The Experts below are selected from a list of 199095 Experts worldwide ranked by ideXlab platform
M V Romalis - One of the best experts on this subject based on the ideXlab platform.
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spin exchange relaxation free magnetometry using elliptically polarized light
Physical Review A, 2009Co-Authors: V Shah, M V RomalisAbstract:Spin-exchange relaxation free alkali-metal magnetometers typically operate in the regime of high optical density, presenting challenges for simple and efficient optical pumping and detection. We describe a high-sensitivity Rb magnetometer using a single elliptically polarized off-resonant laser beam. Circular Component of the light creates relatively uniform spin polarization while the Linear Component is used to measure optical rotation generated by the atoms. Modulation of the atomic spin direction with an oscillating magnetic field shifts the detected signal to high frequencies. Using a fiber-coupled distributed feedback laser, we achieve magnetic field sensitivity of $7\text{ }\text{fT}/\sqrt{\text{Hz}}$ with a miniature $5\ifmmode\times\else\texttimes\fi{}5\ifmmode\times\else\texttimes\fi{}5\text{ }\text{mm}\text{ }\text{Rb}$ vapor cell.
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spin exchange relaxation free magnetometry using elliptically polarized light
arXiv: Atomic Physics, 2009Co-Authors: V Shah, M V RomalisAbstract:Spin-exchange relaxation free alkali-metal magnetometers typically operate in the regime of high optical density, presenting challenges for simple and efficient optical pumping and detection. We describe a high-sensitivity Rb magnetometer using a single elliptically-polarized off-resonant laser beam. Circular Component of the light creates relatively uniform spin polarization while the Linear Component is used to measure optical rotation generated by the atoms. Modulation of the atomic spin direction with an oscillating magnetic field shifts the detected signal to high frequencies. Using a fiber-coupled DFB laser we achieve magnetic field sensitivity of 7 fT/$\sqrt{% \mathrm{Hz}}$ with a miniature $5\times5\times5$ mm Rb vapor cell.
V Shah - One of the best experts on this subject based on the ideXlab platform.
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spin exchange relaxation free magnetometry using elliptically polarized light
Physical Review A, 2009Co-Authors: V Shah, M V RomalisAbstract:Spin-exchange relaxation free alkali-metal magnetometers typically operate in the regime of high optical density, presenting challenges for simple and efficient optical pumping and detection. We describe a high-sensitivity Rb magnetometer using a single elliptically polarized off-resonant laser beam. Circular Component of the light creates relatively uniform spin polarization while the Linear Component is used to measure optical rotation generated by the atoms. Modulation of the atomic spin direction with an oscillating magnetic field shifts the detected signal to high frequencies. Using a fiber-coupled distributed feedback laser, we achieve magnetic field sensitivity of $7\text{ }\text{fT}/\sqrt{\text{Hz}}$ with a miniature $5\ifmmode\times\else\texttimes\fi{}5\ifmmode\times\else\texttimes\fi{}5\text{ }\text{mm}\text{ }\text{Rb}$ vapor cell.
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spin exchange relaxation free magnetometry using elliptically polarized light
arXiv: Atomic Physics, 2009Co-Authors: V Shah, M V RomalisAbstract:Spin-exchange relaxation free alkali-metal magnetometers typically operate in the regime of high optical density, presenting challenges for simple and efficient optical pumping and detection. We describe a high-sensitivity Rb magnetometer using a single elliptically-polarized off-resonant laser beam. Circular Component of the light creates relatively uniform spin polarization while the Linear Component is used to measure optical rotation generated by the atoms. Modulation of the atomic spin direction with an oscillating magnetic field shifts the detected signal to high frequencies. Using a fiber-coupled DFB laser we achieve magnetic field sensitivity of 7 fT/$\sqrt{% \mathrm{Hz}}$ with a miniature $5\times5\times5$ mm Rb vapor cell.
Mathews Jacob - One of the best experts on this subject based on the ideXlab platform.
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a generalized structured low rank matrix completion algorithm for mr image recovery
IEEE Transactions on Medical Imaging, 2019Co-Authors: Xiaohan Liu, Mathews JacobAbstract:Recent theory of mapping an image into a structured low-rank Toeplitz or Hankel matrix has become an effective method to restore images. In this paper, we introduce a generalized structured low-rank algorithm to recover images from their undersampled Fourier coefficients using infimal convolution regularizations. The image is modeled as the superposition of a piecewise constant Component and a piecewise Linear Component. The Fourier coefficients of each Component satisfy an annihilation relation, which results in a structured Toeplitz matrix. We exploit the low-rank property of the matrices to formulate a combined regularized optimization problem. In order to solve the problem efficiently and to avoid the high-memory demand resulting from the large-scale Toeplitz matrices, we introduce a fast and a memory-efficient algorithm based on the half-circulant approximation of the Toeplitz matrix. We demonstrate our algorithm in the context of single and multi-channel MR images recovery. Numerical experiments indicate that the proposed algorithm provides improved recovery performance over the state-of-the-art approaches.
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a generalized structured low rank matrix completion algorithm for mr image recovery
arXiv: Image and Video Processing, 2018Co-Authors: Xiaohan Liu, Mathews JacobAbstract:Recent theory of mapping an image into a structured low-rank Toeplitz or Hankel matrix has become an effective method to restore images. In this paper, we introduce a generalized structured low-rank algorithm to recover images from their undersampled Fourier coefficients using infimal convolution regularizations. The image is modeled as the superposition of a piecewise constant Component and a piecewise Linear Component. The Fourier coefficients of each Component satisfy an annihilation relation, which results in a structured Toeplitz matrix, respectively. We exploit the low-rank property of the matrices to formulate a combined regularized optimization problem. In order to solve the problem efficiently and to avoid the high memory demand resulting from the large-scale Toeplitz matrices, we introduce a fast and memory efficient algorithm based on the half-circulant approximation of the Toeplitz matrix. We demonstrate our algorithm in the context of single and multi-channel MR images recovery. Numerical experiments indicate that the proposed algorithm provides improved recovery performance over the state-of-the-art approaches.
Daming Zhou - One of the best experts on this subject based on the ideXlab platform.
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online remaining useful lifetime prediction of proton exchange membrane fuel cells using a novel robust methodology
Journal of Power Sources, 2018Co-Authors: Daming Zhou, Ahmed Aldurra, Ke Zhang, Alexandre Ravey, Fei GaoAbstract:Abstract This paper proposes a novel robust prognostic approach that contains three phases for degradation prediction of proton exchange membrane fuel cell (PEMFC) performance and its remaining useful lifetime (RUL) estimation. In the first detrending phase, a physical aging model (PAM) is used to remove the non-stationary trend in the original fuel cell degradation data. In the second filtering phase, the order of autoregressive and moving average (ARMA) model is determined by autocorrelation function (ACF), partial ACF and Akaike information criterion. The Linear Component in the stationary time series is then filtered by the identified ARMA model. In the third prediction phase, the remaining nonLinear pattern is used to train the time delay neural network (TDNN), in order to provide the final prediction result. Since the proposed prognostic approach uses appropriate methods to analyze and preprocess the original degradation data (i.e., the PAM maintains stationary trend, and then the identified ARMA filters Linear Component), the remaining nonLinear pattern of stationary time series can thus guarantee a good convergence performance of TDNN. In order to experimentally demonstrate the robustness and prediction accuracy of the proposed approach, degradation tests are performed using two types of PEMFC stack.
Fei Gao - One of the best experts on this subject based on the ideXlab platform.
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online remaining useful lifetime prediction of proton exchange membrane fuel cells using a novel robust methodology
Journal of Power Sources, 2018Co-Authors: Daming Zhou, Ahmed Aldurra, Ke Zhang, Alexandre Ravey, Fei GaoAbstract:Abstract This paper proposes a novel robust prognostic approach that contains three phases for degradation prediction of proton exchange membrane fuel cell (PEMFC) performance and its remaining useful lifetime (RUL) estimation. In the first detrending phase, a physical aging model (PAM) is used to remove the non-stationary trend in the original fuel cell degradation data. In the second filtering phase, the order of autoregressive and moving average (ARMA) model is determined by autocorrelation function (ACF), partial ACF and Akaike information criterion. The Linear Component in the stationary time series is then filtered by the identified ARMA model. In the third prediction phase, the remaining nonLinear pattern is used to train the time delay neural network (TDNN), in order to provide the final prediction result. Since the proposed prognostic approach uses appropriate methods to analyze and preprocess the original degradation data (i.e., the PAM maintains stationary trend, and then the identified ARMA filters Linear Component), the remaining nonLinear pattern of stationary time series can thus guarantee a good convergence performance of TDNN. In order to experimentally demonstrate the robustness and prediction accuracy of the proposed approach, degradation tests are performed using two types of PEMFC stack.