Unordered Error-Correcting Codes and their Applications

We give efficient constructions for error correcting unordered {ECU) codes, i.e., codes such that any pair of codewords are at a certain minimal distance apart and at the same time they are unordered. These codes are used for detecting a predetermined number of (symmetric) errors and for detecting all unidirectional errors. We also give an application in parallel asynchronous communications

Some new EC/AUED codes

A novel construction that differs from the traditional way of constructing systematic EC/AUED/(error-correcting/all unidirectional error-detecting) codes is presented. The usual method is to take a systematic t-error-correcting code and then append a tail so that the code can detect more than t errors when they are unidirectional. In the authors' construction, the t-error-correcting code is modified in such a way that the weight distribution of the original code is reduced. The authors then have to add a smaller tail. Frequently they have less redundancy than the best available systematic t-EC/AUED codes

A coding approach for detection of tampering in write-once optical disks

We present coding methods for protecting against tampering of write-once optical disks, which turns them into a secure digital medium for applications where critical information must be stored in a way that prevents or allows detection of an attempt at falsification. Our method involves adding a small amount of redundancy to a modulated sector of data. This extra redundancy is not used for normal operation, but can be used for determining, say, as a testimony in court, that a disk has not been tampered with

A Computational Framework for Efficient Error Correcting Codes Using an Artificial Neural Network Paradigm.

The quest for an efficient computational approach to neural connectivity problems has undergone a significant evolution in the last few years. The current best systems are far from equaling human performance, especially when a program of instructions is executed sequentially as in a von Neuman computer. On the other hand, neural net models are potential candidates for parallel processing since they explore many competing hypotheses simultaneously using massively parallel nets composed of many computational elements connected by links with variable weights. Thus, the application of modeling of a neural network must be complemented by deep insight into how to embed algorithms for an error correcting paradigm in order to gain the advantage of parallel computation. In this dissertation, we construct a neural network for single error detection and correction in linear codes. Then we present an error-detecting paradigm in the framework of neural networks. We consider the problem of error detection of systematic unidirectional codes which is assumed to have double or triple errors. The generalization of network construction for the error-detecting codes is discussed with a heuristic algorithm. We also describe models of the code construction, detection and correction of t-EC/d-ED/AUED (t-Error Correcting/d-Error Detecting/All Unidirectional Error Detecting) codes which are more general codes in the error correcting paradigm

RFID Key Establishment Against Active Adversaries

We present a method to strengthen a very low cost solution for key agreement with a RFID device. Starting from a work which exploits the inherent noise on the communication link to establish a key by public discussion, we show how to protect this agreement against active adversaries. For that purpose, we unravel integrity $(I)$-codes suggested by Cagalj et al. No preliminary key distribution is required.Comment: This work was presented at the First IEEE Workshop on Information Forensics and Security (WIFS'09) (update including minor remarks and references to match the presented version

Functional diagnosability and recovery from massive faults in digital systems Quarterly progress reports, 17 May - 16 Nov. 1970 /final/

Diagnosability and recovery from massive faults in digital system