Several applications have been proposed for wireless sensor networks, including habitat monitoring, structural health monitoring, pipeline monitoring, and precision agriculture. Among the desirable features of wireless sensor networks, one is the ease of deployment. Since the nodes are capable of self-organization, they can be placed easily in areas that are otherwise inaccessible to or impractical for other types of sensing systems. In fact, some have proposed the deployment of wireless sensor networks by dropping nodes from a plane, delivering them in an artillery shell, or launching them via a catapult from onboard a ship. There are also reports of actual aerial deployments, for example the one carried out using an unmanned aerial vehicle (UAV) at a Marine Corps combat centre in California -- the nodes were able to establish a time-synchronized, multi-hop communication network for tracking vehicles that passed along a dirt road. While this has a practical relevance for some civil applications (such as rescue operations), a more realistic deployment involves the careful planning and placement of sensors. Even then, nodes may not be placed optimally to ensure that the network is fully connected and high-quality data pertaining to the phenomena being monitored can be extracted from the network. This work aims to address the problem of random deployment through two complementary approaches: The first approach aims to address the problem of random deployment from a communication perspective. It begins by establishing a comprehensive mathematical model to quantify the energy cost of various concerns of a fully operational wireless sensor network. Based on the analytic model, an energy-efficient topology control protocol is developed. The protocol sets eligibility metric to establish and maintain a multi-hop communication path and to ensure that all nodes exhaust their energy in a uniform manner. The second approach focuses on addressing the problem of imperfect sensing from a signal processing perspective. It investigates the impact of deployment errors (calibration, placement, and orientation errors) on the quality of the sensed data and attempts to identify robust and error-agnostic features. If random placement is unavoidable and dense deployment cannot be supported, robust and error-agnostic features enable one to recognize interesting events from erroneous or imperfect data.

L’objectif de cette thèse est d’étudier d’une part le problème de sonification d’image et de le solutionner à travers de nouveaux modèles de correspondance entre domaines visuel et sonore. D’autre part d’étudier le problème de la classification de son et de le résoudre avec des méthodes ayant fait leurs preuves dans le domaine de la reconnaissance d’image. La sonification d’image est la traduction de données d’image (forme, couleur, texture, objet) en sons. Il est utilisé dans les domaines de l’assistance visuelle et de l’accessibilité des images pour les personnes malvoyantes. En raison de sa complexité, un système de sonification d’image qui traduit correctement les données d’image en son de manière intuitive n’est pas facile à concevoir. Notre première contribution est de proposer un nouveau système de sonification d’image de bas-niveau qui utilise une approche hiérarchique basée sur les caractéristiques visuelles. Il traduit, à l’aide de notes musicales, la plupart des propriétés d’une image (couleur, gradient, contour, texture, région) vers le domaine audio, de manière très prévisible et donc est facilement ensuite décodable par l’être humain. Notre deuxième contribution est une application Android de sonification de haut niveau qui est complémentaire à notre première contribution car elle implémente la traduction des objets et du contenu sémantique de l’image. Il propose également une base de données pour la sonification d’image. Finalement dans le domaine de l’audio, notre dernière contribution généralise le motif binaire local (LBP) à 1D et le combine avec des descripteurs audio pour faire de la classification de sons environnementaux. La méthode proposée surpasse les résultats des méthodes qui utilisent des algorithmes d’apprentissage automatique classiques et est plus rapide que toutes les méthodes de réseau neuronal convolutif. Il représente un meilleur choix lorsqu’il y a une rareté des données ou une puissance de calcul minimale. / The objective of this thesis is to study on the one hand the problem of image sonification and to solve it through new models of mapping between visual and sound domains. On the other hand, to study the problem of sound classification and to solve it with methods which have proven track record in the field of image recognition. Image sonification is the translation of image data (shape, color, texture, objects) into sounds. It is used in vision assistance and image accessibility domains for visual impaired people. Due to its complexity, an image sonification system that properly conveys the image data to sound in an intuitive way is not easy to design. Our first contribution is to propose a new low-level image sonification system which uses an hierarchical visual feature-based approach to translate, usingmusical notes, most of the properties of an image (color, gradient, edge, texture, region) to the audio domain, in a very predictable way in which is then easily decodable by the human being. Our second contribution is a high-level sonification Android application which is complementary to our first contribution because it implements the translation to the audio domain of the objects and the semantic content of an image. It also proposes a dataset for an image sonification. Finally, in the audio domain, our third contribution generalizes the Local Binary Pattern (LBP) to 1D and combines it with audio features for an environmental sound classification task. The proposed method outperforms the results of methods that uses handcrafted features with classical machine learning algorithms and is faster than any convolutional neural network methods. It represents a better choice when there is data scarcity or minimal computing power.

Dialogue Enhancement (DE) is a process used in audio delivery systems to improve the clarity, intelligibility, and overall quality of the spoken dialogue in audio content. It is primarily used when dialogue is masked by music, surrounding noise, or other audio sources. This thesis project involves experiments to find the optimal time and frequency resolution needed for a DE system. The time resolution focuses on experimenting with various attack/release times for a DE system. The frequency domain analysis investigates whether people prefer a noise spectrum-dependent gain over a conventional full-band gain. The research methodology comprises three main parts. The first part focuses on system setup and choosing content/vectors to be used for the experiments. Next, the experiments are designed for time and frequency resolution. An exponential smoothing model is used to amplify/attenuate the dialogue stream at various times of attack/release. For the frequency counterpart, a banded gain model is designed which uses banded noise levels as input. Subsequently, a modified subjective listening test is designed to evaluate the experiments designed. The responses recorded for various types of content-noise combinations from the listeners are recorded and analyzed. Finally, the main outcome of this research emphasizes the advantages of a DE system. Further, it paves the way for further exploration of DE models and rigorous testing schemes with expert listeners. / Dialogue Enhancement (DE) är en process som används i ljudleveranssystem för att förbättra tydligheten, förståeligheten och den övergripande kvaliteten på den talade dialogen i ljudinnehåll. Det används främst när dialog maskeras av musik, omgivande brus eller andra ljudkällor. Detta examensarbete omfattar experiment för att hitta den optimala tids- och frekvensupplösningen för ett DEsystem. Tidsupplösningsexperimenten fokuserar på olika attack- och releasetider för ett DE-system. Frekvensdomänanalysen undersöker om människor föredrar en brusspektrumberoende förstärkning framför en konventionell fullbandsförstärkning. Forskningsmetodiken består av tre huvuddelar. Den första delen fokuserar på systeminställning och val av innehåll/vektorer som ska användas för experimenten. Därefter designas tids- och frekvensupplösningsexperimenten. En exponentiell tidsenvelopp används för att förstärka/dämpa dialogen vid olika tider för attack/release. För frekvensdomänexperimenten används en bandad förstärkningsmodell som använder bandade brusnivåer som insignal. I den tredje delen utformas ett subjektivt lyssningstest för att utvärdera experimenten. Lyssnarnas svar för olika typer av innehåll-bruskombinationer registreras och analyseras. Det huvudsakliga resultatet av denna forskning betonar fördelarna med ett DEsystem. Vidare banar det väg för utforskning av fler DE-modeller och rigorösa testscheman med expertlyssnare.