International Telemetering Conference Proceedings / October 21, 2002 / Town & Country Hotel and Conference Center, San Diego, California / Feher Keying (FK) signals are clock shaped baseband waveforms with the potential to attain very high spectral efficiencies. Two FK signals which have different level rectangular waveforms (named as FK-1) or sinusoidal waveforms (named as FK-2) for two binary symbols are considered in this paper. These signals have periodic components in the time domain. Therefore they have cyclostationary properties. This means that spectral correlation exists in the frequency domain. For each type of waveforms, spectral correlation has been investigated. FK signals can be expressed mathematically into two parts in the frequency domain – discrete part and continuous part. The discrete part has one or more discrete impulse(s) in their spectra and the continuous part has periodically the same shape of harmonics in their spectra. The correlations of their spectra have been obtained mathematically and by simulation. It is shown that FK signals have high correlation related to the symbol rate. Finally, some suggestions how these properties can be used to improve their performance by devising better demodulators are discussed. These properties can be used for interference rejection at the receiver, which results in low bit error rate performance.

International Telemetering Conference Proceedings / October 22-25, 2001 / Riviera Hotel and Convention Center, Las Vegas, Nevada / Performances of a subclass of a new spectral efficient modulation scheme, designated as Feher Keying [1], or FK, is evaluated. The Power Spectral Density (PSD) and Bit Error Rate (BER) characteristics of FK are presented. FK has ultra high spectral efficiency and satisfies the frequency mask for WLAN defined in FCC part 15, and it has a simple structure for high bit rate implementation.

O Ruído de Baixa Frequência (LFN), tais como o ruído flicker e o Random Telegraph Noise (RTN), são limitadores de performance em muitos circuitos analógicos e digitais. Para transistores diminutos, a densidade espectral de potência do ruído pode variar muitas ordens de grandeza, impondo uma séria limitação na performance do circuito e também em sua confiabilidade. Nesta tese, nós propomos um novo modelo de RTN estatístico para descrever o ruído de baixa frequência em MOSFETs. Utilizando o modelo proposto, pode-se explicar e calcular o valor esperado e a variabilidade do ruído em função das polarizações, geometrias e dos parâmetros físicos do transistor. O modelo é validado através de inúmeros resultados experimentais para dispositivos com canais tipo n e p, e para diferentes tecnologias CMOS. É demonstrado que a estatística do ruído LFN dos dispositivos de canal tipo n e p podem ser descritos através do mesmo mecanismo. Através dos nossos resultados e do nosso modelo, nós mostramos que a densidade de armadilhas dos transistores de canal tipo p é fortemente dependente do nível de Fermi, enquanto para o transistor de tipo n a densidade de armadilhas pode ser considerada constante na energia. Também é mostrado e explicado, através do nosso modelo, o impacto do implante de halo nas estatísticas do ruído. Utilizando o modelo demonstra-se porque a variabilidade, denotado por σ[log(SId)], do RTN/LFN não segue uma dependência 1/√área; e fica demonstrado que o ruído, e sua variabilidade, encontrado em nossas medidas pode ser modelado utilizando parâmetros físicos. Além disso, o modelo proposto pode ser utilizado para calcular o percentil do ruído, o qual pode ser utilizado para prever ou alcançar certo rendimento do circuito. / Low Frequency Noise (LFN) and Random Telegraph Noise (RTN) are performance limiters in many analog and digital circuits. For small area devices, the noise power spectral density can easily vary by many orders of magnitude, imposing serious threat on circuit performance and possibly reliability. In this thesis, we propose a new RTN model to describe the statistics of the low frequency noise in MOSFETs. Using the proposed model, we can explain and calculate the Expected value and Variability of the noise as function of devices’ biases, geometry and physical parameters. The model is validated through numerous experimental results for n-channel and p-channel devices from different CMOS technology nodes. We show that the LFN statistics of n-channel and p-channel MOSFETs can be described by the same mechanism. From our results and model, we show that the trap density of the p-channel device is a strongly varying function of the Fermi level, whereas for the n-channel the trap density can be considered constant. We also show and explain, using the proposed model, the impact of the halo-implanted regions on the statistics of the noise. Using this model, we clarify why the variability, denoted by σ[log(SId)], of RTN/LFN doesn't follow a 1/√area dependence; and we demonstrate that the noise, and its variability, found in our measurements can be modeled using reasonable physical quantities. Moreover, the proposed model can be used to calculate the percentile quantity of the noise, which can be used to predict or to achieve certain circuit yield.

Abstract Cognitive Radio (CR) improves the efficiency of spectrum utilization by allowing non- licensed users to utilize bands when not occupied by licensed users. In this thesis, we address several challenges currently limiting the wide use of cognitive radios. These challenges include identification of unoccupied bands, energy consumption and other technical challenges. Improved accuracy edge detection techniques are developed for CR to mitigate both noise and estimation error variance effects. Next, a reduced complexity Simplified DFT (SDFT) is proposed for use in CR. Then, a sub-Nyquist rate A to D converter is introduced to reduce energy consumption. Finally, a novel multi-resolution PSD estimation based on expectation-maximization algorithm is introduced that can obtain a more accurate PSD within a specified sensing time.

O Ruído de Baixa Frequência (LFN), tais como o ruído flicker e o Random Telegraph Noise (RTN), são limitadores de performance em muitos circuitos analógicos e digitais. Para transistores diminutos, a densidade espectral de potência do ruído pode variar muitas ordens de grandeza, impondo uma séria limitação na performance do circuito e também em sua confiabilidade. Nesta tese, nós propomos um novo modelo de RTN estatístico para descrever o ruído de baixa frequência em MOSFETs. Utilizando o modelo proposto, pode-se explicar e calcular o valor esperado e a variabilidade do ruído em função das polarizações, geometrias e dos parâmetros físicos do transistor. O modelo é validado através de inúmeros resultados experimentais para dispositivos com canais tipo n e p, e para diferentes tecnologias CMOS. É demonstrado que a estatística do ruído LFN dos dispositivos de canal tipo n e p podem ser descritos através do mesmo mecanismo. Através dos nossos resultados e do nosso modelo, nós mostramos que a densidade de armadilhas dos transistores de canal tipo p é fortemente dependente do nível de Fermi, enquanto para o transistor de tipo n a densidade de armadilhas pode ser considerada constante na energia. Também é mostrado e explicado, através do nosso modelo, o impacto do implante de halo nas estatísticas do ruído. Utilizando o modelo demonstra-se porque a variabilidade, denotado por σ[log(SId)], do RTN/LFN não segue uma dependência 1/√área; e fica demonstrado que o ruído, e sua variabilidade, encontrado em nossas medidas pode ser modelado utilizando parâmetros físicos. Além disso, o modelo proposto pode ser utilizado para calcular o percentil do ruído, o qual pode ser utilizado para prever ou alcançar certo rendimento do circuito. / Low Frequency Noise (LFN) and Random Telegraph Noise (RTN) are performance limiters in many analog and digital circuits. For small area devices, the noise power spectral density can easily vary by many orders of magnitude, imposing serious threat on circuit performance and possibly reliability. In this thesis, we propose a new RTN model to describe the statistics of the low frequency noise in MOSFETs. Using the proposed model, we can explain and calculate the Expected value and Variability of the noise as function of devices’ biases, geometry and physical parameters. The model is validated through numerous experimental results for n-channel and p-channel devices from different CMOS technology nodes. We show that the LFN statistics of n-channel and p-channel MOSFETs can be described by the same mechanism. From our results and model, we show that the trap density of the p-channel device is a strongly varying function of the Fermi level, whereas for the n-channel the trap density can be considered constant. We also show and explain, using the proposed model, the impact of the halo-implanted regions on the statistics of the noise. Using this model, we clarify why the variability, denoted by σ[log(SId)], of RTN/LFN doesn't follow a 1/√area dependence; and we demonstrate that the noise, and its variability, found in our measurements can be modeled using reasonable physical quantities. Moreover, the proposed model can be used to calculate the percentile quantity of the noise, which can be used to predict or to achieve certain circuit yield.

O Ruído de Baixa Frequência (LFN), tais como o ruído flicker e o Random Telegraph Noise (RTN), são limitadores de performance em muitos circuitos analógicos e digitais. Para transistores diminutos, a densidade espectral de potência do ruído pode variar muitas ordens de grandeza, impondo uma séria limitação na performance do circuito e também em sua confiabilidade. Nesta tese, nós propomos um novo modelo de RTN estatístico para descrever o ruído de baixa frequência em MOSFETs. Utilizando o modelo proposto, pode-se explicar e calcular o valor esperado e a variabilidade do ruído em função das polarizações, geometrias e dos parâmetros físicos do transistor. O modelo é validado através de inúmeros resultados experimentais para dispositivos com canais tipo n e p, e para diferentes tecnologias CMOS. É demonstrado que a estatística do ruído LFN dos dispositivos de canal tipo n e p podem ser descritos através do mesmo mecanismo. Através dos nossos resultados e do nosso modelo, nós mostramos que a densidade de armadilhas dos transistores de canal tipo p é fortemente dependente do nível de Fermi, enquanto para o transistor de tipo n a densidade de armadilhas pode ser considerada constante na energia. Também é mostrado e explicado, através do nosso modelo, o impacto do implante de halo nas estatísticas do ruído. Utilizando o modelo demonstra-se porque a variabilidade, denotado por σ[log(SId)], do RTN/LFN não segue uma dependência 1/√área; e fica demonstrado que o ruído, e sua variabilidade, encontrado em nossas medidas pode ser modelado utilizando parâmetros físicos. Além disso, o modelo proposto pode ser utilizado para calcular o percentil do ruído, o qual pode ser utilizado para prever ou alcançar certo rendimento do circuito. / Low Frequency Noise (LFN) and Random Telegraph Noise (RTN) are performance limiters in many analog and digital circuits. For small area devices, the noise power spectral density can easily vary by many orders of magnitude, imposing serious threat on circuit performance and possibly reliability. In this thesis, we propose a new RTN model to describe the statistics of the low frequency noise in MOSFETs. Using the proposed model, we can explain and calculate the Expected value and Variability of the noise as function of devices’ biases, geometry and physical parameters. The model is validated through numerous experimental results for n-channel and p-channel devices from different CMOS technology nodes. We show that the LFN statistics of n-channel and p-channel MOSFETs can be described by the same mechanism. From our results and model, we show that the trap density of the p-channel device is a strongly varying function of the Fermi level, whereas for the n-channel the trap density can be considered constant. We also show and explain, using the proposed model, the impact of the halo-implanted regions on the statistics of the noise. Using this model, we clarify why the variability, denoted by σ[log(SId)], of RTN/LFN doesn't follow a 1/√area dependence; and we demonstrate that the noise, and its variability, found in our measurements can be modeled using reasonable physical quantities. Moreover, the proposed model can be used to calculate the percentile quantity of the noise, which can be used to predict or to achieve certain circuit yield.

Abstract Cognitive Radio (CR) improves the efficiency of spectrum utilization by allowing non- licensed users to utilize bands when not occupied by licensed users. In this thesis, we address several challenges currently limiting the wide use of cognitive radios. These challenges include identification of unoccupied bands, energy consumption and other technical challenges. Improved accuracy edge detection techniques are developed for CR to mitigate both noise and estimation error variance effects. Next, a reduced complexity Simplified DFT (SDFT) is proposed for use in CR. Then, a sub-Nyquist rate A to D converter is introduced to reduce energy consumption. Finally, a novel multi-resolution PSD estimation based on expectation-maximization algorithm is introduced that can obtain a more accurate PSD within a specified sensing time.

Abstract: The thesis work is part of a project that aims to develop a reliable CFD model to investigate the fluid-dynamics of a fluidized bubbling bed during gasification of refuse derived fuel (RDF) from sorted municipal solid waste (MSW). Gasification is a thermochemical process that converts carbon-containing materials into syngas. In this specific context scaling up is challenging because it implies dealing with a complex chemistry combined to heat and mass transfer phenomena in a multi-phase fluid environment. CFD modeling could represent a potential tool to predict the impact of the reactor configuration and operating conditions on gas yield, composition and potential contaminants. Validation of CFD simulations for such systems has been so far possible using different sophisticated experimental tools, allowing to link the model with experimental data. However, such high tech equipment may not always be available, especially at industrial scale. Hence, this work focuses on investigating the accuracy and numerical sensitivity of two different CFD models employed in the characterization of dense solid-particle flows in bubbling fluidized beds. The key parameter adopted to describe and quantify the dynamic behavior of this multiphase system is the power spectral density (PSD) distribution of pressure fluctuations. This PSD function was used to assess the accuracy of CFD models using one set of operating condition. The same type of analysis, extended to a wider range of operating conditions, may lead to a robust validation of the numerical models presented in this work. In spite of his measurement simplicity, pressure drop data present a strong connection with the bed fluid-dynamics and its interpretation could help to improve the fluidized bed technologies very fast, pushing CFD models closer to applications. / Résumé : Le but de ce projet est de développer un modèle CFD fiable pour étudier la dynamique des fluides d'un lit fluidisé en régime bullant pendant la gazéification de combustibles solides de récupération (CSR) triés à partir de déchets solides municipaux (DSM). La gazéification est un processus thermochimique qui convertit les matériaux contenant du carbone en gaz de synthèse. La mise à l'échelle est difficile dans ce cas car elle implique une chimie complexe combinée aux phénomènes de transfert de chaleur et de masse dans un environnement fluide multiphasique. La modélisation CFD représente un outil potentiel pour prédire l'impact de la configuration du réacteur et des conditions de fonctionnement sur le rendement, la composition et les contaminants potentiels du gaz. La validation des simulations CFD pour de tels systèmes a été jusqu'à présent possible grâce à l’utilisation de différents outils expérimentaux sophistiqués, permettant de lier le modèle aux données expérimentales. Toutefois, un tel équipement de pointe n’est pas toujours disponible, en particulier à l'échelle industrielle. Par conséquent, ce travail se concentre sur l'étude de la précision et de la sensibilité numérique de deux modèles CFD différents, utilisés dans la caractérisation des flux de particules solides denses dans les lits fluidisés bouillonnants. Le paramètre clé adopté pour décrire et quantifier le comportement dynamique de ce système multiphase est la distribution de la densité spectrale de puissance (DSP) des fluctuations de pression. La fonction DSP a été utilisée pour évaluer la précision des modèles CFD en utilisant un ensemble de conditions de fonctionnement. Le même type d'analyse, étendu à une plus large gamme de conditions de fonctionnement, peut conduire à une validation robuste des modèles numériques présentés dans ce travail. En dépit de sa simplicité de mesure, les données de chute de pression présentent une importante corrélation avec les lits fluidisés, de plus, leur interprétation pourrait aider à améliorer ces technologies très rapidement, poussant les modèles CFD plus près des applications.

This master thesis deals with classification of sleep stages on the base of polysomnographic signals. On several signals was performed analysis and feature extraxtion in time domain and in frequency domain as well. For feature extraxtion was used EEG, EOG and EMG signals. For classification was selected classification models K-NN, SVM and artifical neural network. Accuracy of classifation is different depending on used method and spleep stages split. The best results achieved classification among stages Wake, REM, and N3, with neural network usage. In this case the succes was 93,1 %.