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This paper demonstrates that if the restricted isometry constant δ<i>K</i>+1 of the measurement matrix A satisfies [δ<i>K</i>+1 <; 1 √K+1] then a greedy algorithm called Orthogonal Matching Pursuit (OMP) can recover every K-sparse signal x in K iterations from Ax. By contrast, a matrix is also constructed with the restricted isometry… (More)

- Qun Mo
- ArXiv
- 2015

We shall show that if the restricted isometry constant (RIC) δs+1(A) of the measurement matrix A satisfies δs+1(A) < 1 √ s+ 1 , then the greedy algorithm Orthogonal Matching Pursuit(OMP) will succeed. That is, OMP can recover every s-sparse signal x in s iterations from b = Ax. Moreover, we shall show the upper bound of RIC is sharp in the following sense.… (More)

Let M be a 2 × 2 matrix of Laurent polynomials with real coefficients and symmetry. In this paper, we obtain a necessary and sufficient condition for the existence of four Laurent polynomials (or FIR filters) u1, u2, v1, v2 with real coefficients and symmetry such that [ u1(z) v1(z) u2(z) v2(z) ] [ u1(1/z) u2(1/z) v1(1/z) v2(1/z) ] = M(z) ∀ z ∈ C\{0} and… (More)

Starting from any two compactly supported d-refinable function vectors in ( L2(R) )r with multiplicity r and dilation factor d, we show that it is always possible to construct 2rd wavelet functions with compact support such that they generate a pair of dual d-wavelet frames in L2(R) and they achieve the best possible orders of vanishing moments. When all… (More)

This paper demonstrates theoretically that if the restricted isometry constant $\delta_K$ of the compressed sensing matrix satisfies $$ \delta_{K+1}<\frac{1}{\sqrt{K}+1}, $$ then a greedy algorithm called Orthogonal Matching Pursuit (OMP) can recover a signal with $K$ nonzero entries in $K$ iterations. In contrast, matrices are also constructed with… (More)

Let φ be a compactly supported symmetric real-valued refinable function in L2(R) with a finitely supported symmetric real-valued mask on Z. Under the assumption that the shifts of φ are stable, in this paper we prove that one can always construct three wavelet functions ψ, ψ and ψ such that (i) All the wavelet functions ψ, ψ and ψ are compactly supported,… (More)

- Jinming Wen, Zhengchun Zhou, Jian Wang, Xiaohu Tang, Qun Mo
- 2016 IEEE International Symposium on Information…
- 2016

Support recovery of sparse signals from noisy measurements with orthogonal matching pursuit (OMP) has been extensively studied in the literature. In this paper, we show that for any K-sparse signal x, if the sensing matrix A satisfies the restricted isometry property (RIP) of order K+1 with restricted isometry constant (RIC) δ<sub>K+1</sub> <;… (More)

- BIN HAN, QUN MO, ZUOWEI SHEN
- 2010

In [B. Han and Z. Shen, SIAM J. Math. Anal., 38 (2006), 530–556], a family of univariate short support Riesz wavelets was constructed from uniform B-splines. A bivariate spline Riesz wavelet basis from the Loop scheme was derived in [B. Han and Z. Shen, J. Fourier Anal. Appl., 11 (2005), 615–637]. Motivated by these two papers, we develop in this article a… (More)

- Fan Mo, Qun Mo, +5 authors Biaoyang Lin
- BMC Bioinformatics
- 2009

Quantitative proteomics technologies have been developed to comprehensively identify and quantify proteins in two or more complex samples. Quantitative proteomics based on differential stable isotope labeling is one of the proteomics quantification technologies. Mass spectrometric data generated for peptide quantification are often noisy, and peak detection… (More)

- Jinming Wen, Zhengchun Zhou, Jian Wang, Xiaohu Tang, Qun Mo
- IEEE Transactions on Signal Processing
- 2017

Support recovery of sparse signals from noisy measurements with orthogonal matching pursuit (OMP) has been extensively studied. In this paper, we show that for any <inline-formula><tex-math notation="LaTeX">$K$</tex-math> </inline-formula>-sparse signal <inline-formula><tex-math notation="LaTeX">${\boldsymbol{x}}$</tex-math> </inline-formula>, if a sensing… (More)