DCU Electronic and Computer Engineering
Electronic Engineering Modules
Unlock your career potential: Sign up for an Electronic and Computer Engineering postgrad module pair at DCU!
Overview
In today's ever-evolving technological landscape, staying up-to-date with the latest advancements is crucial for engineering professionals. The field of electronic and computer engineering is constantly evolving, with new technologies and innovations emerging at a rapid pace. However, having the time, support and opportunity to take on a full Masters programme to gain the necessary upskilling is often not possible. At DCU we have a solution to support your flexible part-time short-form learning needs. From our upwards of 30 modules available at postgrad level that represent recent developments in electronic systems and computer engineering, we have selected some module pairs that together can give you a firm foundation in a niche technical or professional area and accelerate your career development.
There would be no academic award for taking these pairs of modules on their own, but you get an official DCU transcript and retain the credits gained for future use if you wish as part of a Grad Diploma or Masters award. All the modules are presented on-campus, but available for remote/online access as well. Depending on the module pairs you chose, they may be taken in the same or successive semesters. Other module pairs are available depending on your interests. There are generally three hours of recorded lectures per week, but you should assume a minimum time commitment for each module of about 10 hours per week for the 12-week semesters. Both September and January starts are possible, but the semesters in which the modules are presented are fixed.
The topics for the module pairs include:
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Engineering Management and Entrepreneurship
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Future Data Networks
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Maths and Wireless Communications
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Photonic Devices and Technologies
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Computer Vision
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Connected Embedded Systems (IoT)
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Foundations of Machine Learning
The module pairs have been selected to equip engineering professionals with the skills and knowledge necessary to excel in their respective fields, and can also be a gateway towards further learning, a Grad Diploma or Masters award.
Combined Module Pair Options
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EE470 |
Introduction to Engineering Management |
Semester 2 |
This course aims to provide a strong understanding and knowledge of engineering management in product development, manufacturing, and other industrial, commercial, and public contexts. Students will learn to recognise different types of engineering projects and the associated project frameworks and methodologies to used. The students will develop an understanding of the connections between business strategy, economics, and engineering operations and gain an enhanced awareness of ethics and compromises related to engineering management.
Core skills such as multidisciplinary project management, project costing, and communication skills are introduced. Students will be introduced to common pitfalls where projects fail and how to avoid them. The theory of key project management skills is core to this course and students will engage with the Six Sigma DMAIC 12 Step Process and the Change Acceleration Process (CAP) for leading effective change and stakeholder management. Challenge-based learning initiatives form an integral part of this module.
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EE507 |
Entrepreneurship for Engineers |
Semester 2 |
This module is designed to provide engineers with the knowledge and skills necessary to develop an entrepreneurial mindset and effectively navigate the world of business and innovation. This module aims to equip students with the fundamental principles of entrepreneurship, empowering them to identify opportunities, develop innovative solutions, and create value within engineering and technology contexts. Through a combination of theoretical concepts, practical exercises, case studies, and real-world examples, students will gain a comprehensive understanding of entrepreneurship and its application in engineering.
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EE521 |
Future Network Architectures |
Semester 2 |
This module delves into advanced concepts and emerging technologies that are shaping the future of network architectures. It aims to equip students with an in-depth understanding of the key trends, challenges, and innovations in network design, protocols, and technologies.
Upon completion of this course, graduates will possess the necessary skills to proficiently operate and design future data networks with aggregate capacities reaching the petabit/s range. The course will delve into the fundamental principles of network architecture design, focusing on achieving optimal scalability and operational efficiency. Additionally, students will develop expertise in a variety of acceleration techniques essential for transmitting and retrieving data at the anticipated wire speeds of tomorrow.
The course will extensively cover resource orchestration and network reconfiguration techniques within data center networks and at the network edge. Students will gain a deep understanding of these concepts and evaluate their practical applications in deploying virtual networks and network slices. By exploring these advanced topics, graduates will be well-prepared to tackle the challenges of next-generation data networks and contribute to their successful implementation.
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EE562 |
Network Stack Implementation |
Semester 2 |
This module introduces learners to the concepts involved in implementing network stacks. Network stacks refer to the software and firmware responsible for implementing computer networking protocol suites, such as TCP/IP over Ethernet.
The aim of the module is to introduce students to the software embedded in network devices such as routers to implement network protocols. Where possible, open-source implementations of protocols used in live networks will be studied. Both the data plane and the control plane will be studied, including data-link layer protocols, network layer protocols and transport layer protocols. Optimisation techniques, hardware acceleration and other approaches to achieving “wire speed” operation will be investigated. Protocols appropriate to the Internet of Things, to data centres, and to the future Internet will also be considered.
Upon completing this course, graduates will be able to describe the principles involved in implementing a network stack in software, decompose the software of “middleboxes”, demonstrate advanced theoretical knowledge of networking, and much more.
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EE488 |
Mathematical Techniques and Problem Solving |
Semester 1 |
In this module, students will explore the fundamental mathematical concepts and techniques that are essential for engineers in their problem-solving endeavors. Mathematics forms the backbone of engineering, providing the tools and language necessary to analyse and solve complex problems in various fields. While this module will cover mathematical techniques that are necessary to help you succeed on other masters-level modules, throughout the module, we will emphasise problem-solving strategies and practical applications, connecting the mathematical concepts to real engineering scenarios.
By the end of this module, students will have developed a strong mathematical foundation and problem-solving skills, enabling them to tackle complex engineering challenges with confidence. Whatever sector you work in, the mathematical techniques and problem-solving strategies covered in this module will be invaluable to your future engineering success.
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EE519 |
Wireless Communications in Fading Channels |
Semester 2 |
This module provides a comprehensive understanding of the challenges and techniques involved in designing and optimising wireless communication systems. During this course, students will explore the impact of fading channels on wireless communication and equip students with the necessary knowledge and skills to overcome these challenges. Fading channels refer to the phenomena in which the wireless signal experiences fluctuations in its strength and quality due to various environmental factors.
Students will gain a deep understanding of fading channels and their characteristics. They will explore the causes and types of fading, including Rayleigh fading, Rician fading, and Nakagami-m fading, among others. Students will examine diversity techniques that enhance the reliability and performance of wireless communication systems in fading channels. Students will delve into diversity combining techniques, such as maximal ratio combining (MRC), equal gain combining (EGC), and selection combining (SC). They will also explore the concept of Multiple-Input Multiple-Output (MIMO) systems and and space-time coding.
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EE506 |
Fundamentals of Photonic Devices |
Semester 1 |
This module provides a comprehensive exploration of semiconductor devices within the realm of photonic devices. Semiconductor devices play a pivotal role in various applications of photonics, including optical communication, sensing, and signal processing. This module aims to equip students with a solid understanding of the principles, design, and applications of semiconductor devices in the field of photonics. This knowledge is essential for engineers and researchers working in areas such as optical communication, photonic systems, and optoelectronic device development, enabling them to contribute to advancements in these rapidly evolving fields.
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EE518 |
Photonic Applications and Technologies |
Semester 2 |
This module will provide the students with knowledge, skills, competencies, and understanding of the telecommunications landscape and the role photonic technologies play in the operation of the heterogenous broadband networks.
Building on the fundamental knowledge on optical communications, a deep insight into the requirements, both from a technology and business/cost point of view, of different network segments and how these are met will be introduced. A series of case scenarios will be presented, providing the students with a practical knowledge of network design, operation and performance metrics.
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EE453 |
Image Processing and Analysis (Plus) |
Semester 1 |
Most people are familiar with the concept of processing an image to improve its quality or the use of image analysis software tools to make basic measurements, but what are the ideas behind such solutions? Why is knowledge of these concepts important in developing successful computer vision applications? This module will answer these questions by focusing on both the theoretical, mathematical and practical issues associated with a wide range of computer vision solutions.
This module will concentrate on helping students to develop the fundamental skills necessary to design, develop and understand a wide range of basic imaging processing (image to image), image analysis (image to feature), image classification (feature to decision), performance characterisation (data to quantitative performance indicators) and computer vision (image to interpretation) solutions. All solutions have limitations and a key element of this module is to focus on how to approach the design, testing and evaluation of successful computer vision applications within an engineering framework. This module will make extensive use of an image analysis development environment to reinforce all the issues covers during the lectures.
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EE544 |
Computer Vision |
Semester 2 |
Computer vision applications have significantly expanded over the last decade and this core skill set is always in high demand by employers. This module will build on the basic concepts with a view to delving deeper into core computer vision, machine learning and deep learning topics. As well as examining traditional computer vision concepts (i.e. feature extraction and machine learning) a key focus of the module will be on deep learning as applied to computer vision.
Students will examine the core concepts behind deep learning for computer vision with a specific focus on Convolutional Neural Networks (CNN). They will learn how to design and tune such networks in a range of practical applications and assignments. Furthermore, this module covers a wide spectrum of deep learning architectures, spanning from the inception of AlexNet to the cutting-edge advancements in this continuously evolving field.
Deep learning-based computer vision forms the core of many of the recent developments in this field and has been widely adopted as a core AI tool by all the key industrial players such as Google, Facebook, IBM, Apple, Baidu, as well as a wide range of highly innovative startups. All computer vision and deep learning concepts will be reinforced by guided practical work and case studies.
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EE402 |
Object Oriented Programming |
Semester 1 |
The Object-oriented Programming module is an essential component in preparing students for the engineering workplace. This module provides students with valuable experience in advanced aspects of object-oriented programming and allows them to apply design concepts in both the C++ and Java programming languages. By working on assignments that involve advanced embedded Linux based embedded systems, such as the BeagleBone, students have the opportunity to gain hands-on experience with systems-on-a-chip devices and TCP socket communications.
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EE513 |
Connected Embedded Systems |
Semester 2 |
Connected embedded systems are the building blocks of the Internet of Things (IoT). These systems not only interface with the physical world through sensors and actuators, but they also have the capability to communicate with each other and with cloud-based Platform-as-a-Service solutions. Embedded systems have undergone a technological revolution, particularly in the 1990s, which paved the way for the emergence of IoT. During this time, embedded system technology combined with the Internet to create a powerful and transformative force. The Internet of Things is an ecosystem where physical objects, embedded with sensors and communication technologies, can exchange data among themselves and with the cloud, enabling new levels of connectivity, automation, and intelligence.
This module aims to expose students to the latest research and solutions in the field of embedded systems for IoT including: embedded Linux, multi-platform SoC solutions, real-time interfacing, telemetry protocols for IoT, and messaging.
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EE488 |
Mathematical Techniques & Problem Solving |
Semester 1 |
In this module, students will explore the fundamental mathematical concepts and techniques that are essential for engineers in their problem-solving endeavors. Mathematics forms the backbone of engineering, providing the tools and language necessary to analyse and solve complex problems in various fields. While this module will cover mathematical techniques that are necessary to help you succeed on other masters-level modules, throughout the module, we will emphasise problem-solving strategies and practical applications, connecting the mathematical concepts to real engineering scenarios.
By the end of this module, students will have developed a strong mathematical foundation and problem-solving skills, enabling them to tackle complex engineering challenges with confidence. Whatever sector you work in, the mathematical techniques and problem-solving strategies covered in this module will be invaluable to your future engineering success.
+
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EE514 |
Data Analysis and Machine Learning |
Semester 1 |
This module is designed to equip students with the necessary skills and knowledge required for data analytics. It covers both fundamental and advanced techniques needed for data analytics, including data management, processing, summarisation, and predictive analytics. Through this module, students will develop a strong theoretical foundation in data analysis and machine learning. They will also gain hands-on experience in applying these techniques to real-world problems. The module focuses on using the Python programming language as a practical tool for demonstrating various techniques.
Requirements
General Entry Requirements
This non-award programme allows applicants from diverse educational and/or employment backgrounds to access Level 8 and Level 9 modules with applications evaluated on a case-by-case basis.
Non-EU Applications
Candidates who are non-native speakers of the English language must satisfy the university of their competency in the English language. For further information on international applications click here. Additionally, non-EU nationals who require a study visa to enter Ireland are ineligible to apply for part-time programmes.
Fees
The fees for the Academic year 2023-2024 are €824 per module.
Full information on DCU Fees, including the fees for the 'Single Module Programme - Engineering and Computing', is available here.
Next Steps
All Applicants must submit:
- All applicants should apply through https://dcuie.elluciancrmrecruit.com/Apply/. Here's a quick step by step guide if you need help with your application.
- Certified Academic Transcripts for each and every year of study to date, with certified English translations if applicable
- Please upload a CV under the "Supplemental Items & Documents" section.
- Personal statement.
- If applicable, evidence of competence in the English language as per DCU entry requirements. Please see link http://www.dcu.ie/registry/english.shtml.
Application Deadlines
Applications will be accepted on a rolling basis until the following dates:
- EU applications: Closing date for DC808 Semester 1 modules is 25th August 2023.
- Non-EU applications: Applications closed for DC808 Semester 1 modules.
- EU applications: Closing date for DC809 Semester 2 modules is 1st December 2023.
- All required documents in support of your application must be provided by this deadline.
Queries
Queries from EU and non-EU applicants should be directed to ee.queries@dcu.ie.
Commencement of Programme
- DC808 commences in September 2023.
- DC809 commences in January 2024.