MEASUREMENT AND MODELING OF HUMIDITY SENSORS

Humidity measurement has been increasingly important in many industries and process control applications. This thesis research focus mainly on humidity sensor calibration and characterization. The humidity sensor instrumentation is briefly described. The testing infrastructure was designed for sensor data acquisition, in order to compensate the humidity sensor’s temperature coefficient, temperature chambers using Peltier elements are used to achieve easy-controllable stable temperatures. The sensor characterization falls into a multivariate interpolation problem. Neuron networks is tried for non-linear data fitting, but in the circumstance of limited training data, an innovative algorithm was developed to utilize shape preserving polynomials in multiple planes in this kind of multivariate interpolation problems

CEOS Intercalibration of Ground-Based Spectrometers and Lidars: Contract Change Notice 2012-2013: Final Report

This document is the final report of the Intercalibration of ground-based spectrometers and Lidars - Extension 2012-2013. It summarizes the activities performed in the period from November 2012 until December 2013 and the main results obtained

Development and accuracy determination of a two-component Doppler Global Velocimeter (DGV)

A two-component Doppler Global Velocimeter (DGV) system was constructed and tested to research problems associated with the accuracy of this unique system. The uniqueness of the system lies in its ability to simultaneously and non-intrusively measure velocities in a laser illuminated plane. A key component of the system is a frequency discriminating optical filter containing iodine vapor which allows direct measurement of the Doppler frequency shift caused by particle motion. Corrections for optical distortions and non-uniform intensities as well as the conversions from intensity data to velocity data are performed by an extensive image processing algorithm. Measurements were made of a 12″ diameter rotating wheel and turbulent pipe/jet flow. Both RMS deviations and velocity range measurement errors from a single component for the rotating wheel with a maximum velocity of 58 m/s were less than 2%, better than most published results, to date, for similar systems. Pipe/jet flow profiles agreed very well with the shape of pitot probe measurements. RMS errors were on the order of 5--10%, but velocity offset error was as much as 10--15% of the 42 m/s velocity range. DGV measured turbulence intensities at the center of the pipe, 4 diameters downstream agreed with hot wire data, with some reservations. Several factors such as repeatability of calibrations, precision of wheel/pipe speed measurement, measurement of viewing angles, and 8-bit camera digitization contributed to the errors in DGV velocity data. Proper techniques for preparing and acquiring correction images are also critical steps toward the goal of producing accurate velocity data

A contribution to characterizing and calibrating the pointing control system of the SOFIA telescope

SOFIA, the Stratospheric Observatory for Infrared Astronomy, is an airborne observatory that will study the universe in the infrared spectrum. A Boeing 747-SP aircraft will carry a 2.5 m telescope designed to make sensitive infrared measurements of a wide range of astronomical objects. It will fly at and above 12 km, where the telescope collects radiation in the waveƠlength range from 0.3 micrometers to 1.6 millimeters of the electromagnetic spectrum. During flight, a door will be opened to allow clear optical observations from the cavity environment where the telescope is mounted. The telescope pointing control is achieved during science observations by an array of sensors including three imagers, gyroscopes and accelerometers. In addition, throughout alignment and calibration of the telescope assembly, the High-speed Imaging Photometer for Occultation (HIPO) is used as a reference instrument. A theoretical concept has been developed to compensate the perturbations in the airborne environment and to correct them within the attitude control loop. A set of Cartesian reference frames is established to describe and manipulate the orientations of the various subsystems, sensor and pointing orientations.thesi

Improvement of the Geospatial Accuracy of Mobile Terrestrial LiDAR Data

Many applications, such as topographic surveying for transportation engineering, have specific high accuracy requirements which MTL may be able to achieve under specific circumstances. Since high rate, immersive (360 FOV), MTL is a relatively new device for the collection and extraction of survey data; the understanding and correction of errors within such systems is under researched. Therefore, the goal of the work presented here is to quantify the geospatial accuracy of MTL data and improve the quality of MTL data products. Quantification of the geospatial accuracy of MTL systems was accomplished through the use of residual analysis, error propagation and conditional variance analysis. Real data from two MTL systems was analyzed using these methods and it was found that the actual errors exceeded the manufacturers estimates of system accuracy by over 10mm. Conditional variance analysis on these systems has shown that the contribution by the interactions among the measured parameters to the variances of the points in MTL point clouds is insignificant. The sizes of the variances for the measurements used to produce a point are the primary sources of error in the output point cloud. Improvement of the geospatial accuracy of MTL data products was accomplished by developing methods for the simultaneous multi-sensor calibration of the systems boresight angles and lever arm offsets, zero error calibration, temperature correction, and both spatial and temporal outlier detection. Evaluation of the effectiveness of these techniques was accomplished through the use of two test cases, employing real MTL data. Test case 1 showed that the residuals between a control field and the MTL point cloud were reduced by 4.4cm for points located on both horizontal and vertical target surfaces. Similarly, test case 2 showed a reduction in the residuals between control points and MTL data of 2~3cm on horizontal surfaces and 1~2cm on vertical surfaces. The most accurate point cloud produced through the use of these calibration and filtering techniques occurred in test case 1 (27mm 26mm). This result is still not accurate enough for certain high accuracy applications such as topographic surveying for transportation engineering (20mm 10mm)

On Improving the Accuracy and Reliability of GPS/INS-Based Direct Sensor Georeferencing

Due to the complementary error characteristics of the Global Positioning System (GPS) and Inertial Navigation System (INS), their integration has become a core positioning component, providing high-accuracy direct sensor georeferencing for multi-sensor mobile mapping systems. Despite significant progress over the last decade, there is still a room for improvements of the georeferencing performance using specialized algorithmic approaches. The techniques considered in this dissertation include: (1) improved single-epoch GPS positioning method supporting network mode, as compared to the traditional real-time kinematic techniques using on-the-fly ambiguity resolution in a single-baseline mode; (2) customized random error modeling of inertial sensors; (3) wavelet-based signal denoising, specially for low-accuracy high-noise Micro-Electro-Mechanical Systems (MEMS) inertial sensors; (4) nonlinear filters, namely the Unscented Kalman Filter (UKF) and the Particle Filter (PF), proposed as alternatives to the commonly used traditional Extended Kalman Filter (EKF). The network-based single-epoch positioning technique offers a better way to calibrate the inertial sensor, and then to achieve a fast, reliable and accurate navigation solution. Such an implementation provides a centimeter-level positioning accuracy independently on the baseline length. The advanced sensor error identification using the Allan Variance and Power Spectral Density (PSD) methods, combined with a wavelet-based signal de-noising technique, assures reliable and better description of the error characteristics, customized for each inertial sensor. These, in turn, lead to a more reliable and consistent position and orientation accuracy, even for the low-cost inertial sensors. With the aid of the wavelet de-noising technique and the customized error model, around 30 percent positioning accuracy improvement can be found, as compared to the solution using raw inertial measurements with the default manufacturer’s error models. The alternative filters, UKF and PF, provide more advanced data fusion techniques and allow the tolerance of larger initial alignment errors. They handle the unknown nonlinear dynamics better, in comparison to EKF, resulting in a more reliable and accurate integrated system. For the high-end inertial sensors, they provide only a slightly better performance in terms of the tolerance to the losses of GPS lock and orientation convergence speed, whereas the performance improvements are more pronounced for the low-cost inertial sensors

Voyager spacecraft phase B, task D. Volume 4 - Engineering tasks. Book 5 - Photo imaging Final report

Alternate methods of performing photoimaging experiments of Martian surface from orbiting Voyager spacecraf

Modeling and Monitoring of the Dynamic Response of Railroad Bridges using Wireless Smart Sensors

Railroad bridges form an integral part of railway infrastructure in the USA, carrying approximately 40 % of the ton-miles of freight. The US Department of Transportation (DOT) forecasts current rail tonnage to increase up to 88 % by 2035. Within the railway network, a bridge occurs every 1.4 miles of track, on average, making them critical elements. In an effort to accommodate safely the need for increased load carrying capacity, the Federal Railroad Association (FRA) announced a regulation in 2010 that the bridge owners must conduct and report annual inspection of all the bridges. The objective of this research is to develop appropriate modeling and monitoring techniques for railroad bridges toward understanding the dynamic responses under a moving train. To achieve the research objective, the following issues are considered specifically. For modeling, a simple, yet effective, model is developed to capture salient features of the bridge responses under a moving train. A new hybrid model is then proposed, which is a flexible and efficient tool for estimating bridge responses for arbitrary train configurations and speeds. For monitoring, measured field data is used to validate the performance of the numerical model. Further, interpretation of the proposed models showed that those models are efficient tools for predicting response of the bridge, such as fatigue and resonance. Finally, fundamental software, hardware, and algorithm components are developed for providing synchronized sensing for geographically distributed networks, as can be found in railroad bridges. The results of this research successfully demonstrate the potentials of using wirelessly measured data to perform model development and calibration that will lead to better understanding the dynamic responses of railroad bridges and to provide an effective tool for prediction of bridge response for arbitrary train configurations and speeds.National Science Foundation Grant No. CMS-0600433National Science Foundation Grant No. CMMI-0928886National Science Foundation Grant No. OISE-1107526National Science Foundation Grant No. CMMI- 0724172 (NEESR-SD)Federal Railroad Administration BAA 2010-1 projectOpe