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Master of Science / Department of Biological and Agricultural Engineering / Naiqian Zhang / In order to meet food, fiber, and bio-fuel needs of a growing world population, crop-breeding methods must be improved and new technologies must be developed. One area under focus is the decoding of the genetic basis of complex traits, such as yield and drought stress tolerance, and predicting these traits from genetic composition of lines or cultivars. In the last three decades, significant advances in genotyping methods have resulted in a wealth of genomic information; however, little improvement has occurred for methods of collecting corresponding plant trait data, especially for agronomic crops. This study developed a mobile, field-based, high-throughput sensor platform for rapid and repeated measurement of plant characteristics. The platform consisted of three sets of sensors mounted on a high-clearance vehicle. Each set of sensors contained two infrared thermometers (IRT), one ultrasonic sensor, one Crop Circle, and one GreenSeeker. Each sensor set measured canopy temperature, crop height, and spectral reflectance. In addition to the sensors, the platform was equipped with an RTK-GPS system that provided precise, accurate position data for georeferencing sensor measurements. Software for collecting, georeferencing, and logging sensor data was developed using National Instruments LabVIEW and deployed on a laptop computer. Two verification tests were conducted to evaluate the phenotyping system. In the first test, data timestamps were analyzed to determine if the system could collect data at the required rate of 10 Hz and 5 Hz for sensor data and position data, respectively. The determination was made that, on average, IRT, ultrasonic, and Crop Circle data are received in intervals of 100 ms (SD = 10 ms), GreenSeeker data are received in intervals of 122 ms(SD=10 ms), and position data are received in intervals of 200 ms (SD = 32 ms). The second test determined that a statistically significant relationship exists between sensor readings and ambient light intensity and ambient temperatures. Whether the relationship is significant from a practical stand point should be determined based on specific application of the sensors.

Motion analysis deals with determining what and how activities are being performed by a subject, through the use of sensors. The process of answering the what question is commonly known as classification, and answering the how question is here referred to as characterization. Frequently, combinations of inertial sensor such as accelerometers and gyroscopes are used for motion analysis. These sensors are cheap, small, and can easily be incorporated into wearable systems. The overall goal of this thesis was to improve the processing of inertial sensor data for the characterization of movements. This thesis presents a framework for the development of motion analysis systems that targets movement characterization, and describes an implementation of the framework for gait analysis. One substantial aspect of the framework is symbolization, which transforms the sensor data into strings of symbols. Another aspect of the framework is the inclusion of human expert knowledge, which facilitates the connection between data and human concepts, and clarifies the analysis process to a human expert. The proposed implementation was compared to state of practice gait analysis systems, and evaluated in a clinical environment. Results showed that expert knowledge can be successfully used to parse symbolic data and identify the different phases of gait. In addition, the symbolic representation enabled the creation of new gait symmetry and gait normality indices. The proposed symmetry index was superior to many others in detecting movement asymmetry in early-to-mid-stage Parkinson's Disease patients. Furthermore, the normality index showed potential in the assessment of patient recovery after hip-replacement surgery. In conclusion, this implementation of the gait analysis system illustrated that the framework can be used as a road map for the development of movement analysis systems.

Scanning probe microscopy was employed to characterize the piezotronic effect in both longitudinal and transverse force sensing modes in CdSe, and GaN nanowires, respectively. Both experimental results show exponential response of their conductivity change to applied forces. Theoretical models are also presented to explain this mechanism and quantify the relationship, where strain induced piezoelectric polarization changes the metal-semiconductor Schottky barrier height. An in-situ method based on SPM is developed to characterize the triboelectric process, including tribo-charge intensity, multi-cycle friction effect, as well as its surface diffusion. Beyond that, effect of external electric field was investigated as an approach to manipulate the polarization and intensity. Finally, a concept of self-powered motion sensing technology is developed and demonstrated experimentally with nanometer resolution, long working distance as well as high robustness. It provides a promising solution for application areas that need ultra-low power consumption devices.

ITC/USA 2008 Conference Proceedings / The Forty-Fourth Annual International Telemetering Conference and Technical Exhibition / October 27-30, 2008 / Town and Country Resort & Convention Center, San Diego, California / This paper explains the development of a wireless network system implemented to streamline grocery store checkout procedures. The design employs a wireless telemetry network consisting of a base station and wireless motes (Micaz MPR2400) that will be located on certain aisles, and attached to shopping carts. This system allows customers to scan items while they shop and uses cashiers for payment purposes only. The objective is to minimize the amount of processing performed by cashiers in order to reduce waiting times in line. The system was tested in a simulation environment and waiting times were reduced by 65%.

With roughly about half of the energy used in buildings attributed to Heating, Ventilation, and Air conditioning (HVAC) systems, there is clearly great potential for energy saving through improved building operations. Accurate knowledge of localised and real-time occupancy numbers can have compelling control applications for HVAC systems. However, existing technologies applied for building occupancy measurements are limited, such that a precise and reliable occupant count is difficult to obtain. For example, passive infrared (PIR) sensors commonly used for occupancy sensing in lighting control applications cannot differentiate between occupants grouped together, video sensing is often limited by privacy concerns, atmospheric gas sensors (such as CO2 sensors) may be affected by the presence of electromagnetic (EMI) interference, and may not show clear links between occupancy and sensor values. Past studies have indicated the need for a heterogeneous multi-sensory fusion approach for occupancy detection to address the short-comings of existing occupancy detection systems. The aim of this research is to develop an advanced instrumentation strategy to monitor occupancy levels in non-domestic buildings, whilst facilitating the lowering of energy use and also maintaining an acceptable indoor climate. Accordingly, a novel multi-sensor based approach for occupancy detection in open-plan office spaces is proposed. The approach combined information from various low-cost and non-intrusive indoor environmental sensors, with the aim to merge advantages of various sensors, whilst minimising their weaknesses. The proposed approach offered the potential for explicit information indicating occupancy levels to be captured. The proposed occupancy monitoring strategy has two main components; hardware system implementation and data processing. The hardware system implementation included a custom made sound sensor and refinement of CO2 sensors for EMI mitigation. Two test beds were designed and implemented for supporting the research studies, including proof-of-concept, and experimental studies. Data processing was carried out in several stages with the ultimate goal being to detect occupancy levels. Firstly, interested features were extracted from all sensory data collected, and then a symmetrical uncertainty analysis was applied to determine the predictive strength of individual sensor features. Thirdly, a candidate features subset was determined using a genetic based search. Finally, a back-propagation neural network model was adopted to fuse candidate multi-sensory features for estimation of occupancy levels. Several test cases were implemented to demonstrate and evaluate the effectiveness and feasibility of the proposed occupancy detection approach. Results have shown the potential of the proposed heterogeneous multi-sensor fusion based approach as an advanced strategy for the development of reliable occupancy detection systems in open-plan office buildings, which can be capable of facilitating improved control of building services. In summary, the proposed approach has the potential to: (1) Detect occupancy levels with an accuracy reaching 84.59% during occupied instances (2) capable of maintaining average occupancy detection accuracy of 61.01%, in the event of sensor failure or drop-off (such as CO2 sensors drop-off), (3) capable of utilising just sound and motion sensors for occupancy levels monitoring in a naturally ventilated space, (4) capable of facilitating potential daily energy savings reaching 53%, if implemented for occupancy-driven ventilation control.

International Telemetering Conference Proceedings / October 26-29, 1992 / Town and Country Hotel and Convention Center, San Diego, California / A fiber optic event timing system was developed for the High Speed Test Track at Holloman Air Force Base, Alamogordo, NM. The system uses fiber optic sensors to detect the passage of rocket sleds by different stations along the track. The sensors are connected by fiber optic cables to an electronics package that records the event time to a resolution of 100 nanoseconds. By use of a GPS receiver as the timebase, the event time is stored to an absolute accuracy of 300 nanoseconds. Custom VMEbus boards were developed for the event timing function, and these boards are controlled by a programmable high speed sequencer, which allows for complicated control functions. Each board has 4 electro-optic channels, and multiple boards can be used in a VMEbus card cage controlled by a single board computer. The system has been tested in a series of missions at the Test Track.

Many properties of polymers can be affected by dissolving gases and supercritical fluids at high temperatures and pressures. Solubility and diffusivity are crucial parameters in polymer processing applications that indicates the content of gases and supercritical fluids in a polymer. Hence, different devices for measuring solubility and diffusivity have been researched, but most of the devices used today are very complex, expensive, and requires long experiment time. In this final thesis, the feasibility of fiber optic sensors as measurement devices for solubility and diffusivity of gas/SCF in polymers have been investigated. Many of the polymers used in polymer processing have high refractive index, from 1.40 to 1.60. However, most of the refractive index sensors based on fiber optics only operate in refractive index ranges of 1 to 1.44 because once the surrounding refractive index becomes greater than that of cladding, the total internal reflection is lost and only small portion of the light propagation occurs. This final thesis first reviews the current methods to measure solubility and diffusivity of gases and supercritical fluids in polymers. In addition, different types of fiber optics sensors used for sensing the refractive index are reviewed. Then, the thesis presents cost efficient, but effective fiber optic refractive index sensors, which are the silver nanoparticle coated LPG sensor, uncoated PCF MZI sensor, silver nanoparticle PCF MZI sensor, and the transmission intensity based gap sensor, to sense the surrounding refractive index in the region greater than the cladding, for the future application of solubility and diffusivity measurement. Moreover, future works that would help in sensing solubility and diffusivity of gas in polymers are also proposed. / Graduate

When foodstuffs are subject to microbial infection a range of volatile organic compounds (VOCs) are released which can be indicative of both the type and severity of the infection. The bacterium Erwinia carotovora, the primary cause of soft rot, is a major problem in the bulk storage of potato tubers. A number of classes of VOCs have been identified above E. carotovora infected potato tubers, but no disease specific marker has been identified. A number of studies have concluded that the best marker of E. carotovora infection is a substantial increase in the concentration of VOCs in the headspace above the tubers. Chemical sensors which are sensitive to low levels of the VOCs identified in the headspace above infected tubers have been developed. The aim was to use these sensors as the basis of a system for the early detection of soft rot in stored potato tubers. The sensors developed fall into two main categories: those which required heating to elevated temperatures, and those which were operated at ambient temperatures. The sensors operated at ambient temperatures included composites of tin dioxide and chemically prepared polypyrroles. The composites exhibited a high sensitivity to a range of organic vapours (1-100 vpm) and were more sensitive than either tin dioxide or polypyrrole at room temperature. Composites of chemically prepared polypyrroles with various thermoplastics were fabricated and were found to exhibit a high sensitivity to a range of volatile amines. Further studies incorporated chemically prepared polypyrroles into a printing ink vehicle, and sensors constructed from these films displayed good sensitivity, high stability and high mechanical strength. The sensors operated at elevated temperatures included a range of evaporated tin oxide films doped with Pt, CuO and ZnO, plus a range of thick film sensors based on tin dioxide, zinc oxide and mixtures of the two materials. The thick film sensors exhibited the highest sensitivity to the vapours of interest and also gave superior reproducibility of fabrication when compared to the sensors based on evaporated thin films. A synergistic effect appeared to be in operation where tin dioxide and zinc oxide were mixed, with sensors incorporating composites of the two materials exhibiting higher sensitivities than either tin dioxide or zinc oxide alone. A GC-MS study to elucidate the surface reactions occuring on exposure to the vapour, suggested that the synergistic effect was in part due to differences in the catalytic activities/pathways of the two materials. A prototype device was produced based on two evaporated tin dioxide film sensors and one thick film tin dioxidelzinc oxide (50/50 mlm) sensor. The device was tested to various quantities of sound tubers with an infected tuber added. The prototype device was capable of detecting one infected tuber amongst 100kg of sound tubers in a simulated storage crate.

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