基于BP神经网络的心电图ST段的模式识别

心电图ST段对心脏疾病的诊断具有重要意义。在正确读取采集于郑州大学一附院的运动心电图数据基础上,利用小波变换更准确地确定其ST段的起始和终止位置,初步探讨了BP神经网络用于心电图ST段识别的方法,并用此方法识别出心电图ST段的三种类型—正常、水平压低和抬高。实验结果较好。     

二进离散小波神经网络在传感器逆向建模中的应用

给出了快速收敛的离散二进小渡神经网络的初始化．构造和权值确定的详细方法。并将这类小波神经网络应用于传感器的非线性校正，并给出了仿真实验结果。相对使用随机贪心算法训练的神经网络，快速收敛小波神经网络利用离散二进小波变换的便利，采用启发式的构造算法；具有构造过程复杂度低，构造完成后高度接近目标模型，训练次数少，并可有效避免陷入局部极小点的优点。有效解决了小波神经网络尺度和平移系数在训练时需对小波函数进行求导而影响网络收敛速度的问题。     

Non-separable lifting scheme with adaptive update step for still and stereo image coding

Many existing works related to lossy-to-lossless multiresolution image compression are based on the lifting concept. It is worth noting that a separable lifting scheme may not appear very efficient to cope with the 2D characteristics of edges which are neither horizontal nor vertical. In this paper, we propose to use 2D non-separable lifting schemes that still enable progressive reconstruction and exact decoding of images. Their relevant advantage is to yield a tractable optimization of all the involved decomposition operators. More precisely, we design the prediction operators by minimizing the variance of the detail coefficients. Concerning the update filters, we propose a new optimization criterion which aims at reducing the inherent aliasing artifacts. A theoretical analysis of the proposed method is conducted in terms of the adaptation criterion considered in the optimization of the update filter. Simulations carried out on still images and residual ones generated from stereo pairs show the benefits which can be drawn from the proposed optimization of the lifting operators.     

Continuous wavelet based linear time-varying system identification

A systematic framework is developed to address the parametric linear time-varying system identification problem, using the continuous wavelet transform (CWT). The system is modeled by a differential equation with unknown parameters and identified via the time-frequency representation, the ratio of the CWTs of the output and the input. The efficient execution of this system identification algorithm requires selecting appropriate scales from the wavelet transform. Scale selection can be formulated as an optimization problem: find a set of scales that contains the most information about the system’s dynamics and has high signal to noise ratio. In addition, selecting scales that have a minimum amount of redundant information is desirable. Three candidate selection metrics are presented that address these criteria and are based on an analytic investigation of the wavelet transform’s probabilistic structure. Finally, a non-linear least squares algorithm, coupled with a scale selection algorithm, is presented to identify the system model. Simulations and experiments verify this algorithm’s capability of tracking different types of model variation.     

Using fractal image analysis to characterize microstructure of low-fat stirred yoghurt manufactured with microparticulated whey protein

Differences in the microstructure of low fat yoghurt manufactured with microparticulated whey proteins used as fat replacer were investigated. Images were obtained by confocal laser scanning microscopy and studied using a technique for image analysis that combines an initial 2D-wavelet compression followed by fractal analysis and inspection of the fractal curves by principal components analysis (PCA). One commercial and three experimental microparticulated ingredients with different chemical characteristics were used in the yoghurt formulations and compared to both full and low fat yoghurts without fat replacer. The results showed that the amount of native and soluble whey proteins present in the microparticles had a positive influence on the structure of the formed gel. The created structure, dominated by dense aggregates and low amount of serum, had an increased degree of self similarity or fractality with yoghurts in which fat was present.     

基于小波变换的SAR图像相干斑噪声消除方法研究

本文提出了一种基于小波变换的合成孔径雷达(SAR)图像相干斑消除滤波器.这种滤波器通过在小波细节子图像中减少小波分解系数的幅度来抑制相干斑噪声,同时利用小波细节子图像中提供的边缘信息来检测边缘和纹理细节,并保留其对应的小波分解系数值.实验结果表明,此方法除了对相干斑噪声有很好的抑制作用外,还保留了尽可能多的目标特性和图像细节,有着良好的图像视觉解译效果.     

Wavelet-based AE characterization of composite materials

This paper addresses an application of recently developed wavelet-based signal processing technique on a composite fracture behavior study. Here, the wavelet methodology is introduced, and a complete procedure of wavelet-based acoustic emission (AE) analysis methods developed by the author are demonstrated. In this research, the AE signals from glass fiber reinforced (GFR) composites are collected during the standard quasi-static tensile testing and transformed by the Daubechies discrete wavelets using the programs developed by the author. Based on AE tests, the fracture behaviors are studied for the material. In the study of GFR composite fracture behavior, classical linear fracture mechanics method is used for comparison. The exponential constant m value used to determine the relationship between stress and stress intensity factor are compared relative to the classical fracture mechanics and the AE techniques. In order to demonstrate the advantage of the wavelet-based AE techniques, the conventional AE analysis is also provided side-by-side for comparison. The results verify that the wavelet-based method better approximates residual strength relative to the classical AE techniques.In this paper, the results of the wavelet-based AE analysis of glass fiber composites are presented. The results indicate that wavelets-based signal processing is an efficient tool in the analysis and the transformation of AE signals.     

一种复合型自适应数字水印算法

提出一种同时嵌入鲁棒性水印和易损性水印的复合型数字水印算法。其中鲁棒性水印的算法,利用图像小波系数的零树结构,基于零树小波编码,构造出重要系数小波树,将水印信息自适应地以不同强度嵌入载体图像的合适位置。实验结果表明,本算法在用于版权认证的同时还能对篡改进行有效的检测,具有较好的双重保护性能。     

数字通信中基于双正交子波的波形成形

本文讨论数字调制技术中的波形成形问题,提出利用双正交子波实现波形成形的思想.本文首先证明了利用双正交子波实现波形成形满足Nyquist准则;然后研究了利用双正交子波进行基带成形的具体数字化实现涉及的问题,并在频谱利用率和误码性能两个方面进行了对比分析,给出了计算机模拟结果,得到了有益的结论.