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Cross correlator systems, analog and clipped input channels, with correlated random narrow band Gaussian noise as inputs are computer simulated. The performance of each system is evaluated on the basis of the output signal-to-noise ratio. The output SNR of each system is compared with theoretical asymptotic approximations computed as a function of the SNR of the input channels. The output of the simulation compares within 3 dB of the asymptotic approximations for the analog correlator for all values of the correlation coefficient tested and for the systems with clipped input channels with uncorrelated inputs. For the systems with clipped input channels, certain combinations of the input SNR with non-zero correlation cause the output SNR to be zero. This causes discontinuities in the dB plot. For the systems with clippers and non-zero correlation of the input channels, the output of the simulation compares within 3 dB of the results of the asymptotic approximations when no discontinuities occur in the output plot.

This dissertation describes methods of mitigating radio frequency interference (RFI) in the frequency range 10-100 MHz, developing and evaluating coherent methods with which RFI is subtracted from the afflicted data, nominally resulting in no distortion of the underlying signals. This approach is of interest in weak signal applications such as radio astronomy, where the signal of interest may have interference-to-noise ratio much less than one, and so can be easily distorted by other methods. Environmental noise in this band is strong and non-white, so a realistic noise model is developed, with which we characterize the performance of signal parameter estimation, a key component of the proposed algorithms. Two classes of methods are considered: "generic" parameter estimation/subtraction (PE/S) and a modulation-specific form known as demodulation-remodulation ("demod--remod") PE/S. It is demonstrated for RFI in the form of narrowband FM and Broadcast FM that generic PE/S has the problem of severely distorting underlying signals of interest and demod-remod PE/S is less prone to this problem. Demod-remod PE/S is also applied and evaluated for RFI in the form of Digital TV signals. In both cases, we compare the performance of the demod-remod PE/S with that of a traditional adaptive canceling method employing a reference antenna, and propose a hybrid method to further improve performance. A new metric for "toxicity" is defined and employed to determine the degree to which RFI mitigation damages the underlying signal of interest. / Ph. D.

Enhanced field emission (EFE) presents the main impediment to higher acceleration gradients in superconducting niobium (Nb) radiofrequency cavities for particle accelerators. The strength, number and sources of EFE sites strongly depend on surface preparation and handling. The main objective of this thesis project is to systematically investigate the sources of EFE from Nb, to evaluate the best available surface preparation techniques with respect to resulting field emission, and to establish an optimized process to minimize or eliminate EFE. To achieve these goals, a scanning field emission microscope (SFEM) was designed and built as an extension to an existing commercial scanning electron microscope (SEM). In the SFEM chamber of ultra high vacuum, a sample is moved laterally in a raster pattern under a high voltage anode tip for EFE detection and localization. The sample is then transferred under vacuum to the SEM chamber equipped with an energy-dispersive x-ray spectrometer for individual emitting site characterization. Compared to other systems built for similar purposes, this apparatus has low cost and maintenance, high operational flexibility, considerably bigger scan area, as well as reliable performance. EFE sources from planar Nb have been studied after various surface preparation, including chemical etching and electropolishing, combined with ultrasonic or high-pressure water rinse. Emitters have been identified, analyzed and the preparation process has been examined and improved based on EFE results. As a result, field-emission-free or near field-emission-free surfaces at ~140 MV/m have been consistently achieved with the above techniques. Characterization on the remaining emitters leads to the conclusion that no evidence of intrinsic emitters, i.e., no fundamental electric field limit induced by EFE, has been observed up to ~140 MV/m. Chemically etched and electropolished Nb are compared and no significant difference is observed up to ~140 MV/m. To address concerns on the effect of natural air drying process on EFE, a comparative study was conducted on Nb and the results showed insignificant difference under the experimental conditions. Nb thin films deposited on Cu present a possible alternative to bulk Nb in superconducting cavities. The EFE performance of a preliminary energetically deposited Nb thin film sample are presented. / Ph. D.

The analysis of the distortion which results when frequency-modulated waves are passed through linear networks is investigated by the Fourier method and the Quasi-steady-state method. The major enphasis is placed on the Fourier method, and extensive digital computer programs are developed to allow this method to be implemented on the modern, high-speed digital computer. In the Fourier method, the frequency-modulated wave which is applied to the input of a linear network is broken up into its Fourier spectrum. Each of the resulting ‘'sideband'' frequencies is then passed through the network and is subjected to alterations in amplitude and phase. The output wave is then synthesized by taking the vector sum of the "weighted" sideband components. In contrast to the single pair of sideband frequencies generated by amplitude modulation, the spectrum of a frequency-modulated wave contains an infinite number of sideband components. Fortunately, only a relatively small number of these sidebands have significant influence on the total makeup of the waveform. The number of significant sidebands is proportional to the value of modulation index. When the modulation index is high, the number of significant sidebands is very large and the number of computations required by the Fourier method becomes enormous. Previously considered to be completely impractical, the Fourier method was usually abandoned in favor of the Quasi-steady-state approach. However, the digital computer techniques developed in the course of this investigation allow for a fast, economical, and convenient analysis based on the Fourier method even when the modulation index is relatively high. Analyses were performed for values of modulation index up to 45 and techniques are discussed for increasing this range. The Quasi-steady-state method is based on the assumption that the frequency of the input wave is changing slowly enough that the frequency of the output wave at any instant is equal to the "instantaneous fregquency' of the input wave. This method is inherently in error since it neglects the transient terms generated by the changing frequency. To compensate for this error, it is the general practice to incorporate correction terms, usually in the form of an infinite series. The Quasi-steady-state method is more effective at low modulating frequencies (high modulation index). While the analysis contained in this paper considers in detail only a first-order correction, the application of higher-order correction terms is discussed. The results obtained from applying both analyses to a complex, multi-section filter indicate that the computer solution of the Fourier method is preferable for intermediate values of modulation index. Experimental verification of the Fourier method is obtained by simulating the system on an analog computer. The advantages of this rather novel approach are discussed in some detail. The agreement between the results predicted by the digital computer and those obtained experimentally leaves no doubt to the validity and accuracy of the analysis. Digital computer programs for analyzing the distortion using each of the above methods are given. Subprograms are also included, some of which can be used independently. Among these are a program that computes Bessel functions of the first kind for positive and negative orders and a program that computes the minimum phase shift of a network from its atténuation. All programs are written in the FORTRAN IV computer language and were executed on the IBM 7040/1401 system. / Ph. D.

M. S.

Yes / Potential demodulating ability in biological tissue exposed to Radio Frequency (RF) signals intrinsically requires an unsymmetrical diode-like nonlinear response in tissue samples. This may be investigated by observing possible generation of the second harmonic in a cavity resonator designed to have fundamental and second harmonic resonant frequencies with collocated antinodes. Such a response would be of interest as being a mechanism that could enable demodulation of information-carrying waveforms having modulating frequencies in ranges that could interfere with cellular processes. Previous work has developed an experimental system to test for such responses: the present work reports an electric circuit model devised to facilitate calibration of any putative nonlinear RF energy conversion occurring within a nonlinear test-piece inside the cavity. The method is validated computationally and experimentally using a well-characterised nonlinear device. The variations of the reflection coefficients of the fundamental and second harmonic responses of the cavity due to adding nonlinear and lossy material are also discussed. The proposed model demonstrates that the sensitivity of the measurement equipment plays a vital role in deciding the required input power to detect any second harmonic signal, which is expected to be very weak. The model developed here enables the establishment of a lookup table giving the level of the second harmonic signal in the detector as a function of the specific input power applied in a measurement. Experimental results are in good agreement with the simulated results. / Engineering and Physical Science Research Council through Grant EP/E022936A

The evolution of technology and gadgets has led to a significant increase in the number of transmitted signals, making RF sensing more complex than ever. Challenges such as signal interference and the lack of prior information about all signal parameters further complicate the task. To address this challenge, researchers have explored machine learning and deep learning approaches to generalize solutions for real-world sensing problems. In this thesis, we focus on two key issues in RF signal detection using deep learning. Firstly, we tackle the problem of increasing signal detection coverage by utilizing multiple resolution eigengram images derived from a bank of channelizers. These channelizers, varying in size, are adept at sensing different types of signals, such as low duration or low bandwidth signals. Channelizer deconfliction is a known challenge in RFML. We use YOLO, a deep learning algorithm, to deconflict the outputs from different channelizers to avoid overreporting. YOLO's ability to handle three channels makes it ideal for our study as we also use three channelizers. While our approach is not dependent on YOLO, it provides a good testing ground for this study. To address signal overlap, we utilize an eigengram image capturing the overlap region between signals. By overlaying this eigengram onto the original, we create a composite image highlighting the overlap. We train another YOLO model using two channels, one for each eigengram, enabling detection even with over 50 percent overlap. This work is versatile and promising, extending to other signal visualizations. It has significant potential for wireless industry applications and sets the stage for further RFML research. / Master of Science / Due to the exponential growth in Radio Frequency (RF) signals over the last few decades, brought about by the proliferation of gadgets, signal detection has become more complex than ever. To address these complexities in signal sensing, adopting a dynamic approach that is not reliant on specific parameters or thresholds is essential. RF approaches using deep learning show great promise in tackling these challenges. Deep learning is the branch of machine learning based on artificial neural networks. An artificial neural network uses layers of interconnected nodes called neurons that work together to process and learn from the input data. The first part of this thesis addresses increasing signal coverage by leveraging different signal perspectives, each capturing unique characteristics. By combining these perspectives into a dataset, we train a deep learning model that incorporates the strengths of each view, resulting in maximum detection coverage. The novelty lies in innovative data preprocessing techniques and using YOLO to deconflict signal views with up to three channelizers. In the second part, we focus on detecting overlapped or occluded signals. We utilize a new dimension of information describing interference regions between signals. By integrating this overlap perspective, we enhance the dataset to identify instances of extensive signal overlap and determine their regions of coverage. This enhancement enables the deep learning network to identify patterns and effectively detect highly overlapped or completely occluded signals.