Probabilistic linear models for multiresolution estimation in gray-level images

A multiresolution analysis of digital gray-level images is presented. A gray-level multi-scale framework is determined from two main assumptions: the gray scale is binary at the finest spatial resolution, and the gray levels of composed regions are obtained additively. In order to interrelate the gray-level histograms of the same image at different resolutions, probabilistic linear models are developed, which are then applied for estimation. Linear-optimization theory is used as a way of constructing such models. A general procedure for image processing is sketched, based on gray-level estimation. A versatile algorithm for nonlinear filtering is derived. Some examples of prospective applications are given.This work was partially supported by grant TIC91-646 from the DGYCIT of the Spanish Government.     

Partial scan design and test sequence generation based on reduced scan shift method

This paper presents a partial scan algorithm, calledPARES (PartialscanAlgorithm based onREduced Scan shift), for designing partial scan circuits. PARES is based on the reduced scan shift that has been previously proposed for generating short test sequences for full scan circuits. In the reduced scan shift method, one determines proch FFs must be controlled and observed for each test vector. According to the results of similar analysis, PARES selects these FFs that must be controlled or observed for a large number of test vectors, as scanned FFs. Short test sequences are generated by reducing scan shift operations using a static test compaction method. To minimize the loss of fault coverage, the order of test vectors is so determined that the unscanned FFs are in the state required by the next test vector. If there are any faults undetected yet by a test sequence derived from the test vectors, then PARES uses a sequential circuit test generator to detect the faults. Experimental results for ISCAS'89 benchmark circuits are given to demonstrate the effectiveness of PARES.     

The performance of a direct-sequence spread-spectrum acquisition system using a linear prediction filter for narrowband interference suppression

The coarse acquisition performance of a direct sequence spread-spectrum receiver is analyzed when a linear prediction filter is used for narrowband interference suppression. We show that once an appropriate matching strategy is identified, the linear prediction filter can provide favorable performance when narrowband interference is present over a considerable range of both interference power and bandwidth. In addition, the presence of the filter dramatically improves the performance over the case where there is no filter, except when the interference bandwidth and the power are both small (i.e., when the processing gain provides sufficient interference immunity without the filter). If long spreading sequences are used with moderately sized observation windows, the acquisition performance can be severely degraded when a parallel acquisition scheme is used due to the linear predication filter. We show, however, that a slower serial receiver will provide reliable performance.This work was supported in part by the Office of Naval Research under contract ONR N00014-91-J-1234, the Army Research Office under contract ARO DAAL03-91-0071, and the NSF Center for Ultra-High Speed Circuits and Systems (ICAS).     

独立不同分布随机变盆序列的部分和的增量有多小

关于独立同分布随机变量部分和的增量有多小问题，目前已有了著名的结果.本文通过与独立同分布情形时完全不同的途径，讨论了独立不同分布情形时相应的问题.在矩母函数存在的条件下，获得了与同分布情形相近的结论.     

Fast and Compact 16 by 16 Cellular Neural Network Implementation

The paper presents a Cellular Neural Network implementation based on a high gain sigmoid operation. The required simplifications to the original theory are described that allow the use of high gain. With this design black and white images can be processed. The basic building blocks in a cell are described. A 16×16 cells network has been designed and processed with 1.2 micron CMOS technology. Measurement results which show the operation of the network are presented.     

钙—铈氟碳酸盐矿物中BmSn型新多型的发现及其微结构研究

利用Philips CM-12分析型透射电镜对四川昌北大型稀土矿床中钙铈氟碳酸盐矿物进行了研究。除证实氟碳钙铈矿(BS)存在3R和6R2种多型外,首次发现和研究了氟碳钙铈矿(BS)存在4H、12H、18R、24R、30R和36R6种新多型;伦琴矿(BS_2)1种6R新多型和B_2S新规则混层矿物2H、12R和24R3种中新多型。电子衍射和晶格象分析表明:该类矿物中众多新多型的产生,主要是由于矿物结构单元层沿(?)轴有序堆垛和[CO_3]的定向周期性变化所致。     

用频域法实现圆环形目标物的计数

研究了用频域法实现圆环形目标物计数的方法。对空域中含有圆环形目标物的二值图像，经傅里叶变换后在频域内进行滤波，再经傅里叶反变换后取一阈值进行处理，则对应于圆环形目标物即变为一个实心体，然后对实心体进行计数即可实现对圆环形目标物的计数。对该方法进行了数学描述，并分别用模拟图像和实际图像进行了实验。结果表明，该方法消除噪声的效果好，对部分封闭圆的检测同样有效，且可用于目标物有粘连的情况。该方法对圆环形目标物的尺寸相差不大的情况更为有效。     

逆序数的应用

本文通过“1 4-1 5”智力玩具和最优戴断切割问题两个实例 ,讨论了逆序数在解决实际问题中的应用     

基于边缘吸引力场正则化的短程线主动轮廓模型

短程线主动轮廓模型是近几年提出的一种有效的多目标轮廓提取算法 .本文在详细分析其动力学过程的基础上 ,针对该模型中存在的局限性和不足 ,提出对边缘吸引力场进行正则化的方法 ,并采用多尺度模型 ,有效的改善了该模型不能对存在断裂轮廓的目标进行正确提取和凹边缘搜索能力弱的缺点 ,增强了抗噪声和虚假边缘干扰的能力 ,使该算法具有更好的鲁棒性和实用性 .     

关于Erdos-Jacobson-Lehel 问题的门槛

设σ(k,n）表示最小的正整数m，使得对于每个n项正可图序列，当其项和至少为m时，有一个实现含k 1个顶点的团作为其子图。Erdos等人猜想：σ(k,n）＝（k-1)(2n-k) 2.Li等人证明了这个猜想对于k≥5，n≥（^k2））＋3是对的，并且提出如下问题：确定最小的整数N（k），使得这个猜想对于n≥N(k）成立。他们同时指出：当k≥5时，[5k-1/2]≤N(k）≤（^k2) 3.Mubayi猜想：当k≥5时，N(k）＝[5k-1/2]。在本文中，我们证明了N（8）＝20，即Mubayi猜想对于k=8是成立的。