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Random sampling is a technique which overcomes the alias problem in regular sampling. The randomization, however, destroys the symmetry property of the transform kernel of the discrete Fourier transform. Hence, when transforming a randomly sampled sequence to its frequency spectrum, the Fast Fourier transform cannot be applied and the computational complexity is N(^2). The objectives of this research project are (1) To devise sampling methods for random sampling such that computation may be reduced while the anti-alias property of random sampling is maintained : Two methods of inserting limited regularities into the randomized sampling grids are proposed. They are parallel additive random sampling and hybrid additive random sampling, both of which can save at least 75% of the multiplications required. The algorithms also lend themselves to the implementation by a multiprocessor system, which will further enhance the speed of the evaluation. (2) To study the auto-correlation sequence of a randomly sampled sequence as an alternative means to confirm its anti-alias property : The anti-alias property of the two proposed methods can be confirmed by using convolution in the frequency domain. However, the same conclusion is also reached by analysing in the spatial domain the auto-correlation of such sample sequences. A technique to evaluate the auto-correlation sequence of a randomly sampled sequence with a regular step size is proposed. The technique may also serve as an algorithm to convert a randomly sampled sequence to a regularly spaced sequence having a desired Nyquist frequency. (3) To provide a rapid spectral estimation using a coarse kernel : The approximate method proposed by Mason in 1980, which trades the accuracy for the speed of the computation, is introduced for making random sampling more attractive. (4) To suggest possible applications for random and pseudo-random sampling : To fully exploit its advantages, random sampling has been adopted in measurement Random sampling is a technique which overcomes the alias problem in regular sampling. The randomization, however, destroys the symmetry property of the transform kernel of the discrete Fourier transform. Hence, when transforming a randomly sampled sequence to its frequency spectrum, the Fast Fourier transform cannot be applied and the computational complexity is N"^. The objectives of this research project are (1) To devise sampling methods for random sampling such that computation may be reduced while the anti-alias property of random sampling is maintained : Two methods of inserting limited regularities into the randomized sampling grids are proposed. They are parallel additive random sampling and hybrid additive random sampling, both of which can save at least 75% , of the multiplications required. The algorithms also lend themselves to the implementation by a multiprocessor system, which will further enhance the speed of the evaluation. (2) To study the auto-correlation sequence of a randomly sampled sequence as an alternative means to confirm its anti-alias property : The anti-alias property of the two proposed methods can be confirmed by using convolution in the frequency domain. However, the same conclusion is also reached by analysing in the spatial domain the auto-correlation of such sample sequences. A technique to evaluate the auto-correlation sequence of a randomly sampled sequence with a regular step size is proposed. The technique may also serve as an algorithm to convert a randomly sampled sequence to a regularly spaced sequence having a desired Nyquist frequency. (3) To provide a rapid spectral estimation using a coarse kernel : The approximate method proposed by Mason in 1980, which trades the accuracy for the speed of the computation, is introduced for making random sampling more attractive. (4) To suggest possible applications for random and pseudo-random sampling : To fully exploit its advantages, random sampling has been adopted in measurement instruments where computing a spectrum is either minimal or not required. Such applications in instrumentation are easily found in the literature. In this thesis, two applications in digital signal processing are introduced. (5) To suggest an inverse transformation for random sampling so as to complete a two-way process and to broaden its scope of application. Apart from the above, a case study of realizing in a transputer network the prime factor algorithm with regular sampling is given in Chapter 2 and a rough estimation of the signal-to-noise ratio for a spectrum obtained from random sampling is found in Chapter 3. Although random sampling is alias-free, problems in computational complexity and noise prevent it from being adopted widely in engineering applications. In the conclusions, the criteria for adopting random sampling are put forward and the directions for its development are discussed.

This thesis proposes a distributed observer design for a class of nonlinear systems that arise in the application of model reduction techniques. Distributed observer design techniques have been proposed in the literature to address estimation problems over sensor networks. In large complex sensor networks, an efficient technique that minimizes the extent of the required communication is highly desirable. This is especially true when sensors have problems caused by physical limitations that result in incorrect information at the local level affecting the estimation of states globally. To address this problem, scalable algorithms for a suitable distributed observer have been developed. Most algorithms are focussed on large linear dynamical systems and they are not directly generalizable to nonlinear systems. In this thesis, scalable algorithms for distributed observers are proposed for a class of large scale observable nonlinear system. Distributed systems models multi-agent systems in which each agents attempts to accomplish local tasks. In order to achieve global objectives, there should be agreement regarding some commonly known variables that depend on the state of all agents. These variables are called consensus states. Once identified, such consensus states can be exploited in the development of distributed consensus algorithms. Consensus algorithms are used to develop information exchange protocols between agents such that global objectives are met through local action. In this thesis, a higher order observer is applied in the distributed sensor network system to design a distributed observer for a class nonlinear systems. Fusion of measurement and covariance information is applied to the higher order filter as the first method. The consensus filter is embedded in the local nonlinear observer for fusion of data. The second method is based on the communication of state estimates between neighbouring sensors rather than fusion of data measurement and covariance. The second method is found to reduce disagreement of the states estimation between each sensor. The performance of these new algorithms is demonstrated by simulation, and the second method is effectively applied over the first method. / Thesis (Master, Chemical Engineering) -- Queen's University, 2012-12-12 11:22:49.113

RFID technology has attracted much attention due to its wide range of applications, such as inventory control and object tracking. Passive RFID tags are battery-less, mobile and lack intercommunication. Hence, they require a central node (the reader) to power them up, organize their replies, and read their data. In the last decade, several proposals have targeted the channel efficiency in RFID systems to improve time and power efficiencies. While such proposals achieve significant performance improvements, they are limited by the backscattering half-duplex channel in which the reader has to wait for the tag to finish its reply (even if the reply is corrupt or redundant). In this thesis, the Modulation Silencing Mechanism (MSM) is proposed as a novel full-duplex-like communication over half-duplex RFID links. With a simple additional circuit at the tag and upgraded software algorithms at the reader, the reader is capable of terminating the tag's non-useful transmissions. Consequently, we propose three schemes that utilize MSM in key application domains where the tag-reader transaction contains a considerable amount of non-useful transmissions. MSM is utilized to enhance tag identification, tag count estimation and tag authentication. First, we propose a Modulation Silencing Anti-collision (MSA) scheme that targets collision time reduction in time slotted anti-collision protocols. In MSA, the time requirements of state of the art identification protocols are significantly reduced. Moreover, we establish a backward compatibility procedure for proper identification of legacy and MSM-enabled tags. Secondly, a Variance- Modulation Silencing Estimation (VMSE) scheme is proposed to increase tag estimation accuracy and to minimize overall estimation time. Variance-to-mean ratio estimator is proposed to determine the most accurate tag count estimate. VMSE combines both, the accuracy of the variance-to-mean ratio estimator and the time efficiency of MSM and delivers rapid, accurate, and anonymous tag estimation that outperform recent estimation schemes for small and large scale tag deployment. Finally, we propose Unique Hash Sequence Authentication (UHSA) scheme for efficient tag authentication. The UHSA is based on hashed key prefetching algorithm at the reader augmented by the MSM circuitry at the tag. UHSA scheme provides higher time efficiency and robustness against tracking and compromising attacks. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2013-04-30 12:38:44.0

For over a century, physicians have prescribed x-ray radiation to destroy or impede the growth of cancerous tumours. Modern radiation therapy machines shape the radiation beam to balance the competing goals of maximizing irradiation of cancerous tissue and minimizing irradiation of healthy tissue, an objective complicated by tumour motion during the treatment and errors positioning the patient to align the tumour with the radiation beam. Recent medical imaging advances have motivated interest in using feedback during radiation therapy to track the tumour in real time and mitigate these complications. This thesis investigates how real-time feedback control can be used to track the tumour and focus the radiation beam tightly around the tumour. Improving on these results, a feedback control system is proposed for intensity modulated radiation therapy which allows a non-uniform radiation dose to be applied to the tumour. Motivated by the results of the proposed control systems, this thesis also examines two theoretical control problems: estimating the output of an unknown system when a sensor delay prevents its direct measurement, and designing a controller to provide an arbitrarily fast nonovershooting step response.

International Telemetering Conference Proceedings / October 26-29, 1992 / Town and Country Hotel and Convention Center, San Diego, California / An improved ionospheric delay correction model for a transionospheric electromagnetic pulse (EMP) is used for estimating the total-electron-content (TEC) profile of the path and accurate ranging of the EMP source. For a known pair of time of arrival (TOA) measurements at two frequency channels, the ionospheric TEC information is estimated using a simple numerical technique. This TEC information is then used for computing ionospheric group delay and pulse broadening effect correction to determine the free space range. The model prediction is compared with the experimental test results. The study results show that the model predictions are in good agreement with the test results.

Approved for public release; distribution is unlimited / The estimation of time delay and Doppler difference of a signal arriving at two physically separated sensors is investigated in this thesis. Usually, modified cross power spectrum coupled with Doppler compensation is used to detect a common, passive signal received at two separated sensors. Another successful approach uses the cross coherence to achieve this goal. This thesis modifies these two techniques to model the Doppler difference via an autoregressive (AR) technique. Analytical results are derived and experimentally verified via a computer simulation. Performance at high and low signal to noise ratios (SNRs) is examined. / http://archive.org/details/armodelingofcohe00leej / Captain, Korea Air Force

Blood pressure is vital sign information that physicians often need as preliminary data for immediate intervention during emergency situations or for regular monitoring of people with cardiovascular diseases. Despite the availability of portable blood pressure meters in the market, they are not regularly carried by people, creating a need for an ultra-portable measurement platform or device that can be easily carried and used at all times. One such device is the smartphone which, according to comScore survey is used by 26.2% of the US adult population. the mass production of these phones with built-in sensors and high computation power has created numerous possibilities for application development in different domains including biomedical. Motivated by this capability and their extensive usage, this thesis focuses on developing a blood pressure measurement platform on smartphones. Specifically, I developed a blood pressure measurement system on a smart phone using the built-in camera and a customized external microphone. the system consists of first obtaining heart beats using the microphone and finger pulse with the camera, and finally calculating the blood pressure using the recorded data. I developed techniques for finding the best location for obtaining the data, making the system usable by all categories of people. the proposed system resulted in accuracies between 90-100%, when compared to traditional blood pressure meters. the second part of this thesis presents a new system for remote heart beat monitoring using the smart phone. with the proposed system, heart beats can be transferred live by patients and monitored by physicians remotely for diagnosis. the proposed blood pressure measurement and remote monitoring systems will be able to facilitate information acquisition and decision making by the 9-1-1 operators.

INTRODUCTION: Sight-resight studies are performed to estimate population sizes, in this case dog populations in rabies endemic areas. AIM: This study compares one- and two-day sight-resight methods with two-day as the standard to explore the feasibility and accuracy of the one-day method in different vaccination campaign strategies and dog population characteristics. METHODS: 2015 household survey data and sight-resight data are analyzed to find the percentage of free roaming and confined dogs in the community and use those to adjust the population estimate formulas. 2016 sight-resight data are analyzed as a two-day campaign and as if it had been a one-day campaign. In a sensitivity analysis, confidence intervals are explored in relation to vaccination coverage. RESULTS: Before missed mark and proportion free-roaming corrections, the one-day method results in slightly underestimated population estimates to the two-day method when the vaccination campaign is central point, overestimated when door-to-door, and far underestimated when capture, vaccinate, release. After corrections door-to-door estimates were accurate whereas central point and capture, vaccinate, release estimates substantially underestimated population sizes. DISCUSSION: Results suggest that the one-day mark-resight method could be used to conserve resources depending on the vaccination method and estimated coverage.

Simultaneous localization and mapping (SLAM) is the problem of mapping your surroundings while simultaneously localizing yourself in the map. It is an important and active area of research for robotics. In this master thesis two approaches are attempted to reduce the drift which appears over time in SLAM algorithms. The first approach tries 3 different motion models for the camera. Two of the models exploit the a priori knowledge that the camera is mounted on a trolley. These two methods are shown to improve the results. The second approach attempts to reduce the drift by reducing noise in the point cloud data used for mapping. This is done by finding planar surfaces in the point clouds. Median filtering is used as an alternative to compare the result for noise reduction. The planes estimation approach is also shown to reduce the drift, while the median estimation makes it worse. / Simultaneous localization and mapping (SLAM) är problemet att kartlägga sin omgivning samtidigt som man lokaliserar sig själv i kartan. Det är ett viktigt och aktivt forskningsområde inom robotik. I det här exjobbet testas två tillvägagångssätt för att minska felet i kameraposition och orientering som uppstår över tiden i SLAM-lösningar. Det första tillvägagångssättet testar 3 olika rörelsemodeller för kameran. Två av modellerna utnyttjar vetskapen om att kameran sitter monterad på en vagn. Dessa två metoder förbättrar resultatet för SLAM-algoritmen. Det andra tillväggagångssättet försöker minska felet genom att reducera bruset i punktmolnsdatan som används i kartläggningen. Det görs genom att hitta plana ytor i punktmolnen. Medianfiltrering används som en alternativ lösning för att jämföra hur bra planestimeringen står sig. Planestimeringen visar sig också minska felet i lösningen, medan medianfiltreringen endast försämrar resultatet.