This dissertation has been motivated by the increasing application of sensing technologies in structural health monitoring. Many wireless sensor techniques exist for structural health monitoring while a challenge faced is the finite lifetime of batteries. The objective of this dissertation is to develop passive wireless technology to provide early warning of conditions that damage the structure. In this dissertation, sensing mechanism is proposed based on time and frequency domain characteristics of magneto-inductive (MI) waves. Experimental results are also presented to demonstrate the sensing mechanism. MI waves are predominantly magnetic waves that are supported in periodic arrays of magnetically coupled resonators and propagate within a narrow frequency band around the resonant frequency. The array is to be embedded in a structure and different types of transducers can be integrated for different sensing applications. With the onset of structure defect, the transducer introduces an impedance discontinuity that generates reflected MI waves along the array, which are monitored and processed by Smoothed Wigner-Ville distribution (WVD) to extract time-of-flight for frequency components in the narrow passband. The transmission and reflection coefficients of MI waves are also investigated based on the lumped-element circuit model of the array. Based on MI waves travel time, amplitude and group velocity, the position and severity of structure defect are decided. The sensing mechanisms for different distribution of defects are proposed. The validity of the sensing mechanism is examined in experiments. The guided wave testing is implemented in one-dimensional square-shaped printed spiral resonators with Q-factor of 161 at 13.6 MHz. It demonstrates that low MI waves propagation loss is achieved with value of 0.098 dB per element at mid-band with center-to-center distance of half an inch. A pitch-catch measurement system is built to capture traveling MI signal in resonant element and extract group velocity, and a pulse-echo measurement system is designed to monitor reflected MI signal and locate structure discontinuity. In both measurement systems, MI waves are excited with wide bandwidth voltage pulse, and a digitizer is attached to sense the MI signal in a specific resonant element circuit. A baseline signal is obtained from the healthy state to use as reference and comparison with the test case using pitch-catch system. The test signal subtracted from baseline signal infers the structure damage information with time and frequency domain characteristics. It can offer an effective method to estimate the structure discontinuity location, severity and type of damage. The experimental results are consistent with the theoretical predictions. At the end, future directions for the research to integrate with other technologies are suggested. / text

Rheumatoid Arthritis (RA) is a disease in which the body has "turned on itself", with its immune system attacking mobility. In RA, an immune mechanism attacks and destroys the joints and limits mobility, in some circumstances to the point of needing replacement of joints. The aim of this research is the development of a less costly, widely accessible, passive sensing technology that provides a quantitative assessment of RA and that monitors the therapeutic effectiveness on joint-debilitating diseases. The proposed solution relies on a quantitative evaluation of human gestures. Such a quantitative assessment supports the comparison between the motion capabilities of a patient and that of a healthy person, using a kinematic model of the human skeleton. Criteria for the classification of severity were established, and tables were generated to classify the levels of severity as a function of the measurements extracted from processed videos of a subject performing predefined movements. This research project, while contributing a new tool to the process of classification of RA level of severity, opens the way for using widely accessible digital imaging for diagnosing and monitoring the evolution of the illness. Replacing MRI or HRUS with a cheaper and more accessible technology would have a major impact on health care services. From the clinical point of view, the proposed techniques based on digital images processing combined with a monitoring approach based on infrared images that was previously developed may provide a utility of care for patients with RA, as well as an alternative and automated approach for early detection of RA and active inflammation at a critical time.

Rheumatoid Arthritis (RA) is a disease in which the body has "turned on itself", with its immune system attacking mobility. In RA, an immune mechanism attacks and destroys the joints and limits mobility, in some circumstances to the point of needing replacement of joints. The aim of this research is the development of a less costly, widely accessible, passive sensing technology that provides a quantitative assessment of RA and that monitors the therapeutic effectiveness on joint-debilitating diseases. The proposed solution relies on a quantitative evaluation of human gestures. Such a quantitative assessment supports the comparison between the motion capabilities of a patient and that of a healthy person, using a kinematic model of the human skeleton. Criteria for the classification of severity were established, and tables were generated to classify the levels of severity as a function of the measurements extracted from processed videos of a subject performing predefined movements. This research project, while contributing a new tool to the process of classification of RA level of severity, opens the way for using widely accessible digital imaging for diagnosing and monitoring the evolution of the illness. Replacing MRI or HRUS with a cheaper and more accessible technology would have a major impact on health care services. From the clinical point of view, the proposed techniques based on digital images processing combined with a monitoring approach based on infrared images that was previously developed may provide a utility of care for patients with RA, as well as an alternative and automated approach for early detection of RA and active inflammation at a critical time.