This diploma thesis deals with the design and implementation of an electronic module for acoustic detection. The module has the task of detecting a predetermined acoustic signals through them learned classification model. The module is used mainly for security purposes. To identify and classify the proposed model using machine learning techniques. Given the possibility of retraining for a different set of sounds, the module becomes a universal sound detector. With acoustic sound using the digital MEMS microphone, for which it is designed and implemented conversion filter. The resulting system is implemented into firmware microcontroller with real time operating system. The various functions of the system are realized with regard to the possible optimization (less powerful MCU or battery power). The module transmits the detection results of the master station via Ethernet network. In the case of multiple modules connected to the network to create a distributed system, which is designed for precise time synchronization using PTP protocol defined by the IEEE-1588 standard.

Time reversal is a method often used to focus sound to a desired location, and works best in a reverberant environment. The effect of focus location within a reverberant environment is presented first, revealing that proximity to reflecting surfaces has a significant effect on the amplitude of the focus both experimentally and when using a modal summation model. These effects are a primary component to creating focus signals at high amplitudes. High-amplitude focusing experiments show that when multiple sources are used simultaneously to generate a focus, a peak amplitude pressure spike of 200 dB can be achieved in air. A pressure spike of this amplitude has multiple nonlinear characteristics, and an investigation into the spatiotemporal features and harmonic content of these signals was conducted. The peak amplitude of the focus signal also increases in amplitude nonlinearly as the loudspeaker volume is linearly increased. This nonlinear increase is the primary subject of investigation in this work. Experimental and computational methods are implemented in order to understand the mechanisms driving the nonlinear increases observed when the sources are combined acoustically as opposed to linear superposition of the contributions from each sound in post-processing. Finally, models of converging high-amplitude waves are generated using the k-Wave© package for MATLAB©. These show a similar nonlinear increase in amplitudes, supporting the hypothesis of a Mach wave coalescence. A COMSOL© finite element model allows visualization of the converging waves with Mach stems forming in free space to cause the nonlinear amplification.

Ambient temperature sensing plays an important role in a number of applications in agriculture, industry, daily health care. In this thesis project, we propose a new acoustic-based ambient temperature sensing method called Mtemp. Mtemp empowers acoustic-enabled IoT devices, smartphones to perform ambient air temperature sensing without additional hardware. Basically, Mtemp utilizes on-board speaker and microphone to calculate the propagation speed of acoustic signal by measuring the phrase of the target signal, thereby estimate the ambient temperature according to a roughly linear relationship between temperature and sound speed. Mtemp is portable and economical, making it competitive compared with traditional thermometers for ubiquitous sensing.

This master's thesis is focused on the noise source identification using microphone arrays and beamforming as the signal processing method. It describes parts of such a system and provides a comparison with other systems that serve a similar purpose (eg. NAH). Various types of microphone arrays are mentioned with their influence on the resulting ability to identify the noise source. We are further focusing on Delay-And-Sum technology, on which we are explaining the basic principles and constraints of beamforming. The practical part describes the implementation of the DAS method in MATLAB and C language, the specific structures of built microphone arrays and assembly of complete systems capable of identifying sources of noise. These systems were tested by performing a practical experiment. Achievements in the form of distribution maps of acoustic energy in the focused space are interpreted in the last chapter.

Vehicle acoustic signals have long been considered as unwanted traffic noise. In this research acoustic signals generated by each vehicle will be used to detect its presence and classify its type. Circular arrays of microphones were designed and built to detect desired signals and suppress unwanted ones. Circular arrays with multiple rings have an interesting and important property that is constant sidelobe levels. A modified genetic algorithm that can work directly with real numbers is used in the circular array design. It offers more effective ways to solve numerical problems than a standard genetic algorithm. In classifier design two main paradigms are considered: multilayer perceptrons and adaptive fuzzy logic systems. A multilayer perceptron is a network inspired by biological neural systems. Even though it is far from a biological system, it possesses the capability to solve many interesting problems in variety fields. Fuzzy logic systems, on the other hand, were inspired by human capabilities to deal with fuzzy terms. Its structures and operations are based on fuzzy set theory and its operations. Adaptive fuzzy logic systems are fuzzy logic systems equipped with training algorithms so that its rules can be extracted or modified from available numerical data similar to neural networks. Both fuzzy logic systems and multilayer perceptrons have been proved to be universal function approximators. Since there are approximations in almost every stage, both of these system types are good candidates for classification systems. In classification problems unequal learning of each class is normally encountered. This unequal learning may come from different learning difficulties and/or unequal numbers of training data from each class. The classifier tends to classify better for a well-learned class while doing poorly for other classes. Classification costs that may be different from class to class can be used to train and test a classifier. An error backpropagation algorithm can be modified so that the classification costs along with unequal learning factors can be used to control classifier learning during its training phase. / Ph. D.

Στο πλαίσιο της εργασίας αυτής πραγματοποιείται ανάλυση και εφαρμογή του διαχωρισμού ακουστικών σημάτων, τα οποία έχουν ληφθεί από το ανθρώπινο σώμα, όταν αυτό βρίσκεται σε κατάσταση ύπνου. Τα σήματα αυτά έχουν ληφθεί με τη βοήθεια μιας συσκευής πιεζοκρυστάλλων και ο διαχωρισμός τους επιτυγχάνεται με τη μέθοδο Ανάλυσης Ανεξάρτητων Συνιστωσών (ICA). Κύριος σκοπός όλων των παραπάνω είναι να χρησιμοποιηθεί η εν λόγω μεθοδολογία στη διάγνωση της αποφρακτικής άπνοιας (OSA). Στο πρώτο κεφάλαιο, παρουσιάζεται αναλυτικά η μέθοδος ICA και το μαθηματικό μοντέλο που την περιγράφει, όπως επίσης και όλα τα βήματα προεπεξεργασίας της. Στη συνέχεια αναλύεται διεξοδικά η λειτουργία του αλγορίθμου FastICA και οι ιδιότητες του, με τον οποίο υλοποιείται το πειραματικό μέρος της εργασίας αυτής. Στο δεύτερο κεφάλαιο, μελετάται η ασθένεια της αποφρακτικής άπνοιας (OSA), οι παράγοντες και η παθολογία της καθώς και το κύριο διαγνωστικό σύμπτωμα της: το ροχαλητό. Ύστερα, πραγματεύεται την διάγνωση και τους γνωστότερους τρόπους θεραπείας αυτής της νόσου και τελικά τη μέθοδο του Snoring Detection. Στο τρίτο κεφάλαιο γίνεται μια εισαγωγή στον πιεζοηλεκτρισμό, και μία μελέτη του πιεζοηλεκτρικού φαινομένου και του μαθηματικού του μοντέλου. Ακολουθεί αναφορά των ειδών πιεζοηλεκτρικών αισθητήρων με τους οποίους λαμβάνονται τα σήματα που εξετάζονται σε αυτή την εργασία. Στο επόμενο κεφάλαιο γίνεται μία σύνδεση των δεδομένων θεωρίας που αναφέρονται στα προηγούμενα κεφάλαια και μία εισαγωγή στην πειραματική μέθοδο. Στο κεφάλαιο πέντε παρατίθενται κάποια παραδείγματα εφαρμογής του αλγορίθμου FastICA με τυχαία σήματα, τα οποία έχουν σκοπό να δοκιμάσουν την απόδοση του. Στο κεφάλαιο έξι, 5 γίνεται η πειραματική διαδικασία όπου τώρα τα σήματα που διαχωρίζονται με τον αλγόριθμο FastICA προέρχονται από το ανθρώπινο σώμα. Η υλοποίηση της γίνεται σε Matlab. Έτσι, γίνεται εξαγωγή του ζητούμενου σήματος ροχαλητού και αναγράφονται κάποια συμπεράσματα για την απόδοση του αλγορίθμου. Στο τέλος της εργασίας παρατίθενται σε ένα παράρτημα όλοι οι κώδικες της MATLAB που χρησιμοποιήθηκαν για την ολοκλήρωση του πειραματικού της μέρους στα κεφάλαια πέντε και έξι. / In this particular thesis, analysis and application of separation of acoustic signals is carried out. These signals have been taken from the human body in a sleeping state. They are obtained by means of a piezocrystallic device and their separation is achieved by the method of Independent Component Analysis (ICA). The main purpose of all this is to use this methodology in order to diagnose the Obstructive Sleep Apnea (OSA). The first chapter presents the method of ICA and the mathematical model that describes it as well as all the pre-processing steps. Then it analyses, in detail, the algorithm FastICA, which is used in the experimental part of this thesis and its properties. The second chapter studies the disease of obstructive sleep apnea (OSA), its factors and its pathology and the major diagnostic symptom: snoring. Then, it discusses the diagnosis and the best known ways of treating this disease and eventually the method of Snoring Detection. The third chapter is an introduction to piezoelectricity and a study of the piezoelectric effect and its mathematical description. This is followed by a reference to the types of piezoelectric sensors which are used to obtain the signals used in this paper. In chapter five we have listed some examplesapplications of the FastICA algorithm with random signals, which are designed to test the performance. Section six is where the experimental procedure takes place. The signals derived from the human body are separated by the algorithm FastICA and the implementation is done in Matlab. In addition, some conclusions regarding the performance of the algorithm. At the end of this paper, all the MATLAB codes used for the completion of the experimental part of the chapters five and six are listed in an Annex.

The development of technologies to reduce turbofan engine noise reveals the fan noise, the first stage of an engine, as a great contributor for the total noise of an airplane. So a better understanding of the fan noise generation came up and motivated the construction of a fan rig test facility at the University of São Paulo in São Carlos by a partnership between the university and EMBRAER S.A.. The fan rig is composed of a long duct (12mlong) comprising a 16-bladed fan rotor and 14-vaned stator. The rotor is powered by an 100 hp electrical motor allowing speed up to 4250 RPM resulting in 0.1Mach axial flow. A 77-microphone wall-mounted array was designed for fan noise analysis. A cooperation with NASA-Glenn allowed data and information exchanging from their similar fan rig setup, the ANCF, grating then the validation of the in-house developed software. A short guide for duct-array is proposed in this work. Complex software was developed to process the data from the microphones array. We performed 3 different types of analysis: power spectral density, noise imaging obtained by acoustic beamforming and modal analysis.We proposed a different technique for modal analysis based on beamforming images in this work.We did not find any similar technique in the references. The results obtained by this technique were validated with data from ANCF-NASA. Comparative results are presented for both fan rigs, such as: power spectral densities for different fan speeds, modal analysis at the blade passing frequency (strong tones generated by the fan), noise imaging obtained by beamforming for rotating and static noise sources. Finally, results achieved in this work are in agreement with those observed in the references consulted. / Como desenvolver de tecnologias para redução de ruído de motores aeronáuticos turbofans, o ruído gerado pelo fan (primeiro estágio do motor) vem se mostrando cada vez mais um grande contribuinte na emissão total de ruído em um avião. Com isso, a necessidade de se estudar mecanismos geradores de ruído nestes motores veio à tona e motivou a construção de uma bancada de experimentos aero-acústicos junto àUniversidade de São Paulo, campus São Carlos, oriundo da parceria entre EMBRAER S.A. e Universidade de São Paulo. A bancada de ensaios compõe um conjunto rotor/estator, sendo que o fan (rotor) é equipado com 16 pás e a estatora 14 pás, conectado a um motor elétrico de 100 hp através de um eixo ao rotor, alcançando 4250 RPM com velocidade de escoamento axial médio de 0,1 Mach. Esta bancada é composta por um longo duto e a seção de ensaio com o fan localiza-se ao centro. Uma antena dispondo de 77 microfones foi especialmente projetada para fazer aquisição do ruído gerado pelo fan. Uma parceria com a NASA-Glenn possibilitou a troca de informações e dados experimentais de sua bancada de experimentos similar (ANCF) ajudando assim a validar os códigos desenvolvidos bem como comparar resultados para ambas as bancadas. Umpequeno roteiro para projeto de antena para análise modal e beamforming em duto é apresentado neste trabalho. Um complexo software foi desenvolvido a fim de processar sistematicamente os dados aquisitados pelos microfones da antena. Três tipos de análise são feitas: Via espectro densidade de potência; Imagem de ruído acústico obtido através da técnica de beamforming, e por último, análise modal. Uma técnica diferente para análise modal baseada em imagens obtidas através de beamforming é proposta neste trabalho. Nada similar foi encontrado nas referências consultadas. Os resultados foram validados com dados de fontes sintéticas produzidas pela bancada ANCF-NASA. Resultados comparativos para ambas as bancadas são exibidas neste trabalho, tais quais: Análise do espectro densidade de potência para diferentes rotações do fan; análise modal nas frequências de passagem das pás (forte ruído tonal gerado pelo fan); imagem acústica do ruído gerado tanto por fontes rotativas quanto para fontes estáticas. Finalmente, os resultados obtidos estão de acordo com o esperado e de antemão observados nas referências consultadas.

Air borne or liquid-laden solid particle transport is a common phenomenon in various industrial applications. Solid particles, transported at severe operating conditions such as high flow velocity, can cause concerns for structural integrity through wear originated from particle impacts with structure. To apply Acoustic Emission (AE) in particle impact monitoring, previous researchers focused primarily on dry particle impacts on dry target plate and/or wet particle impacts on wet or dry target plate. For dry particle impacts on dry target plate, AE events energy, calculated from the recorded free falling or air borne particle impact AE signals, were correlated with particle size, concentration, height, target material and thickness. For a given system, once calibrated for a specific particle type and operating condition, this technique might be sufficient to serve the purpose. However, if more than one particle type present in the system, particularly with similar size, density and impact velocity, calculated AE event energy is not unique for a specific particle type. For wet particle impacts on dry or wet target plate (either submerged or in a flow loop), AE event energy was related to the particle size, concentration, target material, impact velocity and angle between the nozzle and the target plate. In these studies, the experimental arrangements and the operating conditions considered either did not allow any bubble formation in the system or even if there is any at least an order of magnitude lower in amplitude than the sand particle impact and so easily identifiable. In reality, bubble formation can be comparable with particle impacts in terms of AE amplitude in process industries, for example, sand production during oil and gas transportation from reservoir. Current practice is to calibrate an installed AE monitoring system against a range of sand free flow conditions. In real time monitoring, for a specific calibrated flow, the flow generated AE amplitude/energy is deducted from the recorded AE amplitude/energy and the difference is attributed to the sand particle impacts. However, if the flow condition changes, which often does in the process industry, the calibration is not valid anymore and AE events from bubble can be misinterpreted as sand particle impacts and vice versa. In this research, sand particles and glass beads with similar size, density and impact velocity have been studied dropping from 200 mm on a small cylindrical stepped mild steel coupon as a target plate. For signal recording purposes, two identical broadband AE sensors are installed, one at the centre and one 30 mm off centred, on the opposite of the impacting surface. Signal analysis have been carried out by evaluating 7 standard AE parameters (amplitude, energy, rise time, duration, power spectral density(PSD), peak frequency at PSD and spectral centroid) in the time and frequency domain and time-frequency domain analysis have been performed applying Gabor Wavelet Transform. The signal interpretation becomes difficult due to reflections, dispersions and mode conversions caused by close proximity of the boundaries. So, a new signal analysis parameter - frequency band energy ratio - has been proposed. This technique is able to distinguish between population of two very similar groups (in terms of size and mass and energy) of sand particles and glass beads, impacting on mild steel based on the coefficient of variation (Cv) of the frequency band AE energy ratios. To facilitate individual particle impact identification, further analysis has been performed using Support Vector Machine (SVM) based classification algorithm using 7 standard AE parameters, evaluated in both the time and frequency domain. Available data set has been segmented into two parts of training set (80%) and test set (20%). The developed model has been applied on the test data for model performance evaluation purpose. The overall success rate of individually identifying each category (PLB, Glass bead and Sand particle impacts) at S1 has been found as 86% and at S2 as 92%. To study wet particle impacts on wet target surface, in presence of bubbles, the target plate has been sealed to a cylindrical perspex tube. Single and multiple sand particles have been introduced in the system using a constant speed blower to impact the target surface under water loading. Two sensor locations, used in the previous sets of experiments, have been monitored. From frequency domain analysis it has been observed that characteristic frequency for particle impacts are centred at 300-350 kHz and for bubble formations are centred at 135 – 150 kHz. Based upon this, two frequency bands 100 – 200 kHz (E1) and 300 – 400 kHz (E3) and the frequency band energy ratio (E3E1,) have been identified as optimal for identification particle impacts for the given system. E3E1, > 1 has been associated with particle impacts and E3E1, < 1 has been associated with bubble formations. Applying these frequency band energy ratios and setting an amplitude threshold, an automatic event identification technique has been developed for identification of sand particle impacts in presence of bubbles. The method developed can be used to optimize the identification of sand particle impacts. The optimal setting of an amplitude threshold is sensitive to number of particles and noise levels. A high threshold of say 10% will clearly identify sand particle impacts but for multiparticle tests is likely to not detect about 20% of lower (impact) energy particles. A threshold lower than 3% is likely to result in detection of AE events with poor frequency content and wrong classification of the weakest events. Optimal setting of the parameters used in the framework such as thresholds, frequency bands and ratios of AE energy is likely to make identification of sand particle impacts in the laboratory environment within 10% possible. For this technique, once the optimal frequency bands and ratios have been identified, then an added advantage is that calibration of the signal levels is not required.

The development of technologies to reduce turbofan engine noise reveals the fan noise, the first stage of an engine, as a great contributor for the total noise of an airplane. So a better understanding of the fan noise generation came up and motivated the construction of a fan rig test facility at the University of São Paulo in São Carlos by a partnership between the university and EMBRAER S.A.. The fan rig is composed of a long duct (12mlong) comprising a 16-bladed fan rotor and 14-vaned stator. The rotor is powered by an 100 hp electrical motor allowing speed up to 4250 RPM resulting in 0.1Mach axial flow. A 77-microphone wall-mounted array was designed for fan noise analysis. A cooperation with NASA-Glenn allowed data and information exchanging from their similar fan rig setup, the ANCF, grating then the validation of the in-house developed software. A short guide for duct-array is proposed in this work. Complex software was developed to process the data from the microphones array. We performed 3 different types of analysis: power spectral density, noise imaging obtained by acoustic beamforming and modal analysis.We proposed a different technique for modal analysis based on beamforming images in this work.We did not find any similar technique in the references. The results obtained by this technique were validated with data from ANCF-NASA. Comparative results are presented for both fan rigs, such as: power spectral densities for different fan speeds, modal analysis at the blade passing frequency (strong tones generated by the fan), noise imaging obtained by beamforming for rotating and static noise sources. Finally, results achieved in this work are in agreement with those observed in the references consulted. / Como desenvolver de tecnologias para redução de ruído de motores aeronáuticos turbofans, o ruído gerado pelo fan (primeiro estágio do motor) vem se mostrando cada vez mais um grande contribuinte na emissão total de ruído em um avião. Com isso, a necessidade de se estudar mecanismos geradores de ruído nestes motores veio à tona e motivou a construção de uma bancada de experimentos aero-acústicos junto àUniversidade de São Paulo, campus São Carlos, oriundo da parceria entre EMBRAER S.A. e Universidade de São Paulo. A bancada de ensaios compõe um conjunto rotor/estator, sendo que o fan (rotor) é equipado com 16 pás e a estatora 14 pás, conectado a um motor elétrico de 100 hp através de um eixo ao rotor, alcançando 4250 RPM com velocidade de escoamento axial médio de 0,1 Mach. Esta bancada é composta por um longo duto e a seção de ensaio com o fan localiza-se ao centro. Uma antena dispondo de 77 microfones foi especialmente projetada para fazer aquisição do ruído gerado pelo fan. Uma parceria com a NASA-Glenn possibilitou a troca de informações e dados experimentais de sua bancada de experimentos similar (ANCF) ajudando assim a validar os códigos desenvolvidos bem como comparar resultados para ambas as bancadas. Umpequeno roteiro para projeto de antena para análise modal e beamforming em duto é apresentado neste trabalho. Um complexo software foi desenvolvido a fim de processar sistematicamente os dados aquisitados pelos microfones da antena. Três tipos de análise são feitas: Via espectro densidade de potência; Imagem de ruído acústico obtido através da técnica de beamforming, e por último, análise modal. Uma técnica diferente para análise modal baseada em imagens obtidas através de beamforming é proposta neste trabalho. Nada similar foi encontrado nas referências consultadas. Os resultados foram validados com dados de fontes sintéticas produzidas pela bancada ANCF-NASA. Resultados comparativos para ambas as bancadas são exibidas neste trabalho, tais quais: Análise do espectro densidade de potência para diferentes rotações do fan; análise modal nas frequências de passagem das pás (forte ruído tonal gerado pelo fan); imagem acústica do ruído gerado tanto por fontes rotativas quanto para fontes estáticas. Finalmente, os resultados obtidos estão de acordo com o esperado e de antemão observados nas referências consultadas.

Laser-induced breakdown spectroscopy (LIBS) is a fast analytical method, but can also be complicated. This spectroscopic method is used to provide a qualitative and quantitative analysis of a sample. The analysis is carried out by capturing the emission radiation of the generated plasma. The accuracy and stability of the measurement is affected by several parameters, such as stability of the laser, physical and chemical properties of the sample, its homogeneity and others, that can not always be eliminated. For this reason other methods are being added to the LIBS experiment that could improve the quality of this analysis. This diploma thesis is focused on a research of the literature on the standardization of laser-induced plasma signal and the possibility of using an acoustic signal for this purpose. For this reason , it is necessary to perform basic experiments and to verify if the acoustic signal correlates with the emission signal. If these signals correlate together, it would be possible to use the acoustic signal for standardization og the LIBS data, which would improve the accuracy of the analysis. In the theoretical part at the beginning other spectroscopic methods are summarised. Then the work is focused on the description of the LIBS method, possible ways of the analysis, standardization of emission signals and its review in the literature. The experimental part is aimed at the study of the acoustic signal that was performed in the framework of this study. At the beginning, there are introduced results of the basic measurements on steel and brass samples. These results were important for an optimization of the experiment. The following section shows the results obtained from the measurements of the acoustic signal on the samples with the same chemical composition but different hardness. At the end the correlation between the acoustic and emission signals is discussed.