Miao Zhenjiang

Zernike moment-based image shape analysis and its application

This paper presents a novel pattern recognition application, the application in agronomy and proposes a roundness measurement concept to analyze the roundness of rose flower shapes for the new application. The rationality of this concept is theoretically analyzed and its mathematical definition is given. Experiment results show that this measurement is an efficient parameter for image shape analysis.

An extended BAM neural network model

Proposes an extended bidirectional associative memory (BAM) neural network model which can do auto- and hetero-associative memory. The theoretical proof for this neural network models stability is given. Experiments show that this neural network model is much more powerful than the M-P model, discrete Hopfield neural network, continuous Hopfield neural network, discrete bidirectional associative memory neural network, continuous and adaptive bidirectional associative memory neural network, backpropagation neural network and optimal designed nonlinear continuous neural network. Experimental results also show that, when it does auto-associative memory, the power of this model is the same as the loop neural network model which can only do auto-associative memory.

Analysis and optimal design of continuous neural networks with applications to associative memory

The asymptotic stability of a continuous neural network is analyzed for associative memory. An optimal design method is proposed which ensures the highest associative memory speed and guarantees the storage of each desired memory with attractivity. The network asymptotic stability is analyzed by means of a new energy function, and four theorems are obtained. By comparing these theorems with existing ones, it can be shown that in some cases they are consistent, while in others they are not equivalent but complementary to each other. Further study results in two more generalized conclusions, of which the existing conclusions are special cases. The network optimal design method is proposed in terms of an optimal associative memory theorem. Two application examples are presented to demonstrate the defeffectiveness of the optimal design method, which can be used to design the network for many applications.

Loop neural network model for associative memory

Proposes a new associative memory neural net (NN) model called the loop neural network model, and the theoretical proof of this NNs stability is given. Experiments show that this NN model is much more powerful than the McCulloch-Pitts model, the discrete Hopfield NN, the continuous Hopfield NN, the discrete bidirectional associative memory NN, the continuous and adaptive bidirectional associative memory NN, the backpropagation NN, and the optimally designed nonlinear continuous NN.<<ETX>>

Seamless messaging in a multimedia network environment

This paper introduces the first-generation seamless messaging system of the National Research Council and describes its further development version, the multimedia seamless messaging system.

Handwritten character recognition by extended loop neural networks

Presents an extended loop neural network approach to handwritten character recognition. Experiments show that this method is very effective. The recognition rate by this method is higher than that by a backpropagation network.<<ETX>>

Speech recognition by extended loop neural network

Presents an extended loop neural network approach to speech recognition. This speech recognition approach is characterized by the following important properties due to the associative memory neural network. (1) It has the features of great adaptivity and fault tolerance to carry out recognition. (2) The recognition system can be constructed which allows for the formation of arbitrary nonlinear decision surfaces. (3) The recognition system can perform not only the recognition task but also restore the correct information from incomplete even some extent incorrect information at the same time. Experiments are also conducted and the results show that this speech recognition approach has great application potentials.<<ETX>>

Image recognition using extended BAM neural networks

In this paper, we present an extended bidirectional associative memory (BAM) neural network approach to image recognition. This approach is characterized by the following important properties due to its associative memory: 1) it has the features of great adaptivity, robustness and fault tolerance to carry out recognition; 2) the recognition system constructed allows for the formation of arbitrary nonlinear decision surfaces; and 3) the recognition system can perform not only the recognition task but also restore the correct information from incomplete even some extent incorrect information at the same time. Experiments are also conducted and the results show that this approach is very efficient and has great application potentials.<<ETX>>

A Multimedia Information Processing and Analyzing Expert System

Abstract In this paper, the structure and design criteria of a multimedia information processing and analyzing system (MIPAS), which can be used to deal with more complicated intelligent issues, are presented. According to the structure and design criteria, a software environment (SE-MIPAS) which supports the implementation of multimedia information processing and analyzing applications, is implemented and introduced. Under this software environment, a demonstration MIPAS system is constructed. Experiments show that the multimedia information processing and analyzing is much more powerful and effective than single medium case.

Multifrequency remote sensing radar images processing and analysis

The advantages of using multifrequency remote sensing for measuring underlying terrain parameters and radar data interpretation are discussed. Some important peculiarities of image processing and analysis are presented and novel algorithms of two-dimensional image filtering and transformation are proposed. Examples of their application to real data are given. Possible ways of using neural networks on different stages of radar data processing and analysis are shown.<<ETX>>