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In this paper, we study a data aggregation problem in wireless sensor networks. We propose a Compressive Sensing (CS) based strategy which is able to reduce energy consumption and data collection latency. We adopt a random sensing matrix with entries drawn i.i.d. according to strictly subGaussian distributions. Such a matrix have property such that a(More)
In this paper, we represent evaluation information by 2-dimension linguistic labels so as to avoid biased results and achieve high accuracy in multicriteria decision making. We analyze the relationship between a 2-dimension linguistic label and a common linguistic label, and then quantify a certain 2-dimension linguistic label by using a generalized(More)
In this letter, we study signal reconstruction from compressed sensing measurements. We propose new sufficient conditions for stable recovery when partial support information is available. Weighted <i>l</i><sub>1</sub>-minimization is adopted to recover the original signal under three noise models. The proposed approach is to use Ozeki's inequality and(More)
In this paper, we investigate hybrid multiple attribute decision making problems with various forms of attribute values (real numbers, linguistic labels, interval numbers, intuitionistic fuzzy numbers and interval intuitionistic fuzzy numbers). We propose a method based on preference degrees which may take the forms of fuzzy numbers, intuitionistic fuzzy(More)
There may be prioritizations among criteria in some practical multi-criteria decision making (MCDM) problems, which are called prioritized MCDM ones. The investigation of such a kind of problems benefits the development of the MCDM. However, the existing methods of prioritized MCDM cannot cover all situations, so we develop a new method based on the idea of(More)