Engineering Commons^™

Multimodal Fusion For Audio-Image And Video Action Recognition, Muhammad B. Shaikh, Douglas Chai, Syed M. S. Islam, Naveed Akhtar

Research outputs 2022 to 2026

Multimodal Human Action Recognition (MHAR) is an important research topic in computer vision and event recognition fields. In this work, we address the problem of MHAR by developing a novel audio-image and video fusion-based deep learning framework that we call Multimodal Audio-Image and Video Action Recognizer (MAiVAR). We extract temporal information using image representations of audio signals and spatial information from video modality with the help of Convolutional Neutral Networks (CNN)-based feature extractors and fuse these features to recognize respective action classes. We apply a high-level weights assignment algorithm for improving audio-visual interaction and convergence. This proposed fusion-based framework utilizes …

Barriers And Enablers For Older Adults Participating In A Home-Based Pragmatic Exercise Program Delivered And Monitored By Amazon Alexa: A Qualitative Study, Paul Jansons, Jackson Fyfe, Jack Dalla Via, Robin M. Daly, Eugene Gvozdenko, David Scott

Research outputs 2022 to 2026

Background: The remote delivery and monitoring of individually-tailored exercise programs using voice-controlled intelligent personal assistants (VIPAs) that support conversation-based interactions may be an acceptable alternative model of digital health delivery for older adults. The aim of this study was to evaluate the enablers and barriers for older adults participating in a home-based exercise program delivered and monitored by VIPAs. Method: This qualitative study used videoconferencing to conduct semi-structured interviews following a 12-week, prospective single-arm pilot study in 15 adults aged 60 to 89 years living alone in the community. All participants were prescribed an individualized, brief (10 min, 2–4 times …

Computer Vision Based Classification Of Fruits And Vegetables For Self-Checkout At Supermarkets, Khurram Hameed

Theses: Doctorates and Masters

The field of machine learning, and, in particular, methods to improve the capability of machines to perform a wider variety of generalised tasks are among the most rapidly growing research areas in today’s world. The current applications of machine learning and artificial intelligence can be divided into many significant fields namely computer vision, data sciences, real time analytics and Natural Language Processing (NLP). All these applications are being used to help computer based systems to operate more usefully in everyday contexts. Computer vision research is currently active in a wide range of areas such as the development of autonomous vehicles, …

Green Underwater Wireless Communications Using Hybrid Optical-Acoustic Technologies, Kazi Y. Islam, Iftekhar Ahmad, Daryoush Habibi, M. Ishtiaque A. Zahed, Joarder Kamruzzaman

Research outputs 2014 to 2021

Underwater wireless communication is a rapidly growing field, especially with the recent emergence of technologies such as autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs). To support the high-bandwidth applications using these technologies, underwater optics has attracted significant attention, alongside its complementary technology – underwater acoustics. In this paper, we propose a hybrid opto-acoustic underwater wireless communication model that reduces network power consumption and supports high-data rate underwater applications by selecting appropriate communication links in response to varying traffic loads and dynamic weather conditions. Underwater optics offers high data rates and consumes less power. However, due to the severe …

Video Analysis And Verification Of Direct Head Impacts Recorded By Wearable Sensors In Junior Rugby League Players, Lauchlan Carey, Douglas P. Terry, Andrew S. Mcintosh, Peter Stanwell, Grant L. Iverson, Andrew J. Gardner

Research outputs 2014 to 2021

Background: Rugby league is a high-intensity collision sport that carries a risk of concussion. Youth athletes are considered to be more vulnerable and take longer to recover from concussion than adult athletes. Purpose: To review head impact events in elite-level junior representative rugby league and to verify and describe characteristics of X-patchTM-recorded impacts via video analysis. Study Design: Observational case series. Methods: The X-patchTM was used on twenty-one adolescent players (thirteen forwards and eight backs) during a 2017 junior representative rugby league competition. Game-day footage, recorded by a trained videographer from a single camera, was synchronised with X-patchTM-recorded timestamped events. …

Class Distribution-Aware Adaptive Margins And Cluster Embedding For Classification Of Fruit And Vegetables At Supermarket Self-Checkouts, Khurram Hameed, Douglas Chai, Alexander Rassau

Research outputs 2014 to 2021

The complex task of vision based fruit and vegetables classification at a supermarket self-checkout poses significant challenges. These challenges include the highly variable physical features of fruit and vegetables i.e. colour, texture shape and size which are dependent upon ripeness and storage conditions in a supermarket as well as general product variation. Supermarket environments are also significantly variable with respect to lighting conditions. Attempting to build an exhaustive dataset to capture all these variations, for example a dataset of a fruit consisting of all possible colour variations, is nearly impossible. Moreover, some fruit and vegetable classes have significant similar physical …

A Sample Weight And Adaboost Cnn-Based Coarse To Fine Classification Of Fruit And Vegetables At A Supermarket Self-Checkout, Khurram Hameed, Douglas Chai, Alexander Rassau

Research outputs 2014 to 2021

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. The physical features of fruit and vegetables make the task of vision-based classification of fruit and vegetables challenging. The classification of fruit and vegetables at a supermarket self-checkout poses even more challenges due to variable lighting conditions and human factors arising from customer interactions with the system along with the challenges associated with the colour, texture, shape, and size of a fruit or vegetable. Considering this complex application, we have proposed a progressive coarse to fine classification technique to classify fruit and vegetables at supermarket checkouts. The image and weight of …

Application Of Advanced Algorithms And Statistical Techniques For Weed-Plant Discrimination, Saman Akbar Zadeh

Theses: Doctorates and Masters

Precision agriculture requires automated systems for weed detection as weeds compete with the crop for water, nutrients, and light. The purpose of this study is to investigate the use of machine learning methods to classify weeds/crops in agriculture. Statistical methods, support vector machines, convolutional neural networks (CNNs) are introduced, investigated and optimized as classifiers to provide high accuracy at high vehicular speed for weed detection.

Initially, Support Vector Machine (SVM) algorithms are developed for weed-crop discrimination and their accuracies are compared with a conventional data-aggregation method based on the evaluation of discrete Normalised Difference Vegetation Indices (NDVIs) at two different …

Proactive Content Caching In Future Generation Communication Networks: Energy And Security Considerations, Muhammad Ishtiaque Aziz Zahed

Theses: Doctorates and Masters

The proliferation of hand-held devices and Internet of Things (IoT) applications has heightened demand for popular content download. A high volume of content streaming/downloading services during peak hours can cause network congestion. Proactive content caching has emerged as a prospective solution to tackle this congestion problem. In proactive content caching, data storage units are used to store popular content in helper nodes at the network edge. This contributes to a reduction of peak traffic load and network congestion.

However, data storage units require additional energy, which offers a challenge to researchers that intend to reduce energy consumption up to 90% …

Real-Time Classification Of Multivariate Olfaction Data Using Spiking Neural Networks, Arnup Vanarse, Adam Osseiran, Alexander Rassau, Therese O'Sullivan, Jonny Lo, Amanda Devine

Research outputs 2014 to 2021

Recent studies in bioinspired artificial olfaction, especially those detailing the application of spike-based neuromorphic methods, have led to promising developments towards overcoming the limitations of traditional approaches, such as complexity in handling multivariate data, computational and power requirements, poor accuracy, and substantial delay for processing and classification of odors. Rank-order-based olfactory systems provide an interesting approach for detection of target gases by encoding multi-variate data generated by artificial olfactory systems into temporal signatures. However, the utilization of traditional pattern-matching methods and unpredictable shuffling of spikes in the rank-order impedes the performance of the system. In this paper, we present an …

A Hardware-Deployable Neuromorphic Solution For Encoding And Classification Of Electronic Nose Data, Anup Vanarse, Alexander Rassau, Peter Van Der Made

Research outputs 2014 to 2021

In several application domains, electronic nose systems employing conventional data processing approaches incur substantial power and computational costs and limitations, such as significant latency and poor accuracy for classification. Recent developments in spike-based bio-inspired approaches have delivered solutions for the highly accurate classification of multivariate sensor data with minimized computational and power requirements. Although these methods have addressed issues related to efficient data processing and classification accuracy, other areas, such as reducing the processing latency to support real-time application and deploying spike-based solutions on supported hardware, have yet to be studied in detail. Through this investigation, we proposed a spiking …

Effective Plant Discrimination Based On The Combination Of Local Binary Pattern Operators And Multiclass Support Vector Machine Methods, Vi N T Le, Beniamin Apopei, Kamal Alameh

Research outputs 2014 to 2021

Accurate crop and weed discrimination plays a critical role in addressing the challenges of weed management in agriculture. The use of herbicides is currently the most common approach to weed control. However, herbicide resistant plants have long been recognised as a major concern due to the excessive use of herbicides. Effective weed detection techniques can reduce the cost of weed management and improve crop quality and yield. A computationally efficient and robust plant classification algorithm is developed and applied to the classification of three crops: Brassica napus (canola), Zea mays (maize/corn), and radish. The developed algorithm is based on the …

An Entropy-Histogram Approach For Image Similarity And Face Recognition, Mohammed Aljanabi, Zahir Hussain, Song F. Lu

Research outputs 2014 to 2021

Image similarity and image recognition are modern and rapidly growing technologies because of their wide use in the field of digital image processing. It is possible to recognize the face image of a specific person by finding the similarity between the images of the same person face and this is what we will address in detail in this paper. In this paper, we designed two new measures for image similarity and image recognition simultaneously. The proposed measures are based mainly on a combination of information theory and joint histogram. Information theory has a high capability to predict the relationship between …

Application Of 3d Delaunay Triangulation In Fingerprint Authentication System, Wencheng Yang, Ahmed Ibrahim, Junaid Chaudhry, Song Wang, Jiankun Hu, Craig Valli

Research outputs 2014 to 2021

Biometric security has found many applications in Internet of Things (IoT) security. Many mobile devices including smart phones have supplied fingerprint authentication function. However, the authentication performance in such restricted environment has been downgraded significantly. A number of methods based on Delaunay triangulation have been proposed for minutiae-based fingerprint matching, due to some favorable properties of the Delaunay triangulation under image distortion. However, all existing methods are based on 2D pattern, of which each unit, a Delaunay triangle, can only provide limited discrimination ability and could cause low matching performance. In this paper, we propose a 3D Delaunay triangulation based …

Extraction Of Patterns In Selected Network Traffic For A Precise And Efficient Intrusion Detection Approach, Priya Naran Rabadia

Theses: Doctorates and Masters

This thesis investigates a precise and efficient pattern-based intrusion detection approach by extracting patterns from sequential adversarial commands. As organisations are further placing assets within the cyber domain, mitigating the potential exposure of these assets is becoming increasingly imperative. Machine learning is the application of learning algorithms to extract knowledge from data to determine patterns between data points and make predictions. Machine learning algorithms have been used to extract patterns from sequences of commands to precisely and efficiently detect adversaries using the Secure Shell (SSH) protocol. Seeing as SSH is one of the most predominant methods of accessing systems it …

Review Of Deep Learning Methods In Robotic Grasp Detection, Shehan Caldera, Alexander Rassau, Douglas Chai

Research outputs 2014 to 2021

For robots to attain more general-purpose utility, grasping is a necessary skill to master. Such general-purpose robots may use their perception abilities to visually identify grasps for a given object. A grasp describes how a robotic end-effector can be arranged to securely grab an object and successfully lift it without slippage. Traditionally, grasp detection requires expert human knowledge to analytically form the task-specific algorithm, but this is an arduous and time-consuming approach. During the last five years, deep learning methods have enabled significant advancements in robotic vision, natural language processing, and automated driving applications. The successful results of these methods …

A Security Review Of Local Government Using Nist Csf: A Case Study, Ahmed Ibrahim, Craig Valli, Ian Mcateer, Junaid Chaudhry

Research outputs 2014 to 2021

Evaluating cyber security risk is a challenging task regardless of an organisation’s nature of business or size, however, an essential activity. This paper uses the National Institute of Standards and Technology (NIST) cyber security framework (CSF) to assess the cyber security posture of a local government organisation in Western Australia. Our approach enabled the quantification of risks for specific NIST CSF core functions and respective categories and allowed making recommendations to address the gaps discovered to attain the desired level of compliance. This has led the organisation to strategically target areas related to their people, processes, and technologies, thus mitigating …

Development, Manufacture And Application Of A Solid-State Ph Sensor Using Ruthenium Oxide, Wade Lonsdale

Theses: Doctorates and Masters

The measurement of pH is undertaken frequently in numerous settings for many applications. The common glass pH probe is almost ideal for measuring pH, and as such, it is used almost ubiquitously. However, glass is not ideal for all applications due to its relatively large size, fragility, need for recalibration and wet-storage. Therefore, much research has been undertaken on the use of metal oxides as an alternative for the measurement of pH.

Here, a solid-state potentiometric pH sensor is developed using ruthenium metal oxide (RuO2). Initially, pH sensitive RuO2 electrodes were prepared by deposition with radio frequency magnetron sputtering (RFMS) …

Automated Optical Mark Recognition Scoring System For Multiple-Choice Questions, Murtadha Alomran

Theses: Doctorates and Masters

Multiple-choice questions are one of the questions commonly used in assessments. It is widely used because this type of examination can be an effective and reliable way to examine the level of student’s knowledge. So far, this type of examination can either be marked by hand or with specialised answer sheets and scanning equipment. There are specialised answer sheets and scanning equipment to mark multiple-choice questions automatically. However, these are expensive, specialised and restrictive answer sheets and optical mark recognition scanners.

This research aims to design and implement a multiple-choice answer sheet and a reliable image processing-based scoring system that …

Next Generation Wireless Communication Networks: Energy And Quality Of Service Considerations, Md Munjure Mowla

Theses: Doctorates and Masters

The rapid growth in global mobile phone users has resulted in an ever-increasing demand for bandwidth and enhanced quality-of-service (QoS). Several consortia comprising major international mobile operators, infrastructure manufacturers, and academic institutions are working to develop the next generation wireless communication systems fifth generation (5G) - to support high data rates and increased QoS. 5G systems are also expected to represent a greener alternative for communication systems, which is important because power consumption from the information and communication technology (ICT) sector is forecast to increase significantly by 2030. The deployment of ultra-dense heterogeneous small cell networks (SCNs) is expected to …

A Novel Privacy Preserving User Identification Approach For Network Traffic, Nathan Clarke, Fudong Li, Steven Furnell

Research outputs 2014 to 2021

The prevalence of the Internet and cloud-based applications, alongside the technological evolution of smartphones, tablets and smartwatches, has resulted in users relying upon network connectivity more than ever before. This results in an increasingly voluminous footprint with respect to the network traffic that is created as a consequence. For network forensic examiners, this traffic represents a vital source of independent evidence in an environment where anti-forensics is increasingly challenging the validity of computer-based forensics. Performing network forensics today largely focuses upon an analysis based upon the Internet Protocol (IP) address – as this is the only characteristic available. More typically, …

A Feature-Based Structural Measure: An Image Similarity Measure For Face Recognition, Noor A. Shnain, Zahir Hussain, Song F. Lu

Research outputs 2014 to 2021

Facial recognition is one of the most challenging and interesting problems within the field of computer vision and pattern recognition. During the last few years, it has gained special attention due to its importance in relation to current issues such as security, surveillance systems and forensics analysis. Despite this high level of attention to facial recognition, the success is still limited by certain conditions; there is no method which gives reliable results in all situations. In this paper, we propose an efficient similarity index that resolves the shortcomings of the existing measures of feature and structural similarity. This measure, called …

A Sound Idea: An Investigation Into Accessible Video Game Design For The Deaf And Hard Of Hearing, Luke James Brook

Theses: Doctorates and Masters

A widely accepted, and incorrect, assumption towards hearing accessibility in video games is that deaf and hard of hearing (DHH) users are those who encounter the least barriers and are generally well catered for. Rapid advancement in video game technology has seen video game sound evolve from simple blips generated by internal circuitry to fully realised digital audio used to convey critical information. To accommodate the DHH, this information needs to be conveyed in an alternative manner. However, evidence suggests existing accessible design solutions for the DHH lack specificity and are insufficient. Thus, the inability to hear, or hear well, …

Detecting And Tracing Slow Attacks On Mobile Phone User Service, Brian Cusack, Zhuang Tian

Australian Digital Forensics Conference

The lower bandwidth of mobile devices has until recently filtered the range of attacks on the Internet. However, recent research shows that DOS and DDOS attacks, worms and viruses, and a whole range of social engineering attacks are impacting on broadband smartphone users. In our research we have developed a metric-based system to detect the traditional slow attacks that can be effective using limited resources, and then employed combinations of Internet trace back techniques to identify sources of attacks. Our research question asked: What defence mechanisms are effective? We critically evaluate the available literature to appraise the current state of …

The Proceedings Of 14th Australian Digital Forensics Conference, 5-6 December 2016, Edith Cowan University, Perth, Australia, Craig Valli

Australian Digital Forensics Conference

Conference Foreword

This is the fifth year that the Australian Digital Forensics Conference has been held under the banner of the Security Research Institute, which is in part due to the success of the security conference program at ECU. As with previous years, the conference continues to see a quality papers with a number from local and international authors. 11 papers were submitted and following a double blind peer review process, 8 were accepted for final presentation and publication. Conferences such as these are simply not possible without willing volunteers who follow through with the commitment they have initially made, …

Optical Fiber Sensors In Physical Intrusion Detection Systems: A Review, Gary Andrew Allwood, Graham Wild, Steven Hinkley

Research outputs 2014 to 2021

Fiber optic sensors have become a mainstream sensing technology within a large array of applications due to their inherent benefits. They are now used significantly in structural health monitoring, and are an essential solution for monitoring harsh environments. Since their first development over 30 years ago, they have also found promise in security applications. This paper reviews all of the optical fiber-based techniques used in physical intrusion detection systems. It details the different approaches used for sensing, interrogation, and networking, by research groups, attempting to secure both commercial and residential premises from physical security breaches. The advantages and the disadvantages …

Proactive Biometric-Enabled Forensic Imprinting, Abdulrahman Alruban, Nathan L. Clarke, Fudong Li, Steven M. Furnell

Research outputs 2014 to 2021

Threats to enterprises have become widespread in the last decade. A major source of such threats originates from insiders who have legitimate access to the organization's internal systems and databases. Therefore, preventing or responding to such incidents has become a challenging task. Digital forensics has grown into a de-facto standard in the examination of electronic evidence; however, a key barrier is often being able to associate an individual to the stolen data. Stolen credentials and the Trojan defense are two commonly cited arguments used. This paper proposes a model that can more inextricably links the use of information (e.g. images, …

Interfacing Of Neuromorphic Vision, Auditory And Olfactory Sensors With Digital Neuromorphic Circuits, Anup Vanarse

Theses: Doctorates and Masters

The conventional Von Neumann architecture imposes strict constraints on the development of intelligent adaptive systems. The requirements of substantial computing power to process and analyse complex data make such an approach impractical to be used in implementing smart systems.

Neuromorphic engineering has produced promising results in applications such as electronic sensing, networking architectures and complex data processing. This interdisciplinary field takes inspiration from neurobiological architecture and emulates these characteristics using analogue Very Large Scale Integration (VLSI). The unconventional approach of exploiting the non-linear current characteristics of transistors has aided in the development of low-power adaptive systems that can be implemented …

An Investigation Into Off-Link Ipv6 Host Enumeration Search Methods, Clinton Carpene

Theses: Doctorates and Masters

This research investigated search methods for enumerating networked devices on off-link 64 bit Internet Protocol version 6 (IPv6) subnetworks. IPv6 host enumeration is an emerging research area involving strategies to enable detection of networked devices on IPv6 networks. Host enumeration is an integral component in vulnerability assessments (VAs), and can be used to strengthen the security profile of a system. Recently, host enumeration has been applied to Internet-wide VAs in an effort to detect devices that are vulnerable to specific threats. These host enumeration exercises rely on the fact that the existing Internet Protocol version 4 (IPv4) can be exhaustively …

The Social Life Of Big Data - Pawsey Resources, Luke Edwards

The Social Life of Big Data Symposium

The presentation covers the supercomputing facilities and services available at the Pawsey Supercomputing Centre, Western Australia.
The Pawsey Supercomputing Centre is an unincorporated joint venture between CSIRO, Curtin University, Edith Cowan University, Murdoch University and the University of Western Australia and is supported by the Western Australian Government.