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Wireless Communications
, 2005
"... Copyright c ○ 2005 by Cambridge University Press. This material is in copyright. Subject to statutory exception and to the provisions of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University ..."
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Cited by 1129 (32 self)
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Copyright c ○ 2005 by Cambridge University Press. This material is in copyright. Subject to statutory exception and to the provisions of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University
Optimal randomized EREW PRAM algorithms for finding spanning forests
 J. Algorithms
, 2000
"... We present the first randomized O(log n) time and O(m+n) work EREW PRAM algorithm for finding a spanning forest of an undirected graph G = (V; E) with n vertices and m edges. Our algorithm is optimal with respect to time, work and space. As a consequence we get optimal randomized EREW PRAM algori ..."
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Cited by 14 (1 self)
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We present the first randomized O(log n) time and O(m+n) work EREW PRAM algorithm for finding a spanning forest of an undirected graph G = (V; E) with n vertices and m edges. Our algorithm is optimal with respect to time, work and space. As a consequence we get optimal randomized EREW PRAM
Dictionary Compression on the PRAM
, 1994
"... Parallel algorithms for lossless data compression via dictionary compression using optimal, longest fragment first (LFF), and greedy parsing strategies are described. Dictionary compression removes redundancy by replacing substrings of the input by references to strings stored in a dictionary. Given ..."
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Cited by 1 (0 self)
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. Given a static dictionary stored as a suffix tree, we present a CREW PRAM algorithm for optimal compression which runs in O(M + log M log n) time with O(nM 2 ) processors, where it is assumed that M is the maximum length of any dictionary entry. Under the same model, we give an algorithm for LFF
A Description of the Advanced Research WRF Version 2
 AVAILABLE FROM NCAR; P.O. BOX 3000; BOULDER, CO
, 2001
"... ..."
3D Sound for Virtual Reality and Multimedia
, 2000
"... This paper gives HRTF magnitude data in numerical form for 43 frequencies between 0.212 kHz, the average of 12 studies representing 100 different subjects. However, no phase data is included in the tables; group delay simulation would need to be included in order to account for ITD. In 3D sound ..."
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Cited by 282 (5 self)
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This paper gives HRTF magnitude data in numerical form for 43 frequencies between 0.212 kHz, the average of 12 studies representing 100 different subjects. However, no phase data is included in the tables; group delay simulation would need to be included in order to account for ITD. In 3D sound applications intended for many users, we want might want to use HRTFs that represent the common features of a number of individuals. But another approach might be to use the features of a person who has desirable HRTFs, based on some criteria. (One can sense a future 3D sound system where the pinnae of various famous musicians are simulated.) A set of HRTFs from a good localizer (discussed in Chapter 2) could be used if the criterion were localization performance. If the localization ability of the person is relatively accurate or more accurate than average, it might be reasonable to use these HRTF measurements for other individuals. The Convolvotron 3D audio system (Wenzel, Wightman, and Foster, 1988) has used such sets particularly because elevation accuracy is affected negatively when listening through a bad localizers ears (see Wenzel, et al., 1988). It is best when any single nonindividualized HRTF set is psychoacoustically validated using a 113 statistical sample of the intended user population, as shown in Chapter 2. Otherwise, the use of one HRTF set over another is a purely subjective judgment based on criteria other than localization performance. The technique used by Wightman and Kistler (1989a) exemplifies a laboratorybased HRTF measurement procedure where accuracy and replicability of results were deemed crucial. A comparison of their techniques with those described in Blauert (1983), Shaw (1974), Mehrgardt and Mellert (1977), Middlebrooks, Makous, and Gree...
Authentication of LZ77 compressed data
 in Proceedings of the 18th ACM Symposium on Applied Computing
, 2003
"... The formidable dissemination capability allowed by the current network technology makes it increasingly important to devise new methods to ensure authenticity. Nowadays it is common practice to distribute documents in compressed form. In this paper, we propose a simple variation on the classic LZ77 ..."
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Cited by 12 (1 self)
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by an attacker (unless the secret bitstring key is known). Since it can still be decompressed by the original LZ77 algorithm, the embedding is completely "transparent". Preliminary experiments show also the degradation in compression due to the embedding is almost negligible.
XMill: an Efficient Compressor for XML Data
, 1999
"... We describe a tool for compressing XML data, with applications in data exchange and archiving, which usually achieves about twice the compression ratio of gzip at roughly the same speed. The compressor, called XMill, incorporates and combines existing compressors in order to apply them to heterogene ..."
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Cited by 228 (0 self)
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We describe a tool for compressing XML data, with applications in data exchange and archiving, which usually achieves about twice the compression ratio of gzip at roughly the same speed. The compressor, called XMill, incorporates and combines existing compressors in order to apply them
Continuous Speech Recognition by Statistical Methods
 Proceedings of the IEEE 64
, 1976
"... HIS PAPER DESCRIBES statistical methods of automatic recognition (transcription) of continuous speech that have been used successfully by the Speech Processing Group at the IBM Thomas J. Watson Research Center. The ..."
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Cited by 243 (1 self)
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HIS PAPER DESCRIBES statistical methods of automatic recognition (transcription) of continuous speech that have been used successfully by the Speech Processing Group at the IBM Thomas J. Watson Research Center. The
Parallel Algorithmic Techniques: PRAM Algorithms And PRAM Simulations
, 1995
"... PRAM , which is the Priority CRCW PRAM in which each processor can perform arbitrary complex local operations in a single step. Clearly the Abtract PRAM is stronger than the Priority CRCW PRAM, and actually, it is stronger than any other standard (hence we do not take into account the Minimum CRCW ..."
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PRAM , which is the Priority CRCW PRAM in which each processor can perform arbitrary complex local operations in a single step. Clearly the Abtract PRAM is stronger than the Priority CRCW PRAM, and actually, it is stronger than any other standard (hence we do not take into account the Minimum CRCW
Results 1  10
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1,999