Theoretical study of determining orientation and alignment of symmetric top molecule using laser-induced fluorescence

General expressions used for extracting the orientation and alignment parameters of a symmetric top molecule from laser-induced fluorescence (LIF) intensity are derived by employing the density matrix approach. The molecular orientation and alignment are described by molecular state multipoles. Excitation and detection are circularly and linearly polarized lights, respectively. In general cases, the LIF intensity is a complex function of the initial molecular state multipoles, the dynamic factors and the excitation-detection geometrical factors. It contains a population, ten orientation and fourteen alignment multipoles. The problem of how to extract the initial molecular state multipoles from the resolved LIF intensity is discussed.

Theoretical study of the separation mechanism of ionizable compounds in capillary electrochromatography

The migration mechanism of ionizable compounds in capillary electrochromatography (CEC) is more complicated than in high performance liquid chromatography (HPLC) due to the involvement of electrophoresis and the second chemical equilibrium. The separation mechanism of ionizable compounds in CEC has been studied theoretically. The electrochromatographic capacity factors of ions (k *) in CEC and in the pressurized CEC are derived by phenomenological approach. The influence of pH, voltage, pressure on k* is discussed. in addition, the k * of weak acid and weak base are derived based on acid-base equilibrium and the influence of pH on k * is studied theoretically.

Models of Noise and Robust Estimates

Given n noisy observations g; of the same quantity f, it is common use to give an estimate of f by minimizing the function Eni=1(gi-f)2. From a statistical point of view this corresponds to computing the Maximum likelihood estimate, under the assumption of Gaussian noise. However, it is well known that this choice leads to results that are very sensitive to the presence of outliers in the data. For this reason it has been proposed to minimize the functions of the form Eni=1V(gi-f), where V is a function that increases less rapidly than the square. Several choices for V have been proposed and successfully used to obtain "robust" estimates. In this paper we show that, for a class of functions V, using these robust estimators corresponds to assuming that data are corrupted by Gaussian noise whose variance fluctuates according to some given probability distribution, that uniquely determines the shape of V.

View-Based Models of 3D Object Recognition and Class-Specific Invariances

This paper describes the main features of a view-based model of object recognition. The model tries to capture general properties to be expected in a biological architecture for object recognition. The basic module is a regularization network in which each of the hidden units is broadly tuned to a specific view of the object to be recognized.

Learning Shape Descriptions: Generating and Generalizing Models of Visual Objects

We present the results of an implemented system for learning structural prototypes from grey-scale images. We show how to divide an object into subparts and how to encode the properties of these subparts and the relations between them. We discuss the importance of hierarchy and grouping in representing objects and show how a notion of visual similarities can be embedded in the description language. Finally we exhibit a learning algorithm that forms class models from the descriptions produced and uses these models to recognize new members of the class.