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1. Evaluate DSP and microcontroller architectures for an embedded system application.
2. Create mixed programming language projects for the realisation of an embedded DSP application.
3. Build embedded software and hardware for the acquisition and generation of analog signal (A/D & D/A conversion).
4. Produce digital signal processing applications on a microcontroller based system.
Contemporary digital design consists of many subsystems consisting of complex digital hardware interconnected together. Each part has its own requirements and properties. Moreover digital systems need to communicate an interface to the external world. The aim of this unit is to consolidate the theoretical aspects of hardware description languages learned previously with the practical aspect of implementing digital systems. The unit covers the design methodology of high level digital functions and implementation aspects such as timing and power consumption. The unit closes with an in depth review of the latest FPGA technology and trends offered by the major industry players.
This module combines previous knowledge of analog electronics together with other disciplines in order for the student to be able to design a circuit to solve a problem. The student is introduced to advanced techniques which are used in precision design. These types of designs are required for high performance analog circuits. This unit also deals with signals that are buried in noise and deals with the characteristics of noise. An overview of the effects of noise and interference are mitigated is discussed. Many systems operate in the digital domain hence the process of analog to digital conversion and vice versa are described with the relevant effects on the analog signal. Students are introduced to high level design of analog systems using Field Programmable Analog Arrays (FPAA). The main objective of this unit is to show applied theory with the aid of case studies and schematics. A number of experiments are performed to demonstrate these concepts.
Artificial Intelligence (AI) techniques are useful in enhancing research findings or used as tools in finding pieces of information which would have eluded conventional research. The field of artificial Intelligence ranges from the realm of rule based expert systems, machine learning and inference techniques and neural nets who take their inspiration neuroscience. Recently the field of deep learning has found a resurgence with the increase of computing power and large amount of datasets stemming from shared information on the Internet.
Before processing data through deep learning networks the information needs to be suitably processed to remove artifacts and isolate features that are the critical parameters for learning. Hence the need arises for signal conditioning raw information. The signal processing domain requires knowledge in Fourier processing, sampling techniques, Filter design and adaptive filtering.
This unit combines these two fields together so a signal processing chain is created from sampling to pre-processing and subsequent inference. These techniques need to be implemented in a programming language and in this case MATLAB is used.
The unit is split into three parts. It starts with the basics of MATLAB and Simulink, progresses with a detailed look at Artificial Intelligence especially deep neural networks and the final section is composed digital signal processing.