Origin and elimination of spurious solutions of the eight-band k center dot p theory

The origin of spurious solutions in the eight-band envelope function model is examined and it is shown that spurious solutions arise from the additional spurious degeneracies caused by the unphysical bowing of the conduction bands calculated within the eight-band k center dot p model. We propose two approaches to eliminate these spurious solutions. Using the first approach, the wave vector cutoff method, we demonstrate the origin and elimination of spurious solutions in a transparent way without modifying the original Hamiltonian. Through the second approach, we introduce some freedom in modifying the Hamiltonian. The comparison between the results from the various modified Hamiltonians suggests that the wave vector cutoff method can give accurate enough description to the final results.

Interface-related in-plane optical anisotropy in GaAs/AlxGa1-xAs single-quantum-well structures studied by reflectance difference spectroscopy

The in-plane optical anisotropies of a series of GaAs/AlxGa1-xAs single-quantum-well structures have been observed at room temperature by reflectance difference spectroscopy. The measured degree of polarization of the excitonic transitions is inversely proportional to the well width. Numerical calculations based on the envelope function approximation incorporating the effect of C-2v-interface symmetry have been performed to analyze the origin of the optical anisotropy. Good agreement with the experimental data is obtained when the optical anisotropy is attributed to anisotropic-interface structures. The fitted interface potential parameters are consistent with predicted values.

Longitudinal spin transport in diluted magnetic semiconductor superlattices: The effect of the giant Zeeman splitting

Longitudinal spin transport in diluted magnetic semiconductor superlattices is investigated theoretically. The longitudinal magnetoconductivity (MC) in such systems exhibits an oscillating behavior as function of an external magnetic field. In the weak magnetic-field region the giant Zeeman splitting plays a dominant role that leads to a large negative magnetoconductivity. In the strong magnetic-field region the MC exhibits deep dips with increasing magnetic field. The oscillating behavior is attributed to the interplay between the discrete Landau levels and the Fermi surface. The decrease of the MC at low magnetic field is caused by the s-d exchange interaction between the electron in the conduction band and the magnetic ions. The spin polarization increases rapidly with increasing magnetic field and the longitudinal current becomes spin polarized in strong magnetic field. The effect of spin-disorder scattering on MC is estimated numerically for low magnetic fields and found to be neglectible for our system.

Photocurrent derivative spectra of ZnCdSe-ZnSe double multi-quantum wells

Photocurrent (PC) spectra of ZnCdSe-ZnSe double multi-quantum wells are measured at different temperature. Its corresponding photocurrent derivative (PCD) spectra are obtained by computing, and the PCD spectra have greatly enhanced the sensitivity of the relative weak PC signals. The polarization dependence of the PC spectra shows that the transitions observed in the PC spectra are heavy-hole related, and the transition energy coincide well with the results obtained by envelope function approximation including strain. The temperature dependence of the photocurrent curves indicates that the thermal activation is the dominant transport mechanism of the carriers in our samples. The concept of saturation temperature region is introduced to explain why the PC spectra have different temperature dependence in the samples with different structure parameters. It is found to be very useful in designing photovoltaic devices.

A new interface anisotropic potential of zinc-blende semiconductor interface induced by lattice mismatch

A new interface anisotropic potential, which is proportional to the lattice mismatch of interfaces and has no fitting parameter, has been deduced for (001) zinc-blende semiconductor interfaces. The comparison with other interface models is given for GaAs/AlAs and GaAs/InAs interfaces. The strong influence of the interface anisotropic potential on the inplane optical anisotropy of GaAs/AlGaAs low dimensional structures is demonstrated theoretically within the envelope function approximation.

Calculation of the band structure of semiconductor quantum wells using scattering matrices

The transfer-matrix method widely used in the calculation of the band structure of semiconductor quantum wells is found to have limitations due to its intrinsic numerical instability. It is pointed out that the numerical instability arises from free-propagating transfer matrices. A new scattering-matrix method is developed for the multiple-band Kane model within the envelope-function approximation. Compared with the transfer-matrix method, the proposed algorithm is found to be more efficient and stable. A four-band Kane model is used to check the validity of the method and the results are found to be in good agreement with earlier calculations.

Photocurrent derivative spectra of ZnCdSe-ZnSe double multi-quantum wells

Photocurrent (PC) spectra of ZnCdSe-ZnSe double multi-quantum wells are measured at different temperature. Its corresponding photocurrent derivative (PCD) spectra are obtained by computing, and the PCD spectra have greatly enhanced the sensitivity of the relative weak PC signals. The polarization dependence of the PC spectra shows that the transitions observed in the PC spectra are heavy-hole related, and the transition energy coincide well with the results obtained by envelope function approximation including strain. The temperature dependence of the photocurrent curves indicates that the thermal activation is the dominant transport mechanism of the carriers in our samples. The concept of saturation temperature region is introduced to explain why the PC spectra have different temperature dependence in the samples with different structure parameters. It is found to be very useful in designing photovoltaic devices.

基于改进的OFA-MPCA的监控方法

多向主元分析(MPCA)是利用多变量统计方法从纷杂的海量数据信息中提取出能够准确表征数据信息的几个主元,并通过投影法来降低数据的维数,主要应用于间歇生产过程中.在实际的间歇生产过程中,由于各种原因导致各批次异步造成它们运行时间的不一致,而无法直接建立有效的统计模型,正交函数近似(OFA)是一种基于正交基的投影变换技术,通过对原始数据进行OFA处理后,可以用投影系数来描述原始数据所具有的特征,并且可以达到轨迹同步化和压缩数据量的目的.对OFA法进行了部分改进,并结合MPCA法对典型的间歇过程——青霉素发酵过程进行了仿真研究.结果表明,改进的OFA计算速度有了极大的提高,且改进的OFA-MPCA法能完好地对各批次进行同步、建模并得出准确的监视结果.

基于Stewart平台六维力传感器的分区静态标定方法

基于Stewart平台的六维力传感器具有结构紧凑、刚度大、量程宽等特点,它在工业机器人、空间站对接等领域具有广泛的应用前景。好的标定方法是正确使用传感器的基础。由于基于Stewart平台的六维力传感器是一个复杂的非线性系统,所以采用常规的线性标定方法必将带来较大的标定误差从而影响其使用性能。标定的实质是,由测量值空间到理论值空间的映射函数的确定过程。由函数逼近理论可知,当只在已知点集上给出函数值时,可用多项式或分段多项式等较简单函数逼近待定函数。基于上述思想,本文将整个测量空间划分为若干连续的子测量空间,再对每个子空间进行线性标定,从而提高了整个测量系统的标定精度。实验分析结果表明了该标定方法有效。

A Theory of How the Brain Might Work

I wish to propose a quite speculative new version of the grandmother cell theory to explain how the brain, or parts of it, may work. In particular, I discuss how the visual system may learn to recognize 3D objects. The model would apply directly to the cortical cells involved in visual face recognition. I will also outline the relation of our theory to existing models of the cerebellum and of motor control. Specific biophysical mechanisms can be readily suggested as part of a basic type of neural circuitry that can learn to approximate multidimensional input-output mappings from sets of examples and that is expected to be replicated in different regions of the brain and across modalities. The main points of the theory are: -the brain uses modules for multivariate function approximation as basic components of several of its information processing subsystems. -these modules are realized as HyperBF networks (Poggio and Girosi, 1990a,b). -HyperBF networks can be implemented in terms of biologically plausible mechanisms and circuitry. The theory predicts a specific type of population coding that represents an extension of schemes such as look-up tables. I will conclude with some speculations about the trade-off between memory and computation and the evolution of intelligence.

Learning and Example Selection for Object and Pattern Detection

This thesis presents a learning based approach for detecting classes of objects and patterns with variable image appearance but highly predictable image boundaries. It consists of two parts. In part one, we introduce our object and pattern detection approach using a concrete human face detection example. The approach first builds a distribution-based model of the target pattern class in an appropriate feature space to describe the target's variable image appearance. It then learns from examples a similarity measure for matching new patterns against the distribution-based target model. The approach makes few assumptions about the target pattern class and should therefore be fairly general, as long as the target class has predictable image boundaries. Because our object and pattern detection approach is very much learning-based, how well a system eventually performs depends heavily on the quality of training examples it receives. The second part of this thesis looks at how one can select high quality examples for function approximation learning tasks. We propose an {em active learning} formulation for function approximation, and show for three specific approximation function classes, that the active example selection strategy learns its target with fewer data samples than random sampling. We then simplify the original active learning formulation, and show how it leads to a tractable example selection paradigm, suitable for use in many object and pattern detection problems.

Robust fuzzy Gustafson-Kessel clustering for nonlinear system identification

This paper deals with Takagi-Sugeno (TS) fuzzy model identification of nonlinear systems using fuzzy clustering. In particular, an extended fuzzy Gustafson-Kessel (EGK) clustering algorithm, using robust competitive agglomeration (RCA), is developed for automatically constructing a TS fuzzy model from system input-output data. The EGK algorithm can automatically determine the 'optimal' number of clusters from the training data set. It is shown that the EGK approach is relatively insensitive to initialization and is less susceptible to local minima, a benefit derived from its agglomerate property. This issue is often overlooked in the current literature on nonlinear identification using conventional fuzzy clustering. Furthermore, the robust statistical concepts underlying the EGK algorithm help to alleviate the difficulty of cluster identification in the construction of a TS fuzzy model from noisy training data. A new hybrid identification strategy is then formulated, which combines the EGK algorithm with a locally weighted, least-squares method for the estimation of local sub-model parameters. The efficacy of this new approach is demonstrated through function approximation examples and also by application to the identification of an automatic voltage regulation (AVR) loop for a simulated 3 kVA laboratory micro-machine system.

Parallel implementation of a learning algorithm for multilayer perceptrons using transputers

In this paper the parallelization of a new learning algorithm for multilayer perceptrons, specifically targeted for nonlinear function approximation purposes, is discussed. Each major step of the algorithm is parallelized, a special emphasis being put in the most computationally intensive task, a least-squares solution of linear systems of equations.

Sequential Optimal Recovery: A Paradigm for Active Learning

In most classical frameworks for learning from examples, it is assumed that examples are randomly drawn and presented to the learner. In this paper, we consider the possibility of a more active learner who is allowed to choose his/her own examples. Our investigations are carried out in a function approximation setting. In particular, using arguments from optimal recovery (Micchelli and Rivlin, 1976), we develop an adaptive sampling strategy (equivalent to adaptive approximation) for arbitrary approximation schemes. We provide a general formulation of the problem and show how it can be regarded as sequential optimal recovery. We demonstrate the application of this general formulation to two special cases of functions on the real line 1) monotonically increasing functions and 2) functions with bounded derivative. An extensive investigation of the sample complexity of approximating these functions is conducted yielding both theoretical and empirical results on test functions. Our theoretical results (stated insPAC-style), along with the simulations demonstrate the superiority of our active scheme over both passive learning as well as classical optimal recovery. The analysis of active function approximation is conducted in a worst-case setting, in contrast with other Bayesian paradigms obtained from optimal design (Mackay, 1992).

Learning from Incomplete Data

Real-world learning tasks often involve high-dimensional data sets with complex patterns of missing features. In this paper we review the problem of learning from incomplete data from two statistical perspectives---the likelihood-based and the Bayesian. The goal is two-fold: to place current neural network approaches to missing data within a statistical framework, and to describe a set of algorithms, derived from the likelihood-based framework, that handle clustering, classification, and function approximation from incomplete data in a principled and efficient manner. These algorithms are based on mixture modeling and make two distinct appeals to the Expectation-Maximization (EM) principle (Dempster, Laird, and Rubin 1977)---both for the estimation of mixture components and for coping with the missing data.