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Figure 1: We present a computational design framework to construct brick sculptures from pixel art images. Given 2D shapes represented by pixel arts (column 1), our framework optimizes the geometry and color information of the shape (column 2), together with the brick layout (column 3), resulting in appealing, stable, and balanced LEGO brick sculptures that can be built in practice (column 4) (Input images: "Pikachu" c Abstract LEGO R , a popular brick-based toy construction system, provides an affordable and convenient way of fabricating geometric shapes. However, building arbitrary shapes using LEGO bricks with restrictive colors and sizes is not trivial. It requires careful design process to produce appealing, stable and constructable brick sculptures. In this work, we investigate the novel problem of constructing brick sculptures from pixel art images. In contrast to previous efforts that focus on 3D models, pixel art contains rich visual contents for generating engaging LEGO designs. On the other hand, the characteristics of pixel art and corresponding brick sculpture pose new challenges to the design process. We present PIXEL2BRICK, a novel computational framework to automatically construct brick sculptures from pixel art. This is based on implementing a set of design guidelines concerning the visual quality as well as the structural stability of built sculptures. We demonstrate the effectiveness of our framework with various brick sculptures (both real and virtual) generated from a variety of pixel art images. Experimental results show that our framework is efficient and gains significant improvements over state-of-the-arts.

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In this paper, we investigate the problem of spectrum sensing in cognitive radio networks. Compared with related work that aims to propose techniques at different layers of the network protocol stack for detecting primary users, we aim to investigate the capabilities and limitations of different primary user detection techniques from the perspective of network optimization. The goal is to understand the fundamental performance tradeoffs of different primary user detection techniques without being limited by existing cognitive radio software and hardware platforms. To proceed, we first identify several dimensions for designing primary user detection techniques in cognitive radio networks, and then formulate primary user detection techniques using mixedinteger nonlinear programming (MINLP). Evaluation results show the benefits of using the proposed optimization framework for profiling the fundamental characteristics of primary user detection techniques.