辅导IIR filters、讲解Digital Signal、辅导Python程序、讲解Python编程讲解SPSS|辅导Web开发

Assignment 3, Digital Signal processing: IIR filters
Bernd Porr
2018
This assignment covers IIR filters which as before can be low/high/band or stopband
filters.
Your task is to measure a (noisy) physical quantity and use realtime IIR filtering to turn
the raw noisy signal into a smooth, tidy signal.
We have 25 Arduinos, 2 Attys DAQ boards (www.attys.tech) and 6 USB-DUX boards
(www.linux-usb-daq.co.uk). All can operate in realtime under Python. Links to the APIs
and examples are provided on moodle.
The Arduinos are the standard option and work under both Windows and Linux. They
have a sampling rate of 100 Hz.
The USB-DUX boards run under Linux only and offer sampling rates of up to 8 kHz.
They are also electrically isolated and can be used for biomedical applications.
We also have two wireless biomedical bluetooth DAQs called “Attys” which run under
both Linux and Windows.
Examples of noisy signals you can measure:
Temperature using the voltage drop over a diode
Temperature using a thermocouple (i.e. two different metals)
Pulse detection using an light dependent resistor
Atmospheric pressure changes
Distance sensing via a capacitor
Mechanical strain measurements with a piezo sensor
Realtime ECG heartbeat detection during exercise using a bandpass/highpass (biomed
team)
Displaying EEG alpha/beta waves (biomeds team)
1 Detecting the small oscillometric pressure changes in the blood pressure cuff (biomed
team)
Hackaday has a lot of good examples and other Maker pages. You can create your circuit on
a breadboard, matrix board, etc. Feel free to use any component which is in the electronic
component store on level 7 and you can also order components (within a sensible budget!).
Every team needs to measure something differnet. Add your topic to the wiki provided
on moodle as soon as possible.
Again you work in teams of two students and one report is submitted per team.
This task requires planning/initiative before you come to the lab. Think of
a scenario before the lab starts. It’s not the task of the lab demonstrators /
technicians to come up with ideas here and they need to come genuinely from
you. I’d like to see different ideas from every team. Enter you project ideas on
the WIKI on moodle so that others can see what’s already taken. Feel free to
discuss it with us.
1. Present a measurement problem which requires realtime filtering. Marks are given for
initiative, inventiveness and originality (= ideas which haven’t come straight from the
lecturer, lab demonstrators or other groups). Document the experiment with:
photos of the setup
dataflow diagrams
YouTube clip(s)
in addition to your report. How would you like to present the results? Just as a plot
or perhaps a bar graph? QT for Python might be an option to look into. [20%]
2. Design a simple analogue circuit for your measurement. This could be as simple as
two/three components on breadboard or more complex with an instrumentation / operational
amplifier. Generally the aim is to be simple but effective. [10%]
3. Determine the filter response(s) which are required and justify them. Generate the
sos coefficients for the filter(s) either with the help of Python’s high level functions or
solutions shown in the lecture. [20%]
4. Write two classes:
(a) IIR2Filter which implements a 2nd order IIR filter which takes the coefficients
in the constructor and has a method called:
y=IIR2Filter.filter(x)
where y and x are simple scalars (no arrays) as usual. Optimise this class that it
won’t need any arrays for its buffers and coefficients.
(b) a class IIRFilter which directly takes the sos array from the high level IIR design
commands as its constructor argument and which then creates a chain of 2nd
order filter instances of IIR2Filter classes. Thus they form an array of instances
of IIR2Filter. Again implement a function which then filters the signal:y=IIRFilter.filter(x)
and then internally processes the data x by sending it through the chain of 2nd
order IIR2Filter classes.
[30%]
5. Compare your filtered results with the original recordings, show both signals in the
realtime demo (YouTube clip) and discuss if you have been successful. Do a critical
analysis. [20%]
High level design commands are allowed but the actual IIR filtering operations need be
written from scratch as outlined above. Any use of lfilter or other high level python filter
operation will result again in zero marks. Proof of realtime processing in form of a video
needs to be given and the video needs to show clearly what it’s about. If you don’t like
speaking you can also use subtitles or graphics. Please add your link to the wiki.
The report can also be written purely on github which should then contain the software,
the report itself as a README/WIKI and the video clip. In this case please submit a single
page to the teaching office containing the link to github and add the link to github to the
WIKI.
As before I expect sharp figures in vector format in the report. The complete code needs
to be in the appendix and also uploaded to moodle.
Deadline for the report is 17th Dec 3pm. If you leave earlier for the Christmas break
make sure to submit the paper report before you leave

辅导IIR filters、讲解Digital Signal、辅导Python程序、讲解Python编程 讲解SPSS|辅导Web开发

辅导IIR filters、讲解Digital Signal、辅导Python程序、讲解Python编程讲解SPSS|辅导Web开发