Browsing by Author "Yu, G"

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Open Access
Activated carbons prepared from peanut shell and sunflower seed shell for high CO2 adsorption
(Adsorption, 2015-02) Deng, S; Hu, B; Chen, T; Wang, B; Huang, J; Wang, Y; Yu, G
Open Access
Observation of the Kondo effect in a quadruple quantum dot
(Physical Review B - Condensed Matter and Materials Physics, 2015-06-02) Shang, R; Li, HO; Cao, G; Yu, G; Xiao, M; Tu, T; Guo, GC; Jiang, H; Chang, AM; Guo, GP
© 2015 American Physical Society.We investigate the Kondo effect in a quadruple-quantum-dot device of coupled double quantum dots (DQDs), which simultaneously contain intra-DQD and inter-DQD coupling. A variety of novel behaviors are observed. The differential conductance dI/dV is measured in the upper DQDs as a function of source drain bias. It is found to exhibit multiple peaks, including a zero-bias peak, where the number of peaks exceeds five. Alternatively, tuning the lower DQDs yielded regions of four peaks. In addition, a Kondo effect switcher is demonstrated, using the lower DQDs as the controller.
Open Access
Task-driven adaptive statistical compressive sensing of gaussian mixture models
(IEEE Transactions on Signal Processing, 2013-01-21) Duarte-Carvajalino, JM; Yu, G; Carin, L; Sapiro, G
A framework for adaptive and non-adaptive statistical compressive sensing is developed, where a statistical model replaces the standard sparsity model of classical compressive sensing. We propose within this framework optimal task-specific sensing protocols specifically and jointly designed for classification and reconstruction. A two-step adaptive sensing paradigm is developed, where online sensing is applied to detect the signal class in the first step, followed by a reconstruction step adapted to the detected class and the observed samples. The approach is based on information theory, here tailored for Gaussian mixture models (GMMs), where an information-theoretic objective relationship between the sensed signals and a representation of the specific task of interest is maximized. Experimental results using synthetic signals, Landsat satellite attributes, and natural images of different sizes and with different noise levels show the improvements achieved using the proposed framework when compared to more standard sensing protocols. The underlying formulation can be applied beyond GMMs, at the price of higher mathematical and computational complexity. © 1991-2012 IEEE.