The influx of unmanned aerial systems over the last decade has increased need for airspace awareness. Monitoring solutions such as drone detection, tracking, and classification become increasingly important to maintain compliance for regulatory and security purposes, as well as for recognizing aircraft that may not be so. Vision systems offer significant size, weight, power, and cost (SWaP-C) advantages, which motivates exploration of algorithms to further aid with monitoring performance. A method to classify aircraft using vision systems to measure their motion characteristics is explored. It builds on the assumption that at least continuous visual detection or at most visual tracking of an object of interest is already accomplished. Monocular vision is in part limited by range/scale ambiguity, where range and scale information of an object projected onto the image plane of a camera using a pin- hole model is generally lost. In an indirect effort to attempt to recover scale information via identity, classification of aircraft can aid in improvement of. These measured motion characteristics can then be used to classify the perceived object based on its unique motion profile over time, using signal classification techniques. The study is not limited to just unmanned aircraft, but includes full scale aircraft in the simulated dataset used to provide a representative set of aircraft scale and motion. / Doctor of Philosophy / The influx of small drones over the last decade has increased need for airspace awareness to ensure they do not become a nuisance when operated by unqualified or ill-intentioned personnel. Monitoring airspace around locations where drone usage would be unwanted or a security issue is increasingly necessary, especially for more range and endurance capable fixed wing (airplane) drones. This work presents a solution utilizing a single camera to address the classification part of fixed wing drone monitoring, as cameras are extremely common, generally cheap, information rich sensors. Once an aircraft of interest is detected, classifying it can provide additional information regarding its intentions. It can also help improve visual detection and tracking performance since classification can help change expectations of where and how the aircraft may continue to travel. Most existing visual classification works rely on features visible on the aircraft itself or its silhouette shape. This work discusses an approach to classification by characterizing visually perceived motion of an aircraft as it flies through the air. The study is not limited to just drones, but includes full scale aircraft in the simulated dataset used. Video of an airplane is used to extract motion from each frame. This motion is condensed to and expressed as a single time signal, that is then classified using a neural network trained to recognize audio samples using a time-frequency representation called a spectrogram. This transfer learning approach with Resnet based spectrogram classification is able to achieve 90.9% precision on the simulated test set used.

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Oscillatory states in the Electroencephalogram (EEG) reflect the rhythmic synchronous activation in large networks of neurons. Time-frequency methods quantify the spectral content of the EEG as a function of time. As such, they are well suited as tools for the study of spontaneous and induced changes in oscillatory states. We have used time-frequency techniques to analyze the flow of activity patterns between two strongly connected brain structures: the entorhinal cortex and the hippocampus, which are believed to be involved in information storage. EEG was recorded simultaneously from the entorhinal cortex and the hippocampus of behaving rats. During the recording, low-intensity trains of electrical pulses at frequencies between 1 and 40 Hz were applied to the olfactory (piriform) cortex. The piriform cortex projects to the entorhinal cortex, which then passes the signal on to the hippocampus. Several time-frequency methods, including the short-time Fourier transform (STFT), Wigner-Ville distribution (WVD) and multiple window (MW) time-frequency analysis (TFA), were used to analyse EEG signals. To monitor the signal transmission between the entorhinal cortex and hippocampus, the time-frequency coherence functions were used. The analysed results showed that stimulation-related power in both sites peaked near 15 Hz, but the coherence between the EEG signals recorded from these two sites increased monotonically with stimulation frequency. Among the time-frequency methods used, the STFT provided time-frequency distributions not only without cross-terms which were present in the WVD, but also with higher resolutions in both time and frequency than the MW-TFA. The STFT seems to be the most suitable time-frequency method to study the stimulation-induced signals presented in this thesis. The MW-TFA, which gives low bias and low variance estimations of the time-frequency distribution when only one realization of data is given, is suitable for stochastic and nonstationary signals such as spontaneous EEG. We also compared the performance of the MW-TFA using two different window functions: Slepian sequences and Hermite functions. By carefully matching the two window functions, we found no noticeable difference in time-frequency plane between them. / Thesis / Master of Engineering (ME)

Time-delay estimation (TDE) finds application in a variety of problems, be it locating fractures or steering cameras towards the speaker in a multi-participant conference application. Underwater acoustic OFDM source localization is another important application of TDE. Existing underwater acoustic source localization techniques use a microphone array consisting of three or four sensors in order to effectively locate the source. Analog-to-digital (ADC) converters at these sensors call for a non-nominal investment in terms of circuitry and memory. A relatively inexpensive source localization algorithm is needed that works with the output of a single sensor. Since an inexpensive process for estimating the location of the source is desired, the ADC used at the sensor is capable only of a relatively low sampling rate. For a given delay, a low sampling rate leads to sub-sample interval delays, which the desired algorithm must be able to estimate. Prevailing TDE algorithms make some a priori assumptions about the nature of the received signal, such as Gaussianity, wide-sense stationarity, or periodicity. The desired algorithm must not be restrictive in so far as the nature of the transmitted signal is concerned. A time-delay estimation algorithm based on the time-frequency ratio of mixtures (TFRM) method is proposed. The experimental set-up consists of two microphones/sensors placed at some distances from the source. The method accepts as input the received signal which consists of the sum of the signal received at the nearer sensor and the signal received at the farther sensor and noise. The TFRM algorithm works in the time-frequency domain and seeks to perform successive source cancellation in the received burst. The key to performing source cancellation is to estimate the ratio in which the sources combine and this ratio is estimated by means of taking a windowed mean of the ratio of the spectrograms of any two pulses in the received burst. The variance of the mean function helps identify single-source regions and regions in which the sources mix. The performance of the TFRM algorithm is evaluated in the presence of noise and is compared against the Cramer-Rao lower bound. It is found that the variance of the estimates returned by the estimator diverge from the predictions of the Cramer-Rao inequality as the farther sensor is moved farther away. Conversely, the estimator becomes more reliable as the farther sensor is moved closer. The time-delay estimates obtained from the TFRM algorithm are used for source localization. The problem of finding the source reduces to finding the locus of points such that the difference of its distances to the two sensors equals the time delay. By moving the pair of sensors to a different location, or having a second time delay sensor, an exact location for the source can be determined by finding the point of intersection of the two loci. The TFRM method does not rely on a priori information about the signal. It is applicable to OFDM sources as well as sinusoidal and chirp sources. / Master of Science

Proposé dans le cadre d’une triple collaboration entre le Centre de Recherche en Science et Technologie de l'Information et de la Communication (CReSTIC) de l'Université de Reims Champagne-Ardenne (URCA), le Laboratoire d'Ingénierie et Sciences des Matériaux (LISM) de l'Université de Reims Champagne-Ardenne (URCA) et le Laboratoire Electronique et Télécommunication (LET) de l'Université Mohammed 1er, ce projet a pour objectif d’associer les compétences de ces laboratoires, le traitement du signal pour le CReSTIC et le LET et la caractérisation de l’endommagement des agro matériaux composites pour le LISM. Le travail de cette thèse consiste à développer une méthode de Contrôle Non Destructif CND) par Emissions Acoustiques (EA). En effet, durant le processus de dégradation des matériaux composites (sollicitations mécaniques, vieillissement), plusieurs mécanismes d’endommagement à l’échelle microscopique peuvent intervenir selon la nature du composite et de ses constituants (fibres et résine). L’EA permet d’analyser et d’identifier plus en détails ces mécanismes d’endommagement, à l’aide d’une classification. Cette méthode de classification sera basée sur l’utilisation de méthodes à noyaux (typiquement séparateur à vastes marges) dans le domaine temps-fréquence. Il s’agira de déterminer un (ou des) noyau(x) adapté(s) au problème posé, basé sur une mesure de similarité entre les signaux. Ce travail permettra ainsi d’analyser et classifier les mécanismes d’endommagement sans emploi de descripteurs. Cette nouvelle méthode de CND par EA permettra de fournir des informations pertinentes et de vérifier efficacement la fiabilité et l'état de santé en temps réel de structures en service, sans perturber l'exploitation tout en réduisant les coûts de maintenance. / The aims of this thesis consists in developing a method of Non-destructive testing (NDT) by Acoustic Emissions (AE). Indeed, during the process of degradation of composite materials several mechanisms of damage in the microscopic scale can intervene according to the nature of the composite and its constituents (fibers and epoxy). AE allows to analyze and to identify more in detail these damages mechanism, by means of a classification. This method of classification will be based on the use of kernel methods in the time - frequency domain. It will be a question of determining one adapted kernel in the proposed problem. This work will so allow to analyze and to classify mechanisms without using descriptors.

O amortecimento, fenômeno pelo qual energia mecânica em um sistema dinâmico é dissipada, é uma das propriedades mais sensíveis dos materiais quanto a presença de trincas e microtrincas. O estudo do amortecimento já é bem estabelecido em áreas como engenharia civil, em que é de importância na resistência mecânica de um sistema sujeito a abalos sísmicos e vibrações, porém vem sendo cada vez mais estudado na indústria de materiais para analisar e quantificar o dano em concretos refratários que sofrem ciclos de choque térmico. Este trabalho apresenta uma metodologia e algoritmo para a determinação do amortecimento das ressonâncias de um material, para avaliação de danos em concretos refratários, através da análise espectral de tempo-frequência. São também apresentados os resultados obtidos para um sinal simulado, uma barra de alumina densa e um par de barras de concreto refratário comercial de alta alumina, sendo uma com e outra sem dano por choque térmico. Com o uso do método foi possível recuperar o valor do amortecimento e a frequência usada para gerar o sinal simulado. O resultado apresentado para a alumina é compatível com o valor encontrado em literatura e, com o resultado obtido para os concretos refratários, foi possível mostrar o potencial de aplicação do método para caracterização de dano, sendo significativa a diferença do amortecimento do concreto com dano para o concreto sem dano. / Damping, phenomenon by which mechanical energy is reduced in dynamic systems, is one of the most sensible properties of materials in relation to the presence of cracks and micro-cracks. The study of the damping is well stablished in areas such as civil engineering, where it has fundamental importance in the mechanical resistance of a system exposed to seismic waves or vibration, although it is beginning to be used more often in the material industry to analyze and quantify the damage in castables that suffered thermal-shock cycles. This work presents a methodology and an algorithm to determine the resonances damping factors of a material, to evaluate the damage caused by thermal shock in castables, through the use of time-frequency spectral analysis. Results are presented for a simulated signal, an alumina beam, a pair of commercial high alumina with low-aggregate content castable, a pair of high alumina with high-aggregate content castable and a pair of silico-aluminous castable impregnated with coke. Each pair contains one sample with damage through thermal-shock cycle(s) and the other without. By using this method it was possible to retrieve the damping value and frequency used to generate the simulated signal. The result for the alumina beam was in accordance to the literature values and, with the results achieved for the castables, it was possible to expose the potential application of the method to characterize damage: there were a considerable difference between the damping value of the castables with and without damages.

Neste trabalho simulou-se o escoamento do fluxo de ar em um tubo, com um anteparo de formato triangular com arestas cortantes, posicionado no centro do tubo. O objetivo do estudo é a análise do comportamento do número de Strouhal em função do número de Reynolds. Para isto, foi utilizada a técnica da análise tempo-freqüência, baseada na transformada de Fourier e na transformada de Gabor. Os ensaios foram realizados com o fluxo com velocidades médias de escoamento de 3 a 10 m/s, sendo utilizado um sensor de pressão tipo piezo-resistivo para a detecção da flutuação de pressão ocasionada pelo desprendimento e formação dos vórtices. Os ensaios foram realizados em cinco etapas com o objetivo de se verificar a influência dos seguintes parâmetros na coleta de sinais e no fenômeno: ruído da rede elétrica; influência do anteparo e do ruído proveniente do escoamento do fluxo de ar; número de pontos da amostragem na coleta dos dados; do comprimento da tubulação; e posicionamento do sensor. Pode-se observar, a sensibilidade do sistema de medição através do ensaio realizado sem o anteparo, sendo verificada a influência do ruído do escoamento de ar pelo tubo; pode-se observar também uma pequena interferência do ruído da rede elétrica predominantemente para velocidades abaixo de 3 m/s. Apesar das influências citadas, e utilizando a transformada de Gabor para análise dos sinais, observou-se um sinal mais intenso na freqüência dos vórtices para as velocidades de escoamento, podendo-se comprovar que o número de Strouhal permanece quase constante e é independente do número de Reynolds, devendo-se ressaltar que esta conclusão é valida para números de Reynolds compreendidos na faixa de 3000 a 100000. No experimento obteve-se um fator de sensibilidade (freqüência vórtices/velocidade média) de 8,2 Hz/m/s, e número de Strouhal médio de 0,196. / This work concerns the simulation of an air flux through a pipe with a triangular bluff body positioned inside it. In order to study the behavior of the Strouhal number in function of the Reynolds number. For this, the time-frequency analysis technique was used, based on Fourier transform and the Gabor transform. The experiments were carried out with an air flux velocity ranging from 3 to 10 m/s and using a piezoresistive pressure sensor to detect pressure fluctuations caused by the shedding and vortex formation. The experimental procedures were divided in five stages to make it possible to verify the influence of the following parameters in the signal data acquisition: electric network noise, the bluff body presence and the noise generated due to its presence, number of sampling data points, tubing length and sensor positioning. The sensitivity of the experiment could be observed testing the air flowing with no bluff body inside the pipe. Thus, it was possible to investigate the influence of the noise generated due to this flux limiting body. It could be also observed, mainly at 3 m/s or less, the noise generated due to the electric network. Despite the listed influences, and with the use of the Gabor transform, a more intense signal on the vortex frequency for the flow velocity was observed, showing that the Strouhal number remains almost constant and is independent of the Reynolds number. It is important to recall that this conclusion is valid for Reynolds numbers between 3000 and 100000. In the experiments the factor of sensitivity (vortex frequency/mean velocity) obtained was 8,2 Hz/m/s and the mean Strouhal number 0,196.

O amortecimento, fenômeno pelo qual energia mecânica em um sistema dinâmico é dissipada, é uma das propriedades mais sensíveis dos materiais quanto a presença de trincas e microtrincas. O estudo do amortecimento já é bem estabelecido em áreas como engenharia civil, em que é de importância na resistência mecânica de um sistema sujeito a abalos sísmicos e vibrações, porém vem sendo cada vez mais estudado na indústria de materiais para analisar e quantificar o dano em concretos refratários que sofrem ciclos de choque térmico. Este trabalho apresenta uma metodologia e algoritmo para a determinação do amortecimento das ressonâncias de um material, para avaliação de danos em concretos refratários, através da análise espectral de tempo-frequência. São também apresentados os resultados obtidos para um sinal simulado, uma barra de alumina densa e um par de barras de concreto refratário comercial de alta alumina, sendo uma com e outra sem dano por choque térmico. Com o uso do método foi possível recuperar o valor do amortecimento e a frequência usada para gerar o sinal simulado. O resultado apresentado para a alumina é compatível com o valor encontrado em literatura e, com o resultado obtido para os concretos refratários, foi possível mostrar o potencial de aplicação do método para caracterização de dano, sendo significativa a diferença do amortecimento do concreto com dano para o concreto sem dano. / Damping, phenomenon by which mechanical energy is reduced in dynamic systems, is one of the most sensible properties of materials in relation to the presence of cracks and micro-cracks. The study of the damping is well stablished in areas such as civil engineering, where it has fundamental importance in the mechanical resistance of a system exposed to seismic waves or vibration, although it is beginning to be used more often in the material industry to analyze and quantify the damage in castables that suffered thermal-shock cycles. This work presents a methodology and an algorithm to determine the resonances damping factors of a material, to evaluate the damage caused by thermal shock in castables, through the use of time-frequency spectral analysis. Results are presented for a simulated signal, an alumina beam, a pair of commercial high alumina with low-aggregate content castable, a pair of high alumina with high-aggregate content castable and a pair of silico-aluminous castable impregnated with coke. Each pair contains one sample with damage through thermal-shock cycle(s) and the other without. By using this method it was possible to retrieve the damping value and frequency used to generate the simulated signal. The result for the alumina beam was in accordance to the literature values and, with the results achieved for the castables, it was possible to expose the potential application of the method to characterize damage: there were a considerable difference between the damping value of the castables with and without damages.

A presente dissertação tem como objetivo principal estudar a propagação acústica em escoamentos monofásicos. Para tal, são analisados sinais transientes de pressão fornecidos por sensores instalados em posições conhecidas na linha de teste, através do estudo de técnicas de análise de sinais, a fim de investigar se as variações do conteúdo espectral dos sinais são influenciadas pela ocorrência de vazamentos no duto. A análise dos sinais foi realizada nos planos temporal, frequencial, tempo-frequência e estatístico. Os resultados experimentais foram obtidos no oleoduto piloto do NETeF - Núcleo de Engenharia Térmica e Fluidos da USP - Universidade de São Paulo, com uma seção de testes com 1500 metros e diâmetro de 51,2 mm, com escoamento monofásico de água. Os resultados obtidos através da análise tempo-frequência mostraram-se satisfatórios, sendo esta técnica capaz de identificar a composição espectral instantânea de um sinal, ou seja, foi eficiente na identificação de picos de amplitude da frequência ao longo do eixo temporal. Além disso, a análise probabilística, através do desvio-padrão do sinal também mostrou-se eficiente exibindo uma disparidade significativa entre os sinais com e sem vazamento. / The present dissertation reports on the study of the acoustic propagation in single-phase flow. It analyzes the transient signals provided by pressure sensors in known locations in the test line through the study of signal analysis techniques to investigate if the variations in spectral content of the signals are influenced by the occurrence of leaks in the pipe. The analysis of signals was performed in the time, frequency, time-frequency and statistical plans. The experimental results were obtained in a 1500 meter-long and 51.2 millimeter-diameter pilot pipeline at the Center of Thermal Engineering and Fluids, with single-phase flow of water. The results obtained by time-frequency analysis were satisfactory, allowing identifying the spectral composition of an instantaneous signal, i.e., the analysis was effective in identifying the frequency amplitude peaks along the time axis. Moreover, probabilistic analysis using the standard deviation of the signal was also efficient, displaying a significant disparity between the signals with and without leakage.