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Personal Information Agent

Personal Information Agent
Personal Information Agent

Personal Information Agent

Dominik Kuropka1),Thomas Serries2)

1)University of Muenster,dominik.kuropka@wi.uni-muenster.de

2)University of Muenster,thomas.serries@wi.uni-muenster.de

Abstract:Information over?ow is one of the great-est challenges for information focused professions today. This paper presents the Personal Information Agent,an agent based information?ltering prototype.The proto-type has been build to prove the concept of our agent and neuronal network based information?ltering system which is presented in this paper.Further experiences made during implementation and ideas for future work are discussed.

Keywords:Agents,Information Filtering,Neuronal Network

1.Introduction

Beginning with the use of computers more and more structured information were stored digitally.The more desktop computers(PCs)were used in of?ces the more they were used to create unstructured and semi-structured information like reports,letters,etc.More and more un-structured documents were and are generated using com-puters today.Development of the Internet supports this trend.Publishing documents is easier than ever before. The number of available documents/information is grow-ing faster than ever.

By now information has become one of the most im-portant resources for business and research.But the fact that more information is available for everybody than ever before,doesn’t lead to better decisions.Restricted capacity of humans in information processing forces to reduce the amount of presented information.So today one of the greatest challenges is the ef?cient selection of relevant information:information?ltering(IF).

In contradiction to being informed by information?l-tering systems users of information retrieval systems are searching for information actively.The user has an idea of the information she needs.She works with the in-formation retrieval system and starts searching for in-formation(ad-hoc query).Users of information?lter-ing systems are informed about relevant information au-tonomously.So the main task of the system is to?nd out which of the collected information is good enough to support the work of the user without overloading her with useless information.[1]

2.Requirements

Information?ltering systems have to meet at least the fol-lowing four requirements to be of use in a wide range of use cases:

The system should be able to observe several,dif-ferent information sources on its own.To achieve this,it has to cope with different data and document formats.If information sources do not support push-mechanisms to trigger its listeners(e.g.web-sites), the information?ltering system has to scan the in-formation sources for changes regularly.

Messages presumably important for the user should be presented at a glance.

Information?ltering systems will never reach100% correctness in evaluating messages.So unimportant evaluated messages should be accessible,too.

For being accepted by the user an easy-to-use user interface has to be provided by the system.A user should understand the main functionality of the user interface intuitively and unexperienced users should be able to customize their?ltering pro?les.Collect-ing information about user pro?les should require as few interaction with the system as possible.

3.State of the art

Information?ltering has two independent dimensions shown in the morphological framework in?gure1:the classi?cation approach and the classi?cation method used to implement the classi?cation approach.

keyword importance

based based

explicit profile implicit profile without

with pre?classification profile

pre?classification

profile

Figure1:Morpholigical framework for IF

Referring to the?rst dimension,information?ltering

always requires some kind of classi?cation of messages.

In general two different classi?cation approaches exist.

The keyword based approach arranges messages depend-

ing on their content into one or more categories which are

represented by https://www.sodocs.net/doc/159784081.html,ually categories are struc-

tured within a hierarchy or network.The user has to

select those categories she is interested in.Importance

based classi?cation assigns a degree of importance to the

combination of each message and each user.This is an

one dimensional numeric value derived from the content.

The second dimension describes two main types of

classi?cation https://www.sodocs.net/doc/159784081.html,rmation?ltering systems

without pre-classi?cation pro?le in general(also called

collaborative information?ltering systems)sort informa-

tion by date or by user activity or user voting.Alterna-

tively information can be evaluated as important or ap-

pendant to a category if a quorum of other users marked

it accordingly.Public examples for a systems implement-

ing the method of importance based classi?cation without

pre-classi?cation pro?le are the Linux Community web-

site1or the NewsSIEVE2tool for Usenet newsgroups.

Generally systems implementing classi?cation without

pre-classi?cation pro?les loose time between the appear-

ance of a new message and the?rst time this message is

classi?ed by users.This approach is not user speci?c at

all which makes it usable only,if all users have similar

information demands.

In?ltering systems pro?les are used to represent users’

informations demands.By asking the user about her in-

formation demand an explicit pro?le can be created.This

strategy is often implemented in combination with the

keyword based https://www.sodocs.net/doc/159784081.html,monly known systems us-

ing keyword based pre-classi?cation pro?les are e-mail

clients using?ltering rules for example.Other exam-

ples are category based newsletters like the BDW-Agent3

or news-tickers like https://www.sodocs.net/doc/159784081.html,ers select categories

4https://www.sodocs.net/doc/159784081.html,

5ftp://https://www.sodocs.net/doc/159784081.html,/pub/faculty/jrb/rama

tailed look at the functionality of the agent,the used tools

and algorithms the architecture of the system is illustrated

?rst.

4.1.Agent based approach

W OOLDRIDGE and J ENNINGS argue that rational agents

should have the following properties:autonomy,proac-

tiveness,reactivity and social ability[12].These proper-

ties make agents powerful and enable them to meet the

requirements of adaptive information?ltering systems.

Possible speci?cations of these properties for information

?ltering systems are:

autonomy:Information?ltering agents should work

in background;independent from the user.They

collect news-messages and decide about the pre-

sumed relevance for their users.To support au-

tonomy W OOLDRIDGE proposes constructs like be-

liefs,desires and intentions.[13]Information?lter-

ing agents estimate what might be of interest for

their users.They desire to make correct decisions

about the relevance of messages6and to keep their

users informed about important news.Finally agents

have a notion how to achieve their desires,e.g.by

collecting user-feedback or direct questions to the

user.

proactiveness:To achieve their desires agents have

to execute some actions supporting their aims,e.g.

scanning news-sources or contacting the user di-

rectly to inform her about very important news.7

reactivity:Using techniques of arti?cial intelligence

(AI),e.g.arti?cial neural networks or statistical data

analysis,agents adapt their owners’information de-

mands and believes by feedback.

social ability:Information?ltering agents have to

communicate with different news-sources as well as

with users.The user-interface should be easy to use

and give a feeling of working in cooperation with an

intelligent being.8

4.2.Personal Information Agent

The Personal Information Agent(PI-Agent)provides in-

formation?ltering functionality in an agent oriented

9http://www.heise.de/newsticker

Figure3:Steps of linguistic processing known from statistical data analysis.Researches in com-

puter linguistic developed toolsets which are used within the PI-Agent architecture.Figure3illustrates linguistic

analysis of texts.

The?rst step of linguistic processing is to eliminate so called stop words form further processing.These words are used very often within all documents and the fre-quency of usage is nearly constant within different doc-uments.Because of this they do not give any informa-tion about the content of a certain text.Examples for stop words for English are:“and”,“the”,“very”,“by”,or “which”.

Further reduction of data can be achieved by using the knowledge about language speci?c?exion of words.One word(interpreted as meaning)can be written in differ-ent sequences of letters: e.g.‘misunderstand’has the same meaning as‘misunderstood’.Difference in time does not in?uence its meaning.Depending on the used algorithm reduction may lead to loss of information.So two different words may be represented by the same term (e.g.‘mine’)or terms of different words are reduced to the same basic form.The problems resulting from this can only be solved by methods of semantic text analysis.

A collection of different approaches can be found in[9]. None of them is implemented in the PI-Agent yet.

The categorization of documents requires the recog-nition of their meaning.Analyzing basic forms of the used words ensures to recognize different spellings of one word as the same meaning.But different words(basic forms)may have the same meaning or one word is often (only)used in conjunction with an other.To avoid nega-tive effects of correlated words a statistical toolset is used. Alternatively the linguistic analysis can use thesauri to eliminate problems resulting from correlated words.The-sauri may enable further enhancements if additional in-formation like generalization and specialization of words are also taken into account.

Because stop words and?exion analysis are deter-mined by the language these tools have to be provided for every supported language.Availability and quality of these tools are language dependent,too.To avoid these dependencies texts written in a not supported language are translated into the target language.Depending on the quality of the translation tool this may lead to major prob-lems in recognizing the correct meaning of texts.For commercial use the following aspects have to be taken into account for decision if this solution is suitable: How is the translation tool working?Translation tools doing a simple word-by-word translations may tend to false interpretations of some words.Be-cause the PI-Agent does not perform linguistic but statistical analysis,resulting errors will be of mi-nor importance.Wrong translations will be made the same way always.Translation tools based on linguistic analysis will have to be domain speci?c because they may translate words depending on the context into different words of the target language.

How many domains is PI-Agent used in?Depend-ing on the domain of a text one word of the source language may have different translations in the tar-get language.If PI-Agent is used in heterogenous domains this means that translation tools have to be able to recognize the domain of texts and use the corresponding dictionary.

Because usually one message is evaluated by more than one agent it is useful to execute the translation and the stemming algorithms only once for reasons of perfor-mance.Focusing on the main research target the decision was made to implement the linguistic analysis for En-glish https://www.sodocs.net/doc/159784081.html,ed algorithms and methods are taken from [5](stop word list),[14](stemming rules),and[6](cor-relation analysis).Support for non-English documents is realized by the integration a freely available translation service(www.babel?https://www.sodocs.net/doc/159784081.html,).

4.4.Agents and Neuronal Networks

User speci?c agents provide the core functionality of the PI-Agent system.They represent user pro?les and eval-uate messages.Adapting themselves to users’informa-tion demands and using linguistic and statistical tools in-creases quality of relevance presumption.

The evaluation mechanism itself is implemented as an arti?cial neural network[7].The processing elements (neurons)of agent’s neuronal network are perceptrons[8] which are organized in input,output and hidden layers. Similar to the approach described in[2]each neuron of the input layer represents one keyword.For each key-word being part of a message the corresponding neuron is set to the value1,all other neurons are set to the value 0(?gure4).After processing the input the neuronal net-work computes the result in the output layer which con-tains only one neuron.The result range[0;1]of the neu-ron is translated into human readable categories from one

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Figure4:Evaluation of information

to six which represents the predicted,user individual im-portance of a message.

Every message can be evaluated by the user to give feedback to her agent.The agent collects this information and starts the adaption processes regularly.Currently the last150user evaluations are used to recreate and train the neuronal network by the back propagation algorithm as described in[7].

For implementing arti?cal neural networks the pro-gramming paradigm matching the requirements,struc-ture and dynamics at best should be chosen.Neuronal networks consist of a set of data representing the struc-ture of the network and of a set of algorithms working on the data and representing its behavior.Objects–which are the key-components of the object oriented approach –have the same properties:They consist of data which is encapsulated by the object.Methods implement in-terfaces to object’s data and de?ne its behavior,i.e.all other functionality to process its data.[3]So,the object oriented approach–with its distinction between object’s structure and behavior–?ts good into the implementa-tion requirements of neuronal networks.

Just representing the network by an object does not lead to the highest degree of abstraction.For informa-tion?ltering the neuronal network alone is not suf?cient. Linguistic and statistical algorithms are needed as well as access to the training data.The training data is needed for the adaption mechanism being executed autonomously and not necessarily synchronized with other system com-ponents.To abstract from this details and to allow re-usability and?exibility a higher level of abstraction is needed:agents.Section4.1.already showed that the agent concept provides all functionality needed to encap-sulate the complexity of an information?ltering neuronal network.

4.5.Implementation details

The PI-Agent prototype is fully implemented in Java. Persistency is ensured by PostreSQL10database manage-ment system with JDBC as communication interface be-10https://www.sodocs.net/doc/159784081.html, 11https://www.sodocs.net/doc/159784081.html, 12https://www.sodocs.net/doc/159784081.html,

ever new Internet sites of potential interest are inserted into the content of the portal.While mail?ltering re-quires hosting agents by the Internet portal,tracking of the content of the portal can(but does not have to)be realized by external agents.

Within enterprises the PI-Agent technology can be used to realize user-individual bulletin-boards.Exchang-ing knowledge by bulletin boards is a powerful way to share business knowledge within the whole enterprise. As stated in section3.searching and classi?cation of in-formation is not easy.So such systems often lack of ac-ceptance by the users.Supporting them in information retrieval can help to overcome these retentions. Generally PI-Agent systems can be used to create indi-vidual information sources(using push mechanisms)out of a set of non-individual information sources(using pull mechanisms).

6.Related work

Several researches on information?ltering systems have been made in the past.In this section two of the most interesting projects are compared to the PI-Agent system. MINT is an abbreviation for’Management Informa-tion from the Internet’:It implements a prototype of an editorial workbench.[4]The key concept is the support of two different user groups within an enterprise.The?rst group consists of in-house information brokers who pro-vide news to the other group–the information receivers, such as managers.The main tasks of the MINT system are collecting information from different web-sites,sup-porting information brokers in evaluation and categoriza-tion of messages and enabling adequate presentation of information to information receivers.The main differ-ence to the PI-Agent is the use of human resources for evaluation of information.Potentially a better presump-tion quality is reachable but raises the total costs of the system.The engagement of information brokers in small or medium sized business is often more expensive then the achieved improvements reduce costs.

As described in section4.2.the agent layer of the PI-Agent architecture contains a arti?cal neural network as described by B OGER ET.AL.[2]The authors employed their neural network for information?ltering and term se-lection.About1500e-mails from10users where used to train the networks.With this data they reached predic-tion quality of76–99%.S HAPIRA ET.AL.found out that“content-based?ltering”and“sociological?ltering”(and combinations of them)only reach40–70%predic-tion precision.[10]Content-based?ltering is based on correlation of two weighted vectors of terms(one for the user and one for the information).Sociological?ltering de?nes user groups basing on evaluation pro?les of the users.The relevance evaluation for one user is made from her own evaluation rules and the rules of the correspond-ing user group in https://www.sodocs.net/doc/159784081.html,paring quality of these approaches information?ltering using neural networks shows signi?cant advantages.

7.Discussion and future work

The aims of the PI-Agent project are to prove the suit-ability of the described architecture for information?l-tering.For evaluation of presumption quality data from four regularly users of the PI-Agent system were ana-lyzed:The last150user-evaluated messages from each user were used to train one neural net for each user.For a test set consisting of254messages not used for training a presumption precision of80%with a standard devia-tion of11%was reached.A message pre-evaluated by the system was rated as correct if the difference between user evaluation and system evaluation was equal or less than one grade.Starting points to improve quality are: The representation of keywords in input layer of the neuronal networks should be changed from0;1to

-1;1.This makes neuronal networks more power-ful.So,the following situation can be re?ected by networks:’A message is important if keyword but not is part of the message.’

Other training algorithms could improve learning and enable re-usability of networks(maybe in com-bination with genetic algorithms).

Thesauri transform used words into their meaning.

This reduces correlation between choice of word and evaluation.

Integration of pre-classi?cation without pro?les based on statistical analysis and using explicit pro-?les to allow users to describe what information they miss may reduce pre-evaluation errors.

In discussion with users of the system the user inter-face has been the main reason for critics.The system should not require feedback for doing its job.It should record users’behavior to collect information about their information demands.So the user will only have to give feedback in case pre-evaluation was wrong.

On the technical level the agent concept is adequate for abstraction from complexity of the information?ltering problem.But for a seamless implementation powerful agent platforms are needed.A good platform should meet at least the following criteria:

Transparent persistency support:The neuronal net-work represents agent’s knowledge about user’s in-formation demand and is created by a long run-ning process of communication with the user.Data stored within the network is necessary for the sys-tem and hard to restore if a system crashes.A trans-parent persistency layer ensures that agents can be recovered after crashes or after structural changes of the agent-software.It might be usefull to adapt persistency methods known for object oriented sys-tems[11]into agent technology.

Scalability:The resource usage is mainly depending on the number of agents running.Agents are inde-pendent from each other;but PI-Agent architecture provides layers that might be used by them in paral-lel.To avoid implementing same functionality sev-eral times the agent platform should be scalable at least for these layers.

8.Final remarks

As shown in this paper the PI-Agent is an adequate start-ing point for further research in?eld of information?lter-ing.Insuf?cient presumption quality can be enhanced by justi?cation of the training parameter of the neural net-work,implementation of other learning algorithms,addi-tion of further linguistic tools or integration of other clas-si?cation methods.Never the less the PI-Agent architec-ture enables an abstraction from implementation details of the evaluation component.The reached level of ab-straction is suf?cient to integrate the PI-Agent into usual agent platforms or systems.

The PI-Agent systems may even be integrated into other systems like enterprise information portals which do not manage qualitative information only.Here the PI-Agent may be able to support the user in navigating through the set of reference objects,performance?gures and reports.Even seamless integration of qualitative and quantitative information might be possible. References

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tion Filtering and Automatic Keyword Identi?cation by Arti?cal Neural Networks,In:H.R.Hansen,M.Bichler,

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on Information Systems(ECIS2000),V olume1,Vienna 2000,pp.379–385.

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Internet.http://www.wi1.uni-erlangen.de/projects/mint /mint.pdf;downloaded on July,30th2001.

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数据快速压缩算法的C语言实现

价值工程 置,是一项十分有意义的工作。另外恶意代码的检测和分析是一个长期的过程,应对其新的特征和发展趋势作进一步研究,建立完善的分析库。 参考文献: [1]CNCERT/CC.https://www.sodocs.net/doc/159784081.html,/publish/main/46/index.html. [2]LO R,LEVITTK,OL SSONN R.MFC:a malicious code filter [J].Computer and Security,1995,14(6):541-566. [3]KA SP ER SKY L.The evolution of technologies used to detect malicious code [M].Moscow:Kaspersky Lap,2007. [4]LC Briand,J Feng,Y Labiche.Experimenting with Genetic Algorithms and Coupling Measures to devise optimal integration test orders.Software Engineering with Computational Intelligence,Kluwer,2003. [5]Steven A.Hofmeyr,Stephanie Forrest,Anil Somayaji.Intrusion Detection using Sequences of System calls.Journal of Computer Security Vol,Jun.1998. [6]李华,刘智,覃征,张小松.基于行为分析和特征码的恶意代码检测技术[J].计算机应用研究,2011,28(3):1127-1129. [7]刘威,刘鑫,杜振华.2010年我国恶意代码新特点的研究.第26次全国计算机安全学术交流会论文集,2011,(09). [8]IDIKA N,MATHUR A P.A Survey of Malware Detection Techniques [R].Tehnical Report,Department of Computer Science,Purdue University,2007. 0引言 现有的压缩算法有很多种,但是都存在一定的局限性,比如:LZw [1]。主要是针对数据量较大的图像之类的进行压缩,不适合对简单报文的压缩。比如说,传输中有长度限制的数据,而实际传输的数据大于限制传输的数据长度,总体数据长度在100字节左右,此时使用一些流行算法反而达不到压缩的目的,甚至增大数据的长度。本文假设该批数据为纯数字数据,实现压缩并解压缩算法。 1数据压缩概念 数据压缩是指在不丢失信息的前提下,缩减数据量以减少存储空间,提高其传输、存储和处理效率的一种技术方法。或按照一定的算法对数据进行重新组织,减少数据的冗余和存储的空间。常用的压缩方式[2,3]有统计编码、预测编码、变换编码和混合编码等。统计编码包含哈夫曼编码、算术编码、游程编码、字典编码等。 2常见几种压缩算法的比较2.1霍夫曼编码压缩[4]:也是一种常用的压缩方法。其基本原理是频繁使用的数据用较短的代码代替,很少使用 的数据用较长的代码代替,每个数据的代码各不相同。这些代码都是二进制码,且码的长度是可变的。 2.2LZW 压缩方法[5,6]:LZW 压缩技术比其它大多数压缩技术都复杂,压缩效率也较高。其基本原理是把每一个第一次出现的字符串用一个数值来编码,在还原程序中再将这个数值还成原来的字符串,如用数值0x100代替字符串ccddeee"这样每当出现该字符串时,都用0x100代替,起到了压缩的作用。 3简单报文数据压缩算法及实现 3.1算法的基本思想数字0-9在内存中占用的位最 大为4bit , 而一个字节有8个bit ,显然一个字节至少可以保存两个数字,而一个字符型的数字在内存中是占用一个字节的,那么就可以实现2:1的压缩,压缩算法有几种,比如,一个自己的高四位保存一个数字,低四位保存另外一个数字,或者,一组数字字符可以转换为一个n 字节的数值。N 为C 语言某种数值类型的所占的字节长度,本文讨论后一种算法的实现。 3.2算法步骤 ①确定一种C 语言的数值类型。 —————————————————————— —作者简介:安建梅(1981-),女,山西忻州人,助理实验室,研究方 向为软件开发与软交换技术;季松华(1978-),男,江苏 南通人,高级软件工程师,研究方向为软件开发。 数据快速压缩算法的研究以及C 语言实现 The Study of Data Compression and Encryption Algorithm and Realization with C Language 安建梅①AN Jian-mei ;季松华②JI Song-hua (①重庆文理学院软件工程学院,永川402160;②中信网络科技股份有限公司,重庆400000)(①The Software Engineering Institute of Chongqing University of Arts and Sciences ,Chongqing 402160,China ; ②CITIC Application Service Provider Co.,Ltd.,Chongqing 400000,China ) 摘要:压缩算法有很多种,但是对需要压缩到一定长度的简单的报文进行处理时,现有的算法不仅达不到目的,并且变得复杂, 本文针对目前一些企业的需要,实现了对简单报文的压缩加密,此算法不仅可以快速对几十上百位的数据进行压缩,而且通过不断 的优化,解决了由于各种情况引发的解密错误,在解密的过程中不会出现任何差错。 Abstract:Although,there are many kinds of compression algorithm,the need for encryption and compression of a length of a simple message processing,the existing algorithm is not only counterproductive,but also complicated.To some enterprises need,this paper realizes the simple message of compression and encryption.This algorithm can not only fast for tens of hundreds of data compression,but also,solve the various conditions triggered by decryption errors through continuous optimization;therefore,the decryption process does not appear in any error. 关键词:压缩;解压缩;数字字符;简单报文Key words:compression ;decompression ;encryption ;message 中图分类号:TP39文献标识码:A 文章编号:1006-4311(2012)35-0192-02 ·192·

网络安全监控系统

点击文章中飘蓝词可直接进入官网查看 网络安全监控系统 随着网络信息技术的快速发展,网络数据资源变得越来越开放普及,但是随之而来的是信 息安全问题日益突出。同时,网络安全威胁的范围和内容不断扩大和演化,网络安全形势与挑 战日益严峻复杂。所以对于网络安全监控系统,这时候才显示出其无可替代的重要性。网络安 全监控系统有什么样的特点?今天给大家介绍一下。 网络安全监控系统,能够将抽象的网络和系统数据进行可视化呈现,从而对网络中的主机、安全设备、网络设备、应用系统、操作系统等整体环境进行安全状态监测,帮助用户快速掌握 网络状况,识别网络异常、入侵,把握网络安全事件发展趋势,感知网络安全态势。 在一个开放的网络环境中,大量信息流动,全球平均每20秒就发生一起Internet计算机 侵入事件。因此就需要系统对网络安全威胁进行可视化呈现,感知网络安全态势。基于支持二 三维地理空间分布,对主机及关键节点的综合安全信息进行网络态势监控。支持逻辑拓扑层级 结构,从整体安全态势,到信息资产以及安全数据的监测,进行全方位态势监控。支持全网各 节点的信息查询,实时反映节点信息的状态,对节点信息安全进行全面监测。 网络安全监控系统应提供网络威胁入侵检测分析功能,深入分析网络流量信息,对各节点 进行实时监测,并支持多种图表的威胁告警方式,让威胁一目了然。还可查看告警威胁事件的 详细信息,同时支持自定义告警策略,设置告警范围和阀值等策略。基于APT攻击检测系统,对攻击来源、攻击目的、攻击路径进行溯源分析,同时根据安全威胁事件的来源信息和目标信息,结合GIS技术将虚拟的网络威胁和现实世界生动的结合起来,实现网络安全态势的可视化。 南京风城云码软件技术有限公司是获得国家工信部认定的“双软”企业,具有专业的软件 开发与生产资质。多年来专业从事IT运维监控产品及大数据平台下网络安全审计产品研发。开发团队主要由留学归国软件开发人员及管理专家领衔组成,聚集了一批软件专家、技术专家和 行业专家,依托海外技术优势,使开发的软件产品在技术创新及应用领域始终保持在领域上向 前发展。

压缩编码算法设计与实现实验报告

压缩编码算法设计与实现实验报告 一、实验目的:用C语言或C++编写一个实现Huffman编码的程序,理解对数据进行无损压缩的原理。 二、实验内容:设计一个有n个叶节点的huffman树,从终端读入n个叶节 点的权值。设计出huffman编码的压缩算法,完成对n个节点的编码,并写出程序予以实现。 三、实验步骤及原理: 1、原理:Huffman算法的描述 (1)初始化,根据符号权重的大小按由大到小顺序对符号进行排序。 (2)把权重最小的两个符号组成一个节点, (3)重复步骤2,得到节点P2,P3,P4……,形成一棵树。 (4)从根节点开始顺着树枝到每个叶子分别写出每个符号的代码 2、实验步骤: 根据算法原理,设计程序流程,完成代码设计。 首先,用户先输入一个数n,以实现n个叶节点的Huffman 树; 之后,输入n个权值w[1]~w[n],注意是unsigned int型数值; 然后,建立Huffman 树; 最后,完成Huffman编码。 四、实验代码:#include #include #include #define MAX 0xFFFFFFFF typedef struct / /*设计一个结构体,存放权值,左右孩子*// { unsigned int weight; unsigned int parent,lchild,rchild; }HTNode,*HuffmanTree; typedef char** HuffmanCode;

int min(HuffmanTree t,int i) { int j,flag; unsigned int k = MAX; for(j=1;j<=i;j++) if(t[j].parent==0&&t[j].weight s2) { tmp = s1; s1 = s2; s2 = tmp; } } void HuffmanCoding(HuffmanTree &HT,HuffmanCode &HC,int *w,int n,int &wpl) { int m,i,s1,s2,start,j; unsigned int c,f; HuffmanTree p; char *cd; if(n<=1) return; m=2*n-1; HT=(HuffmanTree)malloc((m+1)*sizeof(HTNode)); for(p=HT+1,i=1;i<=n;++i,++p,++w) { (*p).weight=*w; (*p).parent=0; (*p).lchild=0; (*p).rchild=0; }

交互式多模型算法卡尔曼滤波仿真

交互式多模型算法卡尔曼滤波仿真 1 模型建立 分别以加速度u=0、1、2代表三个不同的运动模型 状态方程x(k+1)=a*x(k)+b*w(k)+d*u 观察方程z(k)=c*x(k)+v(k) 其中,a=[1 dt;0 1],b=[dt^2/2;dt],d=[dt^2/2;dt],c=[1 0] 2 程序代码 由两个功能函数组成,imm_main用来实现imm 算法,move_model1用来生成仿真数据,初始化运动参数 function imm_main %交互式多模型算法主程序 %初始化有关参数 move_model %调用运动模型初始化及仿真运动状态生成函数 load movedata %调入有关参数初始值(a b d c u position velocity pmeas dt tg q r x_hat p_var) p_tran=[0.8 0.1 0.1;0.2 0.7 0.1;0.1 0.2 0.7];%转移概率 p_pri=[0.1;0.6;0.3];%模型先验概率 n=1:2:5; %提取各模型方差矩阵 k=0; %记录仿真步数 like=[0;0;0];%视然函数 x_hat_hat=zeros(2,3);%三模型运动状态重初始化矩阵 u_=zeros(3,3);%混合概率矩阵 c_=[0;0;0];%三模型概率更新系数 %数据保存有关参数初始化 phat=[];%保存位置估值 vhat=[];%保存速度估值 xhat=[0;0];%融合和运动状态 z=0;%量测偏差(一维位置) pvar=zeros(2,2);%融合后估计方差 for t=0:dt:tg; %dt为为仿真步长;tg为仿真时间长度 k=k+1;%记录仿真步数 ct=0; %三模型概率更新系数 c_max=[0 0 0];%混合概率规范系数 p_var_hat=zeros(2,6);%方差估计重初始化矩阵, %[x_hat_hat p_var_hat]=model_reinitialization(p_tran,p_pri,x_hat,p_var);%调用重初始化函数,进行混合估计,生成三个模型重初始化后的运动状态、方差 %混合概率计算 for i=1:3 u_(i,:)=p_tran(i,:)*p_pri(i); end for i=1:3 c_max=c_max+u_(i,:); end

数据压缩实验指导书

目 录 实验一用C/C++语言实现游程编码 实验二用C/C++语言实现算术编码 实验三用C/C++语言实现LZW编码 实验四用C/C++语言实现2D-DCT变换13

实验一用C/C++语言实现游程编码 1. 实验目的 1) 通过实验进一步掌握游程编码的原理; 2) 用C/C++语言实现游程编码。 2. 实验要求 给出数字字符,能正确输出编码。 3. 实验内容 现实中有许多这样的图像,在一幅图像中具有许多颜色相同的图块。在这些图块中,许多行上都具有相同的颜色,或者在一行上有许多连续的象素都具有相同的颜色值。在这种情况下就不需要存储每一个象素的颜色值,而仅仅存储一个象素的颜色值,以及具有相同颜色的象素数目就可以,或者存储一个象素的颜色值,以及具有相同颜色值的行数。这种压缩编码称为游程编码,常用(run length encoding,RLE)表示,具有相同颜色并且是连续的象素数目称为游程长度。 为了叙述方便,假定一幅灰度图像,第n行的象素值为: 用RLE编码方法得到的代码为:0@81@38@501@40@8。代码中用黑体表示的数字是游程长度,黑体字后面的数字代表象素的颜色值。例如黑体字50代表有连续50个象素具有相同的颜色值,它的颜色值是8。 对比RLE编码前后的代码数可以发现,在编码前要用73个代码表示这一行的数据,而编码后只要用11个代码表示代表原来的73个代码,压缩前后的数据量之比约为7:1,即压缩比为7:1。这说明RLE确实是一种压缩技术,而且这种编码技术相当直观,也非常经济。RLE所能获得的压缩比有多大,这主要是取决于图像本身的特点。如果图像中具有相同颜色的图像块越大,图像块数目越少,获得的压缩比就越高。反之,压缩比就越小。 译码时按照与编码时采用的相同规则进行,还原后得到的数据与压缩前的数据完全相同。因此,RLE是无损压缩技术。

数据压缩实验

实验二图像预测编码 一、实验题目: 图像预测编码: 二、实验目的: 实现图像预测编码和解码. 三、实验内容: 给定一幅图片,对其进行预测编码,获得预测图像,绝对残差图像, 再利用预测图像和残差图像进行原图像重建并计算原图像和重建图像误差. 四、预备知识: 预测方法,图像处理概论。 五、实验原理: 根据图像中任意像素与周围邻域像素之间存在紧密关系,利用周围邻域四个像素来进行该点像素值预测,然后传输图像像素值与其预测值的差值信号,使传输的码率降低,达到压缩的目的。 六、实验步骤: (1)选取一幅预测编码图片; (2)读取图片内容像素值并存储于矩阵; (3)对图像像素进行预测编码; (4)输出预测图像和残差图像; (5)根据预测图像和残差图像重建图像; (6)计算原预测编码图像和重建图像误差. 七、思考题目: 如何采用预测编码算法实现彩色图像编码和解码. 八、实验程序代码: 预测编码程序1: 编码程序: i1=imread('lena.jpg'); if isrgb(i1) i1=rgb2gray(i1);

end i1=imcrop(i1,[1 1 256 256]); i=double(i1); [m,n]=size(i); p=zeros(m,n); y=zeros(m,n); y(1:m,1)=i(1:m,1); p(1:m,1)=i(1:m,1); y(1,1:n)=i(1,1:n); p(1,1:n)=i(1,1:n); y(1:m,n)=i(1:m,n); p(1:m,n)=i(1:m,n); p(m,1:n)=i(m,1:n); y(m,1:n)=i(m,1:n); for k=2:m-1 for l=2:n-1 y(k,l)=(i(k,l-1)/2+i(k-1,l)/4+i(k-1,l-1)/8+i(k-1,l+1)/8); p(k,l)=round(i(k,l)-y(k,l)); end end p=round(p); subplot(3,2,1); imshow(i1); title('原灰度图像'); subplot(3,2,2); imshow(y,[0 256]); title('利用三个相邻块线性预测后的图像'); subplot(3,2,3); imshow(abs(p),[0 1]); title('编码的绝对残差图像'); 解码程序 j=zeros(m,n); j(1:m,1)=y(1:m,1); j(1,1:n)=y(1,1:n); j(1:m,n)=y(1:m,n);

网络安全设计方案

《某市电子政务工程总体规划方案》主要建设内容为:一个专网(政务通信专网),一个平台(电子政务基础平台),一个中心(安全监控和备份中心),七大数据库(经济信息数据库、法人单位基础信息数据库、自然资源和空间地理信息数据库、人口基础信息库、社会信用数据库、海洋经济信息数据库、政务动态信息数据库),十二大系统(政府办公业务资源系统、经济管理信息系统、政务决策服务信息系统、社会信用信息系统、城市通卡信息系统、多媒体增值服务信息系统、综合地理信息系统、海洋经济信息系统、金农信息系统、金水信息系统、金盾信息系统、社会保障信息系统)。主要包括: 政务通信专网 电子政务基础平台 安全监控和备份中心 政府办公业务资源系统 政务决策服务信息系统 综合地理信息系统 多媒体增值服务信息系统 某市政府中心网络安全方案设计 1.2?安全系统建设目标

本技术方案旨在为某市政府网络提供全面的网络系统安全解决方案,包括安全管理制度策略的制定、安全策略的实施体系结构的设计、安全产品的选择和部署实施,以及长期的合作和技术支持服务。系统建设目标是在不影响当前业务的前提下,实现对网络的全面安全管理。 1)?将安全策略、硬件及软件等方法结合起来,构成一个统一的防御系统,有效阻止非法用户进入网络,减少网络的安全风险; 2)?通过部署不同类型的安全产品,实现对不同层次、不同类别网络安全问题的防护; 3)?使网络管理者能够很快重新组织被破坏了的文件或应用。使系统重新恢复到破坏前的状态。最大限度地减少损失。 具体来说,本安全方案能够实现全面网络访问控制,并能够对重要控制点进行细粒度的访问控制; 其次,对于通过对网络的流量进行实时监控,对重要服务器的运行状况进行全面监控。 1.2.1?防火墙系统设计方案 1.2.1.1?防火墙对服务器的安全保护 网络中应用的服务器,信息量大、处理能力强,往往是攻击的主要对象。另外,服务器提供的各种服务本身有可能成为"黑客"攻击的突破口,因此,在实施方案时要对服务器的安全进行一系列安全保护。

LZ77 压缩算法实验报告 一

LZ77 压缩算法实验报告 一、实验内容:使用 C++编程实现 LZ77 压缩算法的实现。 二、实验目的:用 LZ77 实现文件的压缩。 三、实验环境: 1、软件环境:Visual C++ 6.0 2、编程语言:C++ 四、实验原理: LZ77 算法在某种意义上又可以称为“滑动窗口压缩”,这是 由于该算法将一个虚拟的,可以跟随压缩进程滑动的窗口作为术语字典,要压缩的字符串如果在该窗口中出现,则输出其出现位置和长度。使用固定大小窗口进行术语匹配,而不是在所有已经编码的信息中匹配,是因为匹配算法的时间消耗往往很多,必须限制字典的大小才能保证算法的效率;随着压缩的进程滑动字典窗口,使其中总包含最近编码过的信息,是因为对大多数信息而言,要编码的字符串往往在最近的上下文中更容易找到匹配串。 五、 LZ77 算法的基本流程:1、从当前压缩位置开始,考察未编码的数据,并 试图在滑动窗口中找出最长的匹配字符串,如果找到,则进行步骤2,否则进行步骤 3。 2、输出三元符号组 ( off, len, c )。其中 off 为窗口中匹配字符串相 对窗口边界的偏移,len 为可匹配的长度,c 为下一个字符。然后将窗口向后滑动 len + 1 个字符,继续步骤 1。 3、输出三元符号组 ( 0, 0, c )。其中 c 为下一个字符。然后将窗口向 后滑动 len + 1 个字符,继续步骤 1。 代码如下: #include #include #include #include"lz77.h" ////////////////////////////////////////////////////////////////// // out file format: // 0;flag2;buffer;0;flag2;buffer;...flag1;flag2;bufferlast // flag1 - 2 bytes, source buffer block length // if block size is 65536, be zero // flag2 - 2 bytes, compressed buffer length // if can not compress, be same with flag1 ////////////////////////////////////////////////////////////////// void main(int argc, char* argv[]) { /*if (argc != 4) { puts("Usage: ");

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