Physical Sciences and Mathematics Commons^™

A Spatial Squeezing Approach To Ambisonic Audio Compression, Bin Cheng, Christian Ritz, I. Burnett

Faculty of Informatics - Papers (Archive)

Spatially squeezed surround audio coding (S3AC) has been previously shown to provide efficient coding with perceptually accurate soundfield reconstruction when applied to ITU 5.1 multichannel audio. This paper investigates the application of S3AC to the coding of Ambisonic audio recordings. Traditional ambisonics achieve compression and backward compatibility through the use of the UHJ matrixing approach to obtain a stereo signal. In this paper the relationship to Ambisonic B-format signals is described and alternative approaches that derive a stereo or mono-downmix signal based on S3AC are presented and evaluated. The mono-downmix approach utilizes side information consisting of spatial cues that are …

Binaural Reproduction Of Spatially Squeezed Surround Audio, Bin Cheng, Christian Ritz, Ian Burnett

Faculty of Informatics - Papers (Archive)

Spatially Squeezed Surround Audio Coding (S³AC) has been previously proposed as an efficient approach to multi-channel spatial audio coding with stereo/mono backward compatibility. This paper presents a binaural reproduction scheme that exploits source localisation information in the S³AC squeezed soundfield or S³AC cues to simulate surround audio scene over headphones. The approach utilises interpolated HRTFs to bring the S³AC advantages of accurate, localised sound sources to stereo headphone systems. The integration of the HRTF approach to reproduction also exploits human localisation ability to reduce interpolation complexity. Subjective experiments demonstrate that accurate localisation …

C*-Algebras Associated To Coverings Of K-Graphs, Alexander Kumjian, David A. Pask, Aidan Sims

Faculty of Informatics - Papers (Archive)

A covering of k-graphs (in the sense of Pask-Quigg- Raeburn) induces an embedding of universal C∗-algebras. We show how to build a (k + 1)-graph whose universal algebra encodes this embedding. More generally we show how to realise a direct limit of k-graph algebras under embeddings induced from coverings as the universal algebra of a (k +1)-graph. Our main focus is on computing the K-theory of the (k+1)-graph algebra from that of the component k-graph algebras.

Examples of our construction include a realisation of the Kirchberg algebra Pn whose K-theory is opposite to that of On, and a class of …

Format-Independent Rich Media Delivery Using The Bitstream Binding Language, Joseph Thomas-Kerr, I. Burnett, Christian Ritz

Faculty of Informatics - Papers (Archive)

Several recent standards address virtual containers for rich multimedia content: collections of media with metadata describing the relationships between them and providing an immersive user experience. While these standards - which include MPEG-21 and TVAnytime - provide numerous tools for interacting with rich media objects, they do not provide a framework for streaming or delivery of such content. This paper presents the bitstream binding language (BBL), a format-independent tool that describes how multimedia content and metadata may be bound into delivery formats. Using a BBL description, a generic processor can map rich content (an MPEG-21 digital item, for example) into …

Quality Information Retrieval For The World Wide Web, Milly W. Kc, Markus Hagenbuchner, Ah Chung Tsoi

Faculty of Informatics - Papers (Archive)

The World Wide Web is an unregulated communication medium which exhibits very limited means of quality control. Quality assurance has become a key issue for many information retrieval services on the Internet, e.g. web search engines. This paper introduces some quality evaluation and assessment methods to assess the quality of web pages. The proposed quality evaluation mechanisms are based on a set of quality criteria which were extracted from a targeted user survey. A weighted algorithmic interpretation of the most significant user quoted quality criteria is proposed. In addition, the paper utilizes machine learning methods to produce a prediction of …

Abnormal Behavior Detection Using A Multi-Modal Stochastic Learning Approach, Philippa M. Lyons-Wall, Abdesselam Bouzerdoum, Son Lam Phung, Azeddine Beghdadi

Faculty of Informatics - Papers (Archive)

This paper presents a new approach to trajectory-based abnormal behavior detection (ABD). While existing techniques include position in the feature vector, we propose to estimate the probability distribution locally at each position, hence reducing the dimensionality of the feature vector. Local information derived from accumulated knowledge for a particular position is integrated in the distribution enabling context-based decision for ABD. A stochastic competitive learning algorithm is employed to estimate the local distributions of the feature vector and the location of the distribution modes. The proposed algorithm is tested on the detection of driving under the influence of alcohol. The performance …

A New Approach For Measuring The Line-Width Enhancement Factor, Yanguang Yu, Jiangtao Xi, Joe F. Chicharo, Yan Zhao

Faculty of Informatics - Papers (Archive)

The paper presents a new approach for measuring the line-width enhancement factor (LEF) of semiconductor lasers (SLs). LEF can be measured using a technique called optical feedback self-mixing interferometry (OFSMI). However, the existing OFSMI based approaches require that optical feedback factor (denoted as C) associated with the SL systems within a narrow range. The proposed new method is also based on OFSMI principle. The method does not rely on the C factor, and is suitable for the whole moderate optical feedback regime. The associated simulations and the experiments are carried out for verifying the proposed method.

Ranking Web Pages Using Machine Learning Approaches, Sweah Liang Yong, Markus Hagenbuchner, Ah Chung Tsoi

Faculty of Informatics - Papers (Archive)

One of the key components which ensures the acceptance of web search service is the web page ranker - a component which is said to have been the main contributing factor to the early successes of Google. It is well established that a machine learning method such as the Graph Neural Network (GNN) is able to learn and estimate Google's page ranking algorithm. This paper shows that the GNN can successfully learn many other Web page ranking methods e.g. TrustRank, HITS and OPIC. Experimental results show that GNN may be suitable to learn any arbitrary web page ranking scheme, and …

Vehicle Tracking By Non-Drifting Mean-Shift Using Projective Kalman Filter, Philippe L. Bouttefroy, Abdesselam Bouzerdoum, Son Lam Phung, Azeddine Beghdadi

Faculty of Informatics - Papers (Archive)

Robust vehicle tracking is essential in traffic monitoring because it is the groundwork to higher level tasks such as traffic control and event detection. This paper describes a new technique for tracking vehicles with mean-shift using a projective Kalman filter. The shortcomings of the mean-shift tracker, namely the selection of the bandwidth and the initialization of the tracker, are addressed with a fine estimation of the vehicle scale and kinematic model. Indeed, the projective Kalman filter integrates the non-linear projection of the vehicle trajectory in its observation function resulting in an accurate localization of the vehicle in the image. The …

Local Estimation Of Displacement Density For Abnormal Behavior Detection, Philippe L. Bouttefroy, Abdesselam Bouzerdoum, Son Lam Phung, Azeddine Beghdadi

Faculty of Informatics - Papers (Archive)

Detecting abnormal behavior in video sequences has become a crucial task with the development of automatic video-surveillance systems. Here, we propose an algorithm which locally models the probability distribution of objects behavioral features. A temporal Gaussian mixture with local update is introduced to estimate the local probability distribution. The update of the feature probability distribution is thus temporal and local, allowing a smooth transition for neighboring locations. The integration of local information in the estimation provides a fast adaptation along with an efficient discrimination between normal and abnormal behavior. The proposed technique is evaluated on both synthetic and real data. …

Graphical Modeling And Decoding Of Human Actions, Wanqing Li, Zhengyou Zhang, Zicheng Liu

Faculty of Informatics - Papers (Archive)

This paper presents a graphical model for learning and recognizing human actions. Specifically, we propose to encode actions in a weighted directed graph, referred to as action graph, where nodes of the graph represent salient postures that are used to characterize the actions and shared by all actions. The weight between two nodes measures the transitional probability between the two postures. An action is encoded as one or multiple paths in the action graph. The salient postures are modeled using Gaussian mixture models (GMM). Both the salient postures and action graph are automatically learned from training samples through unsupervised clustering …

Key-Frame Extraction Using Dominant-Set Clustering, Xianglin Zeng, Weiming Hu, Wanqing Li, Xiaoqing Zhang, Bo Xu

Faculty of Informatics - Papers (Archive)

Key frames play an important role in video abstraction. Clustering is a popular approach for key-frame extraction. In this paper, we propose a novel method for key-frame extraction based on dominant-set clustering. Compared with the existing clustering-based methods, the proposed method dynamically decides the number of key frames depending on the complexity of video shots, produces key frames in a progressive manner and requires less computation. Experimental results on different types of video shots have verified the effectiveness of the method.

Strong Shift Equivalence Of C*Correspondences, Paul Muhly, David A. Pask, Mark Tomforde

Faculty of Informatics - Papers (Archive)

We define a notion of strong shift equivalence for C* -correspondences and show that strong shift equivalent C* -correspondences have strongly Morita equivalent Cuntz-Pimsner algebras. Our analysis extends the fact that strong shift equivalent square matrices with non-negative integer entries give stably isomorphic Cuntz-Krieger algebras.

Developing Ontology Revision Framework: A Case Study On The Use Of The Coherence Theory For Semantic Shopping Mall, Seung Hwan Kang, Sim Kim Lau

Faculty of Informatics - Papers (Archive)

Why is ontology revision important? Very often, ontology exists in a particular period of timeline is often designed based on the purpose of a domain of interest at that instance of time. However over time, ontology needs to be revised due to changes in content, environment, requirements, or even structural representation. As a result, revision and updating of necessary components in the pre-defined ontology is unavoidable. When this happens, it is important to ensure that revision is conducted in a consistent manner so that it does not result in unforseen redundancies and inconsistencies. Any revision performed must be accompanied by …

Wireless Interactive System For Patient Healthcare Monitoring Using Mobile Computing Devices, Montserrat Ros, Matthew D'Souza, Adam Postula

Faculty of Informatics - Papers (Archive)

Recently there has been a need to incorporate the use of mobile computing devices in hospital or clinical applications, to enhance patient care. The advancement of wireless technology has created unique mechanisms of interaction that can meet the needs of e-health system robustness, reliability and accuracy requirements. This paper presents an interactive wireless medical information system that allows patient instrumentation output data to be accessed and patient details to be entered by medical practitioners using mobile computing devices. We examine mobile and wireless information technology concepts that can be used to interact with a medical information system for controlling real-time …

Expandable Data-Driven Graphical Modeling Of Human Actions Based On Salient Postures, Wanqing Li, Zhengyou Zhang, Zicheng Liu

Faculty of Informatics - Papers (Archive)

This paper presents a graphical model for learning and recognizing human actions. Specifically, we propose to encode actions in a weighted directed graph, referred to as action graph, where nodes of the graph represent salient postures that are used to characterize the actions and are shared by all actions. The weight between two nodes measures the transitional probability between the two postures represented by the two nodes. An action is encoded as one or multiple paths in the action graph. The salient postures are modeled using Gaussian mixture models (GMMs). Both the salient postures and action graph are automatically learned …

Enhanced Laboratory Diagnosis Of Human Chlamydia Pneumoniae Infection Through Pattern Recognition Derived From Pathology Database Analysis, Alice Richardson, Simon Hawkins, Fariba Shadabi, Dhamendra Sharma, John Fulcher, B. Lidbury

Faculty of Informatics - Papers (Archive)

This study focuses on pattern recognition in pathology data collected from patients tested for Chlamydia pneumoniae (Cp) infection, with co-infection by Mycoplasma pneumoniae (Myco) also considered. Both Cp and Myco are microbes that cause respiratory disease in some infected people. As well as the immunoassay results revealing whether the patient had been infected, or not, an extensive range of other routine pathology data was also available for each patient, allowing the analysis of associations between a positive immunoassay laboratory result for Cp or Myco, and a range of tests for biochemical and cellular markers (e.g. liver enzymes, electrolyte balance, haematological …

Motion Classification Using Dynamic Time Warping, Kevin Adistambha, Christian Ritz, Ian Burnett

Faculty of Informatics - Papers (Archive)

Automatic generation of metadata is an important component of multimedia search-by-content systems as it both avoids the need for manual annotation as well as minimising subjective descriptions and human errors. This paper explores the automatic attachment of basic descriptions (or dasiaTagspsila) to human motion held in a motion-capture database on the basis of a dynamic time warping (DTW) approach. The captured motion is held in the Acclaim ASF/AMC format commonly used in game and movie motion capture work and the approach allows for the comparison and classification of motion from different subjects. The work analyses the bone rotations important to …

A New And Efficient Signature On Commitment Values, Fangguo Zhang, Xiaofeng Chen, Yi Mu, Willy Susilo

Faculty of Informatics - Papers (Archive)

We present a new short signature scheme based on a vari- ant of the Boneh-Boyen's short signatures schemes. Our short signature scheme is secure without requiring the random oracle model. We show how to prove a commit- ted value embedded in our short signature. Using this primitive, we construct an e±cient anonymous credential system.

Visual-Based Interface Using Hand Gesture Recognition And Object Tracking, Abdoleh Chalechale, Farzad Safaei

Faculty of Informatics - Papers (Archive)

This paper presents a novel approach for human-machine interface using visual-based communication. Scene analysis, object recognition and tracking, together with gesture detection and classification, are employed to design and implement a system for real time interaction. A new paradigm is proposed for efficient selection of hand gestures that can be used in other gesturebased environments aiming at interactive multimedia. An inexpensive web camera and readily available tools (such as an ordinary pen) have been used in this approach to render the system cost effective and suitable for general and home-based multimedia applications. Focus is placed on real time applications with …

Mitigating Phishing With Id-Based Online/Offline Authentication, Qiong Ren, Yi Mu, W. Susilo

Faculty of Informatics - Papers (Archive)

Enforcing strong authentication is an option to mitigate phishing. However, existing authentication methods, like traditional digital signatures, require unrealistic full deployment of public key infrastructure(PKI) and destroy email users ’ privacy in that the identity of an email sender is automatically revealed to the public. There have been some works in the literature, where the technology of deniable authentication is adopted and sender’s privacy can be protected. However, the additional computation introduced into the system is obviously a drawback. In this paper, we introduce the notion of online/offline authentication into anti-phishing, in order to construct an efficient and secure anti-phishing …

A Compact Cmos Face Detection Architecture Based On Shunting Inhibitory Convolutional Neural Networks, Xiaoxiao Zhang, Amine Bermak, Farid Boussaid, Abdesselam Bouzerdoum

Faculty of Informatics - Papers (Archive)

In this paper, we present a compact, low cost, real-time CMOS hardware architecture for face detection. The proposed architecture is based on a VLSI-friendly implementation of Shunting Inhibitory Convolutional Neural Networks (SICoNN). Reported experimental results show that the proposed architecture can detect faces with 93% detection accuracy at 5% false alarm rate. A VLSI Systolic architecture was considered to further optimize the design in terms of execution speed, power dissipation and area. Potential applications of the proposed face detection hardware include consumer electronics, security, monitoring and head-counting.

Actor Eco-Systems: Modeling And Configuring Virtual Enterprises, Aditya Ghose, George Koliadis

Faculty of Informatics - Papers (Archive)

Complex business networks such as supply chains, with cross-organizational workflows of even greater complexity, are becoming increasingly common. The problem of engineering cross-organizational processes in a manner that accounts for inter-organizational constraints, and dynamics, has received little attention in the literature. This paper describes an effective framework for addressing the problem. The actor eco-systems approach leverages the ecosystems metaphor to model cross-enterprise constraints, change propagation and equilibria. It describes how cross-enterprise processes can be derived from such models and maintained in the face of dynamic business contexts.

Information Theoretic Bounds On Authentication Systems In Query Model, Reihaneh Safavi-Naini, Peter R. Wild

Faculty of Informatics - Papers (Archive)

Authentication codes provide message integrity guarantees in an information theoretic sense within a symmetric key setting. Information theoretic bounds on the success probability of an adversary who has access to previously authenticated messages have been derived by Simmons and Rosenbaum, among others. In this paper, we consider a strong attack scenario where the adversary is adaptive and has access to authentication and verification oracles. We derive information theoretic bounds on the success probability of the adversary and on the key size of the code. This brings the study of unconditionally secure authentication systems on a par with the study of …

The State Of Public Data Availability In Australia: A Study Of Suppliers Of Critical Infrastructure Information, Roba Abbas

Faculty of Informatics - Papers (Archive)

The purpose of this study is to evaluate the public data availability situation in Australia, and the consequent impact on the nation's critical infrastructure (CI) and the critical infrastructure protection (CIP) process in general, through an evaluation of data supplying bodies in Australia. An assessment of data suppliers was conducted, in order to allow for the categorisation of data supplying entities, and to identify the critical infrastructure data that is available, The public data availability situation in Australia is described, and the concerns associated with having CI-related information available in the public domain are highlighted.

An Ontology-Based Sentiment Classification Methodology For Online Consumer Reviews, Jantima Polpinij, Aditya K. Ghose

Faculty of Informatics - Papers (Archive)

This paper presents a method of ontology-based sentiment classification to classify and analyse online product reviews of consumers. We implement and experiment with a support vector machines text classification approach based on a lexical variable ontology. After testing, it could be demonstrated that the proposed method can provide more effectiveness for sentiment classification based on text content.

Block Spread Ofdma System With Space-Time Coded Mimo Over Frequency Selective Fading Channels, Yiwei Yu, Xiaojing Huang, Eryk Dutkiewicz

Faculty of Informatics - Papers (Archive)

This paper proposes a block spread orthogonal frequency division multiple access (BS-OFDMA) system with a combined space-time coded multiple-input multiple-out (STC-MIMO) scheme called STC-MIMO BS-OFDMA for transmission over frequency selective fading channels. In this system, a novel block spreading approach is firstly applied to effectively achieve precoding in the OFDMA system with lower complexity for improving the multipath diversity performance. The STC-MIMO is then incorporated to take advantage of the spatial diversity. The signal model and architecture of the proposed system are presented, and simulations are carried out to confirm the expected performance improvement.

Adaptive Subband Selection In Ofdm-Based Cognitive Radios For Better System Coexistence, Pingzhou Tu, Xiaojing Huang, Eryk Dutkiewicz

Faculty of Informatics - Papers (Archive)

In an environment of shared radio spectrum, multiple systems may interfere with each other and cause significant impacts on system coexistence. In this paper we propose an adaptive subband selection technique based on orthogonal frequency division multiplexing (OFDM) to avoid interference for better system coexistence when multiple systems are operating in the same unlicensed industrial, scientific and medical (ISM) bands. Under the assumption that the interference power level and the interfered frequency bands are identified at the receiver, interference thresholds, determined over both Gaussian and multipath fading channels, are applied to adaptively select the transmission subbands so that interference is …

Requirements Elicitation Using Goal-Based Organizational Model, Aneesh Krishna, He Lu

Faculty of Informatics - Papers (Archive)

Domain knowledge is crucial for the quality of requirements. The systems analysts, who have adequate knowledge of software often don't understand the organizational context well. The Tropos methodology proposes a set of concepts, which are effective in capturing the organizational context, the interactions between the software system and the human agents. Based on the Tropos methodology, Software System-Business Model (SS-BM), which integrates the Software System Actor (SSA) from early requirements organizational model has been proposed. In this paper, a methodological approach of applying the SS-BM to requirements list elicitation is presented. In the proposed method, requirements items were mapped to …

An Integrated Approach Of Wavelet-Rough Set Technique For Classification Of Power Quality Disturbances, Chandrashekhar Bhende, Satya Mishra

Faculty of Informatics - Papers (Archive)

This paper presents an integrated approach of wavelet and rough set theory for the classification of power quality (PQ) disturbances. Further, the number of features and the rules required for proper classification are decided through rough set approach. Moreover, as the proposed methodology can reduce the number of features extracted through wavelet to a great extent, it will indirectly reduce the memory requirement for the classification procedure. Eleven types of PQ disturbances are considered for classification. The simulation results show that the combination of wavelet and rough set theory can effectively classify different power quality disturbances. Since rule based approach …