Wierk - Wydział Informatyki, Elektroniczne Ramy Kwalifikacji

Course Syllabus

Bialystok University of Technology									Faculty of Computer Science
Field of study		Data Science							Degree level and programme type				Engineer's degree full-time programme
Specialization/ diploma path									Study profile				academic
Course name		no English name yet !						E	Course code				DS1S4ASO
Course name		no English name yet !						E	Course type				obligatory
Forms and number of hours of tuition		L	C	LC	P	SW	FW	S	Semester				4
Forms and number of hours of tuition		30				30			No. of ECTS credits				5
Program valid from											2025/2026
Entry requirements		Linear Algebra 1 (DS1S1AL1), Calculus 1 (DS1S1AM1), no English name yet ! (DS1S3UM1),
Course objectives		Acquisition by students of fundamental knowledge of signal and image analysis and processing methods, with particular emphasis on understanding phenomena and interpreting results. Development of practical skills in the analysis and interpretation of digital signals and images using modern tools. Reference to SFIA framework: Data science DATS - level 3 Data engineering DENG - level 3 Specialist advice TECH - level 2
Framework content
Other information about the course		the course is related to the scientific research conducted at the University
Calculation:		Student workload (in hours):											Total hours	Including contact hours	Including practical hours
		participation in lectures											30	30
		participation in other forms of teaching											30	30	30
		individual support of the learning process, participation in exams and assessments organized outside the schedule of classes											4	4
		preparations for the exam											10
		preparations for classes											51		51
										Cumulative hours:			125	64	81
										Cumulative number of ECTS credits:			5	2.6	3.2
Expected program-specific learning outcomes													Knowledge	Skills	Social competences
													DS1_W01	DS1_U01	DS1_K01
													DS1_W02	DS1_U02
													DS1_W03	DS1_U03
Objectives and framework content formulated by			dr hab. inż. Sławomir Zieliński										Date:		29/05/2025
Implemented in the academic year								2026/2027
Course objectives
		1.	Fundamentals of signal analysis and signal processing in the time and frequency domains. Techniques of signal filtering and fundamentals of image representation and processing. Filtering, transforms, and Fourier analysis of images. Practical aspects of signal and image analysis. Implementation of signal and image analysis methods, including time and frequency domain analysis and filtering techniques. Comprehensive analysis of a selected problem in signal or image processing.
		Lecture
		1.	Introduction to signal analysis. Analog and digital signals. Sampling and quantization. The sampling theorem. Aliasing phenomenon and its effects.
		2.	2 Analysis in the frequency domain. Fourier series. Fourier transform. Interpretation of the signal spectrum. Time windows and their effect on the analysis.
		3.	3 Time-frequency analysis. Spectrograms and their interpretation. Short-time Fourier transform. Wavelet transform - basics.
		4.	4 Filtering of signals. Digital filters and their characteristics. Design of filters. Applications in noise reduction and feature extraction.
		5.	Methods of analysis of multivariate signals. PCA and ICA.
		6.	Methods of signal parameterization.
		7.	7 Automatic classification of signals.
		8.	Fundamentals of image processing. Representation of digital images. Color spaces. Histogram and its interpretation. Basic operations on images.
		9.	9 Image transformations. Point, local, and global operations. Filtering of images. Edge detection. Segmentation of images.
		10.	10 Fourier analysis of images. Two-dimensional Fourier transform. Interpretation of the image spectrum. Applications in filtering and compression.
		11.	Methods of parameterization of images.
		12.	Practical aspects of signal and image analysis. Overview of applications in various fields. Tools for analysis and visualization.
		13.	13 Automatic image recognition. Application of artificial intelligence algorithms for analyzing events and A/V scenes.
		14.	Contemporary trends in the development of methods of signal and image analysis.
		15.	Contemporary trends in the development of methods of signal and image analysis.
		16.	Specialization workshop
		17.	Familiarization with the programming environment and tools for signal and image analysis.
		18.	Fundamentals of signal analysis: working with digital signals; analysis in the time domain; visualization and interpretation of signals.
		19.	Time-frequency analysis: creation and interpretation of spectrograms; analysis of non-stationary signals.
		20.	Signal filtering.
		21.	Image processing: basic operations on images; histogram analysis; filtering and edge detection.
		22.	Parameterization of signals and images.
		23.	Final Project - Part 1. Comprehensive analysis of the selected problem in the field of signal or image processing.
		24.	Final project - Part 2. Formulation of project assumptions, selection of technology.
		25.	Final project - Part 3. Implementation.
		26.	Final project - Part 4. Implementation (cont.).
		27.	Final project - Part 5. Testing and optimization.
		28.	Final project - Part 6. Testing and optimization (cont.).
		29.	Final design - Part 7. Documenting the project.
		30.	Final project - Part 8. Demonstration of the prototype, presentation of the completed project.
		31.	Assessment.
Teaching methods (stationary)
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Teaching methods (remote)
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Assessment methods
		L	written exam with test questions<br>Specialization workshop: class reports, development of project tasks carried out in groups, presentation, demonstration
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Assessment conditions
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Symbol of learning outcome		Intended learning outcomes											Type of tuition during which the outcome is assessed
Symbol of learning outcome		Intended learning outcomes											Knowledge	Skills	Social competences
	Knowledge: the student knows and understands
E1		Fundamental concepts and methods of signal and image analysis											DS1_W01
E2		Principles of analysis in the frequency domain and interpretation of spectra											DS1_W02
E3		Digital image processing and analysis methods											DS1_W03
	Skills: the student can
E4		Analyze and interpret signals in the time and frequency domains												DS1_U01
E5		Apply basic image processing methods												DS1_U02
E6		Select appropriate methods of analysis to solve specific problems												DS1_U03
	Social competences: the student is ready to
E7		Critically evaluate the results of signal and image analysis													DS1_K01
Symbol of learning outcome		Method of learning outcome verification										Form of classes where verification takes place
E1		Written exam										Lecture
E2		Written exam										Lecture
E3		Written exam										Lecture
E4		Submission of laboratory reports, presentation of the project										Specialization workshop
E5		Submission of laboratory reports, presentation of the project										Specialization workshop
E6		Submission of laboratory reports, presentation of the project										Specialization workshop
E7		Project presentation										Specialization workshop
Basic references
		1.	K.D. Toennies, Guide to Medical Image Analysis: Methods and Algorithms, Springer Nature, London, 2017
		2.	O. Alkin, Signals and Systems: A MATLAB Integrated Approach, CRC Press, Boca Rato, 2014
		3.	U. Zölzer (Ed.), DAFX: digital audio effects. John Wiley and Sons, Chichester, 2011
Supplementary references
		1.	M. Fontes, J.D.S. De Almeida, A. Cunha, Application of Example-Based Explainable Artificial Intelligence (XAI) for Analysis and Interpretation of Medical Imaging: A Systematic Review. IEEE Access, vol. 12, pp. 26419-26427, 2024
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Course coordinator:		dr hab. inż. Sławomir Zieliński											Date:		30/05/2025