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Blekinge Institute of Technology
Department of Technology and Aesthetics
Revision: 2
Reg.no: BTH-4.1.14-0472-2024
Course syllabus
Pattern recognition in hybrid radar and camera system
Pattern recognition in hybrid radar and camera system
6 credits (6 högskolepoäng)
Course code: DV2656
Main field of study: Computer Science
Disciplinary domain: Technology
Education level: Second-cycle
Specialization: A1F - Second cycle, has second-cycle course/s as entry requirements
Language of instruction: English
Applies from: 2024-04-23
Approved: 2024-04-23
1. Descision
This course is established by Dean 2023-12-21. The course syllabus is approved by Head of Department of Technology and Aesthetics 2024-04-23 and applies from 2024-04-23.
2. Entry requirements
Admission to the course requires completed course Introduction to Hybrid Systems with Radar and Camera, 6 credits and 6 completed credits in Mathematical Statistics.
3. Objective and content
3.1 Objective
The course covers basic and advanced pattern recognition methods for signal and data classification tasks. A Bayesian approach is used in the course. Simple applications can be the recognition of anomaly in motion. A complex application could be for example in healthcare, such as diagnosing a disease from patient behavioral data.
3.2 Content
• Discrete and continuous random variables in one or more dimensions
• Orientation to multi-dimensional random variables, independence
• Different distributions, especially the Poisson, binomial, exponential and normal distributions and approximations.
• Markovian process and Markov chains
• Markov and hidden Markov models
• Anomaly recognition using hidden Markov models.
• Implementation of hidden Markov models on data from hybrid systems
4. Learning outcomes
The following learning outcomes are examined in the course:
4.1. Knowledge and understanding
On completion of the course, the student will be able to:
- describe the math’s behind Markov chains
- describe how Markov models and hidden Markov models work
- describe and enumerate the different applications of Markov models and hidden Markov models
- recognize a situation, where the basic HMMs can be considered as promising model candidates
- recognize a situation, where the extensions of the HMM can be considered as promising model candidates
- place the HMM in the general picture of statistical learning theory
4.2. Competence and skills
On completion of the course, the student will be able to:
- apply Markov models to any sequence of data, primarily fusion data from hybrid sensors
- write Markov models and hidden Markov models in code
- implement the basic algorithms with appropriate modifications for the data available, primarily fusion data from hybrid sensors
4.3. Judgement and approach
On completion of the course, the student will be able to:
- independently and critically evaluate and analyze their solutions
- write a technical report that describes, in a concise technical prose, the work done to object detection and tracking, analyze and validate in a related hybrid system problem
5. Learning activities
The course is given as a campus course. Teaching consists of lectures, exercises, laboratory sessions and projects.
The lectures present theories and contribute to the theoretical understanding required to complete the course. Exercises, laboratory sessions and projects apply the theory in practical sessions. Projects and laboratory sessions are carried out in groups.
The course assumes that the student has, or acquires, the ability to independently seek information to solve problems and difficulties that arise.
6. Assessment and grading
Modes of examinations of the course
| Code | Module | Credit | Grade |
| 2505 | On-campus Examination | 2.0 credits | AF |
| 2515 | Laboratory Session | 1.0 credits | GU |
| 2525 | Project | 3.0 credits | AF |
The course will be graded A Excellent, B Very good, C Good, D Satisfactory, E Sufficient, FX Fail, supplementation required, F Fail.
The information before a course occasion states the assessment criteria and make explicit in which modes of examination that the learning outcomes are assessed.
An examiner can, after consulting the Disability Advisor at BTH, decide on a customized examination form for a student with a long-term disability to be provided with an examination equivalent to one given to a student who is not disabled.
7. Course evaluation
The course evaluation should be carried out in line with BTH:s course evaluation template and process.
8. Restrictions regarding degree
The course can form part of a degree but not together with another course the content of which completely or partly corresponds with the contents of this course.
9. Course literature and other materials of instruction
Jurafsky, D., & Martin, J. H. (2022). Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition. Pearson.
Koski, Timo.D Hidden Markov models for bioinformatics. Vol. 2. Kluwer Academic Pub, 2001,
Bishop, Christopher M. (2006). Pattern recognition and machine learning. New York :Springer,
selected journal papers
This is not a legal document. If you would like a copy of the legal decision regarding this course plan, contact the registrar at Blekinge Institute of Technology.