Data Structures and Algorithms

1609 Submissions

[3] viXra:1609.0421 [pdf] submitted on 2016-09-29 08:00:08

Complex Boolean Variables and Symmeric Logic Structures.

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

On-Demand Routing Algorithm with Mobility Prediction in the Mobile Ad-hoc Networks

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

Kalman Folding 1.5: Running Statistics

Authors: Brian Beckman
Comments: 7 Pages.

This paper fills in some blanks left between part 1 of this series, Kalman Folding (, 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