Recently, Radio Frequency Identification (RFID) technology has become commonplace in many applications. It is based on storing and remotely retrieving the data embedded on the tags. The tag structure can be chipped or chipless. In chipped tags, an integrated IC attached to the antenna is biased by an onboard battery or interrogating signal. Compared to barcodes, the chipped tags are expensive because of the existence of the chip. That was why chipless RFID tags are demanded as a cheap candidate for chipped RFID tags and barcodes. As its name expresses, the geometry of the tag acts as both modulator and scatterer. As a modulator, it incorporates data into the received electric field launched from the reader antenna and reflects it back to the receiving antenna. The scattered signal from the tag is captured by the antenna and transferred to the reader for the detection process. By employing the singularity expansion method (SEM) and the characteristic mode theory (CMT), a systematic design process is introduced by which the resonant and radiation characteristics of the tag are monitored in the pole diagram versus structural parameters. The antenna is another component of the system. Taking advantage of ultra-wideband (UWB) technology, it is possible to study the time and frequency domain characteristics of the antenna used in chipless RFID system. A new omni-directional antenna element useful in wideband and UWB systems is presented. Then, a new time-frequency technique, called short-time matrix pencil method (STMPM), is introduced as an efficient approach for analyzing various scattering mechanisms in chipless RFID tags. By studying the performance of STMPM in early-time and late-time responses of the scatterers, the detection process is improved in cases of multiple tags located close to each other. A space-time-frequency algorithm is introduced based on STMPM to detect, identify, and localize multiple multi-bit chipless RFID tags in the reader area. The proposed technique has applications in electromagnetic and acoustic-based detection of targets. / Ph. D.

This study introduces new families of wavelets. The first is directly derived from the response of Second Order Underdamped Linear-Time-Invariant (SOULTI) systems, while the second is a generalization of the first to the complex domain and is similar to the Laplace transform kernel function. The first takes the acronym of SOULTI wavelet, while the second is named the Laplace wavelet. The most important criteria for a function or signal to be a wavelet is the ability to recover the original signal back from its continuous wavelet transform. It is shown that it is possible to recover back the original signal once the SOULTI or the Laplace wavelet transform is applied to decompose the signal. It is found that both wavelet transforms satisfy linear differential equations called the reconstructing differential equations, which are closely related to the differential equations that produce the wavelets. The new wavelets can have well defined Time-Frequency resolutions, and they have useful properties; a direct relation between the scale and the frequency, unique transform formulas that can be easily obtained for most elementary signals such as unit step, sinusoids, polynomials, and decaying harmonic signals, and linear relations between the wavelet transform of signals and the wavelet transform of their derivatives and integrals. The defined wavelets are applied to system analysis applications. The new wavelets showed accurate instantaneous frequency identification and modal decomposition of LTI Multi-Degree of Freedom (MDOF) systems and it showed better results than the Short-time Fourier Transform (STFT) and the other harmonic wavelets used in time-frequency analysis. The modal decomposition is applied for modal parameters identification, and the properties of the Laplace and the SOULTI wavelet transforms allows analytical and accurate identification methods. / Ph. D.

Signal processing has been playing a key role in providing solutions to key problems encountered in communications, in general, and in wireless communications, in particular. Time-Frequency Signal Processing (TFSP) provides eective tools for analyzing nonstationary signals where the frequency content of signals varies in time as well as for analyzing linear time-varying systems. This research aimed at exploiting the advantages of TFSP, in dealing with nonstationary signals, into the fundamental issues of signal processing, namely the signal estimation and signal separation. In particular, it has investigated the problems of (i) the Instantaneous Frequency (IF) estimation of Linear Frequency-Modulated (LFM) signals corrupted in complex-valued zero-mean Multiplicative Noise (MN), and (ii) the Underdetermined Blind Source Separation (UBSS) of LFM signals, while focusing onto the fast-growing area of Wireless Communications (WCom). A common problem in the issue of signal estimation is the estimation of the frequency of Frequency-Modulated signals which are seen in many engineering and real-life applications. Accurate frequency estimation leads to accurate recovery of the true information. In some applications, the random amplitude modulation shows up when the medium is dispersive and/or when the assumption of point target is not valid; the original signal is considered to be corrupted by an MN process thus seriously aecting the recovery of the information-bearing frequency. The IF estimation of nonstationary signals corrupted by complex-valued zero-mean MN was investigated in this research. We have proposed a Second-Order Statistics approach, rather than a Higher-Order Statistics approach, for IF estimation using Time-Frequency Distributions (TFDs). The main assumption was that the autocorrelation function of the MN is real-valued but not necessarily positive (i.e. the spectrum of the MN is symmetric but does not necessary has the highest peak at zero frequency). The estimation performance was analyzed in terms of bias and variance, and compared between four dierent TFDs: Wigner-Ville Distribution, Spectrogram, Choi-Williams Distribution and Modified B Distribution. To further improve the estimation, we proposed to use the Multiple Signal Classification algorithm and showed its better performance. It was shown that the Modified B Distribution performance was the best for Signal-to-Noise Ratio less than 10dB. In the issue of signal separation, a new research direction called Blind Source Separation (BSS) has emerged over the last decade. BSS is a fundamental technique in array signal processing aiming at recovering unobserved signals or sources from observed mixtures exploiting only the assumption of mutual independence between the signals. The term "blind" indicates that neither the structure of the mixtures nor the source signals are known to the receivers. Applications of BSS are seen in, for example, radar and sonar, communications, speech processing, biomedical signal processing. In the case of nonstationary signals, a TF structure forcing approach was introduced by Belouchrani and Amin by defining the Spatial Time- Frequency Distribution (STFD), which combines both TF diversity and spatial diversity. The benefit of STFD in an environment of nonstationary signals is the direct exploitation of the information brought by the nonstationarity of the signals. A drawback of most BSS algorithms is that they fail to separate sources in situations where there are more sources than sensors, referred to as UBSS. The UBSS of nonstationary signals was investigated in this research. We have presented a new approach for blind separation of nonstationary sources using their TFDs. The separation algorithm is based on a vector clustering procedure that estimates the source TFDs by grouping together the TF points corresponding to "closely spaced" spatial directions. Simulations illustrate the performances of the proposed method for the underdetermined blind separation of FM signals. The method developed in this research represents a new research direction for solving the UBSS problem. The successful results obtained in the research development of the above two problems has led to a conclusion that TFSP is useful for WCom. Future research directions were also proposed.

The quadratic class of time-frequency distributions (TFDs) forms a set of tools which allow to effectively extract important information from a nonstationary signal. To determine which TFD best represents the given signal, it is a common practice to visually compare different TFDs' time-frequency plots, and select as best the TFD with the most appealing plot. This visual comparison is not only subjective, but also difficult and unreliable especially when signal components are closely-spaced in the time-frequency plane. To objectively compare TFDs, a quantitative performance measure should be used. Several measures of concentration/complexity have been proposed in the literature. However, those measures by being derived with certain theoretical assumptions about TFDs are generally not suitable for the TFD selection problem encountered in practical applications. The non-existence of practically-valuable measures for TFDs' resolution comparison, and hence the non-existence of methodologies for the signal optimal TFD selection, has significantly limited the use of time-frequency tools in practice. In this thesis, by extending and complementing the concept of spectral resolution to the case of nonstationary signals, and by redefining the set of TFDs' properties desirable for practical applications, we define an objective measure to quantify the quality of TFDs. This local measure of TFDs' resolution performance combines all important signal time-varying parameters, along with TFDs' characteristics that influence their resolution. Methodologies for automatically selecting a TFD which best suits a given signal, including real-life signals, are also developed. The optimisation of the resolution performances of TFDs, by modifying their kernel filter parameters to enhance the TFDs' resolution capabilities, is an important prerequisite in satisfying any additional application-specific requirements by the TFDs. The resolution performance measure and the accompanying TFDs' comparison criteria allow to improve procedures for designing high-resolution quadratic TFDs for practical time-frequency analysis. The separable kernel TFDs, designed in this way, are shown to best resolve closely-spaced components for various classes of synthetic and real-life signals that we have analysed.

Malgré la multiplicité des méthodes d'estimation de la VaR, elles souffrent d'une faiblesse fondamentale. En effet, elles ne font aucune distinction entre l'information captée à basse fréquence et celle captée à haute fréquence. Ce qui revient à  supposer de façon implicite que l'information contenue dans les données historiques a la même importance quel que soit l'horizon temporel de l'investisseur c'est-à-dire sa fréquence de trading (intra-journalière, journalière, hebdomadaire, mensuelle,..). Mais, accepter une telle hypothèse revient à supposer que les marchés financiers sont homogènes. Ce qui est contraire à la réalité empirique. En effet, les marchés financiers sont caractérisés par une grande hétérogénéité d'acteurs. L'objet de notre thèse est d'apporter une contribution à l'estimation de la VaR basée sur la décomposition de la volatilité dans le domaine des fréquences. Ce qui nous permet de mette en évidence l'influence de l'hétérogénéité des horizons temporels des acteurs des marchés financiers sur l'estimation de la Value at Risk. Pour cela,nous faisons appel à un outil statistique susceptible de nous procurer de l'information temporelle sur la volatilité et de l'information fréquentielle sur la fréquence de trading des différents acteurs des marchés financiers: l'approche temps-fréquence de la transformée en ondelettes. / Although multiplicity of VaR estimate approaches,they suffer from a fundamental weakness.They don't make any distiction between informations captured in a high frequency and in a low frequency manner.It is an implicit assumption of homogeneity of fiancial markets in contrast to empirical facts. In our thesis, we try to construct a VaR model based on volatility decomposition in the frequency domain.It enables us to show how the time horizons heterogeneity of financial markets participants could influence value at risk estimates.We use a statistical tool able to give us temporal information about volatility and frequencial information about trading frequencies of market participants:the time frequency approach of wavelet transform.

Dans les applications d'écoute active, il est primordial de pouvoir interagir avec les sources individuelles présentes dans le mix, par exemple en changeant leur position spatiale. Dans cette thèse, nous avons proposé des techniques binaurales pour la localisation et la spatialisation, basées sur les différences interaurales en amplitude et en temps d'arrivée. Les techniques sont développées dans le plan temps-fréquence. Elles permettent de localiser et de projeter toute source dans l'espace environnant un auditeur. aussi nous avons mis au point des techniques de séparation binaurale de source basées sur le Maximum de vraisemblance et de masques spatiaux probabilistes. Enfin nous avons étendu les techniques binaurales à des techniques multi-diffusion utilisant un ensemble de haut-parleurs. Les techniques proposées sont éprouvées et comparées à des techniques de référence de la littérature. Pour des performances similaires aux techniques existantes, nos propositions ont un avantage significatif en terme de complexité qui les rendent appropriées aux applications temps-réel. / In active listening applications, it is important to be able to interact with individual sources present in the mix, for example by changing their spatial position. In this thesis, we proposed techniques for binaural localization and spatialization, based on interaural differences in amplitude and in time of arrival. The techniques are developed in the time-frequency plane. They can locate and project sources in the space surrounding a listener. We also developed binaural source separation methods based on the Maximum Likelihood and on spatial probabilistic masks. Finally, we extended binaural spatialization techniques to multi-diffusion techniques which use a set of speakers for diffusion. The proposed techniques are tested and compared to referenced, well-known techniques. For similar performance with the existing ones, our proposed techniques highlight complexity advantages and are suitable for real-time applications.

Approved for public release; distribution in unlimited. / An imaging radar, like ISAR, offers a combatant the capability to perform long range surveillance with high quality imagery for positive target identification. Extending this attractive feature to the battle damage assessment problem (BDA) gives the operator instant viewing of the target's behavior when it is hit. As a consequence, immediate and decisive action can be quickly taken (if required). However, the conventional Fourier processing adopted by most ISAR systems does not provide adequate time resolution to capture the target's dynamic responses during the hit. As a result, the radar image becomes distorted. To improve the time resolution, time-frequency transform (TFT) methods of ISAR imaging have been proposed. Unlike traditional Fourier-based processing, TFT's allows variable time resolution of the entire event that falls within the ISAR coherent integration period to be extracted as part of the imaging process. We have shown in this thesis that the use of linear Short Time-Frequency Transforms allows the translational response of the aircraft caused by a blast force to be clearly extracted. The TFT extracted images not only tell us how the aircraft responds to a blast effect but also provides additional information about the cause of image distortion in the traditional ISAR display.

Cette thèse traite de la variabilité temporelle intrinsèque de l'éclairement solaire reçu au sol. Les échelles caractéristiques de variabilité sont mises en évidence par l'analyse de longues séries temporelles de moyennes journalières de l'éclairement, pour différents endroits du monde, issues de mesures pyranométriques au sol, d'estimations satellitaires ou de réanalyses météorologiques .Compte-tenu de la nature non linéaire et non stationnaire des données, la transformée adaptative de Hilbert-Huang est utilisée comme outil d'analyse pour tenir compte de la diversité de ces échelles temporelles. On montre ainsi la nature variable des échelles caractéristiques et de leur intensité, ainsi que leur dépendance vis-à-vis du climat.L'application d'une technique adaptative de ré-échantillonnage fractionnaire montre la juxtaposition d'une composante déterministe et d'une stochastique. Pour tous les jeux de données, le cycle annuel déterministe représente la plus grande partie de la variabilité. Toutes les séries temporelle contiennent une composante de variabilité stochastique à haute fréquence, qui est modulée en amplitude par le cycle annuel.L'approche permet également d'évaluer, échelle par échelle, les performances des estimations satellitaires ou issues de ré-analyses par comparaison avec des mesures pyranométriques au sol. Une étude de cas confirme que les estimations satellitaires surpassent les ré-analyses à toutes les échelles temporelles. / The center of focus for this PhD thesis is the intrinsic temporal variability of the surface solar irradiance (SSI). The characteristic time-scales of variability are revealed by analysing long-term time-series of daily means of SSI, such as ground measurements, satellite estimates, or radiation products from global atmospheric re-analyses, for different geographical locations around the world.To account for the wide range of the time-scales of variability, and given the non-linear and non-stationary nature of the data, the adaptive, data-driven Hilbert-Huang Transform is employed as an analysis tool. The time-varying nature of the characteristic time-scales of variability, along with variations in intensity, are thus revealed.An adaptive fractional re-sampling technique is used to discriminate between the deterministic and the stochastic variability constituents. For all datasets, the deterministic yearly cycle is found to account for the largest part of variability. Furthermore, all time-series are found to contain a high-frequency stochastic variability component, that exhibit cross-scale amplitude modulation by the yearly cycle.A refinement to existing methods for assessing the fitness for use of surrogate SSI products in lieu of ground measurements is also proposed. A case study confirms that satellite estimates outperform re-analyses across all time-scales.

Orientador: Paulo Roberto de Aguiar / Resumo: O chatter corresponde a movimentos instáveis e caóticos no sistema de usinagem, resultando em flutuação das forças de corte e na impressão de ondulações na superficie da peça usinada. É um fenômeno indesejável ao processo de usinagem, especialmente ao processo de retificação, pois a sua ocorrência acentuada resulta em um produto acabado com tolerâncias dimensionais e geométricas fora dos padrões, ou até mesmo em danos irreversíveis, como por exemplo, alteração na dureza, alta rugosidade e queima superficial da peça usinada. Na literatura, poucos trabalhos tratam da análise e monitoramento do chatter com técnicas de processamento digital de sinais, especialmente de aceleração. O objetivo desse trabalho é propor uma nova técnica de processamento digital utilizando os sinais de aceleração baseados no cálculo da STFT - Short Time Fourier Transform (Transformada de Fourier de curta duração) e na estatística Relação de Potência (ROP – ratio of power), com a finalidade de detecção do fenômeno de chatter na retificação tangencial plana com rebolo superabrasivo de nitreto cúbico de boro (CBN) e óxido de alumínio. Para tanto, ensaios de retificação foram realizados em corpos de prova de aço ABNT 1045. Um acelerômetro piezelétrico foi acoplado ao suporte das peças e sinais de aceleração foram coletados à uma frequência de amostragem de 2MHz. Dentre as variáveis de saída, obteve-se a dureza Vickers (HV), rugosidade média (Ra) e a análise microestrutural das peças retificadas. Os sinais d... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Chatter corresponds to unstable and chaotic movements in the machining system, resulting in fluctuation of the cutting forces. It is a serious and undesired physical phenomenon that occurs in the grinding process during parts manufacturing. The intense occurrence of this phenomenon during machining can generate a finished part outside the dimensional and geometric tolerances or even cause irreversible damage, such as: changes to the hardness, high surface roughness, and thermal damages to the ground part. Few vibration signal processing techniques have been proposed for monitoring chatter during grinding. Thus, the objective of this study is to propose and validate a new vibration signal processing technique based on the short-time Fourier transform (STFT) and the ratio of power (ROP) statistic for the detection of chatter during the tangential surface grinding of ABNT 1045 steel with different grinding wheels. Experimental grinding tests were conducted, and the vibration signals were recorded at 2 MHz. The Vickers hardness (HV), roughness (Ra) and metallography of the ground workpiece surfaces were performed. Subsequently, a digital processing technique based on the STFT and ROP was applied to the vibration signals to extract the characteristics of the chatter in the grinding process. The results show that this technique can be used to characterize over time the spectral patterns of a frequency band related to chatter. The observed patterns have a strong relationship with th... (Complete abstract click electronic access below) / Doutor

De nos jours, les opérations chirurgicales sont impossibles à imaginer sans anesthésie générale, qui implique la perte de conscience, l'immobilité, l'amnésie et l'analgésie. La compréhension des mécanismes sous-jacents de chacun de ces effets garantit un traitement médical bien contrôlé. Cette thèse se concentre sur l'effet analgésique de l'anesthésie générale, précisément, sur la réaction du patient aux stimuli nociceptifs. Nous étudions également les différences des réactions entre différents médicaments anesthésiques. L'étude a été effectuée sur un ensemble de données constituées de 230 signaux EEG : enregistrements pré- et post-incision obtenus sur 115 patients qui ont reçu du desflurane et du propofol. La première phase de l'étude comprend l'analyse spectrale de puissance, qui est une méthode très répandue dans le traitement du signal. L'information spectrale a été décrite en ajustant l'activité de fond, qui exhibe un comportement $1/f$, aux estimations de la densité spectrale de puissance des signaux d'EEG et en mesurant la puissance contenue dans des bandes delta et alpha par rapport à la puissance de l'activité de fond. Une autre amélioration a été réalisée par l'expansion des spectres avec des informations de temps en raison de la nature non stationnaire observée dans les signaux EEG. Pour obtenir les représentations temps-fréquence des signaux nous appliquons trois méthodes différentes: scalogramme (basé sur la transformée en ondelettes continue), spectrogramme classique, et réaffectation de spectrogramme. Celle-ci permet d'améliorer la lisibilité d'une représentation temps-fréquence en réaffectant l'énergie contenue dans le spectrogramme à des positions plus précises. Par la suite, les spectrogrammes obtenus ont été utilisés pour la reconstruction de l'espace de phase, pour l'analyse récurrence et pour sa quantification par une mesure de complexité. L'analyse de récurrence permet de décrire et visualiser les dynamiques récurrentes d'un système et de découvrir des motifs structurels contenus dans les données. Ici, les diagrammes de récurrence ont été utilisés comme réécriture de grammaire pour transformer le signal original en une séquence symbolique, où chaque symbole représente un certain état du système. Trois mesures de complexité différentes sont alors calculées à partir de ces séquences symboliques afin de les utiliser comme éléments de classification. Enfin, en combinant les caractéristiques obtenues avec l'analyse spectrale de puissance et avec l'analyse symbolique de récurrence, nous effectuons la classification des données en utilisant deux méthodes de classification~: l'analyse discriminante linéaire et les machines à vecteurs de support. La classification a été effectuée sur des problèmes à deux classes, la distinction entre les signaux EEG pré- / post-incision, ainsi qu'entre les deux différents médicaments anesthésiques, desflurane et propofol. / Nowadays, surgical operations are impossible to imagine without general anesthesia, which involves loss of consciousness, immobility, amnesia and analgesia. Understanding mechanisms underlying each of these effects guarantees well-controlled medical treatment. This thesis focuses on analgesia effect of general anesthesia, more specifically, on patients reaction to nociceptive stimuli. We also study differences in the reaction between different anesthetic drugs. The study was conducted on dataset consisting of 230 EEG signals: pre- and post-incision recordings obtained form 115 patients, who received desflurane and propofol. The first stage of the study comprise power spectral analysis, which is a widespread approach in signal processing. Spectral information was described by fitting the background activity, that exposes $1/f$ behavior, to power spectral density estimates of the EEG signals and measuring power contained in delta and alpha bands relatively to the power of background activity. A further improvement was done by expanding spectra with time information due to observed non-stationary nature of EEG signals. To obtain time-frequency representations of the signals we apply three different methods: scalogram (based on continuous wavelet transform), conventional spectrogram, and spectrogram reassignment. The latter allows to ameliorate readability of a time-frequency representation by reassigning energy contained in spectrogram to more precise positions. Subsequently, obtained spectrograms were used as phase space reconstruction in recurrence analysis and its quantification by complexity measure. Recurrence analysis allows to describe and visualize recurrent dynamics of a system and discover structural patterns contained in the data. Here, recurrence plots were used as rewriting grammar to turn an original signal into a symbolic sequence, where each symbol represents a certain state of the system. After computing three different complexity measures of resulting symbolic sequences they are used as features for classification. Finally, combining features obtained with power spectral analysis and recurrence symbolic analysis, we perform classification of the data using two classification methods: linear discriminant analysis and support vector machines. Classification was carried out on two-class problem, distinguishing between pre-/post-incision EEG signals, as well as between two different anesthetic drugs, desflurane and propofol.