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This letter investigates the joint recovery of a frequency-sparse signal ensemble sharing a common frequency-sparse component from the collection of their compressed measurements. Unlike conventional arts in compressed sensing, the frequencies follow an off-the-grid formulation and are continuously valued in [0, 1]. As an extension of atomic norm, the(More)
In the design phase of sensor arrays during array signal processing, the estimation performance and system cost are largely determined by array aperture size. In this article, we address the problem of joint direction-of-arrival (DOA) estimation with distributed sparse linear arrays (SLAs) and propose an off-grid synchronous approach based on distributed(More)
The performance of existing approaches to the recovery of frequency-sparse signals from compressed measurements is limited by the coherence of required sparsity dictionaries and the discretization of frequency parameter space. In this paper, we adopt a parametric joint recovery-estimation method based on model selection in spectral compressive sensing.(More)
S1 © 2015 Ann & Joshua Medical Publishing Co. Ltd | Published by Wolters Kluwer Medknow O-001 The Development and Effects of Evidence based Vascular Access Port Management Education Program for Oncology Nurses in South Korea Yunjin Lee, Juhye Lee, Meeyoung Cho, Yoonjung Shin, Soyoung Bae, Jiseon Lee, Jungim Bae, Kyunghi Lee, Keunhwa Lee, Eunhee Lee,(More)
In this paper, we propose a method for lossy audio signal compression via structured sparse decomposition and compressed sensing (CS). In this method, a least absolute shrinkage and selection operator (LASSO) is employed to sparse and structured decompose the audio signals into tonal and transient layers, and then, both resulting layers are compressed by a(More)
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