Cleavage and polyadenylation are essential pre-mRNA processing reactions maturing the 3’end of almost all protein encoding eukaryotic mRNAs. Analysis of the sequences required for cleavage and polyadenylation in the human melanocortin 4 receptor (MC4R) and the human transcription factors JUNB and JUND pre-mRNAs revealed that, at least for some mammalian genes, 3’end processing of the primary transcript is independent of previously described auxiliary sequence elements located upstream or downstream of the core poly(A) sequences. The analysis of the MC4R poly(A) site, contrary to the current understanding of mammalian poly(A) sites, showed that mutations of the AUUAAA hexamer sequence had no effect on 3’end processing levels while mutations in the short DSE severely reduced cleavage efficiency. The MC4R poly(A) site uses a potent DSE and to direct maximal cleavage efficiency requires only a short upstream adenosine rich sequence. Furthermore, analysis of the endogenous A-rich human JUNB poly(A) signal validated upstream A-rich core sequences as genuine 3’end formation directing sequences in human non-canonical 3’end formation signals. The results show that a minimal human poly(A) site, similar to yeast and plants, can be defined by an adenosine rich sequence adjacent to a U/GU-rich sequence element and a cleavage site. These findings further imply that some human non-canonical poly(A) sites may be recognised via a similar DSE-dependent mechanism and may not require additional auxiliary sequence elements. Finally, results on the analysis of the EDF1 poly(A) signal show that, in a spliced environment, A-rich sequences are also 3’end formation effectors but depend on an competent upstream splicing reaction for efficient definition of the 3’end processing site.

Cette thèse propose une méthode d'analyse des signaux triphasés pour la surveillance d'état des systèmes électromécaniques. La méthode proposée repose sur l'utilisation de la transformée en composantes symétriques instantanées et d'outils simples de traitement du signal pour détecter les défauts électriques et mécaniques dans de tels systèmes. Les avantages de cette approche triphasée par rapport à une approche monophasée pour la surveillance d'état sont étudiés en détail. Tout d'abord, pour les défauts électriques, l'utilisation de la transformée triphasée permet de séparer les composantes symétriques et asymétriques, et facilite ainsi la détection d'un déséquilibre électrique. Ensuite, pour les défauts mécaniques, l'approche par transformée en composantes symétriques permet de travailler dans des espaces avec un meilleur rapport signal à bruit. En effet, en appliquant le même traitement à la fois en monophasé et en triphasé sur les composantes symétriques, on observe que certains défauts mécaniques ne sont détectables qu’en utilisant la séquence positive des composantes symétriques. La méthodologie complète et les algorithmes pour calculer les indicateurs de défaut pour les défauts électriques et mécaniques sont donnés et les résultats sont validés sur signaux synthétiques et expérimentaux. En termes d'application, l'accent est mis sur la surveillance d'état des composants de turbines éoliennes. Toutefois, le procédé proposé peut être appliqué à des systèmes électromécaniques en général et peut facilement être étendu à des systèmes polyphasés. / This thesis proposes a three-phase electrical signals analysis method for condition monitoring of electromechanical systems. The proposed method relies on the use of instantaneous symmetrical components (ISCs) transform and simple signal processing tools to detect both electrical and mechanical faults in such systems. The advantages of using this three-phase approach for condition monitoring instead of single-phase ones are thoroughly detailed. Firstly, for electrical faults the use of the three-phase transform separates the balanced and unbalanced components thus making electrical unbalance detection easier. Secondly, for mechanical faults the ISCs approach has better signal-to-noise ratio (SNR). Indeed, by applying the same processing to both single-phase and ISCs, some mechanical faults are only detectable using the positive-sequence ISC. The complete methodology and algorithms to compute fault indicators for both electrical and mechanical faults are given and the results are validated using synthetic and experimental signals. In terms of application, the focus was on condition monitoring of wind turbine components. However, the proposed method can be applied on electromechanical systems in general and can easily be extended to poly-phase systems.

The aim of this thesis is to define a perceptual scale for the ‘Time-Frequency’ analysis of music signals. The equal tempered ‘Bach ’ scale is a suitable scale, since it covers most of the genres of music and the error is equally distributed for each semi-tone. However, it may be necessary to allow a tolerance of around 50 cents or half the interval of the Bach scale, so that the interval can accommodate other common intonation schemes. The thesis covers the formulation of the Bach scale filter-bank as a time-varying model. It makes a comparative study with other commonly used perceptual scales. Two applications for the Bach scale filter-bank are also proposed, namely automated segmentation of speech signals and transcription of singing voice for query-by-humming applications. Even though this filter-bank is suggested with a motivation from music, it could also be applied to speech. A method for automatically segmenting continuous speech into phonetic units is proposed. The results, obtained from the proposed method, show around 82% accuracy for the English and 85% accuracy for the Hindi databases. This is an improvement of around 2 -3% when the performance is compared with other popular methods in the literature. Interestingly, the Bach scale filters perform better than the filters designed for other common perceptual scales, such as Mel and Bark scales. ‘Musical transcription’ refers to the process of converting a musical rendering or performance into a set of symbols or notations. A query in a ‘query-by-humming system’ can be made in several ways, some of which are singing with words, or with arbitrary syllables, or whistling. Two algorithms are suggested to annotate a query. The algorithms are designed to be fairly robust for these various forms of queries. The first algorithm is a frequency selection based method. It works on the basis of selecting the most likely frequency components at any given time instant. The second algorithm works on the basis of finding time-connected contours of high energy in the ‘Time-Frequency’ plane of the input signal. The time domain algorithm works better in terms of instantaneous pitch estimates. It results in an error of around 10 -15%, while the frequency domain method results in an error of around 12 -20%. A song rendered by two different people will have quite a few different properties. Their absolute pitches, rates of rendering, timbres based on voice quality and inaccuracies, may be different. The thesis discusses a method to quantify the distance between two different renderings of musical pieces. The distance function has been evaluated by attempting a search for a particular song from a database of a size of 315, made up of songs sung by both male and female singers and whistled queries. Around 90 % of the time, the correct song is found among the top five best choices picked. Thus, the Bach scale has been proposed as a suitable scale for representing the perception of music. It has been explored in two applications, namely automated segmentation of speech and transcription of singing voices. Using the transcription obtained, a measure of the distance between renderings of musical pieces has also been suggested.

Cette thèse propose trois méthodes de traitement du signal orientées vers la surveillance d’état et le diagnostic. Les techniques proposées sont surtout adaptées pour la surveillance d’état, effectuée à la base de vibrations, des machines tournantes qui fonctionnent dans des conditions d’opération non-stationnaires comme par exemple les éoliennes mais elles ne sont pas limitées à un tel usage. Toutes les méthodes proposées sont des algorithmes automatiques et gérés par les données.La première technique proposée permet de sélectionner la partie la plus stationnaire d’un signal en cadrant la représentation temps-fréquence d’un signal.La deuxième méthode est un algorithme pour l’association des dispositions spectrales, des séries harmoniques et des séries à bandes latérales avec des fréquences caractéristiques provennant du cinématique d'un système analysé. Cette méthode propose une approche unique dédiée à l’élément roulant du roulement qui permet de surmonter les difficultés causées par le phénomène de glissement.La troisième technique est un algorithme de démodulation de bande latérale entière. Elle fonctionne à la base d’un filtre multiple et propose des indicateurs de santé pour faciliter une évaluation d'état du système sous l’analyse.Dans cette thèse, les méthodes proposées sont validées sur les signaux simulés et réels. Les résultats présentés montrent une bonne performance de toutes les méthodes. / This thesis proposes a three signal-processing methods oriented towards the condition monitoring and diagnosis. In particular the proposed techniques are suited for vibration-based condition monitoring of rotating machinery which works under highly non-stationary operational condition as wind turbines, but it is not limited to such a usage. All the proposed methods are automatic and data-driven algorithms.The first proposed technique enables a selection of the most stationary part of signal by cropping time-frequency representation of the signal.The second method is an algorithm for association of spectral patterns, harmonics and sidebands series, with characteristic frequencies arising from kinematic of a system under inspection. This method features in a unique approach dedicated for rolling-element bearing which enables to overcome difficulties caused by a slippage phenomenon.The third technique is an all-sideband demodulation algorithm. It features in a multi-rate filter and proposes health indicators to facilitate an evaluation of the condition of the investigated system.In this thesis the proposed methods are validated on both, simulated and real-world signals. The presented results show good performance of all the methods.

Every auditory event provides an information-rich signal to the brain. The signal constitutes perceptual attributes of pitch, loudness, timbre, and also, conceptual attributes like location, emotions, meaning, etc. In the present work we examine the timbre perception of time-varying signals in particular. While stationary signal timbre, by-itself is complex perceptually, the time-varying signal timbre introduces an evolving pattern, adding to its multi-dimensionality. To characterize timbre, we conduct psycho-acoustic perception tests with normal-hearing human subjects. We focus on time-varying synthetic speech signals(can be extended to music) because listeners are perceptually consistent with speech. Also, we can parametrically control the timbre and pitch glides using linear time-varying models. In order to quantify the timbre change in time-varying signals, we define the JND(Just noticeable difference) of timbre using diphthongs, synthesized using time-varying formant frequency model. The diphthong JND is defined as a two dimensional contour on the plane of percentage change of formant frequencies of terminal vowels. Thus, we simplify the perceptual probing to a lower dimensional space, i.e, 2-D even for a diphthong, which is multi-parametric. We also study the impact of pitch glide on the timbre JND of the diphthong. It is observed that timbre JND is influenced by the occurrence of pitch glide. Focusing on the magnitude of perceptual timbre change, we design a MUSHRA-like listening test using the vowel continuum in the formant-frequency space. We provide explicit anchors for reference: 0% and 100%, thus quantifying the perceptual timbre change on a 1-D scale. We also propose an objective measure of timbre change and observe that there is good correlation between the objective measure and subjective human responses of percentage timbre change. Using the above experimental methodology, we studied the influence of pitch shift on timbre perception and observed that the perceptual timbre change increases with change in pitch. We used vowels and diphthongs with 5 different types of pitch glides-(i) Constant pitch,(ii) 3-semitone linearly-up,(iii) 3 semitone linearly-down, (iv)V–like pitch glide and (v) hat-like pitch glide. The present study shows that timbre change can be measured on a 1-D scale if the perturbation is along one-dimension. We observe that for bright vowels(/a/and/i/), linearly decreasing pitch glide(dull pitch glide)causes more timbre change than linearly increasing pitch glide(bright pitch glide).For dull vowels(/u/),it is vice-versa. To summarize, in congruent pitch glides cause more perceptual timbre change than congruent pitch glides.(Congruent pitch glide implies bright pitch glide in bright vowel or dull pitch glide in dull vowel and in congruent pitch glide implies bright pitch glide in dull vowel or dull pitch glide in bright vowel.) Experiments with quadratic pitch glides show that the decay portion of pitch glide affects timbre perception more than the attack portion in short duration signals with less or no sustained part. In case of time-varying timbre, bright diphthongs show patterns similar to bright vowels. Also, for bright diphthongs(/ai/), perceived timbre change is most with decreasing pitch glide(dull pitch glide). We also observed that listeners perceive more timbre change in constant pitch than in pitch glides, congruent with the timbre or pitch glides with quadratic changes. The main conclusion of this study is that pitch and timbre do interact and in congruent pitch glides cause more timbre change than congruent pitch glides. In the case of quadratic pitch glides, listener perception of vowels is influenced by the decay than the attack in pitch glide in short duration signals. In the case of time-varying timbre also, in congruent pitch glides cause the most timbre change, followed by constant pitch glide. For congruent pitch glides and quadratic pitch glides in time-varying timbre, the listeners perceive lesser timbre change than otherwise.

The role of the South African National Defence Force (SANDF) has changed considerably in the last decade. The emphasis has moved from protecting the country's borders to peacekeeping duties in Central Africa and even further North. Communications between the peacekeeping missions and the military bases back in South Africa is vital to ensure the success of these missions. Currently use is made of satellite as well as High Frequency (HF) communications. There are drawbacks associated with these technologies (high cost and low data rates/interference respectively). Successful long distance ionospheric propagation in the low Very High Frequency (VHF) range will complement the existing infrastructure and enhance the success rate of these missions. This thesis presents a feasibility study to determine under what ionospheric conditions such low VHF communications will be possible. The International Reference Ionosphere (IRI) was used to generate ionospheric data for the reflection point(s) of the signal. The peak height of the ionospheric F2 layer (hmF2) was used to calculate the required antenna elevation angle. Once the elevation angle is known it is possible to calculate the required F2 layer critical frequency (foF2). The required foF2 value was calculated by assuming a Maximum Useable Frequency (MUF) of 20% higher than the planned operational frequency. It was determined that single hop propagation is possible during the daytime if the smoothed sunspot number (SSN) exceeds 15. The most challenging requirement for successful single hop propagation is the need of an antenna height of 23 m. For rapid deployment and semi-mobile operations within a jungle environment it may prove to be a formidable obstacle.

Le travail présenté dans ce mémoire a été effectué dans le cadre du projet FEDER ”Maintenance prédictive des éoliennes et maîtrise des impacts environnementaux”. Un des objectifs du projet a été de développer, dans le Poitou-Charentes, des compétences dans le domaine de l’éolien en lien avec les activités des laboratoires LIAS et LaSIE. Pour le LIAS, il a été décidé de lancer une nouvelle activité de recherche sur le diagnostic de défauts mécaniques. Le cadre du projet concernant l’éolien, les défauts localisés dans les multiplicateurs ont été privilégiés. Par ailleurs, nous avons restreint l’étude au régime stationnaire afin de simplifier l’apprentissage des différents phénomènes mis en jeu et des techniques de traitement du signal utilisées. Dans une première partie, nous avons étudié les signatures de défaut sur les signaux vibratoires. Cette phase a été facilitée par l’utilisation des données expérimentales mise à disposition par le Bearing Data Center de la Case Western Reserve - University de Cleveland. Parmi les méthodes de traitement de signal utilisées, nous avons opté pour l’analyse d’enveloppe mise en oeuvre dans les techniques de type Time Synchronous Analysis (TSA). A cette occasion, nous avons défini une procédure complète de détection de défaut que nous avons conservée tout au long de cette étude en appliquant une technique d’identification de type PNL qui nous a permis d’obtenir des résultats comparables à des méthodes haute résolution de type ESPRIT. Par la suite, nous nous sommes recentrés sur l’application éolienne en réalisant un banc d’essai original permettant d’émuler un défaut au niveau de l’accouplement de deux machines électriques. L’idée principale a été de recenser l’ensemble des signaux exploitables dans le cadre de la détection du défaut émulé et de fournir une classification entre les courants électriques, le couple mécanique et la vitesse des machines. Par ailleurs, un comparatif entre signaux mesurés et signaux estimés a été présenté. Il en ressort qu’il est possible d’obtenir un signal observé plus riche que la mesure directe en terme de composantes spectrales liées au défaut. Cette amélioration est rendue possible par une synthèse adéquate des gains d’observation qui a été obtenue après linéarisation de l’observateur étudié. En marge de l’application éolienne, le cas d’un moteur commandé vectoriellement a été abordé. L’idée a été d’exploiter les performances de la boucle de vitesse afin d’amplifier les composantes recherchées dans les courants électriques. L’ensemble de ces pistes de recherches a été testé en simulation et expérimentalement. / The work presented in this thesis was carried out under the FEDER project ”Maintenance prédictive des éoliennes et maîtrise des impacts environnementaux”. One of the project objectives was to develop, in Poitou-Charentes, expertise in the field of wind power in connection with the activities of LIAS and LaSIE laboratories. For LIAS, it was decided to launch a new research activity on the diagnosis of mechanical faults. The localized defects in gearbox were privileged. Furthermore, we restricted the study to the stationary system to simplify the learning of different phenomena involved and signal processing techniques. In the first part, we studied the fault signatures on the vibration signals. This phase was facilitated by the use of experimental data available from the Bearing Data Center of the Case Western Reserve - Cleveland University. Among the signal processing methods, we opted for envelope analysis implemented in the Synchronous Time Averaging (TSA). On this occasion, we defined a comprehensive fault detection procedure that we have maintained throughout this study by applying a NLP identification technique where we obtained similar results compared to high-resolution methods as ESPRIT. There after, we refocused on wind power applications by making an original test bench capable of emulating a fault in the coupling of two electrical machines. The main idea was to identify all usable signals in the context of emulated fault detection and to provide a classification between electric currents, mechanical torque and speed of the machines. Moreover, a comparison between measured signals and estimated ones was discussed. It shows that it is possible to get an observed signal richer than direct signal measurement in terms of spectral components related to the defect. This improvement is made possible by an appropriate synthesis of gains observer which was obtained after linearization of the studied observer. In the margin of wind application, the case of a motor controlled by vector was also discussed. The idea was to exploit the speed loop performance to amplify the fault components in electrical currents. All these researches have been tested in simulation and experimentally.

Ce travail de thèse s’inscrit dans le domaine de l’informatique affective et plus précisément de l’intelligence artificielle et de l’exploration d’architecture. L’objectif de ce travail est de concevoir un système complet de détection des émotions en utilisant des signaux physiologiques. Ce travail se place donc à l’intersection de l’informatique pour la définition d’algorithme de détection des émotions et de l’électronique pour l’élaboration d’une méthodologie d’exploration d’architecture et pour la conception de nœuds de capteurs. Dans un premier temps, des algorithmes de détection multimodale et instantanée des émotions ont été définis. Deux algorithmes de classification KNN puis SVM, ont été implémentés et ont permis d’obtenir un taux de reconnaissance des émotions supérieurs à 80%. Afin de concevoir un tel système alimenté sur pile, un modèle analytique d’estimation de la consommation à haut niveau d’abstraction a été proposé et validé sur une plateforme réelle. Afin de tenir compte des contraintes utilisateurs, un outil de conception et de simulation d’architecture d’objets connectés pour la santé a été développé, permettant ainsi d’évaluer les performances des systèmes avant leur conception. Une architecture logicielle/matérielle pour la collecte et le traitement des données satisfaisant les contraintes applicatives et utilisateurs a ainsi été proposée. Doté de cette architecture, des expérimentations ont été menées pour la Mnémothérapie musicale. EMOTICA est un système complet de détection des émotions utilisant des signaux physiologiques satisfaisant les contraintes d’architecture, d’application et de l’utilisateur. / This thesis work is part of the field of affective computing and more specifically artificial intelligence and architectural exploration. The goal of this work is to design a complete system of emotions detection using physiological signals. This work is therefore situated at the intersection of computer science for the definition of algorithm of detection of emotions and electronics for the development of an architecture exploration methodology for the design of sensor nodes. At first, algorithms for multimodal and instantaneous detection of emotions were defined. Two algorithms of classification KNN then SVM, were implemented and made it possible to obtain a recognition rate of the emotions higher than 80%. To design such a battery-powered system, an analytical model for estimating the power consumption at high level of abstraction has been proposed and validated on a real platform. To consider user constraints, a connected object architecture design and simulation tool for health has been developed, allowing the performance of systems to be evaluated prior to their design. Then, we used this tool to propose a hardware/software architecture for the collection and the processing of the data satisfying the architectural and applicative constraints. With this architecture, experiments have been conducted for musical Mnemotherapy. EMOTICA is a complete system for emotions detection using physiological signals satisfying the constraints of architecture, application and user.

This thesis investigates out-of-distribution recognition for time-series data of pulsedradar signals. The classifier is a naive Bayesian classifier based on Gaussian mixturemodels and Dirichlet process mixture models. In the mixture models, we model thedistribution of three pulse features in the time series, namely radio-frequency in thepulse, duration of the pulse, and pulse repetition interval which is the time betweenpulses. We found that simple thresholds on the likelihood can effectively determine ifsamples are out-of-distribution or belong to one of the classes trained on. In addition,we present a simple method that can be used for deinterleaving/pulse classification andshow that it can robustly classify 100 interleaved signals and simultaneously determineif pulses are out-of-distribution. / Det här examensarbetet undersöker hur en maskininlärnings-modell kan anpassas för attkänna igen när pulserade radar-signaler inte tillhör samma fördelning som modellen är tränadmed men också känna igen om signalen tillhör en tidigare känd klass. Klassifieringsmodellensom används här är en naiv Bayesiansk klassifierare som använder sig av Gaussian mixturemodels och Dirichlet Process mixture models. Modellen skapar en fördelning av tidsseriedatan för pulserade radar-signaler och specifikt för frekvensen av varje puls, pulsens längd och tiden till nästa puls. Genom att sätta gränser i sannolikheten av varje puls eller sannolikhetenav en sekvens kan vi känna igen om datan är okänd eller tillhör en tidigare känd klass.Vi presenterar även en enkel metod för att klassifiera specifika pulser i sammanhang närflera signaler överlappar och att metoden kan användas för att robust avgöra om pulser ärokända.

This thesis analyzes the kinds of verbal and nonverbal signals elites manifest to show leadership qualities. Launching from Max Weber’s conceptual framework of charisma as a power term and Harold Lasswell’s study of propaganda, this study takes a multidisciplinary approach to studying political leadership with elements of communication methodology and an ontological basis in evolutionary psychology. The study’s goal is to offer a framework for defining and evaluating the diverse signal patterns employed by political elites in three real-life situations. These are the Malta Summit, the 1992 Virginia Presidential Debate, and the 2012 South Carolina Republican Presidential Primary. The cases were chosen because they display a diverse set of signal variations during different types of interactions. The three case studies are evaluated by measuring frequency and patterns of occurrence of the five different interaction constructs (indicator of interest, indicator of disinterest, demonstration of high value, demonstration of low value, and compliance testing) to explain different interaction patterns. A simple frequency distribution of the different signals during a given interaction is used to display the empirical findings and to compare patterns across the case studies. This study reveals that the presence of DLV (demonstration of low value) signals weaken an elite’s position in relation to other elites and the public while the presence of DHV (demonstration of high value) signals strengthen an elite’s position. It is largely the presence, absence, and frequency of these two signals that determines who conveys leadership qualities effectively regardless of leadership style. Studying the signaling patterns of political elites would allow scholars to understand better the kinds of signal patterns and signal frequencies that are used in different types of leadership styles and norm ranges for signals including for political elites belonging to different cultures and subcultures