一种实用的内插FFT信号处理方法

基于信号样本可以重建信号频谱包络,本文提出一种简单、实用的内插方法,将此方法的有效性和FFT的优越性结合起来应用于细化频率分辨率,以适应需要获取信号较多频谱信息的应用场合。     

广义de Bruijn多计算机互连网络

（ｄ，ｍ）ｄｅ Ｂｒｕｉｊｎ多计算机系统互连网络限制网络的规模必须为ｄ＾ｍ。本文把（ｄ，ｍ）ｄｅ Ｂｒｕｉｊｎ网络推广到了任意顶点情形，提出了广义ｄｅ Ｂｒｕｉｊｎ网络，研究了广义ｄｅ Ｂｒｕｉｊｎ网络的自路由性质，设计了路由算法，研究了广义ｄｅ Ｂｒｕｉｊｎ网络顶点度的性质和容错特性。     

基于森林火灾平台的红外图像识别技术研究

森林火灾探测一直是森林资源保护工作中的老大难问题。早期的火灾探测技术多是基于火灾的烟雾和温度特征的,但其判别标准过于单一,误报率较高。随着计算机技术的发展和红外探测器的发明,出现了基于红外图像处理的森林火灾防护技术,该技术主要对红外探测器获得的森林现场的红外图像进行一定的处理后识别图像中是否有火灾出现。本文的主要工作是进行红外图像增强算法的研究,通过对各种算法的描述和仿真实验结果分析,提出一种相对较适合森林背景红外图像的处理算法一将基于频域处理的提升小波变换与直方图修正结合起来的红外图像增强算法。     

A note on circle packing

The problem of packing circles into a domain of prescribed topology is considered. The circles need not have equal radii. The Collins-Stephenson algorithm computes such a circle packing. This algorithm is parallelized in two different ways and its performance is reported for a triangular, planar domain test case. The implementation uses the highly parallel graphics processing unit (GPU) on commodity hardware. The speedups so achieved are discussed based on a number of experiments.     

电导法测定乙酸解离常数实验数据处理软件设计

介绍了用电导法测定乙酸解离常数的实验原理和步骤,给出了用计算机对电导法测定乙酸解离常数的实验数据进行处理的软件设计方法,同时给出了用Visual FoxPro 6.0编写电导法测定乙酸解离常数的处理软件的基本结构和部分程序代码。     

动态规划法求解加工顺序问题的研究

提出动态规划法求解加工顺序问题的方法,分析最优子结构性质,建立最优值的递归关系式,分析Johnson-Bell-man’s Rule并设计算法流程,最后通过构造实例表明了算法的有效性。     

煤炭企业信息化建设问题探讨

企业信息化作为国民经济信息化的基础,是当前推进国民经济信息化的重中之重。伴随全球信息化浪潮,我国煤矿企业信息化进程不断向深度和广度双向推进,但依然存在一些不尽人意的地方。本文首先分析了煤矿企业信息化的现状和制约信息化的原因,在此基础生展望了煤矿企业信息化的发展方向。     

Automatic Channel Detection Using DNN on 2D Seismic Data

Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources. Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs. However, manual channel picking is both time consuming and tedious. Moreover, similar to any other process dependent on human intervention, manual channel picking is error prone and inconsistent. To address these issues, automatic channel detection is both necessary and important for efficient and accurate seismic interpretation. Modern systems make use of real-time image processing techniques for different tasks. Automatic channel detection is a combination of different mathematical methods in digital image processing that can identify streaks within the images called channels that are important to the oil companies. In this paper, we propose an innovative automatic channel detection algorithm based on machine learning techniques. The new algorithm can identify channels in seismic data/images fully automatically and tremendously increases the efficiency and accuracy of the interpretation process. The algorithm uses deep neural network to train the classifier with both the channel and non-channel patches. We provide a field data example to demonstrate the performance of the new algorithm. The training phase gave a maximum accuracy of 84.6% for the classifier and it performed even better in the testing phase, giving a maximum accuracy of 90%.