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Mathematics for Multimedia (MATLD0130), 5 op
Basic information
| Course name: | Mathematics for Multimedia Mathematics for Multimedia |
| Course Winha code: | MATLD0130 |
| Kurre acronym: | MathMM |
| Credits: | 5 |
| Type and level of course: | Basic studies |
| Year of study, semester or study period: | 1.year |
| Implementation: | Spring semester, 3.period, 4.period |
| Semester: | 0607 |
| Language of tuition: | English |
| Teacher: | Jaakko Pitkänen |
| Final assessment: | Grading scale (0-5) |
Descriptions
Prerequisites
Engineering Mathematics
Course contents (core content level)
Complex numbers: representations and rules. Bilinear interpolation. Gradient and directional derivative. Integral function and definite integral. Integration of elementary functions; esp. constant, linear and piecewise constant and piecewise linear functions: definite integral as a sum; integration of data. Idealised image as a mathematical model of an image and a digital image. Image processing with a computer: pixel group operations (masks, filtering, nearest neighbour methods); image enhancement: edges, smoothing, sharpening etc.; analysis of filters.
Course contents (additional)
Integration techniques: integration by parts and by substitution. Numerical methods of integration. Integrals in mathematical models: quantity elements. Filters as mapping functions. Least squares method in image processing. Nearest neighbour and bilinear interpolation in image processing. Processing a coloured image: Bayer filter pattern.
Core content level learning outcomes (knowledge and understanding)
After completing the course the student will understand basic concepts in integration and image processing. Will know several ways to represent data and information. Will understand role of differentiation and integration in engineering applications. Will understand basic methods how to manipulate digital images.
Core content level learning outcomes (skills)
After completing the course the student will be able to solve simple integration problems analytically or numerically, also with the aid of a computer. Will be able to manipulate digital images by mask methods in spatial domain by a computer. Will be able to model simple and practical integration problems and solve them.
Recommended reading
Teaching and learning strategies
Teaching methods and student workload
Lectures
Individual research, reading
Exam
Project
Laboratory assignments
Assessment weighting and grading
Two examinations with approval (at least 40% of the maximum), laboratory exercises and a group work.