Principles of measurement and measurement systems, basic principles of signals and frequency domain representation, Fourier transform, mechanical modeling of measurement systems, probabilistic and statistical methods for measurement data analysis, measurement uncertainty calculation, basics of experiment design, measurement, recording and analysis of force, strain, temperature, humidity, flow, velocity, vibration and acceleration. Use of typical laboratory equipment such as oscilloscopes, frequency analyzers, operational amplifiers, and thermocouples.
İlk dosyayı sen ekleyebilirsin — notlar, geçmiş finaller, çözümler, cheat-sheet, ne varsa. Drive linki / PDF / ZIP / fotoğraf, hepsi olur.
Şu an: mail at, ben düzenleyip yayına alayım. Form/upload UX yakında geliyor (Kimya tasarlıyor).
| Dönem | Course CPA | |
|---|---|---|
| 2024-2025 Fall | 2.82 | 4 sec · 134 öğr |
| 2023-2024 Fall | 2.88 | 4 sec · 198 öğr |
| 2022-2023 Fall | 2.43 | 4 sec · 190 öğr |
| 2021-2022 Fall | 2.33 | 2 sec · 104 öğr |
| 2020-2021 Fall | 1.89 | 2 sec · 96 öğr |
| 2019-2020 Fall | 2.43 | 2 sec · 91 öğr |
| 2018-2019 Fall | 2.39 | 2 sec · 98 öğr |
| 2017-2018 Fall | 2.48 | 2 sec · 125 öğr |
| 2016-2017 Fall | 2.65 | 2 sec · 110 öğr |
| 2015-2016 Fall | 2.19 | 2 sec · 107 öğr |
Aggregate course GPA — Bilkent STARS'tan public data. Hoca-bazlı per-section detayı için STARS evaluation report →. Öğrenci anket cevapları KVKK kapsamında defter'de tutulmaz.
No FZ grade is given in this course.
Students are advised to consult the instructor(s) regarding the use of Generative AI tools and their appropriateness. Responsible use of GenAI is encouraged in accordance with Bilkent University's GenAI Guidelines (https://w3.bilkent.edu.tr/bilkent/generative-artificial-intelligence-genai-guideline).
Signal Transduction and Sensors Static Characteristics of Measurement Systems Data Acquisition Uncertainty Analysis Uncertainty Analysis (Cont.) Dynamic Characteristics of Measurement Systems Intro. to Signal Analysis - amplitude, SNR, dynamic range, resolution Frequency Domain Representation of Signals and Noise Frequency Domain Representation of Signals and Noise (Cont.) Frequency Domain Representation of Signals and Noise (Cont.) Op-Amps for Instrumentation Filtering and Matching Circuits Bridge Circuits & Data Acquisition Data Acquisiton - Revisited ECTS - Workload Table: Activities Number Hours Workload Preparation for Final exam 1 20 20 Course hours 14 2 28 Preparation for Midterm exam 1 10 10 Preparation for Quiz 6 1 6 Laboratory (including preparation) 14 2 28 Midterm exam 1 2 2 Individual or group work 14 1 14 Project (including preparation and presentation if applicable) 1 10 10 Final exam 1 3 3 Report (including preparation and presentation if applicable) 8 4 32 Total Workload: 153 Total Workload / 30: 153 / 30 5.1 ECTS Credits of the Course: 5 Type of Course: Lecture - Laboratory - Project Course Material: PP - Written Teaching Methods: Lecture - Exercises - Presentations - Practical session