EE2S31 Signal Processing
Introduction
Signal processing plays an important role in many applications, such as consumer electronics (mp3 player, mobile phone, CD player, TV (HD)), radar and medical applications. This course covers two topics: an introduction into random signals (following the course Probability and Statistics, EE1M31), and a first course on digital signal processing (following the course Signals and Systems, EE2S11).
For study guide information (teaching goals, etc.), see Study guide.
In this course the following topics are discussed:Digital signal processing
The part on signal processing considers in particular onedimensional signals and discusses digital filter design, filter structures, the DFT spectral analysis, filter implementation, and multirate filters. Repetition: (discretetime) signal processing, poles and zeros, filter functions
 Nonideal sampling and reconstruction
 Sampling in the frequency domain, the Discrete Fourier Transform
 Spectral analysis and filtering using the DFT
 Efficient computation of the DFT: the FFT
 Digital filter structures based on allpass filters
 Quantization and rounding errors in filters
 Analogtodigital conversion using sigmadelta modulation
 Multirate signal processing
Stochastic processes
The part on stochastic processes introduces the concept of stochastic models and random processes to describe systems and signals that are not deterministic in nature. Pairs of random variables
 Random vectors & conditional probability models
 Sums of random variables,
 Derived random variables
 moment generating function
 central limit theorem.
 Deviation of RVs from its expected value:Markov ineq., Chebyshev ineq. and the Chernoff bound.
 Sample mean, unbiased estimators, consistency.
 Estimation of Random variables, blind estimation, conditional estimation, MMSE, MAP and ML estimators.
 Stochastic processes.
 Estimation of autocorrelation functions, ergodicity, the autocorrelation function & signal processing for WSS signals.
 The autocorrelation function & signal processing, PSD, CPSD & frequency domain relationships.
Exam
The examination of the course is conducted in two parts (in the middle and at the end of the quarter). Both partial exams contain a 50/50 distribution of questions on signal processing and stochastic processes.The final grade is the average of the two partial exam results. This calculation is done with one decimal place, and the final grade will be rounded to half a digit. The reexamination is conducted in one part (over all lecture material). The result of the partial exams expires for reexamination and is not transferable to the next year.
Be sure to register for each partial exam on Osiris!
The exams are closed book. You are permitted to bring one A4size page (2 sides) of HANDWRITTEN notes.
Books
 Stochastic processes: R.D. Yates and D.J. Goodman,"Probability and Stochastic Processes, A Friendly Introduction for Electrical and Computer Engineers", 3rd edition, 2014. The TU Delft Library has an ebook version that you can access online (you will need to login using your TU Delft email address).
 Signal Processing supplement, which belongs to Stochastic processes: R.D. Yates and D.J. Goodman,"Probability and Stochastic Processes", 3rd edition, 2014.
 Digital Signal processing: J.G. Proakis and D.G. Manolakis, "Digital signal processing, principle, algorithms and applications", 4th edition (Pearson international edition).
A solution manual including answers to some of the problems in "Probability and Stochastic Processes" can be downloaded from here.
Most classes have been videorecorded in Collegerama in 2016. Since then, some things changed. We are rerecording the lectures in 2022, so these will become available as the course progresses.
Teachers
prof.dr.ir. AlleJan van der Veen (AJV) and dr. Borbala Hunyadi (BoH).
Program
The program for Spring 2022 is as follows:SP refers to classes on stochastic processes, and DSP refers to classes on digital signal processing.
Date  Content  Exercises  Chapter  Slides  Collegerama 2016 
Collegerama 2022 


1.  20/4/2022  AJV  Introduction. Pairs of random variables 
5.1.1, 5.2.1, 5.2.2, 5.3.2, 5.4.1, 5.5.3, 5.5.8, 5.5.9, 5.7.9, 5.7.13, 5.8.3, 5.9.2 
SP: Ch. 5 
welcome SP 1  EE2S31_SP1  EE2S31_01 
2.  22/4/2022  BoH  DSP: Introduction and examples 
6.1, 6.2, 6.3, 6.4, 6.5  DSP: Ch. 1 
DSP 1 
EE2S31_DSP1  EE2S31_02 
3.  25/4/2022 
AJV  Random vectors & conditional probability models 
7.1.1, 7.2.3, 7.2.9, 7.3.1, 7.3.3, 7.3.5, 7.3.9, 7.5.1, 7.5.3, 7.5.5 
SP: 
SP 2  EE2S31_SP2  EE2S31_03 
4.  29/4/2022 
BoH  DSP: (Nonideal) Sampling and Reconstruction 
6.6, 6.9, 6.10, 6.11, 6.12, 6.13, 6.14, 6.15, 6.24 
DSP: 6.1 t/m 6.5 
DSP 2  EE2S31_DSP2  EE2S31_04 
5.  2/5/2022 
AJV  Sums of random variables, derived random variables, moment generating function, central limit theorem. 
6.2.1, 6.2.5, 6.2.7, 9.2.1, 9.2.3, 9.3.3, 9.3.5, 9.3.7 10.2.1, 10.2.3, 10.2.5, 10.3.1 
SP:  SP 3  EE2S31_SP3  EE2S31_05 
6.  4/5/2022 
AJV  Estimation of random variables, blind estimation, conditional estimation, MMSE, MAP and ML estimators 
12.1.312.1.5, 12.2.1, 12.2.3, 12.2.5, 12.3.3, 12.4.3 
SP: 
SP 4  EE2S31_SP4  EE2S31_06 
7.  6/5/2022 
BoH  DSP: Sampling in frequency domain, Discrete Fourier Transform (DFT) 
7.1, 7.7, 7.11, 7.12, 7.13, 7.23 
DSP: 7.1  7.2 
DSP 3  EE2S31_DSP3 from 58:00  EE2S31_07 
8.  9/5/2022 
BoH  DSP: Spectral analysis and filtering using DFT 
7.2, 7.3, 7.6, 7.8, 7.9, 7.14, 7.15, 7.21 
DSP: 7.3, 7.4, 10.2 
DSP 4

EE2S31_DSP4 (from 1:12:45)  EE2S31_08 
9.  11/5/2022 
AJV  Exercise session 

SP 
SP Exercise  EE2S31_09  
10.  13/5/2022  BoH  DSP: Efficient implementation of the DTF: FFT. Exercises 
8.1, 8.3, 8.4, 8.7, 8.8, 8.11, 8.19, 8.20 
DSP: 8.1, 8.2 
DSP 5

EE2S31_DSP5 from 33:20 to 41:30  EE2S31_10 
17/5/2022  Exam (part 1). In 2022: the FFT is examined in part 2. 

11.  20/5/2022  BoH  DSP: Quantization and roundoff effects 
6.18, 9.31, 9.32, 9.33, 9.34, 9.35, 9.38

DSP: 6.3, 9.4  9.6 
DSP 6  EE2S31_DSP7  EE2S31_11 
12.  23/5/2022  AJV  Stochastic processes 
13.1.1, 13.3.1, 13.7.1, 13.7.3, 13.7.5, 13.9.3, 13.9.5, 13.9.7, 13.10.1, 13.10.3 
SP: Ch. 13 (except 13.4 13.6) 
SP 5 
EE2S31_SP5  EE2S31_12 
13.  25/5/2022  AJV  Estimation of autocorrelation functions, ergodicity, the autocorrelation function & signal processing for WSS signals. 
Supplement: 1.1, 1.3, 2.1, 2.3, 2.5, 2.7 
SP: Supplement sections 1 and 2 
SP 6  EE2S31_SP6  
14.  1/6/2022  AJV  The autocorrelation function & signal processing, PSD 
supplement: 5.1, 6.1 
SP: supplement sections 5 and 6 
SP 7  EE2S31_SP7  
15.  3/6/2022  BoH  DSP: Sigmadelta modulation 
6.16, 6.18, 6.20 
DSP: 6.6 
DSP 7  EE2S31_DSP7  
16.  7/6/2022  AJV  PSD, CPSD, frequency domain relationships. 
supplement: 7.1, 8.1, 8.3, 8.5 
SP: supplement sections 7 & 8 
SP 8 
EE2S31_SP8  
17.  10/6/2022  BoH  DSP: Multirate signal processing 
11.1, 11.5, 11.9, 11.10, 11.11, 11.12 
DSP: 11.1 t/m 11.8 
DSP 8 
EE2S31_DSP9  
18.  15/6/2022  AJV  SP: Exercise session 

19.  17/6/2022  BoH  DSP: Exercise session 


20.  cancelled 
DSP: Lattice filter structures (not in 2022) 
9.12,9.13, 9.15 
DSP: 9.2.4, 9.3.5 
DSP 9  
24/6/2022  Exam (part 2)  
20/7/2022  Resit 
Past exams
Exam (complete) of July 2021, with Solutions.Exam (part 2) of July 2021, with Solutions.
Exam (part 1) of May 2021, with Solutions.
Exam (complete) of July 2020, with Solutions.
Part 2 exam of July 2020, with Solutions.
Part 1 exam of May 2020, with Solutions.
Exam (complete) of July 2019, with Solutions.
Part 2 exam of July 2019, with Solutions.
Part 1 exam of May 2019, with Solutions.
Exam (complete) of July 2018, with Solutions.
Part 2 exam of July 2018, with Solutions.
Part 1 exam of May 2018, with Solutions.
Part 2 exam of July 2017, with Solutions.
Part 1 exam of May 2017, with Solutions.
Exam (complete) of July 2016, with Solutions.
Part 2 exam of June 2016, with Solutions.
Part 1 exam of May 2016, with Solutions.