county, random Population coordinates were generated using the complete spatial randomness (CSR) function in S-PLUS. Then, the background information was attached to each individual county based on the county?s distribution for the class of interest. Finally, all counties were merged into a single dataset that describes the whole state
Abstract: This application note explains the hardware of different types of 1-Wire® interfaces and software examples adapted to this hardware with a focus on serial ports. Depending on the types of iButtons required for a project and the type of computer to be used, the most economical interface is easily found. The hardware examples shown are basically two different types: 5V general interface and 12V RS-232 interface. Within the 5V group a common printed circuit board could be used for all circuits described. The variations can be achieved by different Populations of components. The same principal is used for the 12V RS-232 interface. The Population determines if it is a Read all or a Read/Write all type of interface.
There are other possible circuit implementations to create a 1-Wire interface. The circuits described in this application note cover many different configurations. For a custom application, one of the described options can be adapted to meet individual needs.
#if !defined(AFX_GAQUEEN_H__C26AE0A3_F9B4_426F_A324_B460CC7946CB__INCLUDED_)
#define AFX_GAQUEEN_H__C26AE0A3_F9B4_426F_A324_B460CC7946CB__INCLUDED_
#if _MSC_VER > 1000
#pragma once
#endif // _MSC_VER > 1000
class CGAQueen
{
public:
CGAQueen(int nPopulation,int nIteration,float Mutation,int mChBoard)
virtual ~CGAQueen()
void Clear() // to clear chess board with 0 value
void InitialPopulation() // to create the first and initial randomPopulation
void FillArea(int index) // to fill chess board with desired chromosome
int CostFunc(int index) // determine the cost of matrix[index][index]
void PopulationSort() // to sort Population from the best to the worst
void GenerateCrossOverMatrix() // a way to create children from parent is CcrossOver
void Mating() // to create children from parents
void Ap
Particle swarm optimization (PSO) was originally designed and introduced by Eberhart and
Kennedy (Ebarhart, Kennedy, 1995 Kennedy, Eberhart, 1995 Ebarhart, Kennedy, 2001). The
PSO is a Population based search algorithm based on the simulation of the social behavior of
birds, bees or a school of fishes. This algorithm originally intends to graphically simulate the
graceful and unpredictable choreography of a bird folk. Each individual within the swarm is
represented by a vector in multidimensional search space.
The world Population is continuously growing and reached a significant evolution of
the society, where the number of people living in cities surpassed the number of people
in rural areas.
Wireless technology has been evolving at a breakneck speed. The total number of
cell-phones in use (as of 2011) was over 6 billion for a 7 billion world Population [1]
constituting 87% of the world Population. Additionally, with user convenience be-
coming paramount, more and more functions are being implemented wirelessly.
Communication, a word that many associate with modern technology, actually
has nothing to do with technology. At its core, communication involves nothing
more than the spoken or written word, and symbolic languages like art and music.
Technology has become synonymous with communication because technology
has historically been the method by which communication to or by the general
Population takes place.
