Fixing basis mismatch in compressively sampled photonic link

2014 ◽  
Author(s):  
J. M. Nichols ◽  
F. Bucholtz ◽  
C. V. McLaughlin ◽  
A. K. Oh ◽  
R. M. Willett
Keyword(s):  
Basis Mismatch

scholarly journals Reducing Basis Mismatch in Harmonic Signal Recovery via Alternating Convex Search

2014 ◽  
Vol 21 (8) ◽  
pp. 1007-1011 ◽  
Author(s):  
Jonathan M. Nichols ◽  
Albert K. Oh ◽  
Rebecca M. Willett
Keyword(s):  
Harmonic Signal ◽  
Signal Recovery ◽  
Basis Mismatch

Robust sparse signal reconstructions against basis mismatch and their applications

2015 ◽  
Vol 316 ◽  
pp. 1-17 ◽  
Author(s):  
Hongqing Liu ◽  
Yong Li ◽  
Trieu-Kien Truong
Keyword(s):  
Sparse Signal ◽  
Basis Mismatch

scholarly journals Tonal signal detection in passive sonar systems using atomic norm minimization

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Jinhong Kim ◽  
Junhan Kim ◽  
Luong Trung Nguyen ◽  
Byonghyo Shim ◽  
Wooyoung Hong
Keyword(s):  
Frequency Domain ◽  
Frequency Estimation ◽  
Mismatch Error ◽  
Tonal Signal ◽  
Passive Sonar ◽  
Norm Minimization ◽  
Atomic Norm ◽  
Sonar Systems ◽  
Tonal Frequency ◽  
Basis Mismatch

Abstract Frequency estimation of a tonal signal in passive sonar systems is crucial to the identification of the marine object. In the conventional techniques, a basis mismatch error caused by the discretization of the frequency domain is unavoidable, resulting in a severe degradation of the object detection quality. To overcome the basis mismatch error, we propose a tonal frequency estimation technique in the continuous frequency domain. Towards this end, we formulate the frequency estimation problem as an atomic norm minimization problem. From the numerical experiments, we show that the proposed technique is effective in identifying the tonal frequency components of marine objects.

scholarly journals Sparse Plane Wave Approximation of Acoustic Modes to Address Basis Mismatch

2022 ◽  
Vol 12 (2) ◽  
pp. 837
Author(s):  
Jian Xu ◽  
Kean Chen ◽  
Lei Wang ◽  
Jiangong Zhang
Keyword(s):  
Plane Wave ◽  
Bayesian Learning ◽  
Computational Cost ◽  
Low Frequency ◽  
Sound Field ◽  
Approximation Accuracy ◽  
Acoustic Modes ◽  
Wave Approximation ◽  
Basis Mismatch ◽  
Plane Wave Approximation

Low-frequency sound field reconstruction in an enclosed space has many applications where the plane wave approximation of acoustic modes plays a crucial role. However, the basis mismatch of the plane wave directions degrades the approximation accuracy. In this study, a two-stage method combining ℓ1-norm relaxation and parametric sparse Bayesian learning is proposed to address this problem. This method involves selecting sparse dominant plane wave directions from pre-discretized directions and constructing a parameterized dictionary of low dimensionality. This dictionary is used to re-estimate the plane wave complex amplitudes and directions based on the sparse Bayesian framework using the variational Bayesian expectation and maximization method. Numerical simulations show that the proposed method can efficiently optimize the plane wave directions to reduce the basis mismatch and improve acoustic mode approximation accuracy. The proposed method involves slightly increased computational cost but obtains a higher reconstruction accuracy at extrapolated field points and is more robust under low signal-to-noise ratios compared with conventional methods.

EFFICIENT FAULT-TOLERANT QUANTUM SECRET SHARING OVER TWO COLLECTIVE-NOISE CHANNELS

2010 ◽  
Vol 08 (08) ◽  
pp. 1347-1354 ◽  
Author(s):  
XIAOJIAO DUAN ◽  
RIFENG ZHOU ◽  
XIHAN LI
Keyword(s):  
Secret Sharing ◽  
Single Particle ◽  
Quantum Key Distribution ◽  
Fault Tolerant ◽  
Quantum Secret Sharing ◽  
Collective Noise ◽  
Spatial Degree ◽  
Almost All ◽  
Basis Mismatch ◽  
Quantum Key Distribution Protocol

Based on the quantum key distribution protocol [X. H. Li, F. G. Deng and H. Y. Zhou, Phys. Rev. A78 (2008) 022321], we proposed two efficient fault-tolerant quantum secret sharing schemes over two collective-noise channels, in which the sharers can establish a key with the sender only if all the sharers collaborate together. Noiseless subspaces are composed of two Bell states based on the hypothesis of collective noise and the spatial degree of freedom is introduced to insure the security. Although entangled states are used to encode the key bits, the sharers are only required to perform single-particle product operations or single-particle product measurement, making the schemes convenient with current technique. As there is no basis mismatch during the measurement, almost all of the samples can be used to share the key.

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