Optimal HMM processing of differentially phase modulated communication systems

This paper investigates demodulation of differentially phase modulated signals DPMS using optimal HMM filters. The optimal HMM filter presented in the paper is computationally of order N3 per time instant, where N is the number of message symbols. Previously, optimal HMM filters have been of computational order N4 per time instant. Also, suboptimal HMM filters have be proposed of computation order N2 per time instant. The approach presented in this paper uses two coupled HMM filters and exploits knowledge of ...

Optimal HMM filtering and decision feedback equalisation for differential encoded transmission systems

In this paper conditional hidden Markov model (HMM) filters and conditional Kalman filters (KF) are coupled together to improve demodulation of differential encoded signals in noisy fading channels. We present an indicator matrix representation for differential encoded signals and the optimal HMM filter for demodulation. The filter requires O(N3) calculations per time iteration, where N is the number of message symbols. Decision feedback equalisation is investigated via coupling the optimal HMM filter for estimating the message, conditioned on estimates of the channel parameters, and a KF for estimating the channel states, conditioned on soft information message estimates. The particular differential encoding scheme examined in this paper is differential phase shift keying. However, the techniques developed can be extended to other forms of differential modulation. The channel model we use allows for multiplicative channel distortions and additive white Gaussian noise. Simulation studies are also presented.

Frequency-motivated observer design for the prediction of parametric roll resonance control applications in marine systems

This work deals with estimators for predicting when parametric roll resonance is going to occur in surface vessels. The roll angle of the vessel is modeled as a second-order linear oscillatory system with unknown parameters. Several algorithms are used to estimate the parameters and eigenvalues of the system based on data gathered experimentally on a 1:45 scale model of a tanker. Based on the estimated eigenvalues, the system predicts whether or not parametric roll occurred. A prediction accuracy of 100% is achieved for regular waves, and up to 87.5% for irregular waves.

Stochastic output feedback model predictive control

This paper addresses the issue of output feedback model predictive control for linear systems with input constraints and stochastic disturbances. We show that the optimal policy uses the Kalman filter for state estimation, but the resultant state estimates are not utilized in a certainty equivalence control law

Significance of drying periods on nitrate removal in experimental biofilters

Nitrogen is an important nutrient that can impact the quality of aquatic environments when present in high concentration. Even though low concentration levels of ammonium-nitrogen have been observed in laboratory studies in bioretention basins, poor removal or even the production of nitrate-nitrogen within the filter is often recorded in such studies. Ten Perspex biofilter columns of 94 mm (internal diameter) were packed with a filter layer, transition layer and a gravel layer. While the filter layer was packed to a height of 800 mm, transition and gravel layers were packed to a composite height of 220 mm and operated with simulated stormwater in the laboratory. The filter layer contained 8% organic material by weight. A free board of 350 mm provided detention storage and head to facilitate infiltration. The columns were operated with different antecedent dry days (0 d to 21 d) and constant inflow concentration at a feed rate of 100 mL/min. Samples were collected from the outflow at different time intervals, between 2 min and 150 min from the start of outflow, and were tested for nitrate-nitrogen and total organic carbon. Washoff of organic carbon from the filter layer was observed to occur for 30 min of outflow. This indicated washoff of organic carbon from the filter itself. At the same time, a very low concentration of nitrate-nitrogen was recorded at the beginning of the outflow, indicating the effective removal of nitrate-nitrogen. We conclude that the removal of nitrate-nitrogen is insignificant during the wetting phase of a rainfall event and the process of denitrification is more pronounced during the drying phase of a rainfall event. Thus intermittent wetting and drying is crucial for the removal of nitrate-nitrogen in bioretention basins.

Fourier active appearance models

Gaining invariance to camera and illumination variations has been a well investigated topic in Active Appearance Model (AAM) fitting literature. The major problem lies in the inability of the appearance parameters of the AAM to generalize to unseen conditions. An attractive approach for gaining invariance is to fit an AAM to a multiple filter response (e.g. Gabor) representation of the input image. Naively applying this concept with a traditional AAM is computationally prohibitive, especially as the number of filter responses increase. In this paper, we present a computationally efficient AAM fitting algorithm based on the Lucas-Kanade (LK) algorithm posed in the Fourier domain that affords invariance to both expression and illumination. We refer to this as a Fourier AAM (FAAM), and show that this method gives substantial improvement in person specific AAM fitting performance over traditional AAM fitting methods.

The use of new generation geosynthetics in water treatment

The importance of clean drinking water in any community is absolutely vital if we as the consumers are to sustain a life of health and wellbeing. Suspended particles in surface waters not only provide the means to transport micro-organisms which can cause serious infections and diseases, they can also affect the performance capacity of a water treatment plant. In such situations pre-treatment ahead of the main plant is recommended. Previous research carried out using non-woven synthetic as a pre-filter materials for protecting slow sand filters from high turbidity showed that filter run times can be extended by several times and filters can be regenerated by simply removing and washing of the fabric ( Mbwette and Graham, 1987 and Mbwette, 1991). Geosynthetic materials have been extensively used for soil retention and dewatering in geotechnical applications and little research exists for the application of turbidity reduction in water treatment. With the development of new materials in geosynthetics today, it was hypothesized that the turbidity removal efficiency can be improved further by selecting appropriate materials. Two different geosynthetic materials (75 micron) tested at a filtration rate of 0.7 m/h yielded 30-45% reduction in turbidity with relatively minor head loss. It was found that the non-woven geotextile Propex 1701 retained the highest performance in both filtration efficiency and head loss across the varying turbidity ranges in comparison to other geotextiles tested. With 5 layers of the Propex 1701 an average percent reduction of approximately 67% was achieved with a head loss average of 4mm over the two and half hour testing period. Using the data collected for the Propex 1701 a mathematical model was developed for predicting the expected percent reduction given the ability to control the cost and as a result the number of layers to be used in a given filtration scenario.

Integrated marketing communications

Organisations continually use integrated marketing communications to achieve a competitive advantage and meet their marketing objectives. This 4th edition of Integrated Marketing Communications emphasises digital and interactive marketing, the most dynamic and crucial components to a successful IMC campaign today. Incorporating the most up to date theories and practice, the text clearly explains and demonstrates how to best select and co-ordinate all of a brand’s marketing communications elements to effectively engage the target market. Chapters adopt an integrative approach to examine marketing communications from both a consumer’s and marketer’s perspective. A wide range of local and global examples include: Spotify, Pandora, Coca-Cola, Pepsi, Woolworths, Nike, KFC, Victoria Bitter, Tigerair and Air New Zealand. Each new copy of the text also offers 12 month access to wealth of student on-line revision and learning tools: CourseMate Express + Search me! marketing. Unique to the text is a series of end of chapter local videos showing students how key objectives in IMC theory are applied by real businesses.

Convolutional Neural Network-based place recognition

Recently Convolutional Neural Networks (CNNs) have been shown to achieve state-of-the-art performance on various classification tasks. In this paper, we present for the first time a place recognition technique based on CNN models, by combining the powerful features learnt by CNNs with a spatial and sequential filter. Applying the system to a 70 km benchmark place recognition dataset we achieve a 75% increase in recall at 100% precision, significantly outperforming all previous state of the art techniques. We also conduct a comprehensive performance comparison of the utility of features from all 21 layers for place recognition, both for the benchmark dataset and for a second dataset with more significant viewpoint changes.

Mechanical filtering for narrow-band hearing in the weta

This paper constitutes a major attempt to associate tympanic deflections with the mechanoreceptor organ location in an acoustic insect. The New Zealand tree weta (Hemideina thoracica) has tympanal ears located on each of the prothoracic tibiae. The tympana exhibit a sclerotized oval plate, membranous processes bulging out from the tibial cuticle and many loosely suspended ripples. We used microscanning laser Doppler vibrometry to determine how such a tympanal membrane vibrates in response to sound and whether the sclerotized region plays a role in hearing. The tympanum displays a single resonance at the calling frequency of the male, an unusual example of an insect tympana acting as a narrow bandpass filter. Both tympana resonate in phase with the stimulus and with each other. Histological sections show that the tympanal area is divided into two distinct regions, as in other ensiferans. An oval plate lies in the middle of a thickened region and is surrounded by a transparent and uniformly thin region. It is hinged dorsally to the tympanal rim and thus resembles the model of a ‘hinged flap’. The thickened region appears to act as a damping mass on the oscillation of the thin region, and vibration displacement is reduced in this area. The thinner area vibrates with higher amplitude, inducing mechanical pressure on the dorsal area adjacent to the crista acustica. We present a new model showing how the thickened region might confer a mechanical gain onto the activation of the crista acustica sensory neurons during the sound-induced oscillations.

Modeling kinect sensor noise for improved 3D reconstruction and tracking

We contribute an empirically derived noise model for the Kinect sensor. We systematically measure both lateral and axial noise distributions, as a function of both distance and angle of the Kinect to an observed surface. The derived noise model can be used to filter Kinect depth maps for a variety of applications. Our second contribution applies our derived noise model to the KinectFusion system to extend filtering, volumetric fusion, and pose estimation within the pipeline. Qualitative results show our method allows reconstruction of finer details and the ability to reconstruct smaller objects and thinner surfaces. Quantitative results also show our method improves pose estimation accuracy. © 2012 IEEE.

Wide area control of SVCs for first swing stabilisation and damping in longitudinal systems

A non-linear Kalman filter based control strategy for SVCs located in major load groups is presented. This focusses on the limitation and damping of inter-area modes. It does this through treating local modes as noise and uses a tunable nonlinear control algorithm to improve both first swing stability and system damping. Simulation on a four machine system shows that the Kalman filer can successfully lock on to a desired inter-area mode and obtain a 31% improvement in critical clearing time as well as improved damping.

Hydraulic conductivity fields: Gaussian or not?

Hydraulic conductivity (K) fields are used to parameterize groundwater flow and transport models. Numerical simulations require a detailed representation of the K field, synthesized to interpolate between available data. Several recent studies introduced high-resolution K data (HRK) at the Macro Dispersion Experiment (MADE) site, and used ground-penetrating radar (GPR) to delineate the main structural features of the aquifer. This paper describes a statistical analysis of these data, and the implications for K field modeling in alluvial aquifers. Two striking observations have emerged from this analysis. The first is that a simple fractional difference filter can have a profound effect on data histograms, organizing non-Gaussian ln K data into a coherent distribution. The second is that using GPR facies allows us to reproduce the significantly non-Gaussian shape seen in real HRK data profiles, using a simulated Gaussian ln K field in each facies. This illuminates a current controversy in the literature, between those who favor Gaussian ln K models, and those who observe non-Gaussian ln K fields. Both camps are correct, but at different scales.

Dynamics of nitrogen and suspended solids removal in experimental stormwater biofilters under intermittent wetting and drying

This research study comprehensively analyses the dynamics of nitrogen and suspended solids removal in stormwater biofilters. The study focuses on pollutant removal during an event with time, rather than the conventional event-mean analysis. Antecedent dry days (number of days in between rainfall) during which biofilters remain dry and the inflow concentration of pollutants were two other important variables analysed in this study. The research outcome highlights the significance of dry-phase processes and the process of stabilization on filter performance and sets a paradigm shift from the current approach towards an innovative way of performance analysis of biofilters.

BPMN Miner: Automated Discovery of BPMN Process Models with Hierarchical Structure

Existing techniques for automated discovery of process models from event logs gen- erally produce flat process models. Thus, they fail to exploit the notion of subprocess as well as error handling and repetition constructs provided by contemporary process modeling notations, such as the Business Process Model and Notation (BPMN). This paper presents a technique for automated discovery of hierarchical BPMN models con- taining interrupting and non-interrupting boundary events and activity markers. The technique employs functional and inclusion dependency discovery techniques in order to elicit a process-subprocess hierarchy from the event log. Given this hierarchy and the projected logs associated to each node in the hierarchy, parent process and subprocess models are then discovered using existing techniques for flat process model discovery. Finally, the resulting models and logs are heuristically analyzed in order to identify boundary events and markers. By employing approximate dependency discovery tech- niques, it is possible to filter out noise in the event log arising for example from data entry errors or missing events. A validation with one synthetic and two real-life logs shows that process models derived by the proposed technique are more accurate and less complex than those derived with flat process discovery techniques. Meanwhile, a validation on a family of synthetically generated logs shows that the technique is resilient to varying levels of noise.