The Key to this module is to broaden the student's knowledge on Industrial PLC Systems. This module will focus on advanced Ladder logic coupled with Human Machine Interface and Motor Control all of which will be tied into the PLC via a networked system. On Completion the Learner will be able to engage in complex system design and work on debugging and troubleshooting advanced PLC Problems.

1. Explain advanced PLC ladder logic programmes.
2. Design, implement and test HMI applications.
3. Implement motor control in conjunction with variable speed drives and the PLC.
4. Explain the key concepts behind networked system such as Melsec-net, Modbus, Profibus and CAN.
5. Design and test emergency stop functions with integration via the PLC.
6. Explain key areas where automation can be appropriately integrated with robotics.

The aim of this module is to give students the opportunity to undertake an independent research project in a selected area of Embedded Systems. Students will develop and complete a project on a research topic of their choice and will formulate a research plan in consultation with their supervisor. Students will also learn to conduct a critical
literature review and present their results in written and oral forms.The topic must be relevant to the engineering discipline being studied but can be chosen from a very broad range whether it be based in Industry or within the Institute

1. Describe the scientific, mathematical and engineering principles relevant to the project;
2. Conduct a technical literature review;
3. Identify specific research objectives and formulate a research plan;
4. Design an appropriate engineering solution to address the project objective;
5. Manage the execution of an engineering project in a limited time;
6. Collaborate with supervisor, co-workers and/or support staff to achieve a technical solution;
7. Present the research project and results via written technical reports and oral presentation
8. Engage with and observe applicable ethical considerations relating to their project content, personal conduct and the delivery of project components.

The aim of this module is to increase the student’s knowledge of microcontroller systems and peripherals, sensor interfacing, control and data acquisition, USB, I2C and SPI design & digital design using microcontrollers & FPGAs. The module will allow the student to use the latest Digital Design Software tools and fully implement systems using
hardware and software.

1. Employ Microcontroller Peripherals and low power techniques in sensor system designs
2. Design and Implement Microcontroller based state machines
3. Develop solutions to industrial, agricultural and domestic problems
4. Design and Implement USB, I2C and SPI Designs
5. Design and Implement Bluetooth Low Energy based designs
6. Extract appropriate information from data sheets

This module aims to provide the student with the major building blocks required to represent and quantify signals in embedded wireless sensor networks. Firstly, the student is introduced to the technical concepts and challenges that arise out of the growth of wireless sensor networks in our personal, working, and social environments. The student will develop basic probability theory learned through previous modules to use random variables, stochastic processes and information theory to represent wireless signals as random processes to allow signal detection and estimation. Key to designing low power wireless is successful propagation of an RF wireless signal through an environment with line of sight obstructed by buildings, terrain, and people. This module will allow the student to understand and use propagation models to quantify common signal paths. The Medium Access Control (MAC) protocol which allows devices to fairly accessing the radio channel will be studied with a particular emphasis on the MACs of ultra-low power wireless systems: Bluetooth Low Energy, Zigbee (802.15.4) etc. Finally, techniques for efficient use of communication channels for digital communication are explored.

1. Explain the increasing impact on our lives of wireless sensor technology deployed on our bodies and in our personal, working, and social environments.
2. Solve and analyse problems involving wireless signals represented by Ricean, Gaussian, or Rayleigh probability distribution functions.
3. Estimate the range of low power wireless signals through various environments using Path Loss Models.
4. Calculate the power efficiency of low power MAC applied to low power wireless system.
5. Analyse the performance of techniques such as spread spectrum, OFDM, and diversity, to improve the efficiency of digital communications through noisy channels.

Enhance the learner's knowledge of Hardware Description Languages with the main emphasis on VHDL. Allow the student to use Digital Software Design packages to design, test, and implement digital systems using VHDL. Allow the learner to design, write, and test advanced VHDL testbenches, including the use of files for input stimuli and results, testbench self-checking, etc. Explore various options for program data storage/manipulation, structures, pointers, various types of linked lists, etc.

1. Decompose a software problem into simpler/smaller parts. Use user-written subprograms/packages and components in VHDL projects. Reuse code
2. Write scalable/parameterised VHDL descriptions of devices using Generic declarations.
3. Write advanced testbenches, with file input/output and self-checking, to analyse/assess VHDL designs, by simulation
4. Design/Implement complex Finite State Machines in VHDL.
5. Write C programs to manipulate large amounts of data using suitable programming strategies.
6. Document and present their designs.

The Engineering Management module will introduce the learner to the operations management field and how this contributes to the strategic success of an organisation. The learner will explore a variety of techniques used in engineering operations for planning and control. The module will enable the learner to demonstrate knowledge and understanding of a broad range of lean process improvement techniques within a service/manufacturing setting. Learners will develop their ability to visualise and solve problems using quantitative models.

1. Use inventory planning and control techniques to evaluate stock requirements.
2. Recognise and describe the essential elements of quality assured organisations.
3. Develop statistical control charts for attributes and variables to ensure processes are in control.
4. Explain how lean synchronisation and the associated tools can improve operations performance.
5. Measure and manage risk and recovery for an operation.

This module introduces students to a variety of Data Science and Machine Learning techniques, through practical experience of their use, application and evaluation in Engineering. Students will explore the tools and techniques available to read, analyse and create meaningful visualisations from quantitative and qualitative data to facilitate
decision making. Also, this module addresses the key elements of mathematics, Python and algorithms required to understand the field of Machine Learning and its application in Engineering. The module examines supervised and unsupervised approaches to Machine Learning with Regressions, Decision Trees, Random Forests, Ensemble Methods, Support Vector Machines, Clustering, Naive Bayes and Neural Networks encompassed as key topics.

1. Pre-process, manipulate and analyse a broad range of data types using Pandas.
2. Produce appropriate data frames and provide high quality visualisations for decision making.
3. Apply algorithms to develop mathematical models which resolve issues in Engineering.
4. Distinguish between supervised and unsupervised Machine Learning and apply these techniques.
5. Review the theoretical underpinning of Neural Networks and apply these techniques.

The aim of this module is to give students the opportunity to complete the research undertaken in L8 Design Project and to develop all aspects of the project solution. Students will present their work in written, oral and poster formats.

1. Design an appropriate engineering solution to address the project objective
2. Implement solutions through analysis, simulation and/or experimentation
3. Analyse results, discuss critically and draw appropriate conclusions
4. Manage the execution of an engineering project in a limited time
5. Collaborate with supervisor, co-workers and/or support staff to achieve a technical solution
6. Present the research project and results to both specialist and non-specialist audiences through written technical reports, oral presentation and poster presentation
7. Engage with and observe applicable ethical considerations relating to their project content, personal conduct and the delivery of project components.

To increase the student's knowledge of Embedded Systems Design in the areas of low power internet enabled embedded systems, Mixed Signal Array applications, Touch Control and the implementation of a RTOS on a microcontroller. The module will allow the student to use the latest Embedded Processor, Internet Enabled Systems Design Tools and Mixed Array Development Tools to fully implement systems using hardware and software.

1. Develop IoT applications using a recognised Cloud Service.
2. Design and build a low power sensor application with connection to a LPWAN
3. Create and test Mixed Signal Array applications.
4. Design and implement RTOS applications
5. Develop Touch sensing applications
6. Extract appropriate information from data sheets

This module aims to give the student an understanding of the 'Internet of Things' (IoT) by providing an analysis of the existing and emerging low power wireless technologies that underpin these networks. The physical layer of established low power wireless technologies used to connect sensor networks in smart homes, cities, remote health, the environment, and industry 4.0 such as Bluetooth and Zigbee ,will be compared to those of the emerging Low Power Wide Area Network (LPWAN) systems, such as LoRa, NB IoT, SigFox, LTE CAT-M and the future 5G networks. The growth of embedded systems and the Internet of Things is dependent on ever smaller and smarter conformal antenna design. Emerging antennas and design solutions will be investigated in this module. Key to the networking of many very small ultra-low power wireless devices embedded in our lives and environment are new lightweight routing protocols which perform power efficient routing in mobile mesh networks. Underpinning the networking of embedded wireless devices is the understanding of queueing and queuing theory.

1. Quantify and compare the performance the physical layers of ultra-low power wireless systems used in IoT.
2. Explain how the low power wide area networks (LPWANs) aimed at Internet of Things achieves significantly longer range than standard ISM wireless systems.
3. Design and analyse the performance of low power small aperture antennas for wireless embedded applications including BAN applications
4. Model routing protocols required for wireless sensor networks (WSNs).
5. Build and analyse embedded mesh and star networks interfaced with internet.

This module ensures the learner understands project management tasks such as developing, managing, implementing, closing and evaluating the project plan. Students will gain an overview of the ethical and societal issues pertaining to engineering. This will review various intellectual property rights including copyright, patent, trademarks, and patents. Students will learn marketing techniques and practice these techniques by developing an online presence on linked in and other platforms to enhance their own employment opportunities. Students will examine Continuous Professional Development & Lifelong Learning.

1. Apply basic management ideas, principles and skills to the management of projects
2. Plan, schedule and control projects
3. Analyse ethical issues with respect to the design and manufacture of goods
4. Analyse various intellectual property rights including copyright, patents, trademarks and carry out a patent search
5. Explain key marketing concepts, marketing environment and marketing research
6. Develop personal marketing skills using LinkedIn or other platforms to attract employment opportunities