Relative entropy rate based multiple hidden Markov Model Approximation

This paper proposes a novel relative entropy rate (RER) based approach for multiple HMM (MHMM) approximation of a class of discrete-time uncertain processes. Under different uncertainty assumptions, the model design problem is posed either as a min-max optimisation problem or stochastic minimisation problem on the RER between joint laws describing the state and output processes (rather than the more usual RER between output processes). A suitable filter is proposed for which performance results are established which bound conditional mean estimation performance and show that estimation performance improves as the RER is reduced. These filter consistency and convergence bounds are the first results characterising multiple HMM approximation performance and suggest that joint RER concepts provide a useful model selection criteria. The proposed model design process and MHMM filter are demonstrated on an important image processing dim-target detection problem.

Contemporary behavioural influences in an organisational setting and implications for intervention development

Professional drivers and safety Within the industrialised world, work-related crashes are the most common cause of work-related death, injury and reduced productivity (Charbotel et al., 2001; Toscano and Windau, 1994). Likewise in Australia, road crashes are the most common cause of work-related fatalities, injuries and absence from work (Haworth et al., 2000), with the average time lost being greater than any other workplace claim (Stewart-Bogle, 1999). There are obvious costs related to work crashes such as vehicle and property repair costs. There are also many hidden expenses including third party costs, workers compensation, medical costs, rehabilitation, customer-related costs, increased insurance premiums, administrative costs, legal fees and loss of productivity (Collingwood, 1997; Haworth et al, 2000).

In search of a hidden long-term isolated sub-chondritic 142Nd/144Nd reservoir in the deep mantle : implications for the Nd isotope systematics of the Earth

Here we search for evidence of the existence of a sub-chondritic 142Nd/144Nd reservoir that balances the Nd isotope chemistry of the Earth relative to chondrites. If present, it may reside in the source region of deeply sourced mantle plume material. We suggest that lavas from Hawai’i with coupled elevations in 186Os/188Os and 187Os/188Os, from Iceland that represent mixing of upper mantle and lower mantle components, and from Gough with sub-chondritic 143Nd/144Nd and high 207Pb/206Pb, are favorable samples that could reflect mantle sources that have interacted with an Early-Enriched Reservoir (EER) with sub-chondritic 142Nd/144Nd. High-precision Nd isotope analyses of basalts from Hawai’i, Iceland and Gough demonstrate no discernable 142Nd/144Nd deviation from terrestrial standards. These data are consistent with previous high-precision Nd isotope analysis of recent mantle-derived samples and demonstrate that no mantle-derived material to date provides evidence for the existence of an EER in the mantle. We then evaluate mass balance in the Earth with respect to both 142Nd/144Nd and 143Nd/144Nd. The Nd isotope systematics of EERs are modeled for different sizes and timing of formation relative to ε143Nd estimates of the reservoirs in the μ142Nd = 0 Earth, where μ142Nd is ((measured 142Nd/144Nd/terrestrial standard 142Nd/144Nd)−1 * 10−6) and the μ142Nd = 0 Earth is the proportion of the silicate Earth with 142Nd/144Nd indistinguishable from the terrestrial standard. The models indicate that it is not possible to balance the Earth with respect to both 142Nd/144Nd and 143Nd/144Nd unless the μ142Nd = 0 Earth has a ε143Nd within error of the present-day Depleted Mid-ocean ridge basalt Mantle source (DMM). The 4567 Myr age 142Nd–143Nd isochron for the Earth intersects μ142Nd = 0 at ε143Nd of +8 ± 2 providing a minimum ε143Nd for the μ142Nd = 0 Earth. The high ε143Nd of the μ142Nd = 0 Earth is confirmed by the Nd isotope systematics of Archean mantle-derived rocks that consistently have positive ε143Nd. If the EER formed early after solar system formation (0–70 Ma) continental crust and DMM can be complementary reservoirs with respect to Nd isotopes, with no requirement for significant additional reservoirs. If the EER formed after 70 Ma then the μ142Nd = 0 Earth must have a bulk ε143Nd more radiogenic than DMM and additional high ε143Nd material is required to balance the Nd isotope systematics of the Earth.

Enhancing an Incremental Clustering Algorithm for Web Page Collections

With the size and state of the Internet today, a good quality approach to organizing this mass of information is of great importance. Clustering web pages into groups of similar documents is one approach, but relies heavily on good feature extraction and document representation as well as a good clustering approach and algorithm. Due to the changing nature of the Internet, resulting in a dynamic dataset, an incremental approach is preferred. In this work we propose an enhanced incremental clustering approach to develop a better clustering algorithm that can help to better organize the information available on the Internet in an incremental fashion. Experiments show that the enhanced algorithm outperforms the original histogram based algorithm by up to 7.5%.

Thai word segmentation with hidden Markov Model and decision tree

The Thai written language is one of the languages that does not have word boundaries. In order to discover the meaning of the document, all texts must be separated into syllables, words, sentences, and paragraphs. This paper develops a novel method to segment the Thai text by combining a non-dictionary based technique with a dictionary-based technique. This method first applies the Thai language grammar rules to the text for identifying syllables. The hidden Markov model is then used for merging possible syllables into words. The identified words are verified with a lexical dictionary and a decision tree is employed to discover the words unidentified by the lexical dictionary. Documents used in the litigation process of Thai court proceedings have been used in experiments. The results which are segmented words, obtained by the proposed method outperform the results obtained by other existing methods.

A hidden Markov model and relative entropy rate approach to vision-based dim target detection for UAV sense-and-avoid

Uninhabited aerial vehicles (UAVs) are a cutting-edge technology that is at the forefront of aviation/aerospace research and development worldwide. Many consider their current military and defence applications as just a token of their enormous potential. Unlocking and fully exploiting this potential will see UAVs in a multitude of civilian applications and routinely operating alongside piloted aircraft. The key to realising the full potential of UAVs lies in addressing a host of regulatory, public relation, and technological challenges never encountered be- fore. Aircraft collision avoidance is considered to be one of the most important issues to be addressed, given its safety critical nature. The collision avoidance problem can be roughly organised into three areas: 1) Sense; 2) Detect; and 3) Avoid. Sensing is concerned with obtaining accurate and reliable information about other aircraft in the air; detection involves identifying potential collision threats based on available information; avoidance deals with the formulation and execution of appropriate manoeuvres to maintain safe separation. This thesis tackles the detection aspect of collision avoidance, via the development of a target detection algorithm that is capable of real-time operation onboard a UAV platform. One of the key challenges of the detection problem is the need to provide early warning. This translates to detecting potential threats whilst they are still far away, when their presence is likely to be obscured and hidden by noise. Another important consideration is the choice of sensors to capture target information, which has implications for the design and practical implementation of the detection algorithm. The main contributions of the thesis are: 1) the proposal of a dim target detection algorithm combining image morphology and hidden Markov model (HMM) filtering approaches; 2) the novel use of relative entropy rate (RER) concepts for HMM filter design; 3) the characterisation of algorithm detection performance based on simulated data as well as real in-flight target image data; and 4) the demonstration of the proposed algorithm's capacity for real-time target detection. We also consider the extension of HMM filtering techniques and the application of RER concepts for target heading angle estimation. In this thesis we propose a computer-vision based detection solution, due to the commercial-off-the-shelf (COTS) availability of camera hardware and the hardware's relatively low cost, power, and size requirements. The proposed target detection algorithm adopts a two-stage processing paradigm that begins with an image enhancement pre-processing stage followed by a track-before-detect (TBD) temporal processing stage that has been shown to be effective in dim target detection. We compare the performance of two candidate morphological filters for the image pre-processing stage, and propose a multiple hidden Markov model (MHMM) filter for the TBD temporal processing stage. The role of the morphological pre-processing stage is to exploit the spatial features of potential collision threats, while the MHMM filter serves to exploit the temporal characteristics or dynamics. The problem of optimising our proposed MHMM filter has been examined in detail. Our investigation has produced a novel design process for the MHMM filter that exploits information theory and entropy related concepts. The filter design process is posed as a mini-max optimisation problem based on a joint RER cost criterion. We provide proof that this joint RER cost criterion provides a bound on the conditional mean estimate (CME) performance of our MHMM filter, and this in turn establishes a strong theoretical basis connecting our filter design process to filter performance. Through this connection we can intelligently compare and optimise candidate filter models at the design stage, rather than having to resort to time consuming Monte Carlo simulations to gauge the relative performance of candidate designs. Moreover, the underlying entropy concepts are not constrained to any particular model type. This suggests that the RER concepts established here may be generalised to provide a useful design criterion for multiple model filtering approaches outside the class of HMM filters. In this thesis we also evaluate the performance of our proposed target detection algorithm under realistic operation conditions, and give consideration to the practical deployment of the detection algorithm onboard a UAV platform. Two fixed-wing UAVs were engaged to recreate various collision-course scenarios to capture highly realistic vision (from an onboard camera perspective) of the moments leading up to a collision. Based on this collected data, our proposed detection approach was able to detect targets out to distances ranging from about 400m to 900m. These distances, (with some assumptions about closing speeds and aircraft trajectories) translate to an advanced warning ahead of impact that approaches the 12.5 second response time recommended for human pilots. Furthermore, readily available graphic processing unit (GPU) based hardware is exploited for its parallel computing capabilities to demonstrate the practical feasibility of the proposed target detection algorithm. A prototype hardware-in- the-loop system has been found to be capable of achieving data processing rates sufficient for real-time operation. There is also scope for further improvement in performance through code optimisations. Overall, our proposed image-based target detection algorithm offers UAVs a cost-effective real-time target detection capability that is a step forward in ad- dressing the collision avoidance issue that is currently one of the most significant obstacles preventing widespread civilian applications of uninhabited aircraft. We also highlight that the algorithm development process has led to the discovery of a powerful multiple HMM filtering approach and a novel RER-based multiple filter design process. The utility of our multiple HMM filtering approach and RER concepts, however, extend beyond the target detection problem. This is demonstrated by our application of HMM filters and RER concepts to a heading angle estimation problem.

Corrupt police networks : uncovering hidden relationship patterns, functions and roles

This article applies social network analysis techniques to a case study of police corruption in order to produce findings which will assist in corruption prevention and investigation. Police corruption is commonly studied but rarely are sophisticated tools of analyse engaged to add rigour to the field of study. This article analyses the ‘First Joke’ a systemic and long lasting corruption network in the Queensland Police Force, a state police agency in Australia. It uses the data obtained from a commission of inquiry which exposed the network and develops hypotheses as to the nature of the networks structure based on existing literature into dark networks and criminal networks. These hypotheses are tested by entering the data into UCINET and analysing the outcomes through social network analysis measures of average path distance, centrality and density. The conclusions reached show that the network has characteristics not predicted by the literature.

An infinite-horizon robust filter for uncertain hidden Markov Models with conditional relative entropy constraints

We consider a robust filtering problem for uncertain discrete-time, homogeneous, first-order, finite-state hidden Markov models (HMMs). The class of uncertain HMMs considered is described by a conditional relative entropy constraint on measures perturbed from a nominal regular conditional probability distribution given the previous posterior state distribution and the latest measurement. Under this class of perturbations, a robust infinite horizon filtering problem is first formulated as a constrained optimization problem before being transformed via variational results into an unconstrained optimization problem; the latter can be elegantly solved using a risk-sensitive information-state based filtering.

Face recognition across pose on video using eigen light-fields

We propose an approach to employ eigen light-fields for face recognition across pose on video. Faces of a subject are collected from video frames and combined based on the pose to obtain a set of probe light-fields. These probe data are then projected to the principal subspace of the eigen light-fields within which the classification takes place. We modify the original light-field projection and found that it is more robust in the proposed system. Evaluation on VidTIMIT dataset has demonstrated that the eigen light-fields method is able to take advantage of multiple observations contained in the video.