The Session Initiation Protocol (SIP) was designed for wire line networks. It was developed to initiate, modify and terminate sessions between two hosts on a network. When the Internet expanded to include wireless hosts, SIP did not scale well for these wireless hosts because of the nature of the wireless channel. Also, there were issues with mobility and real time communication. This thesis proposes improvements to some of the extensions to SIP, for better performance over wireless channels. We investigate the call setup time for various transport mechanisms viz. TCP and UDP, and study the performance of a dynamic Session Timers compared to the current standard of a periodic refresh mechanism, where the frequency of UPDATEs vary with the condition of the wireless channel. We also propose a handoff algorithm that reduces the handover time with decreased packet losses.

New generations of wireless cellular networks, including 3G and 4G technologies, are envisaged to support more mobile users and a variety of wireless multimedia services. With an increasing demand for wireless multimedia services, the performance of TCP becomes a bottleneck as it cannot differentiate between the losses due to the nature of air as a medium and high data load on the network that leads to congestion. This misinterpretation by TCP leads to a reduction in the congestion window size thereby resulting in reduced throughput of the system. To overcome this scenario Radio Link Protocols are used at a lower layer which hides from TCP the channel related losses and effectively increases the throughput. This thesis proposes enhancements to the radio link protocol that works underneath TCP by identifying decisive frames and categorizing them as {\em crucial} and {\em non-crucial}. The fact that initial frames from the same upper layer segment can afford a few trials of retransmissions and the later frames cannot, motivates this work. The frames are treated differentially with respect to FEC coding and ARQ schemes. Specific cases of FEC and ARQ strategies are then considered and it is shown qualitatively as how the differential treatment of frames can improve the performance of the RLP and in effect that of TCP over wireless networks.

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Thesis's Topic: The Activity Analysis of Rescue of Královehradecký kraj, station Trutnov 2006-2009 Aim of the thesis: To analyze the operation of the Emergeny Medical Service in Trutnov. Method: Research of available sources, data collection and the cooperation of the members of the Emergency Medical Service coming to the patient. Results: All here listed statistics data are base on intervetion documentation of Trutnov center. Therefore the presented results can't be taken, generalled to other centers and assumed that will be comparable. Keywords: Integrated Emergency Services, Emergency Medical Service, rapid response vehicle, advanced life support vehicle, calls for the crew of the Emergency Medical Service.

Cette thèse entre dans le cadre du projet Hélimaintenance ; un project labellisé par le pôle de compétitivité Français Aérospace-Valley, qui vise à construire un centre dédié à la maintenance des hélicoptères civils qui soit capable de lancer des travaux en R&D dans le domaine. Notre travail consiste à prendre en considération les incertitudes dans la planification et l'ordonnancement de projets et résoudre les problèmes Rough Cut Capacity Planning, Resource Leveling Problem et Resource Constraint Project Scheduling Problem sous incertitudes. L'incertitude est modélisée avec l'approche floue/possibiliste au lieu de l'approche stochastique ce qui est plus adéquat avec notre cas d'étude. Trois types de problèmes ont été définis dans cette étude à savoir le Fuzzy Rough Cut Capacity Problem (FRCCP), le Fuzzy Resource Leveling Problem (FRLP) et le Fuzzy Resource Constraint Project Scheduling Problem (RCPSP). Un Algorithme Génétique et un Algorithme "Parallel SGS" sont proposés pour résoudre respectivement le FRLP et le FRCPSP et un Recuit Simulé est proposé pour résoudre le problème FRCCP.

Problems involving optimal sequential making in uncertain dynamic systems arise in domains such as engineering, science and economics. Such problems can often be cast in the framework of Markov Decision Process (MDP). Solving an MDP requires computing the optimal value function and the optimal policy. The idea of dynamic programming (DP) and the Bellman equation (BE) are at the heart of solution methods. The three important exact DP methods are value iteration, policy iteration and linear programming. The exact DP methods compute the optimal value function and the optimal policy. However, the exact DP methods are inadequate in practice because the state space is often large and in practice, one might have to resort to approximate methods that compute sub-optimal policies. Further, in certain cases, the system observations are known only in the form of noisy samples and we need to design algorithms that learn from these samples. In this thesis we study interesting theoretical questions pertaining to approximate and learning algorithms, and also present an interesting application of MDPs in the domain of crowd sourcing. Approximate Dynamic Programming (ADP) methods handle the issue of large state space by computing an approximate value function and/or a sub-optimal policy. In this thesis, we are concerned with conditions that result in provably good policies. Motivated by the limitations of the PBE in the conventional linear algebra, we study the PBE in the (min, +) linear algebra. It is a well known fact that deterministic optimal control problems with cost/reward criterion are (min, +)/(max, +) linear and ADP methods have been developed for such systems in literature. However, it is straightforward to show that infinite horizon discounted reward/cost MDPs are neither (min, +) nor (max, +) linear. We develop novel ADP schemes namely the Approximate Q Iteration (AQI) and Variational Approximate Q Iteration (VAQI), where the approximate solution is a (min, +) linear combination of a set of basis functions whose span constitutes a subsemimodule. We show that the new ADP methods are convergent and we present a bound on the performance of the sub-optimal policy. The Approximate Linear Program (ALP) makes use of linear function approximation (LFA) and offers theoretical performance guarantees. Nevertheless, the ALP is difficult to solve due to the presence of a large number of constraints and in practice, a reduced linear program (RLP) is solved instead. The RLP has a tractable number of constraints sampled from the original constraints of the ALP. Though the RLP is known to perform well in experiments, theoretical guarantees are available only for a specific RLP obtained under idealized assumptions. In this thesis, we generalize the RLP to define a generalized reduced linear program (GRLP) which has a tractable number of constraints that are obtained as positive linear combinations of the original constraints of the ALP. The main contribution here is the novel theoretical framework developed to obtain error bounds for any given GRLP. Reinforcement Learning (RL) algorithms can be viewed as sample trajectory based solution methods for solving MDPs. Typically, RL algorithms that make use of stochastic approximation (SA) are iterative schemes taking small steps towards the desired value at each iteration. Actor-Critic algorithms form an important sub-class of RL algorithms, wherein, the critic is responsible for policy evaluation and the actor is responsible for policy improvement. The actor and critic iterations have deferent step-size schedules, in particular, the step-sizes used by the actor updates have to be generally much smaller than those used by the critic updates. Such SA schemes that use deferent step-size schedules for deferent sets of iterates are known as multitimescale stochastic approximation schemes. One of the most important conditions required to ensure the convergence of the iterates of a multi-timescale SA scheme is that the iterates need to be stable, i.e., they should be uniformly bounded almost surely. However, the conditions that imply the stability of the iterates in a multi-timescale SA scheme have not been well established. In this thesis, we provide veritable conditions that imply stability of two timescale stochastic approximation schemes. As an example, we also demonstrate that the stability of a widely used actor-critic RL algorithm follows from our analysis. Crowd sourcing (crowd) is a new mode of organizing work in multiple groups of smaller chunks of tasks and outsourcing them to a distributed and large group of people in the form of an open call. Recently, crowd sourcing has become a major pool for human intelligence tasks (HITs) such as image labeling, form digitization, natural language processing, machine translation evaluation and user surveys. Large organizations/requesters are increasingly interested in crowd sourcing the HITs generated out of their internal requirements. Task starvation leads to huge variation in the completion times of the tasks posted on to the crowd. This is an issue for frequent requesters desiring predictability in the completion times of tasks specified in terms of percentage of tasks completed within a stipulated amount of time. An important task attribute that affects the completion time of a task is its price. However, a pricing policy that does not take the dynamics of the crowd into account might fail to achieve the desired predictability in completion times. Here, we make use of the MDP framework to compute a pricing policy that achieves predictable completion times in simulations as well as real world experiments.