A new approach to extract phase feature of 3-D objects based on wavelength-scanning digital holography and numerical reconstruction technique is proposed in this paper.A number of holograms are recorded on a digital camera and reconstructed numerically by a computer with different wavelengths spaced at regular intervals.The theoretical analysis and computer simulations demonstrate that those pixels with the same phase of a 3-D object can focus on the same reconstruction plane.And those pixels with different phase of a 3-D object focus on the different reconstruction planes.Therefore the phase information of a 3-D object can be described by a series of reconstructed images on different planes.
SHEN Jin-yuanLI Xian-guoM. K. KimCHANG Sheng-jiang
The main goal of routing solutions is to satisfy the requirements of the Quality of Service (QoS) for every admitted connection as well as to achieve a global efficiency in resource utilization.In this paper proposes a solution based on Hopfield neural network (HNN) to deal with one of representative routing problems in uni-cast routing,i.e.the multi-constrained(MC) routing problem.Computer simulation shows that we can obtain the optimal path very rapidly with our new Lyapunov energy functions.
VBR(Variab le B itRate)视频信号具有时变性、非线性和突发性等特点,实现该信号通信量的高精度预测是提高信息传输速度和提高网络带宽资源利用效率的重要手段.针对以上问题,本文提出了一种用于VBR视频通信量预测的差分输入支持向量机(SVM:SupportVectorM achine)网络模型.该网络模型采用结构风险最小化准则,在最小化经验风险的同时,尽量缩小模型预测误差的上界,从而使网络模型具有更好的推广能力.实验结果表明:支持向量机网络模型的预测误差为0.0018,而梯度径向基函数(G rad ient Rad ial Basis Function:GRBF)神经网络模型的预测误差为0.0029.可以看出,支持向量机网络模型的预测精度要比GRBF网络模型的预测精度高出大约40%.
