One of the most challenging issues facing vehicular networks lies in the design of an efficient MAC protocol due to mobile nature of nodes, delay constraints for safety applications and interference. In this thesis, I propose an efficient Multichannel QoS Cognitive MAC (MQOG). MQOG assesses the quality of channel prior to transmission employing dynamic channel allocation and negotiation algorithms to achieve significant increase in channel reliability, throughput and delay constraints while simultaneously addressing Quality of Service. The uniqueness of MQOG lies in making use of the free unlicensed bands. To consider fair effective sharing of resources I propose a Mobility Based Dynamic Transmit Opportunity (MoByToP) while modifying the 802.11e TXOP (Transmit Opportunity). The proposed protocols were implemented in OMNET++ 4.1, and extensive experiments demonstrated a faster and more efficient reception of safety messages compared to existing VANet MAC Protocols. Finally, improvements in delay, packet delivery ratios and throughput were noticed.

Neste documento apresentamos uma cifra multicanal inspirada nas técnicas de telecomunicações de espalhamento espectral (Spread Spectrum), ou como também conhecido, difusão espectral, para prevenir escuta em uma rede de computadores TCP/IP. Mostramos que dividindo um texto-ilegível ciphertext em blocos de tamanho fixo e transmitindo-os aleatoriamente através de diversos canais estabelecidos entre os agentes (transmissor - receptor) é possível aumentar a complexidade da criptanálise por um suposto adversário não autorizado que esteja escutando a comunicação, dessa forma aumentando relativamente a segurança. Mostramos teoricamente que o tempo de quebra da cifra multicanal cresce em ordem fatorial ou exponencial em função do número de canais utilizados na comunicação. Além disso, adaptado a um esquema de difusão binária herdado do AES (Advanced Encryption Standard) é possível incrementar a segurança da cifra multicanal para maior resiliência contra a criptanálise diferencial. / In this work we present a multichannel cipher inspired on telecommunications Spread Spectrum techniques to prevent eavesdropping in a TCP/IP network. We show that splitting any ciphertext into fixed size blocks and sending them randomly through multiple channels established between the agents (transmitter and receiver) it is possible to increase the complexity of cryptanalysis by a supposed not authorized adversary that might be eavesdropping the communication, in this way, relatively increasing the security. We show theoretically that the brake time of multichannel cipher grows in factorial or exponential order, in function of the number of channel used in the communication. Moreover adapted to binary spread scheme inherited from AES (Advanced Encryption Standard) it is possible to increase the cipher security for more resilience against differential cryptanalysis.

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This thesis provides a performance analysis of Code Division Multiple Access (COMA) systems in non-ideal conditions. Two benefits of CDMA include resistance to multipath fading and graceful performance degradation in the presence of multiple access interference. These benefits have made CDMA especially attractive for cellular telephone systems and other wireless networks. This thesis project is divided into four tasks. The first objective is to implement a channel model incorporating multiple access interference and to verify the results. A method is used that obtains arbitrarily tight upper and lower bounds on the average probability of error without using a Gaussian approximation. The second objective is to model imperfect power control. The third objective is to incorporate multipath effects into this model using a discrete number of multipath components with some known probability distribution. This modeling is based on measurements taken in indoor and outdoor channels. The fourth objective is a performance analysis for a RAKE receiver applied to outdoor channels. / Master of Science

An important problem for economists and policy-makers concerning the cable industry is whether the cable industry faces widespread direct competition. Only one roughly comparable, nation-wide competitor exists, Direct Broadcast Satellite ("DBS"), and there are some indications that DBS does not constrain cable prices. The model presented in this paper indicates that DBS and cable compete imperfectly in terms of monthly prices, if at all, but compete more directly in terms of the premium-level services they offer. Other factors may indicate whether a community is open to advanced services like DBS, such as whether the cable operators offers high-speed Internet access. / Master of Arts

Dans cette thèse, nous traitons le problème de la séparation de sources audio multicanale par réseaux de neurones profonds (deep neural networks, DNNs). Notre approche se base sur le cadre classique de séparation par algorithme espérance-maximisation (EM) basé sur un modèle gaussien multicanal, dans lequel les sources sont caractérisées par leurs spectres de puissance à court terme et leurs matrices de covariance spatiales. Nous explorons et optimisons l'usage des DNNs pour estimer ces paramètres spectraux et spatiaux. À partir des paramètres estimés, nous calculons un filtre de Wiener multicanal variant dans le temps pour séparer chaque source. Nous étudions en détail l'impact de plusieurs choix de conception pour les DNNs spectraux et spatiaux. Nous considérons plusieurs fonctions de coût, représentations temps-fréquence, architectures, et tailles d'ensembles d'apprentissage. Ces fonctions de coût incluent en particulier une nouvelle fonction liée à la tâche pour les DNNs spectraux: le rapport signal-à-distorsion. Nous présentons aussi une formule d'estimation pondérée des paramètres spatiaux, qui généralise la formulation EM exacte. Sur une tâche de séparation de voix chantée, nos systèmes sont remarquablement proches de la méthode de l'état de l'art actuel et améliorent le rapport source-interférence de 2 dB. Sur une tâche de rehaussement de la parole, nos systèmes surpassent la formation de voies GEV-BAN de l'état de l'art de 14%, 7% et 1% relatifs en terme d'amélioration du taux d'erreur sur les mots sur des données à 6, 4 et 2 canaux respectivement / This thesis addresses the problem of multichannel audio source separation by exploiting deep neural networks (DNNs). We build upon the classical expectation-maximization (EM) based source separation framework employing a multichannel Gaussian model, in which the sources are characterized by their power spectral densities and their source spatial covariance matrices. We explore and optimize the use of DNNs for estimating these spectral and spatial parameters. Employing the estimated source parameters, we then derive a time-varying multichannel Wiener filter for the separation of each source. We extensively study the impact of various design choices for the spectral and spatial DNNs. We consider different cost functions, time-frequency representations, architectures, and training data sizes. Those cost functions notably include a newly proposed task-oriented signal-to-distortion ratio cost function for spectral DNNs. Furthermore, we present a weighted spatial parameter estimation formula, which generalizes the corresponding exact EM formulation. On a singing-voice separation task, our systems perform remarkably close to the current state-of-the-art method and provide up to 2 dB improvement of the source-to-interference ratio. On a speech enhancement task, our systems outperforms the state-of-the-art GEV-BAN beamformer by 14%, 7%, and 1% relative word error rate improvement on 6-channel, 4-channel, and 2-channel data, respectively

RTFC is a multichannel publishing tool which has been designed to convert text documents into several accessible formats. You can produce books in common file formats like plain text, HTML and HTML Help as well as large print, Braille, web Braille and DAISY. It implements a standard for e-books which was created at German schools for the blind. This standard makes it possible to get the most out of DAISY even though desktop publishing software often has no capabilities to mark up optional content like annotations or sidebars. Therefore RTFC is especially suitable to convert school books and other non-fiction literature.

The dissertation project consists of four papers. Two papers are concerned with multichannel customer behavior: (a) drivers of competitive webrooming and (b) profitability effects of offline channel additions. The remaining two papers deal with consumer reactions towards different website cookie notifications.

RTFC is a multichannel publishing tool which has been designed to convert text documents into several accessible formats. You can produce books in common file formats like plain text, HTML and HTML Help as well as large print, Braille, web Braille and DAISY. It implements a standard for e-books which was created at German schools for the blind. This standard makes it possible to get the most out of DAISY even though desktop publishing software often has no capabilities to mark up optional content like annotations or sidebars. Therefore RTFC is especially suitable to convert school books and other non-fiction literature.