In order to reduce the hidden danger of noise which can be charactered by singularity spectrum, a new algorithm based on wavelet transform modulus maxima method was proposed. Singularity analysis is one of the most pr...In order to reduce the hidden danger of noise which can be charactered by singularity spectrum, a new algorithm based on wavelet transform modulus maxima method was proposed. Singularity analysis is one of the most promising new approaches for extracting noise hidden information from noisy time series . Because of singularity strength is hard to calculate accurately, a wavelet transform modulus maxima method was used to get singularity spectrum. The singularity spectrum of white noise and aluminium interconnection electromigration noise was calculated and analyzed. The experimental results show that the new algorithm is more accurate than tradition estimating algorithm. The proposed method is feasible and efficient.展开更多
In the strapdown inertial navigation system,the attitude information is obtained through an inertial measurement unit(IMU)device,which mainly includes a triaxial gyroscope,a triaxial accelerometer and a triaxial magne...In the strapdown inertial navigation system,the attitude information is obtained through an inertial measurement unit(IMU)device,which mainly includes a triaxial gyroscope,a triaxial accelerometer and a triaxial magnetometer.However,IMU sensors have system noise and drift errors,and these errors can accumulate over time,which makes it difficult to control the attitude accuracy.In order to solve the problems of gyro drift over time and random errors generated by the surrounding environment,this paper presents an attitude calculation algorithm based on wavelet neural network-extended Kalman filter(WNN-EKF).The wavelet neural network(WNN)is used to optimize the model and compensate the extended Kalman filter’s own model error.Through the semi-physical simulation experiment,the results show that the algorithm improves the accuracy of attitude calculation and enhances the self-adaptability to the environment.展开更多
Based on the good localization characteristic of the wavelet transform both in time and frequency domain, a de-noising method based on wavelet transform is presented, which can make the extraction of visual evoked pot...Based on the good localization characteristic of the wavelet transform both in time and frequency domain, a de-noising method based on wavelet transform is presented, which can make the extraction of visual evoked potentials in single training sample from the EEG background noise in favor of studying the changes between the single sample response happen. The information is probably related with the different function, appearance and pathologies of the brain. At the same time this method can also be used to remove those signal’s artifacts that do not appear with EP within the same scope of time or frequency. The traditional Fourier filter can hardly attain the similar result. This method is different from other wavelet de-noising methods in which different criteria are employed in choosing wavelet coefficient. It has a biggest virtue of noting the differences among the single training sample and making use of the characteristics of high time frequency resolution to reduce the effect of interference factors to a maximum extent within the time scope that EP appear. The experiment result proves that this method is not restricted by the signal-to-noise ratio of evoked potential and electroencephalograph (EEG) and even can recognize instantaneous event under the condition of lower signal-to-noise ratio, as well as recognize the samples which evoked evident response more easily. Therefore, more evident average evoked response could be achieved by de-nosing the signals obtained through averaging out the samples that can evoke evident responses than de-nosing the average of original signals. In addition, averaging methodology can dramatically reduce the number of record samples needed, thus avoiding the effect of behavior change during the recording process. This methodology pays attention to the differences among single training sample and also accomplishes the extraction of visual evoked potentials from single trainings sample. As a result, system speed and accuracy could be improved to a great extent if this methodology is applied to brain-computer interface system based on evoked responses.展开更多
An improved single image dehazing method based on dark channel prior and wavelet transform is proposed. This proposed method employs wavelet transform and guided filter instead of the soft matting procedure to estimat...An improved single image dehazing method based on dark channel prior and wavelet transform is proposed. This proposed method employs wavelet transform and guided filter instead of the soft matting procedure to estimate and refine the depth map of haze images. Moreover, a contrast enhancement method based on just noticeable difference(JND) and quadratic function is adopted to enhance the contrast for the dehazed image, since the scene radiance is usually not as bright as the atmospheric light,and the dehazed image looks dim. The experimental results show that the proposed approach can effectively enhance the haze image and is well suitable for implementing on the surveillance and obstacle detection systems.展开更多
基金Foundation item: National Natural Science Foundation of China(No.60372072)
文摘In order to reduce the hidden danger of noise which can be charactered by singularity spectrum, a new algorithm based on wavelet transform modulus maxima method was proposed. Singularity analysis is one of the most promising new approaches for extracting noise hidden information from noisy time series . Because of singularity strength is hard to calculate accurately, a wavelet transform modulus maxima method was used to get singularity spectrum. The singularity spectrum of white noise and aluminium interconnection electromigration noise was calculated and analyzed. The experimental results show that the new algorithm is more accurate than tradition estimating algorithm. The proposed method is feasible and efficient.
基金National Natural Science Foundation of China(No.61863024)Basic Research Innovation Group Program of Gansu Province(No.1606RJIA327)+2 种基金Higher Education Research Project Funding of Gansu Province(No.2018C-11)Natural Foundation of Gansu Province(No.18JR3RA107)Science and Technology Program Funding of Gansu Province(No.18CX3ZA004)。
文摘In the strapdown inertial navigation system,the attitude information is obtained through an inertial measurement unit(IMU)device,which mainly includes a triaxial gyroscope,a triaxial accelerometer and a triaxial magnetometer.However,IMU sensors have system noise and drift errors,and these errors can accumulate over time,which makes it difficult to control the attitude accuracy.In order to solve the problems of gyro drift over time and random errors generated by the surrounding environment,this paper presents an attitude calculation algorithm based on wavelet neural network-extended Kalman filter(WNN-EKF).The wavelet neural network(WNN)is used to optimize the model and compensate the extended Kalman filter’s own model error.Through the semi-physical simulation experiment,the results show that the algorithm improves the accuracy of attitude calculation and enhances the self-adaptability to the environment.
文摘Based on the good localization characteristic of the wavelet transform both in time and frequency domain, a de-noising method based on wavelet transform is presented, which can make the extraction of visual evoked potentials in single training sample from the EEG background noise in favor of studying the changes between the single sample response happen. The information is probably related with the different function, appearance and pathologies of the brain. At the same time this method can also be used to remove those signal’s artifacts that do not appear with EP within the same scope of time or frequency. The traditional Fourier filter can hardly attain the similar result. This method is different from other wavelet de-noising methods in which different criteria are employed in choosing wavelet coefficient. It has a biggest virtue of noting the differences among the single training sample and making use of the characteristics of high time frequency resolution to reduce the effect of interference factors to a maximum extent within the time scope that EP appear. The experiment result proves that this method is not restricted by the signal-to-noise ratio of evoked potential and electroencephalograph (EEG) and even can recognize instantaneous event under the condition of lower signal-to-noise ratio, as well as recognize the samples which evoked evident response more easily. Therefore, more evident average evoked response could be achieved by de-nosing the signals obtained through averaging out the samples that can evoke evident responses than de-nosing the average of original signals. In addition, averaging methodology can dramatically reduce the number of record samples needed, thus avoiding the effect of behavior change during the recording process. This methodology pays attention to the differences among single training sample and also accomplishes the extraction of visual evoked potentials from single trainings sample. As a result, system speed and accuracy could be improved to a great extent if this methodology is applied to brain-computer interface system based on evoked responses.
基金supported by the National Natural Science Foundation of China(61075013)the Joint Funds of the Civil Aviation(61139003)
文摘An improved single image dehazing method based on dark channel prior and wavelet transform is proposed. This proposed method employs wavelet transform and guided filter instead of the soft matting procedure to estimate and refine the depth map of haze images. Moreover, a contrast enhancement method based on just noticeable difference(JND) and quadratic function is adopted to enhance the contrast for the dehazed image, since the scene radiance is usually not as bright as the atmospheric light,and the dehazed image looks dim. The experimental results show that the proposed approach can effectively enhance the haze image and is well suitable for implementing on the surveillance and obstacle detection systems.
