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What is blocking probability?
Blocking Probability: The blocking probability P is described as the probability like all the servers in system are busy. If all the servers are busy, so no further traffic can be carried by the system and the arriving traffic subscribers is blocked. At same instance, this may appear that the blocking probability is similar measure as the GOS. The possibility that all the servers are busy may well signify the fraction of the calls lost that is what the GOS is all about.
Vector-Scalar Instructions : In this type, when the combination of vector and scalar are fetched and saved in vector register. These instructions are denoted with the many function
Q. Make an instruction execution feasible? What are the functions that a control unit performs to make an instruction execution feasible? Instruction execution is achieved by e
Words having 8-bits are to be stored into computer memory. The number of lines required for writing into memory are ? Ans. 8-bit words needed 8 bit data lines.
What is Traffic Engineering? Traffic engineering gives the basis for analysis and design of telecommunication network to carry a specified traffic at an exact loss probability
Image Segmentation with Minimum Spanning Forests (50 points). Develop a Python 3 implementation of the image segmentation algorithm that we explored on the first day of class. Your
Explain Macro definition and call. Macro: The assembly language programming frequently finds this necessary to repeat certain piece of code several times during the course of
How u can create XML file? To write Dataset Contents out to disk as an XML file use: MyDataset.WriteXML(server.MapPath("MyXMLFile.xml"))
This will be based on presentation of the report, complexity of the task, degree of completion and uniqueness of your problem. As a part of this question, you should also inclu
Introduction to object oriented Analysis & design: tools In case of OOAD, Unified Modelling Language (UML) is a well accepted language. It is used for constructing, visualizin
2) Consider the following neural network for two predictors Thickness and Alignment and two classes Print Quality High and Low. Some weights are shown in the table, including weigh
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