<p>Conventional multiple description coding (MDC) is a source coding technique which provides resilience against packet loss. On the other hand, the correlation introduced between descriptions can be used to combat bit errors as well. While the latter feature of MDC has been attested and exploited in prior work, only few attempts have been made to design MDC with higher bit error resilience ability.</p> <p>This thesis makes some progress in the latter direction by addressing the problem of robust (i.e., bit error resilient) index assignment (IA) design for two description scalar quantizers. Our approach is to start from an initial IA which is known to be good for the conventional two description problem, and then apply permutations to indices in each description to increase a minimum Hamming distance-like performance measure.</p> <p>The criterion of increasing the minimum Hamming distance between valid index pairs (d_min), has been considered in prior work, however an efficient IA construction was presented only for the case of d_min = 2 and low redundancy.</p> <p>The contribution of this thesis is the following. For the scenario when one description is known to be error free, a new measure for IA robustness is proposed, which is termed minimum side Hamming distance (d_side,min). This quantity is defined as the minimum Hamming distance between valid indices of one description for fixed index of the other description. It is further shown that the problem of robust permutations design under the new criterion is closely connected to the anti-bandwidth problem in a certain graph derived from a hypercube. Leveraging this connection, permutations achieving d_side,min = 2 are proposed for all redundancy levels. Furthermore, for general values of d_side,min, a simple construction of permutations achieving d_side,min is presented, based on channel codes of appropriate block length and rate, and with minimum distance d_side,min + 1, respectively, d_side,min, for two types of initial IA (diagonal, respectively, square-based). The application of this result to achieve IA with d_side,min = 3 is further discussed for a wide range of redundancy levels.</p> <p>Finally, for the scenario when both descriptions may carry bit errors, simple constructions of permutations achieving d_min = 3 are proposed for the high redundancy case.</p> / Master of Applied Science (MASc)

On-orbit servicing (OOS) of space systems provides immense benefits by extending their lifetime, by reducing overall cost of space operations, and by adding flexibility to space missions. Refueling is an important aspect of OOS operations. The problem of determining the optimal strategy of refueling multiple satellites in a constellation, by expending minimum fuel during the orbital transfers, is challenging, and requires the solution of a large-scale optimization problem. The conventional notion about a refueling mission is to have a service vehicle visit all fuel-deficient satellites one by one and deliver fuel to them. A recently emerged concept, known as the peer-to-peer (P2P) strategy, is a distributed method of replenishing satellites with fuel. P2P strategy is an integral part of a mixed refueling strategy, in which a service vehicle delivers fuel to part (perhaps half) of the satellites in the constellation, and these satellites, in turn, engage in P2P maneuvers with the remaining satellites. During a P2P maneuver between a fuel-sufficient and a fuel-deficient satellite, one of them performs an orbital transfer to rendezvous with the other, exchanges fuel, and then returns back to its original orbital position. In terms of fuel expended during the refueling process, the mixed strategy outperforms the single service vehicle strategy, particularly with increasing number of satellites in the constellation. This dissertation looks at the problem of P2P refueling problem and proposes new extensions like the Cooperative P2P and Egalitarian P2P strategies. It presents an overview of the methodologies developed to determine the optimal set of orbital transfers required for cooperative and non-cooperative P2P refueling strategies. Results demonstrate that the proposed strategies help in reducing fuel expenditure during the refueling process.

[pt] GRASP (Greedy Randomized Adaptive Search Procedure)é uma metaeurística de partidas múltiplas usada para obter soluções para problemas de otimização combinatória. Nesse trabalho. A metaheurística GRASP tem sido usada para obter soluções de qualidade para muitos problemas de otimização combinatória. Nesse trabalho é proposta uma metodologia para análise do comportamento da metaheurística GRASP. Também são propostas estratégias de hibridização com o religamento de caminhos. Essas estratégias foram desenvolvidas para o problema de atribuição de três índices (AP3) e para o problema de escalonamento de tarefas conhecido na literatura como job-shop schedulling problem (JSP) e são analisadas de acordo com a metodologia proposta. A metodologia para análise do comportamento do método GRASP pode ser usada para prever a partir da versão seqüencial do algoritmo, como a qualidade da solução do algoritmo implementado em paralelo irá variar. Os algoritmos GRASPs desenvolvidos para AP3 e para JSP foram paralelizados e os resultados são comparados aos resultados obtidos usando a metodologia proposta. / [en] GRASP (Greedy Randomized Adaptive Search Procedure) is a multi-start metaheuristic for combinatorial optimization problems. GRASP has been used to find quality solutions of several combinatorial optimization problems. In this work we describe a methodology for analysis of GRASP. Hybrid strategies of GRASP with path relinking are also proposed. These strategies are studied for the 3-index assignment problem (AP3) and for the job-shop schedulling problem (JSP) and are analyzed according to the methodology proposed. The methodology for analysis of GRASP is used to predict qualitatively how the quality of the solution varies in a parallel independent GRASP, using the data of the GRASP sequential version as input. The GRASPs for the AP3 and for the JSP are parallelized and the computational results are compared to the results obtained using the methodology proposed.