Heart rate variability (HRV), the changes in the beat-to-beat heart rate calculated from the electrocardiogram (ECG), is a key indicator of an individual's cardiovascular condition. Assessment of HRV has been shown to aid clinical diagnosis and intervention strategies. However, the variety of HRV estimation methods and contradictory reports in this field indicate that there is a need for a more rigorous investigation of these methods as aids to clinical evaluation. This thesis investigates the development of appropriate HRV signal processing techniques in the context of pilot studies in two fields of potential application, sleep and head-up tilting (HUT). A novel method for characterising normality in the ECG using both timing information and morphological characteristics is presented. A neural network, used to learn the beat-to-beat variations in ECG waveform morphology, is shown to provide a highly sensitive technique for identifying normal beats. Fast Fourier Transform (FFT) based frequency-domain HRV techniques, which require re-sampling of the inherently unevenly sampled heart beat time-series (RR tachogram) to produce an evenly sampled time series, are then explored using a new method for producing an artificial RR tachogram. Re-sampling is shown to produce a significant error in the estimation of an (entirely specified) artificial RR tachogram. The Lomb periodogram, a method which requires no re-sampling and is applicable to the unevenly sampled nature of the signal is investigated. Experiments demonstrate that the Lomb periodogram is superior to the FFT for evaluating HRV measured by the LF/HF-ratio, a ratio of the low to high frequency power in the RR tachogram within a specified band (0.04-0.4 Hz). The effect of adding artificial ectopic beats in the RR tachogram is then considered and it is shown that ectopic beats significantly alter the spectrum and therefore must be removed or replaced. Replacing ectopic beats by phantom beats is compared to the case of ectopic-realted RR interval removal for the FFT and Lomb methods for varying levels of ectopy. The Lomb periodogram is shown to provide a signficantly better estimate of the LF/HF- ratio under these conditions and is a robust method for measuring the LF/HF-ratio in the presence of (a possibly unknown number of) ectpoic beats or artefacts. The Lomb peridogram and FFT-based techniques are applied to a database of sleep apnoeic and normal subjects. A new method of assessing HRV during sleep is proposed to minimise the confounding effects on HRV of changes due to changing mental activity. Estimation of LF/HF-ratio using the Lomb technique is shown to separate these two patient groups more effectively than with FFT-based techniques. Results are also presented for the application of these methods to controlled (HUT) studies on subjects with syncope, an autonomic nervous system problem, which indicate that the techniques developed in this thesis may provide a method for differentiating between sub-classes of syncope.

Thesis (Doctor of Engineering in Electrical Engineering)--Cape Peninsula University of Technology, 2017. / Remote sensing provides a way of frequently observing broad land surfaces. The availability of various earth observation data and their potential exploitation in a wide range of socioeconomic applications stimulated the rapid development of remote sensing technology. Much of the research and most of the publications dealing with remote sensing in the solar spectral domain focus on analysing and interpreting the spectral, spatial and temporal signatures of the observed areas. However, the angular signatures of the reflectance field, known as surface anisotropy, also merit attention. The current research took an exploratory approach to the land surface anisotropy described by the RPV model parameters derived from the MISR-HR processing system (denoted as MISR-HR anisotropy data or MISR-HR RPV data), over a period of 14+ years, for three typical terrestrial surfaces in the Western Cape Province of South Africa: a semi-desert area, a wheat field and a vineyard area. The objectives of this study were to explore (1) to what extent spectral and directional signatures of the MISR-HR RPV data may vary in time and space over the different targets (landscapes), and (2) whether the observed variations in anisotropy might be useful in classifying different land surfaces or as a supplementary method to the traditional land cover classification method. The objectives were achieved by exploring the statistics of the MISR-HR RPV data in each spectral band over the different land surfaces, as well as seasonality and trend in these data. The MISR-HR RPV products were affected by outliers and missing values, both of which influenced the statistics, seasonality and trend of the examined time series. This research proposes a new outlier detection method, based on the cost function derived from the RPV model inversion process. Removed outliers and missing values leave gaps in a MISR-HR RPV time series; to avoid introducing extra biases in the statistics of the anisotropy data, this research kept the gaps and relied on gap-resilient trend and seasonality detection methods, such as the Mann-Kendal trend detection and Lomb-Scargle periodogram methods. The exploration of the statistics of the anisotropy data showed that RPV parameter rho exhibited distinctive over the different study sites; NIR band parameter k exhibits prominent high values for the vineyard area; red band parameter Theta data are not that distinctive over different study sites; variance is important in describing all three RPV parameters. The explorations on trends also demonstrated interesting findings: the downward trend in green band parameter rho data for the semi-desert and vineyard areas; and the upward trend in blue band parameters k and Theta data for all the three study sites. The investigation on seasonality showed that all the RPV parameters had seasonal variations which differed over spectral bands and land covers; the results confirmed expectations in previous literature that parameter varies regularly along the observation time, and also revealed seasonal variations in the parameter rho and Theta data. The explorations on the statistics and seasonality of the MISR-HR anisotropy data show that these data are potentially useful for classifying different landscapes. Finally, the classification results demonstrated that both red band parameter rho data and NIR band parameter k data could successfully separate the three different land surfaces in this research, which fulfilled the second primary objective of this study. This research also demonstrated a classification method using multiple RPV parameters as the classification signatures to discriminate different terrestrial surfaces; significant separation results were obtained by this method.

No description available.

O uso de sistemas denominados Brain Computer Interface, ou simplesmente BCI, para controle de dispositivos tem gerado cada vez mais trabalhos de análise de sinais de EEG, principalmente devido ao fato do desenvolvimento tecnológico dos sistemas de processamento de dados, trazendo novas perspectiva de desenvolvimento de equipamentos que auxiliem pessoas com debilidades motoras. Neste trabalho é abordado o comportamento dos classificadores LDA (Discriminante Linear de Fisher) e o classificador Naive Bayes para classificação de movimento de mão direita e mão esquerda a partir da aquisição de sinais eletroencefalográficos. Para análise destes classificadores foram utilizadas como características de entrada a energia de trechos do sinal filtrados por um passa banda com frequências dentro dos ritmos sensório-motor e também foram utilizadas componentes de energia espectral através do periodograma modificado de Welch. Como forma de pré-processamento também é apresentado o filtro espacial Common Spatial Pattern (CSP) de forma a aumentar a atividade discriminativa entre as classes de movimento. Foram obtidas taxas de acerto de até 70% para a base de dados geradas neste trabalho e de até 88% utilizando a base de dados do BCI Competition II, taxas de acertos compatíveis com outros trabalhos na área. / Brain Computer Interface (BCI) systems usage for controlling devices has increasingly generated research on EEG signals analysis, mainly because the technological development of data processing systems has been offering a new perspective on developing equipment to assist people with motor disability. This study aims to examine the behavior of both Fisher's Linear Discriminant (LDA) and Naive Bayes classifiers in determining both the right and left hand movement through electroencephalographic signals. To accomplish this, we considered as input feature the energy of the signal trials filtered by a band pass with sensorimotor rhythm frequencies; spectral power components from the Welch modified periodogram were also used. As a preprocessing form, the Common Spatial Pattern (CSP) filter was used to increase the discriminative activity between classes of movement. The database created from this study reached hit rates of up to 70% while the BCI Competition II reached hit rates up to 88%, which is consistent with the literature.

O uso de sistemas denominados Brain Computer Interface, ou simplesmente BCI, para controle de dispositivos tem gerado cada vez mais trabalhos de análise de sinais de EEG, principalmente devido ao fato do desenvolvimento tecnológico dos sistemas de processamento de dados, trazendo novas perspectiva de desenvolvimento de equipamentos que auxiliem pessoas com debilidades motoras. Neste trabalho é abordado o comportamento dos classificadores LDA (Discriminante Linear de Fisher) e o classificador Naive Bayes para classificação de movimento de mão direita e mão esquerda a partir da aquisição de sinais eletroencefalográficos. Para análise destes classificadores foram utilizadas como características de entrada a energia de trechos do sinal filtrados por um passa banda com frequências dentro dos ritmos sensório-motor e também foram utilizadas componentes de energia espectral através do periodograma modificado de Welch. Como forma de pré-processamento também é apresentado o filtro espacial Common Spatial Pattern (CSP) de forma a aumentar a atividade discriminativa entre as classes de movimento. Foram obtidas taxas de acerto de até 70% para a base de dados geradas neste trabalho e de até 88% utilizando a base de dados do BCI Competition II, taxas de acertos compatíveis com outros trabalhos na área. / Brain Computer Interface (BCI) systems usage for controlling devices has increasingly generated research on EEG signals analysis, mainly because the technological development of data processing systems has been offering a new perspective on developing equipment to assist people with motor disability. This study aims to examine the behavior of both Fisher's Linear Discriminant (LDA) and Naive Bayes classifiers in determining both the right and left hand movement through electroencephalographic signals. To accomplish this, we considered as input feature the energy of the signal trials filtered by a band pass with sensorimotor rhythm frequencies; spectral power components from the Welch modified periodogram were also used. As a preprocessing form, the Common Spatial Pattern (CSP) filter was used to increase the discriminative activity between classes of movement. The database created from this study reached hit rates of up to 70% while the BCI Competition II reached hit rates up to 88%, which is consistent with the literature.

O uso de sistemas denominados Brain Computer Interface, ou simplesmente BCI, para controle de dispositivos tem gerado cada vez mais trabalhos de análise de sinais de EEG, principalmente devido ao fato do desenvolvimento tecnológico dos sistemas de processamento de dados, trazendo novas perspectiva de desenvolvimento de equipamentos que auxiliem pessoas com debilidades motoras. Neste trabalho é abordado o comportamento dos classificadores LDA (Discriminante Linear de Fisher) e o classificador Naive Bayes para classificação de movimento de mão direita e mão esquerda a partir da aquisição de sinais eletroencefalográficos. Para análise destes classificadores foram utilizadas como características de entrada a energia de trechos do sinal filtrados por um passa banda com frequências dentro dos ritmos sensório-motor e também foram utilizadas componentes de energia espectral através do periodograma modificado de Welch. Como forma de pré-processamento também é apresentado o filtro espacial Common Spatial Pattern (CSP) de forma a aumentar a atividade discriminativa entre as classes de movimento. Foram obtidas taxas de acerto de até 70% para a base de dados geradas neste trabalho e de até 88% utilizando a base de dados do BCI Competition II, taxas de acertos compatíveis com outros trabalhos na área. / Brain Computer Interface (BCI) systems usage for controlling devices has increasingly generated research on EEG signals analysis, mainly because the technological development of data processing systems has been offering a new perspective on developing equipment to assist people with motor disability. This study aims to examine the behavior of both Fisher's Linear Discriminant (LDA) and Naive Bayes classifiers in determining both the right and left hand movement through electroencephalographic signals. To accomplish this, we considered as input feature the energy of the signal trials filtered by a band pass with sensorimotor rhythm frequencies; spectral power components from the Welch modified periodogram were also used. As a preprocessing form, the Common Spatial Pattern (CSP) filter was used to increase the discriminative activity between classes of movement. The database created from this study reached hit rates of up to 70% while the BCI Competition II reached hit rates up to 88%, which is consistent with the literature.

Oceanic microwave remote sensing provides the data necessary for the estimation of significant geophysical parameters such as the near-surface vector wind. To obtain accurate estimates, a precise understanding of the measurements is critical. This work clarifies and quantifies specific uncertainties in the scattered power measured by an active radar instrument. While there are many sources of uncertainty in remote sensing measurements, this work concentrates on three significant, yet largely unstudied effects. With a theoretical derivation of the backscatter from an ocean-like surface, results from this dissertation demonstrate that the backscatter decays with surface roughness with two distinct modes of behavior, affected by the size of the footprint. A technique is developed and scatterometer data analyzed to quantify the variability of spaceborne backscatter measurements for given wind conditions; the impact on wind retrieval is described in terms of bias and the Cramer-Rao lower bound. The probability density function of modified periodogram averages (a spectral estimation technique) is derived in generality and for the specific case of power estimates made by the NASA scatterometer. The impact on wind retrieval is quantified.

This thesis deals with oportunity of ROC curve usage in the description of methods that work with sound signals. Specifically, it focuses on ways of detecting of stress in speech signals. The detection itselfs is done in a range of frequencies of the sound signal. There is also a classifier designed using ROC curves that decides whether the input signal is stressed or not. The output of this thesis are findings gathered from analyses and also some recommendation based on those analyses.

This Master's thesis deals with the influence of breathe to baroreceptor reflex. The first chapter is focused on measuring of blood pressure, in reference to later used method. In the second part is detailed analysis and explanation of term baroreflex, relationship between frequency spectra of systolic blood pressure, heart rate and breathing, mechanisms of regulation of blood pressure and heart rate variability. Third chapter deals with possible ways, how to analyze data, which are needed for estimation of baroreflex sensitivity, analyze of heart rate variability and estimation of power spectral density. Next parts of this thesis is paid attention to technical equipment and methods of acqusition of data, using this equipmnent. Part of this work is also software application, which makes possible from the recorded data estimate baroreflex sensitivity.

Cognitive radio is a technology that aims to maximize the current usage of the licensed frequency spectrum. Cognitive radio aims to provide services for license-exempt users by making use of dynamic spectrum access (DSA) and opportunistic spectrum sharing strategies (OSS). Cognitive radios are defined as intelligent wireless devices capable of adapting their communication parameters in order to operate within underutilized bands while avoiding causing interference to licensed users. An underused band of frequencies in a specific location or time is known as a spectrum hole. Therefore, in order to locate spectrum holes, reliable spectrum sensing algorithms are crucial to facilitate the evolution of cognitive radio networks. Since a large and growing body of literature has mainly focused into the conventional time domain (TD) energy detector, throughout this thesis the problem of spectrum sensing is investigated within the context of a frequency domain (FD) approach. The purpose of this study is to investigate detection based on methods of nonparametric power spectrum estimation. The considered methods are the periodogram, Bartlett's method, Welch overlapped segments averaging (WOSA) and the Multitaper estimator (MTE). Another major motivation is that the MTE is strongly recommended for the application of cognitive radios. This study aims to derive the detector performance measures for each case. Another aim is to investigate and highlight the main differences between the TD and the FD approaches. The performance is addressed for independent and identically distributed (i.i.d.) Rayleigh channels and the general Rician and Nakagami fading channels. For each of the investigated detectors, the analytical models are obtained by studying the characteristics of the Hermitian quadratic form representation of the decision statistic and the matrix of the Hermitian form is identified. The results of the study have revealed the high accuracy of the derived mathematical models. Moreover, it is found that the TD detector differs from the FD detector in a number of aspects. One principal and generalized conclusion is that all the investigated FD methods provide a reduced probability of false alarm when compared with the TD detector. Also, for the case of periodogram, the probability of sensing errors is independent of the length of observations, whereas in time domain the probability of false alarm is increased when the sample size increases. The probability of false alarm is further reduced when diversity reception is employed. Furthermore, compared to the periodogram, both Bartlett method and Welch method provide better performance in terms of lower probability of false alarm but an increased probability of detection for a given probability of false alarm. Also, the performance of both Bartlett's method and WOSA is sensitive to the number of segments, whereas WOSA is also sensitive to the overlapping factor. Finally, the performance of the MTE is dependent on the number of employed discrete prolate spheroidal (Slepian) sequences, and the MTE outperforms the periodogram, Bartlett's method and WOSA, as it provides the minimal probability of false alarm.