Blind identification-blind equalization for Finite Impulse Response (FIR) Multiple Input-Multiple Output (MIMO) channels can be reformulated as the problem of blind sources separation. It has been shown that blind ide...Blind identification-blind equalization for Finite Impulse Response (FIR) Multiple Input-Multiple Output (MIMO) channels can be reformulated as the problem of blind sources separation. It has been shown that blind identification via decorrelating sub-channels method could recover the input sources. The Blind Identification via Decorrelating Sub-channels(BIDS)algorithm first constructs a set of decorrelators, which decorrelate the output signals of subchannels, and then estimates the channel matrix using the transfer functions of the decorrelators and finally recovers the input signal using the estimated channel matrix. In this paper, a new approximation of the input source for FIR-MIMO channels based on the maximum likelihood source separation method is proposed. The proposed method outperforms BIDS in the presence of additive white Gaussian noise.展开更多
The problem of estimating an image corrupted by additive white Gaussian noise has been of interest for practical reasons. Non-linear denoising methods based on wavelets, have become popular but Multiwavelets outperfor...The problem of estimating an image corrupted by additive white Gaussian noise has been of interest for practical reasons. Non-linear denoising methods based on wavelets, have become popular but Multiwavelets outperform wavelets in image denoising. Multiwavelets are wavelets with several scaling and wavelet functions, offer simultaneously Orthogonality, Symmetry, Short support and Vanishing moments, which is not possible with ordinary (scalar) wavelets. These properties make Multiwavelets promising for image processing applications, such as image denoising. The aim of this paper is to apply various non-linear thresholding techniques such as hard, soft, universal, modified universal, fixed and multivariate thresholding in Multiwavelet transform domain such as Discrete Multiwavelet Transform, Symmetric Asymmetric (SA4), Chui Lian (CL), and Bi-Hermite (Bih52S) for different Multiwavelets at different levels, to denoise an image and determine the best one out of it. The performance of denoising algorithms and various thresholding are measured using quantitative performance measures such as, Mean Square Error (MSE), and Root Mean Square Error (RMSE), Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR). It is found that CL Multiwavelet transform in combination with modified universal thresholding has given best results.展开更多
自适应光正交频分复用符号分解串行传输(Adaptive Optical Orthogonal Frequency Division Multiplexing Symbol Decomposition with Serial Transmission,O-OFDM-ASDST)可以抑制O-OFDM系统非线性失真,但在接收端将分解符号合并时会增...自适应光正交频分复用符号分解串行传输(Adaptive Optical Orthogonal Frequency Division Multiplexing Symbol Decomposition with Serial Transmission,O-OFDM-ASDST)可以抑制O-OFDM系统非线性失真,但在接收端将分解符号合并时会增大加性高斯白噪声(Additive White Gaussian Noise,AWGN),因此设计一种能够抑制AWGN的新型接收机.分析了O-OFDM分解符号的结构特征和可观测到的AWGN的最大偏移分量,基于此对接收分解符号进行预处理,尽可能恢复出原本等于限幅门限和零值的时域抽样值,再根据O-OFDM分解符号特征,重构接收分解符号.最后采用蒙特卡洛(Monte Carlo)误码率仿真方法,验证了接收机的有效性.展开更多
基金Supported by the National Natural Science Foundation of China (No.60172048)
文摘Blind identification-blind equalization for Finite Impulse Response (FIR) Multiple Input-Multiple Output (MIMO) channels can be reformulated as the problem of blind sources separation. It has been shown that blind identification via decorrelating sub-channels method could recover the input sources. The Blind Identification via Decorrelating Sub-channels(BIDS)algorithm first constructs a set of decorrelators, which decorrelate the output signals of subchannels, and then estimates the channel matrix using the transfer functions of the decorrelators and finally recovers the input signal using the estimated channel matrix. In this paper, a new approximation of the input source for FIR-MIMO channels based on the maximum likelihood source separation method is proposed. The proposed method outperforms BIDS in the presence of additive white Gaussian noise.
文摘The problem of estimating an image corrupted by additive white Gaussian noise has been of interest for practical reasons. Non-linear denoising methods based on wavelets, have become popular but Multiwavelets outperform wavelets in image denoising. Multiwavelets are wavelets with several scaling and wavelet functions, offer simultaneously Orthogonality, Symmetry, Short support and Vanishing moments, which is not possible with ordinary (scalar) wavelets. These properties make Multiwavelets promising for image processing applications, such as image denoising. The aim of this paper is to apply various non-linear thresholding techniques such as hard, soft, universal, modified universal, fixed and multivariate thresholding in Multiwavelet transform domain such as Discrete Multiwavelet Transform, Symmetric Asymmetric (SA4), Chui Lian (CL), and Bi-Hermite (Bih52S) for different Multiwavelets at different levels, to denoise an image and determine the best one out of it. The performance of denoising algorithms and various thresholding are measured using quantitative performance measures such as, Mean Square Error (MSE), and Root Mean Square Error (RMSE), Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR). It is found that CL Multiwavelet transform in combination with modified universal thresholding has given best results.
文摘自适应光正交频分复用符号分解串行传输(Adaptive Optical Orthogonal Frequency Division Multiplexing Symbol Decomposition with Serial Transmission,O-OFDM-ASDST)可以抑制O-OFDM系统非线性失真,但在接收端将分解符号合并时会增大加性高斯白噪声(Additive White Gaussian Noise,AWGN),因此设计一种能够抑制AWGN的新型接收机.分析了O-OFDM分解符号的结构特征和可观测到的AWGN的最大偏移分量,基于此对接收分解符号进行预处理,尽可能恢复出原本等于限幅门限和零值的时域抽样值,再根据O-OFDM分解符号特征,重构接收分解符号.最后采用蒙特卡洛(Monte Carlo)误码率仿真方法,验证了接收机的有效性.
