A tantárgyleírás hatályossága
Hatályosság kezdete:
2026. March 21.
Hatályosság vége:
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| Subject name (Hungarian, English) |
Méréstechnika
Measurement Technology
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Subject code | BMEVIMIAB01 | ||||||||||||
| Subject type | — | ||||||||||||
| Training Level | — | ||||||||||||
| Course types and hours (weekly/semester) |
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| Assessment type | félévközi érdemjegy | ||||||||||||
| Credits | 5 | ||||||||||||
| Subject coordinator |
DR. Sujbert László
position: egyetemi docens
contact:
sujbert.laszlo@vik.bme.hu
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| Responsible department |
Mesterséges Intelligencia és Rendszertervezés Tanszék
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| Faculty | Villamosmérnöki és Informatikai Kar | ||||||||||||
| Subject website | https://www.mit.bme.hu/eng/oktatas/targyak/vimiab01 | ||||||||||||
| Primary curriculum type | — | ||||||||||||
| Direct prerequisites – Strong prerequisite | none | ||||||||||||
| Direct prerequisites – Weak prerequisite | none | ||||||||||||
| Direct prerequisites – Parallel prerequisite | none | ||||||||||||
| Direct prerequisites – Milestone prerequisite | none | ||||||||||||
| Direct prerequisites – Exclusion | none |
Objectives
Programme
- Introduction. Aim of the subject, main topics. Connection between measurement and modeling. Basic measurement methods. Measurement errors: absolute and relative error.
- Measurement errors: bias and random error. Offset, gain, linearity, hysteresis, and quantization error. Error propagation (1): mathematical model. Addition of errors. Examples.
- Error propagation (2), examples. Overview of probability theory: probability density, probability distribution function, important distributions. Determination of the expected value, variance, etc.
- Properties of the normal distribution. Central limit theorem. Standard normal distribution. Evaluation of measurement data: mathematical model, averaging, variance of the average, sample standard deviation.
- Curve fitting. Fitting of line and polynomial. Confidence calculus (1). Utilization of normal, chi-square, and Student-t distribution. Derivation of the distributions and formulas.
- Confidence calculus (2). Chebyshev inequality. Overview of basic confidence problems. Utilization of confidence calculus for error evaluation. Standard expression of uncertainty in measurement (GUM).
- Measurement of voltage and current (1). Structure of analog and digital meters. Extension of the range, input resistance. Errors of the meters and their evaluation.
- Measurement of voltage and current (2). AC measurement. Representation of AC signals: Fourier series, different mean values, dB scale. Comparison of meters of different measurement principle. Description of the noise, signal to noise ration, noise filtering.
- Signal transformers. Introduction to non-ideal behavior of passive elements (resistor, capacitance, inductance). Voltage dividers: resistive, inductive, and capacitive divider. Compensated resistive divider.
- Signal transformers. Voltage and current transformer. Overview in electronics: basic amplifier circuits, instrumentation amplifiers. Application possibilities.
- Impedance measurement: DC low accuracy methods, series and shunt ohmmeter. AC measurement: impedance models. Connection between mathematical and physical models. AC low accuracy methods. Power measurement.
- Impedance measurement: voltage comparison method. High accuracy methods, Wheatstone-type bridge circuits. Examples. Balancing impedance bridges.
- Ratio transformer and current comparator based bridges. Canceling parasitic impedances. Disturbance sensitivity of measuring circuits: application of shielding.
- Canceling of the effect of cabling and stray impedances. 2, 3, 4, and 5 wires methods. In-circuit measurement. Overview of the complete impedance measurement problem.
- Time and frequency measurement. Counter based frequency, period, and average period meter. Error analysis. Constant gate time period time meter. Digital phase shift measurement.
- Analog and digital oscilloscope. Conditions for displaying a right graph: the role of trigger logic/circuit. Oscilloscope functions. Signal processing overview: sampling theorem and its applications.
- Spectrum analysis. Analog methods: parallel, tuned filter, and heterodyne spectrum analyzer. Application of the discrete Fourier transform. Windowing.
- Analog to digital converters: flash, successive approximation, dual-slope ADCs. Subranging ADC. The role of short time and long time stability. Calculation of conversion time, noise suppression.
- Digital to analog converters: ladder DACs. Switched capacitor DACs. Comparison of different types of ADCs and DACs. Errors of ADCs and DACs: integral and differential nonlinearity.
- Quatization error, the noise model of quantization. Effect of sampling on quantization noise. Calculation of effective number of bits. Structure and operation of delta-sigma ADCs and DACs.
- Reserved for compensation of any delay (fallen lectures, slow pace, etc.)
The aim of the subject is to give insight into
metrology, measurement theory, measurement technology and instrumentation.
Besides the theoretical aspects, the course also prepares students for
laboratory practices. Model building and problem solving skills of the students
are developed. The subject focuses on the measurement of electrical quantities
but emphasizes the analogies with non-electrical problems.
Learning outcomes
Ez a tantárgy a KKK rendeletben meghatározott, következő kompetenciák fejlesztését szolgálja:
Knowledge
No learning outcomes recorded.
Skills
No learning outcomes recorded.
Attitudes
No learning outcomes recorded.
Autonomy and responsibility
No learning outcomes recorded.
Oktatási módszertan
3 lectures and 2 seminars each week.
Tanulástámogató anyagok
Online források
Schnell, L. (Ed.): Technology of Electrical Measurements. Wiley, 1993.
Recommended preliminary knowledge for completing the subject
Knowledge type competencies
(azon előzetes ismeretek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)
Mathematics, physics, digital design, signals and systems, probability theory.
Skill type competencies
(azon előzetes képességek és készségek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)
nincs
Recommended (non-compulsory) preliminary competencies
(azon ajánlott (nem kötelező) előzetesen megszerzendő kompetenciák összessége, amelyek jelentősen hozzájárulnak a tantárgy eredményes teljesítéséhez)
Mathematics, physics, digital design, signals and systems, probability theory.
General rules
Requirements:
During the semester:one mid-term exam must be written with satisfactory results (40%)5 (five) small mid-term exams must be written with satisfactory results (30%)The final result is calculated from the results of the mid-term exam and the small mid-term exams (the weighting is 50-50%). Credits are granted for students achieving 40% final result.
Additional possibilities:
The mid-term exam
can be repeated on an organized repeated mid-term exam during the
semester, and on a 2nd organized repeated mid-term in the repetition
period following the semester.
Assessment methods
In-term assessments
No detailed assessments provided.
Weight of in-term assessments
No weights provided.
Exam-period assessments
No detailed assessments provided.
Weight of exam elements
No weights provided.
Grade calculation
No grade thresholds provided.
Attendance requirements
No attendance requirements provided.
Rules for retake and resubmission
Not provided.
Short description
Not provided.
Detailed description
Not provided.
Recommended courses
Not provided.
Workload to complete the subject
No workload breakdown provided.
Validity of subject requirements
Requirements valid from:
—
Requirements valid until:
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Curriculum placement
No curriculum placements recorded for this subject version.