Application of sample-and-hold circuits in the laser frequency-shifting

A new method of frequency-shifting for a diode laser is realized. Using a sample-and-hold circuit, the error signal can be held by the circuit during frequency shifting. It can avoid the restraint of locking or even lock-losing caused by the servo circuit when we input a step-up voltage into piezoelectric transition (PZT) to achieve laser frequency-shifting.

Multiple maternal origins of chickens: Out of the Asian jungles

Domestic chickens have long been important to human societies for food, religion, entertainment, and decorative uses, yet the origins and phylogeography of chickens through Eurasia remain uncertain. Here, we assessed their origins and phylogeographic hist

Entanglement degree of electron pairs out of Andreev reflection

We study the entanglement degree of electron pairs emitted from an s-wave Superconductor, which Couples to two normal leads via a single-level quantum dot. Within the framework of scattering matrix theory. the concurrence is used to quantify the entanglement. And the result shows that the entanglement degree is generally influenced by the initial separation of the two electrons in a Cooper pair and the normal transmission eigenvalues T-1, T-2. But it is only determined by the eigenvalues in the tunnelling limit, T-1. T-2 << 1, what is more. it is measurable. (C) 2008 Elsevier B.V. All rights reserved.

Diffractive Grating Based Out-of-Plane Coupling between Silicon Nanowire and Optical Fiber

Submitted by 阎军 (yanj@red.semi.ac.cn) on 2010-06-04T07:55:26Z No. of bitstreams: 1 Diffractive Grating Based Out-of-Plane Coupling between Silicon Nanowire and Optical Fiber.pdf: 232805 bytes, checksum: 0bd17756b8a703bf8337dd25bbddaca3 (MD5)

Semisupervised Dimensionality Reduction and Classification Through Virtual Label Regression

Semisupervised dimensionality reduction has been attracting much attention as it not only utilizes both labeled and unlabeled data simultaneously, but also works well in the situation of out-of-sample. This paper proposes an effective approach of semisupervised dimensionality reduction through label propagation and label regression. Different from previous efforts, the new approach propagates the label information from labeled to unlabeled data with a well-designed mechanism of random walks, in which outliers are effectively detected and the obtained virtual labels of unlabeled data can be well encoded in a weighted regression model. These virtual labels are thereafter regressed with a linear model to calculate the projection matrix for dimensionality reduction. By this means, when the manifold or the clustering assumption of data is satisfied, the labels of labeled data can be correctly propagated to the unlabeled data; and thus, the proposed approach utilizes the labeled and the unlabeled data more effectively than previous work. Experimental results are carried out upon several databases, and the advantage of the new approach is well demonstrated.

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.

一种基于改进LLE方法的传感器故障诊断方法研究

针对非线性系统传感器故障诊断难以解决的问题,提出了一种新的基于局部嵌入映射(LLE)的方法,解决了非线性数据的特征映射问题。首先,改进了基于分形维估计的内在维数的估计,通过线性拟合解决了线性区域的自动确定。然后,将故障状态与空间分布结合起来,通过确定数据点在空间超球内的分布完成故障的检测,在这个过程中将超球的确定与LLE算法中基于核函数的样本外数据扩展结合起来,大大减少了计算量,提高了算法的实时性,从而为复杂非线性传感器的故障诊断提供了一种新的有效的方法。