Complex targets are irradiated by UWB radar, not only the mirror scattering echoes but also the multiscattering interacting echoes are included in target echoes. These two echoes can not be distinguished by classical ...Complex targets are irradiated by UWB radar, not only the mirror scattering echoes but also the multiscattering interacting echoes are included in target echoes. These two echoes can not be distinguished by classical frequency spectrum and power spectrurm. Time-domain bispectrum features of UWB radar signals that mingled with noise are analyzed, then processing this kind of signal using the method of time-domain bispectrum is experimented. At last, some UW-B radar returns with different signal noise ratio are simulated using the method of time-domain bispectrum Theoretical analysis and the results of simulation show that the method of extraction partial features of UWB radar targets based on time-domain bispectrum is good, and target classification and recognition can be implemented using those features.展开更多
This paper addresses the problem of adaptively estimating the consistent parameters for non Gaussian nonminimum MA processes with symmetric PDF using the fourth order cumulant of the underlying processes. The process...This paper addresses the problem of adaptively estimating the consistent parameters for non Gaussian nonminimum MA processes with symmetric PDF using the fourth order cumulant of the underlying processes. The processes may be corrupted by additive noise.展开更多
Glaucoma is a chronic and progressive optic neurodegenerative disease leading to vision deterioration and in most cases produce increased pressure within the eye. This is due to the backup of fluid in the eye; it caus...Glaucoma is a chronic and progressive optic neurodegenerative disease leading to vision deterioration and in most cases produce increased pressure within the eye. This is due to the backup of fluid in the eye; it causes damage to the optic nerve. Hence, early detection diagnosis and treatment of an eye help to prevent the loss of vision. In this paper, a novel method is proposed for the early detection of glaucoma using a combination of magnitude and phase features from the digital fundus images. Local binary patterns(LBP) and Daugman’s algorithm are used to perform the feature set extraction.The histogram features are computed for both the magnitude and phase components. The Euclidean distance between the feature vectors are analyzed to predict glaucoma. The performance of the proposed method is compared with the higher order spectra(HOS)features in terms of sensitivity, specificity, classification accuracy and execution time. The proposed system results 95.45% output for sensitivity, specificity and classification. Also, the execution time for the proposed method takes lesser time than the existing method which is based on HOS features. Hence, the proposed system is accurate, reliable and robust than the existing approach to predict the glaucoma features.展开更多
Epilepsy is the most common neuropathology. Statistical studies related to the disease reported that 20% - 25% of epileptic patients with occurrence of seizures were even under treatment with drugs. This article prese...Epilepsy is the most common neuropathology. Statistical studies related to the disease reported that 20% - 25% of epileptic patients with occurrence of seizures were even under treatment with drugs. This article presents a strategy for improved detection of the neuropathology, based on electroencephalogram (EEG), using a classifier built with support vector machines (SVC). The SVC is designed based on feature extraction of higher order spectra of time series derived from the EEG applied to epileptic patients and control patients. As demonstrated in the study presented, the EEG time series are highly nonlinear and non-Gaussian, therefore, exhibit higher order spectra, which are extracted features that improve the accuracy in the performance of SVC. The results of this study suggest the development of highly accurate computational tools for the diagnosis of this dreaded neuropathology.展开更多
基金This work was supported in part by National Defence Science and Technology Foundation (413220402)
文摘Complex targets are irradiated by UWB radar, not only the mirror scattering echoes but also the multiscattering interacting echoes are included in target echoes. These two echoes can not be distinguished by classical frequency spectrum and power spectrurm. Time-domain bispectrum features of UWB radar signals that mingled with noise are analyzed, then processing this kind of signal using the method of time-domain bispectrum is experimented. At last, some UW-B radar returns with different signal noise ratio are simulated using the method of time-domain bispectrum Theoretical analysis and the results of simulation show that the method of extraction partial features of UWB radar targets based on time-domain bispectrum is good, and target classification and recognition can be implemented using those features.
文摘This paper addresses the problem of adaptively estimating the consistent parameters for non Gaussian nonminimum MA processes with symmetric PDF using the fourth order cumulant of the underlying processes. The processes may be corrupted by additive noise.
文摘Glaucoma is a chronic and progressive optic neurodegenerative disease leading to vision deterioration and in most cases produce increased pressure within the eye. This is due to the backup of fluid in the eye; it causes damage to the optic nerve. Hence, early detection diagnosis and treatment of an eye help to prevent the loss of vision. In this paper, a novel method is proposed for the early detection of glaucoma using a combination of magnitude and phase features from the digital fundus images. Local binary patterns(LBP) and Daugman’s algorithm are used to perform the feature set extraction.The histogram features are computed for both the magnitude and phase components. The Euclidean distance between the feature vectors are analyzed to predict glaucoma. The performance of the proposed method is compared with the higher order spectra(HOS)features in terms of sensitivity, specificity, classification accuracy and execution time. The proposed system results 95.45% output for sensitivity, specificity and classification. Also, the execution time for the proposed method takes lesser time than the existing method which is based on HOS features. Hence, the proposed system is accurate, reliable and robust than the existing approach to predict the glaucoma features.
文摘Epilepsy is the most common neuropathology. Statistical studies related to the disease reported that 20% - 25% of epileptic patients with occurrence of seizures were even under treatment with drugs. This article presents a strategy for improved detection of the neuropathology, based on electroencephalogram (EEG), using a classifier built with support vector machines (SVC). The SVC is designed based on feature extraction of higher order spectra of time series derived from the EEG applied to epileptic patients and control patients. As demonstrated in the study presented, the EEG time series are highly nonlinear and non-Gaussian, therefore, exhibit higher order spectra, which are extracted features that improve the accuracy in the performance of SVC. The results of this study suggest the development of highly accurate computational tools for the diagnosis of this dreaded neuropathology.
