The Experts below are selected from a list of 127149 Experts worldwide ranked by ideXlab platform

Maged Marghany - One of the best experts on this subject based on the ideXlab platform.

  • Examining the least square method to retrieve sea surface salinity from MODIS satellite data
    European journal of scientific research, 2010
    Co-Authors: Maged Marghany
    Abstract:

    In this study, we investigate the relative ability of least square algorithm to retrieve sea surface salinity (SSS) from MODIS satellite data. We also examine with comprehensive comparison of the root mean square of bias the difference between second Polynomial Order algorithm and least square algorithm. Both the least squares and algorithm second Polynomial Order algorithm are used to retrieve the sea surface salinity (SSS) from multi MODIS bands data. Thus, the basic linear model has been solved by using second Polynomial Order algorithm and least square estimators. The accuracy of this work has been examined using the root mean square of bias of sea surface salinity retrieved from MODIS satellite data and the in situ measurements that are collected along the east coast of peninsular Malaysia by using hydrolab instrument. The study shows comprehensive relationship between least square method and in situ SSS measurements with high r2 of 0.96 and RMS of bias value of ±0.37 psu. The second Polynomial Order algorithm, however, has lower performance as compared to least square algorithm. Thus, RMS of bias value of ± 7.34 psu has performed with second Polynomial Order algorithm. In conclusions, the least square algorithm can be used to retrieve SSS from MODIS satellite data.

  • ICCSA (1) - Modelling sea surface salinity from MODIS satellite data
    Computational Science and Its Applications – ICCSA 2010, 2010
    Co-Authors: Maged Marghany, Mazlan Hashim, Arthur P. Cracknell
    Abstract:

    In this study, we investigate the relative ability of least square algorithm to retrieve sea surface salinity (SSS) from MODIS satellite data. We also examine with comprehensive comparison of the root mean square of bias the difference between second Polynomial Order algorithm and least square algorithm. Both the least squares algorithm and second Polynomial Order algorithm are used to retrieve the sea surface salinity (SSS) from multi MODIS bands data. Thus, the basic linear model has been solved by using second Polynomial Order algorithm and least square estimators. The accuracy of this work has been examined using the root mean square of bias of sea surface salinity retrieved from MODIS satellite data and the in situ measurements that are collected along the east coast of Peninsular Malaysia by using hydrolab instrument. The study shows comprehensive relationship between least square method and in situ SSS measurements with high r2 of 0.96 and RMS of bias value of ±0.37 psu. The second Polynomial Order algorithm, however, has lower performance as compared to least square algorithm. Thus, RMS of bias value of ± 7.34 psu has performed with second Polynomial Order algorithm. In conclusions, the least square algorithm can be used to retrieve SSS from MODIS satellite data.

Arthur P. Cracknell - One of the best experts on this subject based on the ideXlab platform.

  • ICCSA (1) - Modelling sea surface salinity from MODIS satellite data
    Computational Science and Its Applications – ICCSA 2010, 2010
    Co-Authors: Maged Marghany, Mazlan Hashim, Arthur P. Cracknell
    Abstract:

    In this study, we investigate the relative ability of least square algorithm to retrieve sea surface salinity (SSS) from MODIS satellite data. We also examine with comprehensive comparison of the root mean square of bias the difference between second Polynomial Order algorithm and least square algorithm. Both the least squares algorithm and second Polynomial Order algorithm are used to retrieve the sea surface salinity (SSS) from multi MODIS bands data. Thus, the basic linear model has been solved by using second Polynomial Order algorithm and least square estimators. The accuracy of this work has been examined using the root mean square of bias of sea surface salinity retrieved from MODIS satellite data and the in situ measurements that are collected along the east coast of Peninsular Malaysia by using hydrolab instrument. The study shows comprehensive relationship between least square method and in situ SSS measurements with high r2 of 0.96 and RMS of bias value of ±0.37 psu. The second Polynomial Order algorithm, however, has lower performance as compared to least square algorithm. Thus, RMS of bias value of ± 7.34 psu has performed with second Polynomial Order algorithm. In conclusions, the least square algorithm can be used to retrieve SSS from MODIS satellite data.

André Quinquis - One of the best experts on this subject based on the ideXlab platform.

  • Time-frequency analysis using warped-based high-Order phase modeling
    EURASIP Journal on Advances in Signal Processing, 2005
    Co-Authors: Cornel Ioana, André Quinquis
    Abstract:

    The high-Order ambiguity function (HAF) was introduced for the estimation of Polynomial-phase signals (PPS) embedded in noise. Since the HAF is a nonlinear operator, it suffers from noise-masking effects and from the appearance of undesired cross-terms when multicomponents PPS are analyzed. In Order to improve the performances of the HAF, the multi-lag HAF concept was proposed. Based on this approach, several advanced methods (e.g., product high-Order ambiguity function (PHAF)) have been recently proposed. Nevertheless, performances of these new methods are affected by the error propagation effect which drastically limits the Order of the Polynomial approximation. This phenomenon acts especially when a high-Order Polynomial modeling is needed: representation of the digital modulation signals or the acoustic transient signals. This effect is caused by the technique used for Polynomial Order reduction, common for existing approaches: signal multiplication with the complex conjugated exponentials formed with the estimated coefficients. In this paper, we introduce an alternative method to reduce the Polynomial Order, based on the successive unitary signal transformation, according to each Polynomial Order. We will prove that this method reduces considerably the effect of error propagation. Namely, with this Order reduction method, the estimation error at a given Order will depend only on the performances of the estimation method.

  • Polynomial Phase Signal Modeling Using Warping-Based Order Reduction
    2004
    Co-Authors: André Quinquis, Cornel Ioana, Emanuel Radoi
    Abstract:

    The high-Order ambiguity function (HAF) was introduced for the estimation of Polynomial-phase signals (PPS). Currently the HAF suffers from noise-masking effects and from the appearance of undesired cross terms in the presence of multi-components PPS. The multi-lag product HAF concept was then proposed as a way to improve the performances of the HAF. Nevertheless, performances of the new methods are affected by the error propagation. This effect is due to the technique used for Polynomial Order reduction, common for current approaches : signal multiplication with the complex exponentials formed with the estimated coefficients. In this paper, we introduce an alternative method to reduce the Polynomial Order, based on the successive unitary signal transformation, according to each Polynomial Order. We will prove that this method considerably reduces the effect of error propagation.

  • ICASSP (2) - Polynomial phase signal modeling using warping-based Order reduction
    2004 IEEE International Conference on Acoustics Speech and Signal Processing, 1
    Co-Authors: André Quinquis, Cornel Ioana, Emanuel Radoi
    Abstract:

    The high-Order ambiguity function (HAF) was introduced for the estimation of Polynomial-phase signals (PPS). Currently the HAF suffers from noise-masking effects and from the appearance of undesired cross terms in the presence of multi-components PPS. The multi-lag product HAF concept was then proposed as a way to improve the performance of the HAF. Nevertheless, the performance of the new methods are affected by the error propagation. This effect is due to the technique used for Polynomial Order reduction, common for current approaches: signal multiplication with the complex exponentials formed with the estimated coefficients. In this paper, we introduce an alternative method to reduce the Polynomial Order, based on the successive unitary signal transformation, according to each Polynomial Order. We prove that this method considerably reduces the effect of error propagation.

Junhua Liu - One of the best experts on this subject based on the ideXlab platform.

  • Optimizing Savitzky-Golay Parameters and Its Smoothing Pretreatment for FTIR Gas Spectra
    Guang pu xue yu guang pu fen xi = Guang pu, 2016
    Co-Authors: An-xin Zhao, Xiaojun Tang, Zhong-hua Zhang, Junhua Liu
    Abstract:

    In the smoothing pretreatment for the quantitative analysis of hydrocarbon mixed gases by Fourier transform infrared analysis (FTIR), the Savitzky- Golay filter is usually used as one of the smoothing preprocessing methods in the Fourier transform infrared spectrum data smoothing pretreatment. However, the parameters of the Savitzky-Golay filter such as the Polynomial Order and frame size are not easy to decide. There is no one unified choice basis. Users usually adopt multiple sets in the special data set to try, and then select a set of relatively optimal data as the optimizing parameters of the Savitzky-Golay filter. The optimal selection method of the Savitzky-Golay filter parameters was explored, and the concrete calculation equations were deduced according to the relation among the normalized cut-off frequency, the normalized beginning frequency of the stopband, the normalized first side lobe peak frequency of the stopband, the normalized first side lobe peak amplitude with the Polynomial Order and frame size of the Savitzky-Golay filter parameters. Then when the Polynomial Order and frame size are set as 8 and 11 respectively according the above conclusion and the characteristics of the actual spectral data, the Savitzky - Golay filter smoothing effect is optimum. Through the acquisition the concentration of 0.1%, 0.2%, 0.5%, 1%, 2%, 5% for the actual CH4 spectra, the relative maximum and minimum error of the raw spectra converted absorbance were 17.230 5% and 0.243 0% respectively, and the relative maximum and minimum error of the smooth spectra converted absorbance were 0.088 0% and 0.088 0% respectively in the second absorption peak. The relative error of converted absorbance was basically stable through the Savitzky-Golay filter after the spectral data preprocessing and it was relatively low, so, it laid a foundation for the late spectral data accurate qualitative and quantitative analysis.

  • The parameters optimization selection of Savitzky-Golay filter and its application in smoothing pretreatment for FTIR spectra
    2014 9th IEEE Conference on Industrial Electronics and Applications, 2014
    Co-Authors: Zhao Anxin, Xiaojun Tang, Zhong-hua Zhang, Junhua Liu
    Abstract:

    Gas infrared spectrum should be reconstructed according to the collected spectral data by the spectrum detecting instrument which was used in the online gas Fourier transform infrared spectroscopy measurement. However, due to the influence of the factors such as ambient noise and the spectrum detecting instrument itself noise, spectrum reconstruction was mixed by noise and the results of subsequent analysis may be deviated from the true value. Therefore, the Savitzky-Golay filter was selected to smooth and denoise the spectrum in the procedure of the Fourier transform infrared spectra reconstruction. The main performance index of the Savitzky-Golay smoothing filter was decided by the Polynomial Order and frame size when the Savitzky-Golay filter was used for smoothing. Through the traversal search method, the relation between the main filter design index and the Polynomial Order, frame size were explored for the problems of the Polynomial Order and frame size selected arbitrary. And then, when the Polynomial Order and frame size should be used in the actual smoothing and filter, the above results can be choosed. According to the characteristics of the spectrum which was measured in this paper, the optimal Polynomial Order and frame size were selected as 8 and 11 respectively for smoothing and denoising the spectra data. The smoothing spectra for the actual spectra of 1% concentration of CH4 were very close coincidence to the standard spectra; At the same time, the converted absorbance of standard spectra, raw spectra and smoothed spectra for 1% concentration CH 4 were 26.2720, 25.9017 and 26.2489 respectively, and the relative errors of the raw spectra and smoothed spectra were 1.4095% and 0.0880% respectively, in the second absorption peak area. The treatment effect was more apparent according to the method of the selection the Polynomial Order and frame size for the Savitzky-Golay smoothing filter, thereby a new method was provided for the Savitzky-Golay filter in other areas application.

Emanuel Radoi - One of the best experts on this subject based on the ideXlab platform.

  • Polynomial Phase Signal Modeling Using Warping-Based Order Reduction
    2004
    Co-Authors: André Quinquis, Cornel Ioana, Emanuel Radoi
    Abstract:

    The high-Order ambiguity function (HAF) was introduced for the estimation of Polynomial-phase signals (PPS). Currently the HAF suffers from noise-masking effects and from the appearance of undesired cross terms in the presence of multi-components PPS. The multi-lag product HAF concept was then proposed as a way to improve the performances of the HAF. Nevertheless, performances of the new methods are affected by the error propagation. This effect is due to the technique used for Polynomial Order reduction, common for current approaches : signal multiplication with the complex exponentials formed with the estimated coefficients. In this paper, we introduce an alternative method to reduce the Polynomial Order, based on the successive unitary signal transformation, according to each Polynomial Order. We will prove that this method considerably reduces the effect of error propagation.

  • ICASSP (2) - Polynomial phase signal modeling using warping-based Order reduction
    2004 IEEE International Conference on Acoustics Speech and Signal Processing, 1
    Co-Authors: André Quinquis, Cornel Ioana, Emanuel Radoi
    Abstract:

    The high-Order ambiguity function (HAF) was introduced for the estimation of Polynomial-phase signals (PPS). Currently the HAF suffers from noise-masking effects and from the appearance of undesired cross terms in the presence of multi-components PPS. The multi-lag product HAF concept was then proposed as a way to improve the performance of the HAF. Nevertheless, the performance of the new methods are affected by the error propagation. This effect is due to the technique used for Polynomial Order reduction, common for current approaches: signal multiplication with the complex exponentials formed with the estimated coefficients. In this paper, we introduce an alternative method to reduce the Polynomial Order, based on the successive unitary signal transformation, according to each Polynomial Order. We prove that this method considerably reduces the effect of error propagation.