Wavelength converters based on four-wave mixing in semiconductor optical amplifiers for advanced modulation formats

Les convertisseurs de longueur d’onde sont essentiels pour la réalisation de réseaux de communications optiques à routage en longueur d’onde. Dans la littérature, les convertisseurs de longueur d’onde basés sur le mélange à quatre ondes dans les amplificateurs optiques à semi-conducteur constituent une solution extrêmement intéressante, et ce, en raison de leurs nombreuses caractéristiques nécessaires à l’implémentation de tels réseaux de communications. Avec l’émergence des systèmes commerciaux de détection cohérente, ainsi qu’avec les récentes avancées dans le domaine du traitement de signal numérique, il est impératif d’évaluer la performance des convertisseurs de longueur d’onde, et ce, dans le contexte des formats de modulation avancés. Les objectifs de cette thèse sont : 1) d’étudier la faisabilité des convertisseurs de longueur d’onde basés sur le mélange à quatre ondes dans les amplificateurs optiques à semi-conducteur pour les formats de modulation avancés et 2) de proposer une technique basée sur le traitement de signal numérique afin d’améliorer leur performance. En premier lieu, une étude expérimentale de la conversion de longueur d’onde de formats de modulation d’amplitude en quadrature (quadrature amplitude modulation - QAM) est réalisée. En particulier, la conversion de longueur d’onde de signaux 16-QAM à 16 Gbaud et 64-QAM à 5 Gbaud dans un amplificateur optique à semi-conducteur commercial est réalisée sur toute la bande C. Les résultats démontrent qu’en raison des distorsions non-linéaires induites sur le signal converti, le point d’opération optimal du convertisseur de longueur d’onde est différent de celui obtenu lors de la conversion de longueur d’onde de formats de modulation en intensité. En effet, dans le contexte des formats de modulation avancés, c’est le compromis entre la puissance du signal converti et les non-linéarités induites qui détermine le point d’opération optimal du convertisseur de longueur d’onde. Les récepteurs cohérents permettent l’utilisation de techniques de traitement de signal numérique afin de compenser la détérioration du signal transmis suite à sa détection. Afin de mettre à profit les nouvelles possibilités offertes par le traitement de signal numérique, une technique numérique de post-compensation des distorsions induites sur le signal converti, basée sur une analyse petit-signal des équations gouvernant la dynamique du gain à l’intérieur des amplificateurs optiques à semi-conducteur, est développée. L’efficacité de cette technique est démontrée à l’aide de simulations numériques et de mesures expérimentales de conversion de longueur d’onde de signaux 16-QAM à 10 Gbaud et 64-QAM à 5 Gbaud. Cette méthode permet d’améliorer de façon significative les performances du convertisseur de longueur d’onde, et ce, principalement pour les formats de modulation avancés d’ordre supérieur tel que 64-QAM. Finalement, une étude expérimentale exhaustive de la technique de post-compensation des distorsions induites sur le signal converti est effectuée pour des signaux 64-QAM. Les résultats démontrent que, même en présence d’un signal à bruité à l’entrée du convertisseur de longueur d’onde, la technique proposée améliore toujours la qualité du signal reçu. De plus, une étude du point d’opération optimal du convertisseur de longueur d’onde est effectuée et démontre que celui-ci varie en fonction des pertes optiques suivant la conversion de longueur d’onde. Dans un réseau de communication optique à routage en longueur d’onde, le signal est susceptible de passer par plusieurs étages de conversion de longueur d’onde. Pour cette raison, l’efficacité de la technique de post-compensation est démontrée, et ce pour la première fois dans la littérature, pour deux étages successifs de conversion de longueur d’onde de signaux 64-QAM à 5 Gbaud. Les résultats de cette thèse montrent que les convertisseurs de longueur d’ondes basés sur le mélange à quatre ondes dans les amplificateurs optiques à semi-conducteur, utilisés en conjonction avec des techniques de traitement de signal numérique, constituent une technologie extrêmement prometteuse pour les réseaux de communications optiques modernes à routage en longueur d’onde.

Comparative iss accelerometric analyses

Two accelerometric records coming from the SAMSes es08 sensor in the Columbus module, the so-called Runs 14 and 33 in terms of the IVIDIL experiment, has been studied here using standard digital signal analysis techniques. The principal difference between both records is the vibrational state of IVIDIL, that is to say, during Run 14 the shacking motor of the experiment is active while that in Run 33 this motor is stopped. Identical procedures have been applied to a third record coming from the SAMSII 121f03 sensor located in the Destiny module during an IVIDIL quiescent period. All records have been downloaded from the corresponding public binary accelerometric files from the NASA Principal Investigator Microgravity Services, PIMS website and, in order to be properly compared, have the same number of data. Results detect clear differences in the accelerometric behavior, with or without shaking, despite the care of the designers to ensure the achievement of the ISS pg-vibrational requirements all along the experiments. Copyright © (2012) by the International Astronautical Federation.

Estadiamento do carcinoma do colo do útero por ressonância magnética – comparação com os achados anátomo-patológicos

Objectivo: Avaliar a acuidade da Ressonância Magnética (RM) no estadiamento do carcinoma do colo do útero, comparando os achados em RM com os resultados Anátomo-Patológicos da peça operatória. Material e Métodos: Foi efectuado um estudo retrospectivo que incluiu 41 doentes operadas com o diagnóstico de carcinoma do colo do útero e previamente submetidas a RM para estadiamento, entre Janeiro de 2007 e Dezembro de 2009. Foram analisados os seguintes factores de estadiamento e prognóstico: dimensão do tumor, invasão dos paramétrios, invasão da vagina e metástases ganglionares. A dimensão do tumor determinada por RM foi comparada com a medição na peça operatória através da análise do declive e ordenada na origem de uma recta de regressão entre os dois métodos. Resultados: O tumor foi visualizado por RM na maioria dos casos (35 doentes, 85.4%). Nas restantes 6 doentes a avaliação anátomo-patológica revelou um tumor com menos de 6 mm de diâmetro. A dimensão do tumor foi adequadamente avaliada por RM, sem diferenças estatisticamente significativas entre a medição por RM e na peça operatória. Foi confirmado o elevado valor preditivo negativo da RM na exclusão de invasão dos paramétrios previamente reportado, com apenas 2 falsos negativos em que a anatomia patológica demonstrou apenas invasão microscópica focal. A invasão da vagina foi correctamente avaliada em 30 doentes (85.7%), tendo-se verificado nos restantes casos 2 falsos negativos e 3 falsos positivos. Em relação às metástases ganglionares verificaram-se 4 falsos negativos, no total das 41 doentes avaliadas. Conclusão: A dimensão do tumor, invasão dos paramétrios, invasão da vagina e metástases ganglionares foram adequadamente avaliadas por RM, confirmando a capacidade da RM no estadiamento do carcinoma do colo do útero.

Music Learning Tools for Android Devices

In this paper, a musical learning application for mobile devices is presented. The main objective is to design and develop an application capable of offering exercises to practice and improve a selection of music skills, to users interested in music learning and training. The selected music skills are rhythm, melodic dictation and singing. The application includes an audio signal analysis system implemented making use of the Goertzel algorithm which is employed in singing exercises to check if the user sings the right musical note. This application also includes a graphical interface to represent musical symbols. A set of tests were conducted to check the usefulness of the application as musical learning tool. A group of users with different music knowledge have tested the system and reported to have found it effective, easy and accessible.

Modeling, Simulation, and Characterization of Space Debris in low-Earth Orbit

Every space launch increases the overall amount of space debris. Satellites have limited awareness of nearby objects that might pose a collision hazard. Astrometric, radiometric, and thermal models for the study of space debris in low-Earth orbit have been developed. This modeled approach proposes analysis methods that provide increased Local Area Awareness for satellites in low-Earth and geostationary orbit. Local Area Awareness is defined as the ability to detect, characterize, and extract useful information regarding resident space objects as they move through the space environment surrounding a spacecraft. The study of space debris is of critical importance to all space-faring nations. Characterization efforts are proposed using long-wave infrared sensors for space-based observations of debris objects in low-Earth orbit. Long-wave infrared sensors are commercially available and do not require solar illumination to be observed, as their received signal is temperature dependent. The characterization of debris objects through means of passive imaging techniques allows for further studies into the origination, specifications, and future trajectory of debris objects. Conclusions are made regarding the aforementioned thermal analysis as a function of debris orbit, geometry, orientation with respect to time, and material properties. Development of a thermal model permits the characterization of debris objects based upon their received long-wave infrared signals. Information regarding the material type, size, and tumble-rate of the observed debris objects are extracted. This investigation proposes the utilization of long-wave infrared radiometric models of typical debris to develop techniques for the detection and characterization of debris objects via signal analysis of unresolved imagery. Knowledge regarding the orbital type and semi-major axis of the observed debris object are extracted via astrometric analysis. This knowledge may aid in the constraint of the admissible region for the initial orbit determination process. The resultant orbital information is then fused with the radiometric characterization analysis enabling further characterization efforts of the observed debris object. This fused analysis, yielding orbital, material, and thermal properties, significantly increases a satellite’s Local Area Awareness via an intimate understanding of the debris environment surrounding the spacecraft.

Eulerian-Lagrangian definition of coarse bed-load transport: Theory and verification with low-cost inertial measurement units

Fluvial sediment transport is controlled by hydraulics, sediment properties and arrangement, and flow history across a range of time scales. This physical complexity has led to ambiguous definition of the reference frame (Lagrangian or Eulerian) in which sediment transport is analysed. A general Eulerian-Lagrangian approach accounts for inertial characteristics of particles in a Lagrangian (particle fixed) frame, and for the hydrodynamics in an independent Eulerian frame. The necessary Eulerian-Lagrangian transformations are simplified under the assumption of an ideal Inertial Measurement Unit (IMU), rigidly attached at the centre of the mass of a sediment particle. Real, commercially available IMU sensors can provide high frequency data on accelerations and angular velocities (hence forces and energy) experienced by grains during entrainment and motion, if adequately customized. IMUs are subjected to significant error accu- mulation but they can be used for statistical parametrisation of an Eulerian-Lagrangian model, for coarse sediment particles and over the temporal scale of individual entrainment events. In this thesis an Eulerian-Lagrangian model is introduced and evaluated experimentally. Absolute inertial accelerations were recorded at a 4 Hz frequency from a spherical instrumented particle (111 mm diameter and 2383 kg/m3 density) in a series of entrainment threshold experiments on a fixed idealised bed. The grain-top inertial acceleration entrainment threshold was approximated at 44 and 51 mg for slopes 0.026 and 0.037 respectively. The saddle inertial acceleration entrainment threshold was at 32 and 25 mg for slopes 0.044 and 0.057 respectively. For the evaluation of the complete Eulerian-Lagrangian model two prototype sensors are presented: an idealised (spherical) with a diameter of 90 mm and an ellipsoidal with axes 100, 70 and 30 mm. Both are instrumented with a complete IMU, capable of sampling 3D inertial accelerations and 3D angular velocities at 50 Hz. After signal analysis, the results can be used to parametrize sediment movement but they do not contain positional information. The two sensors (spherical and ellipsoidal) were tested in a series of entrainment experiments, similar to the evaluation of the 111 mm prototype, for a slope of 0.02. The spherical sensor entrained at discharges of 24.8 ± 1.8 l/s while the same threshold for the ellipsoidal sensor was 45.2 ± 2.2 l/s. Kinetic energy calculations were used to quantify the particle-bed energy exchange under fluvial (discharge at 30 l/s) and non-fluvial conditions. All the experiments suggest that the effect of the inertial characteristics of coarse sediments on their motion is comparable to the effect hydrodynamic forces. The coupling of IMU sensors with advanced telemetric systems can lead to the tracking of Lagrangian particle trajectories, at a frequency and accuracy that will permit the testing of diffusion/dispersion models across the range of particle diameters.

Optimizing AI at the Edge: from network topology design to MCU deployment

The first topic analyzed in the thesis will be Neural Architecture Search (NAS). I will focus on two different tools that I developed, one to optimize the architecture of Temporal Convolutional Networks (TCNs), a convolutional model for time-series processing that has recently emerged, and one to optimize the data precision of tensors inside CNNs. The first NAS proposed explicitly targets the optimization of the most peculiar architectural parameters of TCNs, namely dilation, receptive field, and the number of features in each layer. Note that this is the first NAS that explicitly targets these networks. The second NAS proposed instead focuses on finding the most efficient data format for a target CNN, with the granularity of the layer filter. Note that applying these two NASes in sequence allows an "application designer" to minimize the structure of the neural network employed, minimizing the number of operations or the memory usage of the network. After that, the second topic described is the optimization of neural network deployment on edge devices. Importantly, exploiting edge platforms' scarce resources is critical for NN efficient execution on MCUs. To do so, I will introduce DORY (Deployment Oriented to memoRY) -- an automatic tool to deploy CNNs on low-cost MCUs. DORY, in different steps, can manage different levels of memory inside the MCU automatically, offload the computation workload (i.e., the different layers of a neural network) to dedicated hardware accelerators, and automatically generates ANSI C code that orchestrates off- and on-chip transfers with the computation phases. On top of this, I will introduce two optimized computation libraries that DORY can exploit to deploy TCNs and Transformers on edge efficiently. I conclude the thesis with two different applications on bio-signal analysis, i.e., heart rate tracking and sEMG-based gesture recognition.

GUI design for in-car message diagnosis

Electronics needs communication to serve the vehicular world, to let all ECUs to com- municate with each other are needed buses, each bus serves different stuffs and is able to communicate. In the evolution of automotive architecture, integration of multiple func- tionalities in a single ECU will be one of the key aspects. The analysis of in-vehicle data, both for diagnosis and study-case of a particular behaviour, is the basis for future’s technologies and applications. Starting from these ideas, the need of creating a correlation between the Vehicle state and the anomaly. In order to create a link among them, has been created an interface able to simplify the in-car diagnosis and creating a link among vehicle stateand anomaly, looking also to the study-case of the driver's behaviour.

Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques

This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry.

A socio friendly approach to the analysis of emotive speech

This paper describes certain findings of intonation and intensity study of emotive speech with the minimal use of signal processing algorithms. This study was based on six basic emotions and the neutral, elicited from 1660 English utterances obtained from the speech recordings of six Indian women. The correctness of the emotional content was verified through perceptual listening tests. Marked similarity was noted among pitch contours of like-worded, positive valence emotions, though no such similarity was observed among the four negative valence emotional expressions. The intensity patterns were also studied. The results of the study were validated using arbitrary television recordings for four emotions. The findings are useful to technical researchers, social psychologists and to the common man interested in the dynamics of vocal expression of emotions

Residual signal auto-correlation to evaluate speech in Parkinson's disease patients

Objective: To evaluate the maximum residual signal auto-correlation also known as pitch amplitude (PA) values in patients with Parkinson's disease (PD) patients. Method. The signals of 21 Parkinson's patients were compared with 15 healthy individuals, divided according age and gender. Results: Statistical difference was seen between groups for PA, 0.39 for controls and 0.25 for PD. Normal value threshold was set as 0.3; (p <= 0.001). In the Parkinson's group 80.77%, and in the control group only 12.28%, had a PA < 0.3 demonstrating an association between these variables. The dispersion diagram for age and PA for PD individuals showed p=0.01 and r=0.54. There was no significant difference in relation to gender and PA between groups: Conclusion: the significant differences in pitch's amplitude between PD patients and healthy individuals demonstrate the methods specificity.-The results showed the need of prospective controlled studies,to improve the use and indications of residual signal auto-correlation to evaluate speech in PD patients.

Multiresolution analysis (discrete wavelet transform) through Daubechies family for emotion recognition in speech

We propose a study of the mathematical properties of voice as an audio signal -- This work includes signals in which the channel conditions are not ideal for emotion recognition -- Multiresolution analysis- discrete wavelet transform – was performed through the use of Daubechies Wavelet Family (Db1-Haar, Db6, Db8, Db10) allowing the decomposition of the initial audio signal into sets of coefficients on which a set of features was extracted and analyzed statistically in order to differentiate emotional states -- ANNs proved to be a system that allows an appropriate classification of such states -- This study shows that the extracted features using wavelet decomposition are enough to analyze and extract emotional content in audio signals presenting a high accuracy rate in classification of emotional states without the need to use other kinds of classical frequency-time features -- Accordingly, this paper seeks to characterize mathematically the six basic emotions in humans: boredom, disgust, happiness, anxiety, anger and sadness, also included the neutrality, for a total of seven states to identify

Fluorescence and reflectance spectroscopy for identification of atherosclerosis in human carotid arteries using principal components analysis

Objectives: The aim of this work was to verify the differentiation between normal and pathological human carotid artery tissues by using fluorescence and reflectance spectroscopy in the 400- to 700-nm range and the spectral characterization by means of principal components analysis. Background Data: Atherosclerosis is the most common and serious pathology of the cardiovascular system. Principal components represent the main spectral characteristics that occur within the spectral data and could be used for tissue classification. Materials and Methods: Sixty postmortem carotid artery fragments (26 non-atherosclerotic and 34 atherosclerotic with non-calcified plaques) were studied. The excitation radiation consisted of a 488-nm argon laser. Two 600-mu m core optical fibers were used, one for excitation and one to collect the fluorescence radiation from the samples. The reflectance system was composed of a halogen lamp coupled to an excitation fiber positioned in one of the ports of an integrating sphere that delivered 5 mW to the sample. The photo-reflectance signal was coupled to a 1/4-m spectrograph via an optical fiber. Euclidean distance was then used to classify each principal component score into one of two classes, normal and atherosclerotic tissue, for both fluorescence and reflectance. Results: The principal components analysis allowed classification of the samples with 81% sensitivity and 88% specificity for fluorescence, and 81% sensitivity and 91% specificity for reflectance. Conclusions: Our results showed that principal components analysis could be applied to differentiate between normal and atherosclerotic tissue with high sensitivity and specificity.

Normal Versus Pathological Voice Signals Using Wavelet Analysis and Least Squares Support-Vector Machines

State of Sao Paulo Research Foundation (FAPESP)

MODELING THE UNVOICED COMPONENT IN THE CANONICAL REPRESENTATION OF SPEECH

The canonical representation of speech constitutes a perfect reconstruction (PR) analysis-synthesis system. Its parameters are the autoregressive (AR) model coefficients, the pitch period and the voiced and unvoiced components of the excitation represented as transform coefficients. Each set of parameters may be operated on independently. A time-frequency unvoiced excitation (TFUNEX) model is proposed that has high time resolution and selective frequency resolution. Improved time-frequency fit is obtained by using for antialiasing cancellation the clustering of pitch-synchronous transform tracks defined in the modulation transform domain. The TFUNEX model delivers high-quality speech while compressing the unvoiced excitation representation about 13 times over its raw transform coefficient representation for wideband speech.