计算机软件技术在大数据中的应用

随着信息技术的高速发展,人类迎来了大数据时代。在这样的时代下,信息数据呈现出几何式增长趋势,人们对于信息数据分析和处理的要求日益提升。而传统计算机软件技术已经无法满足当代社会发展需求,加强对计算机软件技术的优化和创新已经迫在眉睫。鉴于此,本文主要对计算机软件技术的发展现状、计算机软件技术在大数据中的主要技术、计算机软件技术在大数据中的具体应用、计算机软件技术的发展趋势等方面进行了分析和探讨。     

Symmetry in 3D Geometry: Extraction and Applications

The concept of symmetry has received significant attention in computer graphics and computer vision research in recent years. Numerous methods have been proposed to find, extract, encode and exploit geometric symmetries and high‐level structural information for a wide variety of geometry processing tasks. This report surveys and classifies recent developments in symmetry detection. We focus on elucidating the key similarities and differences between existing methods to gain a better understanding of a fundamental problem in digital geometry processing and shape understanding in general. We discuss a variety of applications in computer graphics and geometry processing that benefit from symmetry information for more effective processing. An analysis of the strengths and limitations of existing algorithms highlights the plenitude of opportunities for future research both in terms of theory and applications.     

研究办公自动化中计算机信息处理技术的应用现状

随着社会文化不断发展,计算机技术逐渐出现在普遍大众的生活之中,各个领域也相继将计算机信息技术引入其中。学校为了实现办公自动化,将计算机信息处理技术应用在学校的方方面面。使学校教职工的办公效率得以提高,办公环境也受到了革新。本文将通过对计算机处理技术的主要功能进行探讨,分析了计算机信息技术在自动化办公之中的优势,并从中提出办公自动化过程中出现的一些现实问题,并给出简单的解决策略,最后就信息处理技术对办公自动化的影响,提出了对未来发展的整体趋向。     

数据库软件的应用

随着科学技术的发展,计算机应用技术已经逐渐深入到了各个领域,对人们的生产和生活造成了十分重要的影响,数据库软件是计算机信息处理中一个十分关键的组成部分,开发和推广数据库软件是进行现代信息化社会资源管理与开发的前提。经过多年对数据库软件的开发与研究,我国有了一定的技术积累,但依旧有很大的提升空间。     

大数据时代计算机信息处理技术分析

在大数据时代下,海量数据呈爆炸式增长,对计算机信息处理提出要了更高的要求。基于此,本文从大数据时 代对计算机处理技术的新要求作为切入点,在此基础上,对当前信息处理技术的原理及其发展趋势展开重点探讨,希望能对相 关人士提供些许参考。     

数据库的图象处理方法

随着电脑应用的深入和普及,在计算机人事信息管理过程中,多媒体信息的处理方法和手段已经成为计算机信息处理的一个热门话题。该文探讨了数据库图像存储、处理以及在程序设计中如何还原和再现的一系列问题,对于学习数据库编程具有非常重要的参考和借鉴意义。     

虚拟现实技术在现代教学中的应用研究

虚拟现实技术是一门综合性信息技术,它融合了数字图像处理、计算机图形学、多媒体技术、传感器技术等多个信息技术,提供了更高级的集成性和交互性,给人以愈发逼真的场景.虚拟现实技术作为新型的教学媒体已广泛应用于教育领域.主要探讨虚拟现实技术的基本特征以及它在现代教学中的作用和应用.     

网络信息处理与安全方面的计算机应用

伴随着目前科学技术水平的逐步提高,网络及计算机被广泛应用到各个领域,并获取了一定的成果。人们在享受互联网进行工作所带来的快速与便捷同时,也需要重视网络信息的安全,采用多种措施提高信息的处理能力,保证信息的安全。本文简要论述了计算机网络信息处理及安全的相关内容,目的在于更好的提高计算机的应用质量,进一步推动社会发展。     

信息技术下机房技术人员应具备的五种职业素质

本文从过硬的思想素质、过硬的业务素质、过硬的计算机和网络素质、过硬的信息素质和过硬的管理素质等五个方面论述新形式下高校计算机机房实验技术人员应具备的职业素质。     

Estimation of word emotions based on part of speech and positional information

Recently, studies on emotion recognition technology have been conducted in the fields of natural language processing, speech signal processing, image data processing, and brain wave analysis, with the goal of letting the computer understand ambiguous information such as emotion or sensibility. This paper statistically studies the features of Japanese and English emotional expressions based on an emotion annotated parallel corpus and proposes a method to estimate emotion of the emotional expressions in the sentence. The proposed method identifies the words or phrases with emotion, which we call emotional expressions, and estimates the emotion category of the emotional expressions by focusing on the three kinds of features: part of speech of emotional expression, position of emotional expression, and part of speech of the previous/next morpheme of the target emotional expression.     

神经计算科学的展望

神经计算科学是从信息科学的角度来研究如何加速神经网络模仿和延伸 人脑的高级精神活动,如联想,记忆,推理,思维及意识等智能行为,这涉及到脑科学,认知科学,神经生物学,非线性科学,计算机科学,数学,物理学诸学科的综合集成,它是综合研究和实现类脑智能信息系统的一个新思想和新策略,本文仅重点论述神经智能信息处理,盲信号处理技术,演化认知与进化计算,混沌信息技术及软科学的开发,由此展望神经计算科学的未来发展及潜力,可带动诸学科及信息技术的美好发展。     

Distributed memory systems for simulating artificial neural networks

In executing tasks involving intelligent information processing, the human brain performs better than the digital computer. The human brain derives its power from a large number [O(10¹¹)] of neurons which are interconnected by a dense interconnection network [O(10⁵) connections per neuron]. Artificial neural network (ANN) paradigms adopt the structure of the brain to try to emulate the intelligent information processing methods of the brain. ANN techniques are being employed to solve problems in areas such as pattern recognition, and robotic processing. Simulation of ANNs involves implementation of large number of neurons and a massive interconnection network. In this paper, we discuss various simulation models of ANNs and their implementation on distributed memory systems. Our investigations reveal that communication-efficient networks of distributed memory systems perform better than other topologies in implementing ANNs.     

Human operators and real-time expert systems

Abstract: This paper describes and examines real-time expert systems from the perspective of their users. It categorizes real-time expert systems according to a three-component system consisting of an expert system, a user and a process being controlled, by considering all possible arrangements of information flow between the components, and uses this classification as a basis for reviewing real-time expert systems. The focus of interest lies in those characteristics of real-time expert systems which impinge on users, especially as they have been discussed by computer scientists. After critically examining these claims, the paper discusses the interaction between systems and users at the perceptual/motor, cognitive and supervisory levels. It concludes by arguing that the successful design and installation of real-time expert systems require the application of ergonomics techniques to provide for efficient and accurate user-system interaction.     

多媒体智能数据库系统MIDS／BUAA的总体设计

1.引言近二十年内,传统数据库(特别是RDB)技术获得了极大发展和广泛应用。但是,自八十年代以来,随着硬件造价的持续下降,人们对数据库提出了更多和更高的需(要)求,促     

Simulations of construction learning for neuron-computer resources

It is well known that information processing in the brain depends on neuron systems. Simple neuron systems are neural networks, and their learning methods have been studied. However, we believe that research on large-scale neural network systems is still incomplete. Here, we propose a learning method for millions of neurons as resources for a neuron computer. The method is a type of recurrent path-selection, so the neural network objective must have nesting structures. This method is executed at high speed. When information processing is executed by analogue signals, the accumulation of errors is a grave problem. We equipped a neural network with a digitizer and AD/DA (Analogue Digital) converters constructed of neurons. They retain all information signals and guarantee precision in complex operations. By using these techniques, we generated an image shifter constructed of 8.6 million neurons. We believe that there is the potential to design a neuron computer using this scheme. This work was presented in part at the Fifth International Symposium on Artificial Life and Robotics, Oita, Japan, January 26–28, 2000     

Perception granular computing in visual haze-free task

In the past decade, granular computing (GrC) has been an active topic of research in machine learning and computer vision. However, the granularity division is itself an open and complex problem. Deep learning, at the same time, has been proposed by Geoffrey Hinton, which simulates the hierarchical structure of human brain, processes data from lower level to higher level and gradually composes more and more semantic concepts. The information similarity, proximity and functionality constitute the key points in the original insight of granular computing proposed by Zadeh. Many GrC researches are based on the equivalence relation or the more general tolerance relation, either of which can be described by some distance functions. The information similarity and proximity depended on the samples distribution can be easily described by the fuzzy logic. From this point of view, GrC can be considered as a set of fuzzy logical formulas, which is geometrically defined as a layered framework in a multi-scale granular system. The necessity of such kind multi-scale layered granular system can be supported by the columnar organization of the neocortex. So the granular system proposed in this paper can be viewed as a new explanation of deep learning that simulates the hierarchical structure of human brain. In view of this, a novel learning approach, which combines fuzzy logical designing with machine learning, is proposed in this paper to construct a GrC system to explore a novel direction for deep learning. Unlike those previous works on the theoretical framework of GrC, our granular system is abstracted from brain science and information science, so it can be used to guide the research of image processing and pattern recognition. Finally, we take the task of haze-free as an example to demonstrate that our multi-scale GrC has high ability to increase the texture information entropy and improve the effect of haze-removing.     

通用信息处理平台的设计研究

结合计算机编程技术和通信技术,总结了各个领域信息处理平台的普遍特征,论述了设计通用信息处理平台的必要性,并介绍了其关键技术要点。     

Parallel computers for region-level image processing

It is well known that parallel computers can be used very effectively for image processing at the pixel level, by assigning a processor to each pixel or block of pixels, and passing information as necessary between processors whose blocks are adjacent. This paper discusses the use of parallel computers for processing images at the region level, assigning a processor to each region and passing information between processors whose regions are related. The basic difference between the pixel and region levels is that the regions (e.g. obtained by segmenting the given image) and relationships differ from image to image, and even for a given image, they do not remain fixed during processing. Thus, one cannot use the standard type of cellular parallelism, in which the set of processors and interprocessor connections remain fixed, for processing at the region level. Reconfigurable cellular computers, in which the set of processors that each processor can communicate with can change during a computation, are more appropriate. A class of such computers is described, and general examples are given illustrating how such a computer could initially configure itself to represent a given decomposition of an image into regions, and dynamically reconfigure itself, in parallel, as regions merge or split.     

Character sets of today and tomorrow

This paper reviews the character sets used in today's computer systems and discusses character sets for future computer systems. Some newer Latin-based character sets are introduced and compared in terms of their suitability for information interchange and information processing. Finally, the character set most appropriate for use in future computer systems is suggested.     

有指向性的视觉注意计算机模型

注意把有限的处理资源优先分配给那些需要精细加工的信息,能提高视觉信息加工中的检测能力和响应速度.基于生物视觉系统的生理结构特点,建立了模拟生物视觉注意系统的有指向性的视觉注意计算机模型.模型首先模拟生物视网膜的成像机制,将视场图像转化为视网膜图像;然后将最大梯度边缘检测和c-均值聚类等方法相结合,对视网膜图像中的目标进行编码,分别提取每个目标的颜色、中心以及边缘点集合等基本信息;最后用知识库中指向性目标的特征来指导注意焦点的转移.实验结果表明,利用此模型能较好地实现注意焦点的转移.