Classification of commercial grade lumber requires a visual inspection of the milled board to determine if the board meets attributes of the various grades of lumber. The lower the number of anomalies, such as knots, the higher the grade of the piece. Knot wood and clear wood radiate and absorb heat at different rates, thereby allowing for the development of a computerized thermal recognition system to detect knot anomalies. This dissertation investigated the use of high power (6 kW) quartz infrared halogen lamp heaters, and high power radio frequency (35 kW) to treat the piece to be inspected. The thermal response was obtained by an infrared image (512 by 480 pixels). Twelve species of wood were investigated. For this dissertation, a computerized thermal recognition system was developed using the optimal derived images to produce a technique to detect anomalies in images. The thermal recognition system to identify anomalies used a soft computing architecture for edge detection in this noisy environment. The principle of soft computing techniques is to arrive at a near-best solution with imprecise, incomplete, and marginal information by adopting a coherent strategy. This methodology recognizes that an optimum solution cannot be obtained but that a near or best-available solution may be just as workable when dealing with real systems. The technology described here evolves an object such that it becomes a replica of the object visualized and constantly compares the view and the image until it is fully-grown. In this research, this approach is referred to as "seedlet technology". Seedlet technology uses soft computational techniques to detect anomalies in images. The seedlet system consists of a discrete cosine transform (DCT) filter, a neural network, a seedlet, and a genetic algorithm. The DCT filter forms a preprocessing module to reduce noise on the image. The neural network provides information about the anomaly in which rules for the seedlet can be developed. In addition, the neural network forms an intelligent selective low-pass filter of the image. The seedlet then grows according to derived rules and by using information provided by the neural network. The seedlets remove noise on the image, and identify the approximate location of anomalies on the image. The genetic algorithm then manipulates parameters of the seedlets to optimize the location of the anomaly. At this point, the location of the anomaly has been determined. This technique was successfully applied for locating knot anomalies in wood.

No description available.

Kidney transplantation is an effective treatment for renal disease that was previously fatal. However, the demand for donor kidneys far exceeds the supply. Due to the scarcity of volunteer donors, the cadaver organs that are retrieved must be optimally utilized. By expanding organ retrieval and sharing pools and improving donor-patient matching algorithms, the utilization of donated organs is enhanced and encouraging medical results are obtained. However, the benefits of enlarged donor and recipient pools may be offset by increasing complexity and decreasing efficiency in the organ distribution process thus increasing cold ischemia time. It is critical to improve distribution process efficiency in order to minimize the time taken to complete the entire process, and thus further enhance patient and graft survival. I attempt to apply supply chain management concepts, agent technologies, mobile communication technologies and decision-making theory to improve the efficiency of the cadaver kidney distribution process. In this thesis I analyze what are the bottlenecks in current cadaver kidney distribution and investigate how agent technology can be applied to improve this process. I propose a distributed multi-agent system operating in a mobile and wireless communication environment to assist transplant coordinators in coordinating with multi-parties in this time-critical distribution process. A prototype system has been developed to help transplanting coordinators in allocating the kidney recipient. / Thesis / Master of Science (MS)

Electronic commerce markets are becoming increasingly popular forums for commerce. As those markets mature, buyers and sellers will both vigorously seek techniques to improve their performance. The Internet lends itself to the use of agents to work on behalf of buyers and sellers. Through simulation, this research examines different implementations of buyers' agents (shopbots) and sellers' agents (pricebots) so that buyers, sellers, and agent builders can capitalize on the evolution of e-commerce technologies. Internet markets bring price visibility to a level beyond what is observed in traditional brick-and-mortar markets. Additionally, an online seller is able to update prices quickly and cheaply. Due to these facts, there are many pricing strategies that sellers can implement via pricebot to react to their environments. The best strategy for a particular seller is dependent on characteristics of its marketplace. This research shows that the extent to which buyers are using shopbots is a critical driver of the success of pricing strategies. When measuring profitability, the interaction between shopbot usage and seller strategy is very strong - what works well at low shopbot usage levels may perform poorly at high levels. If a seller is evaluating strategies based on sales volume, the choice may change. Additionally, as markets evolve and competitors change strategies, the choice of most effective counterstrategies may evolve as well. Sellers need to clearly define their goals and thoroughly understand their marketplace before choosing a pricing strategy. Just as sellers have choices to make in implementing pricebots, buyers have decisions to make with shopbots. In addition to the factors described above, the types of shopbots in use can actually affect the relative performance of pricing strategies. This research also shows that varying shopbot implementations (specifically involving the use of a price memory component) can affect the prices that buyers ultimately pay - an especially important consideration for high-volume buyers. Modern technology permits software agents to employ artificial intelligence. This work demonstrates the potential of neural networks as a tool for pricebots. As discussed above, a seller's best strategy option can change as the behavior of the competition changes. Simulation can be used to evaluate a multitude of scenarios and determine what strategies work best under what conditions. This research shows that a neural network can be effectively implemented to classify the behavior of competitors and point to the best counterstrategy. / Ph. D.

The invention of shape memory alloys spurred a period of intense interest in the area of heat engines in the late 70's and early 80's. It was believed that these engines could use heat from low temperature sources such as solar heated water, geothermal hot water and rejected heat from conventional engines as a significant source of power. The interest has since dwindled, largely because small prototype devices developed in the laboratory could not be scaled up to produce significant power. It is believed that the scaled-up designs failed because they were dependent on friction as the driving mechanism, which led to large energy losses and slip. This thesis proposes a new chain and sprocket driving mechanism that is independent of friction and should therefore allow for large-scale power generation. This thesis begins by presenting properties and applications of shape memory alloys. The proposed design is then described in detail, followed by a review of the evolution that led to the final design. A brief chapter on thermodynamic modeling and a summary chapter suggesting improvements on the current design follow. / Master of Science

In any automotive system, the tires play a very crucial role in defining both the safety and performance of the vehicle. The interaction between the tire and the road surface determines the vehicle's ability to accelerate, decelerate and steer. Having information about this interaction in real-time can be very valuable for the on-board advanced active safety systems to mitigate the risks ahead of time and keep the vehicle stable. The crucial information which can be obtained from the tire includes but are not limited to tire-road friction, tire forces (longitudinal, lateral), normal load, road surface characteristics and tire pressure. This information can be acquired through indirect vehicle dynamics based estimation algorithms or through direct measurements using sensors inside the tire. However, the indirect estimations fail to give an accurate measure of the vehicle state in certain conditions (e.g. side winds, road banking, surface change) and require ABS or VSC activation before the estimation begins. Therefore, to improve the performance of these active stability systems, direct measurement based approaches must be explored. This research expands the applications of Intelligent tire and focuses on using the sensor based measurement approach to develop estimation algorithms relating to tire force measurement. A tri-axial accelerometer is attached to the inner liner of the tire (Intelligent Tire) and two of such tires are placed on an instrumented (MSW, VBox, IMU, Encoders) VW Jetta. Different controlled tests are carried out on the instrumented vehicle and the Intelligent tire signal is analyzed to extract features related to the tire forces and pressure. Due to unavailability of direct force measurements at the wheel, a VW Jetta simulation model is developed in CarSim and the extracted features are validated with a good correlation. / Master of Science / The automotive industry is heading towards autonomous vehicles driven at various levels of autonomy. Autonomous vehicles require a thorough understanding of the vehicle characteristics such as load, current state of the vehicle (speed, heading). It also requires a good grasp of the tire-road interaction to be able to estimate the future state of the vehicle. This research focuses on exploring the tire-road interaction using sensor based approach. The tires are instrumented using a tri- axial accelerometer and different algorithms have been developed using signal processing techniques to estimate parameters such as Tire forces, tire pressure and load of the vehicle. The experiments are conducted on an instrumented VW Jetta vehicle which also has other sensors such as Inertial Measurement Unit, GPS based speed estimation sensor and steering angle measurement sensor. The results obtained from the sensor signal are processed using a code developed in MatLab software and validated using a simulation model in CarSim. Knowing the Tire Characteristics such as Tire force, pressure is essential for accurate estimation of the vehicle state which in turn will refine the autonomous capability of the vehicle.

Sensor-embedded tires, known as intelligent tires, have been widely studied because they are believed to provide reliable and crucial information on tire-road contact characteristics e.g., slip, forces and deformation of tires. Vehicle control systems such as ABS and VSP (Vehicle Stability Program) can be enhanced by leveraging this information since control algorithms can be updated based on directly measured parameters from intelligent tire rather than estimated parameters based on complex vehicle dynamics and on-board sensor measurements. Moreover, it is also expected that intelligent tires can be utilized for the purpose of the analysis of tire characteristics, taking into consideration that the measurements from the sensors inside the tire would contain considerable information on tire behavior in the real driving scenarios. In this study, estimation methods for the tire-road contact features by utilizing intelligent tires are investigated. Also, it was discussed how to identify key tire parameters based on the fusion technology of intelligent tire and tire modeling. To achieve goals, extensive literature reviews on the estimation methods using the intelligent tire system was conducted at first. Strain-based intelligent tires were introduced and tested in the laboratory for this research. Based on the literature review and test results, estimation methods for diverse tire-road contact characteristics such as slippages and contact forces have been proposed. These estimation methods can be grouped into two categories: statistical regressions and model based methods. For statistical regressions, synthetic regressors were proposed for the estimation of contact parameters such as contact lengths, rough contact shapes, test loads and slip angles. In the model-based method, the brush type tire model was incorporated into the estimation process to predict lateral forces. Estimated parameters using suggested methods agreed well with measured values in the laboratory environment. By utilizing sensor measurements from intelligent tires, the tire physical characteristics related to in-plane dynamics of the tire, such as stiffness of the belt and sidewall, contact pressure distribution and internal damping, were identified based on the combination of strain measurements and a flexible ring tire model. The radial deformation of the tread band was directly obtained from strain measurements based on the strain-deformation relationship. Tire parameters were identified by fitting the radial deformations from the flexible ring model to those derived from strain measurements. This approach removed the complex and repeated procedure to satisfy the contact3 constraints between the tread and the road surface in the traditional ring model. For tires with different specifications, identification using the suggested method was conducted and their results are compared with results from conventional methods and tests, which shows good agreements. This approach is available for the tire standing still or rolling at low speeds. For tires rolling at high speeds, advanced tire model was implemented and associated with strain measurements to estimate dynamic stiffness, internal damping effects as well as dynamic pressure distributions. Strains were measured for a specific tire under various test conditions to be used in suggested identification methods. After estimating key tire parameters step by step, dynamic pressure distributions was finally estimated and used to update the estimation algorithm for lateral forces. This updated estimation method predicted lateral forces more accurately than the conventional method. Overall, this research will serve as a stepping stone for developing a new generation of intelligent tire capable of monitoring physical tire characteristics as well as providing parameters for enhanced vehicle controls. / PHD / Tires are very crucial components in a vehicle because they are only objects in contact with the road surface on which the vehicle drive. They support the weight of the vehicle and generate forces which make the vehicle drive, stop and turn. Thus, the improvement of vehicle performances such as handling, ride quality and braking can be achieved by understanding and by optimizing tire properties as well as improving the design of the vehicle itself. These days, diverse vehicle control systems such as anti-lock braking and cornering stability control systems have been widely adopted to improve the stability of the vehicle when it is braked or turned. These stability controls usually require information about slippages and forces occurring between the tire and the road surface. These quantities can be indirectly estimated by monitoring vehicle motions, which are measured by sensors installed on the vehicle frame. Although these traditional methods have worked successively, the control algorithms can be improved further by directly sensing the tire behaviors using sensors embedded in the tire. These sensor-embedded tires are often called as ‘intelligent tire’ because tires themselves serve as the monitoring device on driving conditions as well as conduct traditional functions. Also, the measured quantities inside the tire can be effectively used to understand tire characteristics because they have valuable information on tires, especially, mechanism how the tire deforms and generate contact forces when it rolls over the road surface. In this research, strains are measured at the inner surface of the tire during it rolling and cornering on the flat road surface under different loads on the indoor test rig. A strain represents the relative displacement between particles. Based on experimental results, estimation algorithms for test loads, contact lengths, cornering angles and cornering forces are developed. These estimation methods can be incorporated in the vehicle control algorithm in the real driving scenario for improved vehicle controls. A tire is a complex system comprising various composite materials, so their behaviors or characteristics show sever non-linearity which difficult to understand. They have been simplified and modeled in a various way based on diverse physical principles to understand how they are deflected and generate forces and moments during rolling on the road surface under a vertical load. These models are called ‘physical tire model’. To extract and analyze tire physical characteristics, measured strains at the inner surface are combined with these tire models. In this research, tires are modeled as a flexible ring which is supported by viscoelastic materials and this tire model called as a ‘flexible ring model’ which have been utilized to analyze vibration properties and contact phenomena of tires. Strain measurements were fed into the model and crucial tire characteristics are extracted such as tire stiffness, pressure distributions and internal damping. These properties can be used to analyze the tire performance like wear, rolling resistance, ride qualities and the capacity of cornering forces. Since intelligent tire systems are applied for the real driving situation, tire characteristics extracted in this way would have closer links to vehicle performances rather than those measured in the laboratory. Overall, this research will serve as a stepping stone for developing a new generation of intelligent tire capable of monitoring physical tire characteristics as well as providing parameters for enhanced vehicle controls.

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Intelligent Transportation Systems (ITS) have emerged to use different technologies to promote safety, convenience, and efficiency of transportation networks. Many applications of ITS depend on the availability of the real-time positioning of the vehicles in the network. In this research, the two open challenges in the field of vehicle localization for ITS are introduced and addressed. First, in order to have safe and efficient transportation systems, the locations of the vehicles need to be available everywhere in a network. Conventional localization techniques mostly rely on Global Positioning System (GPS) technology which cannot meet the accuracy requirements for all applications in all situations. This work advances the study of vehicle positioning in ITS by introducing an integrated positioning framework which uses several resources including GPS, vehicle-to-infrastructure and vehicle-to-vehicle communications, radio-frequency identification, and dead reckoning. These technologies are used to provide more reliable and accurate location information. The suggested framework fills the gap between the accuracy of the current vehicle localization techniques and the required one for many ITS applications. Second, different ITS applications have different localization accuracy and latency requirements. A smart positioning algorithm is proposed which enable us to change the positioning accuracy delivered by the algorithm based on different applications. The algorithm utilizes only the most effective resources to achieve the required accuracy, even if more resources are available. In this way, the complexity of the system and the running time decrease while the desired accuracy is obtained. The adjective Smart is selected because the algorithm smartly selects the most effective connection which has the most contribution to vehicle positioning when a connection needs to be added. On the other hand, when a connection should be removed, the algorithm smartly selects the least effective one which has the least contribution to the position estimation. This study also provides an overview about the positioning requirements for different ITS applications. A close-to-real-world scenario has been developed and simulated in MATLAB to evaluate the performance of the proposed algorithms. The simulation results show that the vehicle can acquire accurate location in different environments using the suggested Integrated framework. Moreover, the advantages of the proposed Smart algorithm in terms of accuracy and running time are presented through a series of comprehensive simulations. / Master of Science