摘要：This paper presents a novel approach for human identification at a distance using gait recognition. Recognition of a person from their gait is a biometric of increasing interest. The proposed work introduces a nonlinear machine learning method, kernel Principal Component Analysis （PCA）, to extract gait features from silhouettes for individual recognition. Binarized silhouette of a motion object is first represented by four 1-D signals which are the basic image features called the distance vectors. Fourier transform is performed to achieve translation invariant for the gait patterns accumulated from silhouette sequences which are extracted from different circumstances. Kernel PCA is then used to extract higher order relations among the gait patterns for future recognition. A fusion strategy is finally executed to produce a final decision. The experiments are carried out on the CMU and the USF gait databases and presented based on the different training gait cycles.

摘要：Power-line networks are designed to deliver electricity. They reach most of the domiciles and other buildings nowadays, so most of the people have access to it. On the other hand the backbone for the communications networks is not available in all countries especially the developing ones. A high cost and changing the design for the networks may be needed to construct this backbone. If data can be transmitted over the power-line networks, a recognized cost and time save can be achieved. In Egypt, the infrastructure is not always available for constructing a communications network backbone due to the already designed buildings before the need for these backbones. In this paper, we overcome this problem by designing a reliable Power-line Modem that operates safely on the low voltage grid. The modem is based on the Direct Sequence Spread Spectrum technique. It uses the mains zero crossing as an efficient way for the synchronization between the transmitter and the receiver. The Modem takes into account the problems of the Power-line including noise, attenuation and impedance dismatching.

摘要：焊接工艺参数的确定,是水下焊接研究领域的重要内容,但恶劣的焊接环境使之成为研究的难点.以水下湿法浅水药芯电弧焊(FCAW)为研究对象,将其看成一个多输入多输出(MIMO)的非线性系统,利用支持向量回归机(SVR)提高建模精度和预测速度.首先在正交试验设计的基础上,利用SVR构建水下焊接模型,并与多元非线性回归及BP神经网络所构建的模型进行拟合精度的比较,然后将SVR模型融入进化算法(EA)的全局优化中,利用模型计算个体的评价函数,通过种群进化操作,得到满足要求的最优参数.结果表明,该方案具有使用方便,效率高,精度好的优点,并能推广应用于众多类似领域,具有重要的理论和实践价值.