Data and Signal Processing

Course Information
TitleΕΠΕΞΕΡΓΑΣΙΑ ΣΗΜΑΤΩΝ ΚΑΙ ΔΕΔΟΜΕΝΩΝ / Data and Signal Processing
CodeΜΥΝ730
FacultyAgriculture, Forestry and Natural Environment
SchoolAgriculture
Cycle / Level2nd / Postgraduate, 3rd / Doctorate
Teaching PeriodSpring
CommonYes
StatusActive
Course ID420000894

Class Information
Academic Year2018 – 2019
Class PeriodSpring
Faculty Instructors
Weekly Hours4
Class ID
600121128
Course Type 2016-2020
  • Scientific Area
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
  • Face to face
Digital Course Content
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
  • Greek (Instruction, Examination)
  • English (Examination)
Learning Outcomes
Capability to process signals form all kinds of sensors for obtaining decisions about the status of crops and rural machinery. Development of mathematical models and prediction of phenomena and the behaviour of systems in Matlab programming language.
General Competences
  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Adapt to new situations
  • Make decisions
  • Work autonomously
  • Work in teams
  • Work in an international context
  • Work in an interdisciplinary team
  • Generate new research ideas
  • Design and manage projects
  • Advance free, creative and causative thinking
Course Content (Syllabus)
Signals and systems. Fourier and Z transforms. Sampling of continuous time signals. Discrete stochastic processes and random number production. Noise and digital filters. Applications in data acquisition and data processing in MAtlab and Labview.
Keywords
signals, systems, spectra, moise, filters
Educational Material Types
  • Notes
  • Slide presentations
Use of Information and Communication Technologies
Use of ICT
  • Use of ICT in Course Teaching
  • Use of ICT in Laboratory Teaching
  • Use of ICT in Communication with Students
Description
powerpoint presentations, laboratory exercises
Course Organization
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures
Tutorial
Total
Student Assessment
Description
80% written exams 20% written work
Student Assessment methods
  • Written Exam with Multiple Choice Questions (Formative, Summative)
  • Written Exam with Short Answer Questions (Formative, Summative)
  • Written Assignment (Formative, Summative)
  • Written Exam with Problem Solving (Formative, Summative)
Bibliography
Course Bibliography (Eudoxus)
Σημειώσεις μαθήματος "Σήματα και Συστήματα"
Last Update
30-07-2015