Driving simulators have become useful research tools for the institution and laboratories which a... more Driving simulators have become useful research tools for the institution and laboratories which are studying in different fields of vehicular and transport design to increase road safety. Although classical washout filters are broadly used because of their short processing time, simplicity and ease of adjust, they have some disadvantages such as generation of wrong sensation of motions, false cue motions, and also their tuning process which is focused on the worst case situations leading to a poor usage of the workspace. The aim of this study is to propose a new motion cueing algorithm that can accurately transform vehicle specific force into simulator platform motions at high fidelity within the simula-tor's physical limitations. This method is proposed to compensate wrong cueing motion caused by saturation of tilt coordination rate limit using an adaptive correcting signal based on added fuzzy logic into translational channel to minimize the human sensation error and exploit the platform more efficiently.
2015 IEEE International Conference on Systems, Man, and Cybernetics, 2015
—Ordinary differential equations are used for modelling a wide range of dynamic systems. Even tho... more —Ordinary differential equations are used for modelling a wide range of dynamic systems. Even though there are many graphical software applications for this purpose, a fully customised solution for all problems is code-level programming of the model and solver. In this project, a free and open source C++ framework is designed to facilitate modelling in native code environment and fulfill the common simulation needs of control and many other engineering and science applications. The solvers of this project are obtained from ODEINT and specialised for Armadillo matrix library to provide an easy syntax and a fast execution. The solver code is minimised and its modification for users have become easier. There are several features added to the solvers such as controlling maximum step size, informing the solver about sudden input change and forcing custom times into the results and calling a custom method at these points. The comfort of the model designer, code readability, extendibility and model isolation have been considered in the structure of this framework. The application manages the output results, exporting and plotting them. Modifying the model has become more practical and a portion of corresponding codes are updated automatically. A set of libraries is provided for generation of output figures, matrix hashing, control system functions, profiling, etc. In this paper, an example of using this framework for a classical washout filter model is explained.
The aim of this paper is to provide a washout filter that can accurately produce vehicle motions ... more The aim of this paper is to provide a washout filter that can accurately produce vehicle motions in the simulator platform at high fidelity, within the si-mulators physical limitations. This is to present the driver with a realistic virtual driving experience to minimize the human sensation error between the real driving and simulated driving situation. To successfully achieve this goal, an adap-tive washout filter based on fuzzy logic online tuning is proposed to overcome the shortcomings of fixed parameters, lack of human perception and conservative motion features in the classical washout filters. The cutoff frequencies of high-pass, low-pass filters are tuned according to the displacement information of platform, workspace limitation and human sensation in real time based on fuzzy logic system. The fuzzy based scaling method is proposed to let the platform uses the workspace whenever is far from its margins. The proposed motion cue-ing algorithm is implemented in MATLAB/Simulink software packages and provided results show the capability of this method due to its better performance , improved human sensation and exploiting the platform more efficiently without reaching the motion limitation.
—Driving phenomenon is a repetitive process, that permits sequential learning under identifying t... more —Driving phenomenon is a repetitive process, that permits sequential learning under identifying the proper change periods. Sequential filtering is widely used for tracking and prediction of state dynamics. However, it suffers at abrupt changes, which cause sudden incremental prediction error. We provide a sequential filtering approach using online Bayesian detection of changepoints to decrease prediction error generally, and specifically at abrupt changes. The approach learns from optimally detected segments for identifying driving behaviour. Changepoints detection is done by the Pruned Exact Linear Time algorithm. Computational cost of our approach is bounded by the cost of the implemented sequential filter. This computational performance is suitable to the online nature of motion simulator's delay reduction. The approach was tested on a simulated driving scenario using Vortex by CM Labs. The state dimensions are simulated 2D space coordinates, and velocity. Particle filter was used for online sequential filtering. Prediction results show that change-point detection improves the quality of state estimation compared to traditional sequential filters, and is more suitable for predicting behavioural activities.
—Driving behaviour prediction is a challenging problem due to the nonlinearity of human behaviour... more —Driving behaviour prediction is a challenging problem due to the nonlinearity of human behaviour. Linear and nonlinear techniques have been used to solve this problem, and they provide good results presented in the performance of the current autonomous cars. However, they lack the ability to adapt to abruptness that happens because of the human factor. In this paper, we introduce a method to extract persistent homology barcode statistics. These statistics are useful as a representative of the driving process including the human behaviour. Human factor identification requires finding features that preserve certain properties against scalability, deformation, and abruptness. Topological Data Analysis (TDA) using persistent homology provides these features for driver behaviour prediction. We captured a driver's head motion as an experimental behavioural cue, combined it with captured simulated vehicle data (location and velocities). Barcodes are extracted using JavaPlex, then we extracted descriptive statistics to show the significance of these barcode as features for driver behaviour prediction. The correlation between the extracted features shows a promising start for a behavioural tracking applications using TDA.
—In this note, a novel robust finite-horizon Kalman filter is developed for discrete linear time-... more —In this note, a novel robust finite-horizon Kalman filter is developed for discrete linear time-varying systems with missing measurements and norm-bounded parameter uncertainties. The missing measurements are modelled by a Bernoulli distributed sequence and the system parameter uncertainties are in the state and output matrices. A two stage recursive structure is considered for the Kalman filter and its parameters are determined guaranteeing that the covariances of the state estimation errors are not more than the known upper bound. Finally, simulation results are presented to illustrate the outperformance of the proposed robust estimator compared with the previous results in the literature. Index Terms—robust Kalman filter, miss measurement, state estimation, norm-bounded parameter uncertainties.
Lubrication oil plays an important role in maintaining the health and performance of a land vehic... more Lubrication oil plays an important role in maintaining the health and performance of a land vehicle engine. Accurate condition monitoring of lubrication oil enables an effective predictive maintenance regime to be established. This can extend engine life as well as reduce over or under-servicing and other unnecessary maintenance costs. Machine learning models are useful for mining meaningful patterns from data samples. In this research, through the application of such models, we classify the condition of engine lubrication oil based on data from the Vehicle Health and Usage Monitoring System and laboratory test results of lubrication oil from a cohort of military land vehicles. The oil condition is classified into three categories: normal, degraded, and unsuitable. Feature selection methods are used to identify the best feature set for representing the lubrication oil condition. Importantly, the machine learning models employed provide the predicted output with justification in the form of explanatory rules pertaining to the lubrication oil condition. The findings indicate that (i) a good feature selection method is necessary to reduce the dimensionality of the feature set used for classification; (ii) machine learning provides a viable method for classifying oil condition with understandable justifications.
—In this paper we propose a framework for communicating performance art to deaf, blind and deafbl... more —In this paper we propose a framework for communicating performance art to deaf, blind and deafblind audiences and artists haptically through the sense of touch. This research opens doors for novel artistic trends relying mainly on the sense of touch. The paper investigates the design considerations dictated by solo and group dances as well as stage setup. Implementation scenarios for deafblind audiences and performers are also discussed.
Magnetic Resonance Imaging (MRI) is one of the important imaging techniques. However, it is a tim... more Magnetic Resonance Imaging (MRI) is one of the important imaging techniques. However, it is a time-consuming process. The aim of this study is to make the imaging process efficient. MR images are sparse in the sensing domain and Compressive Sensing exploits this sparsity. Locally sparsified Compressed Sensing is a specialized case of CS which subdivides the image and sparsifies each region separately ; later samples are taken based on sparsity level in that region. In this paper, a new structured approach is presented for defining the size and locality of sub-regions in image. Experiments were done on the regions defined by proposed framework and local sparsity constraints were used to achieve high sparsity level and to reduce the sample set. Experimental results and their comparison with global CS is presented in the paper.
Magnetic Resonance images (MRI) do not only exhibit spar-sity but their sparsity takes a certain ... more Magnetic Resonance images (MRI) do not only exhibit spar-sity but their sparsity takes a certain predictable shape which is common for all kinds of images. That region based localised sparsity can be used to de-noise MR images from random thermal noise. This paper present a simple framework to exploit sparsity of MR images for image de-noising. As, noise in MR images tends to change its shape based on contrast level and signal itself, the proposed method is independent of noise shape and type and it can be used in combination with other methods.
—The fact that medical images have redundant information is exploited by researchers for faster i... more —The fact that medical images have redundant information is exploited by researchers for faster image acquisition. Sample set or number of measurements were reduced in order to achieve rapid imaging. However, due to inadequate sampling, noise artefacts are inevitable in Compressive Sensing (CS) MRI. CS utilizes the transform sparsity of MR images to regenerate images from under-sampled data. Locally sparsified Compressed Sensing is an extension of simple CS. It localises sparsity constraints for sub-regions rather than using a global constraint. This paper, presents a framework to use local CS for improving image quality without increasing sampling rate or without making the acquisition process any slower. This was achieved by exploiting local constraints. Localising image into independent sub-regions allows different sampling rates within image. Energy distribution of MR images is not even and most of noise occurs due to under-sampling in high energy regions. By sampling sub-regions based on energy distribution, noise artefacts can be minimized. Experiments were done using the proposed technique. Results were compared with global CS and summarized in this paper.
—Magnetic Resonance Imaging (MRI) is one of the prominent medical imaging techniques. This proces... more —Magnetic Resonance Imaging (MRI) is one of the prominent medical imaging techniques. This process is time-consuming and can take several minutes to acquire one image. The aim of this research is to reduce the imaging process time of MRI. This issue is addressed by reducing the number of acquired measurements using theory of Compressive Sensing (CS). Compressive Sensing exploits sparsity in MR images. Randomly under sampled k-space generates incoherent noise which can be handled using a nonlinear image reconstruction method. In this paper, a new framework is presented based on the idea to exploit non-uniform nature of sparsity in MR images, where local sparsity constrains were used instead of traditional global constraint, to further reduce the sample set. Experimental results and comparison with CS using global constraint are demonstrated.
Magnetic Resonance Imaging (MRI) is a widely used technique for acquiring images of human organs/... more Magnetic Resonance Imaging (MRI) is a widely used technique for acquiring images of human organs/tissues. Due to its complex imaging process, it consumes a lot of time to produce a high quality image. Compressive Sensing(CS) has been used by researchers for rapid MRI. It uses a global sparsity constraint with variable density random sampling and L1 minimisation. This work intends to speed up the imaging process by exploiting the non-uniform sparsity in the MR images. Locally Sparsified CS suggests that the image can be even better sparsi-fied by applying local sparsity constraints. The image produced by local CS can further reduce the sample set. This paper establishes the basis for a methodology to exploit non-uniform nature of sparsity and to make the MRI process time efficient by using local sparsity constraints. abstract
—Magnetic Resonance Imaging (MRI) is a widely used technique for acquiring images of human organs... more —Magnetic Resonance Imaging (MRI) is a widely used technique for acquiring images of human organs/tissues. Due to its complex imaging process, it consumes a lot of time to produce a high quality image. Compressive Sensing(CS) has been used by researchers for rapid MRI. It uses a global sparsity constraint with variable density random sampling and L1 minimization. This work intends to speed up the imaging process by exploiting the non-uniform sparsity in the MR images. Local CS suggests that the image can be sparsified even better by applying local sparsity constraints. The image produced by local CS can further reduce the sample set. This paper establishes the basis for a methodology to exploit non-uniform nature of sparsity and to make the MRI process time efficient by using local sparsity constraints.
Adaptive filters are now becoming increasingly studied for their suitability in application to co... more Adaptive filters are now becoming increasingly studied for their suitability in application to complex and non-stationary signals. Many adaptive filters utilise a reference input, that is used to form an estimate of the noise in the target signal. In this paper we discuss the application of adaptive filters for high electromyography contaminated electroencephalography data. We propose the use of multiple referential inputs instead of the traditional single input. These references are formed using multiple EMG sensors during an EEG experiment, each reference input is processed and ordered through firstly determining the Pearson's r-squared correlation coefficient, from this a weighting metric is determined and used to scale and order the reference channels according to the paradigm shown in this paper. This paper presents the use and application of the Adaptive-Multi-Reference (AMR) Least Means Square adaptive filter in the domain of electroencephalograph signal acquisition.
The performance of electroencephalograph (EEG)-based brain-computer interface (BCI) systems is su... more The performance of electroencephalograph (EEG)-based brain-computer interface (BCI) systems is susceptible to external influences, typically due to movement of the subject. Static flight simulators are the norm for this type of measurement in reduced risk flight training; however modern day simulators require a new level of realism. Next-generation flight simulators, such as the Deakin University Haptically Enabled Universal Motion Simulator, expose the pilot to external 'G' forces by physical moving the entire cockpit and pilot, motions which increase the likelihood of unwanted EEG artefacts. The filtering techniques are based on a custom designed approach to overcome the dynamic nature of the flight simulator; the techniques are based on Extended Kalman Filters to accommodate for the non-linearity of the EEG acquired signals. In this paper, the effectiveness of the proposed BCI system is presented in the dynamic nature of the simulator. The proposed BCI paradigm is tested and evaluated under real test conditions and the results analysed and compared to that of a static flight simulator.
The work presented in this paper focuses on fitting of a neural mass model to EEG data. Neurophys... more The work presented in this paper focuses on fitting of a neural mass model to EEG data. Neurophysiology inspired mathematical models were developed for simulating brain's electrical activity imaged through Electroencephalography (EEG) more than three decades ago. At the present well informative models which even describe the functional integration of cortical regions also exists. However, a very limited amount of work is reported in literature on the subject of model fitting to actual EEG data. Here, we present a Bayesian approach for parameter estimation of the EEG model via a marginalized Markov Chain Monte Carlo (MCMC) approach.
—Hemodynamic models have a high potential in application to understanding the functional differen... more —Hemodynamic models have a high potential in application to understanding the functional differences of the brain. However, full system identification with respect to model fitting to actual functional magnetic resonance imaging (fMRI) data is practically difficult and is still an active area of research. We present a simulation based Bayesian approach for nonlinear model based analysis of the fMRI data. The idea is to do a joint state and parameter estimation within a general filtering framework. One advantage of using Bayesian methods is that they provide a complete description of the posterior distribution, not just a single point estimate. We use an Auxiliary Particle Filter adjoined with a kernel smoothing approach to address this joint estimation problem.
Driving simulators have become useful research tools for the institution and laboratories which a... more Driving simulators have become useful research tools for the institution and laboratories which are studying in different fields of vehicular and transport design to increase road safety. Although classical washout filters are broadly used because of their short processing time, simplicity and ease of adjust, they have some disadvantages such as generation of wrong sensation of motions, false cue motions, and also their tuning process which is focused on the worst case situations leading to a poor usage of the workspace. The aim of this study is to propose a new motion cueing algorithm that can accurately transform vehicle specific force into simulator platform motions at high fidelity within the simula-tor's physical limitations. This method is proposed to compensate wrong cueing motion caused by saturation of tilt coordination rate limit using an adaptive correcting signal based on added fuzzy logic into translational channel to minimize the human sensation error and exploit the platform more efficiently.
2015 IEEE International Conference on Systems, Man, and Cybernetics, 2015
—Ordinary differential equations are used for modelling a wide range of dynamic systems. Even tho... more —Ordinary differential equations are used for modelling a wide range of dynamic systems. Even though there are many graphical software applications for this purpose, a fully customised solution for all problems is code-level programming of the model and solver. In this project, a free and open source C++ framework is designed to facilitate modelling in native code environment and fulfill the common simulation needs of control and many other engineering and science applications. The solvers of this project are obtained from ODEINT and specialised for Armadillo matrix library to provide an easy syntax and a fast execution. The solver code is minimised and its modification for users have become easier. There are several features added to the solvers such as controlling maximum step size, informing the solver about sudden input change and forcing custom times into the results and calling a custom method at these points. The comfort of the model designer, code readability, extendibility and model isolation have been considered in the structure of this framework. The application manages the output results, exporting and plotting them. Modifying the model has become more practical and a portion of corresponding codes are updated automatically. A set of libraries is provided for generation of output figures, matrix hashing, control system functions, profiling, etc. In this paper, an example of using this framework for a classical washout filter model is explained.
The aim of this paper is to provide a washout filter that can accurately produce vehicle motions ... more The aim of this paper is to provide a washout filter that can accurately produce vehicle motions in the simulator platform at high fidelity, within the si-mulators physical limitations. This is to present the driver with a realistic virtual driving experience to minimize the human sensation error between the real driving and simulated driving situation. To successfully achieve this goal, an adap-tive washout filter based on fuzzy logic online tuning is proposed to overcome the shortcomings of fixed parameters, lack of human perception and conservative motion features in the classical washout filters. The cutoff frequencies of high-pass, low-pass filters are tuned according to the displacement information of platform, workspace limitation and human sensation in real time based on fuzzy logic system. The fuzzy based scaling method is proposed to let the platform uses the workspace whenever is far from its margins. The proposed motion cue-ing algorithm is implemented in MATLAB/Simulink software packages and provided results show the capability of this method due to its better performance , improved human sensation and exploiting the platform more efficiently without reaching the motion limitation.
—Driving phenomenon is a repetitive process, that permits sequential learning under identifying t... more —Driving phenomenon is a repetitive process, that permits sequential learning under identifying the proper change periods. Sequential filtering is widely used for tracking and prediction of state dynamics. However, it suffers at abrupt changes, which cause sudden incremental prediction error. We provide a sequential filtering approach using online Bayesian detection of changepoints to decrease prediction error generally, and specifically at abrupt changes. The approach learns from optimally detected segments for identifying driving behaviour. Changepoints detection is done by the Pruned Exact Linear Time algorithm. Computational cost of our approach is bounded by the cost of the implemented sequential filter. This computational performance is suitable to the online nature of motion simulator's delay reduction. The approach was tested on a simulated driving scenario using Vortex by CM Labs. The state dimensions are simulated 2D space coordinates, and velocity. Particle filter was used for online sequential filtering. Prediction results show that change-point detection improves the quality of state estimation compared to traditional sequential filters, and is more suitable for predicting behavioural activities.
—Driving behaviour prediction is a challenging problem due to the nonlinearity of human behaviour... more —Driving behaviour prediction is a challenging problem due to the nonlinearity of human behaviour. Linear and nonlinear techniques have been used to solve this problem, and they provide good results presented in the performance of the current autonomous cars. However, they lack the ability to adapt to abruptness that happens because of the human factor. In this paper, we introduce a method to extract persistent homology barcode statistics. These statistics are useful as a representative of the driving process including the human behaviour. Human factor identification requires finding features that preserve certain properties against scalability, deformation, and abruptness. Topological Data Analysis (TDA) using persistent homology provides these features for driver behaviour prediction. We captured a driver's head motion as an experimental behavioural cue, combined it with captured simulated vehicle data (location and velocities). Barcodes are extracted using JavaPlex, then we extracted descriptive statistics to show the significance of these barcode as features for driver behaviour prediction. The correlation between the extracted features shows a promising start for a behavioural tracking applications using TDA.
—In this note, a novel robust finite-horizon Kalman filter is developed for discrete linear time-... more —In this note, a novel robust finite-horizon Kalman filter is developed for discrete linear time-varying systems with missing measurements and norm-bounded parameter uncertainties. The missing measurements are modelled by a Bernoulli distributed sequence and the system parameter uncertainties are in the state and output matrices. A two stage recursive structure is considered for the Kalman filter and its parameters are determined guaranteeing that the covariances of the state estimation errors are not more than the known upper bound. Finally, simulation results are presented to illustrate the outperformance of the proposed robust estimator compared with the previous results in the literature. Index Terms—robust Kalman filter, miss measurement, state estimation, norm-bounded parameter uncertainties.
Lubrication oil plays an important role in maintaining the health and performance of a land vehic... more Lubrication oil plays an important role in maintaining the health and performance of a land vehicle engine. Accurate condition monitoring of lubrication oil enables an effective predictive maintenance regime to be established. This can extend engine life as well as reduce over or under-servicing and other unnecessary maintenance costs. Machine learning models are useful for mining meaningful patterns from data samples. In this research, through the application of such models, we classify the condition of engine lubrication oil based on data from the Vehicle Health and Usage Monitoring System and laboratory test results of lubrication oil from a cohort of military land vehicles. The oil condition is classified into three categories: normal, degraded, and unsuitable. Feature selection methods are used to identify the best feature set for representing the lubrication oil condition. Importantly, the machine learning models employed provide the predicted output with justification in the form of explanatory rules pertaining to the lubrication oil condition. The findings indicate that (i) a good feature selection method is necessary to reduce the dimensionality of the feature set used for classification; (ii) machine learning provides a viable method for classifying oil condition with understandable justifications.
—In this paper we propose a framework for communicating performance art to deaf, blind and deafbl... more —In this paper we propose a framework for communicating performance art to deaf, blind and deafblind audiences and artists haptically through the sense of touch. This research opens doors for novel artistic trends relying mainly on the sense of touch. The paper investigates the design considerations dictated by solo and group dances as well as stage setup. Implementation scenarios for deafblind audiences and performers are also discussed.
Magnetic Resonance Imaging (MRI) is one of the important imaging techniques. However, it is a tim... more Magnetic Resonance Imaging (MRI) is one of the important imaging techniques. However, it is a time-consuming process. The aim of this study is to make the imaging process efficient. MR images are sparse in the sensing domain and Compressive Sensing exploits this sparsity. Locally sparsified Compressed Sensing is a specialized case of CS which subdivides the image and sparsifies each region separately ; later samples are taken based on sparsity level in that region. In this paper, a new structured approach is presented for defining the size and locality of sub-regions in image. Experiments were done on the regions defined by proposed framework and local sparsity constraints were used to achieve high sparsity level and to reduce the sample set. Experimental results and their comparison with global CS is presented in the paper.
Magnetic Resonance images (MRI) do not only exhibit spar-sity but their sparsity takes a certain ... more Magnetic Resonance images (MRI) do not only exhibit spar-sity but their sparsity takes a certain predictable shape which is common for all kinds of images. That region based localised sparsity can be used to de-noise MR images from random thermal noise. This paper present a simple framework to exploit sparsity of MR images for image de-noising. As, noise in MR images tends to change its shape based on contrast level and signal itself, the proposed method is independent of noise shape and type and it can be used in combination with other methods.
—The fact that medical images have redundant information is exploited by researchers for faster i... more —The fact that medical images have redundant information is exploited by researchers for faster image acquisition. Sample set or number of measurements were reduced in order to achieve rapid imaging. However, due to inadequate sampling, noise artefacts are inevitable in Compressive Sensing (CS) MRI. CS utilizes the transform sparsity of MR images to regenerate images from under-sampled data. Locally sparsified Compressed Sensing is an extension of simple CS. It localises sparsity constraints for sub-regions rather than using a global constraint. This paper, presents a framework to use local CS for improving image quality without increasing sampling rate or without making the acquisition process any slower. This was achieved by exploiting local constraints. Localising image into independent sub-regions allows different sampling rates within image. Energy distribution of MR images is not even and most of noise occurs due to under-sampling in high energy regions. By sampling sub-regions based on energy distribution, noise artefacts can be minimized. Experiments were done using the proposed technique. Results were compared with global CS and summarized in this paper.
—Magnetic Resonance Imaging (MRI) is one of the prominent medical imaging techniques. This proces... more —Magnetic Resonance Imaging (MRI) is one of the prominent medical imaging techniques. This process is time-consuming and can take several minutes to acquire one image. The aim of this research is to reduce the imaging process time of MRI. This issue is addressed by reducing the number of acquired measurements using theory of Compressive Sensing (CS). Compressive Sensing exploits sparsity in MR images. Randomly under sampled k-space generates incoherent noise which can be handled using a nonlinear image reconstruction method. In this paper, a new framework is presented based on the idea to exploit non-uniform nature of sparsity in MR images, where local sparsity constrains were used instead of traditional global constraint, to further reduce the sample set. Experimental results and comparison with CS using global constraint are demonstrated.
Magnetic Resonance Imaging (MRI) is a widely used technique for acquiring images of human organs/... more Magnetic Resonance Imaging (MRI) is a widely used technique for acquiring images of human organs/tissues. Due to its complex imaging process, it consumes a lot of time to produce a high quality image. Compressive Sensing(CS) has been used by researchers for rapid MRI. It uses a global sparsity constraint with variable density random sampling and L1 minimisation. This work intends to speed up the imaging process by exploiting the non-uniform sparsity in the MR images. Locally Sparsified CS suggests that the image can be even better sparsi-fied by applying local sparsity constraints. The image produced by local CS can further reduce the sample set. This paper establishes the basis for a methodology to exploit non-uniform nature of sparsity and to make the MRI process time efficient by using local sparsity constraints. abstract
—Magnetic Resonance Imaging (MRI) is a widely used technique for acquiring images of human organs... more —Magnetic Resonance Imaging (MRI) is a widely used technique for acquiring images of human organs/tissues. Due to its complex imaging process, it consumes a lot of time to produce a high quality image. Compressive Sensing(CS) has been used by researchers for rapid MRI. It uses a global sparsity constraint with variable density random sampling and L1 minimization. This work intends to speed up the imaging process by exploiting the non-uniform sparsity in the MR images. Local CS suggests that the image can be sparsified even better by applying local sparsity constraints. The image produced by local CS can further reduce the sample set. This paper establishes the basis for a methodology to exploit non-uniform nature of sparsity and to make the MRI process time efficient by using local sparsity constraints.
Adaptive filters are now becoming increasingly studied for their suitability in application to co... more Adaptive filters are now becoming increasingly studied for their suitability in application to complex and non-stationary signals. Many adaptive filters utilise a reference input, that is used to form an estimate of the noise in the target signal. In this paper we discuss the application of adaptive filters for high electromyography contaminated electroencephalography data. We propose the use of multiple referential inputs instead of the traditional single input. These references are formed using multiple EMG sensors during an EEG experiment, each reference input is processed and ordered through firstly determining the Pearson's r-squared correlation coefficient, from this a weighting metric is determined and used to scale and order the reference channels according to the paradigm shown in this paper. This paper presents the use and application of the Adaptive-Multi-Reference (AMR) Least Means Square adaptive filter in the domain of electroencephalograph signal acquisition.
The performance of electroencephalograph (EEG)-based brain-computer interface (BCI) systems is su... more The performance of electroencephalograph (EEG)-based brain-computer interface (BCI) systems is susceptible to external influences, typically due to movement of the subject. Static flight simulators are the norm for this type of measurement in reduced risk flight training; however modern day simulators require a new level of realism. Next-generation flight simulators, such as the Deakin University Haptically Enabled Universal Motion Simulator, expose the pilot to external 'G' forces by physical moving the entire cockpit and pilot, motions which increase the likelihood of unwanted EEG artefacts. The filtering techniques are based on a custom designed approach to overcome the dynamic nature of the flight simulator; the techniques are based on Extended Kalman Filters to accommodate for the non-linearity of the EEG acquired signals. In this paper, the effectiveness of the proposed BCI system is presented in the dynamic nature of the simulator. The proposed BCI paradigm is tested and evaluated under real test conditions and the results analysed and compared to that of a static flight simulator.
The work presented in this paper focuses on fitting of a neural mass model to EEG data. Neurophys... more The work presented in this paper focuses on fitting of a neural mass model to EEG data. Neurophysiology inspired mathematical models were developed for simulating brain's electrical activity imaged through Electroencephalography (EEG) more than three decades ago. At the present well informative models which even describe the functional integration of cortical regions also exists. However, a very limited amount of work is reported in literature on the subject of model fitting to actual EEG data. Here, we present a Bayesian approach for parameter estimation of the EEG model via a marginalized Markov Chain Monte Carlo (MCMC) approach.
—Hemodynamic models have a high potential in application to understanding the functional differen... more —Hemodynamic models have a high potential in application to understanding the functional differences of the brain. However, full system identification with respect to model fitting to actual functional magnetic resonance imaging (fMRI) data is practically difficult and is still an active area of research. We present a simulation based Bayesian approach for nonlinear model based analysis of the fMRI data. The idea is to do a joint state and parameter estimation within a general filtering framework. One advantage of using Bayesian methods is that they provide a complete description of the posterior distribution, not just a single point estimate. We use an Auxiliary Particle Filter adjoined with a kernel smoothing approach to address this joint estimation problem.
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