Thesis (M.Sc.)--Acadia University, 2006. / Includes bibliographical references (leaves 142-143). Also available on the Internet via the World Wide Web.

Thesis (M.C.S.)--Carleton University, 1993. / Also available in electronic format on the Internet.

When designing real-time systems, we want to prove that they will satisfy given timing constraints at run time. The main objective of real-time scheduling theory is to analyze properties of mathematical models that capture the temporal behaviors of such systems. These models typically consist of a collection of computational tasks, each of which generates an infinite sequence of task activations. In this thesis we study different classes of models and their corresponding analysis problems. First, we consider models of mixed-criticality systems. The timing constraints of these systems state that all tasks must meet their deadlines for the run-time scenarios fulfilling certain assumptions, for example on execution times. For the other scenarios, only the most important tasks must meet their deadlines. We study both tasks with sporadic activation patterns and tasks with complicated activation patterns described by arbitrary directed graphs. We present sufficient schedulability tests, i.e., methods used to prove that a given collection of tasks will meet their timing constraints under a particular scheduling algorithm. Second, we consider models where tasks can lock mutually exclusive resources and have activation patterns described by directed cycle graphs. We present an optimal scheduling algorithm and an exact schedulability test. Third, we address a pair of longstanding open problems in real-time scheduling theory. These concern the computational complexity of deciding whether a collection of sporadic tasks are schedulable on a uniprocessor. We show that this decision problem is strongly coNP-complete in the general case. In the case where the asymptotic resource utilization of the tasks is bounded by a constant smaller than 1, we show that it is weakly coNP-complete.

The aim of the thesis is to find out the most suitable channel estimation algorithms for the existing MIMO-OFDM SDR platform. Starting with the analysis of several prevalent channel estimation algorithms, MSE performance are compared under different scenarios. As a result of the hardware independent analysis, the complexvalued matrix computations involved in the algorithms are decomposed to real FLoating-point OPerations (FLOPs). Four feasible algorithms are selected for hardware dependent discussion based on the proposed hardware architecture. The computational latency is exposed as a manner of case study.

A constraint satisfaction problem (CSP) can be represented as two structures: the structure induced by the variables and the structure induced by the constraint language. Both these parameters are amenable to restrictions which affects the complexity of the resulting problems. In this thesis we shall use both constraint language restrictions and structural restrictions in order to create problems that can be solved as efficiently as possible. The language restrictions are based on creating a language that in terms of frozen partial clone theory has the largest number of polymorphic functions. Such a language can according to the Galois connection between functions and relations be implemented by as many languages as possible and is therefore the Boolean language with the lowest complexity. The structural restrictions are mainly based on limiting the number of times a variable is allowed to occur in an instance. We shall prove that the easiest language does not contain a Delta-matroid relation and is NP-complete even with the very restricted structure where no variable can occur in more than two constraints. We also give a branch-and-reduce algorithm for this problem with time complexity O(1.0493^n). This problem is then related to the exponential-time hypothesis, which postulates that k-SAT is not sub-exponential for k >= 3. We show that the exponential-time hypothesis holds if and only if this restricted problem is not sub-exponential, if and only if all NP-complete Boolean languages are not sub-exponential. In the process we also prove a stronger version of Impagliazzo's sparsification lemma for k-SAT; namely that all finite, NP-complete Boolean languages can be sparsified into each other. This should be contrasted with Santhanam's negative result which states that the same does not hold for all infinite Boolean languages.

Computational thinking (CT) is gaining recognition as an important skill for students, both in computer science and other disciplines. Although there has been much focus on this field in recent years, it is rarely taught as a formal course, and there is little consensus on what exactly CT entails and how to teach and evaluate it. This research addresses the lack of resources for integrating CT into the introductory computer science curriculum. The question that we aim to answer is whether CT can be evaluated in a meaningful way. A CT framework that outlines the skills and techniques comprising CT and describes the nature of student engagement was developed; this is used as the basis for this research. An assessment (CT test) was then created to gauge the ability of incoming students, and a CT-specfic computer game was developed based on the analysis of an existing game. A set of problem solving strategies and practice activities were then recommended based on criteria defined in the framework. The results revealed that the CT abilities of first year university students are relatively poor, but that the students' scores for the CT test could be used as a predictor for their future success in computer science courses. The framework developed for this research proved successful when applied to the test, computer game evaluation, and classification of strategies and activities. Through this research, we established that CT is a skill that first year computer science students are lacking, and that using CT exercises alongside traditional programming instruction can improve students' learning experiences.

The thesis deals with knapsack problems variants and possibility of their solving, furthermore with the impact of particular task (instance) special structure on the effciency of tested approach. The thesis also proposes conversion possibility between described tasks and their continuous extension (continuous relaxation). It describes L3 algorithm and superdecreasing knapsack problem solving from the common sort of algorithms and Monte Carlo Method, simulated annealing and genetic algorithms from the sort of probability ones. Other possibilities are also discussed. Integral part of this thesis is the accompanying application, which was used to create groundwork used in the text and which can be also used to solve other instances.

No description available.

No description available.

The motivation of this thesis is to present new lower bounds for important computational problems on strings and graphs, conditioned on plausible conjectures in theoretical computer science. These lower bounds, called conditional lower bounds, are a topic of immense interest in the field of fine-grained complexity, which aims to develop a better understanding of the hardness of problems that can be solved in polynomial time. In this thesis, we give new conditional lower bounds for four interesting computational problems: the median and center string edit distance problems, the pattern matching on labeled graphs problem, and the subtree isomorphism problem. These problems are of interest in the applied topics of computational biology and information retrieval, as well as in theoretical computer science more broadly.