Institution
Hewlett-Packard
Company•Palo Alto, California, United States•
About: Hewlett-Packard is a company organization based out in Palo Alto, California, United States. It is known for research contribution in the topics: Signal & Layer (electronics). The organization has 34663 authors who have published 59808 publications receiving 1467218 citations. The organization is also known as: Hewlett Packard & Hewlett-Packard Company.
Papers published on a yearly basis
Papers
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TL;DR: This paper evaluates acoustic techniques based on Mel-frequency cepstral coefficients and an intermediate ‘anchor space’ of genre classification, and subjective techniques which use data from The All Music Guide, from a survey, from playlists and personal collections, and from web-text mining.
Abstract: music similarity, acoustic measures, evaluation, ground-truth Subjective similarity between musical pieces and artists is an elusive concept, but one that must be pursued in support of applications to provide automatic organization of large music collections. In this paper, we examine both acoustic and subjective approaches for calculating similarity between artists, comparing their performance on a common database of 400 popular artists. Specifically, we evaluate acoustic techniques based on Mel-frequency cepstral coefficients and an intermediate ‘anchor space’ of genre classification, and subjective techniques which use data from The All Music Guide, from a survey, from playlists and personal collections, and from web-text mining.
267 citations
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10 Sep 2007TL;DR: A measurement study shows that the stronger frame can be decoded correctly regardless of the timing relation with the weaker frame, and that the successful capture of a frame involved in a collision is determined through two stages: preamble detection and the frame body FCS check.
Abstract: In wireless networks, a frame collision does not necessarily result in all the simultaneously transmitted frames being lost. Depending on the relative signal power and the arrival timing of the involved frames, one frame can survive the collision and be successfully received by the receiver. Using our IEEE 802.11a wireless network testbed, we carry out a measurement study that shows the terms and conditions (timing, power difference, bit rate) under which this capture effect takes place. Recent measurement work on the capture effect in 802.11 networks [10] argues that the stronger frame can be successfully decoded only in two cases: (1) The stronger frame arrives earlier than the weaker frame, or (2) the stronger frame arrives later than the weaker frame but within the preamble time of the weaker frame. However, our measurement shows that the stronger frame can be decoded correctly regardless of the timing relation with the weaker frame. In addition, when the stronger frame arrives later than the weaker frame's arrival, the physical layer capture exhibits two very distinct patterns based on whether the receiver has been successfully synchronized to the previous weak frame or not. In explaining the distinct cases we observe that the successful capture of a frame involved in a collision is determined through two stages: preamble detection and the frame body FCS check.
266 citations
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TL;DR: This paper broke down the robust reading problem into three subproblems and run competitions for each stage, and also a competition for the best overall system, and described an algorithm for combining the outputs of the individual text locators and showed how the combination scheme improves on any of theindividual systems.
Abstract: This paper describes the robust reading competitions for ICDAR 2003. With the rapid growth in research over the last few years on recognizing text in natural scenes, there is an urgent need to establish some common benchmark datasets and gain a clear understanding of the current state of the art. We use the term `robust reading' to refer to text images that are beyond the capabilities of current commercial OCR packages. We chose to break down the robust reading problem into three subproblems and run competitions for each stage, and also a competition for the best overall system. The subproblems we chose were text locating, character recognition and word recognition. By breaking down the problem in this way, we hoped to gain a better understanding of the state of the art in each of the subproblems. Furthermore, our methodology involved storing detailed results of applying each algorithm to each image in the datasets, allowing researchers to study in depth the strengths and weaknesses of each algorithm. The text-locating contest was the only one to have any entries. We give a brief description of each entry and present the results of this contest, showing cases where the leading entries succeed and fail. We also describe an algorithm for combining the outputs of the individual text locators and show how the combination scheme improves on any of the individual systems.
266 citations
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TL;DR: An inequality for n-particle correlations is found that holds under the most general separability condition and that is violated by some quantum-mechanical states.
Abstract: We analyze the structure of correlations among more than two quantum systems. We introduce a classification of correlations based on the concept of nonseparability, which is different a priori from the concept of entanglement. Generalizing a result of Svetlichny [Phys. Rev. D 35, 3066 (1987)] on three-particle correlations, we find an inequality for n-particle correlations that holds under the most general separability condition and that is violated by some quantum-mechanical states.
266 citations
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TL;DR: Purification and partial amino acid sequencing of the mammalian rsec6/8 complex reveals that it is composed of eight novel proteins with a combined molecular weight of 743 kDa, and suggests a role for the mammalian Rsec6-8 complex in neurotransmitter release via interactions with the core vesicle docking and fusion apparatus.
266 citations
Authors
Showing all 34676 results
Name | H-index | Papers | Citations |
---|---|---|---|
Andrew White | 149 | 1494 | 113874 |
Stephen R. Forrest | 148 | 1041 | 111816 |
Rafi Ahmed | 146 | 633 | 93190 |
Leonidas J. Guibas | 124 | 691 | 79200 |
Chenming Hu | 119 | 1296 | 57264 |
Robert E. Tarjan | 114 | 400 | 67305 |
Hong-Jiang Zhang | 112 | 461 | 49068 |
Ching-Ping Wong | 106 | 1128 | 42835 |
Guillermo Sapiro | 104 | 667 | 70128 |
James R. Heath | 103 | 425 | 58548 |
Arun Majumdar | 102 | 459 | 52464 |
Luca Benini | 101 | 1453 | 47862 |
R. Stanley Williams | 100 | 605 | 46448 |
David M. Blei | 98 | 378 | 111547 |
Wei-Ying Ma | 97 | 464 | 40914 |