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The Digital Signal Processing Group develops signal processing algorithms that span a wide variety of application areas including speech and image processing, sensor networks, communications, radar and sonar. Our primary focus is on algorithm development in general, with the applications serving as motivating contexts. Our approach to new algorithms(More)
In designing discrete-time filters, the length of the impulse response is often used as an indication of computational cost. In systems where the complexity is dominated by arithmetic operations, the number of nonzero coefficients in the impulse response may be a more appropriate metric to consider instead, and computational savings are realized by omitting(More)
This paper considers three problems in sparse filter design, the first involving a weighted least-squares constraint on the frequency response, the second a constraint on mean squared error in estimation, and the third a constraint on signal-to-noise ratio in detection. The three problems are unified under a single framework based on sparsity maximization(More)
This research examines government policies and urban transformation in China through a study of Hangzhou City, which is undergoing dramatic growth and restructuring. As the southern center of the Yangtze River Delta, an emerging global city region of China, Hangzhou has been restlessly searching for strategies to promote economic growth and survive the(More)
Urban land expansion in China has attracted considerable scholarly attention. However, more work is needed to apply spatial modeling to understanding the mechanisms of urban growth from both institutional and physical perspectives. This paper analyzes urban expansion in Shanghai and its development zones (DZs). We find that, as nodes of global-local(More)
This paper examines spatial variations of urban growth patterns in Chinese cities through a case study of Dongguan, a rapidly industrializing city characterized by a bottom-up pattern of development based on townships. We have employed both non-spatial and spatial logistic regression models to analyze urban land conversion. The non-spatial logistic(More)
Non-discrimination is a recognized objective in algorithmic decision making. In this paper, we introduce a novel probabilistic formulation of data pre-processing for reducing discrimination. We propose a convex optimization for learning a data transformation with three goals: controlling discrimination, limiting distortion in individual data samples, and(More)
The sampling of continuous-time signals based on local bandwidth is considered in this thesis. In an intuitive sense, local bandwidth refers to the rate at which a signal varies locally. One would expect that signals should be sampled at a higher rate in regions of higher local bandwidth, and at a lower rate in regions of lower local bandwidth. In many(More)