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Instantaneous Frequency (IF) estimation based on time-varying autoregressive (TVAR) modeling has been shown to perform well in practical scenarios when the IF variation is rapid and/or non-linear and only short data records are available for modeling. A challenging aspect of implementing IF estimation based on TVAR modeling is the efficient computation of the time-varying coefficients by solving a set of linear equations referred to as the generalized covariance equations. Conventional approaches such as Gaussian elimination or direct matrix inversion are computationally inefficient for solving such a system of equations especially when the covariance matrix has a high order. We implement two recursive algorithms for efficiently inverting the covariance matrix. First, we implement the Akaike algorithm which exploits the block-Toeplitz structure of the covariance matrix for its recursive inversion. In the second approach, we implement the Wax-Kailath algorithm that achieves a factor of 2 reduction over the Akaike algorithm in the number of recursions involved and the computational effort required to form the inverse matrix. Although a TVAR model works well for IF estimation of frequency modulated (FM) components in white noise, when the model is applied to a signal containing a finitely correlated signal in addition to the white noise, estimation performance degrades; especially when the correlated signal is not weak relative to the FM components. We propose a decorrelating TVAR (DTVAR) model based IF estimation and a DTVAR model based linear prediction error filter for FM interference rejection in a finitely correlated environment. Simulations show notable performance gains for a DTVAR model over the TVAR model for moderate to high SIRs. / Master of Science

In the past few years, the study of electrical activity in the brain and its interactions with the body has become popular among researchers. One of the hottest topics related to brain activity is the epileptic seizure prediction. Currently, there are several techniques on how to predict a seizure; however, most of the techniques found in research papers are just mathematical models and not system implementations. The seizure prediction approach proposed in this thesis paper is achieved using the EMOTIV Epoc+ headset, MATLAB, and LabVIEW as the analog and digital signal processing devices. In addition, this thesis project incorporates the use of the Hilbert Huang transform (HHT) method to obtain intrinsic mode functions (IMF) and instantaneous frequency components of the transform. From the IMFs, features as variation coefficient (VC) and fluctuation indexes (FI) are extracted to feed a support vector machine that classifies the EEG data as pre-ictal and non-ictal EEGs. Outstanding patterns in non-ictal and pre-ictal are observed and demonstrated by significant differences between both types of EEG signals. In other words, a classification of EEG signals according to a category can be achieved proving that an epileptic seizure prediction technology has a future in engineering and biotechnology fields.

Made available in DSpace on 2016-08-17T14:52:45Z (GMT). No. of bitstreams: 1 Deusdete Brito.PDF: 646824 bytes, checksum: 52d3dc54a7bd1d78f16e7b240f977ce8 (MD5) Previous issue date: 2002-10-11 / Conselho Nacional de Desenvolvimento Científico e Tecnológico / In this work we consider the calculation of the heart instantaneous frequency from the estimate of the weights, gotten through LMS algorithm, (Least Mean Squares) when functioning as spectrum analyzer. It is known that the electrocardiogram (ECG) is a signal that is characterized for a repetitive regularity, which can be called quasi-periodicity. We explore this characteristic to extract the instantaneous frequency of the referred signal. For this, we use the LMS as a spectral analyzer. We use as reference inputs pairs of sines and cosines, inside the frequency band where if it finds the frequency of the desired signal, namely, the heart frequency. The algorithm estimates the frequency desired in real time, with the signal acquired through a data acquisition interface Intel 80C31. The results obtained show that the algorithm can be recommended for this purpose, as besides being easily implemented and generating small computational load it estimates the heart instantaneous frequency with a relative mean error of 0.025 which represent a difference of 18.89% between the two methods. / Neste trabalho propomos o cálculo da frequência instantânea cardíaca a partir da estimativa do espectrograma dos pesos sinápticos, obtidos através do algoritmo LMS, (Least Mean Square) quando funcionando como analisador de espectro. Sabe-se que o eletrocardiograma (ECG) é um sinal que se caracteriza por uma regularidade repetitiva, que se pode chamar quasiperiodicidade. Exploramos aqui essa característica para extrair a frequência instantânea do referido sinal. Para isso, utilizamos o LMS como analisador de espectro. Utilizamos como entradas de referència pares de senos e cossenos, dentro da faixa de frequência em que se encontra a frequência do sinal desejado, no caso, a frequência cardíaca. O algoritmo estima a frequência desejada em tempo real, com obtenção do sinal através de uma interface de aquisição de dados Intel 80C31. Os resultados obtidos mostraram que o algoritmo pode ser recomendado pra esta finalidade, pois além de ser facilmente implementável e por gerar pequena carga computacional ele estimou a frequência instantânea cardíaca com um erro relativo médio de 0.025 que representa uma diferença de 18.89% entre os dois métodos.

Made available in DSpace on 2016-08-17T14:52:55Z (GMT). No. of bitstreams: 1 Marcio de Oliveira Santos.pdf: 375867 bytes, checksum: 9316c23a45525634b808c0315c716985 (MD5) Previous issue date: 2003-12-12 / The patient diagnosis can be made through a analysis of the cardiac variability that, being formed of nervous interactions, give the status of the vagal and sympathetic systems. The main measure to do this analysis is HRV, obtained by RR temporal differences or spectral methods. A major disadvantage we can find in the latter methods is a high sampling tax that yields in lost of information and high storage cost. New methods has been developed to minimize these incovenients, like HIF. This method have two steps: a driver function and a wavelet filter. The proposed algorithm is based on HIF using a auto regressive method as driver function and otimized parameters to the wavelet filter. The obtained results are very promissor and the estimation error is smaller than traditional methods one. / O diagnóstico de um paciente pode ser feito através da análise da variabilidade cardíaca que, por ser resultado de interações nervosas, fornece o estado dos sistemas vagal e simpático. A principal medida utilizada para se fazer esta análise é a taxa de variabilidade cardíaca (HRV) que pode ser obtida por métodos de diferença temporal de ondas R e espectrais. A principal desvantagem que é encontrada nestes métodos é que a alta taxa de amostragem do ECG é herdada por estes métodos, ocasionando perda de informação e o aumento de custo para armazenamento dos dados se torna mais alto. Para que este problema fosse solucionado foram desenvolvidas novas medidas que não apresentassem esses inconvenientes, como o algoritmo HIF. Este algoritmo é composto de duas etapas: a construção de uma função driver e filtragem através de uma wavelet de Gabor. O algoritmo proposto neste trabalho foi baseado no HIF utilizando um novo método autoregressivo (EAR) para função driver e otimizando-se os parâmetros da wavelet de Gabor. Os resultados obtidos mostraram que o algoritmo desenvolvido é bastante promissor e o erro de estimação é bem menor em relação a HIF.

The rise of Renewable Energy Sources (RES) such as wind and solar power, creates new challenges for electric power systems. One of these challenges occur in Frequency Containment Reserves (FCR) on power system because of decreasing system inertia from RES. The purpose of FCR is to regulate the system frequency after a disturbance that gives rise to a Rate of Change of Frequency (RoCoF) and an Instantaneous Frequency Deviation (IFD). Conventional electricity production such as hydro and nuclear power have a contribution for the amount of inertia in the system, while RES lack this contribution of inertia.This paper studies different cases of amount of inertia to understand the impact of lower amount of inertia caused by RES on power system. A power system was simulated and the IFD and SteadyState Frequency Deviation (SSFD) of the system were examined as the nuclear powers were substituted by wind powers. The results showed that a large amount of inertia implies a small IFD and vice versa.Furthermore, this paper also studies Battery Energy Storage System (BESS) as a power support for FCR when using RES. The conclusion for the impact of the battery was to use high injected power and triggering frequency level (TLF) and vice versa to get an acceptable IFD. In other words, this means that it is possible to keep the IFD within predefined limits by using batteries and identify the appropriate range of battery control settings.

This dissertation explores the potential of EMD as analyzing tool for audio and speech processing. This signal expansion into IMFs is adaptive and without any prior assumptions (stationarity and linearity) on the signal to be analyzed. Salient properties of EMD such as dyadic filter bank structure, quasi-symmetry of IMF and fully description of IMF by its extrema, are exploited for denoising, coding and watermarking purposes. In speech signals denoising, we initially proposed a technique based on IMFs thresholding. A comparative analysis of performance of this technique compared to the denoising technique based on the wavelet. Then, to remedy the problem of the MMSE filters which requires an estimation of the spectral properties of noise, we introduced the ACWA filter in the denoising procedure. The proposed approach is consisted to filter all IMFs of the noisy signal by ACWA filter. This filtering approach is implemented in the time domain, and also applicable in the context of colored noise. Finally, to handle the case of hybrid speech frames, that is composed of voiced and unvoiced speech, we introduced a stationarity index in the denoising approach to detect the transition between the mixture of voiced and unvoiced sounds. In audio signals coding, we proposed four compression approaches. The first two approaches are based on the EMD, and the other two approaches exploit the EMD in association with Hilbert transform. In particular, we proposed to use a predictive coding of the instantaneous amplitude and frequency of the IMFs Finally, we studied the problem of audio signals watermarking in context of copyright protection. The number of IMFs can be variable depending on the attack type. The proposed approach involves inserting the mark in the extrema of last IMFs. In addition, we introduced a synchronization code in the procedure in order to facility the extraction of the mark. These contributions are illustrated on synthetic and real data and results compared to well established methods such as MMSE filter, wavelets approach, MP3 and AAC coders showing the good performances of EMD based signal processes. These findings demonstrate the real potential of EMD as analyzing tool (in adaptive way) in speech and audio processing.

In this thesis, RF sections of a multi tier instantaneous frequency measurement (IFM) receiver which can operate in 2 &ndash / 18 GHz frequency band is designed, simulated and partially realized. The designed structure uses one coarse tier, three medium tiers and one fine tier for frequency discrimination. A novel reflective phase shifting technique is developed which enables the design of very wideband phase shifters using stepped cascaded transmission lines. Compared to the classical phase shifters using coupled transmission lines, the new approach came out to be much easier to design and fabricate with much better responses. This phase shifting technique is used in coarse and medium tiers. In fine frequency measurement tier, I/Q discriminator approach is used because reflective phase shifters would necessitate unacceptably long delay lines. Two I/Q discriminators are designed and fabricated using Lange directional couplers that operate in 2-6 GHz and 6-18 GHz, resulting in satisfactory response. Additionally, 6 GHz HP and 6 GHz LP distributed filters are designed and fabricated to be used for these I/Q discriminators in fine tier. In order to eliminate possible ambiguities in coarse tier, a distributed element LP-HP diplexer with 10 GHz crossover frequency is designed and fabricated successfully to be used for splitting the frequency spectrum into 2-10 GHz and 10-18 GHz to ease the design and realization problems. Three power dividers operating in the ranges 2-18 GHz, 2-6 GHz and 6-18 GHz are designed for splitting incoming signals into different branches. All of these dividers are also fabricated with satisfactory response. The fabricated components are all compact and highly reproducible. The designed IFM can tolerate 48 degrees phase margin for resolving ambiguity in the tiers while special precautions are taken in fine tier to help ambiguity resolving process also. The resulting IFM provides a frequency resolution below 1 MHz in case of using an 8-bit sampler with a frequency accuracy of 0.28 MHz rms for 0 dB input SNR and 20 MHz video bandwidth.

Microwave limiting amplifiers are the key components of Instantaneous Frequency Measurement (IFM) systems. Limiting amplifiers provide constant output power level in a wide input dynamic range and over a broad frequency band. Moreover, limiting amplifiers are high gain devices that are used to bring very low input power levels to a constant output power level. Besides, limiting amplifiers are required to provide minimum small signal gain ripple in order not to reduce the sensitivity of the IFM system over the operating frequency band. In this thesis work, a high gain, medium power, 2-18 GHz limiting amplifier module is designed, simulated, fabricated and measured. First, a 3-stage cascaded amplifier with 27 dB small signal gain is designed and fabricated. The 3-stage amplifier is composed of a novel cascaded combination of negative feedback and distributed amplifiers that provides the minimum small signal gain ripple and satisfactory input and output return losses inside 2-18 GHz frequency band. Then, the designed two 3-stage amplifiers and one 4-stage amplifier are cascaded to constitute a limiting amplifier module with minimum 80 dB small signal gain. The designed 10-stage limiting amplifier module also includes an analog voltage controllable attenuator to be used for compensating the gain variations resulting from temperature changes. The fabricated 10-stage limiting amplifier module provides 20 +/- 1.2 dBm output power level and excellent small signal gain flatness, +/- 2.2 dB, over 2-18 GHz frequency range.

abstract: This work examines two main areas in model-based time-varying signal processing with emphasis in speech processing applications. The first area concentrates on improving speech intelligibility and on increasing the proposed methodologies application for clinical practice in speech-language pathology. The second area concentrates on signal expansions matched to physical-based models but without requiring independent basis functions; the significance of this work is demonstrated with speech vowels. A fully automated Vowel Space Area (VSA) computation method is proposed that can be applied to any type of speech. It is shown that the VSA provides an efficient and reliable measure and is correlated to speech intelligibility. A clinical tool that incorporates the automated VSA was proposed for evaluation and treatment to be used by speech language pathologists. Two exploratory studies are performed using two databases by analyzing mean formant trajectories in healthy speech for a wide range of speakers, dialects, and coarticulation contexts. It is shown that phonemes crowded in formant space can often have distinct trajectories, possibly due to accurate perception. A theory for analyzing time-varying signals models with amplitude modulation and frequency modulation is developed. Examples are provided that demonstrate other possible signal model decompositions with independent basis functions and corresponding physical interpretations. The Hilbert transform (HT) and the use of the analytic form of a signal are motivated, and a proof is provided to show that a signal can still preserve desirable mathematical properties without the use of the HT. A visualization of the Hilbert spectrum is proposed to aid in the interpretation. A signal demodulation is proposed and used to develop a modified Empirical Mode Decomposition (EMD) algorithm. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2016