Submitted by Samira Prince (prince@ufpa.br) on 2012-08-28T14:54:01Z No. of bitstreams: 2 Dissertacao_InfluenciasFamiliasWavelets.pdf: 13148962 bytes, checksum: 8cc99d80429abe5dc9a0cdac2447d733 (MD5) license_rdf: 23898 bytes, checksum: e363e809996cf46ada20da1accfcd9c7 (MD5) / Approved for entry into archive by Samira Prince(prince@ufpa.br) on 2012-08-28T14:54:38Z (GMT) No. of bitstreams: 2 Dissertacao_InfluenciasFamiliasWavelets.pdf: 13148962 bytes, checksum: 8cc99d80429abe5dc9a0cdac2447d733 (MD5) license_rdf: 23898 bytes, checksum: e363e809996cf46ada20da1accfcd9c7 (MD5) / Made available in DSpace on 2012-08-28T14:54:38Z (GMT). No. of bitstreams: 2 Dissertacao_InfluenciasFamiliasWavelets.pdf: 13148962 bytes, checksum: 8cc99d80429abe5dc9a0cdac2447d733 (MD5) license_rdf: 23898 bytes, checksum: e363e809996cf46ada20da1accfcd9c7 (MD5) Previous issue date: 2011 / CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico / Essa dissertação tem por objetivo analisar a influência de famílias wavelets e suas ordens no desempenho de um algoritmo de localização de faltas a partir das ondas viajantes de dois terminais de uma linha de transmissão aérea. Tornou-se objetivo secundário a modelagem de um sistema elétrico de potência (SEP) para obtenção de um universo de faltas que validassem o localizador. Para isso, parte de um SEP da Eletrobrás-Eletronorte em 500/230 kV foi modelado no Alternative Transient Program (ATP) utilizando-se parâmetros reais. A Transformada Wavelet, via análise multiresolução (AMR), é empregada valendo-se de sua característica de localização temporal, permitindo caracterizações precisas de instantes de transitórios eletromagnéticos ocasionados por faltas, as quais geram ondas que ao se propagarem em direção aos terminais da linha contêm os tempos de propagação destas do local do defeito a tais terminais e podem ser convenientemente extraídos por tal transformada. Pela metodologia adotada no algoritmo, a diferença entre esses tempos determina com boa exatidão o local de ocorrência da falta sobre a linha. Entretanto, um dos agentes variantes do erro nessa estimação é a escolha da Wavelet usada na AMR dos sinais, sendo, portanto, a avaliação dessa escolha sobre o erro, objetivo principal do trabalho, justificada pela ainda inexistente fundamentação científica que garanta a escolha de uma wavelet ótima a uma certa aplicação. Dentre um leque de Wavelets discretas, obtiveram-se resultados adequados para 16 delas, havendo erros máximos inferiores aos 250 metros estipulados para a precisão. Duas Wavelets, a Db15 e a Sym17, sobressaíram-se ao errarem, respectivamente, 3,5 e 1,1 vezes menos que as demais. A metodologia empregada consta da: exportação dos dados das faltas do ATP para o MATLAB®; aplicação da transformação modal de Clarke; decomposição dos modos alfa e síntese dos níveis 1 de detalhes via AMR; cálculo de suas máximas magnitudes e determinação dos índices temporais; e por fim, a teoria das ondas viajantes equaciona e estima o local do defeito sobre a LT, sendo tudo isso programado no MATLAB e os erros de localização analisados estatisticamente no Microsoft Excell®. Ao final elaborou-se ainda uma GUI (Guide User Interface) para a Interface Homem-Máquina (IHM) do localizador, servindo também para análises gráficas de qualquer das contingências aplicadas ao SEP. Os resultados alcançados demonstram uma otimização de performance em razão da escolha da wavelet mais adequada ao algoritmo e norteiam para uma aplicação prática do localizador. / This dissertation objective to analyze the influence of wavelets families and their orders in the performance of a fault location algorithm through the traveling waves from two ends of an overhead transmission line. Then, a secondary objective is the modelling of an electric power system (EPS) to obtaining of a fault’s universe to validate the algorithm. For that, part of an EPS of the Eletrobrás-Eletronorte on 500/230 kV was modeled in Alternative Transient Program (ATP) using real parameters. The Wavelet Transform, through multiresolution analysis (MRA), it’s used because its time domain localization characteristic to allow characterizations of electromagnetic transitory moments caused by contingencies. The waves generated by faults traveling towards the line’s terminals containing the wave propagation times from defect point at the terminals and these times can be conveniently extracted for such transformed. By means of the methodology adopted in the algorithm, the difference between those times determines the fault occurrence place on the line with good accuracy. However, one of the error variant agents in the estimation it’s the selection of the wavelet used in MRA of the signals, therefore the evaluation of that choice upon those errors, the main objective of this work, it’s justified for the still inexistent scientific bases that assure the choice of a great wavelet to a certain application. Among a diversity of discrete wavelets, appropriated results were obtained for 16 of them with maximum errors smaller than 250 meters, stipulated precision adopted. Two wavelets, Db15 and Sym17, were more exact than the others, respectively, 3.5 and 1.1 times. The methodology to get those results consists of the: export of the fault data files from ATP for MATLAB®; application of the Clarke’s modal transformation of voltages; decomposition of alpha modes and synthesis of the levels 1 of details through AMR; calculation of their maximum magnitudes and time indexes determination; finally, the traveling waves theory is used to formulate the estimate equation of the contingency place on the line. All methodology was programmed in MATLAB and the errors’ statistical analysis was made in Microsoft Excell®. At the end, a GUI (Guide User Interface) for (Human Machine Interface) HMI of locator was elaborated, which is also a graphical analysis interface, no matter which the contingency applied to the EPS. The attained results demonstrate an improved performance in reason of the most appropriate wavelet selection to the algorithm and they suggest a practical application of the locator.

Made available in DSpace on 2016-12-23T14:07:25Z (GMT). No. of bitstreams: 1 Sandra Mara Torres Muller.pdf: 1594751 bytes, checksum: b3abd1c42aa0001991d29eefe9754019 (MD5) Previous issue date: 2006-04-03 / Conselho Nacional de Desenvolvimento Científico e Tecnológico / In this work three incremental adaptation methods for the hidden Markov models (HMM) are studied and implemented, which are based on the Expectation-Maximization (EM), Segmental k-Means and Maximum a Posteriori (MAP) algorithms. These methods, already used in the speech recognition field, are applied here in the electrocardiogram (ECG) segmentation problem. For that, it was used an ECG analysis system able to segment and classify cardiac diseases, like premature ventricular contraction (PVC) and ischemia. The use of these methods allow us to adjust the models to the signal fluctuations commonly met during ambulatory recording. The methods can also be implemented for other kinds of biomedical signals, like electroencephalogram (EEG). / Neste trabalho foram estudadas e implementadas tr^es t¶ecnicas incrementais de adapta»c~ ao de modelos ocultos de Markov (HMM - Hidden Markov Model) baseadas nos algoritmos de treinamento, que s~ ao a esperan»ca da maximiza»c~ ao (expectation maximization - EM), a k-means segmental (segmental k-means) e a m¶aximo a posteriori (Maximum a Posteri- ori -MAP). Essas t¶ecnicas, muito utilizadas em reconhecimento de voz, s~ ao aqui usadas para sinais biom¶edicos, mais precisamente para sinal de eletrocardiograma (ECG). Para tal objetivo, utilizou-se uma plataforma, j¶ a desenvolvida, de segmenta» c~ ao e classi¯ca» c~ ao de ECG, al¶em de detec»c~ oes de anomalias card¶³acas como extra-s¶³stole ventricular (ESV) e isquemia do mioc¶ ardio. Nessa plataforma, os modelos de Markov são empregados na etapa de segmenta»c~ ao do sinal de ECG, tendo em vista a identi¯ca» c~ ao das formas de onda elementares que comp~oem um ciclo card¶³aco. O desenvolvimento dessas t¶ecnicas permite, uma vez que a plataforma esteja funcionando como sistema real, um ajuste aut^ onomo dos modelos µas varia» c~ oes do sinal de ECG ao longo do tempo, assim como a outras varia» c~ oes presentes em um sistema real. As t¶ecnicas foram avaliadas a partir de experimentos usando duas bases de sinais de ECG: QT database e European ST-T database. Os resultados con¯rmam o ganho de desempenho obtido com a adapta»c~ ao, permitindo uma modelagem do sinal ao longo do tempo mais apropriada. As t¶ecnicas desenvolvidas s~ ao indicadas tamb¶em para outros tipos de sinais biom¶edicos, como o sinal de eletroencefalograma (EEG), por exemplo.

Made available in DSpace on 2014-06-12T17:40:50Z (GMT). No. of bitstreams: 2 arquivo7043_1.pdf: 3184867 bytes, checksum: bb5a50f8a46a033e7556cdb1af4fde98 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2005 / Análise de Sinais é uma das partes mais importantes da área de Processamento de Sinais. Esta tese encontra-se dividida em três partes, cada uma abordando um tópico de análise de sinais. Foram endereçadas as seguintes subáreas: (i) métodos aproximados para avaliação espectral; (ii) construção de wavelets e (iii) análise de sinais biomédicos. O problema da estimação espectral sujeita à minimização da complexidade computacional foi abordado por meios de métodos de aproximação. Dois métodos foram utilizados para propor algoritmos eficientes para a transformada discreta de Hartley. O primeiro método introduzido consiste da transformada de Hartley arredondada, um procedimento que utiliza a função de arredondamento para gerar uma matriz de transformação com complexidade multiplicativa nula. A segunda abordagem contempla a proposição da transformada aritmética de Hartley. É demonstrado o papel da interpolação como elemento decisivo na teoria das transformadas aritméticas. Esquemas de interpolação para as transformadas de Hartley, Fourier cosseno e Fourier seno são introduzidos. O foco foi então dirigido para a construção de novas wavelets. Dois procedimentos foram examinados: (i) definição de novas wavelets a partir de equações diferenciais e (ii) construção de wavelets ótimas associadas a uma dada classe de sinais. Da primeira abordagem, foram obtidas duas wavelets propostas nesta tese: a wavelet de Mathieu (baseada nas funções de Mathieu) e a wavelet de Chebyshev (baseada nos polinômios de Chebyshev). Foram examinadas as propriedades de tais wavelets e evidenciadas potenciais aplicações. O segundo método consistiu da proposição de um algoritmo para determinar wavelets ótimas para sinais eletrogastrográficos. Para tal, foram utilizados argumentos de minimização do erro de reconstrução de sinais compactados via wavelet. Na parte final, foi elaborado um algoritmo para classificação de sinais eletrogastrográficos. Foi objetivada a discriminação entre estados de desacoplamento elétrico gástrico e o estado basal

Submitted by FABRÍCIO ELY GOSSLER null (fabricio_ely8@hotmail.com) on 2017-02-09T16:24:59Z No. of bitstreams: 1 Fabrício E. Gossler-Dissertação - Unesp - Feis-PPGEE.pdf: 5023440 bytes, checksum: b5346eb35f509f2283b503acccf22ec3 (MD5) / Approved for entry into archive by LUIZA DE MENEZES ROMANETTO (luizamenezes@reitoria.unesp.br) on 2017-02-14T16:08:30Z (GMT) No. of bitstreams: 1 gossler_fe_me_ilha.pdf: 5023440 bytes, checksum: b5346eb35f509f2283b503acccf22ec3 (MD5) / Made available in DSpace on 2017-02-14T16:08:30Z (GMT). No. of bitstreams: 1 gossler_fe_me_ilha.pdf: 5023440 bytes, checksum: b5346eb35f509f2283b503acccf22ec3 (MD5) Previous issue date: 2016-12-19 / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / Existem diferentes tipos de funções wavelets que podem ser utilizadas na Transformada Wavelet. Na maioria das vezes, a função wavelet escolhida para a análise de um determinado sinal vai ser aquela que melhor se ajusta no domínio tempo-frequência do mesmo. Existem vários tipos de funções wavelets que podem ser escolhidas para certas aplicações, sendo que algumas destas pertencem a conjuntos específicos denominados de famílias wavelets, tais como a Haar, Daubechies, Symlets, Morlet, Meyer e Gaussianas. Nesse trabalho é apresentada uma nova família de funções wavelets geradas a partir de polinômios com coeficientes de Fibonacci (FCPs). Essa família recebe o nome de Golden, e cada membro desta é obtido por uma derivada de ordem n do quociente entre dois FCPs distintos. As Golden wavelets foram deduzidas através das observações de que, em alguns casos, a derivada de ordem n, do quociente entre dois FCPs distintos, resulta em uma função que possui as características de uma onda de duração curta. Como aplicação, algumas wavelets apresentadas no decorrer deste trabalho são utilizadas na classificação de arritmias cardíacas em sinais de eletrocardiograma, que foram extraídos da base de dados do MIT-BIH arrhythmia database. / There exist different types of wavelet functions that can be used in the Wavelet Transform. In most cases, the wavelet function chosen for the analysis of a given signal will be the one that best adjusts in the time-frequency domain of the same signal. There are many types of wavelet functions that can be chosen for certain applications, some of which belong to specific sets called wavelet families, such as Haar, Daubechies, Symlets, Morlet, Meyer, and Gaussians. In this work a new wavelet functions family generated from Fibonacci-coefficients polynomials (FCPs) is presented. This family is called Golden, and each member is obtained by the n-th derivative of the quotient between two distinct FCPs. The Golden wavelets were deduced from the observations that in some cases the n-th derivative of the quotient between two distinct FCPs results in a function that has the characteristics of a short-duration wave. As an application, some wavelets presented in the course of this work are used to cardiac arrhythmia classification in electrocardiogram signals, which were extracted from the MITBIH arrhythmia database. / CNPq: 130123/2015-3

Neste texto são revisados métodos de reamostragem de séries temporais discretas baseados em wavelets, como alternativas as abordagens clássicas, feitas nos domínios do tempo e da frequência. Tais métodos, conhecidos na literatura como wavestrap e wavestrapping fazem uso, respectivamente, das transformadas wavelet discreta (DWT) e wavelet packet discreta (DWPT). Existem poucos resultados sobre a aplicação da DWPT, de forma que este texto pode ser considerado uma contribuição. Aqui mostra-se também, a superioridade do wavestrapping sobre o wavestrap quando aplicados na estimação da densidade espectral de potência de séries temporais sintéticas geradas a partir de modelos autoregressivos. Tais séries possuem uma particularidade interessante que são picos, geralmente acentuados, em sua reapresentação espectral, de tal forma que grande parte dos métodos clássicos de reamostragem apresentam resultados viesados quando aplicados a estes casos. / This paper reviews resampling methods based on wavelets as an alternative to the classic approaches which are, made in the time and frequency domains. These methods, known in the literature as wavestrap and wavestrapping, make use, respectively, of the discrete wavelet transform (DWT) and of the discrete wavelet packet transform (DWPT). Since only few results are avaliable when the DWPT is applied, this text can be considered a contribution to the subject. Here we, also show the superiority of wavestrapping over wavestrap when they are applied to the estimation of power spectral densities of the synthetic time series generated from autoregressive models. These series have an interesting feature that are sharp peaks in their spectral representation, so that most of the traditional methods of resampling lead to biased results.

A necessidade de antecipar e identificar variações de acontecimentos apontam para uma nova direção nos mercados de bolsa de valores e vem de encontro às análises das oscilações de preços de ativos financeiros. Esta necessidade leva a argumentar sobre novas alternativas na predição de séries temporais financeiras utilizando métodos de aprendizado de máquinas e vários modelos têm sido desenvolvidos para efetuar a análise e a previsão de dados de ativos financeiros. Este trabalho tem por objetivo propor o desenvolvimento de um modelo de previsão adaptativo baseado em um sistema SVR-wavelet híbrido, que integra modelos de wavelets e Support Vector Regression (SVR) na previsão de séries financeiras. O método consiste na utilização da Transformada de Wavelet Discreta (DWT) a fim de decompor dados de séries de ativos financeiros que são utilizados como variáveis de entrada do SVR com o objetivo de prever dados futuros de ativos financeiros. O modelo proposto é aplicado a um conjunto de ativos financeiros do tipo Foreign Exchange Market (FOREX), Mercado Global de Câmbio, obtidos a partir de uma base de conhecimento público. As séries são ajustadas gerando-se novas predições das séries originais, que são comparadas com outros modelos tradicionais tais como o modelo Autorregressivo Integrado de Médias Móveis (ARIMA), o modelo Autorregressivo Fracionário Integrado de Médias Móveis (ARFIMA), o modelo Autorregressivo Condicional com Heterocedasticidade Generalizado (GARCH) e o modelo SVR tradicional com Kernel. Além disso, realizam-se testes de normalidade e de raiz unitária para distribuição não linear, tal como testes de correlação, para constatar que as séries temporais FOREX são adequadas para a comprovação do modelo híbrido SVR-wavelet e posterior comparação com modelos tradicionais. Verifica-se também a aderência ao Expoente de Hurst por meio da estatística de Reescalonamento (R/S). / The necessity to anticipate and identify changes in events points to a new direction in the stock exchange market and reaches the analysis of the oscillations of prices of financial assets. This necessity leads to an argument about new alternatives in the prediction of financial time series using machine learning methods. Several models have been developed to perform the analysis and prediction of financial asset data. This thesis aims to propose the development of SVR-wavelet model, an adaptive and hybrid prediction model, which integrates wavelet models and Support Vector Regression (SVR), for prediction of Financial Time Series, particularly Foreign Exchange Market (FOREX), obtained from a public knowledge base. The method consists of using the Discrete Wavelets Transform (DWT) to decompose data from FOREX time series, that are used as SVR input variables to predict new data. The series are adjusted by generating new predictions of the original series, which are compared with other traditional models such as the Autoregressive Integrated Moving Average model (ARIMA), the Autoregressive Fractionally Integrated Moving Average model (ARFIMA), the Generalized Autoregressive Conditional Heteroskedasticity model (GARCH) and the traditional SVR model with Kernel. In addition, normality and unit root tests for non-linear distribution, and correlation tests, are performed to verify that the FOREX time series are adequate for the verification of SVR-wavelet hybrid model and comparison with traditional models. There is also the adherence to the Hurst Exponent through the statistical Rescaled Range (R/S).

In the first part of the dissertation, a novel stochastic averaging technique based on a Hilbert transform definition of the oscillator response displacement amplitude is developed. In comparison to standard stochastic averaging, the requirement of “a priori” determination of an equivalent natural frequency is bypassed, yielding flexibility in the ensuing analysis and potentially higher accuracy. Further, the herein proposed Hilbert transform based stochastic averaging is adapted for determining the time-dependent survival probability and first-passage time probability density function of stochastically excited nonlinear oscillators, even endowed with fractional derivative terms. To this aim, a Galerkin scheme is utilized to solve approximately the backward Kolmogorov partial differential equation governing the survival probability of the oscillator response. Next, the potential of the stochastic averaging technique to be used in conjunction with performance-based engineering design applications is demonstrated by proposing a stochastic version of the widely used incremental dynamic analysis (IDA). Specifically, modeling the excitation as a non-stationary stochastic process possessing an evolutionary power spectrum (EPS), an approximate closed-form expression is derived for the parameterized oscillator response amplitude probability density function (PDF). In this regard, IDA surfaces are determined providing the conditional PDF of the engineering demand parameter (EDP) for a given intensity measure (IM) value. In contrast to the computationally expensive Monte Carlo simulation, the methodology developed herein determines the IDA surfaces at minimal computational cost. In the second part of the dissertation, a novel multiple-input/single-output (MISO) system identification technique is developed for parameter identification of nonlinear and time-variant oscillators with fractional derivative terms subject to incomplete non-stationary data. The technique utilizes a representation of the nonlinear restoring forces as a set of parallel linear sub-systems. Next, a recently developed L1-norm minimization procedure based on compressive sensing theory is applied for determining the wavelet coefficients of the available incomplete non-stationary input-output (excitation-response) data. Several numerical examples are considered for assessing the reliability of the technique, even in the presence of incomplete and corrupted data. These include a 2-DOF time-variant Duffing oscillator endowed with fractional derivative terms, as well as a 2-DOF system subject to flow-induced forces where the non-stationary sea state possesses a recently proposed evolutionary version of the JONSWAP spectrum. In the third part of this dissertation, a joint time-frequency analysis technique based on generalized harmonic wavelets (GHWs) is developed for dynamic cerebral autoregulation (DCA) performance quantification. DCA is the continuous counter-regulation of the cerebral blood flow by the active response of cerebral blood vessels to the spontaneous or induced blood pressure fluctuations. Specifically, various metrics of the phase shift and magnitude of appropriately defined GHW-based transfer functions are determined based on data points over the joint time-frequency domain. The potential of these metrics to be used as a diagnostics tool for indicating healthy versus impaired DCA function is assessed by considering both healthy individuals and patients with unilateral carotid artery stenosis. Next, another application in biomedical engineering is pursued related to the Pulse Wave Imaging (PWI) technique. This relies on ultrasonic signals for capturing the propagation of pressure pulses along the carotid artery, and eventually for prognosis of focal vascular diseases (e.g., atherosclerosis and abdominal aortic aneurysm). However, to obtain a high spatio-temporal resolution the data are acquired at a high rate, in the order of kilohertz, yielding large datasets. To address this challenge, an efficient data compression technique is developed based on the multiresolution wavelet decomposition scheme, which exploits the high correlation of adjacent RF-frames generated by the PWI technique. Further, a sparse matrix decomposition is proposed as an efficient way to identify the boundaries of the arterial wall in the PWI technique.

Wavelet transform provides an alternative to the classical Short-Time Fourier Transform (STFT). In contrast to the STFT, which uses a single analysis window, the Wavelet Transform uses shorter windows at higher frequencies and longer windows at lower frequencies. For some particular wavelet functions, the local maxima of the wavelet transform correspond to the sharp variation points of the signal. As an application, wavelet transform is introduced to the character recognition. Local maximum of wavelet transform is used as a local feature to describe character boundary. The wavelet method performs well in the presence of noise. The maximum of wavelet transform is also an important feature for analyzing the properties of brain wave. In our study, we found the maximum of wavelet transform was related to the P300 latency. It provides an easy and efficient way to measure P300 latency.

Event related potentials (ERP's) carry very important information that relates to the performance of the brain functions of the human being. Further studies have identified that one component, in particular, P 300, is affected by the memory process. Matched filter is used to improved the SNR of signal ERP' s. We use the output of the matched filter to distinguish the difference of the waveforms between normal subjects and memory-impaired subjects. In our study, we found that the peak values of the matched filtering output were different between normal subjects and memoryimpaired subjects. Also, as an application, wavelet transform is introduced to the ERP analysis. Local maximum of wavelet transform was used as a local feature to find the relationship between the sharp variation points and the memory process. A comparison between matched filtering and wavelet transform was made and also the correlation coefficients of the peaks and sharp variation points are calculated to find the relationship between the important moments in a memory process.

The current popularity of WLANs is a testament primarily to their convenience, cost efficiency and ease of integration. Even now the demand for high data rate wireless communications has increased fourfold as consumers demand better multimedia communications over the wireless medium. The next generation of high speed WLANs is expected to meet this increased demand for capacity coupled with high performance and spectral efficiency. The current generation of WLANs utilizes Orthogonal Frequency Division Multiplexing (OFDM) modulation. The next generation of WLAN standards can be made possible either by developing a different modulation technique or combining legacy OFDM with Multiple Input Multiple Output (MIMO) systems to create MIMO-OFDM systems. This dissertation presents two different basis technologies for the next generation of high speed WLANs: OWSS and MIMO-STC-OFDM. OWSS, or Orthogonal Wavelet Division Multiplexed - Spread Spectrum is a new class of wavelet pulses and a corresponding signaling system which has significant advantages over current signaling schemes like OFDM. In this dissertation, CSMA/CA is proposed as the protocol for full data rate multiplexing at the MAC layer for OWSS. The excellent spectral characteristics of the OWSS signal is also studied and simulations show that passband spectrum enjoys a 30-40% bandwidth advantage over OFDM. A novel pre-distortion scheme was developed to compensate for the passband PA non-linearity. Finally for OWSS, the fundamental limits of its system performance were also explored using a multi-level matrix formulation. Simulation results on a 108 Mbps OWSS WLAN system demonstrate the excellent effectiveness of this theory and prove that OWSS is capable of excellent performance and high spectral efficiency in multipath channels. This dissertation also presents a novel MIMO-STC-OFDM system which targets data rates in excess of 100 Mbps and at the same time achieve both high spectral efficiency and high performance. Simulation results validate the superior performance of the new system over multipath channels. Finally as channel equalization is critical in MIMO systems, a highly efficient time domain channel estimation formulation for this new system is also presented. In summary, both OWSS and MIMO-STC-OFDM appear to be excellent candidate technologies for next generation of high speed WLANs.