The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform
Wonjun Lee - One of the best experts on this subject based on the ideXlab platform.
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DeWOZ: Rethinking the Schmidl–Cox Algorithm for Detecting Wi-Fi Out of ZigBee Interference
IEEE Wireless Communications Letters, 2019Co-Authors: Chenglong Shao, Hoorin Park, Wonjun LeeAbstract:This letter presents detect Wi-Fi Signals out of ZigBee (DeWOZ), a clean-slate technique to discern Wi-Fi packets drowning in ZigBee interference. Unlike existing solutions that are restricted to the cases where Wi-Fi Signal strength is much higher than ZigBee, Wi-Fi Signals only interfere with ZigBee payload, or beforehand clear Wi-Fi transmissions are done to obtain transmitter-specific information, DeWOZ aims at achieving the detection of relatively weak Wi-Fi Signal hidden in both ZigBee header and payload portions only based on a single Corrupted Signal. DeWOZ re-explores the Wi-Fi-oriented Schmidl–Cox algorithm for Corrupted Signal synchronization followed by two key operations, autocorrelation anomaly detection and adaptive discrete Fourier transform, to sniff Wi-Fi out of ZigBee header and payload, respectively. The feasibility of DeWOZ is validated via practical experiment.
Anton Amann - One of the best experts on this subject based on the ideXlab platform.
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Removal of CPR Artifacts From the Ventricular Fibrillation ECG by Adaptive Regression on Lagged Reference Signals
IEEE Transactions on Biomedical Engineering, 2008Co-Authors: Klaus Rheinberger, Karl Unterkofler, M. Baubin, Thomas Steinberger, Andreas Klotz, Anton AmannAbstract:Removing cardiopulmonary resuscitation (CPR)-related artifacts from human ventricular fibrillation (VF) electrocardiogram (ECG) Signals provides the possibility to continuously detect rhythm changes and estimate the probability of defibrillation success. This could reduce ldquohands-offrdquo analysis times which diminish the cardiac perfusion and deteriorate the chance for successful defibrillations. Our approach consists in estimating the CPR part of a Corrupted Signal by adaptive regression on lagged copies of a reference Signal which correlate with the CPR artifact Signal. The algorithm is based on a state-space model and the corresponding Kalman recursions. It allows for stochastically changing regression coefficients. The residuals of the Kalman estimation can be identified with the CPR-filtered ECG Signal. In comparison with ordinary least-squares regression, the proposed algorithm shows, for low Signal-to-noise ratio (SNR) Corrupted Signals, better SNR improvements and yields better estimates of the mean frequency and mean amplitude of the true VF ECG Signal. The preliminary results from a small pool of human VF and animal asystole CPR data are slightly better than the results of comparable previous studies which, however, not only used different algorithms but also different data pools. The algorithm carries the possibility of further optimization.
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Removing CPR artifacts from the ventricular fibrillation ECG by enhanced adaptive regression on lagged reference Signals
2006Co-Authors: Klaus Rheinberger, Karl Unterkofler, M. Baubin, Anton AmannAbstract:Removing cardiopulmonary resuscitation (CPR) related artifacts from human ventricular fibrillation (VF) ECG Signals would provide the possibility to continuously detect rhythm changes and estimate the probability of defibrillation success. This would avoid "hands-off" analysis times which diminish the cardiac perfusion and thus deteriorate the chance for a successful defibrillation attempt. Our approach consists in estimating the CPR-part of a Corrupted Signal by an adaptive regression on lagged copies of a reference Signal which correlate with the CPR artifact Signal. The algorithm is based on a state-space model and the corresponding Kalman recursions. The preliminary evaluation based on a small pool of artifact-free VF and asystole CPR data outperform comparable previous studies. In comparison with ordinary least-squares (OLS) regression the proposed algorithm shows improvements for low SNR Corrupted Signals and yields better estimates of the mean frequency of the true VF ECG Signal.
Chenglong Shao - One of the best experts on this subject based on the ideXlab platform.
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DeWOZ: Rethinking the Schmidl–Cox Algorithm for Detecting Wi-Fi Out of ZigBee Interference
IEEE Wireless Communications Letters, 2019Co-Authors: Chenglong Shao, Hoorin Park, Wonjun LeeAbstract:This letter presents detect Wi-Fi Signals out of ZigBee (DeWOZ), a clean-slate technique to discern Wi-Fi packets drowning in ZigBee interference. Unlike existing solutions that are restricted to the cases where Wi-Fi Signal strength is much higher than ZigBee, Wi-Fi Signals only interfere with ZigBee payload, or beforehand clear Wi-Fi transmissions are done to obtain transmitter-specific information, DeWOZ aims at achieving the detection of relatively weak Wi-Fi Signal hidden in both ZigBee header and payload portions only based on a single Corrupted Signal. DeWOZ re-explores the Wi-Fi-oriented Schmidl–Cox algorithm for Corrupted Signal synchronization followed by two key operations, autocorrelation anomaly detection and adaptive discrete Fourier transform, to sniff Wi-Fi out of ZigBee header and payload, respectively. The feasibility of DeWOZ is validated via practical experiment.
Klaus Rheinberger - One of the best experts on this subject based on the ideXlab platform.
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Removal of CPR Artifacts From the Ventricular Fibrillation ECG by Adaptive Regression on Lagged Reference Signals
IEEE Transactions on Biomedical Engineering, 2008Co-Authors: Klaus Rheinberger, Karl Unterkofler, M. Baubin, Thomas Steinberger, Andreas Klotz, Anton AmannAbstract:Removing cardiopulmonary resuscitation (CPR)-related artifacts from human ventricular fibrillation (VF) electrocardiogram (ECG) Signals provides the possibility to continuously detect rhythm changes and estimate the probability of defibrillation success. This could reduce ldquohands-offrdquo analysis times which diminish the cardiac perfusion and deteriorate the chance for successful defibrillations. Our approach consists in estimating the CPR part of a Corrupted Signal by adaptive regression on lagged copies of a reference Signal which correlate with the CPR artifact Signal. The algorithm is based on a state-space model and the corresponding Kalman recursions. It allows for stochastically changing regression coefficients. The residuals of the Kalman estimation can be identified with the CPR-filtered ECG Signal. In comparison with ordinary least-squares regression, the proposed algorithm shows, for low Signal-to-noise ratio (SNR) Corrupted Signals, better SNR improvements and yields better estimates of the mean frequency and mean amplitude of the true VF ECG Signal. The preliminary results from a small pool of human VF and animal asystole CPR data are slightly better than the results of comparable previous studies which, however, not only used different algorithms but also different data pools. The algorithm carries the possibility of further optimization.
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Removing CPR artifacts from the ventricular fibrillation ECG by enhanced adaptive regression on lagged reference Signals
2006Co-Authors: Klaus Rheinberger, Karl Unterkofler, M. Baubin, Anton AmannAbstract:Removing cardiopulmonary resuscitation (CPR) related artifacts from human ventricular fibrillation (VF) ECG Signals would provide the possibility to continuously detect rhythm changes and estimate the probability of defibrillation success. This would avoid "hands-off" analysis times which diminish the cardiac perfusion and thus deteriorate the chance for a successful defibrillation attempt. Our approach consists in estimating the CPR-part of a Corrupted Signal by an adaptive regression on lagged copies of a reference Signal which correlate with the CPR artifact Signal. The algorithm is based on a state-space model and the corresponding Kalman recursions. The preliminary evaluation based on a small pool of artifact-free VF and asystole CPR data outperform comparable previous studies. In comparison with ordinary least-squares (OLS) regression the proposed algorithm shows improvements for low SNR Corrupted Signals and yields better estimates of the mean frequency of the true VF ECG Signal.
Richard Rose - One of the best experts on this subject based on the ideXlab platform.
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a wavelet based thresholding approach to reconstructing unreliable spectrogram components
Speech Communication, 2015Co-Authors: Shirin Badiezadegan, Richard RoseAbstract:Abstract Data imputation approaches for robust automatic speech recognition reconstruct noise Corrupted spectral information by exploiting prior knowledge of the relationship between target speech and background through the use of spectrographic masks. Most of these approaches are model-based techniques that can only provide accurate estimates of the underlying clean speech when the characteristics of the noise Corrupted features do not deviate from those of the model. Discrete wavelet transform (DWT) based de-noising methods can also be used for re-estimating the underlying clean speech from a noise Corrupted Signal, but often require that the background noise is stationary and modeled by a Gaussian distribution. A novel approach is presented here for incorporating the information derived from spectrographic masks in a DWT-based de-noising method. The spectrographic masks are used for deriving thresholds for de-noising wavelet domain coefficients making DWT based de-noising more suitable for non-stationary noise conditions. The results of an experimental study are presented to demonstrate the performance of DWT based data imputation relative to other established techniques on the Aurora 2 noisy speech recognition task. It will be shown that the proposed approach reduces the impact of model mismatch associated with parametric approaches and exploits the robustness of non-parametric wavelet de-noising approach.
