Biomimetic pattern recognition theory and its applications

Biomimetic pattern recogntion (BPR), which is based on "cognition" instead of "classification", is much closer to the function of human being. The basis of BPR is the Principle of homology-continuity (PHC), which means the difference between two samples of the same class must be gradually changed. The aim of BPR is to find an optimal covering in the feature space, which emphasizes the "similarity" among homologous group members, rather than "division" in traditional pattern recognition. Some applications of BPR are surveyed, in which the results of BPR are much better than the results of Support Vector Machine. A novel neuron model, Hyper sausage neuron (HSN), is shown as a kind of covering units in BPR. The mathematical description of HSN is given and the 2-dimensional discriminant boundary of HSN is shown. In two special cases, in which samples are distributed in a line segment and a circle, both the HSN networks and RBF networks are used for covering. The results show that HSN networks act better than RBF networks in generalization, especially for small sample set, which are consonant with the results of the applications of BPR. And a brief explanation of the HSN networks' advantages in covering general distributed samples is also given.

Double synaptic weight neuron theory and its application

In this paper, a novel mathematical model of neuron-Double Synaptic Weight Neuron (DSWN)(l) is presented. The DSWN can simulate many kinds of neuron architectures, including Radial-Basis-Function (RBF), Hyper Sausage and Hyper Ellipsoid models, etc. Moreover, this new model has been implemented in the new CASSANN-II neurocomputer that can be used to form various types of neural networks with multiple mathematical models of neurons. The flexibility of the DSWN has also been described in constructing neural networks. Based on the theory of Biomimetic Pattern Recognition (BPR) and high-dimensional space covering, a recognition system of omni directionally oriented rigid objects on the horizontal surface and a face recognition system had been implemented on CASSANN-II neurocomputer. In these two special cases, the result showed DSWN neural network had great potential in pattern recognition.

Geometrical learning, descriptive geometry, and biomimetic pattern recognition

Studies on learning problems from geometry perspective have attracted an ever increasing attention in machine learning, leaded by achievements on information geometry. This paper proposes a different geometrical learning from the perspective of high-dimensional descriptive geometry. Geometrical properties of high-dimensional structures underlying a set of samples are learned via successive projections from the higher dimension to the lower dimension until two-dimensional Euclidean plane, under guidance of the established properties and theorems in high-dimensional descriptive geometry. Specifically, we introduce a hyper sausage like geometry shape for learning samples and provides a geometrical learning algorithm for specifying the hyper sausage shapes, which is then applied to biomimetic pattern recognition. Experimental results are presented to show that the proposed approach outperforms three types of support vector machines with either a three degree polynomial kernel or a radial basis function kernel, especially in the cases of high-dimensional samples of a finite size. (c) 2005 Elsevier B.V. All rights reserved.

A DSWN-based specific-purpose neural computing system

The Double Synapse Weighted Neuron (DSWN) is a kind of general-purpose neuron model, which with the ability of configuring Hyper-sausage neuron (HSN). After introducing the design method of hardware DSWN synapse, this paper proposed a DSWN-based specific purpose neural computing device-CASSANN-IIspr. As its application, a rigid body recognition system was developed on CASSANN-IIspr, which achieved better performance than RIBF-SVMs system.

基于DSP和FPGA的火腿肠质量检测系统设计

本文研究的主要内容为基于DSP和FPGA的火腿肠质量检测系统设计。论文首先介绍了研究背景及意义和火腿肠质量检测系统原理，接着介绍了传统的专用和通用图像处理系统的结构、特点和模型，并通过分析DSP芯片以及DSP系统的特点，提出了基于DSP和FPGA芯片的实时图像处理系统。该系统不同于传统基于PC机模式的图像处理系统，发挥了DSP和FPGA两者的优势，能更好地提高图像处理系统实时性能。 其次,论述了以TMS320C6416 DSP为核心处理器实时图像处理系统的设计原理与组成，对系统主要部分的电路设计进行了详细的介绍，研究分析了高速电路设计中的几个关键问题。对系统进行了软件开发与调试，包括DSP程序设计和FPGA模块设计，并给出了FPGA各个模块仿真调试结果。经系统调试与实验验证，系统工作稳定可靠,拥有很高的实时性。 最后, 在火腿肠质量检测的图像算法中，对火腿肠的鼓泡问题进行了分析和相关算法的设计。首先实现了FPGA的图像预处理，将流水线处理技术和并行处理等技术应用到电路设计中，提高了处理速度，节省了硬件开销。在DSP中采用了多种算法对火腿肠图像进行了进一步的处理，使其特征更为明显。结果表明，实现的硬件电路能够满足系统功能和处理时间要求，同时有比较高的识别率，具有一定的参考价值。