2005 3rd International Conference on Intelligent Sensing and Information Processing最新文献_第2页

A Set-Covering Approach for Modeling Attacks on Key Predistribution in Wireless Sensor Networks 基于集合覆盖的无线传感器网络密钥预分配攻击建模方法

2005 3rd International Conference on Intelligent Sensing and Information Processing Pub Date : 2005-12-14 DOI: 10.1109/ICISIP.2005.1619445

P. Tague, Jooyoung Lee, R. Poovendran

引用次数: 7

Cryptanalysis of Simplified Data Encryption Standard via Optimization Heuristics 基于优化启发式的简化数据加密标准密码分析

2005 3rd International Conference on Intelligent Sensing and Information Processing Pub Date : 2005-12-14 DOI: 10.1109/ICISIP.2005.1619415

N. Nalini, G. Raghavendra Rao

引用次数: 41

A Secure Image Steganography using LSB, DCT and Compression Techniques on Raw Images 在原始图像上使用LSB、DCT和压缩技术的安全图像隐写

2005 3rd International Conference on Intelligent Sensing and Information Processing Pub Date : 2005-12-14 DOI: 10.1109/ICISIP.2005.1619431

K. B. Raja, C. R. Chowdary, K. Venugopal, L. Patnaik

{"title":"A Secure Image Steganography using LSB, DCT and Compression Techniques on Raw Images","authors":"K. B. Raja, C. R. Chowdary, K. Venugopal, L. Patnaik","doi":"10.1109/ICISIP.2005.1619431","DOIUrl":"https://doi.org/10.1109/ICISIP.2005.1619431","url":null,"abstract":"Steganography is an important area of research in recent years involving a number of applications. It is the science of embedding information into the cover image viz., text, video, and image (payload) without causing statistically significant modification to the cover image. The modern secure image steganography presents a challenging task of transferring the embedded information to the destination without being detected. In this paper we present an image based steganography that combines Least Significant Bit(LSB), Discrete Cosine Transform(DCT), and compression techniques on raw images to enhance the security of the payload. Initially, the LSB algorithm is used to embed the payload bits into the cover image to derive the stego-image. The stego-image is transformed from spatial domain to the frequency domain using DCT. Finally quantization and runlength coding algorithms are used for compressing the stego-image to enhance its security. It is observed that secure images with low MSE and BER are transferred without using any password, in comparison with earlier works.","PeriodicalId":261916,"journal":{"name":"2005 3rd International Conference on Intelligent Sensing and Information Processing","volume":"433 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123561117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 192

Finding Pitch Markers using First Order Gaussian Differentiator 使用一阶高斯微分器寻找音高标记

2005 3rd International Conference on Intelligent Sensing and Information Processing Pub Date : 2005-12-14 DOI: 10.1109/ICISIP.2005.1619426

S. R. Mahadeva Prasanna, A. Subramanian

引用次数: 10

Quantization For Distributed Estimation in Large Scale Sensor Networks 大规模传感器网络中分布式估计的量化

2005 3rd International Conference on Intelligent Sensing and Information Processing Pub Date : 2005-12-14 DOI: 10.1109/ICISIP.2005.1619423

P. Venkitasubramaniam, G. Mergen, L. Tong, A. Swami

引用次数: 23

Predict Protein-Protein Interaction using Heuristic Approaches 使用启发式方法预测蛋白质-蛋白质相互作用

2005 3rd International Conference on Intelligent Sensing and Information Processing Pub Date : 2005-12-14 DOI: 10.1109/ICISIP.2005.1619418

R. Agrawal, A. Mittal, R. C. Joshi

{"title":"Predict Protein-Protein Interaction using Heuristic Approaches","authors":"R. Agrawal, A. Mittal, R. C. Joshi","doi":"10.1109/ICISIP.2005.1619418","DOIUrl":"https://doi.org/10.1109/ICISIP.2005.1619418","url":null,"abstract":"Proteins interact with each other for a common purpose such as cell formation. They come in contact with each other following the biochemical properties. The understanding of protein-protein interactions in metabolic networks is an important aspect of molecular biology and biochemistry. The proposed prediction system uses protein sequence data to learn the interactions. Based on sequence data, the information about the accessible interface and the type of forces that form this interface can be extracted. This solution to the problem offers a great help in the field of drug design. The paper presents an approach which takes into consideration the structure of proteins and also the types of forces that mediate the interactions among the proteins. The heuristic technique has been applied to find the interface surface, which is active in a protein during interaction. Since finding an interface that takes part in the interaction is practically complicated, a similar interface can always be found. The heuristic technique is applied in such a way that considers the protein sequence data minutely in interaction prediction. The system modeled in this paper gives a high value of sensitivity (88%) on the dataset collected from DIP (database of interacting proteins)","PeriodicalId":261916,"journal":{"name":"2005 3rd International Conference on Intelligent Sensing and Information Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130658228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Audio Steganalysis using Ensemble of Autonomous Multi-Agent and Support Vector Machine Paradigm 基于自治多智能体和支持向量机的音频隐写分析

2005 3rd International Conference on Intelligent Sensing and Information Processing Pub Date : 2005-12-14 DOI: 10.1109/ICISIP.2005.1619412

S. Geetha, S.S. Sivatha Sindhu, A. Kannan

引用次数: 6

A Hindi Question Answering system for E-learning documents 用于电子学习文档的印地语问答系统

2005 3rd International Conference on Intelligent Sensing and Information Processing Pub Date : 2005-12-14 DOI: 10.1109/ICISIP.2005.1619416

P. Kumar, S. Kashyap, A. Mittal, S. Gupta

{"title":"A Hindi Question Answering system for E-learning documents","authors":"P. Kumar, S. Kashyap, A. Mittal, S. Gupta","doi":"10.1109/ICISIP.2005.1619416","DOIUrl":"https://doi.org/10.1109/ICISIP.2005.1619416","url":null,"abstract":"To empower the general mass through access to information and knowledge, organized efforts are being made to develop relevant content in local languages and provide local language capabilities to utility software. We have developed a question answering (QA) system for Hindi documents that would be relevant for masses using Hindi as the primary language of education. The user should be able to access information from e-learning documents in a user friendly way, that is by questioning the system in their native language Hindi and the system returns the intended answer (also in Hindi) by searching in context from the repository of Hindi documents. The language constructs, query structure, common words, etc. are completely different in Hindi as compared to English. A novel strategy, in addition to conventional search and NLP techniques, was used to construct the Hindi QA system. The focus is on context based retrieval of information. For this purpose we implemented a Hindi search engine that works on locality-based similarity heuristics to retrieve relevant passages from the collection. It also incorporates language analysis modules like stemmer and morphological analyzer as well as self constructed lexical database of synonyms. The experimental results over corpus of two important domains of agriculture and science show effectiveness of our approach","PeriodicalId":261916,"journal":{"name":"2005 3rd International Conference on Intelligent Sensing and Information Processing","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121209402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 21

Improved Probability Hypothesis Density (PHD) Filter for Multitarget Tracking 多目标跟踪的改进概率假设密度滤波

2005 3rd International Conference on Intelligent Sensing and Information Processing Pub Date : 2005-12-14 DOI: 10.1109/ICISIP.2005.1619438

K. Panta, B. Vo, Sumeetpal S. Singh

{"title":"Improved Probability Hypothesis Density (PHD) Filter for Multitarget Tracking","authors":"K. Panta, B. Vo, Sumeetpal S. Singh","doi":"10.1109/ICISIP.2005.1619438","DOIUrl":"https://doi.org/10.1109/ICISIP.2005.1619438","url":null,"abstract":"The probability hypothesis density (PHD) filter is a practical alternative to the optimal Bayesian multi-target filter based on random finite sets. It propagates the PHD function, the first order moment of the posterior multi-target density, from which the number of targets as well as their individual states can be extracted. Furthermore, the sequential Monte Carlo (SMC) approximation of the PHD filter (also known as particle-PHD filter) is available in the literature in order to overcome its intractability. However, the PHD filter keeps no track of the target identities and hence cannot produce track-valued estimates of individual targets. This work consider the use of an improved implementation, of the particle-PHD filter that gives the track-valued estimates of individual targets and propose a novel way for doing so. The improved PHD filter combines the particles approximation of the posterior PHD function and the peak extraction from the posterior PHD particles to create the target identities of the individual estimates. The improved PHD filter does not affect the convergence results of the particle-PHD filter","PeriodicalId":261916,"journal":{"name":"2005 3rd International Conference on Intelligent Sensing and Information Processing","volume":"151 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122785316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 52

Disulphide Bridge Prediction using Fuzzy Support Vector Machines 基于模糊支持向量机的二硫化物桥预测

2005 3rd International Conference on Intelligent Sensing and Information Processing Pub Date : 2005-12-14 DOI: 10.1109/ICISIP.2005.1619411

Jayavardhana Rama, A. Shilton, Michael M. Parker, Palaniswami M

引用次数: 4