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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 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)
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)
This paper presents an exact algorithm for sparse filter design under a quadratic constraint on filter performance. The algorithm is based on branch-and-bound, a combinatorial optimization procedure that can either guarantee an optimal solution or produce a sparse solution with a bound on its deviation from optimality. To reduce the complexity of(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)
  • Dennis Wei
  • 2009
This paper presents a method for designing sparse FIR filters by means of a sequence of p-norm minimization problems with p gradually decreasing from 1 toward 0. The lack of convexity for p ≪ 1 is partially overcome by appropriately initializing each subproblem. A necessary condition of optimality is derived for the subproblem of p-norm minimization,(More)