Capacity Achieving Code Constructions for Two Classes of (d,k) Constraints

In this paper, we present two low complexity algorithms that achieve capacity for the noiseless (d,k) constrained channel when k=2d+1, or when k-d+1 is not prime. The first algorithm, called symbol sliding, is a generalized version of the bit flipping algorithm introduced by Aviran et al. [1]. In addition to achieving capacity for (d,2d+1) constraints, it comes close to capacity in other cases. The second algorithm is based on interleaving, and is a generalized version of the bit stuffing algorithm introduced by Bender and Wolf [2]. This method uses fewer than k-d biased bit streams to achieve capacity for (d,k) constraints with k-d+1 not prime. In particular, the encoder for (d,d+2^m-1) constraints, 1\le m<\infty, requires only m biased bit streams.Comment: 16 pages, submitted to the IEEE Transactions on Information Theor

Achieving SK Capacity in the Source Model: When Must All Terminals Talk?

In this paper, we address the problem of characterizing the instances of the multiterminal source model of Csisz\'ar and Narayan in which communication from all terminals is needed for establishing a secret key of maximum rate. We give an information-theoretic sufficient condition for identifying such instances. We believe that our sufficient condition is in fact an exact characterization, but we are only able to prove this in the case of the three-terminal source model. We also give a relatively simple criterion for determining whether or not our condition holds for a given multiterminal source model.Comment: A 5-page version of this paper was submitted to the 2014 IEEE International Symposium on Information Theory (ISIT 2014

On the Public Communication Needed to Achieve SK Capacity in the Multiterminal Source Model

The focus of this paper is on the public communication required for generating a maximal-rate secret key (SK) within the multiterminal source model of Csisz{\'a}r and Narayan. Building on the prior work of Tyagi for the two-terminal scenario, we derive a lower bound on the communication complexity, $R_{\text{SK}}$, defined to be the minimum rate of public communication needed to generate a maximal-rate SK. It is well known that the minimum rate of communication for omniscience, denoted by $R_{\text{CO}}$, is an upper bound on $R_{\text{SK}}$. For the class of pairwise independent network (PIN) models defined on uniform hypergraphs, we show that a certain "Type $\mathcal{S}$" condition, which is verifiable in polynomial time, guarantees that our lower bound on $R_{\text{SK}}$ meets the $R_{\text{CO}}$ upper bound. Thus, PIN models satisfying our condition are $R_{\text{SK}}$-maximal, meaning that the upper bound $R_{\text{SK}} \le R_{\text{CO}}$ holds with equality. This allows us to explicitly evaluate $R_{\text{SK}}$ for such PIN models. We also give several examples of PIN models that satisfy our Type $\mathcal S$ condition. Finally, we prove that for an arbitrary multiterminal source model, a stricter version of our Type $\mathcal S$ condition implies that communication from \emph{all} terminals ("omnivocality") is needed for establishing a SK of maximum rate. For three-terminal source models, the converse is also true: omnivocality is needed for generating a maximal-rate SK only if the strict Type $\mathcal S$ condition is satisfied. Counterexamples exist that show that the converse is not true in general for source models with four or more terminals.Comment: Submitted to the IEEE Transactions on Information Theory. arXiv admin note: text overlap with arXiv:1504.0062

New Capacity-Approaching Codes for Run-Length-Limited Channels

Run-Length-Limited (RLL) channels are found in digital recording systems like the Hard Disk Drive (HDD), Compact Disc (CD), and Digital Versatile Disc (DVD). This thesis presents novel encoding algorithms for RLL channels based on a simple technique called bit stuffing. First, two new capacity-achieving variable-rate code constructions are proposed for (d,k) constraints. The variable-rate encoding ideas are then extended to (0,G/I) and other RLL constraints. Since variable-rate codes are of limited practical value, the second half of this thesis focuses on fixed-rate codes. The fixed-rate bit stuff (FRB) algorithm is proposed for the design of simple, high-rate (0,k) codes. The key to achieving high encoding rates with the FRB algorithm lies in a novel, iterative pre-processing of the fixed-length input sequence prior to bit stuffing. Detailed rate analysis for the proposed FRB algorithm is presented, and upper and lower bounds on the asymptotic (in input block length) encoding rate are derived. Several system-level issues of the proposed FRB codes are addressed, and FRB code parameters required to design rate 100/101 and rate 200/201 (0,k) codes are tabulated. Finally, the proposed fixed-rate encoding is extended to (0,G/I) constraints.Ph.D.Committee Chair: McLaughlin, Steven; Committee Member: Barnwell, Thomas; Committee Member: Barry, John; Committee Member: Fekri, Faramarz; Committee Member: Tetali, Prasa

Channel design for probe/mems-based storage

Issued as final reportSeagate Technology Corporatio

Approved for External Publication

Despite the tremendous growth in online video, the consumption interfaces are still modeled on search-and-browse. We propose a solution to create a much richer online video experience modeled on the television channel metaphor. This is especially useful in the emerging market context where people are more likely to consume the video web compared to the text web. Our solution allows users to consume online videos from multiple sources in a personalized manner. We have implemented a number of interactions to facilitate easy consumption such as personalized recommendations, query assistance, similar videos, documents related to videos and video annotations. We believe our solution enables a differentiating online video experience

Finite-State Wiretap Channels: Secrecy Under Memory Constraints

Information-theoretic security offered by the wiretap channel model has been extensively studied for various scenarios recently. One scenario that has not received much attention is secrecy for systems with memory in the form of input constraints or inter-symbol interference (ISI). In this work, we consider finite state wiretap channels (FSWCs), which model the scenario of secrecy with memory. Using results on secrecy capacity for arbitrary wiretap channels, we first arrive at the secrecy capacity of a FSWC. Then, we develop a stochastic algorithm for computing tight lower bounds on the secrecy capacity of a less-noisy FSWC, and illustrate the computation through examples. Our results provide numerical comparisons between secrecy capacities with and without memory, and provide specific targets for code design