EGB242 Assignment 2 25 Orbiting the Red Planet
Released: Friday, 20th of September at 11:59pm Week 9 Group Plan Due: Friday, 27th of September at 11:59pm Week 10 Final Assignment Due: Friday, 18th of October at 11:59pm Week 12
Message from MARS242 Mission Control
The chief engineers at BASA Headquarters are confident in your demonstrated skills dealing with the simulated Mars mission scenarios you have worked on so far. You are now ready to be part of the main engineering team supervising the live MARS242 mission.
Monitoring the Astronauts physical and psychological health will continue to be critical to the success of this first mission to Mars. Furthermore, as the spaceship is now orbiting Mars, identifying an appropriate landing site for the landing module is also a pressing issue. Fuel reserves onboard the spaceship and the effects of planetary alignment require that this process be carried out efficiently and precisely.
You are now called upon to assist with achieving these two high priority objectives. Your contributions will strongly depend on the techniques you have practiced in the simulated tasks codenamed Assignments 1A and 1B.
Section 0Preparation
Read the entire document before attempting the tasks
There is important information in the end of this assignment brief regarding how the assign ment is to be presented.
A unique set of data is provided to you based on the student members of your group. You will need to generate this data in order to complete the assignment.
Extract the files from EGB242 Assignment2.zip
Open and read GenerateAssignment2Data.m
Enter your group members student numbers in the appropriate location.
If your group has fewer than 3 members, enter 0 for the missing members.
Ensure all necessary files are in the same folder and run the script.
Two 2 files will be generated that you will use for this assignmentA2P2Data.mat and A2P3Data.mat.
Two 2 additional template files, A2part2.m and A2part3.m, have been provided to you, corresponding to sections 2 and 3. You are to write all code for this assignment for each section in the respective file.
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Section 1Mars242: Mars Rover
The maths for this section can be either typed or handwritten neatly.
In anticipation for landing on Mars soon, a rover has been gathering and collecting important data. The rover has been communicating directly with stations situated on Earth where BASA Engineers conduct their analysis on the received data and send the results to the astronauts for action.
Unfortunately, a malfunction with the communication system on the rover has meant that this very long distance communication is no longer possible. Because the rovers data consists of information and images of the planet, which are vital for the success of the anticipated Mars landing, a solution has to be found. The best alternative is for the rover to send the data directly to the spaceship and for the astronauts do the analysis themselves.
However, this can only succeed with your help and knowledge of how Time Invariant systems work. You are called upon to assist the astronauts to mathematically model a system that allows reception and processing of the rover data.
Figure 1: A Mars Rover Source: NASA
1.1 The spectrum of the rovers transmitted signals may interfere with the spectrum of the Spaceships own communication module. The Spaceships communication channel can be modelled as a constant magnitude function in the frequency domain, between fbw and fbw. The fbw for your group will be printed out when you generate your data. Mathematically express and sketch this function. CR1a
Your task is to mathematically determine how communication between the spaceship and the rover can occur.
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1.2 The rovers transmission function can be expressed as:
srovt1nA sinc6000tsin10103tm. The magnitude variable A, n and 2
m are printed when you generate your data. Derive an expression for the corresponding representation of this function in the frequency domain, Srovf, and sketch the magnitude spectrum. CR1a, 1c
1.3 Determine the Transfer Function of the ideal filter, Hfiltf , that is required to eliminate the spaceships broadcast information and ensure the rovers signal is received. Addi tionally, name the filter type. Ensure that you select the simplest type of filter for this situation and justify your selection. CR1a,1d
1.4 Evaluate and sketch the final frequency domain function that defines the range of fre quencies you will receive from the rover after applying your filter above. CR1a,1d
1.5 Discuss what impacts applying the filter could have on receiving the rovers information. How might you mitigate these issues? CR1d
Section 2Mars242: EEG signal analysis
All code for section 2 should be entered into the file A2part2.m. Make sure to include appropriate scaling and informative axis labels.
Despite the crews outwardly high morale, an analysis of the astronauts song choices, which you have helped extract from your work in Assignment 1B, pointed to a possible problem. This has prompted BASA psychologists to recommend more quantitative tests of their brain function. Biometric data has been collected in the form of Electroencephalograms EEG via sensors in their spacesuits. The EEG signals have been multiplexed at the spaceship using the Fourier Transform frequency shift property. The result is a Frequency Division Multiplexed FDM data stream transmitted to mission command, similar to the ones you have successfully dealt with in your previous assignment.
Your expertise in examining the EEG signals to identify any abnormalities will assist our BASA medical team with diagnosis as well as provide data for research into human physiology in microgravity environments.
Your task is to extract the EEG signals of each individual astronaut, remove any noise or interference effects from them, and comment on any abnormalities you might be able to observe.
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Electroencephalograph
Spectrum
1
0.8 0.09 0.6 0.08 0.4 0.07 0.2 0.06
0 0.05 0.2 0.04 0.4 0.03 0.6 0.02 0.8 0.01
0.1
Amplitude
Magnitude
1 0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 50 40 30 20 10 0 10 20 30 40 50
Time s Frequency Hz
Figure 2: Electroencephalogram signalLeft: Time domain, Right: Frequency domain
An example of an EEG signal in the time domain, with its corresponding spectrum, is shown in Fig. 2. Notice that most of the energy in the signal is concentrated between 30 Hz and that there is very little spectral leakage outside of that range. We can say that this particular signal is band limited and that it has a bandwidth of B30 Hz.
The multiplexed signal is normally received at command by a modular hardware system which consists of receiver with realtime spectrum analyzer followed by a frequency shifting module and analogue filter. The output of this filter is sampled by an analoguetodigital converter for further manipulation. The block diagram is shown below in Fig. 3.
Figure 3: Block diagram of the modular receiver
2.1 Represent the spectrum analyzerDetermine the sampling period, Ts , and construct a time domain vector, t, that corresponds to muxSignal. Plot muxSignal against this time vector. Compute the Fourier transform of muxSignal , MUX , and construct a suitable frequency vector, k . Plot the magnitude spectrum of MUX . CR 1a, 2a, 2c
2.2 Determine demultiplexing parametersIdentify the frequency shifts and store them in an ascending order in row vector fshift . Find the corresponding magnitude and phase and store in row vectors Mag and Phishift respectively. Note: Remember to scale Mag to account for the sampling rate. CR 1b, 2b
2.3 Remove the frequency shifts for all five astronautsInput variables muxSignal , t , Mag , fshift , and Phishift should be inserted as row vectors. Each row of the xdm matrix will contain the EEG data for one astronaut with frequency shift removed upon successful application of the module. CR 1b, 1d
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The frequency shifting module is provided in FDMDemux.p. This is mathematically equivalent to the hardware module being used and shares the same functionality as FDMDemux.m from Assignment 1B.
The syntax for its usage is
xdmFDMDemuxmuxSignal,t,Mag,fshift,Phishift
2.4 ReviewCompute the Fourier transform for each data stream in xdm . Store the result in the matrix XDM . Plot the magnitude spectrum for each data stream. CR 1a, 2a, 2b
At this time, the filter module is nonoperational and your expertise in using the Laplace Trans form for circuit analysis will be required to select and implement an appropriate filter. Circuit diagrams from the units technical documents have been supplied below along with their corre sponding transfer functions. You must analyse the response of the circuits to determine which filter is the most appropriate for this application. Once your signal has been filtered you should remove any additional unwanted frequency components and proceed with your analysis.
You have been given the four most appropriate filters out of the six shown below. The trans fer functions for these four systems are stored in sys , with the corresponding circuit diagram reference in sysInfo . Each row of the matrix is the description for each of the systems. To display the part name and parameters for the first system, type sysInfo1,: .
PTPSV01
PTPSV02
R1 V V C1 C2 V out in
C1
R2
PTPSV03
V R1 in
V
in
out
R2
R1 C2
C2 R1
PTACT01
R2 V Vin out
C1
V
out
C1C2 R2
5
PTACT03
R2 C1
PTACT02
V R1 R2 in
C1
C2
V
R1
C2
in
Vout
Vout
R4 R3 R5
2.5 Mathematical analysisDisplay the values of each transfer function in MATLAB. De termine the suitability of each system for the task based solely on its numerator and denominator polynomials. Justify your choices. The function factorTF.p has been pro vided to perform factorisation of the polynomials if needed. Use it by passing a transfer function to it, eg. factorTFsys1 . CR 1a, 1b, 1d
2.6 System AnalysisAnalyse each system, to help you understand their behaviour. You may use the LTIviewer or other functionality in MATLAB to do this. You should find information such as the: impulse response, step response, bode plot and pole zero map. CR 1a, 1c, 1d, 2b
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2.7 RecommendMake your final filter selection and provide a short summary between 50 and 100 words justifying your choice based on your mathematical and system analysis performed previously. Describe any implications that may come from using the chosen type of filter. CR 1d, 2b
2.8 Filter the signalsUse the system you have selected to extract the EEG signal for each of the astronauts. The same filter parameters can be used for each signal. Do this by first multiplying the frequency response of the system by each data stream in the frequency domain XDM . Store the result in the matrix EEG .
Convert these back to the time domain and store the result in a matrix called eeg . Each row of both eeg and EEG should contain the time domain and frequency domain of the EEG signal for one astronaut, respectively. Organise the rows so that they correspond to each frequency in fshift . Plot the signals for each of the astronauts in both the time and frequency domains. CR 1b, 1c, 1d
2.9 Equivalence with convolutionConvolution in the time domain is equivalent to multi plication in the frequency domain and vice versa. Demonstrate this by convolving the impulse response impof your chosen system you can use the impulse function to calcu late this with xdm1,: and saving this to variable eqConv . Compare this to the result obtained in 2.8 of the same signal eeg1,: . Comment on and explain any differences you see. CR 1b, 1c, 2b
2.10 CompareCompare the time domain signals and spectra with the example in Fig. 2. Outline any differences, if any, and provide your justification as to why they should be the same or different. CR 1b, 1d
Now that the demultiplexer has been fixed, the EEG can now be received and fed into BASA Headquarters computer network.
2.11 Digital denoisingIt appears that some of the signals have been corrupted by single frequency interference noise. Implement code to remove this noise. Explain what you have done and provide justifications. CR 1b, 1d, 2b
2.12 Visual analysisCompare the EEG signals of the astronauts to each other as well as to the example in Fig 2 and state for each astronaut whether you have identified any variations that may signify a suboptimal mental state. This information will be provided to BASAs medical team for their professional assessment and prescription of any remedial action. CR 1a, 1d
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MATLAB variables that should be included in your workspace for section 1 A2part2.m,
TsSampling period
tTime domain vector
MUXFrequency domain representation of mux
kFrequency vector
fshiftFrequency shifts
MagMagnitude shifts
PhishiftPhase shifts
xdmEEG data
XDMFrequency domain representation of xdm freqResponseFrequency response of systems
impImpulse response of chosen system
EEGFrequency domain representation of Filtered signals eegTime domain of filtered signals
eqConvConvolution equivalence
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Section 3Mars242: Choosing a landing site
All code for section 3 should be entered into the file A2part3.m. Make sure to include appropriate scaling and informative axis labels.
The Mars242 crew must now identify a suitable site for their landing module. The surfacebased rover has transmitted images of some areas it has scouted to the spaceship for human inspection. After all your efforts to enable communication between the rover and the Spaceship in section 1: Mars Rover, you now discover that, the transmission channel between the rover and the spaceship has introduced both periodic and bandlimited random noise to the signal stream. As you can see from the example image shown in Fig. 4 the noise has rendered the image useless for identifying any surface features.
Figure 4: A corrupted image from the Mars exploration rover.
Images in MATLAB are stored as 2D matrices. Each element in the matrix represents a pixel of the image. The numeric value at each matrix index describes the colour intensity of each pixel. Matrix elements for grayscale images are floating point numbers between 0 and 1; which correspond to the colours black zero intensity and white maximum intensity respectively. You will be working with grayscale images in this assignment.
Even though the images are represented by 2D matrices, they are received as a 1D data stream. The first received pixel is placed at the top left corner of the image. Subsequent pixels are used to fill the image columnwise top to bottom from left to right shown below. The size of the images being received are 480640 pixels.
1 4 7 10 1 2 3 4 5 6 7 8 9 10 11 122 5 8 11 3 6 9 12
Your task is to denoise the images and recommend an appropriate landing site.
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The received signals are provided in the rows of the matrix sig . There are 4 images received, and hence 4 signals, with the first stored in the first row of the matrix.
NOTE: For sections 3.13.10 only use the first received image
3.1 View the noisy imageDisplay the first image using the imshow function. You will need to first convert the signal into a 2D matrix using reshape, ie. imshowreshapesig1,: , 480, 640. CR 2a
3.2 Reference vectorsImage data is received from the rover at 1000 pixelssec. Consider each pixel a single sample. Construct a time vector for the input signal i.e. a single image and store this in t . Create an appropriate frequency vector and store in k . CR 2a
3.3 Visualise the received signalPlot the first received signal sig1,:in both the time and frequency domains. Scale the time domain plot to show the first 3 seconds of the signal. Designate the frequency domain data with capitals i.e SIG . Data in SIG should correspond with time domain data in sig i.e. rowwise. Identify graphically both the periodic and bandlimited random noise in both frequency domain plots. Ensure you state the approximate period of the periodic noise and the bandwidth of the bandlimited noise. Rescale the plots if necessary. CR 1b, 2b
The periodic noise in the image can be removed if it is accurately estimated. Engineers at mission control have identified a set of possible values for the period of the corrupting noise. These values are stored in the vector candidateT . Averaging the shape of the waveform for all periods will emphasise the noise component, smoothing away the underlying image into an offset value. The function estimateNoise.p has been provided to you to perform this. It will return a vector one period long representing your periodic noise.
3.4 Estimate the periodic noiseDetermine the period of the noise from the given options and store your selected value as T . The units for T should be seconds. Use estimateNoise to estimate the noise profile and store the result in the variable sigNoise . The syntax for its usage is
outputestimateNoiseinputSignal,periodInSamples
Plot and compare a periodic version sigNoise to the received signal sig1,: , showing only the first 3 seconds. You can use repmat to repeat a vector many times. If the overall shape of the signals do not closely match, you may have an incorrect estimate of the period. CR 1a, 1c, 2d
3.5 Model the periodic noiseModel the noise signal by computing the Fourier coefficients of sigNoise . You can choose to perform this using either the trigonometric or complex exponential Fourier series. Start by computing the DC a0 and the first 6 harmonics an,bn for 1n6 or Cn for 6n6. Remember to account for sampling rate. List these coefficients and explain your process for computing them. CR 1a, 1b, 2a, 2b
3.6 BiasMission control has determined that the mean of the periodic noise signal is 0. Any DC component present in sigNoise is due to the underlying image. Make any changes necessary to your coefficients to take this information into account. List the new coefficients and explain the reasoning behind any modifications you have made. CR 1a, 1d
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3.7 Generate the approximationUsing these Fourier Series coefficients, generate the approx imation of the noise component to correspond with the received signal ie. all 307,200 samples and store it in sigNoise fs . CR 1a, 2b
3.8 Compare the approximationCompare the first T samples of sigNoise fs to sigNoise . Does the number of coefficients used result in a good representation of sigNoise ? CR 2b
3.9 DenoiseUse your previous BASA training from Assignment 1A to denoise the image. Store the result in the first row of an image matrix im1 . Display the image and its spec trum in MATLAB. Include the recovered image and spectrum in your report. Comment on how the spectrum has changed compared with the spectrum of the received image. Also comment on the quality of the denoised image. Has a sufficient amount of noise been removed to identify details of the image? If not, experiment with using more Fourier coefficients to improve the image quality. Justify the number of coefficients you decide on using for removing the noise. CR 1c, 2b
3.10 Remove the bandlimited random noiseRemove the bandlimited random noise. Decide whether to do this in the time or frequency domain, but the final outcome must be in the time domain. Store the filtered signal in the first row of im2 . CR 1d
3.11 Choose a siteMission control has determined the periodic noise profile is consistent across all received data. Using sigNoise fs , repeat the denoising process from 3.9 for the remaining 3 images and store in the corresponding rows of im1 . View the spectrum and remove the bandlimited random noise using the same method used in 3.10. Store the results in corresponding rows of im2. Display the images contained in im2 in a single figure in MATLAB. Of the 4 landing sites photographed, which is an appropriate site to send our lander? Justify your choice. CR 2b
3.12 ResolutionNavigational numbers were marked onto each image at the time they were photographed. What are these numbers? List these in your report. Also include the filtered images in your report, at a scale that allows the navigational numbers to be read. CR 3d
MATLAB variables that should be included in your workspace for section 3 A2part3.m,
tTime domain vector
kFrequency vector
SIGFourier Transform
TSelected candidate T value
sigNoiseEstimated noise signal
c0, cnComplex Fourier series variables, OR, a0, an, bnTrig Fourier series variables sigNoise fsApproximation of noise
im1Image matrix 1 im2Image matrix 2
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Reflection CR 3d
Include a short discussion maximum 300 words long that covers any lessons learned and things that you would have done differently. Marks for this are included as part of the criteria available at the end of this brief.
Academic Integrity Declaration Mandatory
The provided Academic Integrity Declaration and contribution online form must be completed and submitted along with the assignment. Each student from the group will need to complete their own form. Marks may be moderated depending on contributions. Assignments with incomplete or missing declarations will not be marked. Familiarise yourself with the universitys policy regarding plagiarism and collusion. See the file Academic Honesty Slides.pdf posted with this assignment for some useful details.
Report and Code Presentation
This assignment includes elements of writing and coding. This is a group assessment item and you are expected to generate and submit:
One assignment report, The Report,
One set of MATLAB code, The Code including at least A2part2.m and A2part3.m,
and
Two data files A2P2Data.mat and A2P3Data.mat.
The Criteria Reference Assessment CRA sheet has the outlines of the marking standards of this assignment.
The teaching team has put together some pointers for you to consider:
The Report CR3
An outstanding report demonstrates clear knowledge and understanding of the subject through a combination of visual, mathematical and coding elements. Correct information that is not articulated clearly will attract deductions. Remember that you are writing to inform.
Present the report so it can be understood without reference to the assignment brief.
Figures or code referenced should be no more than 1 page turn away.
Avoid the use of see appendix and refer to .m file.
Full working is required in mathematicsbased sections.
Ensure legibility in any handwritten working.
Include a title page that states the unit name, unit code, and your names and student ID
numbers.
Do not include a table of contents, list of figures, nor a list of tables. 12
The MATLAB Code CR 2
Working MATLAB code is expected to be submitted, alongside your report to Blackboard. The code needs to be executable in .m and without runtime errors. No error correction will be made to make your code run.
Code should be fully commented to describe intent. Quality comments encapsulate your un derstanding of the topic.
You may use the code provided in the weekly tutorials to check your solutions. However, you are expected to generate your own code for your assignment. Submitting supplied .p code as your own work constitutes academic misconduct and will not be awarded any marks.
Code for this assignment will be marked with the assistance of an automated marking system. Ensure that you follow given instructions carefully, including naming conventions. Your code submitted will also be checked for academic misconduct.
Interview
Group interviews will take place at the discretion of the teaching team to ensure demonstrated understanding and skills required for this assignment, by the group, and the individual mem bers. You may be selected and contacted to attend an interview if the teaching team requires clarification about how you arrived at your solutions. Interviews will be a casual discussion. These interviews are compulsory and grades are withheld until they are completed. Marks may be deducted for poor demonstration of understanding of content or assignment knowledge. Consult the CRA sheet for the guidelines of what is expected.
Submission Protocol
Assignments are to be submitted in softcopy through QUT Blackboard in three parts
A completed academic integrity and group contribution online form. This form is to be completed individually by every student.
The report. Only ONE group member is required to submit the report to the Turnitin link. Coordinate within your group who this will be.
Your data and code files. Include everything here that your code needs to run. You may submit as either a single zip file, or attach your required files individually.
Some further points:
Submission deadline is on Thursday 18th of October at 11:59pm.
This will be a hard deadline, and late submission will not be accepted. As per QUT policy, late assignments receive 0 marks, unless you have applied for and received approval for extension, as per the university policy.
You do not need to assign your submission with a special name.
You will need to be registered to a group before you can submit your assignment.
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You may submit as many times as you like before the deadline. New submissions overwrite old submissions. Therefore, only the latest submission will be marked.
All documents can be reviewed after submission, and thus it is your responsibility to verify the uploaded documents.
Be aware that the electronic time stamp is placed only after all files have been uploaded successfully.
Dont risk the late penalty and submit early.
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EGB242 Assignment 2: Group Criteria and Standards for Grading
20192
Criteria
Standards
CR1aTheory: Analyse and mathematically represent signals, selecting Fourier series FS, Fourier transform FT, andor Laplace transform LT methods.
Demonstrates exceptional understanding of the theoretical concepts of the FS, FT, and LT and their properties, through the use of graphical and mathematical methods. That are clear, accurate and logical with no errors.
Demonstrates accurate understanding of the theoretical concepts of FS, FT, and LT and their properties, through the use of graphical and mathematical methods that are clear, accurate and logical with one minor error:
Demonstrates mostly correct understanding of the theoretical concepts of FS, FT, and LT and their properties, using graphical and mathematical methods used are clear and logical No critical errors, but with a couple of minor errors or one theoretical error:
Demonstrates sound understanding of the theoretical concepts of FS, FT, and LT and their properties using clear and accurate graphical and mathematical methods No critical errors, but with a couple of minor errors or one theoretical error:
Demonstrates adequate understanding of the theoretical concepts of FS, FT, and LT and their properties, using logical graphical and mathematical methods with many minor representation errors, a few theoretical errors or two critical theoretical errors:
Shows minimal or incoherent understanding of theoretical concepts of FS, FT, and LT and their properties Explanations or calculations have major errors. Graphical representations missingmajor technical errors.
No evidence of understanding of theoretical concepts. Few or no calculations Incorrect interpretation, signal representations or derivations.
CR1bMaths Analysis: Investigate the relationships between time and frequency domain signal representations.
Utilises the mathematical relationship between real and imaginary, FT magnitude and phase, with no errors.
Utilises the mathematical relationship between real and imaginary, with a couple minor errors.
Utilises the mathematical relationship between real and imaginary, with a few minor errors or one misapplication of the relationships.
Utilises the mathematical relationship between real and imaginary, with a several minor errors or a couple misapplications of mathematical relationships.
Utilises the mathematical relationship between real and imaginary, with many minor errors or a few misapplications of mathematical relationships. Comments on FS, FT, LT and uses their characteristics, and identifies effects of shifted spectra. Performs a few mathematical simplifications.
Does not correctly utilise or apply mathematical relationships to given signals.
CR1. Conceptual understanding
7 7 6 5 4 3 21
Derives and expresses correct mathematical relationships between signals time and frequency representations, using Fourier and Laplace analysis.
Derives and expresses correct mathematical relationships between signals time and frequency representations, using Fourier and Laplace analysis.
Derives and expresses correct mathematical relationships between signals time and frequency representations, using Fourier and Laplace analysis.
Derives and expresses correct mathematical relationships between signals time and frequency representations, using Fourier and Laplace analysis.
Derives and expresses mostly correct mathematical relationships between signals time and frequency representations, using Fourier and Laplace analysis.
No attempt to identify Fourier and Laplace characteristics.
Correctly evaluates FS, FT, LT and appropriately uses their characteristics, and identifies effects of shifted spectra, with no errors.
Correctly Evaluates FS, FT, LT and appropriately uses their characteristics, and identifies effects of shifted spectra, with a couple minor errors.
Evaluates FS, FT, LT and appropriately uses their characteristics, and identifies effects of shifted spectra, with a few minor errors or one misapplication of the relationships..
Evaluates FS, FT, LT and appropriately uses their characteristics, and identifies effects of shifted spectra, with a several minor errors or a couple misapplications of mathematical relationships.
Fails to evaluate or comment on signals spectra, Performs no mathematical simplifications.
No comments on signals
Performs all mathematical simplifications necessary.
Performs almost all mathematical simplifications necessary.
representation or spectra.
Performs most mathematical simplifications.
Performs many mathematical simplifications.
45 Weighting
Attempts to derive and express the relationships between signals time, frequency and Laplace representations. Attempts to Utilise the mathematical relationship between real and imaginary, with several major errors.
1
EGB242 Assignment 2: Group
20192
CR1cInterpret theinputoutput
relationships of Linear Time Invariant LTIsystems.
Accurately and correctly represents and analyses LTI systems with no errors.
Performs convolutionmultiplication operations in the time and frequency domains with no errors.
Represents and analyses LTI systems with a couple minor errors
Represents and analysesLTI systems, with a few
minor errors or one misapplication of the relationships
Represents and analyses LTIsystems with a several
minor errors or a couple misapplications of
Represents and analyses LTI systems with many minor errors or a few. misapplications of mathematical relationships. Performs convolutionmultiplication operations in the time and frequency domains with many minor errors or a few. misapplications of mathematical relationships.
Attempts to represent and analyse LTI systems with several misapplications of mathematical relationships.
No evidence of ability to represents or analyse LTI systems or perform convolutionmul tiplication operations.
CR1dSelect andjustify the use of appropriate filters.
Select appropriate filters based on their transfer functions and the relationships between the impulse and frequency responses, with evidence of further research with appropriate references.
Select appropriate filters based on their transfer functions and the relationships between the impulse and frequency responses.
Select appropriate filters based on their transfer functions and the relationships between the impulse and frequency responses.
Select appropriate filters based on their transfer functions and the relationships between the impulse and frequency responses.
Select appropriate filters based on their transfer functions and the relationships between the impulse and frequency responses.
Attempts to select filters.
No evidence of selecting required filters. Fails to correctly justify any selection of parameters, methods, and techniques.
Thoroughly justifies both quantitatively and qualitatively, the selection of parameters, methods, and techniques.
Thoroughly justifies the selection of parameters, methods, and techniques with one minor error.
Justifies the selection of parameters, methods, and techniques with several minor errors.
Justifies the selection of parameters, methods, and techniques with one major error, several minor errors, or has omitted one necessary justification.
Justifies the selection of parameters, methods, and techniques with a few errors or has omitted a couple necessary justifications.
Mostly fails to correctly justify the selection of parameters, methods, and techniques.
Performs convolutionmultiplication operations in the time and frequency domains with a couple minor errors.
Performsconvolutionmultiplication operations in the time and frequency domains with a
mathematical relationships. Performsconvolutionmultiplication operations in the time and frequency domains with a several minor errors or a
couple misapplications of mathematical relationships.
Attempts to perform convolutionmultipli cation operations in several errors exist.
few minor errors or one misapplication of the relationships.
CR2. Application using MATLAB Coding 35 Weighting
CR2aDesign 7 7 6 5 4 3 21
effective code to represent signals and spectra using FS, FT and LT and generate time and frequency vectors.
Wellcommented MATLAB code with all appropriate simplifications and optimisations performed.
Wellcommented MATLAB code with most of the appropriate simplifications.
MATLAB code is commented throughout.
MATLAB ode is commented.
MATLAB code has some structured approach but is difficult to follow.
MATLAB code is unstructured.
Code is difficult to
Little attempt is made to
CR2bEvaluate and interpret signal representations and spectra using Fourier and Laplace analysis and supplied functions.
Correctly calculates all required variables with no errors.
Correctly calculates all required variables with only a couple minor errors.
Correctly calculates all required variables with a few minor errors.
Calculates all required variables with several minor errors or one major error.
Calculates required variables with many minor errors or a couple of major errors. There must be no run time errors in the code.
Attempts to calculate required variables with more than a couple major errors or run time error.
No attempts to calculate
MATLAB functions have been developed to optimise code for repeated processes.
Most optimisations are performed.
Many simplifications and optimisations are performed.
Some simplifications andor optimisations are performed.
Only occasional simplifications performed.
follow and exhibits major flaws.
write MATLAB code.
required variables.
2
EGB242 Assignment 2: Group
7 7 6 5 4 3 21
Assignment solution is presented in a Report format considering:
Professional report format, that is easy to read and core technical ideas are clearly and accurately conveyed. All processes and solution steps are clearly outlined.
Professional report format, that is easy to read and core technical ideas are clearly and accurately conveyed. Almost all appropriate processes and solution steps are clearly outlined.
Professional report format, that is easy to read and core technical ideas are conveyed. Most of the appropriate processes and solution steps are clearly outlined.
Report is mostly easy to read and most core technical ideas are conveyed. Some key explanations of processes and solution steps are missing.
Report is missing some key components of a report. Report is difficult to read in some parts and some core technical ideas are conveyed. Many key explanations of processes and solution steps are missing.
Report is missing multiple key
Report has little to no structure. Large portions of the report are missingnot attempted. Code is incorrect or missing from the report. Figures are almost entirely missing from the report.
CR3aStructure, CR3b Integration, CR3cFigures, graphs and tables CR3dReflections and Contextualisation.
Code is seamlessly integrated into the report and are used effectively to convey ideas in the report. Code snippets are selectively chosen to succinctly demonstrate all necessary key technical ideas.
Code is integrated into the report and are used to convey the ideas in the report. Selected code snippets demonstrate all key technical ideas.
Code is integrated into the report and are used to convey ideas in the report. Selected code snippets demonstrate most key technical ideas. Figures are used throughout the report to demonstrate results and facilitate explanations. Figures are labelled and plots are appropriately scaled and visible. Solution is contextualised and includes reflections. conveying accurate technical knowledge.
Code is integrated into the report and are used to convey ideas in the report. Code snippets are used.
Code is not integrated into the report or is not used to connect with the ideas discussed in the report.
components of a report. Report is difficult to read. A significant number of key explanations of processes and solution steps are missing.
Figures are effectively used throughout the report to demonstrate results and facilitate explanations. Figures are labelled and plots are appropriately scaled and visible.
Figures are effectively used throughout the report to demonstrate results and facilitate explanations. Figures are labelled and plots are appropriately scaled and visible.
Figures are included but do not demonstrate results or facilitate explanations. Some important figures are missing.
Multiple key figures are missing and little effort has been made to link the figures with the report.
Solution is contextualised to the MARS242 mission, and includes reflections conveying accurate technical knowledge and insights.
Solution is contextualised to the MARS242 mission, and includes reflections conveying accurate technical knowledge.
Solution is contextualised and includes reflections.
Solution contains some contextualisation and reflections.
Attempts to contextualise the solution but with no reflections.
Full 5 is given if: Assignment plan is completed, signed by all students, and
Assignment Plan 5 Weighting submitted by the deadline.
Plan not completed before the deadline.
CR3. Effective written communication 15 Weighting
For moderation of overall marks: At the discretion of the teaching team, your group may be selected to attend an interview if the teaching team require clarification about how the group arrived at their solutions, or how individuals contributed to the overall solution. If selected, you will be notified and given details of the location and time of the interview.
Oral interviewGroup
Demonstrate excellent theoretical and applied knowledge retention after assignment submission. All members of the group must be able to correctly explain theory and justify methods used in their submission including MATLAB code.
Demonstrate adequate knowledge retention. If a student fails to show adequate understanding of their assignment and its components, including MATLAB code, hisher overall mark for this assignment may be moderated down by a maximum of 20.
Demonstrate inadequate knowledge retention. If a student does not attend the interview, or fails to demonstrate fundamental knowledge of their submission and its components, including MATLAB code, hisher overall mark may be moderated
By up to 100.
Postsubmission theoretical and applied skills.
Figures are used to demonstrate results and facilitate explanations. Most figures are labelled and plots are appropriately scaled and visible.
Code is not integrated and is mostly a code and figure dump.
20192
No contextualisati on or reflections.
3
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