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EEE 6209 Advanced Signal Processing

创建日期：2024-04-08 14:19:22

所属分类：电气工程electric engineering

标签： EEE 6209

Advanced Signal Processing

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EEE 6209
Advanced Signal Processing
Total Marks: 100
Marks to meet the threshold: 50
The coursework contributes to 40% of the grading assessment
TITLE: Noise Removal
Due Date: 21
st
April 2021 at 16.00 via Blackboard EEE6209 coursework submission link

The aim of the coursework exercise is to explore and apply the signal processing you learned in this module
for denoising a signal. Each of you has been assigned an individual signal. The number of the signal
allocated to you can be found in “My Grades” section in EEE6209 Blackboard pages. Download the
“signals” file from Blackboard coursework folder and choose the MAT file (x.mat, where x is your signal
number). Also download the noise file (noise.MAT) from Blackboard coursework folder. Two variables
can be found in the signal MAT file. They are the signal assigned to you and the signal number, n. The
noise MAT file contains the noise signal (V) to corrupt the signal assigned to you.

Add noise, V, to your test signal, A, to get the corrupted image, B, as follows:
B= A+ V.

You have to complete the following 4 tasks:
1. Analyse the original signal A, to understand its time-domain and frequency domain characteristics.
2. Use the Fourier transform-based techniques to remove the noise in signal, B, to recover the original
signal, A.
3. Explore the Moving Average Filter (MAF) you learned in lectures (Topic 02) to remove noise in
signal, B, to recover the original signal, A.
4. Use the Discrete Cosine Transform (DCT) to remove noise from B to recover the original signal 4

You are required to submit your findings in the following two assessments. For each task in the
assessments 1 and 2, you are required to do the following sub tasks and provide your answers in the
submission. Do not copy text from the task descriptions. Just use the relevant sub task number (e.g., 1.1.1,
1.2.1, etc.)

Assessment 1 (Description of the methodology, discussion of results and conclusions) Not more than 2000
words

1.1.1 Signal ID
1.1.2 The methodology you used to determine time-domain and frequency domain signal characteristics.
(Hint: Firstly, determine the time domain characteristics and use them to segment the signal A into
smaller segments with similar time-domain characteristics. The determine the frequency domain
characteristics for each of the segments)
1.1.3 Discussion of the time domain and frequency domain characteristics of the signal A
1.1.4 Based on 1.1.2 and 1.1.3 explain how you would choose smoothing filters with different parameters
and thresholds for transform domain noise removal for the signal B.

EEE 6209 Advanced Signal Processing
Coursework 2020/21
Dr Charith Abhayaratne

2
1.2.1 The methodology for the Fourier transform-based noise removal. If your technique uses any
parameter values, for example, threshold values, you need to explain how they were chosen.
1.2.2 Discuss the performance of the Fourier transform –based noise removal algorithm and the choice of
its parameter values based on your response to 2.2.1 and 2.2.2.

1.3.1 The methodology for the MAF-based noise removal using the M-point MAF. If your technique uses
any parameter values, for example, filter length (M) and the number of passes, you need to explain how
they were chosen.
1.3.2 Discuss the performance of the MAF–based noise removal algorithm and the choice of its parameter
values, such as, filter length (M) and the number of passes based on your response to 2.3.1 and 2.3.2.

1.4.1 The methodology for the DCT-based noise removal using the N-point DCT. If your technique uses
any parameter values, for example, threshold values and the size (N) of DCT, you need to explain how they
were chosen.
1.4.2 Discuss the performance of the DCT –based noise removal algorithm and the choice of its parameter
values, such as, threshold values and the size (N) of DCT based on your response to 2.4.1 and 2.4.2.

1.5.1 Provide your conclusions based on the summary table in 2.5.1 and any suggestions for further
improvements.

Assessment 2 (Numerical results and plots) – No page/word limit

2.1.1 Signal ID
2.1.2 Show the plots of original and noisy signals
2.1.3 Show plots to support the discussion of the time domain characteristics in 1.1.2 and 1.1.3
2.1.4 Show plots to support the discussion of the frequency domain characteristics in 1.1.2 and 1.1.3
2.1.5 Show the mean square error (MSE) values for the segments of noisy signal B with respect to those of
signal A.

2.2.1 Compute the MSE values for the recovered signal segments in 1.2.1 with respect to those of signal A
for different choices of parameters you have used.
2.2.2 Plot the de-noised signal segments in 1.2.1.

2.3.1 Compute the MSE values for the recovered signal segments in 1.3.1 with respect to those of signal A
for different choices of parameters you have used.
2.3.2 Plot the de-noised signal segments in 1.3.1.

2.4.1 Compute the MSE values for the recovered signal segments in 1.4.1 with respect to those of signal A
for different choices of parameters you have used.
2.4.2 Plot the de-noised signal segments in 1.4.1.

2.5.1. Provide a table summarising the results (MSE for each segment) from above three methods.

The marking scheme is as follows:

Assessment 1:
1.1.1-1.1.3 10 marks
1.1.4 10 marks
1.2.1 10 marks
1.2.2 10 marks
1.3.1 15 marks
EEE 6209 Advanced Signal Processing
Coursework 2020/21
Dr Charith Abhayaratne

3
1.3.2 15 marks
1.4.1 10 marks
1.4.2 10 marks
1.5.1 10 marks

Assessment 2:
2.1.1-2.1.5 (accuracy) 20 marks
2.4.1-2.2.2 (accuracy) 20 marks
2.4.1-2.3.2 (accuracy) 30 marks
2.4.1-2.4.2 (accuracy) 20 marks
Quality of figures/tables 10 marks
Overall marks = 0.7x(Assessment 1) + 0.3x(Assessment 2)

USEFUL ADVICE
- MSE definition

where L is the signal length.

- DO NOT provide Matlab codes with your report. Instead use pseudo codes to explain your
methodology and algorithms.
- DO NOT copy and paste any text from this document. Doing so will result in a matching in
Turnitin reports leading to deduction of marks by the department’s plagiarism assessment
committee. Therefore just use the question numbers, 1.1.3, 1.1.2,….. etc
- All your numerical answers should include the appropriate units. All the plots should include x-axis
label, y-axis label, plot title and a legend if you are showing multiple plots in the same figure. Not
following these instructions may result in penalty marks.

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关键词一关键词二关键词三关键词四关键词五和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 EMESTER1 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FE 825 IERG 5130 STAT 8178 EE161 ADS2 G54FUZ CS326 113B ECN 226 QBUS6860 ELEC3202 COMP9334 MGT11B MET CS 777 STA238 00099F Python SMM306 CMT206 EC116 SP MAST90084 CS480/680 BCPM0091 CMPT 300 ECON2209 FY20 STAT 432 CVEN9625 MATH1712 CMP9771M CMP3750M CIS 325 AI506 MAT344H1S MGT7180 PSTAT 160B CSYS5010 1D STAT 453/558 ME152B STA304H1F CSCI-128 ME428 CO3002 FINE 7640 COMP3234 ECSE 223 EE497 – 3D MATH5905 ECS648U ENG5009 MATH10242 EC665 A CPI221 ISOM5220 MACE40402 MGT B456F COMP9315 AFM 113 STAT 778 CS 564 MA231 CS 230 CS 124 COMP5511 CSC317 ITEC1010 ELEC207 INFT 3019 7CCMFM06 EE 161 5G ECLT5810 MATH 7349 CS526 O2 COMP90054 ELE00063M ACCT5034 ARC1015 CSCI 40 CSCI E40 FIN 524B CS 462 389COM CMT309 MFIN703 COMP4250 STA304 STA304H1S STA 1003 FIN 448 BUFN 732 CS520 CVEN3701 ECMT6006 PHAS0100 CSIT314 MDIA 5028 CSCI 2134 MAT 451 CMPT 459 STAT 457 INFO20003 COMP4141 IELE8066 F540 ECM9 CSE 6321 FIN 430 MATH3911 BFC5916 COMP1011 EEE 6209 STATS 240 CW3 COMP206 COMP1003 4331W ISE 530 CMPT 295 MATH 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25579 1ECE 273 M122 COMP4434 1ELEC ELEC5620M STAT0010 BFF2401 MXB361 MATH3811 CS330 CMT307 FIT1033 CptS 583 UP 431 MATH6005 AF5343 CS2435 ECOM30002 ES50061 CS 2506 ECMT6002 CODE 00099F MT4539 ESSAY ICM317 MATH1231 MFIT5010 BMA547 MATH334 CIVL2201 MATH39032 T-04 CIVE97119 EMESTER CAB302 CAB301 59PM STAT802 ISYS3412 RM3 COSC 2123 AcF 311 MATH2022 1FINAL 4CCS1PHC ICM 142 ENVS3023 Math 4023 I1 ACSE8 FIN B385F CS-275 STA 4109-01 A31021 EE140 MT1 EE124 ECO-6004B Inf2-IADS CS126 IS2184 UL18 MATH5092 ECOM009 COMP 6211 ELEC202 MKT3019 IAB230 FIT5046 584-S21 COMP3210 CO250 A29401 MATH 136 MGT6174 ECMT6007 DATA2001 MAT 443 MATH10222 PHDE0071 ELE00123M COSC1295 MAT00045H MATH32032 1004GRC MAS223 COMP10001 DATA3404 CIVL2110 STAT2008 FNCE10002 EEE6431 COM6515 COMP9120 NBS8185 ECTE942 ECON 405 MATH314301 STAT 2011 COMPSCI 350 COMP20007 CMPSC-132 ECON5002 STA 35 MTH6108 FIT5147 MTH6150 100C FINA 5260 DESC9115 ECON4998 NBS8257 GMM ECON 6070 BSA2 ECE599 STATS 102B STA120 INFS601 BFC3241 MATH20602 COMP532 MAS275 COMP5318 COMP4002 FIT5086 MAST20004 FIT3152 FIT5129 CIS2380 CS521 COMP 1005 EECMS CITS5501 ECE 462 ECON 6002 ENG 4051 CSC 648 CMT654 MECHENG 713 PHYS1002 AMME 2000 FP0060 DDES3200 MECHENG 743 BINF90002 FIT2081 ISYS90081 ELECTENG 722 BUS 360 ECOS3010 BUS2810 ETF2100 ENG2048 STAT5002 COMP2017 CEME 2003 MATS3004 COMP9319 PHYS1001 COMP5349 AMME2261 COMP-381 FIT3173 COSC2757 ENG4025 QBUS2820 COMP9414 MATH2021 EOSC118 MFIN6690 PMATH 321 CSE-141L MATH575A COMPSCI5002 COMP1511 management comp2123 FINS3616 COMP 3331 COMS30034 DTSC 71 AcF 702 AcF702 DTSC71 MAT021 ELE8083 ACFI213 EG-127J 127J FIT5163 FIT2001 ELE00113M CMT310 CS 245 ENGL 1101 MTH6115 EE497 CS 325 Math 124A MTH5130 MBA502 MGT 5380 ARCH 1261 F033800 20LLP109 ELE4009 FINANCIAL COMP3331 Computer ECE4179 20T3 COMP9311 COMP9311 UNSW MFIN6210 ACCT 5930 EE590 ENG2029 ECO100 SIT315 ACCT3610 COMP3600 STK2100 FIT2004 COMP30020 ACTL1101 Stat 101B BANA 200 COSC2473 ENGG1400 AI COMP90043 MMF 1914H PHYS 127 CS584 CIVL2010 ECMT2150 CS 333 CP1406 KIT308 Economics 701 ECON3740 ECE 5759 BFC3340 FINC2012 COMP4500 MATH3871 FIT3080 18-819C Economic ECO202Y5Y PHY100 FINC-780 Econ 100B EC602 IE582 EE450 COMP9517 WRDS 150B ECON 570 LECTURE 2 ECA5101 ECE 4420 ADAD9311 FIT3031 CS 544 MF 703 CS 6140 KIT501 ENGSCI 344 CSC171 IE 332 FINM8007 21S2 COSC 1114 FE545 E471 SIT102 CSC108 MAT301 IAE 101 COP3503 STAT2004 EC 303 CS1026 STAT1203 MATH3171 ISOM5160 IFN657 FIT2014 ACCT2102 DSCI550 STATS 330 ELEC421 COMP3670 QBUS6840 MEES:698B MAST30027 MIET1077 Economics IFT 6758 FIT5122 BUSN5101 CptS 111 HRMT5502 Math2018 PP 312 INFS3200 CZ3005 CSSE1001 SENG 2011 MKTG 553Q BUFN 640 COMP507 ETF5952 DATA 311 ECE4043 BANK3011 finance M3H623544 HW1 INFS 7410 AI6103 STAT3888 INFS3208 ACCT7103 ECOS3013 PSYCH 20B F79MA STAT3006 ENGG952 2X MGSC 7000 ECE4132 MSF 526 MATH 3033 CDS 511 EEEE4120 EEET2465 ECA5103 MATH2070 ELEC3104 BU51019 EEE8147 ENGR20003 COSC 1107 ETF3200 FNCE 435 MSFI 435 ISyE6420 GENG4405 HW ETC5523 IE 7315 MOEC0021 STA 106 ENGG 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EMATM0051 COMP220 BLAW1002 E20 TCP8001 ECON61001 EART40005 ECONM1022 MATH 365 MATH 367 ELEC6218 COMP 5812M EDUC 5061M ELEC ENG 1101 equation MATH6174 ST334 C4 EC9570 A33648 EBUS603 ECON47815 stata WFMS0001 PEM102 INF6060 ionically ELECTRICAL MATH 375 MMW 12 MKTG860 ECON60401 ALY6010 SOST20131 Introduction COM3524 MME2 GGR305 JNB 538 EECS101 COMM5030 IEOR E4601 2B CSC336 EN2315 LAW 432 CSC263 MATH2001 OLET1143 CSCB63 SIT772 COMM1170 STAT 441 MS930 INFT3050 GLBH0030 Stat 120B MAST10007 MGEC61 Machine GGR 252H1S ECON7030 Financial Marketing MGMT30019 ECON5120 SDSC 8009 CSC165H1S DSCI 551 3081W ACM41000 ECA 5304 MATH 1064 ECSE444 DATA 604 COMP7105 MIE1615 ECON4004 ENV200H1S EC481 MKT 306 EC1B1 MATH08051 ALY6140 CS260C CSE30 MSIN0226 CS4103 CSC3064 AM16 COMP11212 CSE4101 PSTAT 174 Math 380W CNT 5106C STAT 310 FIN2028 CS5014 STAT 425 COMP 4102A ELEC2140 MANF9544 QRM 9 QRM 8 ECON 457 COMPSCI 4091 MATH 523 DNSC-4211 PEAS 6000 MSIN0180 COMP2119 A2A HT 2022 IB93F0 MAS316/414 ACCT2522 MSIN0181 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