The objective of the proposed research is to devise a methodology for sensing and tracking environmental variables using a passive wireless sensor based on reflected electro-material signatures. Viability of item level tracking demands the sensor to be extraordinary low cost, thus eliminating the use of any active sensor or memory circuitry. Recent developments of materials whose electrical properties can change significantly with the environmental conditions suggest the possibility of developing a passive sensor that can be interrogated remotely to extract data about the time tracked environmental changes at the sensor. A simple passive sensor, based on the concept of reflected electro-material signatures (REMS), consists of an antenna attached to a microstrip transmission line, which in turn is routed over one or more sections of variable permittivity material before being terminated in a load. The basic idea revolves around sensing the electrical properties of thermotropic liquid crystal (LCs) trapped in a polymer substrate to record the temperature data. As the temperature changes with time, the polymerization process through the material line records the historical temperature profile in the spatial distribution of the electrical properties, thus enabling the system to extract the historical profile of temperature without using any active memory circuitry. This concept can possibly be used to track a variety of variables of interest; however, the proposed research is focused on sensing and extracting the time profile of temperature. The problem of identifying medium properties from waves reflected from a device of this type is a form of the classical one dimensional inverse scattering problem. For profile inversion in a lossy inhomogeneous media, analytical techniques are difficult to implement in most practical situations. In the proposed research, neural networks with a back-propagation algorithm are used to reconstruct the historical temperature profile of the material by extracting the spatially distributed material properties of the electro-material line. After the initial proof of concept for a lossless medium, the methodology is extended to extract spatially distributed properties for a dissipative medium. Finally, for the implementation of REMS sensor concept, a neural network based methodology is developed to reconstruct the spatially distributed permittivity profile of a lossy electro-material line.

A quadrature phase shift keying (QPSK) retrodirective array phase modulator (RAPM) was designed and fabricated to characterize its backscatter signal constellation when placed near objects with varying conductivities and relative permittivities. The signal constellations produced when the RAPM was placed near objects were compared to a constellation in free space to determine relative magnitude and phase changes. When conductors and high permittivity dielectrics were placed close behind the RAPM, constellation points were found to shrink in magnitude by up to twenty percent and shift in phase by up to eight degrees. When conductors were placed between the RAPM and an interrogator, the signal constellation was found to collapse, shrinking by up to 95.6 percent. For materials similar to free space, minimal constellation shrinkage resulted, but signal constellation rotation by up to 68 degrees occurred. The power consumption of a RAPM was also characterized and found to decrease as the number of bits per symbol increased. This result demonstrates that in comparison to conventional backscatter tags, which implement one bit per symbol, the RAPM can implement a greater number of bits per symbol, reduce its power consumption, and increase its range in a passive backscatter communication system. To characterize the beamwidth of the RAPM's retrodirective array, a radar cross section (RCS) measurement of the RAPM was performed over a scan angle range of -90 to +90 degrees. The structural component generated by the RAPM's patch antenna ground plane was found to dominate the antenna mode of the retrodirective array. As a result, a novel homodyne receiver based RCS measurement was performed to filter out the structural RCS component and measure the pure antenna mode of the RAPM.

The future of the aerospace industry in large part relies on two factors: (i) development of advanced damage tolerant materials and (ii) development of advanced smart sensors with the ability to detect and evaluate defects at very early stages of component service life. Laminated composite materials, such as carbon fibre reinforced plastics (CFRP), have emerged as the materials of choice for increasing the performance and reducing the cost and weight of aircrafts, which leads to less fuel consumption and therefore lower CO2 emissions. However, it is well known that these materials exhibit fragile behaviour, poor resistance to impact damage caused by foreign objects and require a relatively slow and labour intensive manufacturing process. These factors prevent the rapid expansion of composite materials in several industrial sectors at the current time. Inspired by the use of rope throughout history and driven by the necessity of creating a lean manufacturing process for composites and enhancing their impact properties, the first part of this work has shown that enhanced damage tolerance and pseudo-ductile behaviour can be achieved with standard CFRP by creatively arranging the fibres into a 3D twisted helical configuration. Through an extensive experimental campaign a new method to arrange fibre reinforcement was presented and its effect investigated. The second part of this PhD work focused on developing a new smart sensor. A spiral passive electromagnetic sensor (SPES) for damage detection on CFRP and glass fibre reinforced plastics (GFRP) is presented in this work. A range of defect types in glass and carbon composite has been considered, such as delamination, perforated holes and cracks. Furthermore, throughout this work, the SPES has been exploited as a multi-sensing device allowing the ability to detect temperature and humidity variation, presence of ice and act as an anti/de-icing device.

The corrosion of steel reinforcement in reinforced concrete structures constitutes an alarming problem. To combat this problem, researchers at the University of Texas at Austin developed two, low-cost, passive, wireless sensors: a threshold, corrosion sensor and an analog conductivity sensor. Today, the basic circuit designs for both sensors are finished and their reliabilities are confirmed. However, multiple problems regarding the durability of the sensors remain. This research project: (a) identifies these problems, (b) proposes enhancements for each type of passive, wireless sensor, (c) tests and evaluates the proposed modifications to the sensors, and (d) proposes potential improvements and areas of research regarding the future development of these two sensors. / text

Locating a radiating source based on range or range measurements obtained from a network of passive sensors has been a subject of research over the past two decades due to the problem’s importance in applications in wireless communications, surveillance, navigation, geosciences, and several other fields. In this thesis, we develop new solution methods for the problem of localizing a single radiating source based on range and range-difference measurements. Iterative re-weighting algorithms are developed for both range-based and range-difference-based least squares localization. Then we propose a penalty convex-concave procedure for finding an approximate solution to nonlinear least squares problems that are related to the range measurements. Finally, the sequential convex relaxation procedures are proposed to obtain the nonlinear least squares estimate of source coordinates. Localization in wireless sensor network, where the RF signals are used to derive the ranging measurements, is the primary application area of this work. However, the solution methods proposed are general and could be applied to range and range-difference measurements derived from other types of signals. / Graduate / 0544 / ismailds@uvic.ca