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http://hdl.handle.net/123456789/8
2019-05-25T16:53:05ZANALYSIS AND DESIGN OF PHOTOVOLTAIC AND FUEL CELL BASED CONVERTER SYSTEMS
http://hdl.handle.net/123456789/14491
Title: ANALYSIS AND DESIGN OF PHOTOVOLTAIC AND FUEL CELL BASED CONVERTER SYSTEMS
Authors: Mehta, Gitanjali
Abstract: The steadily increasing energy consumption, the soaring cost, the exhaustible nature of fossil fuels, and the intensifying concerns over the global environment have created much interest in alternative energy sources such as solar, wind, fuel cell etc. as future energy solution. Alternative energy sources integrated at distribution level is termed as Distributed Generation (DG). Photovoltaic (PV) and Fuel Cells (FC) are the best environmental friendly technologies for DGs and hence receiving increased attention.
The two-stage Power Conditioning Unit (PCU) including the DC-DC boost converter with the Pulse Width Modulated Voltage Source Inverter (VSI) is the state-of-art technology used nowadays worldwide for grid interfacing of the FC. However, it has reduced power conversion efficiency because of the two-stage configuration. Further, it has reduced reliability because of more no of components used in this configuration.
The author has proposed a FC based DG system with a single-stage power conditioning unit. The system is analysed by modelling various units of FCDG system, performing mathematical analysis and simulation studies. The Proton Exchange Membrane Fuel Cell (PEMFC) model used for the simulation studies is based on physical processes inside the PEMFC stack and is modeled using the experimental data obtained from Avista Labs SR-12 0.5 kW PEMFC stack. The inverter controller is designed to control the active power fed to the grid, the reactive power transfer between the inverter and the grid, the DC-link voltage, the quality of the injected power and grid synchronization. The designed control scheme of the inverter consists of a cascade of two independent controllers, where the external voltage controller generates the reference current that is tracked by the inner current controller which generates the pulses for the inverter switches.
Another facet of work done is with respect to solar PV as source of electricity. The conventional grid-interfaced PV systems use a DC-DC converter with Maximum Power Point Tracking (MPPT) control and a DC-AC inverter for grid interfacing. The proposed PV system uses one power conversion stage, thus simplifying the system topology. The single-diode PV circuit model used in the PV system simulation studies is modelled using the experimental values of Kyocera KC200GT 0.2 kW PV module. The MPPT, grid synchronisation, reactive power compensation, output current harmonic reduction is included simultaneously in the control circuit of the VSI connecting the PV to the grid. Due to the utilization of only one energy conversion stage, the single-stage grid-connected PV system proves to be simpler, more efficient and economical than its two-stage counterpart. However, the complexity of the control scheme is somewhat increased.
The increased use of power electronic devices in various loads results in many power quality problems in the power system network. Shunt Active Power Filters (SAPFs) are extensively used to compensate the load current harmonics, reactive power and load unbalance at distribution level. The
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principle of operation of the SAPF is to supply the undesired harmonics and reactive power to the load, so that the mains current is of improved quality. However, its implementation results in additional hardware cost.
With the objective of reducing the cost and increasing the efficiency, grid-interactive FC system have been proposed which includes the functionality of SAPF. A control algorithm is developed such that the features of SAPF have been incorporated in the conventional inverter interfacing the FC to the grid without any additional hardware cost. Thus the grid-interfacing inverter is effectively utilised to perform the following functions: control of active power from the FC source to the grid, load reactive power demand support, current harmonic compensation and current imbalance compensation at PCC. With appropriate control of grid-interfacing inverter all the four objectives can be accomplished either individually or simultaneously. This concept, thus, reduces the overall design and cost of the system.
The motivation for the last part of the work is given below. The cost of FC is too high to justify its widespread use. The PV power generation has large variations in its output power during day and night and during varying weather conditions. Hence a PV-FC hybrid system can prove to be better to provide a reliable power source for grid-connected applications than a system comprising any of these single resources. The last part of the work proposes a utility-interactive hybrid DG system consisting of PV and FC to realize a reliable power supply for a grid connected critical load. These sources can be operated independently or in conjunction as per the requirement. The proposed system ensures maximum utilization of the PV array, and necessary utilisation of FC stack resulting in optimum operational costs.
The power circuit topology consists of two DC-DC boost converters, where one of them is fed by a PV array and the other by an FC stack. The Incremental Conductance MPPT control algorithm is implemented in the DC-DC converter connecting the PV array to the DC-link. This ensures extraction of maximum power from the PV array under all conditions. The difference of required power and the PV power is provided by the FC and governed through proper control of DC-DC converter connecting the FC stack to the DC-link. The outputs of the two DC-DC converters are connected to a common DC-link. The power is fed into the grid through an inverter for which the common DC-link acts as the energy source. The inverter control is so designed that apart from feeding active power into the grid, the system can also provide reactive power and harmonic compensation for PCC load.2014-07-01T00:00:00ZINTELLIGENT ADAPTIVE OPTIMAL CONTROL OF DYNAMICAL SYSTEMS
http://hdl.handle.net/123456789/14490
Title: INTELLIGENT ADAPTIVE OPTIMAL CONTROL OF DYNAMICAL SYSTEMS
Authors: Prasad, Lal Bahadur
Abstract: Most of the dynamical systems such as power systems, missile systems, robotic systems, inverted pendulum, industrial processes, chaotic circuits etc. are highly nonlinear in nature. The control of such systems is a challenging task. Intelligent adaptive optimal control is a viable recent approach. Intelligent adaptive optimal control has been emerged from the integration of adaptive control and optimal control methodologies with intelligent computational techniques. In this research work the performance investigation of intelligent adaptive optimal control of dynamical systems is presented. The applications of control schemes for dynamical systems control are implemented considering certain examples of linear and nonlinear dynamical systems to attempt this research investigation.
The performance of controlled systems is desired to be optimal which should be valid also when applied in the real situation. Adaptive control which is able to deal with uncertainties is generally not optimal. Optimal control is off-line, and needs the knowledge of system dynamics for its design. Thus, to have both features of control design, it is desired to design online adaptive optimal control.
Policy Iteration (PI) is a computational intelligence technique that belongs to a class of reinforcement learning (RL) algorithms; solves Hamilton-Jacobi-Bellman (HJB) equation by direct approach. Based on actor-critic structure, PI algorithm consists of two-step iteration: policy evaluation and policy improvement. These two steps of policy evaluation and policy improvement are repeated until the policy improvement step no longer changes the actual policy and thus converging to the optimal control. PI algorithm starts by evaluating the cost of a given initial admissible (stabilizing) control policy to converge towards state feedback optimal control. The infinite horizon optimal solution using HJB and algebraic Riccati equation (ARE) which gives linear quadratic regulator (LQR) require the complete knowledge of the system dynamics. Also these techniques give offline solution. The online PI algorithm solves online the continuous-time optimal control problem without using the knowledge of system internal dynamics, the information which is extracted from real-time dynamics by online measurement of sampled states along state trajectory. The knowledge of internal state dynamics is not needed for evaluation of cost or the update of control policy; and only the knowledge of input-to-state dynamics is required for updating the control policy. Thus, it is a partially model-free approach. The adaptive critic design (ACD) using online PI technique gives an online infinite horizon adaptive optimal control solution for continuous-time linear time invariant (LTI) systems and continuous-time affine nonlinear systems. By using neural networks to parameterize actor and critic for online implementation, this control scheme becomes a high-level intelligent control scheme.
In the PI algorithm the critic is trained to approximate the solution of Lyapunov equation at the policy evaluation step, and the actor is trained to approximate the control policy at the
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policy improvement step, and the critic and actor are sequentially updated taking other one constant. In the generalized PI algorithm either one or both of the policy evaluation and policy improvement steps are not required to complete before the next step is started. The online synchronous policy iteration algorithm uses simultaneous continuous-time tuning for the actor and critic neural networks. The online synchronous PI algorithm which needs the knowledge of the system dynamics, solve the optimal control problem online using real-time measurements of closed-loop signals. Using neural networks approximations for critic and actor both it gives an online intelligent adaptive optimal control solution for the continuous-time dynamical systems.
This research work contributes by presenting the comprehensive performance investigation of the different control schemes for continuous-time linear time-invariant (LTI) systems and affine nonlinear systems. The following objectives have been considered in this research work.
1. Optimal control of nonlinear inverted pendulum dynamical system using PID controller & LQR.
2. Intelligent control of nonlinear inverted pendulum dynamical system using Mamdani and TSK fuzzy inference systems.
3. Optimal control using LQR for automatic generation control of two-area interconnected power system.
4. Intelligent control using fuzzy-PI controller for automatic generation control of two-area interconnected nonlinear power system.
5. Intelligent control of process system using radial basis function.
6. Adaptive optimal control using policy iteration technique for LTI systems.
7. Adaptive optimal control using policy iteration technique for affine nonlinear systems.
8. Intelligent adaptive optimal control using synchronous policy iteration technique for LTI systems.
9. Intelligent adaptive optimal control using synchronous policy iteration technique for affine nonlinear systems.
These research objectives are briefly described as below.
Linear quadratic regulator (LQR), an optimal control technique and PID control method which are generally used for control of the linear dynamical systems have been used in this research work to control the nonlinear dynamical system. The modeling and control design of nonlinear inverted pendulum-cart dynamic system using PID controller & LQR have been presented for both cases of without and with disturbance input. The simulation results and performance analysis have been presented which justify the comparative advantages of optimal control using PID+LQR method. The pendulum stabilizes in upright position and cart
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reaches the desired position quickly & smoothly even under the continuous disturbance input justify that the control schemes are simple, effective & robust.
Due to the capabilities of generalization, function approximation, learning and adaptation etc. the neural networks are applied for various control, identification, and estimation applications. In this research work the indirect adaptive control of a nonlinear process system using radial basis function neural networks (RBFNN) is presented. The liquid level control problem of a surge tank is considered as a process system. Two RBFNNs are used to model this affine nonlinear system to approximate the internal state dynamic function and input-to-state dynamic function respectively. The RBFNN controller provides a satisfactory response.
Fuzzy control has an impact in the control community because of the simple approach it provides to use heuristic control knowledge for nonlinear control problems. Fuzzy control is an intelligent control technique which uses the human expert knowledge to make the control decisions. In this research work, the modeling, control design and performance analysis of fuzzy control for nonlinear inverted pendulum-cart dynamic system without & with disturbance input are presented. The fuzzy control methods using Mamdani and Takagi-Sugeno-Kang (TSK) fuzzy inference systems have been implemented to control the cart position and stabilize the inverted pendulum in vertically upright position. The comparative performance analysis of these fuzzy control methods have also been done with PID control method. The simulation results justify the comparative advantages of fuzzy control methods. The pendulum stabilizes in vertically upright position and cart approaches the desired position even under the continuous disturbance input justify that the control schemes are effective & robust. The analysis of the responses of the control schemes gives that the performance of PD-FLC using Mamdani type FIS is better than PID controller, and the performance of TSK FLC is better than both. The response of direct fuzzy control using TSK FIS is more smooth & fast than both PID control & Mamdani PD-fuzzy control.
Electrical power systems are complex nonlinear dynamic systems. As the system parameters can’t be completely known under the presence of nonlinearities and uncertainties, the controller designed based on a fixed-parameter linearized model may not work properly for the actual plants. Thus, it is required to take into account the system nonlinearities and parametric uncertainties in the control design. In view of this aspect of investigation, this research work presents the modeling, simulation and performance analysis of automatic generation control (AGC) of two-area interconnected nonlinear power system using fuzzy-PI controller. The conventional integral control is also presented for comparing results. The simulation results and analysis justify the comparative advantages of fuzzy control method.
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In this research work the application of policy iteration technique based adaptive critic scheme for adaptive optimal control of continuous-time LTI dynamical systems is presented. The control scheme is implemented considering various practical examples of LTI systems- general SISO LTI system, higher order LTI system- a mechanical system, load frequency control of power system, automatic voltage regulator of power system, and DC motor speed control system. The systems modeling, analysis, and simulation results are presented for load frequency control of power system for both of system models without and with integral control, automatic voltage regulator of power system for both models of without and with sensor, DC motor speed control system for both of system models without and with integral compensator. Analyzing the simulation results obtained for these applications, it is observed that critic parameter matrix P and actor parameter K obtained from adaptive critic scheme using PI technique are converging adaptively to optimal values which are mostly same to that obtained from LQR approach. Also in case of change in system parameter in real situation the controller adapts it and converges to same optimal values. Thus the actor K and critic P parameters remain unchanged. The structural change introduced in system dynamics by including integral control/compensator is augmenting the system behavior such as of its credit that removing the steady state error in closed loop responses. The structural change in system will not be adapted by the proposed controller but it will adapt the change in system parameters in real situation at any moment of time which is demonstrated by simulating also with change in system parameters at certain instant of time. The comparative performance investigation of adaptive critic control scheme and linear quadratic regulator is also presented. Thus, adaptive optimal control scheme is partially model-free, effective & robust.
In this research work the application of PI technique based adaptive critic scheme for adaptive optimal control of continuous-time affine nonlinear dynamical systems is presented. The cost function approximation using neural network is used for online implementation of PI algorithm. The application of control scheme is implemented considering the state regulation problem for certain general affine nonlinear systems and certain practical examples of affine nonlinear systems- single-link manipulator, inverted pendulum, Vander Pol oscillator. The simulation results and performance analysis are presented from which it is observed that the system states converge towards the equilibrium point at origin, and the control signal remains bounded converging towards zero. The cost function approximation neural networks weights are adjusted to the optimal values which give the critic parameters converging adaptively to optimal values and thus the control policy is adaptive optimal. The online PI algorithm requires an initial stabilizing controller for converging to the optimal solution. The simulation results and performance analysis demonstrate the effectiveness of online policy iteration technique based adaptive critic control scheme.
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In this research work the applications of online synchronous PI technique using neural networks for adaptive optimal control of continuous-time LTI systems and affine nonlinear systems are presented. The application of online synchronous PI based control scheme is implemented for two practical examples of LTI systems- load frequency control of power system, and automatic voltage regulator of power system. The application of online synchronous PI based control scheme is also implemented for affine nonlinear systems considering the state regulation problem for certain general affine nonlinear systems and two practical examples of affine nonlinear systems- single-link manipulator, and Vander Pol oscillator. The simulation results and performance analysis are presented which demonstrate the effectiveness of online synchronous PI based adaptive critic control scheme. The online synchronous PI based adaptive critic design using neural networks provides an intelligent adaptive optimal control of continuous-time dynamical systems.2014-07-01T00:00:00ZROBOT MANIPULATOR CONTROL USING INTELLIGENT AND EVOLUTIONARY COMPUTING TECHNIQUES
http://hdl.handle.net/123456789/14489
Title: ROBOT MANIPULATOR CONTROL USING INTELLIGENT AND EVOLUTIONARY COMPUTING TECHNIQUES
Authors: Chaudhary, Himanshu
Abstract: The rapid development of factory automation involves industrial robot manipulators, which are mostly used in manufacturing processes to increase the productivity and quality of the products. The current thrust of research in robotics is to control robots which can operate in dynamic and partially known environments. Control of an industrial robot includes nonlinearities, uncertainties and external perturbations that should be considered in the design of control laws. Most non-adaptive control schemes for the control of robot manipulator systems, usually assume a full knowledge of the system dynamics. This is an unrealistic assumption in many cases as these complex systems are subjected to the presence of uncertainties. If not dealt appropriately, these uncertainties may have a remarkable effect on the controller performance and even induce instability. The ability of learning provides the robot manipulator with autonomous intelligence to handle such situations. Recent studies strongly stipulate that the learning based intelligent controllers reduce the adverse effects of time-varying behaviour and unmodelled dynamics when the analytical design is tedious or is not satisfactory. Hence, developing model-free adaptive control structure using computationally intelligent soft computing techniques has become an interesting research topic.
The main objectives of the present work are; (a) investigation of the ability of soft computing approaches for addressing various design tasks and issues associated with the control of robot manipulators, (b) development of high performance tracking control algorithms in the presence of time-varying structured and unstructured uncertainties using computationally intelligent soft computing based controllers, (c) Integration and hybridization of fuzzy logic, neural networks, genetic algorithm, and other evolutionary optimization techniques with conventional controllers to overcome each other’s weakness, leading to new approach for solving robot manipulator control problem., (d) investigation of new hybrid adaptive neuro-fuzzy control algorithms for manipulator control with structured and unstructured uncertainties, (e) Fuzzy PD plus Integral (FPD+I) Control of an Industrial Robot manipulator with Velocity Observer, (f) Intelligent hybrid force/position controllers for an Industrial Robot Manipulator with velocity observer (g) Imperialist Competitive Algorithm optimized adaptive neuro fuzzy controller for hybrid force position control of an Industrial Robot Manipulator (h) Using the ability of ANFIS (Adaptive Neuro-Fuzzy Inference System) to provide fast and acceptable solutions of the inverse kinematics problem under kinematical uncertainties, and (i) study of hybrid fuzzy logic based pre-compensation scheme consisting
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of fuzzy PD pre-compensator and a conventional PD Controller for superior steady state and transient performance, good stabilization and tracking performance.
In chapter three, tracking control of a six degrees of freedom robot arm (PUMA Robot) using hybrid Fuzzy PD plus conventional I controller, for unknown joint velocities as well as dynamics of a robot manipulator, is carried out as a new approach. For sensing the unknown joint velocity, a high gain velocity observer is introduced, which estimates the joint velocity from the positional component. The three input and one output fuzzy system is too complex to construct the PID controller. It is very difficult to decide the fuzzy control rules intuitively. Proposed fuzzy controller is mainly focused to mitigate such problems. Complexity of the proposed Fuzzy PD plus conventional I controller is minimized and only two design variables are used to adjust the rate of variations of the proportional gain and derivative gain. Numerical simulation using the dynamic model of six DOF robot arm shows the effectiveness of the approach in trajectory tracking problems. Comparative evaluation with respect to PID and Fuzzy PID controllers are presented to validate the controller design. The results presented emphasize that a satisfactory tracking precision could be achieved using velocity observer based Fuzzy PD plus conventional I controller structure when compared to their respective fuzzy PID only or conventional PID counterparts. An unpaired pooled t-test was executed to prove the statistically significance of the outcome of the proposed velocity observer based fuzzy proportional, derivative plus integral (VOB-FPD+I) controller in comparison with other controllers. Simulation results show the usefulness of the approach.
Neuro-Fuzzy techniques that have emerged from the fusion of neural networks and fuzzy inference systems form a popular framework for solving real world problems. In chapter four, three novel hybrid adaptive neuro-fuzzy control algorithms have been proposed for manipulator control with uncertainties for solving hybrid force/ position control problem in a constrained environment for two cases: (1) when joint velocity is present, (2) when joint velocity is absent. Comparative analysis of simulated performance of some controllers (PID controller, FPID controller, FPD+I controller) is presented to validate the proposed controller design. The output of these controllers is applied to produce the final actuation signal based on current position and velocity errors. Numerical simulation using the dynamic model of six DOF puma robot arm with uncertainties shows the effectiveness of the approach in trajectory tracking problems. The results obtained show that adaptive neuro fuzzy based hybrid force/position controller perform better with uncertainties of the dynamics parameters and friction changes during manipulator operation. Performance indices such as Root Mean
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Square Error (RMSE), Normalised Mean Square Error (NMSE) and Mean Absolute Percentage Error (MAPE) IAE are used for comparison.
Genetic Algorithm, Particle swarm Optimization (PSO) and Imperialist competitive algorithm (ICA), is currently gaining popularity in the research community, for its effectiveness in solving certain real world optimization problems. While the ICA is inspired by social evolution of human species, the GA and PSO are based on biological evolution of species. In chapter five, an ICA based hybrid adaptive neuro fuzzy inference system based force/position controller problem is presented under constrained environment. Numerical simulation using the dynamic model of six degree of freedom robot arm shows the effectiveness of the approach in trajectory tracking problems. Comparative evaluation is presented with respect to genetic algorithm and particle swarm optimization based hybrid adaptive neuro fuzzy inference system based force/position controller. The controller parameters optimized using ICA shows better results in trajectory tracking problems.
Obtaining the joint variables that result in a desired position of the robot end-effector called as inverse kinematics is one of the most important problems in robot kinematics and control. As the complexity of robot increases, obtaining the inverse kinematics solution is difficult and computationally expensive. In chapter six, using the ability of Adaptive Neuro-Fuzzy Inference System (ANFIS), an implementation of a representative fuzzy inference system using a back propagation neural network-like structure, the inverse kinematics problem with limited mathematical representation of the system is solved. Computer simulation conducted on five degree of freedom (DOF) SCORBOT- ER-V PLUS manipulator shows the effectiveness of the developed approach to provide fast and acceptable solutions of the inverse kinematics and thereby making ANFIS as an alternate approach to map the inverse kinematics solution.
The results of the developed intelligent algorithms have been very encouraging and would certainly open new opportunities for dealing with robot control of complex structures in manufacturing industry to increase the productivity and quality.2014-12-01T00:00:00ZPROCESSING OF ECHOCARDIOGRAPHIC IMAGES WITH REFERENCE TO MITRAL REGURGITATION
http://hdl.handle.net/123456789/14488
Title: PROCESSING OF ECHOCARDIOGRAPHIC IMAGES WITH REFERENCE TO MITRAL REGURGITATION
Authors: Saini, Kalpana
Abstract: The present thesis deals with the research work carried out for the automatic severity evaluation of the mitral regurgitation (MR). MR is a common heart valve disorder. In present days, the clinicians carry out this task by extracting the regurgitant jet area from the echocardiographic images, manually. Since, the echocardiographic images primarily suffer from the multiplicative speckle noise, efforts have been made to clean the echocardiographic images to enable clinicians to obtain higher MR assessment accuracies. Here after, in the thesis, only the word images shall be used to refer to the echocardiographic images. Digital image processing has been done in the MATLAB environment.
To facilitate the manual analysis of the MR images by the clinicians the research efforts have been worked out to provide clinicians with the much better contrasted enhanced and almost completely de-speckled images with the view to improve accuracy of his assessment because of the improved visual enhancement. The contrast enhancement has been achieved using the proposed modified log transformation technique and the images has been de-speckled using the newly developed two techniques; the Hybrid Speckle Reducing Anisotropic Diffusion (HSRAD) filter and the Modified Non-linear Complex Diffusion (MNCD) filter for cleaning the images of the multiplicative speckle noises.
For the contrast enhancement the log transformation has been modified to give better contrast enhanced images than the one which are obtainable using conventional log transformation. Results demonstrate that the proposed modified version of log transformation gives better image enhancement one obtainable using conventional log transformation technique. The results are better balanced contrasted images bearing much higher quality indices proving to be more useful for manual processing in assessing the MR severity.
For de-speckling the two newly developed algorithms have been proposed which produce much better results when compared with the results obtainable using existing filters which are also employed on echocardiographic images besides other types of images. These two new filters allow clinicians to carry out flawless diagnosis.
One of the proposed de-speckling filters is a hybrid filter, namely HSRAD filter is a combination of speckle reducing anisotropic diffusion (SRAD) filter and the relaxed median filter.
Processing of echocardiographic images with reference to mitral regurgitation
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Experiments are carried out on a number of images. Results show significant reduction in speckle noise while retaining the edges at the same time. The proposed filter removes speckle noise as well as the impulse noise and enhances the edges. The second proposed de-speckling filter MNCD filter is developed to remove speckle noise more effectively from the images and provides much better enhancement even of the finer details of the image. The performance of the MNCD filter has been found to be much better than the existing de-speckling filters with particular reference to echocardiographic images. The results are compared with the results of the other existing filters.
The clinicians’ assessment of MR, carried out manually, is thus, taken as the ground truth to compare with the results obtained from the following designed and developments of algorithms for ‘Automatic MR Severity Evaluation’ techniques.
The thesis presents the three novel approaches for automatic MR severity grade evaluation. The first approach is the boundary detection of heart’s chambers. Two new algorithms viz. the Fast Region Based Active Contour Model (FRACM) and the New Selective Binary and Gaussian Filtering Regularized Level Set (NSBGFRLS) model have been proposed for the purpose of above stated boundary detection.
The second approach is the detection of MR jet area which employs the two newly proposed techniques; A Region Growing based Mosaic Jet Segmentation (RG-MJS) and the Combined Wavelet and Watershed Transformation based Mosaic Jet Segmentation (CWW-MJS), for carrying out automatically the MR mosaic jet segmentation and evaluation of its area.
The third approach has been the Vena Contracta Width (VCW) detection. A technique which automatically finds out the location of VC and calculates the VCW has been developed and proposed. The results evaluated automatically using the proposed technique and assessed manually by the clinicians are compared. The comparison corroborates results within very close limits.
In case of significant MR, left ventricle has to accommodate both the stroke volume and regurgitant volume with each heart beat so it leads to volume overload of the left ventricle. The left ventricle dilates and becomes hyper-dynamic for compensation. The left atrial and pulmonary venous pressures increase sharply in case of acute severe MR, leading to pulmonary congestion and pulmonary edema. A gradual increase in left atrial size and compliance compensates in chronic MR, so that left atrial and pulmonary venous pressures do not increase until late in the course of the disease. An increase in after load, contractile dysfunction, and heart failure occur in case of progressive left ventricular dilation. This entails the detection of boundaries of heart’s chambers, for which two new models, viz. the Fast Region Active Contour Model (FRACM) and
Abstract
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the Novel Selective Binary Selective Regularized Level Set (NSBGFRLS) have been developed and presented in the thesis. The proposed models the FRACM and the NSBGFRLS are the much faster algorithms than the existing algorithms to detect the boundaries of the heart chambers.
The performance of these two boundary detection models has been experimented and the results are tabulated, plotted and compared with the performance of other existing models which are also employed for boundary detection of echocardiographic images. The performance of the models has been found to be much superior to other existing models.
The second approach to detect the severity grades of MR is to evaluate the value of the MR jet area. The objective is to segment out the color mosaic pattern from the color Doppler image. At present the clinicians draw this area and assess the value of the regurgitation jet area manually. If this area is found out automatically then it will save a lot of time of the clinicians and allow higher measurement accuracies.
The two novel methods, RG-MJS and CWW-MJS have been proposed for the automatic evaluation of the mosaic MR jet area. The methods have been applied on some ten patients who were suffering from varied grades of MR severity from mild to severe. The RG-MJS method is recommended for those patients who are suffering from moderate or severe MR. Whereas the method CWW-MJS is recommended for all the patients who may be suffering from MR of any one of the three severity grades viz. mild, moderate and sever. The results of experiments have been compared with the results of manual assessment of MR assessed by the clinicians as the ground truth.
The third approach to detect the severity grading of MR is to calculate the vena contracta width (VCW) of the MR jet. The narrowest part of the jet that appears just downstream from the orifice is referred to as the vena contracta. High velocity and laminar flow are the characteristics of the vena contracta. A mathematical expression has also been proposed for the calculation of VCW. The VCW is smaller than the anatomic regurgitant orifice to some extent due to boundary effects. It is difficult to locate this narrowest part exactly in TTE or TEE images. It entails to develop a method which should process the MR images to locate and find out the numerical value of VCW. A simpler method than the complexity of the mathematical derivation has been developed and proposed for getting the numerical value of the vena contracta width. The proposed method also automatically finds out the location of the VCW for which it does not require zooming out of the vena contracta width portion of the MR jet images.
Processing of echocardiographic images with reference to mitral regurgitation
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Clinical values have been taken as ground truth for the jet area and vena contracta width comparisons. MR severity comparison has been done between clinical values and values obtained from proposed methods. Results are very much in agreement and within very close limits.
The results of experiments are very encouraging and the proposed developments find clinical applications. To demonstrate application a Graphical User Interface (GUI) which is supported by all the existing and the proposed algorithms has been designed. With the help of the proposed GUI the clinicians or any other user will be able to process images just by selecting the name of the method from the GUI panel; without going in to complexity of the mathematical treatment of the techniques. The GUI facilitates processing of images, provides comparison between different methods, and also displays the measured values of MR jet area and VCW in terms of numerical digits.2013-06-01T00:00:00Z