Phase-frequency characte ristics of approximate sinusoidal geomagnetic signals can be used fo r projectile roll positioning and other high-precision trajectory correction applications.The sinusoidal geomagnetic signal...Phase-frequency characte ristics of approximate sinusoidal geomagnetic signals can be used fo r projectile roll positioning and other high-precision trajectory correction applications.The sinusoidal geomagnetic signal deforms in the exposed and magnetically contaminated environment.In order to preciously recognize the roll information and effectively separate the noise component from the original geomagnetic sequence,based on the error source analysis,we propose a moving horizon based wavelet de-noising method for the dual-observed geomagnetic signal filtering where the captured rough roll frequency value provides reasonable wavelet decomposition and reconstruction level selection basis for sampled sequence;a moving horizon window guarantees real-time performance and non-cumulative calculation amount.The complete geomagnetic data in full ballistic range and three intercepted paragraphs are used for performance assessment.The positioning performance of the moving horizon wavelet de-noising method is compared with the band-pass filter.The results show that both noise reduction techniques improve the positioning accuracy while the wavelet de-noising method is always better than the band-pass filter.These results suggest that the proposed moving horizon based wavelet de-noising method of the dual-observed geomagnetic signal is more applicable for various launch conditions with better positioning performance.展开更多
Falls are a major cause of disability and even death in the elderly,and fall detection can effectively reduce the damage.Compared with cameras and wearable sensors,Wi-Fi devices can protect user privacy and are inexpe...Falls are a major cause of disability and even death in the elderly,and fall detection can effectively reduce the damage.Compared with cameras and wearable sensors,Wi-Fi devices can protect user privacy and are inexpensive and easy to deploy.Wi-Fi devices sense user activity by analyzing the channel state information(CSI)of the received signal,which makes fall detection possible.We propose a fall detection system based on commercial Wi-Fi devices which achieves good performance.In the feature extraction stage,we select the discrete wavelet transform(DWT)spectrum as the feature for activity classification,which can balance the temporal and spatial resolution.In the feature classification stage,we design a deep learning model based on convolutional neural networks,which has better performance compared with other traditional machine learning models.Experimental results show our work achieves a false alarm rate of 4.8%and a missed alarm rate of 1.9%.展开更多
This paper presents a robust filter called the quaternion Hardy filter(QHF)for color image edge detection.The QHF can be capable of color edge feature enhancement and noise resistance.QHF can be used flexibly by selec...This paper presents a robust filter called the quaternion Hardy filter(QHF)for color image edge detection.The QHF can be capable of color edge feature enhancement and noise resistance.QHF can be used flexibly by selecting suitable parameters to handle different levels of noise.In particular,the quaternion analytic signal,which is an effective tool in color image processing,can also be produced by quaternion Hardy filtering with specific parameters.Based on the QHF and the improved Di Zenzo gradient operator,a novel color edge detection algorithm is proposed;importantly,it can be efficiently implemented by using the fast discrete quaternion Fourier transform technique.From the experimental results,we conclude that the minimum PSNR improvement rate is 2.3%and the minimum SSIM improvement rate is 30.2%on the CSEE database.The experiments demonstrate that the proposed algorithm outperforms several widely used algorithms.展开更多
基金funded by National Natural Science Foundation of China(61201391)。
文摘Phase-frequency characte ristics of approximate sinusoidal geomagnetic signals can be used fo r projectile roll positioning and other high-precision trajectory correction applications.The sinusoidal geomagnetic signal deforms in the exposed and magnetically contaminated environment.In order to preciously recognize the roll information and effectively separate the noise component from the original geomagnetic sequence,based on the error source analysis,we propose a moving horizon based wavelet de-noising method for the dual-observed geomagnetic signal filtering where the captured rough roll frequency value provides reasonable wavelet decomposition and reconstruction level selection basis for sampled sequence;a moving horizon window guarantees real-time performance and non-cumulative calculation amount.The complete geomagnetic data in full ballistic range and three intercepted paragraphs are used for performance assessment.The positioning performance of the moving horizon wavelet de-noising method is compared with the band-pass filter.The results show that both noise reduction techniques improve the positioning accuracy while the wavelet de-noising method is always better than the band-pass filter.These results suggest that the proposed moving horizon based wavelet de-noising method of the dual-observed geomagnetic signal is more applicable for various launch conditions with better positioning performance.
文摘Falls are a major cause of disability and even death in the elderly,and fall detection can effectively reduce the damage.Compared with cameras and wearable sensors,Wi-Fi devices can protect user privacy and are inexpensive and easy to deploy.Wi-Fi devices sense user activity by analyzing the channel state information(CSI)of the received signal,which makes fall detection possible.We propose a fall detection system based on commercial Wi-Fi devices which achieves good performance.In the feature extraction stage,we select the discrete wavelet transform(DWT)spectrum as the feature for activity classification,which can balance the temporal and spatial resolution.In the feature classification stage,we design a deep learning model based on convolutional neural networks,which has better performance compared with other traditional machine learning models.Experimental results show our work achieves a false alarm rate of 4.8%and a missed alarm rate of 1.9%.
基金supported in part by the Science and Technology Development Fund,Macao SAR FDCT/085/2018/A2the Guangdong Basic and Applied Basic Research Foundation(2019A1515111185)。
文摘This paper presents a robust filter called the quaternion Hardy filter(QHF)for color image edge detection.The QHF can be capable of color edge feature enhancement and noise resistance.QHF can be used flexibly by selecting suitable parameters to handle different levels of noise.In particular,the quaternion analytic signal,which is an effective tool in color image processing,can also be produced by quaternion Hardy filtering with specific parameters.Based on the QHF and the improved Di Zenzo gradient operator,a novel color edge detection algorithm is proposed;importantly,it can be efficiently implemented by using the fast discrete quaternion Fourier transform technique.From the experimental results,we conclude that the minimum PSNR improvement rate is 2.3%and the minimum SSIM improvement rate is 30.2%on the CSEE database.The experiments demonstrate that the proposed algorithm outperforms several widely used algorithms.
