[3] viXra:1609.0421 [pdf] submitted on 2016-09-29 08:00:08
Authors: Emshanov Dima
Comments: 15 Pages.
This article contains a description representing the logical formula 3-SAT as a conjunction of two polynomial logical formulas.
Category: Data Structures and Algorithms
[2] viXra:1609.0370 [pdf] submitted on 2016-09-26 06:59:01
Authors: Trung Kien Vu, Sungoh Kwon
Comments: Preprint submitted to Computer Networks, 10 pages, 15 figures
In this paper, we propose an ad-hoc on-demand distance vector routing algorithm for mobile ad-hoc networks taking
into account node mobility. Changeable topology of such mobile ad-hoc networks provokes overhead messages in
order to search available routes and maintain found routes. The overheadmessages impede data delivery from sources
to destination and deteriorate network performance. To overcome such a challenge, our proposed algorithm estimates
link duration based neighboring node mobility and chooses the most reliable route. The proposed algorithm also
applies the estimate for route maintenance to lessen the number of overhead messages. Via simulations, the proposed
algorithmis verified in variousmobile environments. In the low mobility environment, by reducing routemaintenance
messages, the proposed algorithm significantly improves network performance such as packet data rate and end-toend
delay. In the high mobility environment, the reduction of route discovery message enhances network performance
since the proposed algorithm provides more reliable routes.
Category: Data Structures and Algorithms
[1] viXra:1609.0044 [pdf] submitted on 2016-09-03 16:15:57
Authors: Brian Beckman
Comments: 7 Pages.
This paper fills in some blanks left between part 1 of this series, Kalman Folding (http://vixra.org/abs/1606.0328), and the rest of the papers in the series. In part 1, we present basic Kalman filtering as a functional fold, highlighting the advantages of this form for hardening code in a test environment. In that paper, we motivated the Kalman filter as a natural extension of the running average and variance, writing both as functional folds computed in constant memory. We expressed the running statistics as recurrence relations, where the new statistic is the old statistic plus a correction. We write the correction as a gain factor times some transform of a residual. The residual is the difference between the current (old) statistic and the incoming (new) observation. In both expressions, for brevity, we left derivations to the reader. Here, we present those derivations in full “school-level” detail, along with some basic explanation of the programming language that mechanizes the computations.
Category: Data Structures and Algorithms