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The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted full papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions,will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.
2021 IEEE Photovoltaic Specialists Conference (PVSC)
Photovoltaic materials, devices, systems and related science and technology
The Annual IEEE PES General Meeting will bring together over 2900 attendees for technical sessions, administrative sessions, super sessions, poster sessions, student programs, awards ceremonies, committee meetings, tutorials and more
AMC2020 is the 16th in a series of biennial international workshops on Advanced Motion Control which aims to bring together researchers from both academia and industry and to promote omnipresent motion control technologies and applications.
The IEEE International Microwave Symposium (IMS) is the world s foremost conference covering the UHF, RF, wireless, microwave, millimeter-wave, terahertz, and optical frequencies; encompassing everything from basic technologies to components to systems including the latest RFIC, MIC, MEMS and filter technologies, advances in CAD, modeling, EM simulation and more. The IMS includes technical and interactive sessions, exhibits, student competitions, panels, workshops, tutorials, and networking events.
Experimental and theoretical advances in antennas including design and development, and in the propagation of electromagnetic waves including scattering, diffraction and interaction with continuous media; and applications pertinent to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques.
Contains articles on the applications and other relevant technology. Electronic applications include analog and digital circuits employing thin films and active devices such as Josephson junctions. Power applications include magnet design as well asmotors, generators, and power transmission
The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...
The IEEE Reviews in Biomedical Engineering will review the state-of-the-art and trends in the emerging field of biomedical engineering. This includes scholarly works, ranging from historic and modern development in biomedical engineering to the life sciences and medicine enabled by technologies covered by the various IEEE societies.
Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007
To go beyond the query-by-example paradigm in image retrieval, there is a need for semantic indexing of large image collections for intuitive text-based image search. Different models have been proposed to learn the dependencies between the visual content of an image set and the associated text captions, then allowing for the automatic creation of semantic indexes for unannotated images. The ...
2009 Second International Symposium on Knowledge Acquisition and Modeling, 2009
Indexing and query multimedia data is a challenging problem due to the high dimension of multimedia data. Clustering-based indexing structures are quite efficient for high-dimensional data indexing. Unfortunately, clustering-based indexing structures are normally static, and the whole structures have to be rebuilt after inserting new data. To resolve this issue, a two-level indexing method, called PASDS plus PPAT method, has ...
2008 27th Chinese Control Conference, 2008
In comparison to traditional graph search, containment search has its own indexing characteristics that have not yet been examined. We propose a scalable contrast subgraph-based indexing model, called csgIndex. Using a redundancy-aware feature selection process, csgIndex can sort out a set of significant and distinctive contrast subgraphs and maximize its indexing capability. Taking this solution as a base indexing model, ...
2008 27th Chinese Control Conference, 2008
LPI is optimal in the sense of local manifold structure. However, LPI is not efficient in time and memory which makes it difficult to be applied to very large data set. In this paper, we propose a optimal algorithm called FLPI. FLPI decomposes the LPI problem as a graph embedding problem plus a regularized least squares problem. Such modification avoids ...
2017 2nd International Conference on Telecommunication and Networks (TEL-NET), 2017
When any query is raised by the user in database, record searching process takes place through keywords into the database which are passed as input to indexing process in order to map keywords to records. In case of misspell keywords the system always bypass the indexing process and go for record by record search to match nearest keywords with the ...
To go beyond the query-by-example paradigm in image retrieval, there is a need for semantic indexing of large image collections for intuitive text-based image search. Different models have been proposed to learn the dependencies between the visual content of an image set and the associated text captions, then allowing for the automatic creation of semantic indexes for unannotated images. The task, however, remains unsolved. In this paper, we present three alternatives to learn a probabilistic latent semantic analysis (PLSA) model for annotated images and evaluate their respective performance for automatic image indexing. Under the PLSA assumptions, an image is modeled as a mixture of latent aspects that generates both image features and text captions, and we investigate three ways to learn the mixture of aspects. We also propose a more discriminative image representation than the traditional Blob histogram, concatenating quantized local color information and quantized local texture descriptors. The first learning procedure of a PLSA model for annotated images is a standard expectation-maximization (EM) algorithm, which implicitly assumes that the visual and the textual modalities can be treated equivalently. The other two models are based on an asymmetric PLSA learning, allowing to constrain the definition of the latent space on the visual or on the textual modality. We demonstrate that the textual modality is more appropriate to learn a semantically meaningful latent space, which translates into improved annotation performance. A comparison of our learning algorithms with respect to recent methods on a standard data set is presented, and a detailed evaluation of the performance shows the validity of our framework.
Indexing and query multimedia data is a challenging problem due to the high dimension of multimedia data. Clustering-based indexing structures are quite efficient for high-dimensional data indexing. Unfortunately, clustering-based indexing structures are normally static, and the whole structures have to be rebuilt after inserting new data. To resolve this issue, a two-level indexing method, called PASDS plus PPAT method, has been developed in this paper. In the PASDS level, clusters and their subspaces can be partially updated, while the indexing trees within the clusters are able to be partially updated at the PPAT level. By choosing proper number of children nodes, the proposed method can balance query accuracy and indexing efficiency. From experiments, the PASDS plus PPAT method is very efficient for updating clusters and inner indexing structures for newly inserted data, while its query accuracy and query time are almost the same with similar dynamic indexing methods.
In comparison to traditional graph search, containment search has its own indexing characteristics that have not yet been examined. We propose a scalable contrast subgraph-based indexing model, called csgIndex. Using a redundancy-aware feature selection process, csgIndex can sort out a set of significant and distinctive contrast subgraphs and maximize its indexing capability. Taking this solution as a base indexing model, we further extend it to accommodate hierarchical indexing methodologies and apply data space clustering and sampling techniques to reduce the index construction time. Experimental results on real test data show that csgIndex achieves near- optimal pruning power on various containment search workloads, and confirms its obvious advantage over indices built for traditional graph search in this new scenario.
LPI is optimal in the sense of local manifold structure. However, LPI is not efficient in time and memory which makes it difficult to be applied to very large data set. In this paper, we propose a optimal algorithm called FLPI. FLPI decomposes the LPI problem as a graph embedding problem plus a regularized least squares problem. Such modification avoids eigen decomposition of dense matrices and can significantly reduce both time and memory cost in computation. Moreover, with a specifically designed graph in supervised situation, LPI only needs to solve the regularized least squares problem which is a further saving of time and memory. Real data experimental results show that FLPI obtains similar or better results comparing to LPI and it is significantly faster.
When any query is raised by the user in database, record searching process takes place through keywords into the database which are passed as input to indexing process in order to map keywords to records. In case of misspell keywords the system always bypass the indexing process and go for record by record search to match nearest keywords with the input keys. This process of bypassing the indexing system is costly in terms of both memory and time. In this paper NLCS based string approximation method is embedded with traversing method of B-tree for searching nearest keyword stored in B-tree so that misspelled keywords can directly used as searching keys without bypassing the indexing system. This proposed mechanism takes benefit of efficient time and space complexity of the B-tree and also provide tolerance against the misspelled keywords to any indexing process.
Extensible indexing is a SQL-based framework that allows users to define domain-specific indexing schemes, and integrate them into the Oracle8i server. Users register a new indexing scheme, the set of related operators, and additional properties through SQL data definition language extensions. The implementation for an indexing scheme is provided as a set of Oracle Data Cartridge Interface (ODCIIndex) routines for index-definition, index- maintenance, and index-scan operations. An index created using the new indexing scheme, referred to as domain index, behaves and performs analogous to those built natively by the database system. The Oracle8i server implicitly invokes user-supplied index implementation code when domain index operations are performed, and executes user-supplied index scan routines for efficient evaluation of domain-specific operators. This paper provides an overview of the framework and describes the steps needed to implement an indexing scheme. The paper also presents a case study of Oracle Cartridges (intermedia text, spatial, and visual information retrieval), and Daylight (Chemical compound searching) Cartridge, which have implemented new indexing schemes using this framework and discusses the benefits and limitations.
In this paper, we propose a novel image indexing platform, so-called INVENIO (INdexing Visual ENvironment for multimedia Items and Objects). Entirely based on the ISO/MPEG-7 normative specification, the INVENIO platform offers, within an integrated system, visual metadata extraction engine, annotation tools, image databases management tools, as well as appropriated, ergonomic user interfaces. In order to validate the INVENIO platform, we have considered an industrial application related to the issue of content re-use within an audio- visual production chain, including both natural and synthetic (i.e. cartoons) image content. The proposed solutions demonstrated that the exploitation of the MPEG-7 visual descriptors makes it possible to obtain significant savings in terms of production time/cost, while ensuring an optimal re-use of content. The INVENIO platform has been validated within the framework of the HD3D-IIO structural project of the French CapDigital competitiveness cluster.
Big Data, true to its name, it deals with large volumes of data characterized by volume, variety and velocity. Big data has made the development of highly capable online search engines nowadays. Search Engine systems are differing by the way of how Indexing and Page Rankings are performed. Without Indexing, Search Engine would require considerable time and computing power. For example, while an index of 10,000 documents can be queried within milliseconds, a sequential scan of every word in 10,000 large documents could take hours. The common indexing methods are not suitable for Search Engine Big Data as it greatly increase the size of the data as well as reduce the scalability. This paper proposes a new method of Indexing Search Engine Big Data called Key Hash Indexing scheme followed by the implementation of Page Rank. A Comprehensive presentation of important technology and factors to achieve efficient Big Data storage, Indexing and Ranking in Web Search Engine are also considered. This system also shows the efficiency of the method with an extensive set of experiments on real data. Experimental results on real- time data sets show that the proposed solution is effective as well as efficient in index generation and ranking.
Our objective is to index talking faces in a TV-Context: build a description of TV-content, in terms of talking people, without any pre-defined dictionary of identities. In TV-content, because of multi-face shots and non-speaking face shots, it is difficult to determine which face is speaking. In this work, a method is proposed which clusters people independently by the audio and by the visual information and combines these clusterings of people (audio and visual) in order to detect sequences of talking faces. The audio indexing system is based on agglomerative clustering with the Bayesian Information Criterion. The visual indexing system is based on costume detection and clustering of color histograms. The combination of both indexes is based on searching for the best match between both clusterings, to obtain a correspondence between the automatic audio labels and the automatic video labels. The talking faces are then determined by the intersection of the segments of the associated audio and video labels. Results of experiments on a TV-Show database show that a high correct detection rate can be achieved by the proposed method.
In recent years, the fast growth of Web pages and the constant evolution of Internet technologies have lead to a significant increase in the number of pedagogical resources. Thus, the indexing and search problems have become crucial. To overcome this problem, it was proposed to use information coming from the norms and standards of educational metadata. However, this solution does not solve completely the problem. Previously, traditional information retrieval systems rely on indexing by keywords for representing pedagogical resources and queries content. This process, based on lexical matching, allows selecting pedagogical resources based on keywords shared with the query, which can reduce the accuracy of search results if the meaning of common words in the query and in the pedagogical resources is different. To overcome this issue and provide a more sophisticated search, adding semantics becomes necessary. Semantic Indexing offers a representation by the meaning of words in order to find pedagogical resources semantically relevant to the user request. We present in this paper the choice of the indexing approach used for the complementary educational resources. The main objective is to integrate this approach in the warehouse model that aims on one hand to store the complementary pedagogical resources, and on the other hand to help the user fill in the description fields of these resources.
The scope of this trial-use standard is the definition of an exchange format, using eXtensible Markup Language (XML), for identifying all of the hardware, software, and documentation that may be used to test and diagnose a UUT on an automatic test system (ATS).