Spin Relaxation of Electrons in Single InAs Quantum Dots

By using polarization-resolved photoluminescence spectra, we study the electron spin relaxation in single InAs quantum dots (QDs) with the configuration of positively charged excitons X+ (one electron, two holes). The spin relaxation rate of the hot electrons increases with the increasing energy of exciting photons. For electrons localized in QDs the spin relaxation is induced by hyperfine interaction with the nuclei. A rapid decrease of polarization degree with increasing temperature suggests that the spin relaxation mechanisms are mainly changed from the hyperfine interaction with nuclei into an electron-hole exchange interaction.

Geometry property of multi-degree of freedom neurons

In this paper, we study a problem of geometric inequalities for a Multi-degree of Freedom Neurons. Some new geometric inequalities for a Multi-degree of Freedom Neurons are established. As special cases, some known inequalities are deduced.

Photoinduced spin alignment of the magnetic ions in (Ga,Mn)As

Time-resolved Kerr rotation measurement in the (Ga,Mn)As diluted magnetic semiconductor allows direct observation of the dynamical properties of the spin system of the magnetic ions and the spin-polarized holes. Experimental results show that the magnetic ions can be aligned by the polarized holes, and the time scales of spin alignment and relaxation take place in tens and hundreds of picoseconds, respectively. The Larmor frequency and effective g factor obtained in the Voigt geometry show an unusual temperature dependence in the vicinity of the Curie temperature due to the exchange coupling between the photoexcited holes and magnetic ions. Such a spin coherent precession can be amplified or destructed by two sequential excitation pulses with circularly copolarized or oppositely polarized helicity, respectively. (c) 2006 American Institute of Physics.

Research on multi-degree-of-freedom neurons with weighted graphs

In this paper, we redefine the sample points set in the feature space from the point of view of weighted graph and propose a new covering model - Multi-Degree-of-Freedorn Neurons (MDFN). Base on this model, we describe a geometric learning algorithm with 3-degree-of-freedom neurons. It identifies the sample points secs topological character in the feature space, which is different from the traditional "separation" method. Experiment results demonstrates the general superiority of this algorithm over the traditional PCA+NN algorithm in terms of efficiency and accuracy.

New method for the growth of highly uniform quantum dots

A new method is realized for the growth of self-formed quantum dots. We identify that dislocation-free islands can be formed by the strain from the strained superlattice taken as a whole. Unlike the Stranski-Krastanow (S-K) growth mode, the islands do not form during the growth of the corresponding strained single layers. Highly uniform quantum dots can be self-formed via this mechanism. The low temperature spectra of self-formed InGaAs/GaAs quantum dot superlattices grown on a (001) GaAs substrate have a full width at half maximum of 26-34 meV, indicating a better uniformity of quantum dot size than those grown in the S-K mode. This method can provide great degrees of freedom in designing possible quantum dot devices. 1998 Published by Elsevier Science B.V. All rights reserved.

Face recognition based on PCA and multi-degree of freedom neurons

An algorithm of PCA face recognition based on Multi-degree of Freedom Neurons theory is proposed, which based on the sample sets' topological character in the feature space which is different from "classification". Compare with the traditional PCA+NN algorithm, experiments prove its efficiency.

Research on multi-degree-of-freedom neurons with weighted graphs

In this paper, we redefine the sample points set in the feature space from the point of view of weighted graph and propose a new covering model - Multi-Degree-of-Freedorn Neurons (MDFN). Base on this model, we describe a geometric learning algorithm with 3-degree-of-freedom neurons. It identifies the sample points secs topological character in the feature space, which is different from the traditional "separation" method. Experiment results demonstrates the general superiority of this algorithm over the traditional PCA+NN algorithm in terms of efficiency and accuracy.

Dynamics of Multi-Stage Bladed Disks Systems

This paper presents a new and original method for dynamical analysis of multistage cyclic structures such as turbomachinery compressors or turbines. Each stage is modeled cyclically by its elementary sector and the interstage coupling is achieved through a cyclic recombination of the interface degrees of freedom. This method is quite simple to set up; it allows us to handle the finite element models of each stage's sector directly and, as in classical cyclic symmetry analysis, to study the nodal diameter problems separately. The method is first validated on a simple case study which shows good agreements with a complete 360 deg reference calculation. An industrial example involving two HP compressor stages is then presented. Then the forced response application is presented in which synchronous engine order type excitations are considered.

New method for the growth of highly uniform quantum dots

A new method is realized for the growth of self-formed quantum dots. We identify that dislocation-free islands can be formed by the strain from the strained superlattice taken as a whole. Unlike the Stranski-Krastanow (S-K) growth mode, the islands do not form during the growth of the corresponding strained single layers. Highly uniform quantum dots can be self-formed via this mechanism. The low temperature spectra of self-formed InGaAs/GaAs quantum dot superlattices grown on a (001) GaAs substrate have a full width at half maximum of 26-34 meV, indicating a better uniformity of quantum dot size than those grown in the S-K mode. This method can provide great degrees of freedom in designing possible quantum dot devices. 1998 Published by Elsevier Science B.V. All rights reserved.

Reduction-Based Model Updating of a Scaled Offshore Platform Structure

This paper attempts to develop a reduction-based model updating technique for jacket offshore platform structure. A reduced model is used instead of the direct finite-element model of the real structure in order to circumvent such difficulties as huge degrees of freedom and incomplete experimental data that are usually civil engineers' trouble during the model updating. The whole process consists of three steps: reduction of FE model, the first model updating to minimize the reduction error, and the second model updating to minimize the modeling error of the reduced model and the real structure. According to the performance of jacket platforms, a local-rigidity assumption is employed to obtain the reduced model. The technique is applied in a downscale model of a four-legged offshore platform where its effectiveness is well proven. Furthermore, a comparison between the real structure and its numerical models in the following model validation shows that the updated models have good approximation to the real structure. Besides, some difficulties in the field of model updating are also discussed.