The Experts below are selected from a list of 13848 Experts worldwide ranked by ideXlab platform
Johan Schoukens - One of the best experts on this subject based on the ideXlab platform.
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A Rigorous Analysis of Least Squares Sine Fitting Using Quantized Data: the Random Phase Case
arXiv: Signal Processing, 2018Co-Authors: Paolo Carbone, Johan SchoukensAbstract:This paper considers least-square based estimation of the amplitude and square amplitude of a quantized sine wave, done by considering random initial record phase. Using amplitude- and frequency-domain modeling techniques, it is shown that the estimator is inconsistent, biased and has a variance that may be underestimated if the simple model of Quantization is applied. The effects of both sine wave offset values and additive Gaussian noise are taken into account. General estimator properties are derived, without making simplifying assumptions on the role of the Quantization Process, to allow assessment of measurement uncertainty, when this least-square procedure is used.
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A Rigorous Analysis of Least Squares Sine Fitting Using Quantized Data: The Random Phase Case
IEEE Transactions on Instrumentation and Measurement, 2014Co-Authors: Paolo Carbone, Johan SchoukensAbstract:This paper considers least square (LS) based estimation of the amplitude and square amplitude of a quantized sine wave, done by considering random initial record phase. Using amplitude- and frequency-domain modeling techniques, it is shown that the estimator is inconsistent, biased, and has a variance that may be underestimated if the simple model of Quantization is applied. The effects of both sine wave offset values and additive Gaussian noise are considered. General estimator properties are derived, without making simplifying assumptions on the role of the Quantization Process, to allow assessment of measurement uncertainty, when this LS procedure is used.
Paolo Carbone - One of the best experts on this subject based on the ideXlab platform.
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A Rigorous Analysis of Least Squares Sine Fitting Using Quantized Data: the Random Phase Case
arXiv: Signal Processing, 2018Co-Authors: Paolo Carbone, Johan SchoukensAbstract:This paper considers least-square based estimation of the amplitude and square amplitude of a quantized sine wave, done by considering random initial record phase. Using amplitude- and frequency-domain modeling techniques, it is shown that the estimator is inconsistent, biased and has a variance that may be underestimated if the simple model of Quantization is applied. The effects of both sine wave offset values and additive Gaussian noise are taken into account. General estimator properties are derived, without making simplifying assumptions on the role of the Quantization Process, to allow assessment of measurement uncertainty, when this least-square procedure is used.
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A Rigorous Analysis of Least Squares Sine Fitting Using Quantized Data: The Random Phase Case
IEEE Transactions on Instrumentation and Measurement, 2014Co-Authors: Paolo Carbone, Johan SchoukensAbstract:This paper considers least square (LS) based estimation of the amplitude and square amplitude of a quantized sine wave, done by considering random initial record phase. Using amplitude- and frequency-domain modeling techniques, it is shown that the estimator is inconsistent, biased, and has a variance that may be underestimated if the simple model of Quantization is applied. The effects of both sine wave offset values and additive Gaussian noise are considered. General estimator properties are derived, without making simplifying assumptions on the role of the Quantization Process, to allow assessment of measurement uncertainty, when this LS procedure is used.
Peilin Zhao - One of the best experts on this subject based on the ideXlab platform.
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local features are not lonely laplacian sparse coding for image classification
Computer Vision and Pattern Recognition, 2010Co-Authors: Shenghua Gao, Ivor W Tsang, Liangtien Chia, Peilin ZhaoAbstract:Sparse coding which encodes the original signal in a sparse signal space, has shown its state-of-the-art performance in the visual codebook generation and feature Quantization Process of BoW based image representation. However, in the feature Quantization Process of sparse coding, some similar local features may be quantized into different visual words of the codebook due to the sensitiveness of Quantization. In this paper, to alleviate the impact of this problem, we propose a Laplacian sparse coding method, which will exploit the dependence among the local features. Specifically, we propose to use histogram intersection based kNN method to construct a Laplacian matrix, which can well characterize the similarity of local features. In addition, we incorporate this Laplacian matrix into the objective function of sparse coding to preserve the consistence in sparse representation of similar local features. Comprehensive experimental results show that our method achieves or outperforms existing state-of-the-art results, and exhibits excellent performance on Scene 15 data set.
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CVPR - Local features are not lonely – Laplacian sparse coding for image classification
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010Co-Authors: Shenghua Gao, Ivor W Tsang, Liangtien Chia, Peilin ZhaoAbstract:Sparse coding which encodes the original signal in a sparse signal space, has shown its state-of-the-art performance in the visual codebook generation and feature Quantization Process of BoW based image representation. However, in the feature Quantization Process of sparse coding, some similar local features may be quantized into different visual words of the codebook due to the sensitiveness of Quantization. In this paper, to alleviate the impact of this problem, we propose a Laplacian sparse coding method, which will exploit the dependence among the local features. Specifically, we propose to use histogram intersection based kNN method to construct a Laplacian matrix, which can well characterize the similarity of local features. In addition, we incorporate this Laplacian matrix into the objective function of sparse coding to preserve the consistence in sparse representation of similar local features. Comprehensive experimental results show that our method achieves or outperforms existing state-of-the-art results, and exhibits excellent performance on Scene 15 data set.
Mohammad R. Taghizadeh - One of the best experts on this subject based on the ideXlab platform.
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Optimized Quantization for diffractive phase elements by use of uneven phase levels
Optics letters, 2001Co-Authors: Karsten Ballüder, Mohammad R. TaghizadehAbstract:Many applications of diffractive phase elements involve the calculation of a continuous phase profile, which is subsequently quantized for fabrication. The Quantization Process maps the continuous range of phase values to a limited number of discrete steps. We present a new scheme with unevenly spaced levels for the design of diffractive elements and apply it to the design of intracavity mode-selecting elements. We show that this modified Quantization can produce significantly better results than are possible with a regular or even the bias-phase-optimized Quantization scheme that we reported here earlier. In principle this Process can be employed to a greater or lesser extent in any Quantization Process, allowing the fabrication of diffractive elements with much improved performance.
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Optimized phase Quantization for diffractive elements by use of a bias phase
Optics letters, 1999Co-Authors: Karsten Ballüder, Mohammad R. TaghizadehAbstract:Many applications of diffractive phase elements involve the calculation of a continuous phase profile that is subsequently quantized for fabrication. The Quantization Process maps the continuous range of phase values to a limited number of discrete steps. We report our observation of the influence of this Quantization Process on the performance of mode-selecting diffractive elements and show that the Quantization Process produces significantly better results by use of an optimized bias phase. In principle this Process can be employed to a greater or lesser extent in any Quantization Process.
Chokri Ben Amar - One of the best experts on this subject based on the ideXlab platform.
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ICASSP - Laplacian Tensor sparse coding for image categorization
2014 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2014Co-Authors: Mouna Dammak, Mahmoud Mejdoub, Chokri Ben AmarAbstract:To generate the visual codebook, a step of Quantization Process is obligatory. Several works have proved the efficiency of sparse coding in feature Quantization Process of BoW based image representation. Furthermore, it is an important method which encodes the original signal in a sparse signal space. Yet, this method neglects the relationships among features. To reduce the impact of this issue, we suggest in this paper, a Laplacian Tensor sparse coding method, which will aim to profit from the relationship among the local features. Precisely, we propose to apply the similarity of tensor descriptors to create a Laplacian Tensor similarity matrix, which can better present in the same time the closeness of local features in the data space and the topological relationship among the spatially near local descriptors. Moreover, we integrate statistical analysis applied to the local features assigned to each visual word in the pooling step. Our experimental results prove that our method prevails or exceeds existing background results.
