Multi-level simulator for VLSI on the parallel object-oriented machine

Author(s):  
E. Aposporidis ◽  
F. Lohnert
Keyword(s):  
2010 ◽  
Vol 171-172 ◽  
pp. 596-599
Author(s):  
Zhi Hui Wang ◽  
Shang Fu Hao ◽  
Yuan Qiang Wang

The virtual reality technology is a three-dimensional virtual environment composed by computer hardware, software and various of sensors. It creates an unprecedented wealth of content and more fantastic teaching environment which reflects strong advantage in experiment and practice. In accordance with the needs of experiment teaching, multi-level interrupt virtual experiment environment is studied combining with the characteristics of its principle. The experiment environment is based on Tec-xp test box designed and developed by Tsinghua University computer factory. Microsoft Visual C++6.0 language is adopted for software platform development. Seen from the system needs analysis, object-oriented simulation method is adopted for modeling in this experiment environment. Meanwhile combining with computer system features and the characteristics of Computer Organization Principle teaching, the basic theory and methods of discrete event system simulation are used, which make the operation of virtual environment the same as on real device.


2014 ◽  
pp. 20-28
Author(s):  
Mitica Craus ◽  
Laurentiu Rudeanu

This article deals with the pyramidal framework designed to be used in the parallelization of the ant-like algorithms. Such algorithms have several things in common: they run in cycles and the process can be divided among different "processing units". The parallel implementation of the Ant Colony Optimization algorithm for the Traveling Salesman Problem is an application of this system. The topology of the framework architecture is similar to a B-tree and contains three types of processing nodes: a single master (the root), several sub-masters corresponding to the internal nodes of the tree and several slaves as leaves. First the master reads the problem instance, wraps it up in a message that is sent to all the other processing nodes and initializes the central data structures. Then, the slaves take over the control by starting the algorithm while the master and the sub-masters are waiting for requests to update the data. The framework has an object-oriented design and was implemented in C++, using the MPI library.


Author(s):  
C. K. Li ◽  
W. Fang ◽  
X. J. Dong

With the development of remote sensing technology, the spatial resolution, spectral resolution and time resolution of remote sensing data is greatly improved. How to efficiently process and interpret the massive high resolution remote sensing image data for ground objects, which with spatial geometry and texture information, has become the focus and difficulty in the field of remote sensing research. An object oriented and rule of the classification method of remote sensing data has presents in this paper. Through the discovery and mining the rich knowledge of spectrum and spatial characteristics of high-resolution remote sensing image, establish a multi-level network image object segmentation and classification structure of remote sensing image to achieve accurate and fast ground targets classification and accuracy assessment. Based on worldview-2 image data in the Zangnan area as a study object, using the object-oriented image classification method and rules to verify the experiment which is combination of the mean variance method, the maximum area method and the accuracy comparison to analysis, selected three kinds of optimal segmentation scale and established a multi-level image object network hierarchy for image classification experiments. The results show that the objectoriented rules classification method to classify the high resolution images, enabling the high resolution image classification results similar to the visual interpretation of the results and has higher classification accuracy. The overall accuracy and Kappa coefficient of the object-oriented rules classification method were 97.38%, 0.9673; compared with object-oriented SVM method, respectively higher than 6.23%, 0.078; compared with object-oriented KNN method, respectively more than 7.96%, 0.0996. The extraction precision and user accuracy of the building compared with object-oriented SVM method, respectively higher than 18.39%, 3.98%, respectively better than the object-oriented KNN method 21.27%, 14.97%.


Author(s):  
H. Wang ◽  
D. Xue

Abstract A zone-based delivery scheduling approach is introduced in this research using artificial intelligence techniques. In this approach, delivery scheduling is conducted at three different levels: (1) prediction of customer delivery demand using a multi-level pattern clustering and matching method, (2) creation of delivery zones based on customer delivery demand, and (3) identification of the optimal sequence and timing parameters of delivery tasks. The system was implemented using Smalltalk, an object oriented programming language.


1997 ◽  
Vol 23 (2) ◽  
pp. 97-117 ◽  
Author(s):  
Ahmad Baraani-Dastjerdi ◽  
Josef Pieprzyk ◽  
Reihaneh Safavi-Naini

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