Description:
Hand vein biometric is an important modality for human authentication and liveness detection in many applications. Reliable feature extraction is vital to any biometric system. Over the past years, two major categories of vein features, namely vein structures and vein image textures, were proposed for hand dorsal vein based biometric identification. Of them, texture features seem important as it can combine skin micro-textures along with vein properties. In this study, we have performed a comparative study to identify potential texture features and feature-classifier combination that produce efficient vein biometric systems. Seven texture features (HOG, GABOR, GLCM, SSF, DWT, WPT, and LBP) and three multiclass classifiers (LDA, ESVM, and KNN) were explored towards the supervised identification of human from vein images. An experiment with 400 infrared (IR) hand images from 40 adults indicates the superior performance of the histogram of oriented gradients (HOG) and simple local statistical feature (SSF) with LDA and ESVM classifiers in terms of average accuracy (> 90%), average Fscore (> 58%) and average specificity (>93%). The decision-level fusion of the LDA and ESVM classifier with single texture features showed improved performances (by 2.2 to 13.2% of average Fscore) over individual classifier for human identification with IR hand vein images.
Description:
Proceedings - International Computer Software and Applications Conference
Description:
Hand vein biometric is an important modality for human authentication and liveness detection in many applications. Reliable feature extraction is vital to any biometric system. Over the past years, two major categories of vein features, namely vein structures and vein image textures, were proposed for hand dorsal vein based biometric identification. Of them, texture features seem important as it can combine skin micro-textures along with vein properties. In this study, we have performed a comparative study to identify potential texture features and feature-classifier combination that produce efficient vein biometric systems. Seven texture features (HOG, GABOR, GLCM, SSF, DWT, WPT, and LBP) and three multiclass classifiers (LDA, ESVM, and KNN) were explored towards the supervised identification of human from vein images. An experiment with 400 infrared (IR) hand images from 40 adults indicates the superior performance of the histogram of oriented gradients (HOG) and simple local statistical feature (SSF) with LDA and ESVM classifiers in terms of average accuracy (> 90%), average Fscore (> 58%) and average specificity (>93%). The decision-level fusion of the LDA and ESVM classifier with single texture features showed improved performances (by 2.2 to 13.2% of average Fscore) over individual classifier for human identification with IR hand vein images.
Description:
Proceedings - International Computer Software and Applications Conference
Description:
The aim of this paper is to demonstrate the importance of technological investment as an innovation creates benefits either in macro and micro economics. In particular, adopting information and communication technology (ICT) for East Java's SMAES can create value within the agribusiness sub-systems by digitalized coordination.
Description:
This paper presents a proposed adaptive technique for nonholonomic wheeled mobile robot (NWMR) using the sliding-mode control (SMC) method. The proposed control system based on the backstepping kinematic controller and PI sliding mode dynamic control. With an adaptive fuzzy logic to adjust adaptation gain of SMC for trajectory tracking control of nonholonomic mobile robot. Parametric and nonparametric uncertainties of mobile robot can be solved by using the proposed control which take advantages of stability and robustness in sliding mode control. The adaptation gain of SMC is adjusted by using Mamdani type inference system with adaptive tuning algorithm, which improves the adaptability for uncertainness and eliminate input chattering of the SMC. The stability and convergence of the control system are proved using Lypanouve criteria, and the comparison of the proposed controller with the other controllers ensures the validity and superiority of my own controller
Description:
In the classrooms of tomorrow, beginning teachers encounter a wide array of students from diverse linguistic, cultural, religious and socio-economic backgrounds. The global community is reflected in the classroom and teachers are expected to prepare their students for global citizenship. Practising and pre-service teachers studying education programs at open access regional universities in Australia often have limited experience of travel and few opportunities to develop global mindedness. Drawing on Bourdieu’s work on social and cultural capital, this qualitative research explores how intercultural experiences contribute to the individual’s developing teacher identity and, in particular, whether unfamiliar professional experience settings promote socially and culturally inclusive teaching in a global context. The study examines whether pre-service teachers develop intercultural empathy, improve intercultural communication and assemble values that reflect membership of a global community. Critical pedagogy principles have been applied to the research and participants are encouraged to critically reflect on their professional experience and their personal journeys as they build their teacher identities. Participant stories, collected through interviews and reflective writing, are examined through narrative inquiry. Additionally, questionnaires are used to provide observer evaluation of the extent to which participants are being and becoming socially and culturally inclusive teachers in a global context. This paper reports on the experiences of the first cohort in the study who have travelled from Federation University Australia to complete professional experience placements in China, Timor-Leste and Papua New Guinea in 2015. Initial findings indicate that these experiences may increase the individual’s cross-cultural empathy, understandings and communication skills, thus enhancing their capacity for socially and culturally inclusive teaching. In addition university mentoring and guidance through the reflection process can increase the learning that occurs including understanding cultural diversity in the classroom, becoming responsible and globally informed teachers and deeply reflective practitioners.
Description:
A unified model is proposed for general optimization problems in multi-scale complex systems. Based on this model and necessary assumptions in physics, the canonical duality theory is presented in a precise way to include traditional duality theories and popular methods as special applications. Two conjectures on NP-hardness are proposed, which should play important roles for correctly understanding and efficiently solving challenging real-world problems. Applications are illustrated for both nonconvex continuous optimization and mixed integer nonlinear programming.
Description:
A unified model is proposed for general optimization problems in multi-scale complex systems. Based on this model and necessary assumptions in physics, the canonical duality theory is presented in a precise way to include traditional duality theories and popular methods as special applications. Two conjectures on NP-hardness are proposed, which should play important roles for correctly understanding and efficiently solving challenging real-world problems. Applications are illustrated for both nonconvex continuous optimization and mixed integer nonlinear programming.
Description:
In a distributed, dynamic and volatile operating environment, runtime anomalies occurring in service-oriented systems (SOSs) must be located and fixed in a timely manner in order to guarantee successful delivery of outcomes in response to user requests. Monitoring all component services constantly and inspecting the entire SOS upon a runtime anomaly are impractical due to excessive resource and time consumption required, especially in large-scale scenarios. We present a spectrum-based approach that goes through a five-phase process to quickly localize runtime anomalies occurring in SOSs based on end-to-end system delays. Upon runtime anomalies, our approach calculates the similarity coefficient for each basic component (BC) of the SOS to evaluate their suspiciousness of being faulty. Our approach also calculates the delay coefficients to evaluate each BC's contribution to the severity of the end-to-end system delays. Finally, the BCs are ranked by their similarity coefficient scores and delay coefficient scores to determine the order of them being inspected. Extensive experiments are conducted to evaluate the effectiveness and efficiency of the proposed approach. The results indicate that our approach significantly outperforms random inspection and the popular Ochiai-based inspection in localizing single and multiple runtime anomalies effectively. Thus, our approach can help save time and effort for localizing runtime anomalies occuring in SOSs.
Description:
Malware authors are known to reuse existing code, this development process results in software evolution and a sequence of versions of a malware family containing functions that show a divergence from the initial version. This paper proposes the term evolved similarity to account for this gradual divergence of similarity across the version history of a malware family. While existing techniques are able to match functions in different versions of malware, these techniques work best when the version changes are relatively small. This paper introduces the concept of evolved similarity and presents automated Evolved Similarity Techniques (EST). EST differs from existing malware function similarity techniques by focusing on the identification of significantly modified functions in adjacent malware versions and may also be used to identify function similarity in malware samples that differ by several versions. The challenge in identifying evolved malware function pairs lies in identifying features that are relatively invariant across evolved code. The research in this paper makes use of the function call graph to establish these features and then demonstrates the use of these techniques using Zeus malware.
Description:
Manufacturing industries are facing tough competition due to increasing raw material cost and depleting natural resources. There is great pressure on the industry to produce environmental friendly products using environmental friendly processes. To address these issues modern manufacturing industries are focusing on sustainable manufacturing. To develop more sustainable societies, industries need to better understand how to respond to environmental, economic and social challenges. This paper proposed some framework and tools that accelerate the transition towards a sustainable system. The developed framework will be beneficial for sustainability assessment comparing different plans alongside material properties, ultimately helping the manufacturing industries to reduce the carbon emissions and material waste, besides improving energy efficiency. It is expected that this would be highly beneficial for determination of environmental impact of a process at early design stages. Therefore, it would greatly help the manufacturing industries for selection of process plan based on sustainable indices. Overall objective of this paper would have good impact on reducing air emissions and protecting environment. We expect this work to contribute to the development of a standard reference methodology to help further sustainability in the manufacturing sector.
Description:
Manufacturing industries are facing tough competition due to increasing raw material cost and depleting natural resources. There is great pressure on the industry to produce environmental friendly products using environmental friendly processes. To address these issues modern manufacturing industries are focusing on sustainable manufacturing. To develop more sustainable societies, industries need to better understand how to respond to environmental, economic and social challenges. This paper proposed some framework and tools that accelerate the transition towards a sustainable system. The developed framework will be beneficial for sustainability assessment comparing different plans alongside material properties, ultimately helping the manufacturing industries to reduce the carbon emissions and material waste, besides improving energy efficiency. It is expected that this would be highly beneficial for determination of environmental impact of a process at early design stages. Therefore, it would greatly help the manufacturing industries for selection of process plan based on sustainable indices. Overall objective of this paper would have good impact on reducing air emissions and protecting environment. We expect this work to contribute to the development of a standard reference methodology to help further sustainability in the manufacturing sector.
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:
The unbalanced conditions are taken into account in the Reactive Power/Voltage control of distribution system. The aim of the control is to simultaneously minimize energy loss and improve voltage profile. The control is carried out by optimal dispatch of load tap changers (LTC) and shunt capacitors considering unbalanced conditions. A genetic algorithm (GA) is developed to determine the load curve partition for effective LTC scheduling and switching constraint satisfaction. GA is also appointed to determine the optimal dispatch schedule of the devices and to check the fulfillment of switching constraints prior to performing calculations. For power flow analyses under unbalanced conditions, a forward/backward propagation algorithm is developed. The optimization is implemented on the IEEE 34-bus unbalanced distribution system, and the presented system improvements are highlighted. The main contribution is inclusion of unbalanced system conditions into the optimal dispatch problem considering different daily load curves for the three phases of distribution system.
Description:
Based on a large scale crime scene investigation (CSI) image database, an effective and efficient CSI image retrieval system has been proposed to empower the investigative work of the police force. The main contribution of this paper includes: (1) a DCT domain texture feature extraction algorithm is proposed for CSI images, which is shown to be simple and effective. (2) the use of GIST descriptor on CSI images for the first time and combined with color histogram and the DCT domain texture feature as a fused feature, which describes CSI images from different aspects including color, texture, and scene content. Experimental results prove that the proposed method is effective for CSI image retrieval.
Description:
Three high-temperature solar receiver design concepts are being evaluated as part of the Australian Solar Thermal Research Initiative (ASTRI): a flux-optimised sodium receiver, a falling particle receiver, and an expanding-vortex particle receiver. Preliminary results from performance modelling of each concept are presented. For the falling particle receiver, it is shown how particle size and flow rate have a significant influence on absorptance. For the vortex receiver, methods to reduce particle deposition on the window and increase particle residence time are discussed. For the sodium receiver, the methodology for geometry optimisation is discussed, as well as practical constraints relating to containment materials
Description:
The Macquarie Marshes (NSW, Australia) cover approximately 200 square km of the Macquarie River flood-plains. The marshes are one of the largest remaining inland semi-permanent wetlands in southeastern Australia. Diversity of fauna and flora has decreased in the wetlands while the flood-drought cycles controlling these ecosystems have been affected by recent human activity. An Electrical Resistivity Tomography survey has been carried out to provide insight into the surface water/ groundwater interactions occurring at the north-western part of the marshes and to identify potential recharge areas of the aquifer systems. In the resistivity sections three main units can be identified: 1. A top unit of low-resistivity (1 to 6 ohm.m) with about 5 meter thick on average. 2. A middle unit of higher electrical resistivity (6 to 20 ohm.m) that continues to a depth of approximately 20 metres and is discontinuous laterally. 3. A bottom unit below a depth of 20 to 25 metres with resistivity decreasing to values similar to those of the top unit. The resistivity results has allowed to identify clay dominated and sand dominated materials. The groundwater is recharged from surface water following sandy windows in the clay created by modern channels on the surface of the marshes.
Description:
22nd European Meeting of Environmental and Engineering Geophysics, Near Surface Geoscience 2016
Description:
While the main body of literature regarding small-to-medium enterprises is focused on formation and growth, there is insufficient research about the role of both (a) firm size and (b) location on economic growth. The role of firm size and industrial structure on economic growth has been examined by some researchers. Pagano (2003) and Pagano and Schivardi (2000) identified a positive association between average firm size and growth and Carree and Thurik (1999) found evidence that the low number of large firms in an industry could lead to a higher value added growth. The current study attempts to investigate the impact of industry structure and businesses operating within these industries on economic growth. This paper uses “k-means” clustering algorithm to cluster Statistical Local Areas. Regression analysis is utilised to identify drivers of economic growth. Preliminary results suggest that size of business may act as a driver of economic growth but the impact could vary based on location.
Description:
While the main body of literature regarding small-to-medium enterprises is focused on formation and growth, there is insufficient research about the role of both (a) firm size and (b) location on economic growth. The role of firm size and industrial structure on economic growth has been examined by some researchers. Pagano (2003) and Pagano and Schivardi (2000) identified a positive association between average firm size and growth and Carree and Thurik (1999) found evidence that the low number of large firms in an industry could lead to a higher value added growth. The current study attempts to investigate the impact of industry structure and businesses operating within these industries on economic growth. This paper uses “k-means” clustering algorithm to cluster Statistical Local Areas. Regression analysis is utilised to identify drivers of economic growth. Preliminary results suggest that size of business may act as a driver of economic growth but the impact could vary based on location.