When a traffic signals malfunction monitoring unit detects a problem with a traffic signal such as the simultaneous display of green indications to conflicting movements or loss of power to some signal heads, the signal is automatically placed into flash mode as a safety precaution. Signals can have either red/red malfunction flash mode or yellow/red malfunction flash mode, and the mode cannot change by time of day or day of week. This study analyzed traffic operation at 34 instances of yellow/red malfunction flash and 9 instances of red/red malfunction flash in the Atlanta, Georgia area. Many of these instances were during high volume periods. A high level of driver confusion exists at malfunction flash intersections. The rate at which through major street drivers (i.e. those facing a flashing yellow signal) stopped exceeded 75 percent at some yellow/red flash intersections. This creates a safety hazard for other major street drivers who are not expecting vehicles to stop, and for minor street drivers who cannot tell what type of control is being presented to cross traffic or do not understand that vehicles are not required to stop when approaching a flashing yellow indication. Furthermore, high stopping rates at a flashing yellow signal eliminate many of the operational benefits that yellow/red flash is assumed to have over red/red flash. Based on the findings of this study, the use of red/red flash should be the primary flash mode and possibly used exclusively. Requiring all vehicles to stop will improve safety conditions and not have large operational impacts at intersections where a majority of major street vehicles are already stopping at a flashing yellow signal. Yellow/red flash may be an acceptable malfunction flash mode at the intersection of a very large street and a very small street, but additional measures would be required at these intersections to address potential driver confusion.

In the case of wideband (aperiodic) signals, the classical signal and noise measures used to characterize stochastic resonance do not work because their way of distinguishing signal from noise fails. In a study published earlier (L. B. Kish, 1996), a new way of measuring and identifying noise and aperiodic (wideband) signals during strongly nonlinear transfer was introduced. The method was based on using cross-spectra between the input and the output. According to the study, in the case of linear transfer and sinusoidal signals, the method gives the same results as the classical method and in the case of aperiodic signals it gives a sensible measure. In this paper we refine the theory and present detailed simulations which validate and refine the conclusions reached in that study. As neural and ion channel signal transfer are nonlinear and aperiodic, the new method has direct applicability in membrane biology and neural science (S.M. Bezrukov and I. Vodyanoy, 1997).

Thesis (M.S.)--University of Wyoming, 2008. / Title from PDF title page (viewed on August 9, 2009). Includes bibliographical references (p. 96-99).

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The purpose of this thesis is to explore how the installations of traffic signals affect crash experience at intersections, to identify those factors which help predict crashes after a signal is installed, and to develop a crash prediction model. It is the intent of this thesis to supplement the Manual on Uniform Traffic Control Devices Signal Warrant procedure and aid the traffic engineer in the signal installation decision making process. Crash data, as well as operational and geometric factors were examined for 32 state road intersections in the northeast Florida area before and after signal installation. Signal warrant studies were used as sources for traffic volumes, geometric information and crash history, before signal installation. The Florida Department of Transportation's Crash Analysis Reporting System (CARS) was used to gather crash data for the time period after signal installation. On average, the 32 intersections experienced a 12% increase in the total number of crashes and a 26% reduction in crash rate after signals were installed. The change in the number of crashes was not significant, but the rate change was significant with 90% confidence. Angle crash frequency dropped by 60% and the angle crash rate dropped by 66%, both are significant. Left-turn crashes dropped by 8% and their rate by 16%, although neither was significant. Rear-end crashes increased by 86% and the rear-end crash rate decreased by 5%. Neither of these changes was statistically significant. When crash severity was examined, it was found that the number of injury crashes increased by 64.8% and the rate by only 0.02%. Neither change was significant. Both the number of fatal crashes and the rate decreased by 100% and were significant. Property Damage Only (PDO) crashes increased by 96%, after signalization, but this change was not significant. The PDO rate, however, decreased by 46.5% and is significant. Operational factors such as AADT, turning movement counts, and speed limits; and geometric factors such as medians, turn lanes and numbers of lanes were considered to determine their effect on crashes at signalized intersections. Smaller roads, with low AADT, fewer lanes, and a rural character were found to benefit from signalization more than busier urbanized roads, in terms of crash rate reduction. The AADT, roadway cross section, number of lanes, medians, speed limit and left turn volume were all found to be important factors influencing crash rates. This thesis recommends: 1) the use of crash prediction models to supplement the MUTCD Crash Warrant, 2) the addition of a left-turn warrant to the MUTCD signal warranting procedure, and 3) development of an intersection database containing crash data as well as operational and geometric information to aid in future research.

This dissertation explores the realm of high-accuracy localization through the utilization of ranging-based techniques, encompassing a spectrum of signals ranging from low-frequency ultrasound acoustic signals to more intricate high-frequency signals like Wireless Fidelity (Wi-Fi) IEEE 802.11az, 5G New Radio (NR), and 6G. Moreover, another contribution is the conception of a novel timing mechanism and synchronization protocol grounded in tunable quantum photonic oscillators. In general, our primary focus is to facilitate precise indoor localization, where conventional GPS signals are notably absent. To showcase the significance of this innovation, we present two vital use cases at the forefront: drone localization and metaverse user positioning. In the context of indoor drone localization, the spectrum of applications ranges from recreational enthusiasts to critical missions requiring pinpoint accuracy. At the hobbyist level, drones can autonomously navigate intricate indoor courses, enriching the recreational experience. As a finer illustration of a hobbyist application, consider the case of ``follow me drones". These specialized drones are tailored for indoor photography and videography, demanding an exceptionally accurate autonomous flight capability. This precision is essential to ensure the drone can consistently track and capture its designated subject, even as it moves within the confined indoor environment. Moving on from hobby use cases, the technology extends its profound impact to more crucial scenarios, such as search and rescue operations within confined spaces. The ability of drones to localize with high precision enhances their autonomy, allowing them to maneuver seamlessly, even in environments where human intervention proves challenging. Furthermore, the technology holds the potential to revolutionize the metaverse. Within the metaverse, where augmented and virtual realities converge, the importance of high-accuracy localization is amplified. Immersive experiences like Augmented/Virtual/Mixed Reality (AR/VR/MR) gaming rely heavily on precise user positioning to create seamless interactions between digital and physical environments. In entertainment, this innovation sparks innovation in narrative design, enhancing user engagement by aligning virtual elements with real-world surroundings. Beyond entertainment, applications extend to areas like telemedicine, enabling remote medical procedures with virtual guidance that matches physical reality. In light of all these examples, the imperative for an advanced high-accuracy localization system has become increasingly pronounced. The core objective of this dissertation is to address this pressing need by engineering systems endowed with exceptional precision in localization. Among the array of potential techniques suitable for GPS-absent scenarios, we have elected to focus on ranging-based methods. Specifically, our methodologies are built upon the fundamental principles of time of arrival, time difference of arrival, and time of flight measurements. In essence, each of our devised systems harnesses the capabilities of beacons such as ultrasound acoustic sensors, 5G femtocells, or Wi-Fi access points, which function as the pivotal positioning nodes. Through the application of trilateration techniques, based on the calculated distances between these positioning nodes and the integrated sensors on the drone or metaverse user side, we facilitate robust three-dimensional localization. This strategic approach empowers us to realize our ambition of creating localization systems that not only compensate for the absence of GPS signals but also deliver unparalleled accuracy and reliability in complex and dynamic indoor environments. A significant challenge that we confronted during our research pertained to the disparity in z-axis localization performance compared to that of the x-y plane. This nuanced yet pivotal concern often remains overlooked in much of the prevailing state-of-the-art literature, which predominantly emphasizes two-dimensional localization methodologies. Given the demanding context of our work, where drones and metaverse users navigate dynamically across all three dimensions, the imperative for three-dimensional localization became evident. To address this, we embarked on a comprehensive analysis, encompassing mathematical derivations of error bounds for our proposed localization systems. Our investigations unveiled that localization errors trace their origins to two distinct sources: errors induced by ranging-based factors and errors stemming from geometric considerations. The former category is chiefly influenced by factors encompassing the quality of measurement devices, channel quality in which the signal communication between the sensor on the user and the positioning nodes takes place, environmental noise, multipath interference, and more. In contrast, the latter category, involving geometry-induced errors, arises primarily from the spatial configuration of the positioning nodes relative to the user. Throughout our journey, we dedicated efforts to mitigate both sources of error, ensuring the robustness of our system against diverse error origins. Our approach entails a two-fold strategy for each proposed localization system. Firstly, we introduce innovative techniques such as Frequency-Hopping Spread Spectrum (FHSS) and Frequency-Hopping Code Division Multiple Access (FH-CDMA) and incorporate devices such as Reconfigurable Intelligent Surfaces (RIS) and photonic oscillators to fortify the system against errors stemming from ranging-related factors. Secondly, we devised novel evolutionary-based optimization algorithms, adept at addressing the complex NP-Hard challenge of optimal positioning node placement. This strategic placement mitigates the impact of geometry-induced errors on localization accuracy across the entire environmental space. By meticulously addressing both these sources of error, our localization systems stand as a testament to comprehensive robustness and accuracy. Our methodologies not only extend the frontiers of three-dimensional localization but also equip the systems to navigate the intricacies of indoor environments with precision and reliability, effectively fulfilling the evolving demands of drone navigation and metaverse user interaction. / Doctor of Philosophy / In this dissertation, we first explore some promising substitutes for the Global Positioning System (GPS) for the autonomous navigation of drones and metaverse user positioning in indoor spaces. Then, we will make the scope of research more comprehensive and try to explore substitutes to GPS for autonomous navigation of drones in general, both in indoor environments and outdoors. For the first part, we make our small indoor GPS. Similar to GPS, in our system, a receiver onboard the drone or the metaverse user can receive signals from our small semi-satellites in the room, and with that, it can localize itself. The idea is very similar to how the well-known GPS works, with some modifications. Unlike the GPS, we are using acoustic ultrasound signals or some RF signal based on 5G or Wi-Fi for transmission. Also, we have more freedom compared to GPS because, in GPS, they have to transmit signals from far ahead distances, whereas, in our scenario, it is just a room in which we put all of our semi-satellite transmitters. Moreover, we can put them anywhere we want in the room. This is, in fact important, because the positions of these semi-satellites have a huge effect on the accuracy of our system. Also, we can decide how many of them we need to cover every point in the room and not have any blind spots. We propose our novel techniques for finding the optimal placement to improve localization accuracy. In GPS, they propose a technique that is suitable for the case of those satellites and their distance to the targets. Similarly, we offer our novel techniques to have a robust transmission against noise and other factors and guarantee a localization scheme with high accuracy. All being said, our proposed system for indoor localization of drones and metaverse users in three dimensions has considered all the possible sources of error and proposed solutions to conquer them; hence a robust system with high accuracy in three-dimensional space.

<p>I studied acoustic and visual communication in the Smooth-billed Ani, a joint-nesting, cooperatively breeding cuckoo. I describe vocal repertoire of this species using both qualitative and quantitative methods. In this first, formal description of the species’ repertoire, I provide verbal descriptions of each call type, the contexts in which each call is produced, spectrograms, and acoustic measurements for each call type. I used multivariate statistics to show that call types can be correctly classified based on acoustic measurements alone. Smooth-billed Anis are capable of complex communication, including the use of functionally referential alarms and signals of aggression that reliably predict attack. Functionally referential signals are produced in response to a specific set of stimuli and elicit predictable, appropriate responses in signal receivers, even in the absence of any other cues. I show that anis produce two distinct signal types, <em>chlurps</em> and <em>ahnee</em> <em>alarms</em>, in response to two different predator classes, aerial and terrestrial, respectively. I also show that receiver responses to playback of these alarm signals are distinct and appropriate to evade predation from aerial and terrestrial attackers. Aggressive signals should increase in aggressive contexts, predict subsequent aggression and elicit responses from signal receivers. I show that <em>hoots</em>, an acoustic signal, and throat inflation, a visual signal, both increase in aggressive contexts and reliably predict aggressive escalation in the form of direct attacks on a mount. The receiver response to <em>hoots</em> and throat inflation remains to be tested. In the synthesis, I provide suggestions for future research.</p> / Master of Science (MSc)

For most of the naturally occurring signals, especially biomedical signals, the underlying physical process generating the signal is often not fully known, making it difficult to obtain a parametric model. Therefore, signal processing techniques are used to analyze the signal for non-parametrically characterizing the underlying system from which the signals are produced. Most of the real life systems are nonlinear and time varying, which poses a challenge while characterizing them. Additionally, multiple sensors are used to extract signals from such systems, resulting in multichannel signals which are inherently coupled. In this thesis, we counter this challenge by using Recurrence Plot based techniques for characterizing biomedical systems such as heart or brain, using signals such as heart rate variability (HRV), electroencephalogram(EEG) or functional magnetic resonance imaging (fMRI), respectively, extracted from them. In time series analysis, it is well known that a system can be represented by a trajectory in an N-dimensional state space, which completely represents an instance of the system behavior. Such a system characterization has been done using dynamical invariants such as correlation dimension, Lyapunov exponent etc. Takens has shown that when the state variables of the underlying system are not known, one can obtain a trajectory in ‘phase space’ using only the signals obtained from such a system. The phase space trajectory is topologically equivalent to the state space trajectory. This enables us to characterize the system behavior from only the signals sensed from them. However, estimation of correlation dimension, Lyapunov exponent, etc, are vulnerable to non-stationarities in the signal and require large number of sample points for accurate computation, both of which are important in the case of biomedical signals. Alternatively, a technique called Recurrence Plots (RP) has been proposed, which addresses these concerns, apart from providing additional insights. Measures to characterize RPs of single and two channel data are called Recurrence Quantification Analysis (RQA) and cross RQA (CRQA), respectively. These methods have been applied with a good measure of success in diverse areas. However, they have not been studied extensively in the context of experimental biomedical signals, especially multichannel data. In this thesis, the RP technique and its associated measures are briefly reviewed. Using the computational tools developed for this thesis, RP technique has been applied on select single channel, multichannel and multimodal (i.e. multiple channels derived from different modalities) biomedical signals. Connectivity analysis is demonstrated as post-processing of RP analysis on multichannel signals such as EEG and fMRI. Finally, a novel metric, based on the modification of a CRQA measure is proposed, which shows improved results. For the case of single channel signal, we have considered a large database of HRV signals of 112 subjects recorded for both normal and abnormal (anxiety disorder and depression disorder) subjects, in both supine and standing positions. Existing RQA measures, Recurrence Rate and Determinism, were used to distinguish between normal and abnormal subjects with an accuracy of 58.93%. A new measure, MLV has been introduced, using which a classification accuracy of 98.2% is obtained. Correlation between probabilities of recurrence (CPR) is a CRQA measure used to characterize phase synchronization between two signals. In this work, we demonstrate its utility with application to multimodal and multichannel biomedical signals. First, for the multimodal case, we have computed running CPR (rCPR), a modification proposed by us, which allows dynamic estimation of CPR as a function of time, on multimodal cardiac signals (electrocardiogram and arterial blood pressure) and demonstrated that the method can clearly detect abnormalities (premature ventricular contractions); this has potential applications in cardiac care such as assisted automated diagnosis. Second, for the multichannel case, we have used 16 channel EEG signals recorded under various physiological states such as (i) global epileptic seizure and pre-seizure and (ii) focal epilepsy. CPR was computed pair-wise between the channels and a CPR matrix of all pairs was formed. Contour plot of the CPR matrix was obtained to illustrate synchronization. Statistical analysis of CPR matrix for 16 subjects of global epilepsy showed clear differences between pre-seizure and seizure conditions, and a linear discriminant classifier was used in distinguishing between the two conditions with 100% accuracy. Connectivity analysis of multichannel EEG signals was performed by post-processing of the CPR matrix to understand global network-level characterization of the brain. Brain connectivity using thresholded CPR matrix of multichannel EEG signals showed clear differences in the number and pattern of connections in brain connectivity graph between epileptic seizure and pre-seizure. Corresponding brain headmaps provide meaningful insights about synchronization in the brain in those states. K-means clustering of connectivity parameters of CPR and linear correlation obtained from global epileptic seizure and pre-seizure showed significantly larger cluster centroid distances for CPR as opposed to linear correlation, thereby demonstrating the efficacy of CPR. The headmap in the case of focal epilepsy clearly enables us to identify the focus of the epilepsy which provides certain diagnostic value. Connectivity analysis on multichannel fMRI signals was performed using CPR matrix and graph theoretic analysis. Adjacency matrix was obtained from CPR matrices after thresholding it using statistical significance tests. Graph theoretic analysis based on communicability was performed to obtain community structures for awake resting and anesthetic sedation states. Concurrent behavioral data showed memory impairment due to anesthesia. Given the fact that previous studies have implicated the hippocampus in memory function, the CPR results showing the hippocampus within the community in awake state and out of it in anesthesia state, demonstrated the biological plausibility of the CPR results. On the other hand, results from linear correlation were less biologically plausible. In biological systems, highly synchronized and desynchronized systems are of interest rather than moderately synchronized ones. However, CPR is approximately a monotonic function of synchronization and hence can assume values which indicate moderate synchronization. In order to emphasize high synchronization/ desynchronization and de-emphasize moderate synchronization, a new method of Correlation Synchronization Convergence Time (CSCT) is proposed. It is obtained using an iterative procedure involving the evaluation of CPR for successive autocorrelations until CPR converges to a chosen threshold. CSCT was evaluated for 16 channel EEG data and corresponding contour plots and histograms were obtained, which shows better discrimination between synchronized and asynchronized states compared to the conventional CPR. This thesis has demonstrated the efficacy of RP technique and associated measures in characterizing various classes of biomedical signals. The results obtained are corroborated by well known physiological facts, and they provide physiologically meaningful insights into the functioning of the underlying biological systems, with potential diagnostic value in healthcare.

This thesis examines the origins and development of signals intelligence in the Royal Australian Navy, during the period 1914 to 1945, within the context of an Australian contribution to Imperial defence. In doing so it demonstrates how the development of this capability was shaped by national, Imperial and international forces and events. The thesis thus fills a gap in the historiography of imperial defence and of early twentieth century signals intelligence. It also constitutes a case study of the development of a niche military capability by a small to medium power in the context of great power alliances and major historical events. The thesis is based principally upon the investigation of documents in the Australian, US and UK national archives, some of which have been newly declassified for this purpose. During the First World War the RAN undertook a minor cryptographic effort focused on intercepting and breaking coded messages from the German Pacific Squadron. After the War, and at the request of the RN, the RAN began to develop a signals intelligence capability aimed at the Imperial Japanese Navy. This capability was seen as part of the RAN contribution to Imperial defence. The commitment, made without Australian political approval, would see the RAN conduct two covert intelligence collection operations against the Japanese Mandated Territories. After the Japanese attack on Pearl Harbor and subsequent defeat of the Western Powers in Asia, the RAN signals intelligence organisation became, as a consequence of agreements between Britain and the USN, part of the USN organisation in the Pacific. At no stage however, was the RAN involved in the discussions which accompanied these arrangements nor did it have any subsequent say in the strategic direction of this capability. As a consequence, when the Pacific War was drawing to a close the future of the RAN's cryptographic organisation came in to question. By the time the Japanese surrendered this issue had still not been resolved. Beyond the history of the origins and development of signals intelligence in the RAN, and of its involvement in the signals intelligence war against Japan, the thesis highlights the importance of committed individuals in small military organisations and how they can greatly influence the success or otherwise of these organisations. The ability of personnel from different nations to work together in signals intelligence is reflective of the functioning of the alliance as a whole. The development of such a niche capability by a small to medium power can have an effect on that nation's standing, in the context of alliance relationships, as it did in this case. As the RAN found however, such capabilities do not provide for automatic access to strategic decision making within an alliance.