The literature on compressive parameter estimation has been mostly focused on the use of sparsity dictionaries that encode a discretized sampling of the parameter space; these dictionaries, however, suffer from coherence issues that must be controlled for successful estimation. To bypass such issues with discretization, we propose the use of statistical parameter estimation methods within the Approximate Message Passing (AMP) algorithm for signal recovery. Our method leverages the recently proposed use of custom denoisers in place of the usual thresholding steps (which act as denoisers for sparse signals) in AMP. We introduce the design of analog denoisers that are based on statistical parameter estimation algorithms, and we focus on two commonly used examples: frequency estimation and bearing estimation, coupled with the Root MUSIC estimation algorithm. We first analyze the performance of the proposed analog denoiser for signal recovery, and then link the performance in signal estimation to that of parameter estimation. Numerical experiments show significant improvements in estimation performance versus previously proposed approaches for compressive parameter estimation.

Health applications are usually complicated and low end devices do not benefit from them. The focus of this thesis is on expandable health services platform for low end cell phones. Large number of mobile phones in the world are incapable using web or modern operating systems and pre-installed SMS application is the preferred communication medium. Moreover, SMS does not require a stable connection so text-based health information can still be available even during natural disasters. Although our platform is accessible through communication forms other than SMS. We propose a scalable platform for light weight health applications, providing novel and proactive client communication. Using cloud we assure the scalability, elasticity and reliability of the server side. Our multi-layered architecture provides separation of concerns and decoupling of communication and business logic. Furthermore, plug-ins can expand and customize functionalities.

The use of natural gas as a primary energy source has increased over time and is expected to increase even further in the near future. Cyprus and Israel, two countries in the Eastern Mediterranean, have recently made major offshore discoveries of natural gas, sufficient enough to cover their own needs for at least the next few decades and use an even greater amount for export. In this project, the software MESSAGE was used to conduct modelling of the two countries’ energy systems. Projections were made until 2050 of the electricity generation in each country from each major energy source under different scenarios and the possibility of exporting electricity, liquefied natural gas (LNG) or gas-to-liquid products (GTL) was assessed.

Augmented reality, defined as a real-time direct or indirect view of a physical real-world environment that has been enhanced by adding digital computer generated information to it, is rapidly developing in terms of associated hardware (wearable displays, wireless mobile devices) and software (development platforms). AR enhanced instruction has been shown to provide cognitive and psychomotor support during procedural learning and has been shown to use both words and pictures when delivering instructional content.  A set of message design guidelines, created using a design and development research approach, can be used by novice designers to effectively manage the use of words and pictures while developing instructional applications for AR. / Ph. D.

Just nine months after the World Health Organization declared the outbreak of SARS-CoV-2 a global pandemic, the Food and Drug Administration granted emergency use authorization (EUA) for the Pfizer-BioNtech and Moderna vaccines in December 2020, followed by EUA for the Johnson & Johnson vaccine in February 2021. Although achieving herd immunity through vaccinations is the greatest hope for ending the pandemic, the COVID-19 vaccination effort has been plagued by misinformation and mistrust. Given the urgency to vaccinate the population, public health officials must construct messages that encourage individuals to obtain the COVID-19 vaccine. The current study examines the impact of linguistic assignment of agency on an individual’s desire to get vaccinated. Guided by the EPPM, participants (N= 296) were randomly assigned to receive either a virus agentic message or a human agentic message. The researchers discovered that the virus agentic message resulted in a greater intention to obtain the vaccine. Further, participants who received the virus agentic message reported a stronger sense of perceived self-efficacy and perceived susceptibility. Additionally, participants who perceived the societal reaction to the pandemic to be appropriate, as well as those who knew at least one person who had died from the virus, were more likely to express an intention to get vaccinated.

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No description available.

This project investigates the effects of social identity and personal characteristics on foreign policy decision-making. The two main US political parties, Democrats and Republicans, were used as markers for social identity. Participants were first instructed to read a scenario regarding a hostile fictional country followed by a speech regarding the same fictional country. Two speeches were created using a content analysis of speeches from Democrats and Republicans prior to the 2003 invasion of Iraq. Participants were randomly placed into one of four conditions. One condition paired a Democratic-themed speech with a Republican speaker, another paired a Democratic speech with a Democratic speaker, the third paired a Republican speech with a Democratic speaker, and the last paired a Republican speech with a Republican speaker. Participants then answered a survey that asked about their policy preferences regarding the fictional country. Manipulated variables were found to be insignificant, whereas the personality characteristics presented different levels of significance based on models of regression analysis. The data indicates that personality characteristics may be a more salient predictor of foreign policy preferences than social identity.

This thesis addresses the problem of unit testing components in message-passing systems. A message-passing system is one that comprises components communicating with each other solely via the exchange of messages. Testing aids developers in detecting and fixing potential errors and with unit testing in particular, the focus is on independently verifying the correctness of single components, such as functions and methods, in a system whose behavior is well understood. With the aid of unit testing frameworks such as those of the xUnit family, this process can not only be automated and done iteratively, but easily interleaved with the development process, facilitating rapid feedback and early detection of errors in the system. However, such frameworks work in an imperative manner and as such, are unsuitable for verifying message-passing systems where the behavior of a component is encoded in its stream of exchanged messages. In this work, we recognise that similar to streams of symbols in the field of formal languages and abstract machines, one can specify properties of a component’s message stream such that they form a language. Unit testing a component thus becomes the description of an automaton that recognizes such a specified language. We propose a platform-independent, language-recognition approach to creating unit testing frameworks for describing and verifying the behavior of message-passing components, and use this approach in creating a prototype implementation for the Kompics component model. We show that this approach can be used to perform both black box and white box testing of components, and that it is easy to work with while preventing common mistakes in practice.

The emergence of connected vehicles paradigm has made secure communication a key concern amongst the connected vehicles. Communication between the vehicles and Road Side Units (RSUs) is critical to disseminate message among the vehicles. We focus on secure message transmission in connected vehicles using multi-hop social networks environment to deliver the message with varying trustworthiness. We proposed a Geographic Social Trust Routing (GSTR) approach; messages are propagated using multiple hops and by considering the various available users in the vehicular network. GSTR is proposed in an application perspective with an assumption that the users are socially connected. The users are selected based on trustworthiness as defined by social connectivity. The route to send a message is calculated based on the highest trust level of each node by using the node's social network connections along the path in the network. GSTR determines the shortest route using the trusted nodes along the route for message dissemination. GSTR is made delay tolerant by introducing message storage in the cloud if a trustworthy node is unavailable to deliver the message. We compared the proposed approach with Geographic and Traffic Load based Routing (GTLR), Greedy Perimeter Stateless Routing (GPSR), Trust-based GPSR (T-GPSR). The performance results obtained show that GSTR ensures efficient resource utilization, lower packet losses at high vehicle densities.