Multi Pattern Dynamic Time Warping For Automatic Speech Recognition

We are addressing the problem of jointly using multiple noisy speech patterns for automatic speech recognition (ASR), given that they come from the same class. If the user utters a word K times, the ASR system should try to use the information content in all the K patterns of the word simultaneously and improve its speech recognition accuracy compared to that of the single pattern based speech recognition. T address this problem, recently we proposed a Multi Pattern Dynamic Time Warping (MPDTW) algorithm to align the K patterns by finding the least distortion path between them. A Constrained Multi Pattern Viterbi algorithm was used on this aligned path for isolated word recognition (IWR). In this paper, we explore the possibility of using only the MPDTW algorithm for IWR. We also study the properties of the MPDTW algorithm. We show that using only 2 noisy test patterns (10 percent burst noise at -5 dB SNR) reduces the noisy speech recognition error rate by 37.66 percent when compared to the single pattern recognition using the Dynamic Time Warping algorithm.

Multi-Hypotheses Tracking using the Dempster–Shafer Theory, application to ambiguous road context

This paper presents a Multi-Hypotheses Tracking (MHT) approach that allows solving ambiguities that arise with previous methods of associating targets and tracks within a highly volatile vehicular environment. The previous approach based on the Dempster–Shafer Theory assumes that associations between tracks and targets are unique; this was shown to allow the formation of ghost tracks when there was too much ambiguity or conflict for the system to take a meaningful decision. The MHT algorithm described in this paper removes this uniqueness condition, allowing the system to include ambiguity and even to prevent making any decision if available data are poor. We provide a general introduction to the Dempster–Shafer Theory and present the previously used approach. Then, we explain our MHT mechanism and provide evidence of its increased performance in reducing the amount of ghost tracks and false positive processed by the tracking system.

Processing of Stimulus Repetition and Change in the Somatosensory System: Recordings of Electrical and Magnetic Brain Responses

In the present work, effects of stimulus repetition and change in a continuous stimulus stream on the processing of somatosensory information in the human brain were studied. Human scalp-recorded somatosensory event-related potentials (ERPs) and magnetoencephalographic (MEG) responses rapidly diminished with stimulus repetition when mechanical or electric stimuli were applied to fingers. On the contrary, when the ERPs and multi-unit a ctivity (MUA) were directly recorded from the primary (SI) and secondary (SII) somatosensory cortices in a monkey, there was no marked decrement in the somatosensory responses as a function of stimulus repetition. These results suggest that this rate effect is not due to the response diminution in the SI and SII cortices. Obviously the responses to the first stimulus after a long "silent" period are nhanced due to unspecific initial orientation, originating in more broadly distributed and/or deeper neural structures, perhaps in the prefrontal cortices. With fast repetition rates not only the late unspecific but also some early specific somatosensory ERPs were diminished in amplitude. The fast decrease of the ERPs as a function of stimulus repetition is mainly due to the disappearance of the orientation effect and with faster repetition rates additively due to stimulus specific refractoriness. A sudden infrequent change in the continuous stimulus stream also enhanced somatosensory MEG responses to electric stimuli applied to different fingers. These responses were quite similar to those elicited by the deviant stimuli alone when the frequent standard stimuli were omitted. This enhancement was obviously due to the release from refractoriness because the neural structures generating the responses to the infrequent deviants had more time to recover from the refractoriness than the respective structures for the standards. Infrequent deviant mechanical stimuli among frequent standard stimuli also enhanced somatosensory ERPs and, in addition, they elicited a new negative wave which did not occur in the deviants-alone condition. This extra negativity could be recorded to deviations in the stimulation site and in the frequency of the vibratory stimuli. This response is probably a somatosensory analogue of the auditory mismatch negativity (MMN) which has been suggested to reflect a neural mismatch process between the sensory input and the sensory memory trace.

Multi-Disciplinary Design and Simulation of a Fuel Cell Engine for a UAV

This report describes a methodology for the design and coupling of a proton exchange membrane (PEM) Fuel Cell to an Unmanned Aerial Vehicle (UAV). The report summarizes existing work in the field, the type of UAV and the mission requirements, design the fuel cell system, simulation environment, and compares endurance and range to when the aircraft is fitted with a conventional internal combustion engine (ICE).

MT10049 Pheromone component of a multi target approach to fruitspotting bug management

A pheromone-based trapping system will be developed for both A. lutescens and A. nitida to improve insecticide timing and to rationalise use.

Multi-time scale analysis applied to long time simulation of power systems

The results are presented of applying multi-time scale analysis using the singular perturbation technique for long time simulation of power system problems. A linear system represented in state-space form can be decoupled into slow and fast subsystems. These subsystems can be simulated with different time steps and then recombined to obtain the system response. Simulation results with a two-time scale analysis of a power system show a large saving in computational costs.

Robustness of the partitions in grouping multi-item inventories

A recent work obtained closed-form solutions to the.problem of optimally grouping a multi-item inventory into subgroups with a common order cycle per group, when the distribution by value of the inventory could be described by a Pareto function. This paper studies the sensitivity of the optimal subgroup boundaries so obtained. Closed-form expressions have been developed to find intervals for the subgroup boundaries for any given level of suboptimality. Graphs have been provided to aid the user in selecting a cost-effective level of aggregation and choosing appropriate subgroup boundaries for a whole range of inventory distributions. The results of sensitivity analyses demonstrate the availability of flexibility in the partition boundaries and the cost-effectiveness of any stock control system through three groups, and thus also provide a theoretical support to the intuitive ABC system of classifying the items.

A multi-field bio-economic model of irrigated grain-cotton farming systems.

We present a participatory modelling framework that integrates information from interviews and discussions with farmers and consultants, with dynamic bio-economic models to answer complex questions on the allocation of limited resources at the farm business level. Interviews and discussions with farmers were used to: describe the farm business; identify relevant research questions; identify potential solutions; and discuss and learn from the whole-farm simulations. The simulations are done using a whole-farm, multi-field configuration of APSIM (APSFarm). APSFarm results were validated against farmers' experience. Once the model was accepted by the participating farmers as a fair representation of their farm business, the model was used to explore changes in the tactical or strategic management of the farm and results were then discussed to identify feasible options for improvement. Here we describe the modelling framework and present an example of the application of integrative whole farm system tools to answer relevant questions from an irrigated farm business case study near Dalby (151.27E - 27.17S), Queensland, Australia. Results indicated that even though cotton crops generates more farm income per hectare a more diversified rotation with less cotton would be relatively more profitable, with no increase in risk, as a more cotton dominated traditional rotation. Results are discussed in terms of the benefits and constraints from developing and applying more integrative approaches to represent farm businesses and their management in participatory research projects with the aim of designing more profitable and sustainable irrigated farming systems.

A Modified Heffron-Phillip's Model for The Design of Power System Stabilizers

In this paper a modified Heffron-Phillip's (K-constant) model is derived for the design of power system stabilizers. A knowledge of external system parameters, such as equivalent infinite bus voltage and external impedances or their equivalent estimated values is required for designing a conventional power system stabilizer. In the proposed method, information available at the secondary bus of the step-up transformer is used to set up a modified Heffron-Phillip's (ModHP) model. The PSS design based on this model utilizes signals available within the generating station. The efficacy of the proposed design technique and the performance of the stabilizer has been evaluated over a range of operating and system conditions. The simulation results have shown that the performance of the proposed stabilizer is comparable to that could be obtained by conventional design but without the need for the estimation and computation of external system parameters. The proposed design is thus well suited for practical applications to power system stabilization, including possibly the multi-machine applications where accurate system information is not readily available.

NMR Spectroscopy of Multi-domain Proteins : Immunoglobulin-like Domains of Human Filamin A

Proteins are complex biomacromolecules playing fundamental roles in the physiological processes of all living organisms. They function as structural units, enzymes, transporters, process regulators, and signal transducers. Defects in protein functions often derive from genetic mutations altering the protein structure, and impairment of essential protein functions manifests itself as pathological conditions. Proteins operate through interactions, and all protein functions depend on protein structure. In order to understand biological mechanisms at the molecular level, one has to know the structures of the proteins involved. This thesis covers structural and functional characterization of human filamins. Filamins are actin-binding and -bundling proteins that have numerous interaction partners. In addition to their actin-organizing functions, filamins are also known to have roles in cell adhesion and locomotion, and to participate in the logistics of cell membrane receptors, and in the coordination of intracellular signaling pathways. Filamin mutations in humans induce severe pathological conditions affecting the brain, bones, limbs, and the cardiovascular system. Filamins are large modular proteins composed of an N-terminal actin-binding domain and 24 consecutive immunoglobulin-like domains (IgFLNs). Nuclear magnetic resonance (NMR) spectroscopy is a versatile method of gaining insight into protein structure, dynamics and interactions. NMR spectroscopy was employed in this thesis to study the atomic structure and interaction mechanisms of C-terminal IgFLNs, which are known to house the majority of the filamin interaction sites. The structures of IgFLN single-domains 17 and 23 and IgFLN domain pairs 16-17 and 18-19 were determined using NMR spectroscopy. The structures of domain pairs 16 17 and 18 19 both revealed novel domain domain interaction modes of IgFLNs. NMR titrations were employed to characterize the interactions of filamins with glycoprotein Ibα, FilGAP, integrin β7 and dopamine receptors. Domain packing of IgFLN domain sextet 16 21 was further characterized using residual dipolar couplings and NMR relaxation analysis. This thesis demonstrates the versatility and potential of NMR spectroscopy in structural and functional studies of multi-domain proteins.

On various types of decoupling for time-varying system

This paper deals with the problem of decoupling a class of linear time-varying multi-variable systems, based on the defining property that the impulse response matrix of a decoupled system is diagonal. Depending on the properties of the coefficient matrices of the vector differential equation of the open-loop system, the system may be uniformly or totally decoupled. The necessary and sufficient conditions that permit a system to be uniformly or totally decoupled by state variable feedback are given. The main contribution of this paper is the precise definition of these two classes of decoupling and a rigorous derivation of the necessary and sufficient conditions which show the necessity of requiring that the system be of constant ordered rank with respect to observability. A simple example illustrates the importance of having several definitions of decoupling. Finally, the results are specialized to the case of time invariant systems.

Exploiting the beta distribution-based reputation model in recommender system

Reputation systems are employed to measure the quality of items on the Web. Incorporating accurate reputation scores in recommender systems is useful to provide more accurate recommendations as recommenders are agnostic to reputation. The ratings aggregation process is a vital component of a reputation system. Reputation models available do not consider statistical data in the rating aggregation process. This limitation can reduce the accuracy of generated reputation scores. In this paper, we propose a new reputation model that considers previously ignored statistical data. We compare our proposed model against state-of the-art models using top-N recommender system experiment.

Statistical analysis of spectral data: A methodology for designing an intelligent monitoring system for the diabetic foot

Early detection of (pre-)signs of ulceration on a diabetic foot is valuable for clinical practice. Hyperspectral imaging is a promising technique for detection and classification of such (pre-)signs. However, the number of the spectral bands should be limited to avoid overfitting, which is critical for pixel classification with hyperspectral image data. The goal was to design a detector/classifier based on spectral imaging (SI) with a small number of optical bandpass filters. The performance and stability of the design were also investigated. The selection of the bandpass filters boils down to a feature selection problem. A dataset was built, containing reflectance spectra of 227 skin spots from 64 patients, measured with a spectrometer. Each skin spot was annotated manually by clinicians as "healthy" or a specific (pre-)sign of ulceration. Statistical analysis on the data set showed the number of required filters is between 3 and 7, depending on additional constraints on the filter set. The stability analysis revealed that shot noise was the most critical factor affecting the classification performance. It indicated that this impact could be avoided in future SI systems with a camera sensor whose saturation level is higher than 106, or by postimage processing.

A new multi-objective model to optimise rail transport scheduler

The sugarcane transport system plays a critical role in the overall performance of Australia’s sugarcane industry. An inefficient sugarcane transport system interrupts the raw sugarcane harvesting process, delays the delivery of sugarcane to the mill, deteriorates the sugar quality, increases the usage of empty bins, and leads to the additional sugarcane production costs. Due to these negative effects, there is an urgent need for an efficient sugarcane transport schedule that should be developed by the rail schedulers. In this study, a multi-objective model using mixed integer programming (MIP) is developed to produce an industry-oriented scheduling optimiser for sugarcane rail transport system. The exact MIP solver (IBM ILOG-CPLEX) is applied to minimise the makespan and the total operating time as multi-objective functions. Moreover, the so-called Siding neighbourhood search (SNS) algorithm is developed and integrated with Sidings Satisfaction Priorities (SSP) and Rail Conflict Elimination (RCE) algorithms to solve the problem in a more efficient way. In implementation, the sugarcane transport system of Kalamia Sugar Mill that is a coastal locality about 1050 km northwest of Brisbane city is investigated as a real case study. Computational experiments indicate that high-quality solutions are obtainable in industry-scale applications.

Open source computer-vision based guidance system for UAVs on-board decision making

The use of UAVs for remote sensing tasks; e.g. agriculture, search and rescue is increasing. The ability for UAVs to autonomously find a target and perform on-board decision making, such as descending to a new altitude or landing next to a target is a desired capability. Computer-vision functionality allows the Unmanned Aerial Vehicle (UAV) to follow a designated flight plan, detect an object of interest, and change its planned path. In this paper we describe a low cost and an open source system where all image processing is achieved on-board the UAV using a Raspberry Pi 2 microprocessor interfaced with a camera. The Raspberry Pi and the autopilot are physically connected through serial and communicate via MAVProxy. The Raspberry Pi continuously monitors the flight path in real time through USB camera module. The algorithm checks whether the target is captured or not. If the target is detected, the position of the object in frame is represented in Cartesian coordinates and converted into estimate GPS coordinates. In parallel, the autopilot receives the target location approximate GPS and makes a decision to guide the UAV to a new location. This system also has potential uses in the field of Precision Agriculture, plant pest detection and disease outbreaks which cause detrimental financial damage to crop yields if not detected early on. Results show the algorithm is accurate to detect 99% of object of interest and the UAV is capable of navigation and doing on-board decision making.