Abstract not found

LT-Codes are a new class of codes introduced in [1] for the purpose of scalable and fault-tolerant distribution of data over computer networks. In this paper we introduce Raptor Codes, an extension of LT-Codes with linear time encoding and decoding. We will exhibit a class of universal Raptor codes

program source code. Bandera takes as input Java source code and generates a program model in the input language of one of several existing verification tools; Bandera also maps verifier outputs back to the original source code. We discuss the major components of Bandera and give an overview of how it can

Abstract-Let {(X,, Y,J}r = 1 be a sequence of independent drawings of a pair of dependent random variables X, Y. Let us say that X takes values in the finite set 6. It is desired to encode the sequence {X,} in blocks of length n into a binary stream*of rate R, which can in turn be decoded as a sequence { 2k}, where zk E %, the reproduction alphabet. The average distorjion level is (l/n) cl = 1 E[D(X,,z&, where D(x, $ 2 0, x E I, 2 E J, is a pre-assigned distortion measure. The special assumption made here is that the decoder has access to the side information {Yk}. In this paper we determine the quantity R*(d). defined as the infimum of rates R such that (with E> 0 arbitrarily small and with suitably large n) communication is possible in the above setting at an average distortion level (as defined above) not exceeding d + E. The main result is that R*(d) = inf[Z(X,Z)- Z(Y,Z)], where the infimum is with respect to all auxiliary random variables Z (which take values in a finite set 3) that satisfy: i) Y,Z conditiofally independent given X; ii) there exists a functionf: “Y x E +.%, such that E[D(X,f(Y,Z))] 5 d. Let Rx, y(d) be the rate-distortion function which results when the encoder as well as the decoder has access to the side information {Y,}. In nearly all cases it is shown that when d> 0 then R*(d)> Rx, y(d), so that knowledge of the side information at the encoder permits transmission of the {X,} at a given distortion level using a smaller transmission rate. This is in contrast to the situation treated by Slepian and Wolf [5] where, for arbitrarily accurate reproduction of {X,}, i.e., d = E for any E> 0, knowledge of the side information at the encoder does not allow a reduction of the transmission rate.

With network coding, intermediate nodes between the source and the receivers of an end-to-end communication session are not only capable of relaying and replicating data messages, but also of coding incoming messages to produce coded outgoing ones. Recent studies have shown that network coding

This paper describes proof-carrying code (PCC), a mechanism by which a host system can determine with certainty that it is safe to execute a program supplied (possibly in binary form) by an untrusted source. For this to be possible, the untrusted code producer must supply with the code a safety

the approaches based on the notion of mobile code. Second, it introduces criteria and guidelines that support the developer in the identification of the classes of applications that can leverage off of mobile code, in the design of these applications, and, finally, in the selection of the most appropriate

Abstract- This paper deals with a new class of convolutional codes called Turbo-codes, whose performances in terms of Bit Error Rate (BER) are close to the SHANNON limit. The Turbo-Code encoder is built using a parallel concatenation of two Recursive Systematic Convolutional codes

This paper proposes COPE, a new architecture for wireless mesh networks. In addition to forwarding packets, routers mix (i.e., code) packets from different sources to increase the information content of each transmission. We show that intelligently mixing packets increases network throughput. Our

that|like the other methods|the error-correcting code technique can provide reliable class probability estimates. Taken together, these results demonstrate that error-correcting output codes provide a general-purpose method for improving the performance of inductive learning programs on multiclass