| Week |
Subject |
Related Preparation |
| 1) |
• Syllabus.
• Filters
• Signals and their frequency domain representation, Bode plots.
• Digital Filters, analog filters
• Low pass, band pass and high pass filters
• Band stop filters
• Real filters and their characteristics: passband ripple, stopband ripple, transition region.
|
Review the Syllabus.
Acquire a copy of the book.
Review some of the topics covered in the first half of the book, in particular section 4.5 and Chapter 8
|
| 2) |
Structures for realization of discrete-time systems
• Structures for FIR systems
o Direct-form structures
o Cascade-form structures
o Frequency-sampling structures
o Lattice structure
• Structures for IIR systems
o Direct-form structures
o Signal flow graphs and transposed structures
o Cascade-form structures
o Parallel-form structures
o Frequency-sampling structures
o Lattice and lattice-ladder structures for IIR systems |
Begin reading chapter 7.
Review problems solved in class and other problems in the book
|
| 3) |
• Review of the z-transform.
• State-space system analysis and structures
o State-Space Descriptions of Systems Characterized by Difference Equations
o Solution of the State-Space Equations.
o Relationships Between Input-Output and State-Space Descriptions,
o State-Space Analysis in the z-Domain,
o Additional State-Space Structures. |
Continue reading chapter 7, also review chapter 3.
Review problems solved in class and other problems in the book.
|
| 4) |
• Representation of Numbers
o Fixed-Point Representation of Numbers
o Binary Floating-Point Representation of Numbers.
o Errors Resulting from Rounding and Truncation.
• Quantization of Filter Coefficients.
o Analysis of Sensitivity to Quantization of Filter Coefficients.
o Quantization of Coefficients in FIR Filters.
• Round-off effects in digital filters
o Limit-Cycle Oscillations in Recursive Systems.
o Scaling to Prevent Overflow,
o Statistical Characterization of Quantization Effects in Fixed-Point Realizations of Digital Filters.
|
Finish reading chapter 7.
Review problems solved in class and other problems in the book.
|
| 5) |
• Random variables
• Random processes
• probability density functions,
• expectation,
• autocorrelation,
• stationary random processes
• ergodic random processes |
Read Appendix A and B in the textbook
Review problems solved in class.
|
| 6) |
• Sampling of Bandpass Signals
o Representation of Bandpass Signals
o Sampling of Bandpass Signals
o Discrete-Time Processing of Continuous-Time Signals
• Analog-to-Digital Conversion
o Sample-and-Hold.
o Quantization and Coding, |
Start reading chapter 9 in the textbook
Review problems solved in class, review all problems in chapter 9
|
| 7) |
• Analog-to-Digital Conversion
o Analysis of Quantization Errors,
o Oversampling A/D Converters,
• Digital-to-Analog Conversion
o Sample and Hold,
o First-Order Hold.
o Linear Interpolation with Delay,
o Oversampling D/A Converters, |
Continue reading chapter 9 in the textbook
Review problems solved in class, review all problems in chapter 9
|
| 8) |
Multirate signal processing
• Introduction to multirate digital signal processing
• Decimation by a factor D
• Interpolation by a factor I
• Sampling rate conversion by a rational factor
|
Read chapter 10 in the textbook
Review problems solved in class, review all problems in chapter 10.
|
| 9) |
• Filter design and implementation for sampling rate conversion
o Direct form FIR filter structures
o Polyphase filter structures
o Time-variant filter structures
• Multistage implementation of sampling rate conversion
• Sampling rate conversion of bandpass signals
• Sampling rate conversion by an arbitrary factor
• Application of multirate signal processing |
Read chapter 10 in the textbook
Review problems solved in class, review all problems in chapter 10.
|
| 10) |
Linear Prediction and Optimum Linear Filters
• Innovations representation of a stationary random process
o Rational power spectra
o Relationships between the filter parameters and the autocorrelation sequence.
• Forward and backward linear prediction
o Forward linear prediction
o Backward linear prediction
o The Optimum Reflection Coefficients for the Lattice Forward and backward predictors
o Relationship of an AR Process to Linear Prediction.
• Solution of the normal equations
o The Levinson-Durbin Algorithm.
o The Schiir Algorithm.
• Properties of the linear prediction error filters
|
Start reading chapter 11 in the textbook
Review problems solved in class, review all problems in Chapter 11 |
| 11) |
• Explain what AR, MA and ARMA stand for, and why a process may be modeled as such.
• Describe a Wiener filter.
• Derive the coefficients of an FIR Wiener filter for a given process.
• Define the Linear mean-square estimation problem.
• Explain the orthogonality principle and how it is used to find the coefficients of the LMMSE estimator.
• Describe the IIR and non-causal Wiener filters and how these are derived.
|
Read chapter 11 in the textbook
Review problems solved in class, review all problems in Chapter 11
|
| 12) |
Power spectrum estimation
• Estimation of spectra from finite-duration observations of signals
o Computation of the Energy Density Spectrum.
o Estimation of the Autocorrelation and Power Spectrum of Random Signals: The Periodogram.
o The Use of the DFT in Power Spectrum Estimation,
• Nonparametric methods for power spectrum estimation
o The Bartlett Method: Averaging Periodograms,
o The Welch Method: Averaging Modified Periodograms,
o The Blackman and Tukey Method: Smoothing the Periodogram,
o Performance Characteristics of Nonparametric Power Spectrum Estimators,
o Computational Requirements of Nonparametric Power Spectrum Estimates,
|
Read chapter 12 and the handouts in the textbook
Review problems solved in class, and all problems in Chapter 12.
|
| 13) |
• Parametric Methods for Power Spectrum Estimation
o Relationships Between the Autocorrelation and the Model Parameters,
o The Yule-Walker Method for the AR Model Parameters,
o The Burg Method for the AR Model Parameters,
o Unconstrained Least-Squares Method for the AR Model Parameters,
o Sequential Estimation Methods for the A R Model Parameters,
o Selection of AR Model Order,
o MA Model for Power Spectrum Estimation,
o ARMA Model for Power Spectrum Estimation,
o Some Experimental Results,
• Minimum Variance Spectral Estimation |
|
| 14) |
• Eigenanalysis Algorithms for Spectrum Estimation
o Pisarenko Harmonic Decomposition Method,
o Eigen-decomposition of the Autocorrelation Matrix for Sinusoids in White Noise,
o MUSIC Algorithm.
o ESPRIT Algorithm,
o Order Selection Criteria.
o Experimental Results,
|
Read chapter 12 and the handouts in the textbook
Review problems solved in class, and all problems in Chapter 12.
|
| 15) |
• Unitary Transforms, Wavelets and Their Applications
o Karhunen-Loeve Transform
o Discrete Cosine Transform
o Wavelet Transforms
o Subband coding |
Read chapter 12 and the handouts in the textbook
Review problems solved in class, and all problems in Chapter 12. |
| |
Program Outcomes |
Level of Contribution |
| 1) |
Awareness of professional and ethical responsibility. |
|
| 2) |
Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied information in these areas to model and solve engineering problems. |
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| 3) |
Ability to communicate effectively i Turkish, both orally and in writing; knowledge of a minimum of one foreign language. |
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| 4) |
Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development. |
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| 5) |
Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way so as to meet the desired result; ability to apply modern design methods for this purpose. (Realistic constraints and conditions may include factors such as economic and environmental issues, sustainability, manufacturability, ethics, health, safety isuues, and social and political issues according to the nature of the design.)
|
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| 6) |
Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually. |
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| 7) |
Ability to devise, select, and use modern techniques and tools needed for engineering practice; ability to employ information technologies effectively. |
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| 8) |
Ability to identify, formulate, and solve complex engineering problems; ability to select and apply proper analysis and modelling methods for this purpose. |
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| 9) |
Knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of engineering solutions. |
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| 10) |
Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself. |
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| 11) |
Ability to design and conduct experiments, gather data, analyse and interpret results for investigating engineering problems. |
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ISTANBUL OKAN UNIVERSITY INFORMATION PACKAGE / COURSE CATALOGUE
