红外双色目标识别技术

介绍了一种红外双色目标识别算法。该算法将通过二维傅立叶小波变换对图像提取的特征作为特征向量,这组特征向量具有旋转、平移和尺度不变性。利用Dempster-Shafer(D-S)证据理论进行图像融合,并使用神经网络分类器进行分类。实验结果表明,该算法提高了对近似物体的识别能力,能对飞机、舰船等目标进行有效的识别。

Theoretical analysis of adaptive harmonic window and its application in frequency extraction of vibration signal

The goal of this paper is to find an excellent adaptive window function for extracting the weak vibration signal and high frequency vibration signal under strong noise.The relationship between windowing transform andfiltering is analyzed first in the paper.The advantage of adjustable time-frequency window of wavelet transform is introduced.Secondly the relationship between harmonic wavelet and multiple analytic band-pass filter is analyzed.The coherence of the multiple analytic band-pass filter and harmonic wavelet base function is discussed,and the characteristic that multiple analytic band-pass filter included in the harmonic wavelet transform is founded.Thirdly,by extending the harmonic wavelet transform,the concept of the adaptive harmonic window and its theoretical equation without decomposition are put forward in this paper.Then comparing with the Hanning window,the good performance of restraining side-lobe leakage possessed by adaptive harmonic window is shown,and the adaptive characteristics of window width changing and analytical center moving of the adaptive harmonic window are presented.Finally,the proposed adaptive harmonic window is applied to weak signal extraction and high frequency orbit extraction of high speed rotor under strong noise,and the satisfactory results are achieved.The application results show that the adaptive harmonic window function can be successfully applied to the actual engineering signal processing.

sEMG Feature Analysis on Forearm Muscle Fatigue During Isometric Contractions

In order to detect and assess the muscle fatigue state with the surface electromyography(sEMG) characteristic parameters,this paper carried out a series of isometric contraction experiments to induce the fatigue on the forearm muscles from four subjects,and recorded the sEMG signals of the flexor carpi ulnaris.sEMG's median frequency(MDF) and mean frequency(MF) were extracted by short term Fourier transform(STFT),and the root mean square(RMS) of wavelet coefficients in the frequency band of 5—45 Hz was obtained by continuous wavelet transform(CWT).The results demonstrate that both MDF and MF show downward trends within 1 min; however,RMS shows an upward trend within the same time.The three parameters are closely correlated with absolute values of mean correlation coefficients greater than 0.8.It is suggested that the three parameters above can be used as reliable indicators to evaluate the level of muscle fatigue during isometric contractions.

Convergence of Riemannian sums of inverse wavelet transforms

We study the approximation of the inverse wavelet transform using Riemannian sums.We show that when the Fourier transforms of wavelet functions satisfy some moderate decay condition,the Riemannian sums converge to the function to be reconstructed as the sampling density tends to infinity.We also study the convergence of the operators introduced by the Riemannian sums.Our result improves some known ones.

Parseval Frame Wavelet Multipliers in L^2(R^d)

Let A be a d x d real expansive matrix. An A-dilation Parseval frame wavelet is a function φ E n2 （Rd）, such that the set {｜det A｜n/2φ（Ant -l） ：n ∈ Z, l∈ Zd} forms a Parseval frame for L2 （Rd）. A measurable function f is called an A-dilation Parseval frame wavelet multiplier if the inverse Fourier transform of fφ is an A-dilation Parseval frame wavelet whenever φ is an A-dilation Parseval frame wavelet, where φ denotes the Fourier transform of φ. In this paper, the authors completely characterize all A-dilation Parseval frame wavelet multipliers for any integral expansive matrix A with ｜ det（A）｜= 2. As an application, the path-connectivity of the set of all A-dilation Parseval frame wavelets with a frame MRA in L2（Rd） is discussed.