Thesis (M.S. in Electrical Engineering) Naval Postgraduate School, December 1996. / "December 1996." Thesis advisor(s): Gurnam S. Gill. Includes bibliographical references (p. 45). Also available online.

Thesis (M.S.)--Cleveland State University, 2009. / Abstract. Title from PDF t.p. (viewed on Dec. 2, 2009). Includes bibliographical references (p. 65-72). Available online via the OhioLINK ETD Center and also available in print.

In the field of aviation forecasting is used, among other things, to determine the number of passengers to expect for each flight. This is beneficial in the practice of revenue management, as the forecast is used as a base when setting the price for each flight. In this study, a forecast evaluation has been done on seven different routes with a total of 61 different flights, using four different methods. These are: Mean Absolute Scaled Error (MASE), Mean Absolute Percentage Error (MAPE), Tracking Signal, and a goodness of fit test to determine if the forecast errors are normally distributed. The MASE has been used to determine if the passenger forecasts are better or worse than a naïve forecast, while the MAPE provides an error value for internal comparisons between the flights. The Tracking Signal and the normal distribution test have been used in order to determine whether a flight has bias or not towards under- or overforecasting. The results point towards a general underforecast across all studied flights. A total of 89 % of the forecasts perform better than the naïve forecast, with an average MASE value of 0,78. As such, the forecast accuracy is better than that of the naïve forecast. There are however large error values among the observed flights, affecting the MAPE average. The MAPE average is 38,53 % while the median is 30,60 %. The measure can be used for internal comparisons, and one such way is to use the average value as a benchmark in order to focus on improving those forecasts with a higher than average MAPE. The authors have found that the MASE and MAPE are useful in measuring forecast accuracy and as such the recommendation of the authors is that these two error measures can be used together to evaluate forecast accuracy at frequent intervals. In addition to this there is value in examining the error distribution in conjunction with the Mean Error when searching for bias, as this will indicate if there is systematic error present.

Stroke is the largest cause of severe adult complex disability, caused when the blood supply to the brain is interrupted, either by a clot or a burst blood vessel. It is characterised by deficiencies in movement and balance, changes in sensation, impaired motor control and muscle tone, and bone deformity. Clinically applied stroke management relies heavily on the observational opinion of healthcare workers. Despite the proven validity of a few clinical outcome measures, they remain subjective and inconsistent, and suffer from a lack of standardisation. Motion capture of the upper limb has also been used in specialised laboratories to obtain accurate and objective information, and monitor progress in rehabilitation. However, it is unsuitable in environments that are accessible to stroke patients (for example at patients’ homes or stroke clubs), due to the high cost, special set-up and calibration requirements. The aim of this research project was to validate and assess the sensitivity of a relatively low cost, wearable, compact and easy-to-use monitoring system, which uses inertial sensors in order to obtain detailed analysis of the forearm during simple functional exercises, typically used in rehabilitation. Forearm linear and rotational motion were characterised for certain movements on four healthy subjects and a stroke patient using a motion capture system. This provided accuracy and sensitivity specifications for the wearable monitoring system. With basic signal pre-processing, the wearable system was found to report reliably on acceleration, angular velocity and orientation, with varying degrees of confidence. Integration drift errors in the estimation of linear velocity were unresolved. These errors were not straightforward to eliminate due to the varying position of the sensor accelerometer relative to gravity over time. The cyclic nature of rehabilitation exercises was exploited to improve the reliability of velocity estimation with model-based Kalman filtering, and least squares optimisation techniques. Both signal processing methods resulted in an encouraging reduction of the integration drift in velocity. Improved sensor information could provide a visual display of the movement, or determine kinematic quantities relevant to the exercise performance. Hence, the system could potentially be used to objectively inform patients and physiotherapists about progress, increasing patient motivation and improving consistency in assessment and reporting of outcomes.

This thesis presents a body of work on the theme of millimetre-wave FMCW radar, for the purposes of security screening and remote sensing. First, the development of an optimised software radar signal processor will be outlined. Through use of threading and GPU acceleration, high data processing rates were achieved using standard PC hardware. The flexibility of this approach, compared to specialised hardware (e.g. DSP, FPGA etc…), allowed the processor to be rapidly adapted and has produced a significant performance increase in a number of advanced real-time radar systems. An efficient tracker was developed and was successfully deployed in live trials for the purpose of real-time wave detection in an autonomous boat control system. Automated radar operation and remote data telemetry functions were implemented in a terrain mapping radar to allow continuous monitoring of the Soufrière Hills volcano on the Caribbean island of Montserrat. This work concluded with the installation of the system 3 km from the volcano. Hardware modifications were made to enable coherent measurement in a number of existing radar systems, allowing phase sensitive measurements, including range-Doppler, to be performed. Sensitivity to displacements of less than 200 nm was demonstrated, which is limited by the phase noise of the system. Efficient compensation techniques are presented which correct for quadrature mixer imbalance, FMCW chirp non-linearity, and scanner drive distortions. In collaboration with the Home Office, two radar systems were evaluated for the stand-off detection of concealed objects. Automatic detection capability, based on polarimetric signatures, was developed using data gathered under controlled conditions. Algorithm performance was assessed through blind testing across a statistically significant number of subjects. A detailed analysis is presented, which evaluates the effect of clothing and object type on detection efficiency.