School of Engineering, Computer and Mathematical Sciences - Te Kura Mātai Pūhanga, Rorohiko, Pāngarau

Permanent link for this collection

https://hdl.handle.net/10292/553

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;

Browse

Now showing 1 - 5 of 1343

Optimizing Wireless Connectivity: A Deep Neural Network-Based Handover Approach for Hybrid LiFi and WiFi Networks
(MDPI AG, 2024-03-22) Khan, Mohammad Usman Ali; Babar, Mohammad Inayatullah; Rehman, Saeed Ur; Komosny, Dan; Chong, Peter Han Joo
A Hybrid LiFi and WiFi network (HLWNet) integrates the rapid data transmission capabilities of Light Fidelity (LiFi) with the extensive connectivity provided by Wireless Fidelity (WiFi), resulting in significant benefits for wireless data transmissions in the designated area. However, the challenge of decision-making during the handover process in HLWNet is made more complex due to the specific characteristics of electromagnetic signals' line-of-sight transmission, resulting in a greater level of intricacy compared to previous heterogeneous networks. This research work addresses the problem of handover decisions in the Hybrid LiFi and WiFi networks and treats it as a binary classification problem. Consequently, it proposes a handover method based on a deep neural network (DNN). The comprehensive handover scheme incorporates two sets of neural networks (ANN and DNN) that utilize input factors such as channel quality and the mobility of users to enable informed decisions during handovers. Following training with labeled datasets, the neural-network-based handover approach achieves an accuracy rate exceeding 95%. A comparative analysis of the proposed scheme against the benchmark reveals that the proposed method considerably increases user throughput by approximately 18.58% to 38.5% while reducing the handover rate by approximately 55.21% to 67.15% compared to the benchmark artificial neural network (ANN); moreover, the proposed method demonstrates robustness in the face of variations in user mobility and channel conditions.
Electron Beam Powder Bed Fusion Additive Manufacturing of Ti6Al4V Alloy Lattice Structures: Orientation-Dependent Compressive Strength and Fracture Behavior
(Springer, 2024-04-09) Huang, Yawen; Chen, ZW; Wan, ARO; Schmidt, K; Sefont, P; Singamneni, S
High porosity level lattice structures made using electron beam powder bed fusion additive manufacturing (EBPBF) need to be sufficiently strong and the understanding of the mechanical anisotropy of the structures is important for the design of orthopedic implants. In this work, the combined effects of loading direction (LD), cell orientation, and strut irregularity associated with EBPBF of Ti6Al4V alloy lattices on the mechanical behavior of the lattices under compressive loading have been studied. Three groups of simple cubic unit cell lattices were EBPBF made, compressively tested, and examined. The three groups were [001]//LD lattices, [011]//LD lattices, and [111]//LD lattices. Simulation has also been conducted. Yield strength (σy-L) values of all lattices determined experimentally have been found to be comparable to the values predicted by simulation; thus, EBPBF surface defects do not affect σy-L. σy-L of [001]//LD lattices is 1.8–2.0 times higher than those of [011]//LD and [111]//LD lattices. The reason for this is shown to be due to the high stress concentrations in non-[001]//LD samples, causing yielding at low loading levels. Furthermore, plastic strain (εp) at ultimate compression strength of [001]//LD samples has been determined to be 4–6 times higher than the values of non-[001]//LD samples. Examining the tested samples has shown cracks more readily propagating from EBPBF micro-notches in non-[001]//LD samples, resulting in low εp.
Impact and Significance of Human Factors in Digital Information Security
(innove, 2024-03-31) Ahmed, M; Kambam, HR; Lin, Y; Jaidka, S; Petrova, Krassimira
In this paper, we present a study on the impact and significance of human factors in digital information security. The study focuses on digital data breaches and seeks to find out how human factors within the context of data breaches in cyberspace impact information security. Data breach in cyberspace is a major privacy and security concern that affects the integrity of information security, and thus the underlying reasons for such data breaches demand investigation. An incident of data breach may occur due to several reasons. The root cause for a data breach may yield either from technological or human factors, or both. While technological factors are mostly predictable, human factors may not be. Besides, human factors are dynamic and cannot be fully quantified. This opens the opportunity for an attacker to compromise systems by exploiting human factors. The presented study seeks to find the extent to which human factors are contributors for data breaches. Analyses on 101 real life incidents of data breaches are carried out, and the reasons behind those breaches are explored to understand the implications of human factors in these breaches.
Guest Editorial: Special Issue on Explainable AI Empowered for Indoor Positioning and Indoor Navigation
(Institution of Engineering and Technology (IET), 2023-12-03) Kim, KI; Cherukuri, AK; Li, XJ; Ahmad, T; Rafiq, M; Chaudhry, SA
Transactive Energy Management of Solar-based Range Anxious Electric Vehicle Integrated Parking Lots
(Elsevier BV, 2024-03) Mohammad, Asaad; Zamora, Ramon; Lie, Tek Tjing
Electric vehicles (EVs) are regarded as essential solutions for alleviating climate change and energy crises. EVs can store excess Photovoltaic (PV) generation and transfer energy to other EVs, reducing distribution network upgrade costs. However, the limited range of EVs coupled with inadequate charging infrastructure leads to range anxiety among EV users, thereby becoming a barrier to implementing a transactive energy management system. This research quantifies the range anxiety among EV users and proposes a novel trading mechanism for transactive trading between workplace EVs. The case study solved for a commercial region in Auckland shows a 3–10% reduction in charging cost compared to a conventional V2G system. Further analysis shows that public charging stations can also result in cost savings from 1% to 5%. Still, their impact is limited compared to the number of discharging EVs participating in transactive trading. The uncertainty analysis of PV generation under different scenarios also shows the cost savings of the proposed strategy. The simulation results verify the feasibility and effectiveness of the proposed strategy while alleviating range anxiety among EV users.

Browse

Recent Submissions