Diego Ayala

Diego Ayala, Daniel Durini, Jose Rangel-Magdaleno

Different space-filling curves (SFCs) are briefly reviewed in this paper, and a new one is proposed. A century has passed between the inception of this kind of curves, since then they have been found useful in computer science, particularly in data storage and indexing due to their clustering properties, being Hilbert curve the most well-known member of the family of fractals. The proposed Aztec curve, with similar characteristics to the Hilbert's curve, is introduced in this paper, accompanied by a grammatical description for its construction. It yields the possibility of creating bi-dimensional clusters, not available for Hilbert nor Peano curves. Additional to this, a case of application on the scope of Compressed Sensing is implemented, in which the use of Hilbert curve is contrasted with Aztec curve, having a similar performance, and positioning the Aztec curve as viable and a new alternative for future exploitation on applications that make use of SFC's.

Diego Ayala, Danilo Chavez, Leopoldo Altamirano Robles

This paper describes the development of an embedded vision system for detection, location, and tracking of a color object; it makes use of a single 32-bit microprocessor to acquire image data, process, and perform actions according to the interpreted data. The system is intended for applications that need to make use of artificial vision for detection, location and tracking of a color object and its objective is to have achieve at reduced terms of size, power consumption, and cost.