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We consider the problem of direction of arrival (DOA) estimation using a recently proposed structure of nonuniform linear arrays, referred to as co-prime arrays. By exploiting the second order statistical information of the received signals, co-prime arrays exhibit O(MN) degrees of freedom with only M+N sensors. A sparsity-based recovery algorithm is(More)
In this letter, we consider the problem of direction of arrival estimation using sparsity enforced reconstruction methods. Co-prime arrays with M + N sensors are utilized to increase the degrees of the freedom from O(M + N) to O(MN). The key to the success of sparse-based direction of arrival estimation is that every target must fall on the predefined grid.(More)
In traditional compressed sensing theory, the dictionary matrix is given a priori, whereas in real applications this matrix suffers from random noise and fluctuations. In this paper, we consider a signal model where each column in the dictionary matrix is affected by a structured noise. This formulation is common in direction-of-arrival (DOA) estimation of(More)
We consider algorithms and recovery guarantees for the analysis sparse model in which the signal is sparse with respect to a highly coherent frame. We consider the use of a monotone version of the fast iterative shrinkage-thresholding algorithm (MFISTA) to solve the analysis sparse recovery problem. Since the proximal operator in MFISTA does not have a(More)
Chronic hepatitis B virus (HBV) carrier status has a critical impact on clinical management of patients with IgA nephropathy (IgAN) who are treated with corticosteroids, because corticosteroids may enhance HBV replication. This study compared corticosteroids and antivirals combined therapy with corticosteroids monotherapy on patients of IgAN who were also(More)
In traditional compressed sensing theory, the dictionary matrix is given a priori, whereas in real applications this matrix suffers from random noise and fluctuations. In this paper we consider a signal model where each column in the dictionary matrix is affected by a structured noise. This formulation is common in problems such as radar signal processing(More)
In this paper, we propose a new model of demand response management for the future smart grid that integrates plug-in electric vehicles and renewable distributed generators. A price scheme considering fluctuation cost is developed. We consider a market where users have the flexibility to sell back the energy generated from their distributed generators or(More)
BACKGROUND Very limited data are available on factors predictive of corticosteroids plus cyclophosphamide treatment efficacy on IgA nephropathy (IgAN). The aim of the study was to research the clinical factors predictive of treatment efficacy in IgAN. METHODS One hundred and fifty-nine patients with IgAN (proteinuria ≥2 g/d and estimated glomerular(More)
In traditional compressed sensing theory, the dictionary matrix is given a priori, while in real applications this matrix suffers from random noise and fluctuations. This paper considers a signal model where each column in the dictionary matrix is affected by a structured noise. This formulation is common in radar related applications and(More)
OF THE DISSERTATION Discrete and Continuous Sparse Recovery Methods and Their Applications by Zhao Tan Doctor of Philosophy in Electrical Engineering Washington University in St. Louis, 2015 Professor Arye Nehorai, Chair Low dimensional signal processing has drawn an increasingly broad amount of attention in the past decade, because prior information about(More)