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課程目錄： 增強現實入門培訓

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增強現實入門培訓

 

 

 

Welcome

Welcome to the "Getting started with Augmented Reality" MOOC! We are excited

to have you in the class. Let's meet the course team and introduce yourself to the community.

 

Introducing Mixed and Augmented Reality

In this module we will explain the key concepts and techniques

at work in Mixed and Augmented Reality. We will also spend some time explaining some business aspects

of augmented reality: the AR market, the potential applications and the value chain. At the end of this module,

we’ll identify the main characteristics of AR systems and we’ll specify the main components of an AR architecture.

 

Augmented Books

In this module you will create an AR application for printed media:

an Augmented Book. For this, in the first part of the module,

we shall use an authoring tool and an AR browser. In the second part,

we will have a closer look at the code produced by the authoring tool.

If you want to go deeper and learn how to “program” it and change the behavior

of the application, you can access the Honors material.

 

Augment Your City Map

In this module you will learn how to use the image recognition

and tracking process to enrich an image with a 3D object.

At the end of this module, you will have an AR application that will augment a city map with a 3D model.

If you have difficulties with the assignments, you should post on the Discussions section to ask for help.

To get started, please jump into the first lesson below!

Augmented Reality with Geolocation

This module concerns the geolocation in Augmented Reality and spans 2 weeks.

This week, we walk you step by step in the creation of an AR geolocalized quiz game.

 

Customizing an Augmented Reality Game

In this module, you will have a closer look at the code of the

AR Quiz produced by the authoring tool in the previous module.

You will spend almost the entire module in understanding the ARAF format for AR Quiz and then

you will learn how to “program” it and change the behavior of the application.

定量模型檢驗培訓

 

 

 

Module 1: Computational Tree Logic

We introduce Labeled Transition Systems (LTS),

the syntax and semantics of Computational Tree Logic (CTL) and discuss the model checking algorithms

that are necessary to compute the satisfaction set for specific CTL formulas.

Discrete Time Markov Chains

We enhance transition systems by discrete time and add probabilities

to transitions to model probabilistic choices. We discuss important properties of DTMCs,

such as the memoryless property and time-homogeneity. State classification can be used to

determine the existence of the limiting and / or stationary distribution.

Probabilistic Computational Tree Logic

We discuss the syntax and semantics of Probabilistic Computational

Tree logic and check out the model checking algorithms that are necessary

to decide the validity of different kinds

of PCTL formulas. We shortly discuss the complexity of PCTL model checking.

Continuous Time Markov Chains

We enhance Discrete-Time Markov Chains with real time and discuss how

the resulting modelling formalism evolves over time. We compute the steady-state

for different kinds of CMTCs and discuss how the transient probabilities

can be efficiently computed using a method called uniformisation.

 

Continuous Stochastic Logic

We introduce the syntax and semantics of Continuous Stochastic

Logic and describe how the different kinds of CSL formulas can be model checked. Especially,

model checking the time bounded until operator requires applying the concept

of uniformisation, which we have discussed in the previous module.

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