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Course Catalog 2011-2012
SGN-9206 Signal Processing Graduate Seminar II, 3-8 cr |
Additional information
Topic of the seminar will be determined and advertised in due time.
Suitable for postgraduate studies
Person responsible
Serkan Kiranyaz, Moncef Gabbouj
Lessons
| Study type | P1 | P2 | P3 | P4 | Summer | Implementations | Lecture times and places |
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Requirements
Active participation in the seminar, one seminar presentation, and final exam.
Principles and baselines related to teaching and learning
-book/article reading, slide presentations, and possible demos.
Learning outcomes
Seminar topics: signal processing and related topics, with emphasis on algorithms. After completing the seminar, students should become familiar with the seminar topic and able to exploit the methodologies learned in their own research field.
Content
| Content | Core content | Complementary knowledge | Specialist knowledge |
| 1. | Graduate course whose topic will be announced at the beginning of the term. The core content will be described by the instructor when the scope of the seminar is defined. | Investigating topics extending the scope of the seminar. | Master the topic of the seminar and become fluent in its various research aspects. |
Evaluation criteria for the course
Attendance, presentations and exams will be used to assess performance.
Assessment scale:
Evaluation scale passed/failed will be used on the course
Prerequisite relations (Requires logging in to POP)
Correspondence of content
There is no equivalence with any other courses
More precise information per implementation
| Implementation | Description | Methods of instruction | Implementation |
| General Programming on Graphics Processing Units (GPGPU) is an effective method for addressing computationally intensive tasks. Modern GPU processors are sophisticated parallel computing platforms. Their capabilities transcend their traditional role in computer graphics, and entertainment software. This processor architecture is especially well suited for computationally intensive tasks in dealing with large amounts of homogeneous data, as often encountered in various areas of signal processing. GPU based algorithm implementations offer multi-fold speedups in comparison to standard CPU based approaches, even in comparison to latest multi-core processors. In addition, in recent years, GPUs have become increasingly cheap and evermore ubiquitous, as they have become available in almost every desktop and laptop PC configuration. GPU based solutions have been applied in wide variety of fields such as, image processing, computer vision, medical image processing, audio signal processing, computational systems biology, physics, chemistry, statistical modeling, computational neuroscience, data mining, machine learning, etc. The aim of this seminar is to give an introduction into the concept of GPGPU in general, and into two existing standards in this area, CUDA and OpenCL. It also aims to entice the participants to think how their own research can benefit from the parallel processing power of GPUs. |