The principal portfolio approach is an emerging method in signal-based trading. However, these principal portfolios may not be diversified to explore the key features of the prediction matrix or robust to different situations. To address this problem, we propose a novel linear trading position with sparse spectrum that can explore a larger spectral region of the prediction matrix. We also develop a Krasnosel'skii-Mann fixed-point algorithm to optimize this trading position, which possesses the descent property and achieves a linear convergence rate in the objective value. This is a new theoretical result for this type of algorithms. Extensive experiments show that the proposed method achieves good and robust performance in various situations.

Low-cost high-solution multispectral imagery is attractive to various applications. This paper presents our ongoing research on developing Band-reconfigurable Unmanned Airship on Board Imaging System and focuses on the automatic registration of multispectral imageries at its each exposure. First, the overall architecture of developed Multispectral Imaging system as well as its main functions and core components are described. After that, motion compensation among collected multispectral imageries is discussed and based on Least Square technique, multi-homography model is developed to undo the motions deduced in multispectral imageries as well. Next, to reliably obtain control points for the estimation of motion parameters, one FFT-induced Multispectral Imagery Matching framework is proposed. Finally, experiments of registering multispectral imageries with different motion models are implemented, compared and analyzed as well as conclusions are conducted.