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Basically, my dissertation focuses on License Auctions. Four chapters of my dissertation are theoretical analysis of license auctions. Broadly speaking, I analyze the effects of different auction rules on revenue, efficiency and social welfare. The first chapter studies the flaw in the design of the 2000 Turkish GSM auction. In this auction, the Turkish government wants to raise as much revenue as possible and to increase competition in the cell-phone market by selling two licenses to new firms via a sequential auction, but it ends up with only one license sold. I identify this auction design failure. And I also show that if the auction were designed as a “simultaneous auction”, the government would sell two licenses and receive more revenue. In the second chapter, I show that if the cost asymmetry between the bidding firms is large enough, then having fewer firms in the market will surprisingly result in higher social welfare. This result is contrast to the common or general case in which “social welfare” will be higher if there are more firms competing in the market. In the third chapter, I characterize the optimal bidding strategies of local and global bidders for two heterogeneous licenses in a multi-unit simultaneous ascending auction with synergies. I determine the optimal bidding strategies in the presence of an exposure problem and show that global bidders may accept a loss even when they win all licenses and moreover, if a “bid-withdrawal” rule is introduced to the auction, the exposure problem disappears, and the simulation results show that revenue will be higher. In the last chapter, I study the Canadian AWS auction in which 40 percent spectrum are set aside for new firms. I characterize the effect of spectrum set-aside auctions on seller's revenue, consumer surplus and social welfare. I show that a spectrum set aside may not only encourage new entry and increase competition in the downstream market, but also under some circumstance, decreases the seller's revenue and consumer surplus. But a spectrum set aside results in inefficient allocation, and this inefficient entry further reduces social welfare.

No description available.

No description available.

Customization of processors with instruction set extensions (ISEs) is a technique that improves performance through parallelization with a reasonable area overhead, in exchange for additional design effort. This thesis presents a collection of novel techniques that reduce the design effort and cost of generating ISEs by advancing automation and reconfigurability. In addition, these techniques maximize the perfomance gained as a function of the additional commited resources. Including ISEs into a processor design implies development at many levels. Most prior works on ISEs solve separate stages of the design: identification, selection, and implementation. However, the interations between these stages also hold important design trade-offs. In particular, this thesis addresses the lack of interaction between the hardware implementation stage and the two previous stages. Interaction with the implementation stage has been mostly limited to accurately measuring the area and timing requirements of the implementation of each ISE candidate as a separate hardware module. However, the need to independently generate a hardware datapath for each ISE limits the flexibility of the design and the performance gains. Hence, resource sharing is essential in order to create a customized unit with multi-function capabilities. Previously proposed resource-sharing techniques aggressively share resources amongst the ISEs, thus minimizing the area of the solution at any cost. However, it is shown that aggressively sharing resources leads to large ISE datapath latency. Thus, this thesis presents an original heuristic that can be parameterized in order to control the degree of resource sharing amongst a given set of ISEs, thereby permitting the exploration of the existing implementation trade-offs between instruction latency and area savings. In addition, this thesis introduces an innovative predictive model that is able to quickly expose the optimal trade-offs of this design space. Compared to an exhaustive exploration of the design space, the predictive model is shown to reduce by two orders of magnitude the number of executions of the resource-sharing algorithm that are required in order to find the optimal trade-offs. This thesis presents a technique that is the first one to combine the design spaces of ISE selection and resource sharing in ISE datapath synthesis, in order to offer the designer solutions that achieve maximum speedup and maximum resource utilization using the available area. Optimal trade-offs in the design space are found by guiding the selection process to favour ISE combinations that are likely to share resources with low speedup losses. Experimental results show that this combined approach unveils new trade-offs between speedup and area that are not identified by previous selection techniques; speedups of up to 238% over previous selection thecniques were obtained. Finally, multi-cycle ISEs can be pipelined in order to increase their throughput. However, it is shown that traditional ISE identification techniques do not allow this optimization due to control flow overhead. In order to obtain the benefits of overlapping loop executions, this thesis proposes to carefully insert loop control flow statements into the ISEs, thus allowing the ISE to control the iterations of the loop. The proposed ISEs broaden the scope of instruction-level parallelism and obtain higher speedups compared to traditional ISEs, primarily through pipelining, the exploitation of spatial parallelism, and reducing the overhead of control flow statements and branches. A detailed case study of a real application shows that the proposed method achieves 91% higher speedups than the state-of-the-art, with an area overhead of less than 8% in hardware implementation.

A system for visual guidance in patient set-up for external-beam radiotherapy procedures was developed using augmented reality. The system uses video cameras to obtain views of the linear accelerator, and the live images are displayed on a monitor in the treatment room. A 3D model of the patient's external surface, obtained from planning CT data, is superimposed onto the treatment couch in the camera images. The augmented monitor can then be viewed, and alignment performed against the virtual contour. The system provides an intuitive method for set-up guidance, and allows non-rigid deformations to patient pose to be visualised. It also allows changes to patient geometry between treatment fractions to become observable, and can remain in operation throughout the treatment procedure, so that patient motion becomes apparent. Coordinate registration between the camera view and the linac is performed using a cube which is aligned with the linac isocentre using room lasers or cone-beam CT. The AR tracking software detects planar fiducial tracking markers attached to the cube faces, and determines their positions in order to perform pose estimation of the 3D model on-screen. Experimental results with an anthropomorphic phantom in a clinical environment have shown that the system can be used to position a rigid-body with a translational error of 3 mm, and a rotational error of 0.19 degrees, 0.06 degrees and 0.27 degrees, corresponding to pitch, roll and yaw respectively. With further developments to optimise the system accuracy and its interface, it could be made into a valuable tool for radiotherapy clinics. The outcome of the project has been encouraging, and has shown that augmented reality for patient set-up guidance has great potential.

We present data structures and algorithms for sets and some generalizations thereof (fuzzy sets, multisets, and fuzzy multisets) available for R through the sets package. Fuzzy (multi-)sets are based on dynamically bound fuzzy logic families. Further extensions include user-definable iterators and matching functions. (author´s abstract) / Series: Research Report Series / Department of Statistics and Mathematics

Basically, my dissertation focuses on License Auctions. Four chapters of my dissertation are theoretical analysis of license auctions. Broadly speaking, I analyze the effects of different auction rules on revenue, efficiency and social welfare. The first chapter studies the flaw in the design of the 2000 Turkish GSM auction. In this auction, the Turkish government wants to raise as much revenue as possible and to increase competition in the cell-phone market by selling two licenses to new firms via a sequential auction, but it ends up with only one license sold. I identify this auction design failure. And I also show that if the auction were designed as a “simultaneous auction”, the government would sell two licenses and receive more revenue. In the second chapter, I show that if the cost asymmetry between the bidding firms is large enough, then having fewer firms in the market will surprisingly result in higher social welfare. This result is contrast to the common or general case in which “social welfare” will be higher if there are more firms competing in the market. In the third chapter, I characterize the optimal bidding strategies of local and global bidders for two heterogeneous licenses in a multi-unit simultaneous ascending auction with synergies. I determine the optimal bidding strategies in the presence of an exposure problem and show that global bidders may accept a loss even when they win all licenses and moreover, if a “bid-withdrawal” rule is introduced to the auction, the exposure problem disappears, and the simulation results show that revenue will be higher. In the last chapter, I study the Canadian AWS auction in which 40 percent spectrum are set aside for new firms. I characterize the effect of spectrum set-aside auctions on seller's revenue, consumer surplus and social welfare. I show that a spectrum set aside may not only encourage new entry and increase competition in the downstream market, but also under some circumstance, decreases the seller's revenue and consumer surplus. But a spectrum set aside results in inefficient allocation, and this inefficient entry further reduces social welfare.