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ELEC3106 metal proximity detector

Creation date：2024-05-23 15:49:21

Belonging category：电气工程electric engineering

Tag ELEC3106

metal proximity detector

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ELEC3106
Electronics
Lab project: metal proximity detector
Objective
The objective of this laboratory task is to give the students experience with a small open-ended
design problem in which they can benefit from the knowledge gained throughout the course.
Components
For this laboratory exercise, you will need
2 Probes
1 Bread-board
LEDs
Logic gates
Resistors
Capacitors
Transistors
Other components
Wires
Lab-book
Components available from the school workshop (or equivalent substitutions) may be used in
this design project. Use of other components require the permission from the course coordinator.
Requirements
In this laboratory exercise, you need to, in pairs of two students, design and characterise a metal
proximity detector which makes use of an air coil as a sensor. For example: the specific coil
showed in Figure 1(a), which has a diameter of about 5cm, has an inductance of 5µH; when a
sheet of metal is placed just next to this coil (parallel to the coil plane), the coil inductance drops
to 3.5µH.
The designed metal proximity detector must be able to detect a 5cm× 5cm metal sheet held
next to (but not touching) the sensor coil and indicate to a user – for instance by means of an
LED – the presence of the metal sheet. The metal priximity detector may make use of laboratory
signal generators and power supplies.
Other than these, there are no requirements to the design – but you may want to consider
how to exceed the basic requirements by for instance 1) using a 3 V power supply as the only
laboratory equipment emulating battery operation 2) measuring and displaying the distance to
the metal sheet with 2 or more bits resolution or 3) making a very sensitive detector that can
detect a “small” piece of metal or a metal sheet “far away”.
Pre-lab work
It is essential that you do your circuit designs before each laboratory class, complete with initial
component values: there will only be enough time during the class to measure, optimise and
debug your circuit, not designing it. Use your lab time wisely and start design early. It is
probably a good idea to also assemble the circuit on your bread-board before the class. You will
likely need to do some experimentation in the lab to implement a good solution or try out ideas,
so be prepared for that. Think about the requirements. How do you sense the coil inductance?
How do you make the sensed signal into a form that you can further work with? How do you
drive your proximity indicator?
Measurements
In your laboratory session, carry out the measurements in this section in pairs of two students to
characterise your circuit. Make sure that you show your result to the laboratory demonstrator,
and that both of you record everything neatly in your lab-book. In your last design laboratory
class (week 10), you must demonstrate your design to your laboratory demonstrator. Your test
set-up should look somewhat like what is shown in the figure below:
(a)
function
generator
power
supply
metal
detector internal nodes
for observation
probe
display
detector
coil
metal
sheet
(b)
Figure 1: Air coil (a). Metal proximity detector measurement set-up (b).
Characterisation
To verify that your circuit meets the specified requirements, your circuit characterisation need
to include the measurements that answers the following questions: What is the circuit power
consumption? What is longest distance D (see Figure 1(b)) between coil and metal sheet you
can reliably detect? How many different distance (D) can you reliably detect? What is the range
of input supply voltages over which the circuit operates? You must capture CRO waveforms of
voltages on suitable nodes in your circuit that demonstrate the circuit operation. It is up to you to
identify which voltages waveforms on which nodes to capture to best demonstrate your design.
You may want to do further characterisation, making sure that you collect suitable evidence of
your characterisation.
Report
A short report (5 pages max) on the laboratory exercise must be prepared and uploaded as a
.pdf-file on the course Moodle site by Monday week 11. The report should include answers
to all questions asked above, and brief comments on the results. It should also include a brief
explanation of your circuit design and reasoning for choice of component values. Make sure you
point out any clever designs ideas you have employed. The full schematic of your circuit must
be included in the report, not counting towards the page limit. Submit one report per two-student
group and make sure that both student’s names and student ID appear clearly on the report.

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LAW3016 CS371 STAT 334 FIN0008 ACC3004 STAT 371 COMP1921 MTH201 ACCT2000 UN3412 CIV6000 MATHS 3021 MATE7014 ECON1 CIBC6035 LAW121G COMM 321 CSC8204R TTM2033 CSCI 2600 MAE 115 LNG302 CENV6175W1 ECON30019 CPS2015 BST969 DDES9015 AMATH242 CS 1151 N1592 ACCT600 EG503G SMM519 EC318 Ecstatistics EEE 471 MARK2051 Topology COM00037H ACCT3000 MTHM501 EG501V COM00055H BUSN9085 GGR273 MATH 2B ITD102 MA214 ECON0060 ECE3114 ECMM447 Mechanics CCT361H5S BSAN3210 WELF2001 Fin3001 RESP5001 STA 137 Linguistics 101 CSCI368 CMPSC497 LUBS3655 OMBA 5355 BUSM2562 BFF5902 CYBR7001 CULT2005 ECON 7720 MTRX3760 FSE598 LUE1001 MGSC 7100 CS3240 COSC7500 ACCT3091 INFOGR TRAN 2080 DESIGN CIVE215001 CIVE2360 CPSC 8430 FNCE2003 Stoichiometry INFS6023 Quantitative Method Math 21D ECE5179 Design ECON8022 Chemical DMS 213 ADM1370 BFF5902 BFF2701 AGRI10051 AMN400 ECON7322 FINC5090 MATH1052 SciComp Elec4622 FINS5516 ENG 101 MMM143 CS431CA2 BSAN2201 EBSC7070 ES9v9-10 PLIN0009 DATA 8 MATH 101 BCPM0097 MAT 237 BISM2201 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SVM. (10 points) Consider an SVM classifier using RBF kernel, and finish the following python code to search for the optimum kernel parameter γ. You are required to calculate the RBF function and search yourself, instead of using functions in Scikit-Learn (sklearn). def parameter search(gamma list ,Xtr,ytr,Xts,yts ,...): """ Parameters gamma list: A list of RBF kernel parameter gamma to search for Xtr: Training data, shape (ntr,nrow,ncol) ytr: Training labels, shape (ntr,) Xts: Test data, shape (nts,nrow,ncol) yts: Test labels, shape (nts,) ... Add other parameters as needed Returns: gamma optimum: The gamma with minimum error on Xts, yts """ ... return gamma optimum

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