonlinearity is included in the model. In this thesis, an identification method with this option is presented for nonlinear autoregressive systems with external inputs. Using this method, models with a parametric linear part and a nonparametric Lipschitz continuous nonlinear part can be estimated by solving a convex optimization problem.</p> / <p>Linjära tidsinvarianta approximationer av olinjära system har många användningsområden och kan tas fram på flera sätt. Om man har mätningar av in- och utsignalerna från ett olinjärt system kan man till exempel använda systemidentifiering och prediktionsfelsmetoden för att skatta en linjär modell utan att ta hänsyn till att systemet egentligen är olinjärt. Ett av huvudmålen med den här avhandlingen är att beskriva egenskaper för sådana approximativa modeller.</p><p>Framförallt studeras linjära tidsinvarianta modeller som är optimala approximationer i meningen att de minimerar ett kriterium baserat på medelkvadratfelet. Brusmodeller kan inkluderas i dessa modelltyper och både fallet med och utan brusmodell studeras här. Modeller som är optimala i medelkvadratfelsmening visar sig kunna uppvisa ett antal intressanta, men ibland oönskade, egenskaper. Bland annat visas det att en optimal linjär modell kan vara mycket känslig för små olinjäriteter. Denna känslighet är inte önskvärd i de flesta tillämpningar och innebär att en linjär approximation av ett nästan linjärt system kan vara oanvändbar för till exempel robust reglerdesign. Vidare visas det att en del valideringsmetoder som är framtagna för linjära system inte alltid kan användas för validering av linjära approximationer av olinjära system.</p><p>Man kan dock göra de optimala linjära modellerna mer användbara genom att välja lämpliga insignaler. Bland annat visas det att minfasfiltrerat vitt brus i många avseenden är ett bra val av insignal. Klassen av separabla signaler detaljstuderas också. Denna klass innehåller till exempel alla gaussiska signaler och just dessa signaler visar sig vara speciellt användbara för att ta fram approximationer av generaliserade wiener-hammerstein-system. Dessutom visas det att en viss typ av slumpmässiga multisinussignaler är separabel. Några teoretiska resultat om nästan linjära system presenteras också.</p><p>De flesta metoder för robust reglerdesign kan bara användas om storleken på modellfelet är känd för alla tänkbara insignaler. Detta är emellertid ofta inte realistiskt när ett olinjärt system approximeras med en linjär modell. I denna avhandling beskrivs därför ett alternativt sätt att göra en robust reglerdesign baserat på en tidsdiskret modell och en modellfelsmodell som bara är giltig för begränsade insignaler.</p><p>Ibland skulle det vara önskvärt om en linjär modell av ett system inte påverkades av förekomsten av små olinjäriteter i systemet. Denna oönskade påverkan kan i vissa fall reduceras om en liten olinjär term tas med i modellen. En identifieringsmetod för olinjära autoregressiva system med externa insignaler där denna möjlighet finns beskrivs här. Med hjälp av denna metod kan modeller som består av en parametrisk linjär del och en ickeparametrisk lipschitzkontinuerlig olinjär del skattas genom att man löser ett konvext optimeringsproblem.</p>

Linear time-invariant approximations of nonlinear systems are used in many applications and can be obtained in several ways. For example, using system identification and the prediction-error method, it is always possible to estimate a linear model without considering the fact that the input and output measurements in many cases come from a nonlinear system. One of the main objectives of this thesis is to explain some properties of such approximate models. More specifically, linear time-invariant models that are optimal approximations in the sense that they minimize a mean-square error criterion are considered. Linear models, both with and without a noise description, are studied. Some interesting, but in applications usually undesirable, properties of such optimal models are pointed out. It is shown that the optimal linear model can be very sensitive to small nonlinearities. Hence, the linear approximation of an almost linear system can be useless for some applications, such as robust control design. Furthermore, it is shown that standard validation methods, designed for identification of linear systems, cannot always be used to validate an optimal linear approximation of a nonlinear system. In order to improve the models, conditions on the input signal that imply various useful properties of the linear approximations are given. It is shown, for instance, that minimum phase filtered white noise in many senses is a good choice of input signal. Furthermore, the class of separable signals is studied in detail. This class contains Gaussian signals and it turns out that these signals are especially useful for obtaining approximations of generalized Wiener-Hammerstein systems. It is also shown that some random multisine signals are separable. In addition, some theoretical results about almost linear systems are presented. In standard methods for robust control design, the size of the model error is assumed to be known for all input signals. However, in many situations, this is not a realistic assumption when a nonlinear system is approximated with a linear model. In this thesis, it is described how robust control design of some nonlinear systems can be performed based on a discrete-time linear model and a model error model valid only for bounded inputs. It is sometimes undesirable that small nonlinearities in a system influence the linear approximation of it. In some cases, this influence can be reduced if a small nonlinearity is included in the model. In this thesis, an identification method with this option is presented for nonlinear autoregressive systems with external inputs. Using this method, models with a parametric linear part and a nonparametric Lipschitz continuous nonlinear part can be estimated by solving a convex optimization problem. / Linjära tidsinvarianta approximationer av olinjära system har många användningsområden och kan tas fram på flera sätt. Om man har mätningar av in- och utsignalerna från ett olinjärt system kan man till exempel använda systemidentifiering och prediktionsfelsmetoden för att skatta en linjär modell utan att ta hänsyn till att systemet egentligen är olinjärt. Ett av huvudmålen med den här avhandlingen är att beskriva egenskaper för sådana approximativa modeller. Framförallt studeras linjära tidsinvarianta modeller som är optimala approximationer i meningen att de minimerar ett kriterium baserat på medelkvadratfelet. Brusmodeller kan inkluderas i dessa modelltyper och både fallet med och utan brusmodell studeras här. Modeller som är optimala i medelkvadratfelsmening visar sig kunna uppvisa ett antal intressanta, men ibland oönskade, egenskaper. Bland annat visas det att en optimal linjär modell kan vara mycket känslig för små olinjäriteter. Denna känslighet är inte önskvärd i de flesta tillämpningar och innebär att en linjär approximation av ett nästan linjärt system kan vara oanvändbar för till exempel robust reglerdesign. Vidare visas det att en del valideringsmetoder som är framtagna för linjära system inte alltid kan användas för validering av linjära approximationer av olinjära system. Man kan dock göra de optimala linjära modellerna mer användbara genom att välja lämpliga insignaler. Bland annat visas det att minfasfiltrerat vitt brus i många avseenden är ett bra val av insignal. Klassen av separabla signaler detaljstuderas också. Denna klass innehåller till exempel alla gaussiska signaler och just dessa signaler visar sig vara speciellt användbara för att ta fram approximationer av generaliserade wiener-hammerstein-system. Dessutom visas det att en viss typ av slumpmässiga multisinussignaler är separabel. Några teoretiska resultat om nästan linjära system presenteras också. De flesta metoder för robust reglerdesign kan bara användas om storleken på modellfelet är känd för alla tänkbara insignaler. Detta är emellertid ofta inte realistiskt när ett olinjärt system approximeras med en linjär modell. I denna avhandling beskrivs därför ett alternativt sätt att göra en robust reglerdesign baserat på en tidsdiskret modell och en modellfelsmodell som bara är giltig för begränsade insignaler. Ibland skulle det vara önskvärt om en linjär modell av ett system inte påverkades av förekomsten av små olinjäriteter i systemet. Denna oönskade påverkan kan i vissa fall reduceras om en liten olinjär term tas med i modellen. En identifieringsmetod för olinjära autoregressiva system med externa insignaler där denna möjlighet finns beskrivs här. Med hjälp av denna metod kan modeller som består av en parametrisk linjär del och en ickeparametrisk lipschitzkontinuerlig olinjär del skattas genom att man löser ett konvext optimeringsproblem.

With the advent of high-throughput genomic and proteomic technologies, in conjunction with the difficulty in obtaining even moderately sized samples, small-sample classifier design has become a major issue in the biological and medical communities. Training-data error estimation becomes mandatory, yet none of the popular error estimation techniques have been rigorously designed via statistical inference or optimization. In this investigation, we place classifier error estimation in a framework of minimum mean-square error (MMSE) signal estimation in the presence of uncertainty, where uncertainty is relative to a prior over a family of distributions. This results in a Bayesian approach to error estimation that is optimal and unbiased relative to the model. The prior addresses a trade-off between estimator robustness (modeling assumptions) and accuracy. Closed-form representations for Bayesian error estimators are provided for two important models: discrete classification with Dirichlet priors (the discrete model) and linear classification of Gaussian distributions with fixed, scaled identity or arbitrary covariances and conjugate priors (the Gaussian model). We examine robustness to false modeling assumptions and demonstrate that Bayesian error estimators perform especially well for moderate true errors. The Bayesian modeling framework facilitates both optimization and analysis. It naturally gives rise to a practical expected measure of performance for arbitrary error estimators: the sample-conditioned mean-square error (MSE). Closed-form expressions are provided for both Bayesian models. We examine the consistency of Bayesian error estimation and illustrate a salient application in censored sampling, where sample points are collected one at a time until the conditional MSE reaches a stopping criterion. We address practical considerations for gene-expression microarray data, including the suitability of the Gaussian model, a methodology for calibrating normal-inverse-Wishart priors from unused data, and an approximation method for non-linear classification. We observe superior performance on synthetic high-dimensional data and real data, especially for moderate to high expected true errors and small feature sizes. Finally, arbitrary error estimators may be optimally calibrated assuming a fixed Bayesian model, sample size, classification rule, and error estimation rule. Using a calibration function mapping error estimates to their optimally calibrated values off-line, error estimates may be calibrated on the fly whenever the assumptions apply.