Many applications use multiple cameras to simultaneously capture imagery of a scene from different vantage points on a rigid, moving camera system over time. Multiple cameras often provide unique viewing angles but also additional levels of detail of a scene at different spatio-temporal resolutions. However, in order to benefit from this added information the sources must be temporally aligned. As a result of cost and physical limitations it is often impractical to synchronize these sources via an external clock device. Most methods attempt synchronization through the recovery of a constant scale factor and offset with respect to time. This limits the generality of such alignment solutions. We present an unsupervised method that utilizes a content-based clustering mechanism in order to temporally align multiple non-synchronized image sequences of different and varying spatio-temporal resolutions. We show that the use of temporal constraints and dynamic programming adds robustness to changes in capture rates, field of view, and resolution.

Reinforcement learning algorithms hold promise in many complex domains, such as resource management and planning under uncertainty. Most reinforcement learning algorithms are iterative - they successively approximate the solution based on a set of samples and features. Although these iterative algorithms can achieve impressive results in some domains, they are not sufficiently reliable for wide applicability; they often require extensive parameter tweaking to work well and provide only weak guarantees of solution quality. Some of the most interesting reinforcement learning algorithms are based on approximate dynamic programming (ADP). ADP, also known as value function approximation, approximates the value of being in each state. This thesis presents new reliable algorithms for ADP that use optimization instead of iterative improvement. Because these optimization-based algorithms explicitly seek solutions with favorable properties, they are easy to analyze, offer much stronger guarantees than iterative algorithms, and have few or no parameters to tweak. In particular, we improve on approximate linear programming - an existing method - and derive approximate bilinear programming - a new robust approximate method. The strong guarantees of optimization-based algorithms not only increase confidence in the solution quality, but also make it easier to combine the algorithms with other ADP components. The other components of ADP are samples and features used to approximate the value function. Relying on the simplified analysis of optimization-based methods, we derive new bounds on the error due to missing samples. These bounds are simpler, tighter, and more practical than the existing bounds for iterative algorithms and can be used to evaluate solution quality in practical settings. Finally, we propose homotopy methods that use the sampling bounds to automatically select good approximation features for optimization-based algorithms. Automatic feature selection significantly increases the flexibility and applicability of the proposed ADP methods. The methods presented in this thesis can potentially be used in many practical applications in artificial intelligence, operations research, and engineering. Our experimental results show that optimization-based methods may perform well on resource-management problems and standard benchmark problems and therefore represent an attractive alternative to traditional iterative methods.

High-speed railways provide fast, comfortable and environmentally friendly transportation for passengers. However, they are fraught with high investment costs that decrease the willingness of governments to construct new lines. This thesis proposes a new way of building high-speed railways to decrease investment costs, where power from overhead wire is, for the majority of a journey, substituted with power from batteries located on the train sets. Not only can the construction and maintenance costs for the overhead wire itself be reduced; as tunnels can be narrowed when no overhead wire is needed, tunnelling costs decrease as well. An algorithm using dynamic programming is devised which calculates the optimal placement of overhead wire given a velocity and altitude profile of a journey, which is then applied on the planned Swedish high-speed line Ostlänken. The cost savings amount in total to approximately 5.689 billion SEK. It is a significant reduction in absolute terms, but is likely even higher, as the cost estimates for electrification are conservative. More studies on this aspect is needed to obtain more exact estimates. / Höghastighetståg erbjuder snabba, bekväma och miljövänliga passagerartransporter. Dock kräver dess banor höga investeringskostnader som kan avskräcka för nybyggnationer. För att minska dessa kostnader föreslår denna uppsats ett nytt sätt att bygga höghastighetsbanor på, där kontaktledning byggs på endast delar av sträckan. Där kontaktledning inte byggs drivs tågen av batterier placerade ombord. Utöver lägre kostnader för konstruktion och underhåll av själva kontaktledningen kan också kostnaden för tunneldrivning reduceras, då diametern på tunnlar kan minskas när ingen kontaktledning krävs. I rapporten presenteras en metod med dynamisk programmering för att beräkna den optimala placeringen av kontaktledningssträckor givet en hastighets- och höjdkurva för en bana. Metoden appliceras på den planerade höghastighetsbanan Ostlänken, där kostnadsbesparingarna beräknas uppgå till ungefär 5,689 miljarder SEK. Det är en signifikant besparing i absoluta mått som troligen är ännu större i verkligheten, eftersom de uppskattade kostnaderna för elektrifiering är konservativa. Det behövs fler studier kring dessa kostnader för att kunna erhålla mer exakta uppskattningar av kostnadsbesparingar.

Bayesian network is a popular machine learning tool for modeling uncertain dependence relationships among the random factors of a domain. It represents the relations qualitatively by using a directed acyclic graph (DAG) and quantitatively by using a set of conditional probability distributions. Several exact algorithms for learning optimal Bayesian networks from data have been developed recently. However, these algorithms are still inefficient to some extent. This is not surprising because learning Bayesian network has been proven to be an NP-Hard problem. Based on a critique of these algorithms, this thesis introduces a new algorithm based on heuristic search for learning optimal Bayesian.

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