Description:
It is well known that breast density in mammograms may hinder the accuracy of diagnosis of breast cancer. Although the dense breasts should be processed in a special manner, most of the research has treated dense breast almost the same as fatty. Consequently, the dense tissues in the breast are diagnosed as a developed cancer. In contrast, dense-fatty should be clearly distinguished before the diagnosis of cancerous or not cancerous breast. In this paper, we develop such a system that will automatically analyze mammograms and identify significant features. For feature extraction, we develop a novel system by combining a two-dimensional discrete cosine transform (2D-DCT) and a principal component analysis (PCA) to extract a minimal feature set of mammograms to differentiate breast density. These features are fed to three classifiers: Backpropagation Multilayer Perceptron (MLP), Support Vector Machine (SVM) and K Nearest Neighbour (KNN). A majority voting on the outputs of different machine learning tools is also investigated to enhance the classification performance. The results show that features extracted using a combination of DCT-PCA provide a very high classification performance while using a majority voting of classifiers outputs from MLP, SVM, and KNN.
Description:
It is well known that breast density in mammograms may hinder the accuracy of diagnosis of breast cancer. Although the dense breasts should be processed in a special manner, most of the research has treated dense breast almost the same as fatty. Consequently, the dense tissues in the breast are diagnosed as a developed cancer. In contrast, dense-fatty should be clearly distinguished before the diagnosis of cancerous or not cancerous breast. In this paper, we develop such a system that will automatically analyze mammograms and identify significant features. For feature extraction, we develop a novel system by combining a two-dimensional discrete cosine transform (2D-DCT) and a principal component analysis (PCA) to extract a minimal feature set of mammograms to differentiate breast density. These features are fed to three classifiers: Backpropagation Multilayer Perceptron (MLP), Support Vector Machine (SVM) and K Nearest Neighbour (KNN). A majority voting on the outputs of different machine learning tools is also investigated to enhance the classification performance. The results show that features extracted using a combination of DCT-PCA provide a very high classification performance while using a majority voting of classifiers outputs from MLP, SVM, and KNN.
Description:
Australia has ample geothermal resource, however, it is of low-grade heat and requires Enhanced Geothermal Systems (EGS). Integrating heat recovered via EGS into a lignocellulosic biorefinery opens the avenue for countless opportunities in biomass to products industries. In this study, a biorefinery is modelled that uses heat from a supercritical CO
Description:
Australia has ample geothermal resource, however, it is of low-grade heat and requires Enhanced Geothermal Systems (EGS). Integrating heat recovered via EGS into a lignocellulosic biorefinery opens the avenue for countless opportunities in biomass to products industries. In this study, a biorefinery is modelled that uses heat from a supercritical CO
Description:
Unbound Granular Material (UGM) used in the base/subbase layers of a flexible pavement structure constitutes the vast majority of the material found in roads around the world. The permanent deformation of a compacted UGM layer due to cyclic deviatoric loading has a significant effect on the performance of the pavement structure. The accurate prediction of the magnitude of accumulated permanent strain at varying load cycles and stress levels plays an important role in improving the design and maintenance of flexible pavements. In this study, samples of two road base UGMs are tested to evaluate the characteristics of permanent deformation using the laboratory Repeated Load Triaxial (RLT) test. Three permanent deformation models are used to predict the magnitude of strain accumulation of the studied UGMs. The permanent strain results predicted by the models are compared against those measured by laboratory RLT tests to evaluate the prediction ability of each model.
Description:
Bearing Capacity of Roads, Railways and Airfields - Proceedings of the 10th International Conference on the Bearing Capacity of Roads, Railways and Airfields, BCRRA 2017
Description:
This paper proposes a classifier-free method for extraction of power line wires from aerial point cloud data. It combines the advantages of both grid- and point-based processing of the input data. In addition to the non-ground point cloud data, the input to the proposed method includes the pylon locations, which are automatically extracted by a previous method. The proposed method first counts the number of wires in a span between the two successive pylons using two masks: vertical and horizontal. Then, the initial wire segments are obtained and refined iteratively. Finally, the initial segments are extended on both ends and each individual wire points are modelled as a 3D polynomial curve. Experimental results show both the object-based completeness and correctness are 97%, while the point-based completeness and correctness are 99% and 88%, respectively.
Description:
2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018
Description:
Texture image classification is very useful in many domains. It has been tried using statistical, spectral and structural approaches. A novel Pixel N-grams technique has emerged for image feature extraction recently. The aim of this paper is to analyse the efficacy of Pixel N-grams technique for texture image classification in comparison with the traditional techniques namely Intensity histogram, Haralick’s features based on co-occurrence matrix and state-of-the-art Bag-of-Visual-Words (BoVW). The experiments were carried out on the benchmark UIUC texture dataset using SVM classifier. The classification performance was compared using Fscore, Recall and Precision. The classification results using Pixel N-gram were significantly better than that using Intensity histogram and Haralick features whereas, they were comparable with the BoVW approach.
Description:
In this paper, an improved algorithm for cuboid image segmentation is proposed. To address the two main limitations of the recently proposed cuboid segmentation algorithm, the improved algorithm substitutes colour quantization in HCL colour space with infinity norm distance in RGB colour space along with a different way to impose area thresholding. We also propose a new metric to evaluate the quality of segmentation. Experimental results show that the proposed cuboid segmentation algorithm significantly outperforms the existing cuboid segmentation algorithm in terms of quality of segmentation.
Description:
International Conference Image and Vision Computing New Zealand
Description:
In this paper, an improved algorithm for cuboid image segmentation is proposed. To address the two main limitations of the recently proposed cuboid segmentation algorithm, the improved algorithm substitutes colour quantization in HCL colour space with infinity norm distance in RGB colour space along with a different way to impose area thresholding. We also propose a new metric to evaluate the quality of segmentation. Experimental results show that the proposed cuboid segmentation algorithm significantly outperforms the existing cuboid segmentation algorithm in terms of quality of segmentation.
Description:
International Conference Image and Vision Computing New Zealand
Description:
Application of high tensile steel in various structural forms such as rolled or fabricated sections is currently increasing due its mechanical and economic advantages. Experimental investigations show the distinct plastic behavior of high tensile steel with grades greater than 700 MPa damaged under reversed tension-compression loading scenarios compared to the hardening performance of lower grades of steel. This paper investigates a combined nonlinear plastic model to predict the stress-strain equations of grades 800 and 1200 steel extracted from circular tubes under very low cycle structural damage. In the numerical modelling phase, relevant parameters are calibrated and model is verified against hysteretic experimental results. The numerical results provide a simulation tool for structures consisting of high tensile steel tubes under seismic loads.
Description:
Scoring systems such as the Glasgow-Coma scale used to assess consciousness AusDrisk to assess the risk of diabetes, are prevalent in clinical practice. Scoring systems typically include relevant variables with ordinal values where each value is assigned a weight. Weights for selected values are summed and compared to thresholds for health care professionals to rapidly generate a score. Scoring systems are prevalent in clinical practice because they are easy and quick to use. However, most scoring systems comprise many variables and require some time to calculate an final score. Further, expensive population-wide studies are required to validate a scoring system. In this article, we present a new approach for the generation of a scoring system. The approach uses a search procedure invoking iterative decision tree induction to identify a suite of scoring rules, each of which requires values on only two variables. Twelve scoring rules were discovered using the approach, from an Australian screening program for the assessment of Type 2 Diabetes risk. However, classifications from the 12 rules can conflict. In this paper we argue that a simple rule preference relation is sufficient for the resolution of rule conflicts.
Description:
Data transmission in delay tolerant networks (DTNs) is a challenging problem due to the lack of continuous network connectivity and nondeterministic mobility of the nodes. Epidemic routing and spray-and-wait methods are two popular mechanisms that are proposed for DTNs. In order to reduce the transmission delay in DTNs, some previous works combine intra-flow network coding with the routing protocols. In this paper, we propose two routing mechanisms using systematic joint inter- and intra-flow network coding for the purpose of data exchange between the nodes. We discuss the reasons why inter-flow network coding helps to reduce the delivery delay of the packets, and we also analyze the delays related with only using intra-flow coding, and joint inter- and intra-flow coding methods. We empirically show the benefit of joint coding over just intra-flow coding. Based on our simulation, joint coding can reduce the delay up to 40%, compared to only intra-flow coding.
Description:
2018 IEEE 37th International Performance Computing and Communications Conference, IPCCC 2018
Description:
Data transmission in delay tolerant networks (DTNs) is a challenging problem due to the lack of continuous network connectivity and nondeterministic mobility of the nodes. Epidemic routing and spray-and-wait methods are two popular mechanisms that are proposed for DTNs. In order to reduce the transmission delay in DTNs, some previous works combine intra-flow network coding with the routing protocols. In this paper, we propose two routing mechanisms using systematic joint inter- and intra-flow network coding for the purpose of data exchange between the nodes. We discuss the reasons why inter-flow network coding helps to reduce the delivery delay of the packets, and we also analyze the delays related with only using intra-flow coding, and joint inter- and intra-flow coding methods. We empirically show the benefit of joint coding over just intra-flow coding. Based on our simulation, joint coding can reduce the delay up to 40%, compared to only intra-flow coding.
Description:
2018 IEEE 37th International Performance Computing and Communications Conference, IPCCC 2018
Description:
Prognostic models for maintenance decisions have inherent limitations due to quality quantity of historical data, assumptions made, and time required in validating models. In this paper, Preventive Maintenance (PM) Intervals, Failure events, cost and maintenance records from Computerized Maintenance Management System (CMMS) are analyzed for reducing downtimes and Operating Expenditure (OPEX). The proposed methodologies for maintenance intervals and replacements with acceptable level of confidence are articulated to asset maintenance of a City Council of Australian Local Government organisation as a case of improved decision making under limited information.
Description:
IEEE International Conference on Industrial Engineering and Engineering Management
Description:
Prognostic models for maintenance decisions have inherent limitations due to quality quantity of historical data, assumptions made, and time required in validating models. In this paper, Preventive Maintenance (PM) Intervals, Failure events, cost and maintenance records from Computerized Maintenance Management System (CMMS) are analyzed for reducing downtimes and Operating Expenditure (OPEX). The proposed methodologies for maintenance intervals and replacements with acceptable level of confidence are articulated to asset maintenance of a City Council of Australian Local Government organisation as a case of improved decision making under limited information.
Description:
IEEE International Conference on Industrial Engineering and Engineering Management
Description:
Event-based social networks (EBSNs) are the newly emerging social platforms for users to publish events online and attract others to attend events offline. The content information of events plays an important role in event recommendation. However, the content-based approaches in existing event recommender systems cannot fully represent the preference of each user on events since most of them focus on exploiting the content information from events' perspective, and the bag-of-words model, commonly used by them, can only capture word frequency but ignore word orders and sentence structure. In this paper, we shift the focus from events' perspective to users' perspective, and propose a Deep User Modeling framework for Event Recommendation (DUMER) to characterize the preference of users by exploiting the contextual information of events that users have attended. Specifically, we utilize convolutional neural network (CNN) with word embedding to deeply capture the contextual information of a user's interested events and build up a user latent model for each user. We then incorporate the user latent model into probabilistic matrix factorization (PMF) model to enhance the recommendation accuracy. We conduct experiments on the real-world dataset crawled from a typical EBSN, Meetup.com, and the experimental results show that DUMER outperforms the compared benchmarks.
Description:
Traffic systems and signals are used to improve traffic flow, reduce congestion, increase travel time consistency and ensure safety of road users. Malicious interruption or manipulation of traffic signals may cause disastrous instants including huge delays, financial loss and loss of lives. Intrusion into traffic signals by hackers can create such interruption whose consequences will only increase with the introduction of driverless vehicles. Recently, many traffic signals across the world are reported to have intruded, highlighting the importance of accurate detection. To reduce the impact of an intrusion, in this paper, we introduce an intrusion detection technique using the flow rate and phase time of a traffic signal as evidential information to detect the presence of an intrusion. The information received from flow rate and phase time are fused with the Dempster Shaffer (DS) theory. Historical data are used to create the probability mass functions for both flow rate and phase time. We also developed a simulation model using a traffic simulator, namely SUMO for many types of real traffic situations including intrusion. The performance of the proposed Intrusion Detection System (IDS) is appraised with normal traffic condition and induced intrusions. Simulated results show our proposed system can successfully detect intruded traffic signals from normal signals with significantly high accuracy (above 91%).
Description:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Description:
Image sensors are generating limitless digital images every day. Image forgery like splicing and copy-move are very common type of attacks that are easy to execute using sophisticated photo editing tools. As a result, digital forensics has attracted much attention to identify such tampering on digital images. In this paper, a passive (blind) image tampering identification method based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP) has been proposed. First, the chroma components of an image is divided into fixed sized non-overlapping blocks and 2D block DCT is applied to identify the changes due to forgery in local frequency distribution of the image. Then a texture descriptor, LBP is applied on the magnitude component of the 2D-DCT array to enhance the artifacts introduced by the tampering operation. The resulting LBP image is again divided into non-overlapping blocks. Finally, summations of corresponding inter-cell values of all the LBP blocks are computed and arranged as a feature vector. These features are fed into a Support Vector Machine (SVM) with Radial Basis Function (RBF) as kernel to distinguish forged images from authentic ones. The proposed method has been experimented extensively on three publicly available well-known image splicing and copy-move detection benchmark datasets of color images. Results demonstrate the superiority of the proposed method over recently proposed state-of-the-art approaches in terms of well accepted performance metrics such as accuracy, area under ROC curve and others.
Description:
2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018
Description:
Image sensors are generating limitless digital images every day. Image forgery like splicing and copy-move are very common type of attacks that are easy to execute using sophisticated photo editing tools. As a result, digital forensics has attracted much attention to identify such tampering on digital images. In this paper, a passive (blind) image tampering identification method based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP) has been proposed. First, the chroma components of an image is divided into fixed sized non-overlapping blocks and 2D block DCT is applied to identify the changes due to forgery in local frequency distribution of the image. Then a texture descriptor, LBP is applied on the magnitude component of the 2D-DCT array to enhance the artifacts introduced by the tampering operation. The resulting LBP image is again divided into non-overlapping blocks. Finally, summations of corresponding inter-cell values of all the LBP blocks are computed and arranged as a feature vector. These features are fed into a Support Vector Machine (SVM) with Radial Basis Function (RBF) as kernel to distinguish forged images from authentic ones. The proposed method has been experimented extensively on three publicly available well-known image splicing and copy-move detection benchmark datasets of color images. Results demonstrate the superiority of the proposed method over recently proposed state-of-the-art approaches in terms of well accepted performance metrics such as accuracy, area under ROC curve and others.
Description:
2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018
Description:
Complex network analysis has gained research interests in a wide range of fields. Network motif, which is one of the most popular network properties, is a statistically significant network subgraph. In this paper, we propose a fast methodology, called Four-node Motif Detection Algorithm (FMDA), to extract four-node motifs in complex networks. Specifically, we employ a two-way spectral clustering method to cut big networks into small sub-graphs, and then identify motifs by recognition algorithm to reduce the computational complexity. After that, we use three isomorphic four-node motifs to analyze network structure by American Physical Society (APS) data set.
Description:
Sensing technologies provide system applications with the awareness of environmental conditions, customer behaviours, object movements, etc. Further, with such capability, system applications can be smart to intelligently adapt their responses to the changing conditions. With regard to business operations, these system applications ensure that business processes can run more intelligently and adaptively. These features will undoubtedly improve customer experience, enhance the reliability of service delivery and lower the operational cost for a more competitive and sustainable business. To enable context awareness to business process management, this paper proposes a conceptual method of depicting the context of a business process and the related mechanism of perceiving the contextual dynamics. A running example demonstrates the applicability of the proposed method and the improvements to process performance are evaluated using process simulations.
Description:
This work aims to maximize the energy efficiency of a downlink cloud radio access network (C-RAN), where data is transferred from a baseband unit in the core network to several remote radio heads via a set of edge routers over capacity-limited fronthaul links. The remote radio heads then send the received signals to their users via radio access links. We formulate a new mixed-integer nonlinear problem in which the ratio of network throughput and total power consumption is maximized. This challenging problem formulation includes practical constraints on routing, predefined minimum data rates, fronthaul capacity and maximum RRH transmit power. By employing the successive convex quadratic programming framework, an iterative algorithm is proposed with guaranteed convergence to a Fritz John solution of the formulated problem. Significantly, each iteration of the proposed algorithm solves only one simple convex program. Numerical examples with practical parameters confirm that the proposed joint optimization design markedly improves the C-RAN's energy efficiency compared to benchmark schemes.
Description:
In this paper, we further investigate our recently proposed cuboid image segmentation algorithm for effective image retrieval. Instead of using all cuboids (i.e. segments), we have proposed two approaches to choose different subsets of cuboids appropriately. With the experimental results on eBay dataset, we have shown that our proposals outperform retrieval performance of the existing technique. In addition, we have investigated how many segments are required for the most effective image retrieval and provide a quick method to determine the suitable number of cuboids.
Description:
2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018
Description:
In this paper, we further investigate our recently proposed cuboid image segmentation algorithm for effective image retrieval. Instead of using all cuboids (i.e. segments), we have proposed two approaches to choose different subsets of cuboids appropriately. With the experimental results on eBay dataset, we have shown that our proposals outperform retrieval performance of the existing technique. In addition, we have investigated how many segments are required for the most effective image retrieval and provide a quick method to determine the suitable number of cuboids.
Description:
2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018
Description:
Image registration has received great attention from researchers over the last few decades. SIFT (Scale Invariant Feature Transform), a local descriptor-based technique is widely used for registering and matching images. To establish correspondences between images, SIFT uses a Euclidean Distance ratio metric. However, this approach leads to a lot of incorrect matches and eliminating these inaccurate matches has been a challenge. Various methods have been proposed attempting to mitigate this problem. In this paper, we propose a scale and orientation harmony-based pruning method that improves image matching process by successfully eliminating incorrect SIFT descriptor matches. Moreover, our technique can predict the image transformation parameters based on a novel adaptive clustering method with much higher matching accuracy. Our experimental results have shown that the proposed method has achieved averages of approximately 16% and 10% higher matching accuracy compared to the traditional SIFT and a contemporary method respectively.
Description:
2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018