|Unit Name||Data Acquisition|
|Unit Duration||12 week|
Doctor of Engineering
Duration 3 years
|Unit Creator / Reviewer||Dr Milind Siddhpura and Dr Yuanyuan Fan|
Total Program Credit Points 120
|Mode of Delivery||Online or on-campus.|
10 hours per week:
Lecture - 1 hour
Tutorial - 1 hour
Assessments / Practical / Lab - 1 hour (where applicable)
Personal Study recommended - 7 hours (guided and unguided)
Unit Description and General Aims
This unit covers concepts related to data acquisition, enabling students to understand the issues required to achieve high-quality measurements. Students will better understand issues such as accuracy, precision, repeatability, calibration, uncertainty and noise. Measurements from a range of sensors (mechanical, optical, electrical) are introduced and subsequent signal conditioning (operational amplifiers, instrumentation amplifiers) are addressed with the aim of maximising signal quality. Statistical methods are discussed to better understand noise processes and how noise can be minimised. Methods to improve signal quality (signal to noise ratio) are discussed. Measurement signal and noise are analysed in both the time and frequency domain to better understand the connection between two domains and the importance of measurement bandwidth. Sampling is reviewed to understand the impact of moving from continuous-time (CT) to discrete-time (DT), including discussion of the Nyquist rate and aliasing. The conversion between the analogue and digital domains including CT-DT, system transfer functions, spectral analysis and the construction of finite and infinite impulse response filters to reduce noise are discussed
On successful completion of this Unit, students are expected to be able to:
- Evaluate measurement concepts obtained through team-based learning.
Bloom’s Level 5
- Generate and analyse data (numerically and in graphical form) in a manner that includes the uncertainty associated with the measurement.
Bloom’s Level 6
- Produce measurements with minimised noise by moving between the time and frequency domains.
Bloom’s Level 6
- Critically analyse and judge instrumentation characteristics that affect data collection in achieving high-quality measurements.
Bloom’s Level 5
- Construct high quality experimental design.
Bloom’s Level 6
The cognitive domain levels of Bloom’s Taxonomy:
|Bloom's level||Bloom's category||Description|
|1||Remember||Retrieve relevant knowledge from long-term memory by recognising, identifying, recalling and retrieving.|
|2||Understand||Construct meaning from instructional messages by interpreting, classifying, summarising, inferring, comparing, contrasting, mapping and explaining.|
|3||Apply||Carrying out or using a procedure in a given situation by executing, implementing, operating, developing, illustrating, practicing and demonstrating.|
|4||Analyse||Deconstruct material and determine how the parts relate to one another and to an overall structure or purpose by differentiating, organising and attributing.|
|5||Evaluate||Make judgments based on criteria and standards by checking, coordinating, evaluating, recommending, validating, testing, critiquing and judging.|
|6||Create||Put elements together to form a coherent pattern or functional whole by generating, hypothesising, designing, planning, producing and constructing.|
|Assessment Type||When assessed||Weighting (% of total unit marks)||Learning Outcomes Assessed|
Type: Multiple-Choice quiz
Word length: n/a
Questions from the content covered over the first four weeks. Including: Measurement terminology; Sensors; Signal conditioning; Probability and statistics
Type: Report and Presentation (Mid-project)
Word length: 3000
Consider a project that requires measuring to collect and analyse the data. The uncertainty in measurement should be expressed. The data should be analysed in both time and frequency domains. The data collection and analysis process is to be presented.
Type: Report (Final Project)
Word length: 4000
Consider a project that focuses on measurements with noise. Identify the devices required for and the uncertainty in the measurements. Describe the entire data processing of the project, including measuring, noise minimising, digitization, sampling and analysis.
Prescribed and Recommended Readings
The required text book provides important references in each chapter which are relevant to the subject matter. These references and those provided by the instructor will form the basis of the study material. The following textbook provides a study guide, and a student’s future reference book for statistical theory, numerous research methods, calculations and visuals.
- Suggested textbook: Measurement and Data Analysis for Engineering and Science, Fourth Edition, By Patrick F Dunn, Michael P. Davis.
In addition to the above textbook, there are several useful reference material may be obtained on-line from published journals, and websites. Some recommendations are:
- High Frequency Measurements and Noise in Electronic Circuits by Douglas C. Smith
- Electrical Measurement, Signal Processing, and Displays edited by John G. Webster
- Measurement Systems and Sensors, Second Edition by Waldemar Nawrocki
- Digital Signal Processing for Measurement Systems: Theory and Applications by Gabriele D'Antona, Alessandro Ferrero
Software Reference Material
Software can be applied in the processing of data and the professional presentation of computed results. There are numerous software packages which can be applied. For convenience and affordability, the Office .xls ‘add-on’ software XLSTAT-Base is proposed.
- The proposed XLSTAT-Base solution software is for data mining, machine learning, tests, data modelling and visualization. This software tool can be applied for data preparation and visualization, parametric and nonparametric tests, modelling methods (ANOVA, regression, generalized linear models, mixed models, nonlinear models), data mining features (principal component analysis, correspondence analysis) and clustering methods (Agglomerative Hierarchical Clustering, K-means). XLSTAT-Base also features machine learning methods (association rules, regression and classification trees and K-Nearest Neighbours), partial least square regression and more. It is IET’s viewpoint that XLSTST-Base will be a comprehensive and affordable research tool for the candidate’s final research project. Further reading can be obtained from website: (https://www.xlstat.com/en/solutions/base).
- Alternative software may be applied such as Maple, Quantum XL, MATLAB.
This topic reviews measurement terminology associated with static and dynamic instrument characteristics.
- Commonly used measurement terminology
- Static Characteristics: Accuracy, Precision, Calibration, Resolution, Threshold, Offset/Bias, Sensitivity, Reproducibility, Repeatability
- Dynamic characteristics: Parameters that define 0th, 1st and 2nd order systems, response time, time to steady state
- Datasheet specifications
- Static and dynamics systems and their response
This topic reviews sensors including accelerometers, pressure sensors, flow meters and temperature sensors. The output quantity of sensors reviewed includes voltage, current, resistance, mechanical force and capacitance. Linear and non-linear (including logarithmic) sensor outputs are considered.
- Sensor physical mechanisms
- Output quantities of sensors
- Sensor characteristics
- Types of Sensors and applications
This topic introduces the basics of detecting a signal, amplifying it and transforming it into an appropriate form. This process is known as signal conditioning.
- Unbalanced and balanced sensor arrangements
- Gain and bandwidth issues
- Offset elimination
- Temperature correction
Probability and Statistics
This topic introduces cumulative and probability density functions, (specifically the Gaussian distribution) as well as random variables. Means and variances of samples and populations are explored. The transformations of distributions is analysed using several different tool sets.
- Means, sample and population statistics
- Understanding probability density functions (PDF) and cumulative density functions (CDFs)
- Reading standard normal tables
- Identification of outliers
- Convolution of probability distributions
- Generate random variables with any desired probability density functions
This topic reviews how to take the data that has been analysed and present it in a rigorous manner, suitable for engineering technical reports and publications.
- Methods to presenting data in plots and tables.
- Transforming data for appropriate presentation/plotting
- Presenting figures and text in documents
- The difference between different plot types
The guide to the expression of uncertainty in measurement (The GUM)
This topic reviews and uses the ISO Guide 98-3 “Uncertainty of measurement – Part 3: Guide to the expression of uncertainty in measurement (GUM:1995)” and the corresponding “Australian National Measurement institute Monography 1: Uncertainty in Measurement: The ISO guide.”
- Type A and Type B uncertainties
- Determining expanded uncertainty, standard uncertainty and coverage factors
- Ability to apply the student’s ‘t’ table
- Apply the law of Propagation of Uncertainties (UPL)
- Build and analyse uncertainty analysis tables
- Ability to state result in GUM format [word for word is not required, but key elements are needed]
Least squares data analysis
This topic presents the fitting of data to a model using least squares data analysis. The consideration will be on how uncertainties can be determined from the fits to the data, which would be subsequently used for specifying measurement uncertainty. Linear and non-linear least squares is considered.
- Importance of a model
- Linear regression
- Overfitting issues
- Interpreting statistical data
- How uncertainty is related to linear regression
Time and frequency analysis of random variables (Random processes)
This topic reviews signals that are deterministic and random. It looks at the tools to analyse deterministic signals in the time and frequency domain and under what conditions these tools can be used to analyse random signals.
- Time and frequency spectrum – typical transforms
- Power and energy in the time and frequency domains
- Periodic, aperiodic and random signals and their analysis
- Relevance of the Bandwith-Time product and application
- Random processes
This topic reviews the concepts and mathematics associated with the conversion of a continuous time (CT) signal into a discrete time (DT) signal.
- Why digital forms are important
- Effects of sampling in the time and frequency domains
- Difference between discrete time and digitized
- ADC and DAC issues
This topic reviews correlation of time series and looks at the links between convolution and filters.
- Correlation forms (auto, cross, continuous and digital)
- Uses of correlation
- Convolution and Linear Time Invariant (LTI) systems
- Convolution and multiplication in the time and frequency domains
Sampled data analysis
This topic focuses on analysis of sampled data. All data dealt with computationally is in the form of x[n]. Key functions which can be performed are filtering and analysis in the time and frequency domain.
- Discrete Fourier Transform (DFT)
- Discrete time integration and differentiation
- Difference equations and z transforms
- Smoothing and averaging
- Digital filters (FIR and IIR)
In the final week, students will have an opportunity to review the contents covered so far. Opportunity will be provided for a review of student work and to clarify any outstanding issues. Instructors/facilitators may choose to cover a specialized topic if applicable to that cohort.
- Software: N/A
- Version: N/A
- Instructions: N/A
- Additional resources or files: N/A