This research focuses on the development of intelligent fastening tool tracking systems for the automotive industry to identify the fastened bolts. In order to accomplish such a task, the position of the tool tip must be identified because the tool tip position coincides with the head of the fastened bolt while the tool fastens the bolt. The proposed systems utilize an inertial measurement unit (IMU) and another sensor to track the position and orientation of the tool tip. To minimize the position and orientation calculation error, an IMU needs to be calibrated as accurately as possible. This research presents a novel triaxial accelerometer calibration technique that offers a high accuracy. The simulation and experimental results of the accelerometer calibration are presented. To identify the fastening action, an expert system is developed based on the sensor measurements. When a fastening action is identified, the system identifies the fastened bolt by using an expert system based on the position and orientation of the tool tip and the position and orientation of the bolt. Since each fastening procedure needs different accuracies and requirements, three different systems are proposed. The first system utilizes a triaxial magnetometer and an IMU to identify the fastened bolt. This system calculates the position and orientation by using an IMU. An expert system is used to identify the initial position, stationary state, and the fastened bolt. When the tool fastens a bolt, the proposed expert system detects the fastening action by triaxial accelerometer and triaxial magnetometer measurements. When the fastening action is detected, the system corrects the velocity and position error using zero velocity update (ZUPT). By using the corrected tool tip position and orientation, the system can identify the fastened bolts. Then, with the fastened bolt position, the position of the IMU is corrected. When the tool is stationary, the system corrects linear velocity error and reduces the position error. The experimental results demonstrate that the proposed system can identify fastened bolts if the angles of the bolts are different or the bolts are not closely placed. This low cost system does not require a line of sight, but has limited position accuracy. The second system utilizes an intelligent system that incorporates Kalman filters (KFs) and a fuzzy expert system to track the tip of a fastening tool and to identify the fastened bolt. This system employs one IMU and one encoder-based position sensor to determine the orientation and the centre of mass location of the tool. When the KF is used, the orientation error increases over time due to the integration step. Therefore, a fuzzy expert system is developed to correct the tilt angle error and orientation error. When the tool fastens a bolt, the system identifies the fastened bolt by applying the fuzzy expert system. When the fastened bolt is identified, the 3D orientation error of the tool is corrected by using the location and the orientation of the fastened bolt and the position sensor outputs. This orientation correction method results in improved reliability in determining the tool tip location. The fastening tool tracking system was experimentally tested in a lab environment, and the results indicate that such a system can successfully identify the fastened bolts. This system not only has a low computational cost but also provides good position and orientation accuracy. The system can be used for most applications because it provides a high accuracy. The third system presents a novel position/orientation tracking methodology by hybridizing one position sensor and one factory calibrated IMU with the combination of a particle filter (PF) and a KF. In addition, an expert system is used to correct the angular velocity measurement errors. The experimental results indicate that the orientation errors of this method are significantly reduced compared to the orientation errors obtained from an EKF approach. The improved orientation estimation using the proposed method leads to a better position estimation accuracy. The experimental results of this system show that the orientation of the proposed method converges to the correct orientation even when the initial orientation is completely unknown. This new method was applied to the fastening tool tracking system. This system provides good orientation accuracy even when the gyroscopes (gyros hereafter) include a small error. In addition, since the orientation error of this system does not grow over time, the tool tip position drift is limited. This system can be applied to the applications where the bolts are closely placed. The position error comparison results of the second system and the third system are presented in this thesis. The comparison results indicate that the position accuracy of the third system is better than that of the second system because the orientation error does not increase over time. The advantages and limitations of all three systems are compared in this thesis. In addition, possible future work on fastening tool tracking system is described as well as applications that can be expanded by using the KF/PF combination method.

This research focuses on the development of intelligent fastening tool tracking systems for the automotive industry to identify the fastened bolts. In order to accomplish such a task, the position of the tool tip must be identified because the tool tip position coincides with the head of the fastened bolt while the tool fastens the bolt. The proposed systems utilize an inertial measurement unit (IMU) and another sensor to track the position and orientation of the tool tip. To minimize the position and orientation calculation error, an IMU needs to be calibrated as accurately as possible. This research presents a novel triaxial accelerometer calibration technique that offers a high accuracy. The simulation and experimental results of the accelerometer calibration are presented. To identify the fastening action, an expert system is developed based on the sensor measurements. When a fastening action is identified, the system identifies the fastened bolt by using an expert system based on the position and orientation of the tool tip and the position and orientation of the bolt. Since each fastening procedure needs different accuracies and requirements, three different systems are proposed. The first system utilizes a triaxial magnetometer and an IMU to identify the fastened bolt. This system calculates the position and orientation by using an IMU. An expert system is used to identify the initial position, stationary state, and the fastened bolt. When the tool fastens a bolt, the proposed expert system detects the fastening action by triaxial accelerometer and triaxial magnetometer measurements. When the fastening action is detected, the system corrects the velocity and position error using zero velocity update (ZUPT). By using the corrected tool tip position and orientation, the system can identify the fastened bolts. Then, with the fastened bolt position, the position of the IMU is corrected. When the tool is stationary, the system corrects linear velocity error and reduces the position error. The experimental results demonstrate that the proposed system can identify fastened bolts if the angles of the bolts are different or the bolts are not closely placed. This low cost system does not require a line of sight, but has limited position accuracy. The second system utilizes an intelligent system that incorporates Kalman filters (KFs) and a fuzzy expert system to track the tip of a fastening tool and to identify the fastened bolt. This system employs one IMU and one encoder-based position sensor to determine the orientation and the centre of mass location of the tool. When the KF is used, the orientation error increases over time due to the integration step. Therefore, a fuzzy expert system is developed to correct the tilt angle error and orientation error. When the tool fastens a bolt, the system identifies the fastened bolt by applying the fuzzy expert system. When the fastened bolt is identified, the 3D orientation error of the tool is corrected by using the location and the orientation of the fastened bolt and the position sensor outputs. This orientation correction method results in improved reliability in determining the tool tip location. The fastening tool tracking system was experimentally tested in a lab environment, and the results indicate that such a system can successfully identify the fastened bolts. This system not only has a low computational cost but also provides good position and orientation accuracy. The system can be used for most applications because it provides a high accuracy. The third system presents a novel position/orientation tracking methodology by hybridizing one position sensor and one factory calibrated IMU with the combination of a particle filter (PF) and a KF. In addition, an expert system is used to correct the angular velocity measurement errors. The experimental results indicate that the orientation errors of this method are significantly reduced compared to the orientation errors obtained from an EKF approach. The improved orientation estimation using the proposed method leads to a better position estimation accuracy. The experimental results of this system show that the orientation of the proposed method converges to the correct orientation even when the initial orientation is completely unknown. This new method was applied to the fastening tool tracking system. This system provides good orientation accuracy even when the gyroscopes (gyros hereafter) include a small error. In addition, since the orientation error of this system does not grow over time, the tool tip position drift is limited. This system can be applied to the applications where the bolts are closely placed. The position error comparison results of the second system and the third system are presented in this thesis. The comparison results indicate that the position accuracy of the third system is better than that of the second system because the orientation error does not increase over time. The advantages and limitations of all three systems are compared in this thesis. In addition, possible future work on fastening tool tracking system is described as well as applications that can be expanded by using the KF/PF combination method.

A novel technology, the Magneto-articulography for the Assessment of Speech Kinematics (MASK) system, which measures brain activity and oromotor movement simultaneously, was validated for its speech tracking capabilities. MASK has not been systematically tested, so its movement tracking accuracy and practicality for research was still unknown. An error testing and mapping protocol is developed to validate MASK accuracy against established electromagnetic articulography (EMA) speech tracking systems. Data from human speech experiments are also compared. MASK exhibited higher positional error and fluctuation than EMA, and more inconsistent distribution of errors. Error mapping and potential error correction protocols were also developed. MASK spatial and temporal resolutions were found insufficient for precise tracking of small and quick articulatory movements. MASK requires much improvement to reach the capabilities of EMA. Further investigation into numerical instabilities of the position calculation algorithms is encouraged. This project provides the first assessment of MASK, which may advance speech research for future applications.

A novel technology, the Magneto-articulography for the Assessment of Speech Kinematics (MASK) system, which measures brain activity and oromotor movement simultaneously, was validated for its speech tracking capabilities. MASK has not been systematically tested, so its movement tracking accuracy and practicality for research was still unknown. An error testing and mapping protocol is developed to validate MASK accuracy against established electromagnetic articulography (EMA) speech tracking systems. Data from human speech experiments are also compared. MASK exhibited higher positional error and fluctuation than EMA, and more inconsistent distribution of errors. Error mapping and potential error correction protocols were also developed. MASK spatial and temporal resolutions were found insufficient for precise tracking of small and quick articulatory movements. MASK requires much improvement to reach the capabilities of EMA. Further investigation into numerical instabilities of the position calculation algorithms is encouraged. This project provides the first assessment of MASK, which may advance speech research for future applications.

No description available.

Real-time, virtual and mixed reality systems have diverse uses for real-world data visualization, representation, and remote collaboration in distant learning settings, especially in universities. Design of such systems involves challenges in mapping the real world data and physical world structure accurately to digital form of physical space, also called as virtual models. Researchers have created similar systems using multiple cameras, stereo cameras, accelerometers, and motion detectors. This report presents a platform to detect and track real-time locations of people present in buildings and map their location information into virtual models as avatars using omni-directional cameras installed in the physical space. These models were created as part of the Mirror Worlds project. The project infrastructure is funded by National Science Foundation. This infrastructure enables users to connect virtual and physical aspects of the environment through a coordinate-based data networking system to enable interaction with the rest of the system including environment objects and other users. This is an interdisciplinary project where students from various departments have worked on the development of virtual model of the Moss Art Center and Torgersen Hall in Unity / X3D. Some students from the Department of Computer Science have developed a coordinate-based data networking system. The prototype of a detection and tracking algorithm to extract the location information was developed using background subtraction in MATLAB. The proposed approach was developed using the combination of background subtraction and neural networks along with heuristics based on spatial information about the physical space. The system was scaled to work across multiple buildings, extract the location information of people present in the physical space, and map location information into shared virtual space as an avatar. The concept of remote presence was extended to create a collaborative object manipulation application using Leap Motion controller. Effects of fidelity were evaluated to perform the collaborative object manipulation task in shared virtual space based on user study conducted for this application. Since no annotated people video dataset is publicly available with overhead view from omni-directional cameras, three videos were annotated manually to test the performance of the approach. The current approach almost works at near real-time rates. All three video sequences were evaluated to compute frame based detection accuracy. Precision and recall obtained for the first video sequence of people detection is 93.85% and 95.06% respectively. / Master of Science

There is suggestion of obtaining exact position in this work, based on GPS and GSM modules controlled by PIC microcontroller. Layout of this work is electronic schematic, PCB footprint and theoretical analysis of used modules. The practical part of work is also dealing with software for both microprocessor and web interface. The function prototype will be created and tested in next phases of this project.

<div>Over the past few decades, the focus of wireless communication technology has been shrinking in terms of coverage area. It started with WMAN (Wireless Metropolitan Area Network), moved to WLAN (Wireless Local Area Network) and WPAN (Wireless Personal Area Network), and is soon expected to move to WBAN (Wireless Body Area Network). Wireless positioning/location services present a perfect analogy to wireless communication services. It started with the use of GPS (Global Positioning System), is moving to Local Area Positioning System (LPS) and will be soon moving to Personal and Body Area Positioning Systems (BPS) in the future.</div><div><br></div>This thesis presents the development of a high-speed and high-accuracy wireless magnetic positioning system which can locate the position and orientation of the sensor in real-time with a sub-mm level accuracy in body area. The system consists of an antenna (transmitter) and one or multiple sensors (receivers). The sensor module consists of a tri-axis AC magnetic field sensor, an orientation sensor, a micro-controller and a communication unit. The system is robust to multi-path, low-power, low-cost and provides complete location privacy to its users. Possible implementations of this technology could be in the field of gaming, media entertainment, security, robotics, bio medical, motion-capture and home-automation. The ultra-low latency of the system and its ability to track the sensor anywhere around the antenna without occlusion makes it a perfect candidate to be used as a Virtual/Augment Reality (VR/AR) input device.

This diplom work entitled "Monitoring system for the detection of motility and position of laboratory animals after anesthesia" focuses on the design and implementation of non-contact detection of the rat or mouse position in the enclosure with a transparent cover. The aim of this semester paper is to find suitable methods of realizing contactless detection of the position of a laboratory rat or mouse. This automatic positioning of the animal will be used as the basis for controlling the irradiator in the next follow-up work, which will "shade" animal movement and aim at the scar on the animal's back. The rat that is located inside our enclosure is either standard or movable after anesthesia. In this work I first deal with searches of automatic monitoring systems for detecting the position of animals in the enclosure. Then, in the practical part, I test three types of cameras for image detection of rat position. Evaluation software for motion analysis will largely be solved in the follow-up diploma thesis.Project made like monitoring and detecting software based on OpenCV.

No description available.