Matching Items (45)

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Electromagnetic Models of Electric Field Imaging

Description

The field of computed tomography involves reconstructing an image from lower dimensional projections. This is particularly useful for visualizing the inner structure of an object. Presented here is an imaging

The field of computed tomography involves reconstructing an image from lower dimensional projections. This is particularly useful for visualizing the inner structure of an object. Presented here is an imaging setup meant for use in computed tomography applications. This imaging setup relies on imaging electric fields through active interrogation. Models designed in Ansys Maxwell are used to simulate this setup and produce 2D images of an object from 1D projections to verify electric field imaging as a potential route for future computed tomography applications. The results of this thesis show reconstructed images that resemble the object being imaged using a filtered back projection method of reconstruction. This work concludes that electric field imaging is a promising option for computed tomography applications.

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  • 2016-12

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Adaptive Radar Matched Filter Saddlepoint Approximation Study

Description

Radar systems seek to detect targets in some search space (e.g. volume of airspace, or area on the ground surface) by actively illuminating the environment with radio waves. This illumination

Radar systems seek to detect targets in some search space (e.g. volume of airspace, or area on the ground surface) by actively illuminating the environment with radio waves. This illumination yields a return from targets of interest as well as highly reflective terrain features that perhaps are not of interest (called clutter). Data adaptive algorithms are therefore employed to provide robust detection of targets against a background of clutter and other forms of interference. The adaptive matched filter (AMF) is an effective, well-established detection statistic whose exact probability density function (PDF) is known under prevalent radar system model assumptions. Variations of this approach, however, lead to tests whose PDFs remain unknown or incalculable. This project will study the effectiveness of saddlepoint methods applied to approximate the known pdf of the clairvoyant matched filter, using MATLAB to complete the numerical calculations. Specifically, the approximation was used to compute tail probabilities for a range of thresholds, as well as compute the threshold and probability of detection for a specific desired probability of false alarm. This was compared to the same values computed using the known exact PDF of the filter, with the comparison demonstrating high levels of accuracy for the saddlepoint approximation. The results are encouraging, and justify further study of the approximation as applied to more strained or complicated scenarios.

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  • 2020-05

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Multi-Static Space-Time-Frequency Channel Modeling

Description

Radio communication has become the dominant form of correspondence in modern society. As the demand for high speed communication grows, the problems associated with an expanding consumer base and limited

Radio communication has become the dominant form of correspondence in modern society. As the demand for high speed communication grows, the problems associated with an expanding consumer base and limited spectral access become more difficult to address. One communications system in which people commonly find themselves is the multiple access cellular network. Users operate within the same geographical area and bandwidth, so providing access to every user requires advanced processing techniques and careful subdivision of spectral access. This is known as the multiple access problem. This paper addresses this challenge in the context of airborne transceivers operating at high altitudes and long ranges. These operators communicate by transmitting a signal through a target scattering field on the ground without a direct line of sight to the receiver. The objective of this investigation is to develop a model for this communications channel, identify and quantify the relevant characteristics, and evaluate the feasibility of using it to effectively communicate.

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Date Created
  • 2015-12

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Edge Detection from Spectral Phase Data

Description

The detection and characterization of transients in signals is important in many wide-ranging applications from computer vision to audio processing. Edge detection on images is typically realized using small, local,

The detection and characterization of transients in signals is important in many wide-ranging applications from computer vision to audio processing. Edge detection on images is typically realized using small, local, discrete convolution kernels, but this is not possible when samples are measured directly in the frequency domain. The concentration factor edge detection method was therefore developed to realize an edge detector directly from spectral data. This thesis explores the possibilities of detecting edges from the phase of the spectral data, that is, without the magnitude of the sampled spectral data. Prior work has demonstrated that the spectral phase contains particularly important information about underlying features in a signal. Furthermore, the concentration factor method yields some insight into the detection of edges in spectral phase data. An iterative design approach was taken to realize an edge detector using only the spectral phase data, also allowing for the design of an edge detector when phase data are intermittent or corrupted. Problem formulations showing the power of the design approach are given throughout. A post-processing scheme relying on the difference of multiple edge approximations yields a strong edge detector which is shown to be resilient under noisy, intermittent phase data. Lastly, a thresholding technique is applied to give an explicit enhanced edge detector ready to be used. Examples throughout are demonstrate both on signals and images.

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Date Created
  • 2016-05

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Perturbation and Sparsification of a Uniform Linear Array

Description

In modern remote sensing, arrays of sensors, such as antennas in radio frequency (RF) systems and microphones in acoustic systems, provide a basis for estimating the direction of arrival of

In modern remote sensing, arrays of sensors, such as antennas in radio frequency (RF) systems and microphones in acoustic systems, provide a basis for estimating the direction of arrival of a narrow-band signal at the sensor array. A Uniform linear array (ULA) is the most well-studied array geometry in that its performance characteristics and limitations are well known, especially for signals originating in the far field. In some instances, the geometry of an array may be perturbed by an environmental disturbance that actually changes its nominal geometry; such as, towing an array behind a moving vehicle. Additionally, sparse arrays have become of interest again due to recent work in co-prime arrays. These sparse arrays contain fewer elements than a ULA but maintain the array length. The effects of these alterations to a ULA are of interest. Given this motivation, theoretical and experimental (i.e. via computer simulation) processes are used to determine quantitative and qualitative effects of perturbation and sparsification on standard metrics of array performance. These metrics include: main lobe gain, main lobe width and main lobe to side lobe ratio. Furthermore, in order to ascertain results/conclusions, these effects are juxtaposed with the performance of a ULA. Through the perturbation of each element following the first element drawn from a uniform distribution centered around the nominal position, it was found that both the theoretical mean and sample mean are relatively similar to the beam pattern of the full array. Meanwhile, by using a sparsification method of maintaining all the lags, it was found that this particular method was unnecessary. Simply taking out any three elements while maintaining the length of the array will produce similar results. Some configurations of elements give a better performance based on the metrics of interest in comparison to the ULA. These results demonstrate that a sparsified, perturbed or sparsified and perturbed array can be used in place of a Uniform Linear Array depending on the application.

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Date Created
  • 2016-05

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Risk Factors of China's Nuclear Energy Ambitions

Description

China's rapid growth was fueled by an unsustainable method: trade environment for GDP. Air pollution has reached dangerous levels and has taken a serious toll on China's economic progress. The

China's rapid growth was fueled by an unsustainable method: trade environment for GDP. Air pollution has reached dangerous levels and has taken a serious toll on China's economic progress. The World Bank estimates that in 2013, China lost about 10% of its GDP to pollution. As the cost of burning fossil fuels and public dismay continue to mount, the government is taking steps to reduce carbon emissions and appease the people. The rapidly growing nuclear energy program is one of the energy solutions that China is using to addressing carbon emissions. While China has built a respectable amount of renewable energy capacity (such as wind and solar), much of that capacity is not connected to the power grid. Nuclear energy on the other hand, provides a low-emission alternative that operates independently of weather and sunlight. However, the accelerated pace of reactor construction in recent years presents challenges for the safe operation of nuclear energy in China. It is in China's (and the world's) best interest that a repeat of the Fukushima accident does not occur. In the wake of the Fukushima nuclear accident, public support for nuclear energy in China took a serious hit. A major domestic nuclear accident would be detrimental to the development of nuclear energy in China and diminish the government's reliability in the eyes of the people. This paper will outline those risk factors such as regulatory efforts, legal framework, technological issues, spent fuel disposal, and public perception and provide suggestions to decrease the risk of a major nuclear accident.

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Date Created
  • 2018-05

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Maximum Entropy Surrogation in Multiple Channel Signal Detection

Description

Multiple-channel detection is considered in the context of a sensor network where data can be exchanged directly between sensor nodes that share a common edge in the network graph. Optimal

Multiple-channel detection is considered in the context of a sensor network where data can be exchanged directly between sensor nodes that share a common edge in the network graph. Optimal statistical tests used for signal source detection with multiple noisy sensors, such as the Generalized Coherence (GC) estimate, use pairwise measurements from every pair of sensors in the network and are thus only applicable when the network graph is completely connected, or when data are accumulated at a common fusion center. This thesis presents and exploits a new method that uses maximum-entropy techniques to estimate measurements between pairs of sensors that are not in direct communication, thereby enabling the use of the GC estimate in incompletely connected sensor networks. The research in this thesis culminates in a main conjecture supported by statistical tests regarding the topology of the incomplete network graphs.

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Date Created
  • 2014-05

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The Role of Fourier Phase in Image Representation and Reconstruction

Description

The Fourier representation of a signal or image is equivalent to its native representation in the sense that the signal or image can be reconstructed exactly from its Fourier transform.

The Fourier representation of a signal or image is equivalent to its native representation in the sense that the signal or image can be reconstructed exactly from its Fourier transform. The Fourier transform is generally complex-valued, and each value of the Fourier spectrum thus possesses both magnitude and phase. Degradation of signals and images when Fourier phase information is lost or corrupted has been studied extensively in the signal processing research literature, as has reconstruction of signals and images using only Fourier magnitude information. This thesis focuses on the case of images, where it examines the visual effect of quantifiable levels of Fourier phase loss and, in particular, studies the merits of introducing varying degrees of phase information in a classical iterative algorithm for reconstructing an image from its Fourier magnitude.

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Date Created
  • 2021-05

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Edge Detection from Non-Uniform Fourier Data via a Modified Method of Convolutional Gridding

Description

The recovery of edge information in the physical domain from non-uniform Fourier data is of importance in a variety of applications, particularly in the practice of magnetic resonance imaging (MRI).

The recovery of edge information in the physical domain from non-uniform Fourier data is of importance in a variety of applications, particularly in the practice of magnetic resonance imaging (MRI). Edge detection can be important as a goal in and of itself in the identification of tissue boundaries such as those defining the locations of tumors. It can also be an invaluable tool in the amelioration of the negative effects of the Gibbs phenomenon on reconstructions of functions with discontinuities or images in multi-dimensions with internal edges. In this thesis we develop a novel method for recovering edges from non-uniform Fourier data by adapting the "convolutional gridding" method of function reconstruction. We analyze the behavior of the method in one dimension and then extend it to two dimensions on several examples.

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Date Created
  • 2013-05

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Estimation Theory on Random Graphs for Offset Detection in Sensor Networks

Description

A distributed sensor network (DSN) is a set of spatially scattered intelligent sensors designed to obtain data across an environment. DSNs are becoming a standard architecture for collecting data over

A distributed sensor network (DSN) is a set of spatially scattered intelligent sensors designed to obtain data across an environment. DSNs are becoming a standard architecture for collecting data over a large area. We need registration of nodal data across the network in order to properly exploit having multiple sensors. One major problem worth investigating is ensuring the integrity of the data received, such as time synchronization. Consider a group of match filter sensors. Each sensor is collecting the same data, and comparing the data collected to a known signal. In an ideal world, each sensor would be able to collect the data without offsets or noise in the system. Two models can be followed from this. First, each sensor could make a decision on its own, and then the decisions could be collected at a ``fusion center'' which could then decide if the signal is present or not. The fusion center can then decide if the signal is present or not based on the number true-or-false decisions that each sensor has made. Alternatively, each sensor could relay the data that it collects to the fusion center, and it could then make a decision based on all of the data that it then receives. Since the fusion center would have more information to base its decision on in the latter case--as opposed to the former case where it only receives a true or false from each sensor--one would expect the latter model to perform better. In fact, this would be the gold standard for detection across a DSN. However, there is random noise in the world that causes corruption of data collection, especially among sensors in a DSN. Each sensor does not collect the data in the exact same way or with the same precision. We classify these imperfections in data collections as offsets, specifically the offset present in the data collected by one sensor with respect to the rest of the sensors in the network. Therefore, reconsider the two models for a DSN described above. We can naively implement either of these models for data collection. Alternatively, we can attempt to estimate the offsets between the sensors and compensate. One could see how it would be expected that estimating the offsets within the DSN would provide better overall results than not finding estimators. This thesis will be structured as follows. First, there will be an extensive investigation into detection theory and the impact that different types of offsets have on sensor networks. Following the theory, an algorithm for estimating the data offsets will be proposed correct for the offsets. Next, we will look at Monte Carlo simulation results to see the impact on sensor performance of data offsets in comparison to a sensor network without offsets present. The algorithm is then implemented, and further experiments will demonstrate sensor performance with offset detection.

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Created

Date Created
  • 2016-05