How can a building and the landscape be integrated in such a way that the interior and exterior begin to infiltrate each other, blurring and in some cases breaking the threshold between them? A threshold is a place, time, or event that indicates the passage from one state to another. Instead of a simple dividing line, thresholds should be understood as a choreographed sequence of spaces, both inside and outside the building envelope. This project will explore the means to strategically break thresholds between different conditions to create a building intimately bound to the physical, social, and mental landscape it inhabits. / Master of Architecture

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This thesis is concerned with a novel local threshold segmentation algorithm for digital images incorporating shape information. In image segmentation, most local threshold algorithms are based only on intensity analysis. In many applications where an image contains objects with a similar shape, in addition to the intensity information, some prior known shape attributes could be exploited to improve the segmentation. The goal of this work is to design a local threshold algorithm that includes shape information to enhance the segmentation quality. The algorithm adaptively selects a local threshold. Shape attribute distributions are learned from typical objects in ground truth images. Local threshold for each object in an image to be segmented is chosen to maximize probabilities in these shape attributes distributions. Then for the application of the oil sand image segmentation, a supervised classifier is introduced to further enhance the segmentation accuracy. The algorithm applies a supervised classifier trained by shape features to reject unwanted fragments. To meet different image segmentation intents in practical applications, we investigate a variety of combination of shape attributes and classifiers, and also look for the optimal one. Experiments on oil sand images have shown that the proposed algorithm has superior performance to local threshold approaches based on intensity information in terms of segmentation quality.

Quantum error correcting codes have been introduced to encode the data bits in extra redundant bits in order to accommodate errors and correct them. However, due to the delicate nature of the quantum states or faulty gate operations, there is a possibility of catastrophic spread of errors which might render the error correction techniques ineffective. Hence, in this thesis we concentrate on how various operations can be carried out fault-tolerantly so that the errors are not propagated in the same block. We prove universal fault-tolerance for nonbinary CSS codes. This thesis is focussed only on nonbinary quantum codes and all the results pertain to nonbinary codes. Efficient error detection and correction techniques using fault-tolerant techniques can help as long as we ensure that the gate error probability is below a certain threshold. The calculation of this threshold is therefore important to see if quantum computations are realizable or not, even with fault-tolerant operations. We derive an expression to compute the gate error threshold for nonbinary quantum codes and test this result for different classes of codes, to get codes with best threshold results.

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Thesis (Ph.D.) -- McMaster University, 2001. / Includes bibliographical references. Also available via World Wide Web.

Threshold logic gates have the capability of realizing complex Boolean functions with smaller number of logic gates [1]. These gates are very sensitive to their weight values which may change during manufacturing process. So Threshold logic gates should be carefully designed to allow for maximum deviation from desired design weight values without affecting its functionality . This maximum allowable deviation is known as Fault Tolerance of the gate. ILP is one of the methods to find the optimum weight values with fault tolerance. But ILP has inability to solve the threshold functions with large inputs. This thesis presents two methods to overcome this difficulty.First one is the Combination method which combines the procedures of both decomposition method and ILP method .Second one is the Variable collapsing method which uses the principle of Variable Collapsing to find weights values with fault tolerance for large input functions.