CISR PhD and Postdoctoral Positions

This research aims to investigate expand low cost motion tracking gaming consoles to improve user interaction in the living room. An occlusion free multi hyper skeleton motion tracking system will be designed and implemented. The anticipated outcomes of this research will facilitate living room rehabilitation, social networking, microt-rageting advertising and autistic recovery.

This research aims to provide visually impaired audience the ability to perceive and understand the dance movements. The research takes place in two directions. First, introducing tactile track to composers and choreographers. This track will require a vibrating hardware fitted in the venue seats. The second direction is facilitating dance to visually impaired actors. This will require a wearable hardware to be fitted on the visually impaired actors.

The aim of the project is to address limitations of the Design of Experiments (DOE) methods by developing modelling methodology and tools for the process optimisation based on the theory of nonlinear dynamical systems including excitability and attractors. In particular the aim is to determine the desirable process attractor in regards to process parameters and material properties that would allow to quickly and accurately determining critical process parameters and optimal process settings in regards to product quality and/or productivity.

This project involves the analysis of haptics devices manufactured by different vendors. This analysis and the relevant framework developed will provide a benchmarking tool to compare different devices in terms of quantitative and qualitative performance. This research will further generate a performance matrix highlighting the suitability/feasibility of existing off-the-shelf devices for any particular application. The factors involved in the performance comparison are peak static and peak continuous force reflection, work envelope, force and position control, accuracy and resolution, etc. In addition, this research will provide grounds for the validation of vendors' claims about the device.

This project involves the development of framework and methodology that will allow interaction with and manipulation of flexible materials in real time reflecting the properties of the materials such as tension, torsion, bending, surface friction and forces at any certain point. Currently the focus of this particular research is the modelling of 1D objects such as cables, hoses, harnesses, etc. This focus will be extended to 2D analysis providing grounds for the modelling of Visco-Elastic materials as an end goal.

This project involves the development of a methodology and framework to interface haptics technology with Atomic Force Microscope (AFM). The end product of the research work will open new horizons for the nano-scientists and will provide a direct and intuitive way of nano-manipulation of materials at atomic level. The haptics system will reflect all the interactive forces exerted on the cantilever tip of the microscope in real time during manipulation.

Scientific computing is the field that studies scientific phenomena, model them into computer and mathematical systems, and adapt these models to design new families of algorithms, data structures, and mathematical solutions. Ant colony optimization, genetic algorithms, neural networks, simulated annealing are all typical results of scientific computing theories. However, the objective of all these solutions is mainly optimization, tuning and searching for the best approximate solution. They simulate the problem on the physical level rather than the semantic and understanding level. This is why adapting these methods to handle and learn highly semantic problems is challenging.

This research studies these soft computing algorithms/structures and upgrades them into algebraic ones to maintain high semantics interpretation. It develops an algebraic framework that binds the concepts of genetic programming (not algorithms), agent programming, and concept oriented programming to span a space of programs. This research provides a new area of understanding algorithms. It allows analytical study and design of algorithms and data structures. A programs space spanned by algebraic structures defined on a set of independent concepts provides a framework for designing self-repairing programs (interpolation) and generating new programs (extrapolation). Programs hyper-surfaces can be derived and searched for local minimum where the optimum algorithm/data structure lie.

This research extends the known techniques of augmented reality into a generic framework for developing augmented reality applications, scenes, and media production. ARML is a tag language through which colored barcodes/tags can be translated into 3D objects, animations, and movies on top of a news paper, a book, or in a class room. The research is subdivided into three phases. First, a generic framework for augmented reality is to be defined and implemented. This phase includes identifying basic instruction set. Then, a colored barcode is to be developed for each instruction, system hardware is to be put together, and software API and rendering engines are to be developed on mobile phones. Finally, OCR features can be added to allow the user to search through the text of a book or newspaper.

Efficient collision detection is one of the very challenging problems facing force feedback (Haptic) rendering. Researchers managed to achieve efficient runtime rendering using optimized data structures, caching, force shading, and hybrid surface representations. However, these solutions lack the ability to adapt with deformable surfaces due to the time overhead to be spent on restructuring the geometry representation in the memory.

Multi-point collisions, surface deformations, and multipoint haptics (1000 fps each) adds more complexity to the problem and put conventional data structures techniques out of business. Implicit surfaces had a potential in representing, deforming, and recovering 3D meshes. The main benefit of using implicit surfaces is developing one equation the represents the whole surface which means that collision points are where these equations intersect. Solving the equations of two surfaces does the job. Furthermore, incorporating conventional structuring and rendering techniques, minimizes the complexity of the surface equation.

This research studies heterogeneous image fusion from an algebraic perspective. It develops a heterogeneous algebra for fusing images, color maps, interpretation, properties, and dimensionalities. The framework also declares limits of the fusion process and a set of constraints that govern the duality between fusion operators and quality metrics and allows a criteria upon which an automated algorithm can select the quality metric that best suits every fusion algorithm and vice versa.

This PhD project will investigate novel design and improvements of Body weight support system (BWSS) to achieve higher performance and force control. This research will study new methodologies for the lower limb robotic rehabilitation using a system with the ability to control the weight that the person must endure. Several methods for BWSS are going to be studied, allowing to the applicant to choose the best option and improve it and to control the weight of the patient.

The main goal of the proposed PhD project is to design a synchronized cooperative control system of modular robots for lower limb rehabilitation tasks. The mechanism will consist of three adjustable force controlled robots that cooperatively help the patient in walking stability and legs motions. In this project, the PhD candidate will derive a mathematical model for the lower limb robotic rehabilitation system. Then after, the derived model will be utilized to simulate, and finally design a control system . The fundamental questions that are investigated in this research are 1) Development of an accurate mathematical model, 2) Systems identification of the model, 3) Design a synchronized cooperative control system for the robots system.

The aim of this PhD project is to design a cooperative and modular robotic system for lower limb rehabilitation. The mechanism will consist of three adjustable force controlled robots that cooperatively help the patient in walking stability and legs motions. In this project, the PhD candidate will design, simulate, evaluate and finally fabricate the complementary mechanical and electronic parts of the system to use robots for rehabilitation applications. The fundamental questions that are investigated in this research are 1) Design of efficient mechanical mechanisms for the system with special considerations to maximize the performance, mobility, safety, and reliability of the robot while minimizing the size and cost and 2) Identification and verification of the dynamic model of the designed system.

The aim of this research project is to tele-operates a semi-autonomous surgical system using a dynamic robust adaptive control approach. The trajectory and force profile of the surgical system are going to be commanded by the remote operator. The controlled system must have two level control systems. The higher level controller should produce the required path and force profile signals as the control commands for the system. The lower level controller transforms the higher level control signals to the motor voltage, current or frequency commands respectively.

This PhD project aims to inestigate robust control stragies of CISR's motion simulator. In order to do this, appropriate fault prediction and identification features are developed for the motion simulator and high-level robust control systems will be implemented. Artificial intelligence approach will be deployed for development of fault prediction and identification and robust control approach will be used to implement robust high-level motion planning and control for the motion simulator.

In this PhD project, the applicant studies fault-tolerance for medical robotics by using model predictive control methods and considers both sensor and joint failures. The aim is to enables a medical robot to perform a task including maintaining the path and compliance or perform safety routines for any fault scenario to ensure the human safety during robot operation. The project will result to an improved robot control system with higher reliability and safety.

In this research, the applicant will use data analysis and pattern recognition methods for analysing the energy consumption profile of householders in order to identify the wrong/low efficient energy consumption behaviours. The results are then used and to develop appropriate strategies to improve the energy efficiency of the householders.

This research aims to study functional near infrared (fNIR) brain signals in order to design an intuitive brain computer interface (BCI). The anticipated outcome of this research is modular BCI system that communicates with Google GlassesTM. Applications are limitless. The analysed signals will facilitate interaction with wearable computers, wheelchair control, and provide safer driving experience.

This project aims to fuse high resolution detailed x-ray images with live ultrasound videos to produce live fluoroscopic experience with minimal radiation doses. The proposed algorithm will enhance the freeze frame technology by minimising the rate at which frozen x-ray frames are obtained during a fluoroscopic-guided procedure. This research will have a great impact on minimally invasive fluoroscopic interventional procedures carried out on the abdominal part of the human body. This impact can be measured in terms of reduced radiation dose or lengthened fluoroscopic procedures at the same radiation level of current fluoroscopic systems. The anticipated outcome of this research will also open doors for carrying out safe CT-fluoroscopic procedures.

This project aims to collect images from different mobile devices recording a particular phenomena. The main outcome is a bigger picture publicly available for all participating mobile devices. The main motivation deriving this project is to capture every little detail monitored by the crowd witnessing this phenomena. The project has a potential to be scaled up to maintain videos. Applications are limitless. They range from parties, concerts, natural phenomenas, riots and revolutions. The outcome of this project can also facilitate situational awareness for autonomous mobile robots.

This research is focused on the design and understanding of the influence of neuroplasticity on the behaviour of neuron networks and the flow of information. How neurons reconnect themselves when there is a network break down such as after physical injury. Multielectrode technology will be used for this research and will have significant impact on the efforts of neuro-rehabilitation after physical head injuries.

The research work involves the design and development of innovative microelectrode arrays to interface with living neurons. Current microelectrodes use planner structures which are relatively easy to fabricate however lead to very low signal to noise ratio compromising the accurate detection of the neural activities. This research is about the optimisation of the parameters that could lead to higher signal to noise ration and involves the enhancement of geometric structures of the electrode, electrical conductivity and endurance of the electrode material, adhesion with the cells and the electrode density for better neuron to electrode mapping.

In vitro multichannel recordings from neurons have been used as important evidence in neuroscientific studies to understand the fundamentals of neural network mechanisms in the brain. Accurate detection and sorting of neural activity waveforms becomes a key requirement for creating meaningful machine brain interfaces and to understand the working principles of neural networks. In this work we propose a unified framework for unsupervised neural spike clustering. Proposed framework exploits the features of wavelets scale-space representation and time-frequency localisation as well as multiscale principle component analysis to minimise the dimensionality of the raw data at different scales prior to clustering.

In vitro multichannel recordings from neurons have been used as important evidence in neuroscientific studies to understand the fundamentals of neural network mechanisms in the brain. Accurate detection and sorting of neural activity waveforms becomes a key requirement for creating meaningful machine brain interfaces and to understand the working principles of neural networks. This work is about the design of new classification algorithms to employ the prior knowledge of neuron cells distribution around the electrode in N sources to 1 sink information flow configuration. This research will enhance the quality of understanding of the information flow using multielectrode architecture.

This research work involves the design and development of novel experimental setup and algorithms to map and predict neural behaviour and information flow in response to external stimulations. This research will provide insight about the activity of the brain and will shed light how neurons pass information through the network and what makes them stop communicating.

This project will develop methods to optimise and schedule networks of smart and traditional cameras in a manufacturing environment, enabling knowledge capture, manage performance and identify causes of quality degradation. This research will assist Australian manufacturers to stay competitive in the dynamic global market.

This project will utilise event simulation-based meta-modelling capability, coupled with optimisation to address systems optimisation and simulation challenges to provide solutions to regional decision-makers. The research challenge is in accurate estimation, time series prediction and the integration of effective optimisation methods. Such problems in most real world applications are large scale and they may involve nonsmooth nonconvex functions.

The study of constitutive laws and physical behaviours of advanced materials, from organs and skin to nano surfaces, needs to consider non-smoothness and multi-scale effects. Modelling, design, and simulation of these advanced materials must deal with nonconvexity and large-scale deformation, which produce fundamental challenging problems in both theoretical analysis and scientific computations. This investigation will be coupled with advanced visualisation for effective communication and analysis.

Regional industries faces challenging supply chain problems, from the minimisation of inventory costs, to demand forecasting and adjustment, matching sales to capacity, production planning, and not least transport planning and scheduling and environmental impact. The research challenges in optimising regional supply chains include modelling, reinforcement learning, continuous and discrete optimisation and the integration of optimisation methods for the different sub-problems.

The aim of this research program is to investigate applicability of and exploit advanced Artificial Intelligence (AI) methods for uncertainty quantification. Uncertainties associated with values predicted by AI models will be quantified and measured through construction of Prediction Intervals (PIs). This research project focuses on developing new techniques for generation of reliable PIs using AI methods such as neural networks and fuzzy systems. In particular, advanced type-2 fuzzy logic system will be investigated due to their excellent capability in dealing with uncertainties.

The aim of this research program is to investigate applicability of and exploit advanced Artificial Intelligence (AI) methods for uncertainty quantification. Uncertainties associated with values predicted by AI models will be quantified and measured through construction of Prediction Intervals (PIs). This research project considers how to improve the quality of PIs. The idea is to develop PIs that are more informative (narrower) and theoretically correct (a coverage probability above the confidence level). The PI quality improvement is challenging, as the optimisation problem is multi-objective (formulation and solution).

The aim of this research program is to investigate applicability of and exploit advanced Artificial Intelligence (AI) methods for uncertainty quantification. Uncertainties associated with values predicted by AI models will be quantified and measured through construction of Prediction Intervals (PIs). This research project will examine how PIs can be used in real world decision making processes.. PIs will be used for operational planning and scheduling in a variety of fields, such as manufacturing, energy systems, transportation system, logistic networks, and so on.