ࡱ> (*%&'W bjbjJJD(a(a)&aaaaauuu8Iu%";""]"]"]"8#8#8#HJJJJJJJJa8#8#8#8#8#Jaa]"]"˪---8#ra]"a]"H-8#H-- :Μ]"bP/'jb40N(ΜΜ~aL 8#8#-8#8#8#8#8#JJ*8#8#8#8#8#8#8#8#8#8#8#8#8#8#8#8#m ': NATIONAL UNIVERSITY OF LIFE AND ENVIRONMENTAL SCIENCES OF UKRAINE Machines and equipment design department APPROVED Faculty of design and engineering 10 June 2025 CURRICULUM OF ACADEMIC DISCIPLINE DIGITAL SIGNAL PROCESSING (title) Field of knowledge: G "Engineering, production and construction" Specialty: G11 "Mechanical Engineering (by specializations)" Academic programme "Robotic systems and complexes of agricultural production" Orientation of the program: educational and professional Faculty of design and engineering Developers: doctor of technical sciences, professor Romasevych Y.O., candidate of technical sciences, docent Krushelnytskyi V.V. Kyiv 2025 Course description DIGITAL SIGNAL PROCESSING (title) Digital signal processing (DSP) is an integral part of modern industrial engineering and plays an important role in various aspects of product design, production and optimization. The study of the discipline of DSP is of great importance for specialists in the field of mechanical engineering for the following reasons: 1) signal processing in modern systems (most modern systems in mechanical engineering include electronics and sensors that generate signals. The study of DSP allows you to understand how these signals can be processed, filter, analyze and use for system management and optimization); 2) control and automation systems (control and automation systems are widely used in mechanical engineering. They are based on signal processing to detect errors, adjust parameters and make decisions based on input data); 3) signals from sensors (in modern machine-building solutions, much attention is paid to the collection of data from various sensors and sensors. DSP helps to process this data, extract useful information and allows for accurate analysis of the system state). Area of knowledge, specialty, academic programme, academic degreeAcademic degree MasterSpecialty G11 Mechanical Engineering (by specializations)Academic programmeRobotic systems and complexes of agricultural productionCharacteristics of the disciplineTypeSelectiveGeneral volume of hours150Number of credits ECTS 5Number of modules2Course project-Control formExamIndicators of the discipline for full-time forms of university studyYear of study1Term2Lectures30 h.Practical classes and seminars-Laboratory classes30 h.Self-study90 h.Number of hours per week for full-time students4 h. 1. Aim, competences and expected learning outcomes of the discipline The purpose of the course is the formation of theoretical understanding and practical skills of working with digital signals that are generated, transmitted and processed in mechatronic systems of agricultural machines. The tasks of the discipline consist in teaching: the main theoretical principles on which the methods of digital signal processing are based, their application to work with signals; provisions regarding the selection of DSP methods and their setting; the use of software for the implementation of DSP. Competences acquired: integral competence: the ability to solve complex tasks and problems of industrial mechanical engineering, which involve research and/or innovation and are characterized by uncertainty of conditions and requirements. general competences (GC): GC2. Ability to learn and master modern knowledge. GC 4. Ability to be critical and self-critical. GC 6. Ability to generate new ideas (creativity). special (professional) competencies (SC): SC3. Ability to create new equipment and technologies in the field of mechanical engineering. SC4. Awareness of the promising tasks of modern production aimed at meeting the needs of consumers, mastery of trends in innovative development of technologies in the industry. SC7. Ability to use intelligent technologies to ensure the sustainable development of robotic systems of agricultural production. Expected learning outcomes (ELO): ELO2. Knowledge and understanding of mechanics and mechanical engineering and the prospects for their development. ELO4. Perform engineering calculations to solve complex tasks and practical problems in the field of mechanical engineering. ELO6. Find the necessary scientific and technical information in available sources, in particular, in a foreign language, analyze and evaluate it. ELO8. Knowledge of the production advantages and features of the use of robotic systems and complexes in the agricultural sector. Programme and structure of the discipline Modules and topicsNumber of hoursfull-timetotalincludinglplabinds.stweeksModule 1. General properties of digital signalsTopic 1. Introduction. Types of signals22----1Topic 2. Mathematical apparatus for describing digital signals244---201-2Topic 3. Kotelnikov's theorem and its consequences. Signal recovery262-4-203-4Topic 4. Signal quantization effects82-6--5-6Total for module 16010-10-40-Module 2. Digital filters for robotic systemsTopic 5. The simplest digital filters for robotic systems82-6--7-8Topic 6. Basic characteristics of digital filters122---108Topic 7. Recursive and non-recursive digital filters3810-8-209-12Topic 8. Design of digital filters for robotic systems226-6-2013-15Total for module 29020-20-50-Total hours15030-30-90- 3. Topics of lectures !TopicHours1Introduction. Types of signals22Mathematical apparatus for describing digital signals43Kotelnikov's theorem and its consequences. Signal recovery24Signal quantization effects25The simplest digital filters for robotic systems26Basic characteristics of digital filters27Recursive and non-recursive digital filters108Design of digital filters for robotic systems6 4. Topic of laboratory (practical, seminars) classes !TopicHours1Recovery of an analog signal from a discrete one42Study of effects of signal quantization of sensor systems of robots63Study of the operation of average and median running filters24Study of the operation of the Savitsky-Goley filter. Differentiation of a discrete signal. Robot link velocity calculation45Study of the operation of non-recursive filters46Study of the work of recursive filters47Design of digital filters for robotic systems6Topics of self-study !TopicHours1Discrete Fourier transform202z-transformation203Classification of digital filters104Bessel filter105Elliptical filter106Software products for the implementation of digital filters20 6. Methods of assessing expected learning outcomes: When teaching this discipline, the following diagnostic tools are used: oral interview; exam; module tests; defense of laboratory work. 7. Teaching methods: When teaching this discipline, the following methods are used: problem-based learning method; practice-oriented learning method; research-based learning method; educational discussions and debates method; teamwork and brainstorming method. Results assessment The knowledge of a higher education applicant is assessed on a 100-point scale, which is translated into a national assessment in accordance with the current "Regulations on Examinations and Tests at the Vlog of Ukraine." Distribution of points by types of educational activities Type of training activitiesResults teachingEvaluationModule 1. General properties of digital signalsSelf-study work 1ELO2, ELO4, ELO8.To know the advantages of digital signals and main means of their processing. To be able to discrete analog signals and avoid signal spoiling. 20Self-study work 220Lab 115Lab 215Module 1 test-30Overall on 1st module-100Module 2. Digital filters for robotic systemsLab 3ELO2, ELO4, ELO6, ELO8. To know the main features of digital filters. To be able to select proper digital filter and to tune it settings (order, cut-off frequency, etc.). To be able to use proper software to process a digital signal.10Lab 410Self-study work 35Lab 510Lab 610Self-study work 45Self-study work 55Lab 710Self-preparation work 65Module 2 test-30Overall on 2nd module-100Class work-(1+2)/2*0,7 d"70Exam-30Overall-(Class work+Exam)d"100 8.2 Scale for assessing student s knowledge Student s rating, pointsNational grading (exam/credits)90-100excellent74-89good60-73satisfactory0-59unsatisfactory 8.3 Assessment policy Deadlines and exam retaking rules works that are submitted late without valid reasons will be assessed with a lower grade. Module tests may be retaken with the permission of the lecturer if there are valid reasons (e.g. a sick leave). Academic integrity rulescheating during tests and exams is prohibited (including using mobile devices). Term papers and essays must have correct references to the literature usedAttendance rulesattendance is compulsory. 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Recommended sources of information A=>28 B0 <5B>48 F8D@>2>W >1@>1:8 A83=0;V2: 2V4 B5>@VW 4> ?@0:B8:8: =02G. ?>AV1=8: / ... #H5=:>, .!. 02@8;O:, .. "0;0E, .. 2>@60:.  '5@=V2FV : '5@=V25FL:89 =0F. C=-B V<. .. $54L:>28G0, 2021. - 308 A. ;3>@8B<8 B0 70A>18 >1@>1:8 A83=0;V2: =02G. ?>AV1=. / 02@C: ., 0H:> ., >?>28G .  L2V2: !, 2021.  240 A. Digital Signal Processing  Complete Guide With Examples [;5:B@>==89 @5AC@A]  568< 4>ABC?C 4> @5AC@AC: https://www.softwaretestinghelp.com/ digital-signal-processing-tutorial/. &8D@>289 A83=0; (>1@>1:0 A83=0;V2) [;5:B@>==89 @5AC@A]  568< 4>ABC?C 4> @5AC@AC: https://uk.wikipedia.org/wiki/&8D@>289_ A83=0;_(>1@>1:0_A83=0;V2). fV B     &`#$gdikh]hgddd &`#$gdtO $^a$gdc$ & F.0`0a$gdc $^a$gdKTeaching and Learning with Jupyter [;5:B@>==89 @5AC@A]  568< 4>ABC?C 4> @5AC@AC: https://jupyter4edu.github.io/jupyter-edu-book/. A Beginner's Guide to Digital Signal Processing (DSP) [;5:B@>==89 @5AC@A]  568< 4>ABC?C 4> @5AC@AC: https://www.analog.com/en/design-center/landing-pages/001/beginners-guide-to-dsp.html. Digital Signal Processing Tutorial [;5:B@>==89 @5AC@A]  568< 4>ABC?C 4> @5AC@AC: https://www.tutorialspoint.com/digital_signal_processing/index.htm.     PAGE  PAGE 6 ĹĬĹhSD0JCJmHnHuhtOh]J0JCJjhtOh]J0JCJUh]J h]J0Jjh]J0JUh0jh0UhshiaJmH sH  $^a$gdch]hgdTN &`#$gdddB 00P1h:p`. 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