SOA-based polarity-preserving all-optical wavelength conversion at 80Gbit/s: Wide conversion range, well dynamic characteristics and polarization insensitive

This letter presents the effective design of a tunable 80 Gbit/s wavelength converter with a simple configuration consisting of a single semiconductor optical amplifier (SOA) and an optical bandpass filter (OBPF). Based on both cross-gain and cross-phase modulation in SOA, the polarity-preserved, ultrafast wavelength conversion is achieved by appropriately filtering the blue-chirped spectral component of a probe light. Moreover, the experiments are carried out to investigate into the wavelength tunability and the maximum tuning range of the designed wavelength converter. Our results show that a wide wavelength conversion range of nearly 35 nm is achieved with 21-nm downconversion and 14-nm upconversion, which is substantially limited by the operation wavelength ranges of a tunable OBPF and a tunable continuous-wave laser in our experiment. We also exploited the dynamics characteristics of the wavelength converter with variable input powers and different injection current of SOA. (C) 2008 Wiley Periodicals, Inc.

Discriminative Orthogonal Neighborhood-Preserving Projections for Classification

Orthogonal neighborhood-preserving projection (ONPP) is a recently developed orthogonal linear algorithm for overcoming the out-of-sample problem existing in the well-known manifold learning algorithm, i.e., locally linear embedding. It has been shown that ONPP is a strong analyzer of high-dimensional data. However, when applied to classification problems in a supervised setting, ONPP only focuses on the intraclass geometrical information while ignores the interaction of samples from different classes. To enhance the performance of ONPP in classification, a new algorithm termed discriminative ONPP (DONPP) is proposed in this paper. DONPP 1) takes into account both intraclass and interclass geometries; 2) considers the neighborhood information of interclass relationships; and 3) follows the orthogonality property of ONPP. Furthermore, DONPP is extended to the semisupervised case, i.e., semisupervised DONPP (SDONPP). This uses unlabeled samples to improve the classification accuracy of the original DONPP. Empirical studies demonstrate the effectiveness of both DONPP and SDONPP.

Elliptic Object Detection Based on Shape Preserving and Active Contour

本文通过形状约束方程(组)与一般主动轮廓模型结合,将目标形状与主动轮廓模型融合到统一能量泛函模型中,提出了一种形状保持主动轮廓模型即曲线在演化过程中保持为某一类特定形状。模型通过参数化水平集函数的零水平集控制演化曲线形状,不仅达到了分割即目标的目的,而且能够给出特定目标的定量描述。根据形状保持主动轮廓模型,建立了一个用于椭圆状目标检测的统一能量泛函模型,导出了相应的Euler-Lagrange常微分方程并用水平集方法实现了椭圆状目标检测。此模型可以应用于眼底乳头分割,虹膜检测及相机标定。实验结果表明,此模型不仅能够准确的检测出给定图像中的椭圆状目标,而且有很强的抗噪、抗变形及遮挡性能。