# II1303 Signal Processing 7.5 credits

### Offering and execution

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## Course information

### Content and learning outcomes

#### Course contents *

• Sinusoidal Signals
• Amplitude, Phase & Frequency
• Complex Exponential Representation (Phasors)
• Spectrum Representation of Signals
• Sinusoids, Harmonics Other Synthesis Examples: e.g., Chirp (FM) Signals
• Fourier Series: Synthesis & Analysis
• Digital Signals and Sampling
• Aliasing & Folding
• Reconstruction from Samples
• Moving Average Filters
• Finite-Length Impulse Response (FIR)
• Convolution
• Linearity & Time-Invariance
• Frequency Response
• Magnitude & Phase Responses
• Lowpass, Highpass & Bandpass Filters
• Z-Transform
• Method for FIR
• Zeros of the Transfer Function Polynomial
• Relationship to Frequency ResponseRecursive Filters
• Feedback Difference Equations
• Discretizing Differential Equations
• Impulse Response
• Z-transform for Recursive Filters
• Fourier Transform (Continuous-Time)
• Discrete-Time Fourier Transform
• FFT Algorithm
• Relationship between Continuous-Time and Discrete-Time Frequency Domains
• Short-Time Fourier Analysis & Spectrograms
• Bandpass Filter Banks

#### Intended learning outcomes *

Course Objectives

As part of this course, students will:

• understand mathematical representation of discrete-time and continuous-time signals
• will be introduced to signal processing and characterization techniques, such as filtering, frequency response, and transforms
• gain laboratory experience in computer-based signal processing.

Course Outcomes

Upon successful completion of this course, students should be able to:

• express signal processing systems in mathematical form
• write MatLab code describing a signal processing system
• analyze signals in terms of their frequency content.
• describe system behavior in terms of frequency content
• describe system behavior in terms of frequency response
• describe system behavior in terms of the Fourier Transform
• analyze mixed analog-digital systems with sampling operations and digital filters
• utilize the z
• transform to analyze discrete-time systems in terms of poles and zeroes
• use complex exponential notation to describe signals and systems
• describe how signal processing is used in applications (e.g., audio and digital image processing).

#### Course Disposition

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### Literature and preparations

#### Specific prerequisites *

Completed upper secondary education including documented proficiency in Swedish corresponding to Swedish B and English corresponding to English A. For students who received/will receive their final school grades after 31 December 2009, there is an additional entry requirement for mathematics as follows: documented proficiency in mathematics corresponding to Mathematics A. Specific requirements corresponding to Physics B and Chemistry A and mathematics corresponding to courses IX1303 and IX1304.

#### Recommended prerequisites

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#### Equipment

No information inserted

#### Literature

Signal Processing First, McClellan, Schafer, YoderUpplaga:  Förlag: Pearson Prentice Hall År: ISBN: 0-13-120265-0

### Examination and completion

A, B, C, D, E, FX, F

#### Examination *

• LAB1 - Laboratory Work, 3.0 credits, Grading scale: A, B, C, D, E, FX, F
• TEN1 - Examination, 4.5 credits, Grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

#### Opportunity to complete the requirements via supplementary examination

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#### Opportunity to raise an approved grade via renewed examination

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### Further information

#### Course web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Course web II1303

#### Offered by

EECS/Intelligent Systems

Technology

First cycle