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GLOMIS Curriculum > Pai chai University > Curriculum

Curriculum

Modules and Courses

The four semester lasting Master program comprises a workload of 120 credits. The program is structured modularly. In the following table, the examination subjects are specified by means of title, course type, credits (ECTS) and semester hours. For the three partner universities of the University of Graz only the module names and sub-module names are given.

In the column “O/OS/E” it is specified whether a module/a course is obligatory (O), obligatory selected (OS) or elective (E). Obligatory selected modules/courses are modules/courses which must be selected from a list/contingent of offered modules/courses. The description of the modules can be found in the following.

The first academic year takes place at the home university (modules A1 – A4), the mandatory stay abroad is in the second academic year (modules B1 – B4).

  Module title/examination subject Course type O/OS/E credits semester hours
A4 COMPUTER SCIENCE (only for students from Pai Chai University in the 1st academic year) 60  
Module A4.a Selected Topics on Computer Science   OS 60  
Module A4.a1 Software Engineering   OS 0-30  
Module A4.a2 Information Processing   OS 0-30  
Module A4.a3 Image Processing   OS 0-20  
Module A4.a4 Multimedia Technology   OS 0-40  
 
  Module title/examination subject Course type O/OS/E credits semester hours
B2 COMPUTER SCIENCE (only for students from the University of Graz and the University of Hildesheim at Pai Chai University in the 2nd academic year) 60  
Module B2.a Selected Topics on Computer Science   OS 30  
Module B2.a1 Software Engineering   OS 0-30  
Module B2.a2 Information Processing   OS 0-30  
Module B2.a3 Image Processing   OS 0-20  
Module B2.a4 Multimedia Technology   OS 0-40  
Module B2.b Korean Language and Korean History and Culture   OS 10  
Master thesis     20  

Description of the modules provided by Pai Chai University

Module A4.a: Selected Topics on Computer Science

Module A4.a1 and module B2.a: Software Engineering

    Content:
      Advanced Software Engineering:
        In this course, students learn the software engineering technique; life cycle models, software requirements and specification; conceptual model design; detailed design; validation and verification; design quality assurance; software design / development environments and project management.
      Operating System Design:
        Analysis of algorithms in computer operating systems; sequencing and control algorithms supporting concurrent processes; scheduling algorithms to minimize execution times and mean flow times; algorithms for allocating tasks to processors. Allocation of memory (virtual and real); direct access device schedules; auxiliary and buffer storage models.
      Computer User Interface Design:
        This course is designed to cover information and communication system design based on software ergonomics. Design of human centered interface, implementation of artificial intelligence technologies like user modeling.
    Learning Objectives:
      To apply the knowledge of a disciplined approach to the development of software and the management of the software productivity and quality
    Teaching Methods:
      lectures, exercises, projects.
    Presumed prior Knowledge:
      -
    Offered:
      each academic year.

Module A4.a2 and module B2.b: Information Processing

    Content:
      Databases and Information Retrieval:
        This course is designed to cover information and communication system design based on software ergonomics. Design of human centered interface, implementation of artificial intelligence technologies like user modeling
      Advanced Database System:
        In this course, students learn the advanced database system technologies.
      Expert Systems:
        In this course, students learn about technique of expert system, and practice to programming of expert systems.
    Learning Objectives:
      The ability to information processing technologies such as problem solving and program design, information processing, databases managements and Information retrieval.
    Teaching Methods:
      lectures, exercises, projects.
    Presumed prior Knowledge:
      -
    Offered:
      each academic year.

Module A4.a3 and module B2.c: Image Processing

    Content:
      Digital Image Processing:
        Introduction to the basic techniques of image processing. Topics include image formation and perception, digitization, Fourier transform domain processing, compression and decompression, hardware and software designs of applied system.
      Pattern Recognition:
        A study of image pattern recognition techniques and computer-based methods for Bayes determination theory, supervised and unsupervised learning system, scene analysis, including discriminate function, fixture extraction, classification strategies, edge detection and Fourier image processing. Application and current results will be covered.
      Computer Vision:
        Analysis of images by computers. Specific attention is given to analysis of the geometric features of objects in images, such as region size, connectedness and topology. Topics covered include segmentation, template matching, motion analysis, boundary detection, region growing, shape representation, 3-D object recognition including graph matching.
      Computer Graphics:
        An overview of the hardware, software, and appropriate techniques used in vector, raster and graphics. Multi-dimensional presentation, geometrical transformation.
      Multidimensional Digital Signal Processing:
        The objective of this course is to make students understand basic principles of multidimensional digital signal processing, particularly applied to digital image processing. It deals with contents such as several types of transforms, their applications, design and implementation of two dimensional filter, stabilization, the estimation of two dimensional Fourier spectrum, and finally topics related to an image processing, etc.
    Learning Objectives:
      Students have an overview of the field of image processing, understand the fundamental algorithms and how to implement them as well as gain experience in applying image processing algorithms to real problems.
    Teaching Methods:
      lectures, exercises, projects.
    Presumed prior Knowledge:
      -
    Offered:
      on demand.

Module A4.a4 and module B2.d: Multimedia Technology

    Content:
      Multimedia Information Processing:
        A study of techniques and algorithms of digital voice and video representations. Topics include multidimensional transforms, multidimensional digital systems, computational structures, transmissions, storages and retrievals for multimedia information.
      Visual Communication Systems:
        A study of techniques and algorithms of effective compressions of still images and videos. Topics include theory, hardware design and software design of visual communication systems.
      Real-Time Multimedia Communication:
        Taxonomy of real-time computer systems; scheduling algorithms for static and dynamic real-time tasks; hard real-time communications protocols; programming languages and environments for real-time systems; case studies of real-time multimedia systems.
      Cyber Space Communication:
        Comprehensive coverage of Hypertext/Hypermedia; basic concepts and definitions; fundamental components, architectures and models; problems and current solutions; design and implementation issues; and research issues.
    Learning Objectives:
      Students will be able to apply basic and intermediate multimedia design skills, and to use multimedia tools.
    Teaching Methods:
      lectures, exercises, projects.
    Presumed prior Knowledge:
      -
    Offered:
      on demand.