Detection Strategies and Intercept Metrics for Intra-Pulse Radar-Embedded Communications

This thesis presents various detection strategies and intercept metrics to evaluate and design an intra-pulse radar-embedded communication system. This system embeds covert communication symbols in masking interference provided by the reflections of a pulsed radar emission. This thesis considers the case where the communicating device is a transponder or tag present in an area that is illuminated by a radar. The radar is considered to be the communication receiver. As with any communication system, performance (as measured by reliability and data rate) should be maximized between the tag and radar. However, unlike conventional communication systems, the symbols here should also have a low-probability of intercept (LPI). This thesis examines the trade-offs associated with the design of a practical radar-embedded communication system. A diagonally-loaded decorrelating receiver is developed and enhanced with a second stage based on the Neyman-Pearson criterion. For a practical system, the communication symbols will likely encounter multipath. The tag may then use a pre-distortion strategy known as time-reversal to improve the signal-to-noise ratio at the radar receiver thereby enhancing communication performance. The development of several intercept metrics are shown and the logic behind the design evolutions are explained. A formal analysis of the processing gain by the desired receiver relative to the intercept receivers is given. Finally, simulations are shown for all cases, to validate the design metrics

Signal Processing for Non-Gaussian Statistics: Clutter Distribution Identification and Adaptive Threshold Estimation

We examine the problem of determining a decision threshold for the binary hypothesis test that naturally arises when a radar system must decide if there is a target present in a range cell under test. Modern radar systems require predictable, low, constant rates of false alarm (i.e. when unwanted noise and clutter returns are mistaken for a target). Measured clutter returns have often been fitted to heavy tailed, non-Gaussian distributions. The heavy tails on these distributions cause an unacceptable rise in the number of false alarms. We use the class of spherically invariant random vectors (SIRVs) to model clutter returns. SIRVs arise from a phenomenological consideration of the radar sensing problem, and include both the Gaussian distribution and most commonly reported non-Gaussian clutter distributions (e.g. K distribution, Weibull distribution). We propose an extension of a prior technique called the Ozturk algorithm. The Ozturk algorithm generates a graphical library of points corresponding to known SIRV distributions. These points are generated from linked vectors whose magnitude is derived from the order statistics of the SIRV distributions. Measured data is then compared to the library and a distribution is chosen that best approximates the measured data. Our extension introduces a framework of weighting functions and examines both a distribution classification technique as well as a method of determining an adaptive threshold in data that may or may not belong to a known distribution. The extensions are then compared to neural networking techniques. Special attention is paid to producing a robust, adaptive estimation of the detection threshold. Finally, divergence measures of SIRVs are examined

Impact on Epidemic Measles of Vaccination Campaigns Triggered by Disease Outbreaks or Serosurveys: A Modeling Study.

BACKGROUND: Routine vaccination supplemented by planned campaigns occurring at 2-5 y intervals is the core of current measles control and elimination efforts. Yet, large, unexpected outbreaks still occur, even when control measures appear effective. Supplementing these activities with mass vaccination campaigns triggered when low levels of measles immunity are observed in a sample of the population (i.e., serosurveys) or incident measles cases occur may provide a way to limit the size of outbreaks. METHODS AND FINDINGS: Measles incidence was simulated using stochastic age-structured epidemic models in settings conducive to high or low measles incidence, roughly reflecting demographic contexts and measles vaccination coverage of four heterogeneous countries: Nepal, Niger, Yemen, and Zambia. Uncertainty in underlying vaccination rates was modeled. Scenarios with case- or serosurvey-triggered campaigns reaching 20% of the susceptible population were compared to scenarios without triggered campaigns. The best performing of the tested case-triggered campaigns prevent an average of 28,613 (95% CI 25,722-31,505) cases over 15 y in our highest incidence setting and 599 (95% CI 464-735) cases in the lowest incidence setting. Serosurvey-triggered campaigns can prevent 89,173 (95% CI, 86,768-91,577) and 744 (612-876) cases, respectively, but are triggered yearly in high-incidence settings. Triggered campaigns reduce the highest cumulative incidence seen in simulations by up to 80%. While the scenarios considered in this strategic modeling exercise are reflective of real populations, the exact quantitative interpretation of the results is limited by the simplifications in country structure, vaccination policy, and surveillance system performance. Careful investigation into the cost-effectiveness in different contexts would be essential before moving forward with implementation. CONCLUSIONS: Serologically triggered campaigns could help prevent severe epidemics in the face of epidemiological and vaccination uncertainty. Hence, small-scale serology may serve as the basis for effective adaptive public health strategies, although, in high-incidence settings, case-triggered approaches are likely more efficient

Greenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly Detection

This short paper describes our ongoing research on Greenhouse - a zero-positive machine learning system for time-series anomaly detection

Precision and Recall for Range-Based Anomaly Detection

Classical anomaly detection is principally concerned with point- based anomalies, anomalies that occur at a single data point. In this paper, we present a new mathematical model to express range- based anomalies, anomalies that occur over a range (or period) of time

Why do some coronaviruses become pandemic threats when others do not?

Despite multiple spillover events and short chains of transmission on at least 4 continents, Middle East Respiratory Syndrome Coronavirus (MERS-CoV) has never triggered a pandemic. By contrast, its relative, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has, despite apparently little, if any, previous circulation in humans. Resolving the unsolved mystery of the failure of MERS-CoV to trigger a pandemic could help inform how we understand the pandemic potential of pathogens, and probing it underscores a need for a more holistic understanding of the ways in which viral genetic changes scale up to population-level transmission

Seven challenges in modeling vaccine preventable diseasesC

Vaccination has been one of the most successful public health measures since the introduction of basic sanitation. Substantial mortality and morbidity reductions have been achieved via vaccination against many infections, and the list of diseases that are potentially controllable by vaccines is growing steadily. We introduce key challenges for modelling in shaping our understanding and guiding policy decisions related to vaccine preventable diseases

Quantum teleportation on a photonic chip

Quantum teleportation is a fundamental concept in quantum physics which now finds important applications at the heart of quantum technology including quantum relays, quantum repeaters and linear optics quantum computing (LOQC). Photonic implementations have largely focussed on achieving long distance teleportation due to its suitability for decoherence-free communication. Teleportation also plays a vital role in the scalability of photonic quantum computing, for which large linear optical networks will likely require an integrated architecture. Here we report the first demonstration of quantum teleportation in which all key parts - entanglement preparation, Bell-state analysis and quantum state tomography - are performed on a reconfigurable integrated photonic chip. We also show that a novel element-wise characterisation method is critical to mitigate component errors, a key technique which will become increasingly important as integrated circuits reach higher complexities necessary for quantum enhanced operation.Comment: Originally submitted version - refer to online journal for accepted manuscript; Nature Photonics (2014

'Signs of churning': Muslim Personal Law and public contestation in twenty-first century India

Copyright © Cambridge University Press 2009. Published version reproduced with the permission of the publisher.For many Indian Muslims, the preservation of Muslim Personal Law has been the touchstone of their capacity to defend their Muslim identity. This article examines public debate over Muslim Personal Law less as a subject uniting Indian Muslims, but rather as a site in which a varied array of individuals, schools and organisations have sought to assert their individual identities. This is done through a discussion of the evolution of the All India Muslim Personal Law Board, the most authoritative such organisation since the 1970s, with particular focus on its recent fragmentation at the hands of a number of alternative legal councils formed by feminist, clerical and other groups. These organisations have justified their existence through criticism of the Board’s alleged attempts to standardisation of Islamic law and its Deobandi dominance. In truth, however, this process of fragmentation owes to a complex array of embryonic and interlinked personal, political and ideological competitions, indicative of the increasingly paradoxical process of consensus-building in contemporary Indian Muslim society