阐述了基于IEEE1394总线的VXI零槽扩展基器件的基本原理,给出了1394/VXI零槽控制器的逻辑结构、软硬件设计和实现过程。试验结果表明该系统可以稳定地工作，能够和HP系列的VXI 总线设备进行互连和集成。

A new Fuzzy ART neural network model based on dual competition and resonance technique is proposed. This new model takes the competition and resonance method of the input nodes into the output nodes, putting the input nodes and output nodes competition and resonance together, solves the over-segmentation of the FART algorithm due to the vigilance's increase in the application of the image segmentation. Experimental results have shown that compared with the original FART, this algorithm has a better segmentation and antinoise performance.

According to the principle of human discrimination of small targets from a natural scene that there is a signature of discontinuity between the object and its neighboring regions, we develop an efficient method for multiscale small target detection using template matching based on a dissimilarity measure, which is called an average gray absolute difference maximum map (AGADMM), and infer the criterion of recognizing multiscale small objects from the properties of the AGADMM of the natural scene, which is a spatially independent and stable Gaussian randomeld. We explain how the AGADMM increases the ratio of the signal of object-to-background perturbations, improves the detectable probability, and keeps the false alarm probability very low. We analyze the complexity of computing an AGADMM and justify the validity and efficiency. Experiments with images of a natural scene such as a sky and sea surface have shown the great potential of the proposed method for distinguishing multiscale small objects from a natural scene. 1996 Society of Photo-Optical Instrumentation Engineers.

Autonomous real-time fingerprint verification, how to judge whether two fingerprints come from the same finger or not, is an important and difficult problem in AFIS (Automated Fingerprint Identification system). In addition to the non-linear deformation, two fingerprints from the same finger may also be dissimilar due to translation or rotation, all these factors do make the dissimilarities more great and lead to misjudgment, thus the correct verification rate highly depends on the deformation degree. In this paper, we present a new fast simple algorithm for fingerprint matching, derived from the Chang et al.'s method, to solve the problem of optimal matches between two fingerprints under nonlinear deformation. The proposed algorithm uses not only the feature points of fingerprints but also the multiple information of the ridge to reduce the computational complexity in fingerprint verification. Experiments with a number of fingerprint images have shown that this algorithm has higher efficiency than the existing of methods due to the reduced searching operations.

A fast object detection method based on object region dissimilarity and 1-D AGADM(one dimensional average gray absolute difference maximum) between object and background is proposed for real-time defection of small oshore targets. Then computational complexity, antinoise performance, the signal-to-noise ratio (SNR) gain between original images and their results as a function of SNR of original images and receiver operating characteristic (ROC) curve are analyzed and compared with those existing methods of small target detection such as two dimensional average gray absolute difference maximum (2-D AGADM), median contrastlter algorithm and multi-levellter algorithm. Experimental results and theoretical analysis have shown that the proposed method has faster speed and more adaptability to small object shape and also yields improved SNR performance. Small target detection, average gray absolute difference maximum, computational