Module Specification

The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
Title Advanced Signal Processing
Code ELEC474
Coordinator Dr AF Garcia-Fernandez
Electrical Engineering and Electronics
Angel.Garcia-Fernandez@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2021-22 Level 7 FHEQ Whole Session 15

Aims

To develop higher level signal processing techniques and apply them to some problems.

To develop different types filters and demonstrate their applications.


Pre-requisites before taking this module (other modules and/or general educational/academic requirements):

ELEC309 SIGNAL PROCESSING & DIGITAL FILTERING 

Co-requisite modules:

 

Learning Outcomes

(LO1) On successful completion of this module the student should be able to explain concepts of time and frequency domain descriptions of signals.

(LO2) On successful completion of this module the student should be able to describe,use and design 'fixed' filter for different types of noise reduction tasks.

(LO3) On successful completion of this module the student should be able to explainand use auto-correlation and cross-correlation.

(LO4) On successful completion of this module the student should be able to describe,use and design linear predictor and matched filter, and explain theirapplications.

(LO5) On successful completion of this module the student should be able to describe,use and design FIR Wiener filters for different tasks, and explain theirapplications.

(LO6) On successful completion of this module the student should be able to describe,use and design FIR adaptive filters, and explain their applications.

(LO7) On successful completion of this module the student should be able to describe,use and design Kalman filters.

(S1) Critical thinking and problem solving - Critical analysis

(S2) Numeracy/computational skills - Problem solving

(S3) Improving own learning/performance - Self-awareness/self-analysis

(S4) Research skills - Awareness of /commitment to academic integrity


Syllabus

 

Semester one
Chapter one: Knowledge Preparation
Chapter two-three: Noise Reduction
Chapter four: Auto-correlation and Cross-correlation
Chapter five: Linear Prediction and Matched Filter

Semester 2:
Chapter six: Knowledge Preparation
Chapter seven: Wiener Filter
Chapter eight: Linear Prediction and Noise Cancellation
Chapter nine: Adaptive Filter
Chapter 10 Kalman Filter


Teaching and Learning Strategies

Due to Covid-19, one or more of the following delivery methods will be implemented based on the current local conditions and the situation of registered students. It is anticipated that both a) & b) will be in operation for semester 1.

(a) Hybrid delivery, with social distancing on Campus

Teaching Method 1 - On-line asynchronous lectures
Description: Lectures to explain the material
Attendance Recorded: No
Notes: On average one per week

Teaching Method 2 - Synchronous face to face tutorials
Description: Tutorials on the Assignments and Problem Sheets
Attendance Recorded: Yes
Notes: On average one per fortnight

(b) Fully online delivery and assessment

Teaching Method 1 - On-line asynchronous lectures
Description: Lectures to explain the material
Attendance Recorded: No
Notes: On average one per week

Teaching Method 2 - On-line synchronous tutorials
Descrip tion: Tutorials on the Assignments and Problem Sheets
Attendance Recorded: Yes
Notes: On average one per fortnight

(c) Standard on-campus delivery with minimal social distancing

Teaching Method 1 - Lecture
Description: Lectures
Attendance Recorded: Yes

Teaching Method 2 - Tutorial
Description: Tutorials
Attendance Recorded: Yes


Teaching Schedule

  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours 24

  20

    4

48
Timetable (if known)              
Private Study 102
TOTAL HOURS 150

Assessment

EXAM Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
(474) Assessment 2 This is an anonymous assessment. Assessment Schedule (When) :Semester 2 examination period    75       
CONTINUOUS Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
(474.1) Assessment 1 This is an anonymous assessment. Assessment Schedule (When) :Semester 1    25       

Reading List

Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.