The Experts below are selected from a list of 10251 Experts worldwide ranked by ideXlab platform

Anbo Wang - One of the best experts on this subject based on the ideXlab platform.

  • fast white light interferometry Demodulation algorithm for low finesse fabry perot sensors
    IEEE Photonics Technology Letters, 2015
    Co-Authors: Zhihao Yu, Anbo Wang
    Abstract:

    Over decades, the Signal Demodulation techniques of low-finesse Fabry–Perot interferometer (FPI) sensors have been either slow with wide dynamic range and absolute measurement capability, or fast with narrow dynamic range and relative measurement capability. The tradeoff between the speed and the measurement capability has greatly limited the application of FPI-based sensors. In this letter, a novel high-speed white light interferometry (WLI) Demodulation algorithm for low-finesse FPI has been developed. By realizing high-speed absolute Demodulation utilizing full spectra, the new algorithm has the advantage of spectral drift immunity, high precision, and simultaneous ac and dc Signal measurement capability, such as acoustic and temperature. A 70-kHz real-time WLI Demodulation experiment was conducted in lab, in which the speed was limited only by the spectrometer hardware.

  • toward eliminating Signal Demodulation jumps in optical fiber intrinsic fabry perot interferometric sensors
    Journal of Lightwave Technology, 2011
    Co-Authors: Evan M Lally, Anbo Wang
    Abstract:

    Fiber optic Fabry-Perot sensors are commonly interrogated by spectral interferometric measurement of optical path difference (OPD). Spurious jumps in sensor output, previously attributed to noise, are often observed in OPD-based measurements. Through analysis and experimentation based on intrinsic Fabry-Perot interferometric (IFPI) sensors, we show that these discontinuities are actually caused by a time-varying interferogram phase term. We identify several physical causes for varying initial phase and derive a threshold value at which it begins to cause errors in the sensor output. Finally, we present a total phase measurement method as an alternative to OPD-based techniques to reduce the occurrence of output Signal jumps.

  • Mode power distribution effect in white-light multimode fiber extrinsic Fabry-Perot interferometric sensor systems
    Optics Letters, 2006
    Co-Authors: Ming Han, Anbo Wang
    Abstract:

    Theoretical and experimental results have shown that mode power distribution (MPD) variations could significantly vary the phase of spectral fringes from multimode fiber extrinsic Fabry-Perot interferometric (MMF-EFPI) sensor systems, owing to the fact that different modes introduce different extra phase shifts resulting from the coupling of modes reflected at the second surface to the lead-in fiber end. This dependence of fringe pattern on MPD could cause measurement errors in Signal Demodulation methods of white-light MMF-EFPI sensors that implement the phase information of the fringes.

Shiyin Li - One of the best experts on this subject based on the ideXlab platform.

  • deep learning for Signal Demodulation in physical layer wireless communications prototype platform open dataset and analytics
    IEEE Access, 2019
    Co-Authors: Hongmei Wang, Zhenzhen Wu, Songtao Lu, Han Zhang, Guoru Ding, Shiyin Li
    Abstract:

    In this paper, we investigate deep learning (DL)-enabled Signal Demodulation methods and establish the first open dataset of real modulated Signals for wireless communication systems. Specifically, we propose a flexible communication prototype platform for measuring real modulation dataset. Then, based on the measured dataset, two DL-based demodulators, called deep belief network (DBN)-support vector machine (SVM) demodulator and adaptive boosting (AdaBoost)-based demodulator, are proposed. The proposed DBN-SVM based demodulator exploits the advantages of both DBN and SVM, i.e., the advantage of DBN as a feature extractor and SVM as a feature classifier. In DBN-SVM based demodulator, the received Signals are normalized before being fed to the DBN network. Furthermore, an AdaBoost-based demodulator is developed, which employs the $k$ -nearest neighbor as a weak classifier to form a strong combined classifier. Finally, the experimental results indicate that the proposed DBN-SVM based demodulator and AdaBoost-based demodulator are superior to the single classification method using DBN, SVM, and maximum likelihood-based demodulator.

  • Signal Demodulation with machine learning methods for physical layer visible light communications prototype platform open dataset and algorithms
    IEEE Access, 2019
    Co-Authors: Songtao Lu, Han Zhang, Hang Li, Chun Du, Shiyin Li
    Abstract:

    In this paper, we investigate the design and implementation of machine learning (ML)-based Demodulation methods in the physical layer of visible light communication (VLC) systems. We build a flexible hardware prototype of an end-to-end VLC system, from which the received Signals are collected as the real data. The dataset is available online, which contains eight types of modulated Signals. Then, we propose three ML demodulators based on convolutional neural network (CNN), the deep belief network (DBN), and adaptive boosting (AdaBoost), respectively. Specifically, the CNN-based demodulator converts the modulated Signals to images and recognizes the Signals by the image classification. The proposed DBN-based demodulator contains three restricted Boltzmann machines to extract the modulation features. The AdaBoost method includes a strong classifier that is constructed by the weak classifiers with the k -nearest neighbor algorithm. These three demodulators are trained and tested by our online open dataset. The experimental results show that the Demodulation accuracy of the three data-driven demodulators drops as the transmission distance increases. A higher modulation order negatively influences the accuracy for a given transmission distance. Among the three ML methods, the AdaBoost modulator achieves the best performance.

Thierry Bastogne - One of the best experts on this subject based on the ideXlab platform.

  • A Signal Demodulation-based method for the early detection of Cheyne-Stokes respiration
    PLoS ONE, 2020
    Co-Authors: Pauline Guyot, El-hadi Djermoune, Bruno Chenuel, Thierry Bastogne
    Abstract:

    Cheyne-Stokes respiration (CSR) is a sleep-disordered breathing characterized by recurrent central apneas alternating with hyperventilation exhibiting a crescendo-decrescendo pattern of tidal volume. This respiration is reported in patients with heart failure, stroke or damage in respiratory centers. It increases mortality for patients with severe heart failure as it has adverse impacts on the cardiac function. Early stage of CSR, also called periodic breathing, is often undiagnosed as it only provokes hypopneas instead of apneas, which are much more difficult to detect. This paper demonstrates the proof of concept of a new method devoted to the early detection of CSR. The proposed approach relies on a Signal Demodulation technique applied to ventilation Signals measured on 15 patients with chronic heart failure whose respiration goes from normal to severe CSR. Based on a modulation index and its instantaneous frequency, oscillation zones are detected and classified into three categories: CSR, periodic breathing and no abnormal pattern. The modulation index is used as an efficient indicator to quantify the degree of certainty of the pathology for each patient. Results show high correlation with experts' annotations with sensitivity and specificity values of 87.1% and 89.8% respectively. A final decision leads to a classification which is confirmed by the experts' conclusions.

  • a Signal Demodulation based method for the early detection of cheyne stokes respiration
    bioRxiv, 2019
    Co-Authors: Pauline Guyot, El-hadi Djermoune, Bruno Chenuel, Thierry Bastogne
    Abstract:

    Abstract Cheyne-Stokes respiration (CSR) is a sleep-disordered breathing characterized by recurrent central apneas alternating with hyperventilation exhibiting a crescendo-decrescendo pattern of tidal volume. This respiration is reported in patients with heart failure, stroke or damage in respiratory centers. It increases mortality for patients with severe heart failure as it has adverse impacts on the cardiac function. Early stage of CSR, also called periodic breathing, is often undiagnosed as it only provokes hypopneas instead of apneas, which are much more difficult to detect. This paper demonstrates the proof of concept of a new method devoted to the early detection of CSR. The proposed approach relies on a Signal Demodulation technique applied to ventilation Signals measured on 15 patients with chronic heart failure whose respiration goes from normal to severe CSR. Based on a modulation index and its instantaneous frequency, oscillation zones are detected and classified into three categories: CSR, periodic breathing and no abnormal pattern. The modulation index is used as an efficient biomarker to quantify the severity of the pathology for each patient. Results show high correlation with experts’ annotations with sensitivity and specificity values of 87.1% and 89.8% respectively. A final decision leads to a classification which is confirmed by the experts’ conclusions.

Songtao Lu - One of the best experts on this subject based on the ideXlab platform.

  • deep learning for Signal Demodulation in physical layer wireless communications prototype platform open dataset and analytics
    IEEE Access, 2019
    Co-Authors: Hongmei Wang, Zhenzhen Wu, Songtao Lu, Han Zhang, Guoru Ding, Shiyin Li
    Abstract:

    In this paper, we investigate deep learning (DL)-enabled Signal Demodulation methods and establish the first open dataset of real modulated Signals for wireless communication systems. Specifically, we propose a flexible communication prototype platform for measuring real modulation dataset. Then, based on the measured dataset, two DL-based demodulators, called deep belief network (DBN)-support vector machine (SVM) demodulator and adaptive boosting (AdaBoost)-based demodulator, are proposed. The proposed DBN-SVM based demodulator exploits the advantages of both DBN and SVM, i.e., the advantage of DBN as a feature extractor and SVM as a feature classifier. In DBN-SVM based demodulator, the received Signals are normalized before being fed to the DBN network. Furthermore, an AdaBoost-based demodulator is developed, which employs the $k$ -nearest neighbor as a weak classifier to form a strong combined classifier. Finally, the experimental results indicate that the proposed DBN-SVM based demodulator and AdaBoost-based demodulator are superior to the single classification method using DBN, SVM, and maximum likelihood-based demodulator.

  • Signal Demodulation with machine learning methods for physical layer visible light communications prototype platform open dataset and algorithms
    IEEE Access, 2019
    Co-Authors: Songtao Lu, Han Zhang, Hang Li, Chun Du, Shiyin Li
    Abstract:

    In this paper, we investigate the design and implementation of machine learning (ML)-based Demodulation methods in the physical layer of visible light communication (VLC) systems. We build a flexible hardware prototype of an end-to-end VLC system, from which the received Signals are collected as the real data. The dataset is available online, which contains eight types of modulated Signals. Then, we propose three ML demodulators based on convolutional neural network (CNN), the deep belief network (DBN), and adaptive boosting (AdaBoost), respectively. Specifically, the CNN-based demodulator converts the modulated Signals to images and recognizes the Signals by the image classification. The proposed DBN-based demodulator contains three restricted Boltzmann machines to extract the modulation features. The AdaBoost method includes a strong classifier that is constructed by the weak classifiers with the k -nearest neighbor algorithm. These three demodulators are trained and tested by our online open dataset. The experimental results show that the Demodulation accuracy of the three data-driven demodulators drops as the transmission distance increases. A higher modulation order negatively influences the accuracy for a given transmission distance. Among the three ML methods, the AdaBoost modulator achieves the best performance.

Changzhi Li - One of the best experts on this subject based on the ideXlab platform.

  • random body movement cancellation in doppler radar vital sign detection
    IEEE Transactions on Microwave Theory and Techniques, 2008
    Co-Authors: Changzhi Li
    Abstract:

    The complex Signal Demodulation and the arctangent Demodulation are studied for random body movement cancellation in quadrature Doppler radar noncontact vital sign detection. This technique can be used in sleep apnea monitor, lie detector, and baby monitor to eliminate the false alarm caused by random body movement. It is shown that if the dc offset of the baseband Signal is accurately calibrated, both Demodulation techniques can be used for random body movement cancellation. While the complex Signal Demodulation is less likely to be affected by a dc offset, the arctangent Demodulation has the advantage of eliminating harmonic and intermodulation interference at high carrier frequencies. When the dc offset cannot be accurately calibrated, the complex Signal Demodulation is more favorable. Ray-tracing model is used to show the effects of constellation deformation and optimum/null detection ambiguity caused by the phase offset due to finite antenna directivity. Experiments have been performed using 4-7 GHz radar to verify the theory.