scholarly journals Parallel Rank of Two Sandpile Models of Signed Integer Partitions

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
G. Chiaselotti ◽  
T. Gentile ◽  
G. Marino ◽  
P. A. Oliverio
Keyword(s):  
Finite Lattice ◽  
Convergence Time ◽  
Dynamical Model ◽  
Integer Partitions ◽  
Graded Poset ◽  
Fundamental Sequence ◽  
Sandpile Models

We introduce the concept of fundamental sequence for a finite graded posetXwhich is also a discrete dynamical model. The concept of fundamental sequence is a refinement of the concept of parallel convergence time for these models. We compute the parallel convergence time and the fundamental sequence whenXis the finite lattice of all the signed integer partitions such that , where , and whenXis the sublattice of all the signed integer partitions of having exactlydnonzero parts.

scholarly journals Sand Piles Models of Signed Partitions with Piles

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
C. Bisi ◽  
G. Chiaselotti ◽  
P. A. Oliverio
Keyword(s):  
Rank Function ◽  
Convergence Time ◽  
Dynamical Model ◽  
Integer Partitions ◽  
Integer Partition ◽  
Basic Properties ◽  
Generic Element ◽  
Sand Piles

Let be nonnegative integers. In this paper we study the basic properties of a discrete dynamical model of signed integer partitions that we denote by . A generic element of this model is a signed integer partition with exactly all distinct nonzero parts, whose maximum positive summand is not exceeding and whose minimum negative summand is not less than . In particular, we determine the covering relations, the rank function, and the parallel convergence time from the bottom to the top of by using an abstract Sand Piles Model with three evolution rules. The lattice was introduced by the first two authors in order to study some combinatorial extremal sum problems.

scholarly journals A Discrete Dynamical Model of Signed Partitions

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
G. Chiaselotti ◽  
G. Marino ◽  
P. A. Oliverio ◽  
D. Petrassi
Keyword(s):  
Partially Ordered Set ◽  
Ordered Set ◽  
Dynamical Model ◽  
Integer Partitions ◽  
Partially Ordered

We use a discrete dynamical model with three evolution rules in order to analyze the structure of a partially ordered set of signed integer partitions whose main properties are actually not known. This model is related to the study of some extremal combinatorial sum problems.

Extension of the Spatial PDI Model to Three Dimensions

1997 ◽  
Vol 84 (1) ◽  
pp. 176-178
Author(s):  
Frank O'Brien
Keyword(s):  
Population Density ◽  
Dimensional Space ◽  
Finite Lattice ◽  
Three Dimensional ◽  
Upper Bounds ◽  
Three Dimensions ◽  
Lower And Upper Bounds ◽  
Density Index ◽  
Three Dimensional Space

The author's population density index ( PDI) model is extended to three-dimensional distributions. A derived formula is presented that allows for the calculation of the lower and upper bounds of density in three-dimensional space for any finite lattice.

BUILDING A DYNAMIC MODEL OF A HELICOPTER POWER SUPPLY SYSTEM

2019 ◽  
Author(s):  
Dmitriy Krasnov ◽  
Vyacheslav Novoselsky
Keyword(s):  
Dynamic Model ◽  
Power Supply ◽  
Power Supply System ◽  
Supply System ◽  
Dynamical Model ◽  
Matlab Simulink

This work is devoted to the analysis of the possibility of constructing a dynamical model of a helicopter power supply system in the MATLAB SIMULINK program.

Performance enhancement of Echo Cancellation Using a Combination of Partial Update ( PU) Methods and New Variable Length LMS (NVLLMS) Algorithm

2018 ◽  
Vol 24 (5) ◽  
pp. 66
Author(s):  
Thamer M. Jamel ◽  
Faez Fawzi Hammood
Keyword(s):  
Mean Square Error ◽  
Return Loss ◽  
Performance Enhancement ◽  
Variable Length ◽  
Echo Cancellation ◽  
Convergence Time ◽  
Length Variation ◽  
Mean Square ◽  
Step Size ◽  
Echo Return Loss Enhancement

In this paper, several combination algorithms between Partial Update LMS (PU LMS) methods and previously proposed algorithm (New Variable Length LMS (NVLLMS)) have been developed. Then, the new sets of proposed algorithms were applied to an Acoustic Echo Cancellation system (AEC) in order to decrease the filter coefficients, decrease the convergence time, and enhance its performance in terms of Mean Square Error (MSE) and Echo Return Loss Enhancement (ERLE). These proposed algorithms will use the Echo Return Loss Enhancement (ERLE) to control the operation of filter's coefficient length variation. In addition, the time-varying step size is used.The total number of coefficients required was reduced by about 18% , 10% , 6%, and 16% using Periodic, Sequential, Stochastic, and M-max PU NVLLMS algorithms respectively, compared to that used by a full update method which  is very important, especially in the application of mobile communication since the power consumption must be considered. In addition, the average ERLE and average Mean Square Error (MSE) for M-max PU NVLLMS are better than other proposed algorithms.  

A Comparative Study of Computational Intelligence Algorithms for Sensor Localization

2019 ◽  
Vol 9 (2) ◽  
pp. 224-236
Author(s):  
Vaishali R. Kulkarni ◽  
Veena Desai ◽  
Raghavendra Kulkarni
Keyword(s):  
Firefly Algorithm ◽  
Heuristic Algorithms ◽  
Computing Time ◽  
Sensor Nodes ◽  
Convergence Time ◽  
Localization Error ◽  
Location Awareness ◽  
Cultural Algorithm ◽  
Localization Accuracy ◽  
Multi Stage

Background & Objective: Location of sensors is an important information in wireless sensor networks for monitoring, tracking and surveillance applications. The accurate and quick estimation of the location of sensor nodes plays an important role. Localization refers to creating location awareness for as many sensor nodes as possible. Multi-stage localization of sensor nodes using bio-inspired, heuristic algorithms is the central theme of this paper. Methodology: Biologically inspired heuristic algorithms offer the advantages of simplicity, resourceefficiency and speed. Four such algorithms have been evaluated in this paper for distributed localization of sensor nodes. Two evolutionary computation-based algorithms, namely cultural algorithm and the genetic algorithm, have been presented to optimize the localization process for minimizing the localization error. The results of these algorithms have been compared with those of swarm intelligence- based optimization algorithms, namely the firefly algorithm and the bee algorithm. Simulation results and analysis of stage-wise localization in terms of number of localized nodes, computing time and accuracy have been presented. The tradeoff between localization accuracy and speed has been investigated. Results: The comparative analysis shows that the firefly algorithm performs the localization in the most accurate manner but takes longest convergence time. Conclusion: Further, the cultural algorithm performs the localization in a very quick time; but, results in high localization error.

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