On Algebraic Decoding of qq-ary Reed-Muller and Product-Reed-Solomon Codes

We consider a list decoding algorithm recently proposed by Pellikaan-Wu \cite{PW2005} for $q$-ary Reed-Muller codes $\mathcal{RM}_q(\ell, m, n)$ of length $n \leq q^m$ when $\ell \leq q$. A simple and easily accessible correctness proof is given which shows that this algorithm achieves a relative error-correction radius of $\tau \leq (1 - \sqrt{{\ell q^{m-1}}/{n}})$. This is an improvement over the proof using one-point Algebraic-Geometric codes given in \cite{PW2005}. The described algorithm can be adapted to decode Product-Reed-Solomon codes. We then propose a new low complexity recursive algebraic decoding algorithm for Reed-Muller and Product-Reed-Solomon codes. Our algorithm achieves a relative error correction radius of $\tau \leq \prod_{i=1}^m (1 - \sqrt{k_i/q})$. This technique is then proved to outperform the Pellikaan-Wu method in both complexity and error correction radius over a wide range of code rates.Comment: 5 pages, 5 figures, to be presented at 2007 IEEE International Symposium on Information Theory, Nice, France (ISIT 2007

Airworthiness Requirements for Type Certification of NALLA Report No. 3- Dynamic Tests and Analysis)

This Report is Third in the series of the TEN Reports prepared by NALLA Airworthiness Group to assist the NALLA design team in providing ready information on airworthiness requirements to be met while considering Dynamic Tests and Analysis aspects in the aircraft design

Airworthiness Requirements for Type Certification of NALLA (Report No.5-Aircraft Materials and Fabrication Aspects)

This report is Fifth in the series of the TEN Report prepared by NALLA Airworthiness Group to assist the NALLA design team in providing ready information on airworthiness requirements to be met while selecting/ using materials as well as adapting fabrication methods for a light aircraft

Bayesian Analysis for Component Manufacturing Processes

In manufacturing processes various machines are used to produce the same product. Based on the age, make, etc., of the machines the output may not always follow the same distribution. An attempt is made to introduce Bayesian techniques for a two machine problem. Two cases are presented in this article