Electrical and Electronic Engineering - Theses
Permanent URI for this collection
Search Results
1 - 3 of 3
-
ItemAutomatic analysis of 4D laryngeal CT scans to assist diagnosing of voice disorders( 2017)Vocal folds are the two smooth bands of muscles located in larynx just above the trachea. Humans produce voice by vibrating the vocal folds using the air coming from the lungs. This abduction and adduction of vocal folds are controlled by the muscles connected to thyroid cartilage, cricothyroid cartilages and arytenoid cartilages. When vocal muscles are misused or excessively used, they can be strained or damaged and voice disorders may occur. Furthermore, vocal folds can be damaged and the connecting cartilages and muscles can be affected due to the effect of other illnesses like Parkinson's disease (PD), multiple sclerosis (MS), myasthenia gravis (MG), strokes or tumours. PD is a neuro-degenerative disease which currently has neither cure nor any pathological tests to detect. The disease progresses very slowly over the years and symptoms appear when approximately 70% of the neuron cells have ceased to function. Usual symptoms are tremors and stiffness in the body muscles which results in difficulty moving most of the body parts externally as well as internally. Consequently, vocal folds and laryngeal muscles get affected and PD patients suffer from vocal impairments. Furthermore, previous studies carried out using laryngoendoscopy, laryngostroboscopy and laryngeal electromyography of PD patients found that those patients have an abnormal phase closure and abnormal laryngeal muscle activity. Moreover, in 2014, a study carried out using a group of early PD patients demonstrated increased glottis area and reduced inter-arytenoid distance in subjects. Therefore, laryngeal measurements could be used as a biomarker for early detection of PD. However, segmenting the vocal folds region from volumetric laryngeal computed tomography (CT) images is a tedious task, when it is done manually. Manual segmentation schemes require lot of expert knowledge and time, and often provide poor objective and reproducible results. In this project, we hope to develop a novel automated algorithm to segment the vocal folds region and measure the laryngeal parameters. This thesis consists of two major parts; first it proposes a fully automated segmentation method for segmenting the rima glottidis from 3D laryngeal CT scans and generates the time series for rima glottidis areas, which in future can be used to develop an automated diagnosis tool for voice disorders. The gray-level difference features are learnt through a support vector machine classifier and several post processing algorithms are introduced to refine the final segmentation result. Second, a fully automated method to estimate the vocal plane position in a 3D laryngeal CT volume using computer vision algorithms and techniques is proposed. Vocal plane position is identified using anatomical markers like thyroid cartilage and vertebral bones and these markers are segmented using gray-scale and edge-based features. The experiments are conducted using a private data set from the Movement Disorder Clinic at Monash Medical Centre The detailed implementations of the two methods including feature extraction, kernel selection, post processing and validation are explained in this thesis.
-
ItemAdaptive control of voltage sourced inverters in microgrid( 2017)The microgrid is a new scheme of future distribution power grid with small scale and the integration of distributed generation (DG). Generally, the interface between the DG and the utility grid is a voltage sourced inverter (VSI) and a power filter, aiming to (i) regulate the electricity injection in grid-connected mode and the additional voltage \& frequency in islanding mode, (ii) eliminate the high-frequency harmonics caused by pulse width modulation (PWM) controlled VSI, respectively. The coefficients of VSI controller and the parameters of power filter should be designed properly to achieve system stability and to prevent harmonic injection and excessive power loss. In the equivalent structure of VSI based DG unit, the grid impedance is another component that exists between the filter and the ideal grid, which is an aggregate impedance of the whole network. However, it is usually seen to be uncertain or unknown in the practical system that will highly affect the controller design and output performance of VSI, both in grid-connected and islanded modes. Imprudent selection of filter parameters may cause worse filtering outcomes and significant power losses. The rationale for the need for optimal filter design is that conventional designs methods are only able to provide a range of values for each parameter and therefore are unable to guarantee performance. This thesis proposes an optimal design method of power filter with passive damping to address these problems. The novelty of the proposed method stems from using multi-objective optimisation approach to find optimal values of filter parameters using the genetic algorithm. The objective of the optimisation is to attain high harmonic attenuation performance and small switching frequency ripple while achieving low power consumption. The proposed method is verified through simulation studies carried out on a three-phase grid-connected VSI based DG system, using the parameter values obtained from the proposed design method. The simulation results demonstrate that the new method can achieve a higher level of ripple reduction, greater harmonic attenuation, and higher system efficiency than existing design methods. Controller design for grid-connected and islanded VSI is based on the knowledge of equivalent grid impedance (or network impedance). Grid impedance is determined by experience or calculated through the technical manuals of overhead line or underground cable in the system. However, the actual line impedance may vary due to the variation of temperature, humidity, and ageing. Any inconsistency of grid impedance between the control loop and the real value will lead to poor output performance and even instability of VSI. By using the information of grid topology and multiple measurement scenarios of bus voltage and power, a network impedance estimation (NIE) is proposed to calculate every line impedance of the grid based on reverse power flow. The Newton-Raphson iterative method is employed to solve the proposed NIE problem, while the corresponding Jacobian matrix is formulated. Then the impedance of every line in the network can be obtained iteratively. The NIE method is verified through three benchmark systems. Estimation results show that great accuracy and fast iteration of the proposed method can be realised. The proposed NIE process can operate online to provide the estimated value of network impedance continuously. Based on the knowledge of network topology, the equivalent grid impedance of every DG unit can be computed subsequently. When the \emph{LCL} filter is properly designed by the proposed optimisation method, the accurate model of VSI can be obtained. For grid-connected mode, an adaptive state feedback controller integrated with NIE process is presented. The state feedback gain is well designed by using the estimated impedance values of NIE. In islanded mode, the output feedback controller with droop control is more suggested since its simplicity and good power sharing. However, sharing performance is very sensitive concerning the value of impedance. Hence, an adaptive controller with virtual impedance is proposed based on the NIE method to achieve the desired performance. Then the output impedance of every VSI can be adequately designed. Grid-connected and islanded simulations are carried out to verify the proposed control method in a 14-bus benchmark microgrid. Results demonstrate the effectiveness and superiority of the NIE based adaptive methods, which are better in comparison with the conventional method in both grid-connected and islanded operations. Moreover, it also shows that the circulating current among multiple islanded DG units is eliminated and the stability of system frequency can be preserved during the process of impedance variation.
-
ItemA low-power minaturised intracranial pressure monitoring microsystem( 2017)The ultimate goal of this work is realisation of a fully implantable chronic intracranial pressure (ICP) monitoring system. Due to the required mm-scale form factor of the implantable device, the available power is scarce. This calls for investigation of new circuit and sensor integration techniques to decrease the total power consumption of the system down to a few hundreds of nano watts. So the main focus of this work is design of an ultra-low power integrated circuit (IC) for measuring ICP. Power consumption minimization of the sensing system proposed in this work paves the way for integration of an RF-power scavenger or biological fuel cells. The proposed sensing system also takes full advantage of Invensense MEMS-CMOS process to heterogeneously integrate the sensor and interface. This integration type requires no post-processing and results in sub-pF sensor-interface parasitic interconnection capacitance Cp which is an order of magnitude smaller than previously reported Cp’s.