School of Engineering, Computer and Mathematical Sciences - Te Kura Mātai Pūhanga, Rorohiko, Pāngarau
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AUT is home to a number of renowned research institutes in engineering, and computer and mathematical sciences. The School of Engineering, Computer and Mathematical Sciences strong industry partnerships and the unique combination of engineering, computer and mathematical sciences within one school stimulates interdisciplinary research beyond traditional boundaries.
Current research interests include:
- Artificial Intelligence; Astronomy and Space Research;
- Biomedical Technologies;
- Computer Engineering; Computer Vision; Construction Management;
- Data Science;
- Health Informatics and eHealth;
- Industrial Optimisation, Modelling & Control;
- Information Security;
- Mathematical Sciences Research; Materials & Manufacturing Technologies;
- Networking, Instrumentation and Telecommunications;
- Parallel and Distributed Systems; Power and Energy Engineering;
- Software Engineering; Signal Processing; STEM Education;
- Wireless Engineering;
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Recent Submissions
- ItemEnhancing Critical Infrastructure Security: Unsupervised Learning Approaches for Anomaly Detection(Springer Science and Business Media LLC, 2024-09-10) Pinto, A; Herrera, LC; Donoso, Y; Gutierrez, JATraditional security detection methods face challenges in identifying zero-day attacks in critical infrastructures (CIs) integrated with the industrial internet of things (IIoT). These attacks exploit unknown vulnerabilities and are difficult to detect due to their connection to physical systems. The integration of legacy ICS networks with modern computing and networking technologies has significantly expanded the attack surface, making these systems more susceptible to cyber-attacks. Despite existing security measures, attackers continually find ways to breach these operating networks. Anomaly detection systems are critical in protecting these CIs from current cyber threats. This study investigates the effectiveness of unsupervised anomaly detection models in detecting operational anomalies that could lead to cyber-attacks, thereby disrupting and negatively impacting quality of life. We preprocess the data with a focus on cybersecurity and chose the SWAT dataset because it accurately represents the types of attack vectors that critical infrastructures commonly encounter. We evaluated the performance of isolation forest (IF), local outlier factor (LOF), one-class SVM (OCSVM), and Autoencoder algorithms—trained exclusively on normal data—in enhancing cybersecurity within IIoT environments. Our comprehensive analysis includes an assessment of each model’s detection capabilities. The findings highlight the VAE-LSTM model’s potential to identify cyber-attacks within seconds in a high-frequency dataset, suggesting near real-time detection capability. The final model combines the reconstruction ability of the variational autoencoder (VAE) with regularization using the Kullback–Leibler divergence, reflecting the non-Gaussian nature of industrial system data. Our model successfully detected 23 out of 26 attack scenarios in the SWAT dataset, demonstrating its effectiveness in improving the security of IIoT-based CIs.
- ItemOne-Pot Fabrication of Hydrophobic, Superelastic, Harakeke-Derived Nanocellulose Aerogels with Excellent Shape Recovery for Oil Adsorption and Water-in-Oil Emulsion Separation(Elsevier, 2024-09-10) Zhai, Yitong; Yuan, XiaowenCellulose-based aerogels have attracted significant attention for oil/water separation due to their high porosity, large specific surface area and high adsorption capacity. However, their intrinsic hydrophilicity, and inadequate mechanical properties have often limited their practical applications. Traditional freeze-dried cellulose aerogels exhibit unsatisfactory elasticity and require a separate surface modification process to adjust the surface wettability. In this study, we present a novel one-pot fabrication strategy which simultaneously achieves the crosslinking of individual cellulose nanofibers and the hydrophobic modification of the surface wettability. Following directional freeze-drying, hydrophobic, superelastic, and anisotropic cellulose-based aerogel was prepared from the 2,2,6,6-tetramethylpiperidin-1-oxyl (TEMPO)-oxidized cellulose nanofibers, isolated from harakeke (New Zealand native flax). The resulting aerogel exhibits a high water contact angle of 142°, good compressive recovery performance (85 % recovery of the original height after 100 compression cycles at 70 % strain), and outstanding adsorption capacity for various types of oil and organic solvents (80-105 g/g). Furthermore, the aerogel could also be used as a filter to separate the surfactant stabilized water-in-oil emulsions with high flux (782 L m-2 h-1) and high separation efficiency (98.7-99.2 %). The novel aerogel prepared in this study is expected to have great potential for practical applications in oily wastewater remediation.
- ItemExtended Context-Based Semantic Communication System for Text Transmission(Elsevier BV, 2022-10-08) Liu, Yueling; Jiang, Shengteng; Zhang, Yichi; Cao, Kuo; Zhou, Li; Seet, Boon-Chong; Zhao, Haitao; Wei, JiboContext information is significant for semantic extraction and recovery of messages in semantic communication. However, context information is not fully utilized in the existing semantic communication systems since relationships between sentences are often ignored. In this paper, we propose an Extended Context-based Semantic Communication (ECSC) system for text transmission, in which context information within and between sentences is explored for semantic representation and recovery. At the encoder, self-attention and segment-level relative attention are used to extract context information within and between sentences, respectively. In addition, a gate mechanism is adopted at the encoder to incorporate the context information from different ranges. At the decoder, Transformer-XL is introduced to obtain more semantic information from the historical communication processes for semantic recovery. Simulation results show the effectiveness of our proposed model in improving the semantic accuracy between transmitted and recovered messages under various channel conditions.
- ItemBio-Inspired Energy-Efficient Cluster-Based Routing Protocol for the IoT in Disaster Scenarios(MDPI AG, 2024-08-19) Ahmed, Shakil; Hossain, Md Akbar; Chong, Peter Han Joo; Ray, Sayan KumarThe Internet of Things (IoT) is a promising technology for sensing and monitoring the environment to reduce disaster impact. Energy is one of the major concerns for IoT devices, as sensors used in IoT devices are battery-operated. Thus, it is important to reduce energy consumption, especially during data transmission in disaster-prone situations. Clustering-based communication helps reduce a node's energy decay during data transmission and enhances network lifetime. Many hybrid combination algorithms have been proposed for clustering and routing protocols to improve network lifetime in disaster scenarios. However, the performance of these protocols varies widely based on the underlying network configuration and the optimisation parameters considered. In this research, we used the clustering parameters most relevant to disaster scenarios, such as the node's residual energy, distance to sink, and network coverage. We then proposed the bio-inspired hybrid BOA-PSO algorithm, where the Butterfly Optimisation Algorithm (BOA) is used for clustering and Particle Swarm Optimisation (PSO) is used for the routing protocol. The performance of the proposed algorithm was compared with that of various benchmark protocols: LEACH, DEEC, PSO, PSO-GA, and PSO-HAS. Residual energy, network throughput, and network lifetime were considered performance metrics. The simulation results demonstrate that the proposed algorithm effectively conserves residual energy, achieving more than a 17% improvement for short-range scenarios and a 10% improvement for long-range scenarios. In terms of throughput, the proposed method delivers a 60% performance enhancement compared to LEACH, a 53% enhancement compared to DEEC, and a 37% enhancement compared to PSO. Additionally, the proposed method results in a 60% reduction in packet drops compared to LEACH and DEEC, and a 30% reduction compared to PSO. It increases network lifetime by 10-20% compared to the benchmark algorithms.
- ItemSoftware Developers and Collective Empathy - Can They Be Disposed to Care?(Association for Computing Machinery (ACM), 2024-08-21) Clear, TonySoftware Developers and Collective Empathy --- Can They Be Disposed to Care?