A compressed full-text self-index represents a text in a compressed form and still answers queries efficiently. This represents a significant advancement over the (full-)text indexing techniques of the previous decade...
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A compressed full-text self-index represents a text in a compressed form and still answers queries efficiently. This represents a significant advancement over the (full-)text indexing techniques of the previous decade, whose indexes required several times the size of the text. Although it is relatively new, this algorithmic technology has matured up to a point where theoretical research is giving way to practical developments. Nonetheless this requires significant programming skills, a deep engineering effort, and a strong algorithmic background to dig into the research results. To date only isolated implementations and focused comparisons of compressed indexes have been reported, and they missed a common API, which prevented their re-use or deployment within other applications. The goal of this article is to fill this gap. First, we present the existing implementations of compressed indexes from a practitioner's point of view. Second, we introduce the Pizza & Chili site, which offers tuned implementations and a standardized API for the most successful compressed full-text self-indexes, together with effective test-beds and scripts for their automatic validation and test. Third, we show the results of our extensive experiments on these codes with the aim of demonstrating the practical relevance of this novel algorithmic technology. Categories and Subject Descriptors: F.2.2 [Analysis of Algorithms and Problem Complexity]: Nonnumerical Algorithms and Problems-Pattern matching, computations on discrete structures, sorting and searching;H.2.1 [Database Management]: Physical Design-Access methods;H.3.2 [information Storage and Retrieval]: information Storage-File organization;H.3.3 [information Storage and Retrieval]: information Search and Retrieval-Search process General Terms: Algorithms Additional Key Words and Phrases: Text indexing, text compression, data structures, data storage representation, coding and information theory, indexing methods, textual databases, bi
"Lightweight Hierarchical Error Control coding ( LHECC)" is a new class of nonlinear block codes that is designed to increase noise immunity and decrease error rate for high-performance chip-to-chip and on-c...
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"Lightweight Hierarchical Error Control coding ( LHECC)" is a new class of nonlinear block codes that is designed to increase noise immunity and decrease error rate for high-performance chip-to-chip and on-chip interconnects. LHECC is designed such that its corresponding encoder and decoder logic may be tightly integrated into compact, high-speed, and low-latency I/O interfaces. LHECC operates over a new channel technology called Multi-Bit Differential Signaling ( MBDS). MBDS channels utilize a physical-layer channel code called "N choose M ( nCm)" encoding, where each channel is restricted to a symbol set such that half of the bits in each symbol are set to one. These symbol sets have properties that are utilized by LHECC to achieve error correction capability while requiring low or zero relative information overhead. In addition, these codes may be designed such that the latency and size of the corresponding decoders are tightly bounded. The effectiveness of these codes is demonstrated by modeling error behavior of MBDS interconnects over a range of transmission rates and noise characteristics.
Reed Solomon (RS) codes are widely used to protect information from errors in transmission and storage systems. Most of the RS codes are based on GFd(2(8)) Galois Fields and use a byte to encode a symbol providing cod...
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Reed Solomon (RS) codes are widely used to protect information from errors in transmission and storage systems. Most of the RS codes are based on GFd(2(8)) Galois Fields and use a byte to encode a symbol providing codewords up to 255 symbols. Codewords with more than 255 symbols can be obtained by using GFd(2(m)) Galois Fields with m > 8, but this choice increases the complexity of the encoding and decoding algorithms. This limitation can be superseded by introducing Parity Sharing ( PS) RS codes that are characterized by a greater flexibility in terms of design parameters. Consequently, a designer can choose between different PS code implementations in order to meet requirements such as Bit Error Rate (BER), hardware complexity, speed, and throughput. This paper analyzes the performance of PS codes in terms of BER with respect to the code parameters, taking into account either random error or erasure rates as two independent probabilities. This approach provides an evaluation that is independent of the communication channel characteristics and extends the results to memory systems in which permanent faults and transient faults can be modeled, respectively, as erasures and random errors. The paper also provides hardware implementations of the PS encoder and decoder and discusses their performances in terms of hardware complexity, speed, and throughput.
A symbol permutation invariant balanced (SPI-balanced) code over the alphabet ZZ(m) = {0, 1,..., m - 1} is a block code over ZZm such that each alphabet symbol occurs as many times as any other symbol in every codewor...
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A symbol permutation invariant balanced (SPI-balanced) code over the alphabet ZZ(m) = {0, 1,..., m - 1} is a block code over ZZm such that each alphabet symbol occurs as many times as any other symbol in every codeword. For this reason, every permutation among the symbols of the alphabet changes an SPI-balanced code into an SPI-balanced code. This means that SPI-balanced words are "the most balanced" among all possible m-ary balanced word types and this property makes them very attractive from the application perspective. In particular, they can be used to achieve m-ary DC-free communication, to detect/correct asymmetric/unidirectional errors on the m-ary asymmetric/unidirectional channel, to achieve delay-insensitive communication, to maintain data integrity in digital optical disks, and so on. This paper gives some efficient methods to convert ( encode) m-ary information sequences into m-ary SPI-balanced codes whose redundancy is equal to roughly double the minimum possible redundancy r(min). It is proven that r(min) similar or equal to vertical bar(m - 1)/2] log(m)n - (1/2) [1/log(2 pi)m)m-(1/log(2 pi)m) for any code which converts k information digits into an SPI-balanced code of length n = k + r. For example, the first method given in the paper encodes k information digits into an SPI-balanced code of length n = k + r, with r = (m - 1) log(m) k + O(m log(m) log(m) k). A second method is a recursive method, which uses the first as base code and encodes k digits into an SPI-balanced code of length n = k + r, with r similar or equal to (m - 1) log(m) n - log(m) [(m - 1)!].
1. INTRODUCTION With the increasing deployment of wireless networks (802. 11 architecture) in enterprise environments, IT enterprises are working to implement security mechanisms that are equivalent to those existing ...
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ISBN:
(数字)9780387298450
ISBN:
(纸本)9780387954257;9781489977120
1. INTRODUCTION With the increasing deployment of wireless networks (802. 11 architecture) in enterprise environments, IT enterprises are working to implement security mechanisms that are equivalent to those existing today for wire-based networks. An important aspect of this is the need to provide secure access to the network for valid users. Existing wired network jacks are located inside buildings already secured from unauthorized access through the use of keys, badge access, and so forth. A user must gain physical access to the building in order to plug a client computer into a network jack. In contrast, a wireless access point (AP) may be accessed from off the premises if the signal is detectable (for instance, from a parking lot adjacent to the building). Thus, wireless networks require secure access to the AP and the ability to isolate the AP from the internal private network prior to user authentication into the network domain. Furthermore, as enterprises strive to provide better availability of mission-critical wireless data, they also face the challenge of maintaining that data's security and integrity. While each connection with a client, a supplier or a enterprise partner can improve responsiveness and efficiency, it also increases the vulnerability of enterprise wireless data to attack. In such an environment, wireless network security is becoming more important every day. Also, with the growing reliance on e-commerce, wireless network-based services and the Internet, enterprises are faced with an ever-increasing responsibility to protect their systems from attack.
Appendices 133 A Mathematical Results 133 A.1 Singularities of the Displacement Error Covariance Matrix 133 A.2 A Class of Matrices and their Eigenvalues 134 A.3 Inverse of the Power Spectral Density Matrix 134 A.4 Po...
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ISBN:
(数字)9781402077654
ISBN:
(纸本)9781402077593;9781441954329
Appendices 133 A Mathematical Results 133 A.1 Singularities of the Displacement Error Covariance Matrix 133 A.2 A Class of Matrices and their Eigenvalues 134 A.3 Inverse of the Power Spectral Density Matrix 134 A.4 Power Spectral Density of a Frame 136 Glossary 137 References 141 Index 159 Preface This book aims to capture recent advances in motion compensation for - ficient video compression. It investigates linearly combined motion comp- sated signals and generalizes the well known superposition for bidirectional prediction in B-pictures. The number of superimposed signals and the sel- tion of reference pictures will be important aspects of the discussion. The application oriented part of the book employs this concept to the well known ITU-T Recommendation H.263 and continues with the improvements by superimposed motion-compensated signals for the emerging ITU-T R- ommendation H.264 and ISO/IEC MPEG-4 (Part 10). In addition, it discusses a new approach for wavelet-based video coding. This technology is currently investigated by MPEG to develop a new video compression standard for the mid-term future.
Reporting the state of the art of color image processing, this monograph fills an existing gap in the literature on digital signal and image processing. It can serve the needs of different users at different levels: a...
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ISBN:
(数字)9783662041864
ISBN:
(纸本)9783540669531;9783642086267
Reporting the state of the art of color image processing, this monograph fills an existing gap in the literature on digital signal and image processing. It can serve the needs of different users at different levels: as a textbook which covers a graduate image processing course, as a up-to-date reference for researchers since it offers a broad survey of the relevant literature, and as a relevant information source for development engineers who work in the design and the implementation of various image processing tasks. Part of the material in the book was the basis of seminars at the University of Toronto. The book contains numerous examples and pictures of color image processing results, as well as tables which summarize the results of the analysis. Algorithms implemented in JAVA can be downloaded from the author's website .
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