Página 1 dos resultados de 40 itens digitais encontrados em 0.026 segundos

Contribuições à verificação funcional ajustada por cobertura para núcleos de hardware de comunicação e multimídia.; Contribuitions to coverage-driven verification of communication and multimedia IP-cores.

Romero Tobar, Edgar Leonardo
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 29/06/2010 PT
Relevância na Pesquisa
36.03%
Tornar a verificação funcional mais eficiente, em termos de gasto de recursos de computação e tempo, é necessário para a contínua evolução dos sistemas digitais. A verificação funcional com geração de casos de teste aleatória ajustada por cobertura é uma das alternativas identificadas nos últimos anos para acelerar a execução de testbenches. Várias abordagens têm sido testadas com sucesso na verificação funcional de núcleos de hardware, no domínio de aplicação dos processadores de propósito geral, porém, influenciada por características específicas do domínio, dos modelos de cobertura e do espaço possível de casos de teste. Por outro lado, pouca atenção tem sido dispensada à verificação ajustada por cobertura em outros domínios de aplicação como nos de sistemas de comunicação e de sistemas multimídia. Estes casos são tratados no presente estudo, com os fatores específicos que influenciam os resultados dos testbenches com geração ajustada. Entre os fatores relevantes para isto, foram identificados o tamanho do espaço de casos de teste e a distribuição da ocorrência dos eventos de cobertura, sendo necessária para o desenvolvimento do presente trabalho, a realização de várias alterações na construção de testbenches com ajuste. A geração de casos de teste ajustada por cobertura é realizada a partir da realimentação da informação do estado da cobertura...

Imersão de espaços métricos em espaços multidimensionais para indexação de dados usando detecção de agrupamentos; Embedding of metric spaces in multidimensional spaces for data indexing using cluster detection

Paterlini, Adriano Arantes
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 28/03/2011 PT
Relevância na Pesquisa
55.86%
O sucesso dos Sistemas de Gerenciamento de Banco de Dados (SGBDs) em aplicações envolvendo dados tradicionais (números e textos curtos) encorajou o seu uso em novos tipos de aplicações, que exigem a manipulação de dados complexos. Séries temporais, dados científicos, dados multimídia e outros são exemplos de Dados Complexos. Inúmeras áreas de aplicação têm demandado soluções para o gerenciamento de dados complexos, dentre as quais a área de informática médica. Dados complexos podem também ser estudos com técnicas de descoberta de conhecimentos, conhecidas como KDD (Knowledge Discovery in Database), usando alguns algoritmos de detecção de agrupamentos apropriados. Entretanto, estes algoritmos possuem custo computacional elevado, o que dificulta a sua utilização em grandes conjuntos de dados. As técnicas já desenvolvidas na Área de Bases de Dados para indexação de espaços métricos usualmente consideram o conjunto de maneira uniforme sem levar em conta a existência de agrupamentos nos dados, por isso as estruturas buscam maximizar a eficiência das consultas para todo o conjunto simultaneamente. No entanto muitas vezes as consultas por similaridade estão limitadas a uma região específica do conjunto de dados. Neste contexto...

Um sistema de recomendação para páginas web sobre a cultura da cana-de-açúcar; A recommender system for web pages regarding sugarcane crop

Flavio Margarito Martins de Barros
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 22/02/2013 PT
Relevância na Pesquisa
45.94%
Sistemas de informação web oferecem informações em quantidade elevada, tal que a tarefa de encontrar a informação de interesse torna-se desafiadora. A Agencia de Informação Embrapa e um sistema web com o objetivo de organizar, tratar, armazenar e divulgar informações técnicas e conhecimentos gerados pela EMBRAPA (Empresa Brasileira de Pesquisa Agropecuária). O portal esta estruturado como uma arvore hierárquica, denominada Arvore de Conhecimento, a qual compreende centenas de paginas web, artigos, planilhas e materiais multimídia. Diariamente o site recebe milhares de acessos tal que os registros dessas visitas são armazenados em um banco de dados. Em domínios onde estão disponíveis informações em quantidade elevada, armazenadas em bancos de dados, as ferramentas de Mineração de Dados são promissoras, pois apresentam recursos para analise e extração de padrões de uso do site para fazer recomendações. Recomendações personalizadas de conteúdo melhoram a usabilidade de sistemas, agregam valor aos serviços, poupam tempo e fidelizam usuários. O objetivo desse trabalho foi projetar, desenvolver e implantar um sistema de recomendação web, baseado em regras de associação, que ofereça recomendações automaticamente de conteúdos da cultura da cana-de-açúcar...

Proposta para visualização de dados no website de carpooling www.rotapartilhada.com

Sousa, João Pedro Lopes de
Fonte: Universidade do Porto Publicador: Universidade do Porto
Tipo: Dissertação Formato: 103 f., 30 cm; application/pdf
POR
Relevância na Pesquisa
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Tese de mestrado. Multimédia. Faculdade de Engenharia. Universidade do Porto. 2009

Validating Co-Training Models for Web Image Classification

Zhang, Dell; Lee, Wee Sun
Fonte: MIT - Massachusetts Institute of Technology Publicador: MIT - Massachusetts Institute of Technology
Tipo: Artigo de Revista Científica Formato: 148397 bytes; application/pdf
EN
Relevância na Pesquisa
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Co-training is a semi-supervised learning method that is designed to take advantage of the redundancy that is present when the object to be identified has multiple descriptions. Co-training is known to work well when the multiple descriptions are conditional independent given the class of the object. The presence of multiple descriptions of objects in the form of text, images, audio and video in multimedia applications appears to provide redundancy in the form that may be suitable for co-training. In this paper, we investigate the suitability of utilizing text and image data from the Web for co-training. We perform measurements to find indications of conditional independence in the texts and images obtained from the Web. Our measurements suggest that conditional independence is likely to be present in the data. Our experiments, within a relevance feedback framework to test whether a method that exploits the conditional independence outperforms methods that do not, also indicate that better performance can indeed be obtained by designing algorithms that exploit this form of the redundancy when it is present.; Singapore-MIT Alliance (SMA)

Exploiting Captions for Web Data Mining by Neil C. Rowe

Rowe, Neil C.
Fonte: Monterey, California. Naval Postgraduate School Publicador: Monterey, California. Naval Postgraduate School
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
45.96%
This article is to appear in Web Mining: Applications and Techniques ed. A. Scime, 2004.; We survey research on using captions in data mining from the Web. Captions are text that describes some other information (typically, multimedia). Since text is considerably easier to index and manipulate than non-text (being usually smaller and less ambiguous), a good strategy for accessing non-text is to index its captions. However, captions are not often obvious on the Web as there are few standards. So caption references can reside within paragraphs near a media reference, in clickable text or display text for it, on names of media files, in headings or titles on the page, and in explicit references arbitrarily far from the media. We discuss the range of possible syntactic clues (such as HTML tags) and semantic clues (such as meanings of particular words). We discuss how to quantify their strength and combine their information to arrive at a consensus. We then discuss the problem of mapping information in captions to information in media objects. While it is hard, classes of mapping schemes are distinguishable, and segmentation of the media can be matched to a parsing of the caption by constraint-satisfaction methods. Active work is addressing the issue of automatically learning the clues for mapping from examples.

Exploiting Captions for Multimedia Data Mining / Chapter in Encyclopedia of Multimedia Technology and Networking

Rowe, Neil C.
Fonte: Monterey, California. Naval Postgraduate School Publicador: Monterey, California. Naval Postgraduate School
Tipo: Parte de Livro
Relevância na Pesquisa
56.18%
This is a chapter in the Encyclopedia of Multimedia Technology and Networking, ed. M. Pagani, Hershey, PA: The Idea Group, 2005.; Captions are essential accompaniments to multimedia data objects as a way to facilitate their data mining. This article describes the kinds of possible captions and the task of recognizing them. It then discusses the forms of the caption-object relationship and the ways in which components of the two can be matched. Automatic generation of captions is also discussed. While no breakthroughs are expected on this subject, captioned multimedia will become increasingly important in future systems as increases in processing speeds enable it to be more easily used.

ESA-EUSC-JRC 2011 - The proceedings of the Seventh Conference on Image Information Mining: Geospatial Intelligence from Earth Observation

Fonte: Publications Office of the European Union Publicador: Publications Office of the European Union
Tipo: Books Formato: Online
ENG
Relevância na Pesquisa
36.13%
Today the analysis of a few, very high resolution, multi-spectral images and Synthetic Aperture Radar (SAR) can be complex and challenging. In addition, the emerging needs from major applications (e.g.: mapping, global monitoring, disaster management support, non proliferation, etc.) and large programmes / initiatives (e.g.: GEMS, GEO, GEOSS), and the continuous increase in archives' size and EO sensors' variety, require new methodologies and tools for information mining and management, supported by shared knowledge. The manual process performed by experts to mine information from images is currently too complex and expensive to be applied systematically on even a small subset of the acquired scenes. This limits the full exploitation of the petabytes of archived or new data. The issue might become even more challenging in future since more missions - including constellations - are being planned, with broader sensor variety, higher data rates and increasing complexity. As an example, ENVISAT, Sentinels 1 and 2, or the ESA third party missions. Since the problem are common to other fields, contributions from multimedia, medicine, astronomy, etc.are also expected. Results from current R&D activity might ease the access to the imagery (today mostly retrieved using spatio-temporal and a few more attributes) also through their information content. The need to access information also in large volumes of image data has stimulated the research in the field of content-based image retrieval during last decade. Many new concepts have been developed and prototyped. However the dramatic increase in volume...

A multimedia indexing and retrieval framework for multimedia database systems

Zhang, Chengcui
Fonte: FIU Digital Commons Publicador: FIU Digital Commons
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
36.1%
The main challenges of multimedia data retrieval lie in the effective mapping between low-level features and high-level concepts, and in the individual users' subjective perceptions of multimedia content. ^ The objectives of this dissertation are to develop an integrated multimedia indexing and retrieval framework with the aim to bridge the gap between semantic concepts and low-level features. To achieve this goal, a set of core techniques have been developed, including image segmentation, content-based image retrieval, object tracking, video indexing, and video event detection. These core techniques are integrated in a systematic way to enable the semantic search for images/videos, and can be tailored to solve the problems in other multimedia related domains. In image retrieval, two new methods of bridging the semantic gap are proposed: (1) for general content-based image retrieval, a stochastic mechanism is utilized to enable the long-term learning of high-level concepts from a set of training data, such as user access frequencies and access patterns of images. (2) In addition to whole-image retrieval, a novel multiple instance learning framework is proposed for object-based image retrieval, by which a user is allowed to more effectively search for images that contain multiple objects of interest. An enhanced image segmentation algorithm is developed to extract the object information from images. This segmentation algorithm is further used in video indexing and retrieval...

Knowledge assisted data management and retrieval in multimedia database systems

Chen, Min
Fonte: FIU Digital Commons Publicador: FIU Digital Commons
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
36.15%
With the proliferation of multimedia data and ever-growing requests for multimedia applications, there is an increasing need for efficient and effective indexing, storage and retrieval of multimedia data, such as graphics, images, animation, video, audio and text. Due to the special characteristics of the multimedia data, the Multimedia Database management Systems (MMDBMSs) have emerged and attracted great research attention in recent years. ^ Though much research effort has been devoted to this area, it is still far from maturity and there exist many open issues. In this dissertation, with the focus of addressing three of the essential challenges in developing the MMDBMS, namely, semantic gap, perception subjectivity and data organization, a systematic and integrated framework is proposed with video database and image database serving as the testbed. In particular, the framework addresses these challenges separately yet coherently from three main aspects of a MMDBMS: multimedia data representation, indexing and retrieval. In terms of multimedia data representation, the key to address the semantic gap issue is to intelligently and automatically model the mid-level representation and/or semi-semantic descriptors besides the extraction of the low-level media features. The data organization challenge is mainly addressed by the aspect of media indexing where various levels of indexing are required to support the diverse query requirements. In particular...

Pattern Mining in Visual Concept Streams

Xie, Lexing; Chang, Shih-Fu
Fonte: Institute of Electrical and Electronics Engineers (IEEE Inc) Publicador: Institute of Electrical and Electronics Engineers (IEEE Inc)
Tipo: Conference paper
Relevância na Pesquisa
46.02%
Pattern mining algorithms are often much easier applied than quantitatively assessed. In this paper we address the pattern evaluation problem by looking at both the capability of models and the difficulty of target concepts. We use four different data min

A Survey on Web Multimedia Mining

Kamde, Pravin M.; Algur, Dr. Siddu. P.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 06/09/2011
Relevância na Pesquisa
46.41%
Modern developments in digital media technologies has made transmitting and storing large amounts of multi/rich media data (e.g. text, images, music, video and their combination) more feasible and affordable than ever before. However, the state of the art techniques to process, mining and manage those rich media are still in their infancy. Advances developments in multimedia acquisition and storage technology the rapid progress has led to the fast growing incredible amount of data stored in databases. Useful information to users can be revealed if these multimedia files are analyzed. Multimedia mining deals with the extraction of implicit knowledge, multimedia data relationships, or other patterns not explicitly stored in multimedia files. Also in retrieval, indexing and classification of multimedia data with efficient information fusion of the different modalities is essential for the system's overall performance. The purpose of this paper is to provide a systematic overview of multimedia mining. This article is also represents the issues in the application process component for multimedia mining followed by the multimedia mining models.; Comment: 13 Pages; The International Journal of Multimedia & Its Applications (IJMA) Vol.3...

An Effective Method of Image Retrieval using Image Mining Techniques

Kannan, A.; Mohan, V.; Anbazhagan, N.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 01/12/2010
Relevância na Pesquisa
46.02%
The present research scholars are having keen interest in doing their research activities in the area of Data mining all over the world. Especially, [13]Mining Image data is the one of the essential features in this present scenario since image data plays vital role in every aspect of the system such as business for marketing, hospital for surgery, engineering for construction, Web for publication and so on. The other area in the Image mining system is the Content-Based Image Retrieval (CBIR) which performs retrieval based on the similarity defined in terms of extracted features with more objectiveness. The drawback in CBIR is the features of the query image alone are considered. Hence, a new technique called Image retrieval based on optimum clusters is proposed for improving user interaction with image retrieval systems by fully exploiting the similarity information. The index is created by describing the images according to their color characteristics, with compact feature vectors, that represent typical color distributions [12].

Low-rank data modeling via the Minimum Description Length principle

Ramírez, Ignacio; Sapiro, Guillermo
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 28/09/2011
Relevância na Pesquisa
45.83%
Robust low-rank matrix estimation is a topic of increasing interest, with promising applications in a variety of fields, from computer vision to data mining and recommender systems. Recent theoretical results establish the ability of such data models to recover the true underlying low-rank matrix when a large portion of the measured matrix is either missing or arbitrarily corrupted. However, if low rank is not a hypothesis about the true nature of the data, but a device for extracting regularity from it, no current guidelines exist for choosing the rank of the estimated matrix. In this work we address this problem by means of the Minimum Description Length (MDL) principle -- a well established information-theoretic approach to statistical inference -- as a guideline for selecting a model for the data at hand. We demonstrate the practical usefulness of our formal approach with results for complex background extraction in video sequences.

Recent Trends and Research Issues in Video Association Mining

V, Vijayakumar; R, Nedunchezhian
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 09/12/2011
Relevância na Pesquisa
46.02%
With the ever-growing digital libraries and video databases, it is increasingly important to understand and mine the knowledge from video database automatically. Discovering association rules between items in a large video database plays a considerable role in the video data mining research areas. Based on the research and development in the past years, application of association rule mining is growing in different domains such as surveillance, meetings, broadcast news, sports, archives, movies, medical data, as well as personal and online media collections. The purpose of this paper is to provide general framework of mining the association rules from video database. This article is also represents the research issues in video association mining followed by the recent trends.; Comment: 13 pages; 1 Figure; 1 Table

Novel Metaknowledge-based Processing Technique for Multimedia Big Data clustering challenges

Bari, Nima; Vichr, Roman; Kowsari, Kamran; Berkovich, Simon Y.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 01/03/2015
Relevância na Pesquisa
36.08%
Past research has challenged us with the task of showing relational patterns between text-based data and then clustering for predictive analysis using Golay Code technique. We focus on a novel approach to extract metaknowledge in multimedia datasets. Our collaboration has been an on-going task of studying the relational patterns between datapoints based on metafeatures extracted from metaknowledge in multimedia datasets. Those selected are significant to suit the mining technique we applied, Golay Code algorithm. In this research paper we summarize findings in optimization of metaknowledge representation for 23-bit representation of structured and unstructured multimedia data in order to; Comment: IEEE Multimedia Big Data (BigMM 2015)

A language independent web data extraction using vision based page segmentation algorithm

YesuRaju, P; KiranSree, P
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 24/10/2013
Relevância na Pesquisa
35.96%
Web usage mining is a process of extracting useful information from server logs i.e. users history. Web usage mining is a process of finding out what users are looking for on the internet. Some users might be looking at only textual data, where as some others might be interested in multimedia data. One would retrieve the data by copying it and pasting it to the relevant document. But this is tedious and time consuming as well as difficult when the data to be retrieved is plenty. Extracting structured data from a web page is challenging problem due to complicated structured pages. Earlier they were used web page programming language dependent; the main problem is to analyze the html source code. In earlier they were considered the scripts such as java scripts and cascade styles in the html files. When it makes different for existing solutions to infer the regularity of the structure of the Web Pages only by analyzing the tag structures. To overcome this problem we are using a new algorithm called VIPS algorithm i.e. independent language. This approach primary utilizes the visual features on the webpage to implement web data extraction.; Comment: arXiv admin note: text overlap with arXiv:1201.0385 by other authors without attribution

Query engine of novelty in video streams

Kang, James
Fonte: Rochester Instituto de Tecnologia Publicador: Rochester Instituto de Tecnologia
Tipo: Tese de Doutorado Formato: 19999 bytes; 2932580 bytes; 234559 bytes; 1 bytes; 2061 bytes; 698 bytes; 5468 bytes; 49 bytes; 19999 bytes; 2932580 bytes; application/pdf; application/pdf; text/plain; text/plain; text/plain; application/octet-stream; application/octet-stream; applicati
EN_US
Relevância na Pesquisa
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Prior research on novelty detection has primarily focused on algorithms to "detect" novelty for a given application domain. Effective storage, indexing and retrieval of novel events (beyond detection) are largely ignored as a problem in itself. In light of the recent advances in counter-terrorism efforts and link discovery initiatives, the need for effective data management of novel events assumes apparent importance. Automatically detecting novel events in video data streams is an extremely challenging task. The aim of this thesis is to provide evidence to the fact that the notion of novelty in video as perceived by a human is extremely subjective and therefore algorithmically illdefined. Though it comes as no surprise that current machine-based parametric learning systems to accurately mimic human novelty perception are far from perfect such systems have recently been very successful in exhaustively capturing novelty in video once the novelty function is well-defined by a human expert. So, how truly effective are these machine based novelty detection systems as compared to human novelty detection? In this paper we outline an experimental evaluation of the human vs machine based novelty systems in terms of qualitative performance. We then quantify this evaluation using a variety of metrics based on location of novel events...

Structural advances for pattern discovery in multi-relational databases

Kanodia, Juveria
Fonte: Rochester Instituto de Tecnologia Publicador: Rochester Instituto de Tecnologia
Tipo: Tese de Doutorado Formato: 15299 bytes; 1045261 bytes; 148995 bytes; 1 bytes; 2061 bytes; 698 bytes; 5468 bytes; 49 bytes; 15299 bytes; 1045261 bytes; application/pdf; application/pdf; text/plain; text/plain; text/plain; application/octet-stream; application/octet-stream; applicati
EN_US
Relevância na Pesquisa
46.15%
With ever-growing storage needs and drift towards very large relational storage settings, multi-relational data mining has become a prominent and pertinent field for discovering unique and interesting relational patterns. As a consequence, a whole suite of multi-relational data mining techniques is being developed. These techniques may either be extensions to the already existing single-table mining techniques or may be developed from scratch. For the traditionalists, single-table mining algorithms can be used to work on multi-relational settings by making inelegant and time consuming joins of all target relations. However, complex relational patterns cannot be expressed in a single-table format and thus, cannot be discovered. This work presents a new multi-relational frequent pattern mining algorithm termed Multi-Relational Frequent Pattern Growth (MRFP Growth). MRFP Growth is capable of mining multiple relations, linked with referential integrity, for frequent patterns that satisfy a user specified support threshold. Empirical results on MRFP Growth performance and its comparison with the state-of-the-art multirelational data mining algorithms like WARMR and Decentralized Apriori are discussed at length. MRFP Growth scores over the latter two techniques in number of patterns generated and speed. The realm of multi-relational clustering is also explored in this thesis. A multi-Relational Item Clustering approach based on Hypergraphs (RICH) is proposed. Experimentally RICH combined with MRFP Growth proves to be a competitive approach for clustering multi-relational data. The performance and iii quality of clusters generated by RICH are compared with other clustering algorithms. Finally...

Exploring Hidden Coherent Feature Groups and Temporal Semantics for Multimedia Big Data Analysis

Yang, Yimin
Fonte: FIU Digital Commons Publicador: FIU Digital Commons
Tipo: text Formato: application/pdf
Relevância na Pesquisa
66.31%
Thanks to the advanced technologies and social networks that allow the data to be widely shared among the Internet, there is an explosion of pervasive multimedia data, generating high demands of multimedia services and applications in various areas for people to easily access and manage multimedia data. Towards such demands, multimedia big data analysis has become an emerging hot topic in both industry and academia, which ranges from basic infrastructure, management, search, and mining to security, privacy, and applications. Within the scope of this dissertation, a multimedia big data analysis framework is proposed for semantic information management and retrieval with a focus on rare event detection in videos. The proposed framework is able to explore hidden semantic feature groups in multimedia data and incorporate temporal semantics, especially for video event detection. First, a hierarchical semantic data representation is presented to alleviate the semantic gap issue, and the Hidden Coherent Feature Group (HCFG) analysis method is proposed to capture the correlation between features and separate the original feature set into semantic groups, seamlessly integrating multimedia data in multiple modalities. Next, an Importance Factor based Temporal Multiple Correspondence Analysis (i.e....