Réduction de l'égo-bruit de robots

En robotique, il est désirable d’équiper les robots du sens de l’audition afin de mieux interagir avec les utilisateurs et l’environnement. Cependant, le bruit causé par les actionneurs des robots, nommé égo-bruit, réduit considérablement la qualité des segments audios. Conséquemment, la performance des techniques de reconnaissance de la parole et de détection d’évènements sonores est limitée par la quantité de bruit que le robot produit durant ses mouvements. Le bruit généré par les robots diffère considérablement selon l’environnement, les moteurs, les matériaux utilisés et même selon l’intégrité des différentes composantes mécaniques. L’objectif du projet est de concevoir un modèle de réduction d’égo-bruit robuste utilisant plusieurs microphones et d’être capable de le calibrer rapidement sur un robot mobile. Ce mémoire présente une méthode de réduction de l’égo-bruit combinant l’apprentissage de gabarit de matrice de covariance du bruit à un algorithme de formation de faisceau de réponses à variance minimum sans distorsion. L’approche utilisée pour l’apprentissage des matrices de covariances permet d’enregistrer les caractéristiques spatiales de l’égo-bruit en moins de deux minutes pour chaque nouvel environnement. L’algorithme de faisceau permet, quant à lui, de réduire l’égo-bruit du signal bruité sans l’ajout de distorsion nonlinéaire dans le signal résultant. La méthode est implémentée sous Robot Operating System pour une utilisation simple et rapide sur différents robots. L’évaluation de cette nouvelle méthode a été effectuée sur un robot réel dans trois environnements différents : une petite salle, une grande salle et un corridor de bureau. L’augmentation du ratio signal-bruit est d’environ 10 dB et est constante entre les trois salles. La réduction du taux d’erreur des mots de la reconnaissance vocale se situe entre 30 % et 55 %. Le modèle a aussi été testé pour la détection d’évènements sonores. Une augmentation de 7 % à 20 % de la précision moyenne a été mesurée pour la détection de la musique, mais aucune augmentation significative pour la parole, les cris, les portes qui ferment et les alarmes. La méthode proposée permet une utilisation plus accessible de la reconnaissance vocale sur des robots bruyants. De plus, une analyse des principaux paramètres a permis de valider leurs impacts sur la performance du système. Les performances sont meilleures lorsque le système est calibré avec plus de bruit du robot et lorsque la longueur des segments utilisés est plus longue. La taille de la Transformée de Fourier rapide à court terme (Short-Time Fourier Transform) peut être réduite pour réduire le temps de traitement du système. Cependant, la taille de cette transformée impacte aussi la résolution des caractéristiques du signal résultant. Un compromis doit être faire entre un faible temps de traitement et la qualité du signal en sortie du système

Nonnegative matrix factorization of phonocardiograms for heart rate detection

Phonocardiograms (PCGs) are recordings of the sounds and murmurs made by the heart detected through specialized microphones placed on a patient's thorax. Alongside electrocardiograms (ECGs), they are a tool used in a medical environment to assess patients' conditions relative to their cardiac rhythm. Unlike the latter, in which, during each cardiac cycle, only one main peak can be detected within the voltage-over-time graph (the so called R wave), in PCGs two distinct peaks can be observed. These peaks are associated to the first and second heart sound (S1 and S2), generated by the closure of specific valves within the heart. In the following we shall refer to R waves and S1, S2 sounds as 'cardiac events'. In order to extrapolate the heart's activity from ECGs or PCGs, one needs to detect all cardiac events within the signal of choice. When it comes to ECGs, this process is relatively straightforward since only one R wave ought to be identified during each cycle. Moreover, such signals usually contain very low levels of noise, mainly caused by powerline interference, which can be easily removed using notch filters. On the other hand, event detection within PCGs is a quite challenging task. Indeed, not only do we need to detect two sounds each cycle, but also the signal itself is often severely contaminated by many different types of noise, such as the patient's movement, ambient sources, microphone movement or other body-related murmurs. As a consequence, the analysis of PCGs is often carried out with the aid of a synchronous ECG signal and requires a careful denoising of the audio file through digital filtering and signal envelope estimation. The objective of the dissertation was to develop a method of detecting cardiac events within PCG signals that does not rely on the knowledge of ECGs. In particular, we achieved our goal by leveraging the modelling and learning capabilities of Nonnegative Matrix Factorization (NMF) applied to the spectrogram of PCGs.Phonocardiograms (PCGs) are recordings of the sounds and murmurs made by the heart detected through specialized microphones placed on a patient's thorax. Alongside electrocardiograms (ECGs), they are a tool used in a medical environment to assess patients' conditions relative to their cardiac rhythm. Unlike the latter, in which, during each cardiac cycle, only one main peak can be detected within the voltage-over-time graph (the so called R wave), in PCGs two distinct peaks can be observed. These peaks are associated to the first and second heart sound (S1 and S2), generated by the closure of specific valves within the heart. In the following we shall refer to R waves and S1, S2 sounds as 'cardiac events'. In order to extrapolate the heart's activity from ECGs or PCGs, one needs to detect all cardiac events within the signal of choice. When it comes to ECGs, this process is relatively straightforward since only one R wave ought to be identified during each cycle. Moreover, such signals usually contain very low levels of noise, mainly caused by powerline interference, which can be easily removed using notch filters. On the other hand, event detection within PCGs is a quite challenging task. Indeed, not only do we need to detect two sounds each cycle, but also the signal itself is often severely contaminated by many different types of noise, such as the patient's movement, ambient sources, microphone movement or other body-related murmurs. As a consequence, the analysis of PCGs is often carried out with the aid of a synchronous ECG signal and requires a careful denoising of the audio file through digital filtering and signal envelope estimation. The objective of the dissertation was to develop a method of detecting cardiac events within PCG signals that does not rely on the knowledge of ECGs. In particular, we achieved our goal by leveraging the modelling and learning capabilities of Nonnegative Matrix Factorization (NMF) applied to the spectrogram of PCGs

Cumulative index to NASA Tech Briefs, 1986-1990, volumes 10-14

Tech Briefs are short announcements of new technology derived from the R&D activities of the National Aeronautics and Space Administration. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This cumulative index of Tech Briefs contains abstracts and four indexes (subject, personal author, originating center, and Tech Brief number) and covers the period 1986 to 1990. The abstract section is organized by the following subject categories: electronic components and circuits, electronic systems, physical sciences, materials, computer programs, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences

Aerospace Medicine and Biology. an Annotated Bibliography. 1958-1961 Literature, Volumes VII-X, Part 2

Abstracts on aerospace medicine and biology - bibliography on environmental factors, safety and survival, personnel, pharmacology, toxicology, and life support system

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen

Performance Analysis For Wireless G (IEEE 802.11 G) And Wireless N (IEEE 802.11 N) In Outdoor Environment

This paper described an analysis the different capabilities and limitation of both IEEE technologies that has been utilized for data transmission directed to mobile device. In this work, we have compared an IEEE 802.11/g/n outdoor environment to know what technology is better. the comparison consider on coverage area (mobility), through put and measuring the interferences. The work presented here is to help the researchers to select the best technology depending of their deploying case, and investigate the best variant for outdoor. The tool used is Iperf software which is to measure the data transmission performance of IEEE 802.11n and IEEE 802.11g

Performance analysis for wireless G (IEEE 802.11G) and wireless N (IEEE 802.11N) in outdoor environment

This paper described an analysis the different capabilities and limitation of both IEEE technologies that has been utilized for data transmission directed to mobile device. In this work, we have compared an IEEE 802.11/g/n outdoor environment to know what technology is better. The comparison consider on coverage area (mobility), throughput and measuring the interferences. The work presented here is to help the researchers to select the best technology depending of their deploying case, and investigate the best variant for outdoor. The tool used is Iperf software which is to measure the data transmission performance of IEEE 802.11n and IEEE 802.11g