Mechatronics Systems Engineering - Theses, Dissertations, and other Required Graduate Degree Essays

Receive updates for this collection

Mechanical Characterization of Breast Tissue Constituents for Cancer Assessment

Author: 
Date created: 
2014-02-25
Abstract: 

Breast elastography is a method of cancer detection that uses the response of soft tissue to deformations, leading to discovery of abnormalities. The methods of Clinical Breast Examination and Breast Self-Examination are based primarily on stiffness and, hence, on the mechanics of tissue constituents examined by palpation (Goodson, 1996). However, little is known about the mechanical characteristics of breast tissue under compression and the contribution of tissue mechanics to breast cancer detection. This study focuses therefore on tissue characterization and on identification of the relationship between tissue properties and pathological mechanics via offering an elastography technique based on the Yeoh hyperelastic model. The strength of the Yeoh model has been validated through compression testing of breast phantoms (small and large sizes), animal tissues, and in-vivo human tissues. The proposed method provides thresholds for the mechanical properties of soft tissues, which are useful in medical applications.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Farid Golnaraghi
Department: 
Applied Sciences: School of Mechatronic Systems Engineering
Thesis type: 
(Thesis) M.A.Sc.

A Dental Assisting System for Procedures Performed by Air–Turbine Handpieces

Author: 
Date created: 
2013-08-13
Abstract: 

The present thesis introduces a dental assisting system (DAS) for procedures that are performed by air–turbine dental handpieces. Dental restoration is a process that begins with removing carries and affected tissues to retain the functionality of tooth structures. Air–turbine dental handpieces are high–speed rotary cutting tools that are widely used by dentists during this operation. The next stage in the process is filling the cavity with appropriate restorative materials. “Amalgam” and “composite” are two dental restorative materials that are extensively used by dentists. Most old restorations eventually fail and need to be replaced. One of the difficulties in replacing failing restorations is discerning the boundary of the restorative materials. Dentists may remove healthy tooth structures while replacing tooth–colored composites. Although the visibility issue is less challenging for amalgam materials, replacing them still results in loss of healthy tooth layers. Developing an objective and sensor–based method is a promising approach to monitor restorative operations and prevent removal of healthy tooth structures. The designed DAS uses the audio signals of ATDH during the cutting process. Audio signals are rich sources of information and can be analysed to identify a particular zone of cutting. Support vector machine (SVM), a powerful algorithm for classification, is employed to differentiate the tooth structures from composite/amalgam samples based on their cutting sounds. The averaged short–time Fourier transform coefficients are selected as the features; and the performance of the SVM classifier is evaluated from different aspects such as number of features, feature scaling methods, and the utilized kernels. The obtained results indicated capability and efficiency of the proposed scheme. The developed DAS can also measure the speed of ATDH, and maintain it during loaded conditions. An indirect speed measurement method is introduced based on the vibration/sound of ATDH. This measurement technique is explained theoretically based on the rotating unbalance concept and the vibration of a fixed–free beam. To control the speed, a proportional–integral controller is designed and tested. The feasibility of this controller in maintaining the speed in the loaded conditions was confirmed by simulations and experiments.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Siamak Arzanpour
Department: 
Applied Sciences: School of Mechatronic Systems Engineering
Thesis type: 
(Thesis) Ph.D.

Implementation of EKF-SLAM on NAO Humanoid Robot

Date created: 
2013-07-08
Abstract: 

Autonomous navigation is among the most important areas within the robotic community and has gained significant interest in the last three decades. At its core, autonomous navigation depends on whether or not the Simultaneous Localization And Mapping (SLAM) problem is properly addressed. SLAM is the mobile robot’s ability to navigate in an unknown (indoor or outdoor) environment. The process includes building a map of the environment while concurrently the mobile robot position is being updated. This thesis focuses on the implementation of SLAM on NAO humanoid robot. The ubiquitous method of Extended Kalman Filter (EKF) is employed in this project. The central focus of the thesis is on simulation and the real time implementation of EKF-SLAM with the entire coding in python. I have adopted the feature-based map (landmarks) along with updating the robot position in an indoor area. The main contribution of this work is the real time implementation of EKF-SLAM on the NAO humanoid robot, which is manufactured by Aldebaran Robotics. The NAO humanoid robot has been programmed in python through the interfacing with the main software of NAO that is called NAOqi. The details of implementing EKF-SLAM on NAO are explained and discussed in this thesis. The main two sensors used for addressing SLAM on the NAO humanoid robot were odometry and laser sensors. In addition, this thesis has practically solved the data association problem, which is considered one of the main issues of EKF-SLAM, using Mahalanobis Distance likelihood technique. Data association is the ability of the robot to correspond the detected landmarks from sensors at any time with the pre-observed landmarks in the map. Throughout the experimental studies, I have detected and successfully resolved several problems. First, the deviation in NAO robot motion function is solved by designing a closed loop motion control of the robot. Second, the Agglomerative Hierarchical Clustering (AHC) method is used to let the laser sensor detects and differentiates between different landmarks at one time. The laser in NAO generates the world around it via 683 points without differentiation between different objects in the scene. Third, obstacle avoidance function is designed.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Ahmad Rad
Department: 
Applied Sciences: School of Mechatronic Systems Engineering
Thesis type: 
(Thesis) M.A.Sc.