Currently, many adaptive filtering algorithms are available for the non-Gaussian environment, namely, least mean square (LMS) algorithm, recursive least square (RLS) algorithm, least mean fourth (LMF) algorithm, and subspace minimum norm (SMN) algorithm. Most of them can converge to the steady-state, but face various constraints in the presence of alpha (α)-stable noises. To solve the problem, this paper aims to develop an adaptive filtering algorithm for non-Gaussian signals in α-stable distribution, drawing on the merits of existing adaptive filtering algorithms. Firstly, the authors introduced the theory of α-stable distribution, the central limit theorem and fractional lower-order statistics (FLOS). Next, two classic adaptive filtering algorithms, RLS and LMS, were summarized, and compared through tests. On this basis, the FLOS-SMN algorithm was designed in the light of the features of the LMS and the SMN, which applies to the filtering of non-Gaussian signals in αstable distribution. Finally, the proposed algorithm was proved as faster, more stable and more adaptable than the traditional method.
The traditional multipath channel algorithms cannot estimate low signal-to-noise ratio (SNR) in an accurate manner, which drags down the quality of the entire channel. To solve the problem, this paper puts forward a novel low SNR estimation algorithm for multipath channels. The core idea of the algorithm is as follows: the noise variance of received signals is estimated based on the good autocorrelation of periodical sequences; the signal amplitude is solved according to the statistical features of white Gaussian noise (WGN); finally, the SNR is estimated despite its low level. Matlab simulations show that our algorithm greatly outperformed the classic SNR estimation algorithms in accuracy, and its advantage increases with the decline in the SNR. The proposed algorithm has a great potential in the field of channel detection.
The multiple signal classification, MUSIC algorithm, introduces linear space to direction estimation, which realizes the breakthrough of the sound source direction resolution and lays an important foundation for the method and theory of spacial spectrum estimation. In order to better use the algorithm to estimate the direction of the sound source signal, this paper illustrates equally spaced linear array based on the MUSIC algorithm model and carries out the comparative simulation study on the narrow-band and the broad-band sound source signal direction estimation. Computer simulation shows that MUSIC algorithm can achieve super resolution direction estimation of the narrowband signals, and when MUSIC algorithm is used to estimate the direction of broadband signals, its performance is greatly degraded. And finally the conclusion is that in the narrowband sound source signal direction estimation, MUSIC algorithm functions remarkably, and when handling voice and other broadband source signal direction estimation, it needs to be further improved.
Abstract. Objective: Development of orthopedic 3D image computer aided diagnosis system can provide the help in the diagnosis of the orthopedic diseases. Methods: 150 patients from January 2014 to January 2016 were chosen as study samples. According to CT scan and X-ray image data, we got 3D reconstruction to the injured parts for the patients with fractures, and conducted evaluation according to the clinical data. Results: Comparing the examination result of the patients with fractures with the computer three-dimensional reconstruction, P>0.05, the difference is not of statistical significance. Conclusion: Orthopedic 3D image computer aided diagnosis system can help to reconstruct 3D images of damaged fracture parts, which is a very helpful diagnos and treatment asistant method for bone injuries, and will have very broad clinical application prospect.
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