3225 Chapters
Medium 9789380386546

CH10-1

Gandharba Swain Laxmi Publications PDF

Chapter

10

INTERACTION AND

INTERACTION DIAGRAM

10.1 INTERACTIONS

A

n interaction is a behavior that comprises a set of messages exchanged among a set of objects within a context to accomplish a purpose. A message is a specification of a communication between objects that conveys information transfer.

You may find an interaction wherever objects are linked to one another. You will find interactions in the collaboration of objects that exist in the context of your system or subsystem. You will also find interactions in the context of an operation. You will find interactions in the context of a class.

Most often you will find interactions in the collaboration of objects that exist in the context of your system or subsystem as a whole. You will also find interactions among objects in the implementation of an operation. You can use interactions to visualize, specify, construct, and document the semantics of a class.

10.1.1 Objects and Roles

The objects that participate in an interaction are either concrete things or prototypical things.

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Medium 9788131805220

ALLC12-3

Manish Goyal Laxmi Publications PDF

STATISTICAL COMPUTATION

9.

699

Assuming that we conduct an experiment with 8 fields planted with corn, four fields having no nitrogen fertiliser and four fields having 80 kgs of nitrogen fertilizer. The resulting corn yields are shown in table in bushels per acre :

Field

:

1

2

3

4

5

6

7

8

Nitrogen (kgs) x :

0

0

0

0

80

80

80

80

Corn yield y

:

120

360

60

180

1280

1120

1120

760

(acre)

(a) Compute a linear regression equation of y on x.

(b) Predict corn yield for a field treated with 60 kgs of fertilizer.

10. The means of a bivariate frequency distribution are at (3, 4) and r = 0.4. The line of regression of y on x is parallel to the line y = x. Find the two lines of regression and estimate value of x when y = 1.

11. The following results were obtained in the analysis of data on yield of dry bark in ounces (y) and age in years (x) of 200 cinchona plants : x y

Average

:

9.2

16.5

Standard deviation

:

2.1

4.2

Correlation coefficient = 0.84

Construct the two lines of regression and estimate the yield of dry bark of a plant of age 8 years.

12. Given N = 50, Mean of y = 44

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Medium 9781601323620

SESSION Poster Papers

Hamid Arabnia, Leonidas Deligiannidis, CSREA Press PDF

Int'l Conf. Health Informatics and Medical Systems | HIMS'16 |

SESSION

POSTER PAPERS

Chair(s)

TBA

ISBN: 1-60132-437-5, CSREA Press ©

149

150

Int'l Conf. Health Informatics and Medical Systems | HIMS'16 |

ISBN: 1-60132-437-5, CSREA Press ©

Int'l Conf. Health Informatics and Medical Systems | HIMS'16 |

151

MedInternet: Application of Artificial Intelligence for Medical Data Collection and Analysis

1

Babək Murad-Kəngərli 1,

MedEffect LTD, Baku, Azerbaijan

Abstract - MedInternet is a program of general medical information character and can embrace all areas of medicine.

It allows you to register via the Internet, organize and analyze the processes taking place in the world of medicine.

MedInternet creates a strong basis on which to develop the entire computer medicine. The program will have its own think tank composed of top doctors and programmers, as well as scientists and experts in other similar areas. MedInternet has a potential to unite and centralize the whole world of medicine accentuating the application of artificial intelligence on information features and systems.

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Medium 9781683921899

Embedded Systems, FPGA, Novel Applications and Tools

Edited by Hamid R. Arabnia, Leonidas Deligiannidis, Fernando G. Tinetti CSREA Press PDF

Int'l Conf. Embedded Systems, Cyber-physical Systems, & Applications | ESCS'17 |

SESSION

EMBEDDED SYSTEMS, FPGA, NOVEL

APPLICATIONS AND TOOLS

Chair(s)

TBA

ISBN: 1-60132-455-3, CSREA Press ©

33

34

Int'l Conf. Embedded Systems, Cyber-physical Systems, & Applications | ESCS'17 |

ISBN: 1-60132-455-3, CSREA Press ©

Int'l Conf. Embedded Systems, Cyber-physical Systems, & Applications | ESCS'17 |

35

Transforming Ladder Logic to Verilog for FPGA

Realization of Programmable Logic Controllers

Giancarlo Corti

Drake Brunner, Naoki Mizuno, and Peter Jamieson

Department Mechanical and

Manufacturing Engineering

Miami University

Oxford, Ohio 45056

Email: corticlg@miamioh.edu

Department of Electrical and

Computer Engineering

Miami University

Oxford, Ohio 45056

Email: jamiespa@miamioh.edu

Abstract—Programmable Logic Controllers (PLCs) are used in many industrial settings to control and automate machinery in a manufacturing process. Typically, these devices are programmed in ladder logic, which is used to define the logical control of connected machines in parallel. The resulting system needs to control machines in the millisecond time domain, and therefore, PLCs can implement what appears to be millisecond parallel control by emulating the logic with GHz processing capabilities of modern processors. This system solution works, but

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Medium 9781601323248

Performance Analysis of Face Detection Algorithms for Efficient Comparison of Prediction Time and Accuracy

Hamid R. Arabnia, Leonidas Deligiannidis Joan Lu, Fernando G. Tinetti, Jane You, George Jandieri, Gerald Schaefer, and Ashu M.G. Solo CSREA Press PDF

Int'l Conf. IP, Comp. Vision, and Pattern Recognition | IPCV'14 |

121

Performance Analysis of Face Detection Algorithms for

Efficient Comparison of Prediction Time and Accuracy

Seunghui Cha, Jong Wook Kwak, and Wookhyun Kim

Department of Computer Engineering

Yeungnam University, Gyeongsan, 712-749, Republic of Korea

Abstract - Face detection is one of challenges in image processing. It is necessary to compare two or more face detection algorithm to effectively select candidate algorithms based on their detection time and accuracy. In this paper we analyze three face detection algorithms and then provide accuracy and performance of each algorithm. Candidate algorithms for face detection method are skin color, haar feature and facial feature. Based on our analysis, we have checked that each algorithm has their unique characteristics and our experimental results show that, depend on algorithms, their detection accuracy varies 66%, 87%, 93%, respectively.

Keywords: face detection, face extraction, skin color detection, haar feature, adaboost, facial feature, classifier evaluation.

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