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6

90 hrs

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COMP6161 

This module describes a new design and planning approach to manage the impact of smart city urban development by integrating those technological advances with living systems and natural processes to enhance the health, livability, and equality in cities. Smart Cities are complex challenges for governments because along with the benefits come negatives such as uncontrolled development, traffic congestion, waste management, complicated access to resources, and crime. Therefore, creation of sustainable smart cities should be the main focus of following years and the development of smart cities if they are to play a fundamental role in the models of economic development. In these terms sustainable development of smart cities is considered as for urban cities that could take advantage of all the possibilities that Information and Communications Technologies (ICT) could offer to improve their residents’ life quality, but always taking care of the environment, energy, waste management, and sustainability of life.

3

45 hrs

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COMP6162 

Recent advances in sensing technology and smart cities have led to the rapid explosion of data. The ability to derive insights from big data is crucial for understanding complex phenomena in various environmental contexts. This learning module provides an overview of common data analytical techniques, including statistical inference and data visualization. It also discusses implementation issues related to environmental data analytics projects, including challenges in data collection, data cleaning and data analysis. As part of a group project, students will need to demonstrate their ability to analyze a given case study and relevant dataset, applying these techniques to address specific questions related to urban environments.

3

45 hrs

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COMP6163 

Artificial Intelligence (AI) has become deeply integrated into our daily lives, often in ways we may not even realize. At the forefront of AI is Machine Learning (ML), a branch of AI that enables computers to learn and adapt without explicit programming. Over the past decade, ML has revolutionized academics and industries with breakthroughs such as autonomous vehicles, speech and image recognition, financial market analysis, machine translation, and game strategies like AlphaGo. This module covers some of the key machine learning techniques, including decision tree, neural networks, deep learning, etc. The aim of the module is to equip students with both the theoretical foundation and practical skills to apply these methods on environmental issues, such as classification, regression, etc. Student will work on environmental data and help stakeholders make informed decisions and take effective action about environments, urban planning, and smart cities.

3

45 hrs

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COMP6164 

In the module, basics of remote sensing, Geographical Information System (GIS), Global Navigation Satellite System (GNSS), and the relevance to smart cities are covered. GIS are tools for managing, describing, analyzing, and presenting information about the relationships between where features are (location, size and shape) and what they are like (descriptive information - attribute data). Mapping is the common technique used to represent social and environmental data. Basic principles of remote sensing (Earth observation sensors and platforms, thermal remote sensing, spectral signatures) of different land cover features are discussed. Satellites and aerial vehicles are tools for capturing images. Signal processing and interpretation could be important in both detection and prediction. The technological principles of GNSS are discussed with focus on GNSS receivers, GNSS data processing methods, errors and accuracy. Advanced GNSS processing, applications such as GPS signal characteristics, data formats (broadcast, precise ephemeris), and mobile mapping may be discussed. Skill will be developed in using remote sensing software tool, such as the widely popular ArcGIS Pro software. The module also explores case studies of smart cities utilizing remote sensing.

3

45 hrs

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COMP6165 

Environmental Intelligence explores the intersection of environmental sciences, artificial intelligence (AI), and data analytics to address and solve complex environmental challenges. This module is designed for graduate students who aim to utilize advanced computational tools and techniques in environmental research and policy-making. Students will gain a foundational understanding of how AI technologies such as machine learning, remote sensing, and big data analytics can be leveraged for environmental monitoring, resource management, and climate change mitigation. It covers how AI can be used in data analysis, predictive modeling, and system optimization to make more informed decisions for environmental management. This module perfectly suited for any graduate student interested in how advanced technology can be harnessed to support and enhance environmental stewardship and sustainability. This comprehensive introduction encourages students to engage with technological solutions that have the potential to address some of the most pressing ecological issues of our time, preparing them to contribute thoughtfully and effectively in diverse professional roles that intersect with environmental and technological domains.

3

45 hrs

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COMP6298 

Students are required to apply the techniques and technologies which they have learned in a significant advanced project. Under the supervision of an advisor, the students shall focus on a contemporary research topic or technological problem and make use of the leading-edge techniques to produce new research findings or solutions. Upon completion, the Project Report is to be submitted and evaluated using the standard criteria for advanced project.

9

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COMP6131

This module provides a comprehensive overview of the Internet of Things (IoT), and introduces the design fundamentals of the IoT. An IoT environment should facilitate interactions among intelligent machines, smart devices, ubiquitous computing systems, interactive physical objects and human user communications. Numerous essentials and underlying technologies enabling IoT are discussed, starting different recommended system and middleware architectures, material science of sensing technologies, wireless sensor networks, machine-to-machine communications, Cloud computing technologies, etc. Topics such as RFID, MQTT, CoAP are discussed. In order to obtain more hands-on experience in building IoT applications, project-based system constructions through interconnecting different smart sensing devices and coding the latest popular single board computers (SBCs) will be covered.

3

45 hrs

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COMP6166

This module provides a comprehensive introduction to the key principles and issues within environmental science. Students will explore the underlying causes of environmental problems and examine strategies for achieving sustainable societies. The module covers ecosystem dynamics, focusing on the energy and matter flow essential for life. Additionally, the module delves into the significance of biodiversity, the factors influencing it, and methods to sustain it. Students will also investigate the environmental impacts of urbanization and human population growth. Furthermore, the module evaluates economic tools and environmental policies designed to implement sustainable approaches, preparing students to address and mitigate environmental challenges effectively.

3

45 hrs

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COMP6167

This advanced module builds on the foundational knowledge of environmental science and delves deeper into issues surrounding the consumption of natural resources within Earth's ecosystems, how these practices contribute to global change, and the challenges associated with sustainable resource usage. Students will investigate the natural resources that sustain life and critically examine the environmental impacts of food production, freshwater use, and mineral extraction, while evaluating sustainable resource consumption strategies. The module will explore the causes and impacts of air and water pollution, with a special focus on climate change. Additionally, students will compare major energy sources, advocating for transitions to more sustainable energy systems. The module also identifies environmental hazards impacting human health and assesses the challenges posed by solid and hazardous waste, proposing methods to achieve a low-waste society. This module prepares students to analyze complex environmental issues and develop practical solutions for sustainability in a changing world.

3

45 hrs

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COMP6168

This module provides an introduction to the concepts, techniques, and tools used in modeling and simulating smart cities. Students will learn the fundamentals of smart city infrastructure, data collection and analysis, and simulation methodologies. They will explore various modeling techniques and simulation platforms to gain insights into the planning, design, and evaluation of smart city systems.

3

45 hrs

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COMP6169

The selected topics are designed to accommodate new, advanced and state-of-the-art technologies that are not included in this curriculum. One example is environmental data mining. Data Mining is one of the most popular research fields in Computer Science. The aim of this is to give an applicable understanding of the usage of data mining as of decision making. In this module, several essential fields would be discussed, including the classes of different algorithms and models, and the methodology of how to choose a suitable algorithm. Classification, pattern recognition and different learning types would be discussed and covered. Besides, other interdisciplinary topics, such as mathematical and statistical modelling in transportation systems, can also be covered.

3

45 hrs

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COMP6170

The selected topics are designed to accommodate new, advanced and state-of-the-art technologies that are not included in this curriculum. One example is environmental data mining. Data Mining is one of the most popular research fields in Computer Science. The aim of this is to give an applicable understanding of the usage of data mining as of decision making. In this module, several essential fields would be discussed, including the classes of different algorithms and models, and the methodology of how to choose a suitable algorithm. Classification, pattern recognition and different learning types would be discussed and covered. Besides, other interdisciplinary topics, such as mathematical and statistical modelling in transportation systems, can also be covered.

3

45 hrs

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