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Articles 1 - 25 of 25
Full-Text Articles in Physical Sciences and Mathematics
Novel Image-Dependent Quality Assessment Measures, Asaad Hashim, Zahir Hussain
Novel Image-Dependent Quality Assessment Measures, Asaad Hashim, Zahir Hussain
Research outputs 2014 to 2021
The image is a 2D signal whose pixels are highly correlated in a 2D manner. Hence, using pixel by pixel error what we called previously Mean-Square Error, (MSE) is not an efficient way to compare two similar images (e.g., an original image and a compressed version of it). Due to this correlation, image comparison needs a correlative quality measure. It is clear that correlation between two signals gives an idea about the relation between samples of the two signals. Generally speaking, correlation is a measure of similarity between the two signals. An important step in image similarity was introduced by …
Windows Surface Rt Tablet Forensics, Asif Iqbal, Hanan Al Obaidli, Andrew Marrington, Andy Jones
Windows Surface Rt Tablet Forensics, Asif Iqbal, Hanan Al Obaidli, Andrew Marrington, Andy Jones
Research outputs 2014 to 2021
Small scale digital device forensics is particularly critical as a result of the mobility of these devices, leading to closer proximity to crimes as they occur when compared to computers. The Windows Surface tablet is one such device, combining tablet mobility with familiar Microsoft Windows productivity tools. This research considers the acquisition and forensic analysis of the Windows Surface RT tablet. We discuss the artifacts of both the Windows RT operating system and third-party applications. The contribution of this research is to provide a road map for the digital forensic examination of Windows Surface RT tablets.
Hybrid Intelligent Model For Software Maintenance Prediction, Abdulrahman Ahmed Bobakr Baqais, Mohammad Alshayeb, Zubair A. Baig
Hybrid Intelligent Model For Software Maintenance Prediction, Abdulrahman Ahmed Bobakr Baqais, Mohammad Alshayeb, Zubair A. Baig
Research outputs 2014 to 2021
Maintenance is an important activity in the software life cycle. No software product can do without undergoing the process of maintenance. Estimating a software’s maintainability effort and cost is not an easy task considering the various factors that influence the proposed measurement. Hence, Artificial Intelligence (AI) techniques have been used extensively to find optimized and more accurate maintenance estimations. In this paper, we propose an Evolutionary Neural Network (NN) model to predict software maintainability. The proposed model is based on a hybrid intelligent technique wherein a neural network is trained for prediction and a genetic algorithm (GA) implementation is used …
Frequency Estimation Of Single-Tone Sinusoids Under Additive And Phase Noise, Asmaa Nazar Almoosawy, Zahir Hussain, Fadel A. Murad
Frequency Estimation Of Single-Tone Sinusoids Under Additive And Phase Noise, Asmaa Nazar Almoosawy, Zahir Hussain, Fadel A. Murad
Research outputs 2014 to 2021
We investigate the performance of main frequency estimation methods for a single-component complex sinusoid under complex additive white Gaussian noise (AWGN) as well as phase noise (PN). Two methods are under test: Maximum Likelihood (ML) method using Fast Fourier Transform (FFT), and the autocorrelation method (Corr). Simulation results showed that FFT-method has superior performance as compared to the Corr-method in the presence of additive white Gaussian noise (affecting the amplitude) and phase noise, with almost 20dB difference.
Free-Space 120 Gb/S Reconfigurable Card-To-Card Optical Wireless Interconnects With 16-Cap Modulation, Ke Wang, Ampalavanapillai Nirmalathas, Christina Lim, Efstratios Skafidas, Kamal Alameh
Free-Space 120 Gb/S Reconfigurable Card-To-Card Optical Wireless Interconnects With 16-Cap Modulation, Ke Wang, Ampalavanapillai Nirmalathas, Christina Lim, Efstratios Skafidas, Kamal Alameh
Research outputs 2014 to 2021
In this paper, we propose and experimentally demonstrate reconfigurable card-to-card optical wireless interconnects architecture with 16-Carrierless-Amplitude/Phase modulation. Results show that 3×40 Gb/s interconnection is achieved with 2 mW transmission power.
Application Of Cellular Neural Networks And Naive Bayes Classifier In Agriculture, Oluleye H. Babatunde, Leisa Armstrong, Jinsong Leng, Dean Diepeveen
Application Of Cellular Neural Networks And Naive Bayes Classifier In Agriculture, Oluleye H. Babatunde, Leisa Armstrong, Jinsong Leng, Dean Diepeveen
Research outputs 2014 to 2021
This article describes the use of Cellular Neural Networks (a class of Ordinary Differential Equation (ODE)), Fourier Descriptors (FD) and NaiveBayes Classifier (NBC) for automatic identification of images of plant leaves. The novelty of this article is seen in the use of CNN for image segmentation and a combination FDs with NBC. The main advantage of the segmentation method is the computation speed compared with other edge operators such as canny, sobel, Laplacian of Gaussian (LoG). The results herein show the potential of the methods in this paper for examining different agricultural images and distinguishing between different crops and weeds …
Integrating Soil And Plant Tissue Tests And Using An Artificial Intelligence Method For Data Modelling Is Likely To Improve Decisions For In-Season Nitrogen Management, Andreas Neuhaus, Leisa Armstrong, Jinsong Leng, Dean Diepeveen, Geoff Anderson
Integrating Soil And Plant Tissue Tests And Using An Artificial Intelligence Method For Data Modelling Is Likely To Improve Decisions For In-Season Nitrogen Management, Andreas Neuhaus, Leisa Armstrong, Jinsong Leng, Dean Diepeveen, Geoff Anderson
Research outputs 2014 to 2021
This paper hypothesizes that there is value in combining soil, climate and plant tissue data to give more reliable advice on nitrogen top-ups in-season when compared with models that are currently available. The benefit of soil and climate data is to factor in N mineralisation and potential yield while plant test data is a more direct approach of yield estimates when considering firstly plant N uptake from the whole soil profile and secondly biomass (important yield component). Plant test data are closer to yield in time and space than soil test data, shortening the time period for any yield prognosis …
Why Penetration Testing Is A Limited Use Choice For Sound Cyber Security Practice, Craig Valli, Andrew J. Woodward, Peter Hannay, Michael N. Johnstone
Why Penetration Testing Is A Limited Use Choice For Sound Cyber Security Practice, Craig Valli, Andrew J. Woodward, Peter Hannay, Michael N. Johnstone
Research outputs 2014 to 2021
Penetration testing of networks is a process that is overused when demonstrating or evaluating the cyber security posture of an organisation. Most penetration testing is not aligned with the actual intent of the testing, but rather is driven by a management directive of wanting to be seen to be addressing the issue of cyber security. The use of penetration testing is commonly a reaction to an adverse audit outcome or as a result of being penetrated in the first place. Penetration testing used in this fashion delivers little or no value to the organisation being tested for a number of …
An Information-Theoretic Image Quality Measure: Comparison With Statistical Similarity, Asmhan F. Hassan, Dong Cailin, Zahir M. Hussain
An Information-Theoretic Image Quality Measure: Comparison With Statistical Similarity, Asmhan F. Hassan, Dong Cailin, Zahir M. Hussain
Research outputs 2014 to 2021
We present an information-theoretic approach for structural similarity for assessing gray scale image quality. The structural similarity measure SSIM, proposed in 2004, has been successflly used and verfied. SSIM is based on statistical similarity between the two images. However, SSIM can produce confusing results in some cases where it may give a non-trivial amount of similarity for two different images. Also, SSIM cannot perform well (in detecting similarity or dissimilarity) at low peak signal to noise ratio (PSNR). In this study, we present a novel image similarity measure, HSSIM, by using information - theoretic technique based on joint histogram. The …
Mobile Applications For Indian Agriculture Sector: A Case Study, Pratik Shah, Niketa Gandhi, Leisa Armstrong
Mobile Applications For Indian Agriculture Sector: A Case Study, Pratik Shah, Niketa Gandhi, Leisa Armstrong
Research outputs 2014 to 2021
Government, private agencies and the general public are often interested in the decisions made by the Indian farmers as they have large influences beyond the farm boundary. Over many years, the process of adoption of new technologies and policies in the Indian agricultural sector has received considerable academic attention highlighting the role of many social, financial and other influences on their decision making. The Indian government and other development agencies promote income generating projects as a way of encouraging growth through increased agricultural production and the protection of the natural resource base. The impact of new technology to economic growth …
Geospatial Data Pre-Processing On Watershed Datasets: A Gis Approach, Sreedhar Nallan, Leisa Armstrong, Barry Croke, Amiya K. Tripathy
Geospatial Data Pre-Processing On Watershed Datasets: A Gis Approach, Sreedhar Nallan, Leisa Armstrong, Barry Croke, Amiya K. Tripathy
Research outputs 2014 to 2021
Spatial data mining helps to identify interesting patterns from the spatial data sets. However, geo spatial data requires substantial data pre-processing before data can be interrogated further using data mining techniques. Multi-dimensional spatial data has been used to explain the spatial analysis and SOLAP for pre-processing data. This paper examines some of the methods for pre-processing of the data using Arc GIS 10.2 and Spatial Analyst with a case study dataset of a watershed.
A Network That Really Works - The Application Of Artificial Neural Networks To Improve Yield Predictions And Nitrogen Management In Western Australia, Jinsong Leng, Andreas Neuhaus, Leisa Armstrong
A Network That Really Works - The Application Of Artificial Neural Networks To Improve Yield Predictions And Nitrogen Management In Western Australia, Jinsong Leng, Andreas Neuhaus, Leisa Armstrong
Research outputs 2014 to 2021
Yield predictions are notorious for being difficult due to many interdependent factors such as rainfall, soil properties, plant health, plant density etc. This study is based upon the author’s previously published work and extends its findings by further investigating the best mathematical solution to this dilemma. Artificial intelligence (AI) techniques have been applied to a large set of soil, plant, rainfall, and yield data from CSBP’s field research trial program. Here we further differentiate by investigate two ANN techniques, a genetic algorithm with back propagation neural networks (GA-BP-NN) and a particle swarm optimization with back propagation neural networks (PSO-BP-NN). Results …
An Artificial Neural Network For Predicting Crops Yield In Nepal, Tirtha Ranjeet, Leisa Armstrong
An Artificial Neural Network For Predicting Crops Yield In Nepal, Tirtha Ranjeet, Leisa Armstrong
Research outputs 2014 to 2021
This paper examines the application of artificial neural networks (ANNs) for predicting crop yields for an agricultural region in Nepal. The neural network algorithm has become an effective data mining tool and the outcome produced by this algorithm is considered to be less error prone than other computer science techniques. The backpropagation algorithm which iteratively finds a suitable weight value is considered for computing the error derivative. Agricultural data was collected from thirteen years from paddy field cultivation in the Siraha district, an eastern region in Nepal, and used for this investigation of neural networks. Additionally, climatic parameters including rainfall, …
A Survey Of Image Processing Techniques For Agriculture, Lalit Saxena, Leisa Armstrong
A Survey Of Image Processing Techniques For Agriculture, Lalit Saxena, Leisa Armstrong
Research outputs 2014 to 2021
Computer technologies have been shown to improve agricultural productivity in a number of ways. One technique which is emerging as a useful tool is image processing. This paper presents a short survey on using image processing techniques to assist researchers and farmers to improve agricultural practices. Image processing has been used to assist with precision agriculture practices, weed and herbicide technologies, monitoring plant growth and plant nutrition management. This paper highlights the future potential for image processing for different agricultural industry contexts.
Decision Support System Data For Farmer Decision Making, Pornchai Taechatanasat, Leisa Armstrong
Decision Support System Data For Farmer Decision Making, Pornchai Taechatanasat, Leisa Armstrong
Research outputs 2014 to 2021
The capacity of farmers and agricultural scientists to be able to make in-season decisions is dependent on accurate climate, soil and plant data. This paper will provide a review of the types of environmental and crop data that can be collected by sensors which can used for decision support systems (DSS) or be further interrogated for real time data mining and analysis. This paper also presents a review of the data requirements for agricultural decision making by firstly reviewing decision support frameworks and agricultural DSSs, data acquisition, sensors for data acquisition and examples of data incorporation for agricultural DSSs.
Genetic Algorithm With Logistic Regression For Prediction Of Progression To Alzheimer's Disease, Piers Johnson, Luke Vandewater, William Wilson, Paul Maruff, Greg Savage, Petra Graham, Lance S. Macaulay, Kathryn A. Ellis, Cassandra Szoeke, Ralph N. Martins, Christopher Rowe, Colin L. Masters, David Ames, Ping Zhang
Genetic Algorithm With Logistic Regression For Prediction Of Progression To Alzheimer's Disease, Piers Johnson, Luke Vandewater, William Wilson, Paul Maruff, Greg Savage, Petra Graham, Lance S. Macaulay, Kathryn A. Ellis, Cassandra Szoeke, Ralph N. Martins, Christopher Rowe, Colin L. Masters, David Ames, Ping Zhang
Research outputs 2014 to 2021
Assessment of risk and early diagnosis of Alzheimer's disease (AD) is a key to its prevention or slowing the progression of the disease. Previous research on risk factors for AD typically utilizes statistical comparison tests or stepwise selection with regression models. Outcomes of these methods tend to emphasize single risk factors rather than a combination of risk factors. However, a combination of factors, rather than any one alone, is likely to affect disease development. Genetic algorithms (GA) can be useful and efficient for searching a combination of variables for the best achievement (eg. accuracy of diagnosis), especially when the search …
Small To Medium Enterprise Cyber Security Awareness: An Initial Survey Of Western Australian Business, Craig Valli, Ian C. Martinus, Michael N. Johnstone
Small To Medium Enterprise Cyber Security Awareness: An Initial Survey Of Western Australian Business, Craig Valli, Ian C. Martinus, Michael N. Johnstone
Research outputs 2014 to 2021
Small to Medium Enterprises (SMEs) represent a large proportion of a nation’s business activity. There are studies and reports reporting the threat to business from cyber security issues resulting in computer hacking that achieve system penetration and information compromise. Very few are focussed on SMEs. Even fewer are focussed on directly surveying the actual SMEs themselves and attempts to improve SME outcomes with respect to cyber security. This paper represents research in progress that outlines an approach being undertaken in Western Australia with SMEs in the northwest metropolitan region of Perth, specifically within the large local government catchments of Joondalup …
Zernike Moments And Genetic Algorithm : Tutorial And Application, Oluleye H. Babatunde, Leisa Armstrong, Jinsong Leng, Dean Diepeveen
Zernike Moments And Genetic Algorithm : Tutorial And Application, Oluleye H. Babatunde, Leisa Armstrong, Jinsong Leng, Dean Diepeveen
Research outputs 2014 to 2021
Aims/ objectives: To demontrate effectiveness of Zernike Moments for Image Classification. Zernike moment(ZM) is an excellent region-based moment which has attracted the attentions of many image processing researchers since its first application to image analysis. Many papers have been published on several works done on ZM but no single paper ever give a detailed information of how the computation of ZM is done from the time the image is captured to the computation of ZM. This work showed how to effectively apply ZM on RGB images. We have demonstrated the effectiveness of Zernike moment in image classification system. A neuro-genetic …
Tego - A Framework For Adversarial Planning, Daniel Ashlock, Philip Hingston
Tego - A Framework For Adversarial Planning, Daniel Ashlock, Philip Hingston
Research outputs 2014 to 2021
This study establishes a framework called ∗-Tego for a situation in which two agents are each given a set of players for a competitive game. Each agent places their players in an order. Players on each side at the same position in the order play one another, with the agent's score being the sum of their player's scores. The planning agents are permitted to simultaneous reorder their players in each of several stages. The reordering is termed competitive replanning. The resulting framework is scalable by changing the number of players and the complexity of the replanning process. The framework is …
Editorial - The Changing Face Of Ehealth Security, Patricia Williams, Lizzie Coles-Kemp
Editorial - The Changing Face Of Ehealth Security, Patricia Williams, Lizzie Coles-Kemp
Research outputs 2014 to 2021
No abstract provided.
Cloud Security Meets Telemedicine, Michael N. Johnstone
Cloud Security Meets Telemedicine, Michael N. Johnstone
Research outputs 2014 to 2021
Medical systems are potentially one domain where security is seen as an impediment to patient care and not as an essential part of a system. This is an issue for safety-critical systems where reliability and trust are essential for successful operation. Cloud computing services offer a seamless means to allow medical data to be transferred from patient to medical specialist, whilst maintaining security requirements. This paper uses a case study to investigate the use of cloud computing in a mobile application to assist with diagnostics for patients with Parkinson Disease. It was found that the developers of the app ignored …
Text Extraction In Natural Scenes Using Region-Based Method, Zhihu Huang, Jinsong Leng
Text Extraction In Natural Scenes Using Region-Based Method, Zhihu Huang, Jinsong Leng
Research outputs 2014 to 2021
Text in images is a very important clue for image indexing and retrieving. Unfortunately, it is a challenging work to accurately and robustly extract text from a complex background image. In this paper, a novel region-based text extraction method is proposed. In doing so, the candidate text regions are detected by 8-connected objects detection algorithm based on the edge image. Then the non-text regions are filtered out using shape, texture and stroke width rules. Finally, the remaining regions are grouped into text lines. Since stroke width is the intrinsic and particular characteristics of the text, the accuracy of the non-text …
Local And Semi-Global Feature-Correlative Techniques For Face Recognition, Asaad Noori Hashim, Zahir Hussain
Local And Semi-Global Feature-Correlative Techniques For Face Recognition, Asaad Noori Hashim, Zahir Hussain
Research outputs 2014 to 2021
Face recognition is an interesting field of computer vision with many commercial and scientific applications. It is considered as a very hot topic and challenging problem at the moment. Many methods and techniques have been proposed and applied for this purpose, such as neural networks, PCA, Gabor filtering, etc. Each approach has its weaknesses as well as its points of strength. This paper introduces a highly efficient method for the recognition of human faces in digital images using a new feature extraction method that combines the global and local information in different views (poses) of facial images. Feature extraction techniques …
An Information-Theoretic Measure For Face Recognition: Comparison With Structural Similarity, Asmhan Flieh Hassan, Zahir Hussain, Dong Cai-Lin
An Information-Theoretic Measure For Face Recognition: Comparison With Structural Similarity, Asmhan Flieh Hassan, Zahir Hussain, Dong Cai-Lin
Research outputs 2014 to 2021
Automatic recognition of people faces is a challenging problem that has received significant attention from signal processing researchers in recent years. This is due to its several applications in different fields, including security and forensic analysis. Despite this attention, face recognition is still one among the most challenging problems. Up to this moment, there is no technique that provides a reliable solution to all situations. In this paper a novel technique for face recognition is presented. This technique, which is called ISSIM, is derived from our recently published information - theoretic similarity measure HSSIM, which was based on joint histogram. …
A Genetic Algorithm-Based Feature Selection, Oluleye H. Babatunde, Leisa Armstrong, Jinsong Leng, Dean Diepeveen
A Genetic Algorithm-Based Feature Selection, Oluleye H. Babatunde, Leisa Armstrong, Jinsong Leng, Dean Diepeveen
Research outputs 2014 to 2021
This article details the exploration and application of Genetic Algorithm (GA) for feature selection. Particularly a binary GA was used for dimensionality reduction to enhance the performance of the concerned classifiers. In this work, hundred (100) features were extracted from set of images found in the Flavia dataset (a publicly available dataset). The extracted features are Zernike Moments (ZM), Fourier Descriptors (FD), Lengendre Moments (LM), Hu 7 Moments (Hu7M), Texture Properties (TP) and Geometrical Properties (GP). The main contributions of this article are (1) detailed documentation of the GA Toolbox in MATLAB and (2) the development of a GA-based feature …