ECE 4522/5514: Digital Signal Processing

Course Syllabus
Contact Information:

Lecture	W: 17:30 PM - 20:20 PM   (ENGR 309)
Lecturer	Joseph Picone, Professor     Office: EA 703A     Office Hours: (M) 8 AM - 10 AM, (W) 8 AM - 11 AM, (F) 8 AM - 9 AM, (F) 11 AM - 12 PM     Phone: 215-204-4841     Email: picone@temple.edu     Skype: joseph.picone
Social Media	https://www.facebook.com/groups/temple.engineering.ece4522/     temple.engineering.ece4522@groups.facebook.com
Email	http://groups.google.com/group/temple-engineering-ece4522     temple-engineering-ece4522@googlegroups.com
Website	http://www.isip.piconepress.com/courses/temple/ece_4522
Textbook	S. Orfanidis   Introduction to Signal Processing     URL: http://www.ece.rutgers.edu/~orfanidi/intro2sp/
Reference Textbooks	D. Manolakis and V. Ingle   Applied Digital Signal Processing, 2nd Edition     Cambridge University Press (1st Edition)     November 21, 2011, 1008 pages     ISBN: 978-0521110020     URL: Applied Digital Signal Processing (1st Edition)     A. Oppenheim and R. Shaffer   Discrete-time Signal Processing, 3rd Edition     Prentice-Hall     August 28, 2009, 1120 pages     ISBN: 978-0131988422     URL: Discrete-time Signal Processing (3rd Edition)     J. G. Proakis and D. G. Manolakis   Digital Signal Processing: Principles, Algorithms, and Applications     Prentice-Hall     April 7, 2006, 1006 pages     URL: Digital Signal Processing: Principles, Algorithms, and Applications (4th Edition)
Other Reference Materials	ECE 4773: Digital Signal Processing     MIT Open Courseware: Digital Signal Processing     dspGuru
Prerequisites	D- or better in ECE 3522

Lecture Grading Policies:

Item	Weight
Exam No. 1	10%
Exam No. 2	10%
Exam No. 3	10%
Final Exam	20%
Computer Assignments	40%
Homework Assignments	10%
TOTAL:	100%

In your undergraduate Signals and Systems course, you should have learned the basics of discrete-time systems (e.g., the Sampling Theorem, Fourier Analysis, digital filtering). In this course, we take a more advanced theoretical view of these topics and also reinforce them using C/C++ programming examples. Digital Signal Processing (DSP) is pervasive - any multimedia device or information source uses many aspects of DSP to achieve real-time information transmission. The goal of this course is to make you feel comfortable learning implementing new algorithms in run-time efficient systems. This requires a firm grasp of the theory and knowledge of how to efficiently implement that theory.

We will have three in-class exams in this course and a comprehensive final. Each in-class exam will be closed books and notes. You will be allowed one page (double-sided) of notes for the in-class exams. For the final exam you will be allowed four pages of notes, presumably the same notes you used for the in-class exams. The exams will resemble the homework problems, so it is important that you thoroughly study the homework problems.

Unannounced quizzes will be given periodically throughout the course to encourage you to attend lecture classes and keep up with the daily work. If you miss a quiz without a prior excuse from the instructor, you receive a zero for that quiz with no exception. Make-up quizzes will not be given. The same policy applies to in-class exams and the final exam as well.

Detailed homework solutions will be prepared in an 8.5"x11" notebook. You are required to use a three-ring binder with tab dividers labeled by the homework assignment number and type (e.g., "HW #7" or "CA #3"). These will be turned in during each exam and graded. Students are expected to prepare detailed solutions that include a problem statement and a well-documented step-by-step solution. Students can collaborate on homework solutions, but the solutions you provide must be unique. Grading will take into account the accuracy of your solution as well as the quality of your explanation. Simply providing answers with no explanations gets no credit.

There will be weekly computer assignments that involve C/C++ programming, and verification of your results in MATLAB. These will be based on real world data. A template for the solutions to be turned in is provided here: here. You must conform to the template provided. The easiest way to do this is to start an assignment by editing the template provided and to use the format painter tool in Word. Consult with your Technical Communications instructor if you have questions about how to do various things in Word or have questions about what is expected for content.

Lecture Schedule:

Though this course is scheduled for a once-a-week lecture, we will split this into three 50-minute lectures. We will cover the following topics:

   

Class	Date	Sections	Topic(s)
1	01/13	1.1 - 1.4	Sampling and Reconstruction:       Basic Components of a DSP System       Review of Analog Signals       The Sampling Theorem       Sampling of Sinusoids
2	01/13	1.5	The Spectra of Sampled Signals:       Discrete-Time Fourier Transform       Spectrum Replication       Antialiasing Filters
3	01/13	1.6 - 1.8	Analog Reconstruction:       Ideal Reconstruction       Ideal Interpolation and Sample Rate Conversion       Staircase Reconstructors and Postfiltering
4	01/20	2.1, 2.2	Quantization:       The Quantization Process       Oversampling and Noise-Shaping
5	01/20	2.4, 2.5	A/D Conversion:       Successive Approximation       Analog and Digital Dither
6	01/20	2.3	D/A Converters:       Unipolar Natural Binary Converter       Two's Complement Codes       Binary Data Files in Unix       Quiz
7	01/27	3.1 - 3.6	Discrete-Time Systems:       Linearity and Time-Invariance       Impulse Response       FIR and IIR Filters       Causality and Stability
8	01/27	4.1	FIR Filtering and Convolution:       Convolution       Direct Form       Graphical Convolution
9	01/27	4.2	Real-Time Filtering:       Programming Considerations       Buffering       Hardware Realizations
10	02/03	5.1 - 5.3	Z-Transforms:       Basic Properties       Region of Convergence       Causality and Stability
11	02/03	5.4, 5.5	Z-Transforms:       Frequency Spectrum       Inverse Z-Transforms
12	02/03	6.1 - 6.3	Transfer Functions:       Definitions       Sinusoidal Response
13	02/10	6.4	Pole/Zero Designs:       First-Order Filters       Parametric Resonators       Notch and Comb Filters
14	02/10	6.5	Deconvolution:       Inverse Filters       Stability
15	02/10	Exam No. 1	Chapters 1-4
16	02/17	7.1 - 7.4	Binary Representations:       ASCII and Binary Revisited       Finite Precision Filter Coefficients       Finite Precision Math
17	02/17	7.1 - 7.4	Digital Filter Realizations:       Direct Form       Canonical Form       Cascade Form
18	02/17	7.5, 7.6	Hardware Realizations:       DSP Architectures       Circular Buffers       Quantization Effects
19	02/24	8.1	Signal Processing Applications:       Waveform Generators       Table Lookups       Delays and Echoes
20	02/24	8.2	Digital Audio Effects:       Flanging, Chorusing and Phasing       Reverberation       Compressors and Expanders
21	02/24	8.3	Noise Reduction and Signal Enhancement:       Noise Reduction Filters       Notch and Comb Filters
--	03/02	Spring Break
--	03/02	Spring Break
--	03/02	Spring Break
22	03/09	8.3	Noise Reduction and Signal Enhancement:       Signal Averaging       Golay Filters
23	03/09	9.1 - 9.3	DFT / FFT Algorithms:       Frequency Resolution and Windowing       DTFT Computation       Resolution Revisitied
24	03/09	9.4 - 9.7	DFT / FFT Algorithms:       The Matrix Form       Modulo-N Reduction       Inverse DFT
25	03/16	9.8, 9.9	DFT / FFT Algorithms:       The Fast Fourier Transform (FFT)       Radix 2 Transforms       Bit Reversal and Shuffling       Fast Convolution
26	03/16	10.1 - 10.4	FIR Filter Design:       The Window Method       The Kaiser Window Method       Frequency Sampling Methods
27	03/16	11.1, 11.2	IIR Filter Design:       Bilinear Transform       First-Order Lowpass Filters       First-Order Highpass Filters
28	03/23	11.3, 11.4	IIR Filter Design Using Analog Prototypes:       Derivatives       Impulse Invariance       Bilinear Transform       Notch Filters       Equalizers
29	03/23	11.5, 11.6	IIR Filter Design Using Analog Prototypes:       Butterworth       Chebyshev       Elliptic Filters       Transformations
30	03/23	Exam No. 2	Chapters 5-8
31	03/30	12.1 - 12.4	Interpolation:       Sinc Functions (Review)       Linear Filtering       Polyphase Filters
32	03/30	12.5 - 12.7	Oversampling:       Noise Shaping       Differential Sampling       Sigma Delta Converters
33	03/30	N/A	Multirate Signal Processing:       Filter Banks       Downsampling       Quadrature Mirror Filters
34	04/06	N/A	Random Signals and Systems:       Random Processes       Correlation and Covariance       Gaussian Distributions
35	04/06	N/A	Linear Prediction:       Derivation
36	04/06	N/A	Linear Prediction:       The Autocorrelation Method       Prediction Error
37	04/13	N/A	Linear Prediction:       Digital Filter Interpretation       Lattice Methods       Coefficient Transformations       Quantization
38	04/13	N/A	Homomorphic Processing:       Transformations       The Complex Cepstrum       Relationship to Linear Prediction       Blind Source Separation
39	04/13	Exam No. 3	Chapters 9-12
40	04/20	N/A	Review:       Linear Systems       Z-Transforms       Fourier Transforms       Filters
41	04/20	N/A	Pattern Recognition and Machine Learning:       Introduction       Speech Recognition       Image Recognition
42	04/20	N/A	EEG Processing:       Feature Extraction       Event Detection       Spatial and Temporal Postprocessing
43	05/04	Final Exam	17:45 PM - 19:45 PM (Comprehensive)

Please note that the dates above are fixed since they have been arranged to optimize a number of constraints. If you have conflicts with other classes, such as too many exams within the same week, we need to resolve that the first week of classes.

Homework:

The textbook homework schedule is as follows:

HW	Due Date	Item(s)
1	01/20	1.2, 1.6, 1.7, 1.10
2	01/27	2.1, 2.3, 2.4, 2.6, 2.9, 2.10
3	02/03	3.1, 3.2, 3.3, 3.4
4	02/03	4.1, 4.3, 4.5, 4.15
5	02/10	5.2, 5.3, 5.8, 5.9
6	02/17	6.1, 6.2, 6.5, 6.13
7	02/24	7.1, 7.3, 7.4, 7.5
8	03/09	8.1, 8.2, 8.9, 8.17
9	03/16	9.1, 9.2, 9.3, 9.9
10	03/23	10.1, 10.5, 10.11, 10.15
11	03/30	11.1, 11.3, 11.11, 11.12
12	TBD	TBD
13	TBD	TBD

The computer assignment schedule is as follows:

CA	Due Date	Item(s)
1	01/20	Real-Time Audio
2	01/27	Signal Interpolation
3	02/03	Real-Time Filtering
4	02/10	Transfer Functions
5	02/17	Digital Filter Realizations
6	02/24	Digital Audio Effects
7	03/09	Spectral Analysis
8	03/16	Digital Filter Design
9	TBD	TBD
10	TBD	TBD
11	TBD	TBD
12	TBD	TBD

University Policy Statements:

• Any student who has a need for accommodation based on the impact of a disability should contact me privately to discuss the specific situation as soon as possible. Contact Disability Resources and Services at 215-204-1280 in 100 Ritter Annex to coordinate reasonable accommodations for students with documented disabilities.


• Freedom to teach and freedom to learn are 2 inseparable facets of academic freedom. The University has a policy on Student and Faculty and Academic Rights and Responsibilities (Policy #03.70.02) which can be accessed through the following link: http://policies.temple.edu/getdoc.asp?policy_no=03.70.02.


• Please review the university's academic dishonesty policy Students who are caught plagiarizing or cheating will receive a grade of 0 on the corresponding assignment for a first offense. A second offense will result in failure of the course.