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    Improved Upper Bounds on Stopping Redundancy

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    ์ƒˆ๋กœ์šด ์†Œ์‹ค ์ฑ„๋„์„ ์œ„ํ•œ ์ž๊ธฐ๋™ํ˜• ๊ตฐ ๋ณตํ˜ธ๊ธฐ ๋ฐ ๋ถ€๋ถ„ ์ ‘์† ๋ณต๊ตฌ ๋ถ€ํ˜ธ ๋ฐ ์ผ๋ฐ˜ํ™”๋œ ๊ทผ ํ”„๋กœํ† ๊ทธ๋ž˜ํ”„ LDPC ๋ถ€ํ˜ธ์˜ ์„ค๊ณ„

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2019. 2. ๋…ธ์ข…์„ .In this dissertation, three main contributions are given asi) new two-stage automorphism group decoders (AGD) for cyclic codes in the erasure channel, ii) new constructions of binary and ternary locally repairable codes (LRCs) using cyclic codes and existing LRCs, and iii) new constructions of high-rate generalized root protograph (GRP) low-density parity-check (LDPC) codes for a nonergodic block interference and partially regular (PR) LDPC codes for follower noise jamming (FNJ), are considered. First, I propose a new two-stage AGD (TS-AGD) for cyclic codes in the erasure channel. Recently, error correcting codes in the erasure channel have drawn great attention for various applications such as distributed storage systems and wireless sensor networks, but many of their decoding algorithms are not practical because they have higher decoding complexity and longer delay. Thus, the AGD for cyclic codes in the erasure channel was introduced, which has good erasure decoding performance with low decoding complexity. In this research, I propose new TS-AGDs for cyclic codes in the erasure channel by modifying the parity check matrix and introducing the preprocessing stage to the AGD scheme. The proposed TS-AGD is analyzed for the perfect codes, BCH codes, and maximum distance separable (MDS) codes. Through numerical analysis, it is shown that the proposed decoding algorithm has good erasure decoding performance with lower decoding complexity than the conventional AGD. For some cyclic codes, it is shown that the proposed TS-AGD achieves the perfect decoding in the erasure channel, that is, the same decoding performance as the maximum likelihood (ML) decoder. For MDS codes, TS-AGDs with the expanded parity check matrix and the submatrix inversion are also proposed and analyzed. Second, I propose new constructions of binary and ternary LRCs using cyclic codes and existing two LRCs for distributed storage system. For a primitive work, new constructions of binary and ternary LRCs using cyclic codes and their concatenation are proposed. Some of proposed binary LRCs with Hamming weights 4, 5, and 6 are optimal in terms of the upper bounds. In addition, the similar method of the binary case is applied to construct the ternary LRCs with good parameters. Also, new constructions of binary LRCs with large Hamming distance and disjoint repair groups are proposed. The proposed binary linear LRCs constructed by using existing binary LRCs are optimal or near-optimal in terms of the bound with disjoint repair group. Last, I propose new constructions of high-rate GRP LDPC codes for a nonergodic block interference and anti-jamming PR LDPC codes for follower jamming. The proposed high-rate GRP LDPC codes are based on nonergodic two-state binary symmetric channel with block interference and Nakagami-mm block fading. In these channel environments, GRP LDPC codes have good performance approaching to the theoretical limit in the channel with one block interference, where their performance is shown by the channel threshold or the channel outage probability. In the proposed design, I find base matrices using the protograph extrinsic information transfer (PEXIT) algorithm. Also, the proposed new constructions of anti-jamming partially regular LDPC codes is based on follower jamming on the frequency-hopped spread spectrum (FHSS). For a channel environment, I suppose follower jamming with random dwell time and Rayleigh block fading environment with M-ary frequnecy shift keying (MFSK) modulation. For a coding perspective, an anti-jamming LDPC codes against follower jamming are introduced. In order to optimize the jamming environment, the partially regular structure and corresponding density evolution schemes are used. A series of simulations show that the proposed codes outperforms the 802.16e standard in the presence of follower noise jamming.์ด ๋…ผ๋ฌธ์—์„œ๋Š”, i) ์†Œ์‹ค ์ฑ„๋„์—์„œ ์ˆœํ™˜ ๋ถ€ํ˜ธ์˜ ์ƒˆ๋กœ์šด ์ด๋‹จ ์ž๊ธฐ๋™ํ˜• ๊ตฐ ๋ณตํ˜ธ๊ธฐ , ii) ๋ถ„์‚ฐ ์ €์žฅ ์‹œ์Šคํ…œ์„ ์œ„ํ•œ ์ˆœํ™˜ ๋ถ€ํ˜ธ ๋ฐ ๊ธฐ์กด์˜ ๋ถ€๋ถ„ ์ ‘์† ๋ณต๊ตฌ ๋ถ€ํ˜ธ(LRC)๋ฅผ ์ด์šฉํ•œ ์ด์ง„ ํ˜น์€ ์‚ผ์ง„ ๋ถ€๋ถ„ ์ ‘์† ๋ณต๊ตฌ ๋ถ€ํ˜ธ ์„ค๊ณ„๋ฒ•, ๋ฐ iii) ๋ธ”๋ก ๊ฐ„์„ญ ํ™˜๊ฒฝ์„ ์œ„ํ•œ ๊ณ ๋ถ€ํšจ์œจ์˜ ์ผ๋ฐ˜ํ™”๋œ ๊ทผ ํ”„๋กœํ† ๊ทธ๋ž˜ํ”„(generalized root protograph, GRP) LDPC ๋ถ€ํ˜ธ ๋ฐ ์ถ”์  ์žฌ๋ฐ ํ™˜๊ฒฝ์„ ์œ„ํ•œ ํ•ญ์žฌ๋ฐ ๋ถ€๋ถ„ ๊ท ์ผ (anti-jamming paritally regular, AJ-PR) LDPC ๋ถ€ํ˜ธ๊ฐ€ ์—ฐ๊ตฌ๋˜์—ˆ๋‹ค. ์ฒซ๋ฒˆ์งธ๋กœ, ์†Œ์‹ค ์ฑ„๋„์—์„œ ์ˆœํ™˜ ๋ถ€ํ˜ธ์˜ ์ƒˆ๋กœ์šด ์ด๋‹จ ์ž๊ธฐ๋™ํ˜• ๊ตฐ ๋ณตํ˜ธ๊ธฐ๋ฅผ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ตœ๊ทผ ๋ถ„์‚ฐ ์ €์žฅ ์‹œ์Šคํ…œ ํ˜น์€ ๋ฌด์„  ์„ผ์„œ ๋„คํŠธ์›Œํฌ ๋“ฑ์˜ ์‘์šฉ์œผ๋กœ ์ธํ•ด ์†Œ์‹ค ์ฑ„๋„์—์„œ์˜ ์˜ค๋ฅ˜ ์ •์ • ๋ถ€ํ˜ธ ๊ธฐ๋ฒ•์ด ์ฃผ๋ชฉ๋ฐ›๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋งŽ์€ ๋ณตํ˜ธ๊ธฐ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ๋†’์€ ๋ณตํ˜ธ ๋ณต์žก๋„ ๋ฐ ๊ธด ์ง€์—ฐ์œผ๋กœ ์ธํ•ด ์‹ค์šฉ์ ์ด์ง€ ๋ชปํ•˜๋‹ค. ๋”ฐ๋ผ์„œ ๋‚ฎ์€ ๋ณตํ˜ธ ๋ณต์žก๋„ ๋ฐ ๋†’์€ ์„ฑ๋Šฅ์„ ๋ณด์ผ ์ˆ˜ ์žˆ๋Š” ์ˆœํ™˜ ๋ถ€ํ˜ธ์—์„œ ์ด๋‹จ ์ž๊ธฐ ๋™ํ˜• ๊ตฐ ๋ณตํ˜ธ๊ธฐ๊ฐ€ ์ œ์•ˆ๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํŒจ๋ฆฌํ‹ฐ ๊ฒ€์‚ฌ ํ–‰๋ ฌ์„ ๋ณ€ํ˜•ํ•˜๊ณ , ์ „์ฒ˜๋ฆฌ ๊ณผ์ •์„ ๋„์ž…ํ•œ ์ƒˆ๋กœ์šด ์ด๋‹จ ์ž๊ธฐ๋™ํ˜• ๊ตฐ ๋ณตํ˜ธ๊ธฐ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆํ•œ ๋ณตํ˜ธ๊ธฐ๋Š” perfect ๋ถ€ํ˜ธ, BCH ๋ถ€ํ˜ธ ๋ฐ ์ตœ๋Œ€ ๊ฑฐ๋ฆฌ ๋ถ„๋ฆฌ (maximum distance separable, MDS) ๋ถ€ํ˜ธ์— ๋Œ€ํ•ด์„œ ๋ถ„์„๋˜์—ˆ๋‹ค. ์ˆ˜์น˜ ๋ถ„์„์„ ํ†ตํ•ด, ์ œ์•ˆ๋œ ๋ณตํ˜ธ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ๊ธฐ์กด์˜ ์ž๊ธฐ ๋™ํ˜• ๊ตฐ ๋ณตํ˜ธ๊ธฐ๋ณด๋‹ค ๋‚ฎ์€ ๋ณต์žก๋„๋ฅผ ๋ณด์ด๋ฉฐ, ๋ช‡๋ช‡์˜ ์ˆœํ™˜ ๋ถ€ํ˜ธ ๋ฐ ์†Œ์‹ค ์ฑ„๋„์—์„œ ์ตœ๋Œ€ ์šฐ๋„ (maximal likelihood, ML)๊ณผ ๊ฐ™์€ ์ˆ˜์ค€์˜ ์„ฑ๋Šฅ์ž„์„ ๋ณด์ธ๋‹ค. MDS ๋ถ€ํ˜ธ์˜ ๊ฒฝ์šฐ, ํ™•์žฅ๋œ ํŒจ๋ฆฌํ‹ฐ๊ฒ€์‚ฌ ํ–‰๋ ฌ ๋ฐ ์ž‘์€ ํฌ๊ธฐ์˜ ํ–‰๋ ฌ์˜ ์—ญ์—ฐ์‚ฐ์„ ํ™œ์šฉํ•˜์˜€์„ ๊ฒฝ์šฐ์˜ ์„ฑ๋Šฅ์„ ๋ถ„์„ํ•œ๋‹ค. ๋‘ ๋ฒˆ์งธ๋กœ, ๋ถ„์‚ฐ ์ €์žฅ ์‹œ์Šคํ…œ์„ ์œ„ํ•œ ์ˆœํ™˜ ๋ถ€ํ˜ธ ๋ฐ ๊ธฐ์กด์˜ ๋ถ€๋ถ„ ์ ‘์† ๋ณต๊ตฌ ๋ถ€ํ˜ธ (LRC)๋ฅผ ์ด์šฉํ•œ ์ด์ง„ ํ˜น์€ ์‚ผ์ง„ ๋ถ€๋ถ„ ์ ‘์† ๋ณต๊ตฌ ๋ถ€ํ˜ธ ์„ค๊ณ„๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ดˆ๊ธฐ ์—ฐ๊ตฌ๋กœ์„œ, ์ˆœํ™˜ ๋ถ€ํ˜ธ ๋ฐ ์—ฐ์ ‘์„ ํ™œ์šฉํ•œ ์ด์ง„ ๋ฐ ์‚ผ์ง„ LRC ์„ค๊ณ„ ๊ธฐ๋ฒ•์ด ์—ฐ๊ตฌ๋˜์—ˆ๋‹ค. ์ตœ์†Œ ํ•ด๋ฐ ๊ฑฐ๋ฆฌ๊ฐ€ 4,5, ํ˜น์€ 6์ธ ์ œ์•ˆ๋œ ์ด์ง„ LRC ์ค‘ ์ผ๋ถ€๋Š” ์ƒํ•œ๊ณผ ๋น„๊ตํ•ด ๋ณด์•˜์„ ๋•Œ ์ตœ์  ์„ค๊ณ„์ž„์„ ์ฆ๋ช…ํ•˜์˜€๋‹ค. ๋˜ํ•œ, ๋น„์Šทํ•œ ๋ฐฉ๋ฒ•์„ ์ ์šฉํ•˜์—ฌ ์ข‹์€ ํŒŒ๋ผ๋ฏธํ„ฐ์˜ ์‚ผ์ง„ LRC๋ฅผ ์„ค๊ณ„ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๊ทธ ์™ธ์— ๊ธฐ์กด์˜ LRC๋ฅผ ํ™œ์šฉํ•˜์—ฌ ํฐ ํ•ด๋ฐ ๊ฑฐ๋ฆฌ์˜ ์ƒˆ๋กœ์šด LRC๋ฅผ ์„ค๊ณ„ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ œ์•ˆ๋œ LRC๋Š” ๋ถ„๋ฆฌ๋œ ๋ณต๊ตฌ ๊ตฐ ์กฐ๊ฑด์—์„œ ์ตœ์ ์ด๊ฑฐ๋‚˜ ์ตœ์ ์— ๊ฐ€๊นŒ์šด ๊ฐ’์„ ๋ณด์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, GRP LDPC ๋ถ€ํ˜ธ๋Š” Nakagami-mm ๋ธ”๋ก ํŽ˜์ด๋”ฉ ๋ฐ ๋ธ”๋ก ๊ฐ„์„ญ์ด ์žˆ๋Š” ๋‘ ์ƒํƒœ์˜ ์ด์ง„ ๋Œ€์นญ ์ฑ„๋„์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์ฑ„๋„ ํ™˜๊ฒฝ์—์„œ GRP LDPC ๋ถ€ํ˜ธ๋Š” ํ•˜๋‚˜์˜ ๋ธ”๋ก ๊ฐ„์„ญ์ด ๋ฐœ์ƒํ–ˆ์„ ๊ฒฝ์šฐ, ์ด๋ก ์  ์„ฑ๋Šฅ์— ๊ฐ€๊นŒ์šด ์ข‹์€ ์„ฑ๋Šฅ์„ ๋ณด์—ฌ์ค€๋‹ค. ์ด๋Ÿฌํ•œ ์ด๋ก  ๊ฐ’์€ ์ฑ„๋„ ๋ฌธํ„ฑ๊ฐ’์ด๋‚˜ ์ฑ„๋„ outage ํ™•๋ฅ ์„ ํ†ตํ•ด ๊ฒ€์ฆํ•  ์ˆ˜ ์žˆ๋‹ค. ์ œ์•ˆ๋œ ์„ค๊ณ„์—์„œ๋Š”, ๋ณ€ํ˜•๋œ PEXIT ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ํ™œ์šฉํ•˜์—ฌ ๊ธฐ์ดˆ ํ–‰๋ ฌ์„ ์„ค๊ณ„ํ•œ๋‹ค. ๋˜ํ•œ AJ-PR LDPC ๋ถ€ํ˜ธ๋Š” ์ฃผํŒŒ์ˆ˜ ๋„์•ฝ ํ™˜๊ฒฝ์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์ถ”์  ์žฌ๋ฐ์ด ์žˆ๋Š” ํ™˜๊ฒฝ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ๋‹ค. ์ฑ„๋„ ํ™˜๊ฒฝ์œผ๋กœ MFSK ๋ณ€๋ณต์กฐ ๋ฐฉ์‹์˜ ๋ ˆ์ผ๋ฆฌ ๋ธ”๋ก ํŽ˜์ด๋”ฉ ๋ฐ ๋ฌด์ž‘์œ„ํ•œ ์ง€์† ์‹œ๊ฐ„์ด ์žˆ๋Š” ์žฌ๋ฐ ํ™˜๊ฒฝ์„ ๊ฐ€์ •ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์žฌ๋ฐ ํ™˜๊ฒฝ์œผ๋กœ ์ตœ์ ํ™”ํ•˜๊ธฐ ์œ„ํ•ด, ๋ถ€๋ถ„ ๊ท ์ผ ๊ตฌ์กฐ ๋ฐ ํ•ด๋‹น๋˜๋Š” ๋ฐ€๋„ ์ง„ํ™” (density evolution, DE) ๊ธฐ๋ฒ•์ด ํ™œ์šฉ๋œ๋‹ค. ์—ฌ๋Ÿฌ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ๋Š” ์ถ”์  ์žฌ๋ฐ์ด ์กด์žฌํ•˜๋Š” ํ™˜๊ฒฝ์—์„œ ์ œ์•ˆ๋œ ๋ถ€ํ˜ธ๊ฐ€ 802.16e์— ์‚ฌ์šฉ๋˜์—ˆ๋˜ LDPC ๋ถ€ํ˜ธ๋ณด๋‹ค ์„ฑ๋Šฅ์ด ์šฐ์ˆ˜ํ•จ์„ ๋ณด์—ฌ์ค€๋‹ค.Contents Abstract Contents List of Tables List of Figures 1 INTRODUCTION 1.1 Background 1.2 Overview of Dissertation 1.3 Notations 2 Preliminaries 2.1 IED and AGD for Erasure Channel 2.1.1 Iterative Erasure Decoder 2.1.1 Automorphism Group Decoder 2.2. Binary Locally Repairable Codes for Distributed Storage System 2.2.1 Bounds and Optimalities of Binary LRCs 2.2.2 Existing Optimal Constructions of Binary LRCs 2.3 Channels with Block Interference and Jamming 2.3.1 Channels with Block Interference 2.3.2 Channels with Jamming with MFSK and FHSS Environment. 3 New Two-Stage Automorphism Group Decoders for Cyclic Codes in the Erasure Channel 3.1 Some Definitions 3.2 Modification of Parity Check Matrix and Two-Stage AGD 3.2.1 Modification of the Parity Check Matrix 3.2.2 A New Two-Stage AGD 3.2.3 Analysis of Modification Criteria for the Parity Check Matrix 3.2.4 Analysis of Decoding Complexity of TS-AGD 3.2.5 Numerical Analysis for Some Cyclic Codes 3.3 Construction of Parity Check Matrix and TS-AGD for Cyclic MDS Codes 3.3.1 Modification of Parity Check Matrix for Cyclic MDS Codes . 3.3.2 Proposed TS-AGD for Cyclic MDS Codes 3.3.3 Perfect Decoding by TS-AGD with Expanded Parity Check Matrix for Cyclic MDS Codes 3.3.4 TS-AGD with Submatrix Inversion for Cyclic MDS Codes . . 4 New Constructions of Binary and Ternary LRCs Using Cyclic Codes and Existing LRCs 4.1 Constructions of Binary LRCs Using Cyclic Codes 4.2 Constructions of Linear Ternary LRCs Using Cyclic Codes 4.3 Constructions of Binary LRCs with Disjoint Repair Groups Using Existing LRCs 4.4 New Constructions of Binary Linear LRCs with d โ‰ฅ 8 Using Existing LRCs 5 New Constructions of Generalized RP LDPC Codes for Block Interference and Partially Regular LDPC Codes for Follower Jamming 5.1 Generalized RP LDPC Codes for a Nonergodic BI 5.1.1 Minimum Blockwise Hamming Weight 5.1.2 Construction of GRP LDPC Codes 5.2 Asymptotic and Numerical Analyses of GRP LDPC Codes 5.2.1 Asymptotic Analysis of LDPC Codes 5.2.2 Numerical Analysis of Finite-Length LDPC Codes 5.3 Follower Noise Jamming with Fixed Scan Speed 5.4 Anti-Jamming Partially Regular LDPC Codes for Follower Noise Jamming 5.4.1 Simplified Channel Model and Corresponding Density Evolution 5.4.2 Construction of AJ-PR-LDPC Codes Based on DE 5.5 Numerical Analysis of AJ-PR LDPC Codes 6 Conclusion Abstract (In Korean)Docto

    Stopping Set Distributions of Some Linear Codes

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    Stopping sets and stopping set distribution of an low-density parity-check code are used to determine the performance of this code under iterative decoding over a binary erasure channel (BEC). Let CC be a binary [n,k][n,k] linear code with parity-check matrix HH, where the rows of HH may be dependent. A stopping set SS of CC with parity-check matrix HH is a subset of column indices of HH such that the restriction of HH to SS does not contain a row of weight one. The stopping set distribution {Ti(H)}i=0n\{T_i(H)\}_{i=0}^n enumerates the number of stopping sets with size ii of CC with parity-check matrix HH. Note that stopping sets and stopping set distribution are related to the parity-check matrix HH of CC. Let Hโˆ—H^{*} be the parity-check matrix of CC which is formed by all the non-zero codewords of its dual code CโŠฅC^{\perp}. A parity-check matrix HH is called BEC-optimal if Ti(H)=Ti(Hโˆ—),i=0,1,...,nT_i(H)=T_i(H^*), i=0,1,..., n and HH has the smallest number of rows. On the BEC, iterative decoder of CC with BEC-optimal parity-check matrix is an optimal decoder with much lower decoding complexity than the exhaustive decoder. In this paper, we study stopping sets, stopping set distributions and BEC-optimal parity-check matrices of binary linear codes. Using finite geometry in combinatorics, we obtain BEC-optimal parity-check matrices and then determine the stopping set distributions for the Simplex codes, the Hamming codes, the first order Reed-Muller codes and the extended Hamming codes.Comment: 33 pages, submitted to IEEE Trans. Inform. Theory, Feb. 201

    Cooperating error-correcting codes and their decoding

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    Integrating protein structural information

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    Dissertaรงรฃo apresentada para obtenรงรฃo de Grau de Doutor em Bioquรญmica,Bioquรญmica Estrutural, pela Universidade Nova de Lisboa, Faculdade de Ciรชncias e TecnologiaThe central theme of this work is the application of constraint programming and other artificial intelligence techniques to protein structure problems, with the goal of better combining experimental data with structure prediction methods. Part one of the dissertation introduces the main subjects of protein structure and constraint programming, summarises the state of the art in the modelling of protein structures and complexes, sets the context for the techniques described later on, and outlines the main points of the thesis: the integration of experimental data in modelling. The first chapter, Protein Structure, introduces the reader to the basic notions of amino acid structure, protein chains, and protein folding and interaction. These are important concepts to understand the work described in parts two and three. Chapter two, Protein Modelling, gives a brief overview of experimental and theoretical techniques to model protein structures. The information in this chapter provides the context of the investigations described in parts two and three, but is not essential to understanding the methods developed. Chapter three, Constraint Programming, outlines the main concepts of this programming technique. Understanding variable modelling, the notions of consistency and propagation, and search methods should greatly help the reader interested in the details of the algorithms, as described in part two of this book. The fourth chapter, Integrating Structural Information, is a summary of the thesis proposed here. This chapter is an overview of the objectives of this work, and gives an idea of how the algorithms developed here could help in modelling protein structures. The main goal is to provide a flexible and continuously evolving framework for the integration of structural information from a diversity of experimental techniques and theoretical predictions. Part two describes the algorithms developed, which make up the main original contribution of this work. This part is aimed especially at developers interested in the details of the algorithms, in replicating the results, in improving the method or in integrating them in other applications. Biochemical aspects are dealt with briefly and as necessary, and the emphasis is on the algorithms and the code

    Opportunistic Routing with Network Coding in Powerline Communications

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    Opportunistic Routing (OR) can be used as an alternative to the legacy routing (LR) protocols in networks with a broadcast lossy channel and possibility of overhearing the signal. The power line medium creates such an environment. OR can better exploit the channel than LR because it allows the cooperation of all nodes that receive any data. With LR, only a chain of nodes is selected for communication. Other nodes drop the received information. We investigate OR for the one-source one-destination scenario with one traffic flow. First, we evaluate the upper bound on the achievable data rate and advocate the decentralized algorithm for its calculation. This knowledge is used in the design of Basic Routing Rules (BRR). They use the link quality metric that equals the upper bound on the achievable data rate between the given node and the destination. We call it the node priority. It considers the possibility of multi-path communication and the packet loss correlation. BRR allows achieving the optimal data rate pertaining certain theoretical assumptions. The Extended BRR (BRR-E) are free of them. The major difference between BRR and BRR-E lies in the usage of Network Coding (NC) for prognosis of the feedback. In this way, the protocol overhead can be severely reduced. We also study Automatic Repeat-reQuest (ARQ) mechanism that is applicable with OR. It differs to ARQ with LR in that each sender has several sinks and none of the sinks except destination require the full recovery of the original message. Using BRR-E, ARQ and other services like network initialization and link state control, we design the Advanced Network Coding based Opportunistic Routing protocol (ANChOR). With the analytic and simulation results we demonstrate the near optimum performance of ANChOR. For the triangular topology, the achievable data rate is just 2% away from the theoretical maximum and it is up to 90% higher than it is possible to achieve with LR. Using the G.hn standard, we also show the full protocol stack simulation results (including IP/UDP and realistic channel model). In this simulation we revealed that the gain of OR to LR can be even more increased by reducing the head-of-the-line problem in ARQ. Even considering the ANChOR overhead through additional headers and feedbacks, it outperforms the original G.hn setup in data rate up to 40% and in latency up to 60%.:1 Introduction 2 1.1 Intra-flow Network Coding 6 1.2 Random Linear Network Coding (RLNC) 7 2 Performance Limits of Routing Protocols in PowerLine Communications (PLC) 13 2.1 System model 14 2.2 Channel model 14 2.3 Upper bound on the achievable data rate 16 2.4 Achieving the upper bound data rate 17 2.5 Potential gain of Opportunistic Routing Protocol (ORP) over Common Single-path Routing Protocol (CSPR) 19 2.6 Evaluation of ORP potential 19 3 Opportunistic Routing: Realizations and Challenges 24 3.1 Vertex priority and cooperation group 26 3.2 Transmission policy in idealized network 34 3.2.1 Basic Routing Rules (BRR) 36 3.3 Transmission policy in real network 40 3.3.1 Purpose of Network Coding (NC) in ORP 41 3.3.2 Extended Basic Routing Rules (BRR) (BRR-E) 43 3.4 Automatic ReQuest reply (ARQ) 50 3.4.1 Retransmission request message contents 51 3.4.2 Retransmission Request (RR) origination and forwarding 66 3.4.3 Retransmission response 67 3.5 Congestion control 68 3.5.1 Congestion control in our work 70 3.6 Network initialization 74 3.7 Formation of the cooperation groups (coalitions) 76 3.8 Advanced Network Coding based Opportunistic Routing protocol (ANChOR) header 77 3.9 Communication of protocol information 77 3.10 ANChOR simulation . .79 3.10.1 ANChOR information in real time .80 3.10.2 Selection of the coding rate 87 3.10.3 Routing Protocol Information (RPI) broadcasting frequency 89 3.10.4 RR contents 91 3.10.5 Selection of RR forwarder 92 3.10.6 ANChOR stability 92 3.11 Summary 95 4 ANChOR in the Gigabit Home Network (G.hn) Protocol 97 4.1 Compatibility with the PLC protocol stack 99 4.2 Channel and noise model 101 4.2.1 In-home scenario 102 4.2.2 Access network scenario 102 4.3 Physical layer (PHY) layer implementation 102 4.3.1 Bit Allocation Algorithm (BAA) 103 4.4 Multiple Access Control layer (MAC) layer 109 4.5 Logical Link Control layer (LLC) layer 111 4.5.1 Reference Automatic Repeat reQuest (ARQ) 111 4.5.2 Hybrid Automatic Repeat reQuest (HARQ) in ANChOR 114 4.5.3 Modeling Protocol Data Unit (PDU) erasures on LLC 116 4.6 Summary 117 5 Study of G.hn with ANChOR 119 5.1 ARQ analysis 119 5.2 Medium and PHY requirements for โ€œgoodโ€ cooperation 125 5.3 Access network scenario 128 5.4 In-home scenario 135 5.4.1 Modeling packet erasures 136 5.4.2 Linear Dependence Ratio (LDR) 139 5.4.3 Worst case scenario 143 5.4.4 Analysis of in-home topologies 145 6 Conclusions . . . . . . . . . . . . . . . 154 A Proof of the neccessity of the exclusion rule 160 B Gain of ORPs to CSRPs 163 C Broadcasting rule 165 D Proof of optimality of BRR for triangular topology 167 E Reducing the retransmission probability 168 F Calculation of Expected Average number of transmissions (EAX) for topologies with bi-directional links 170 G Feedback overhead of full coding matrices 174 H Block diagram of G.hn physical layer in ns-3 model 175 I PER to BER mapping 17

    Characterizing Spoken Discourse in Individuals with Parkinson Disease Without Dementia

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    Background: The effects of disease (PD) on cognition, word retrieval, syntax, and speech/voice processes may interact to manifest uniquely in spoken language tasks. A handful of studies have explored spoken discourse production in PD and, while not ubiquitously, have reported a number of impairments including: reduced words per minute, reduced grammatical complexity, reduced informativeness, and increased verbal disruption. Methodological differences have impeded cross-study comparisons. As such, the profile of spoken language impairments in PD remains ambiguous. Method: A cross-genre, multi-level discourse analysis, prospective, cross-sectional between groups study design was conducted with 19 PD participants (Mage = 70.74, MUPDRS-III = 30.26) and 19 healthy controls (Mage = 68.16) without dementia. The extensive protocol included a battery of cognitive, language, and speech measures in addition to four discourse tasks. Two tasks each from two discourse genres (picture sequence description; story retelling) were collected. Discourse samples were analysed using both microlinguistic and macrostructural measures. Discourse variables were collapsed statistically to a primal set of variables used to distinguish the spoken discourse of PD vs. controls. Results: Participants with PD differed significantly from controls along a continuum of productivity, grammar, informativeness, and verbal disruption domains including total words F(1,36) = 3.87, p = .06; words/minute F(1,36) = 7.74, p = .01 , % grammatical utterances F(1,36) = 11.92, p = .001, total CIUs F(1,36) = 13.30, p = .001, % CIUs (Correct Information Units) F(1,36) = 9.35, p = .004, CIUs/minute F(1,36) = 14.06, p = .001, and verbal disruptions/100 words F(1,36) = 3.87, p = .06 (ฮฑ = .10). Discriminant function analyses showed that optimally weighted discourse variables discriminated the spoken discourse of PD vs. controls with 81.6% sensitivity and 86.8% specificity. For both discourse genres, discourse performance showed robust, positive, correlations with global cognition. In PD (picture sequence description), more impaired discourse performance correlated significantly with more severe motor impairment, more advanced disease staging, and higher doses of PD medications. Conclusions: The spoken discourse in PD without dementia differs significantly and predictably from controls. Results have both research and clinical implications
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