The Virginia Tech Center for Wireless Telecommunications' (CWT) Public Safety Cognitive Radio (PSCR) addresses the radio interoperability issues that plague many of the existing public safety radios — disparate frequency bands, incompatible modulation schemes and lack of active channel detection features. The PSCR allows the operator to scan for active channels, classify the detected channels, connect to any of the recognized waveforms and begin analog audio communication as well as bridge two incompatible waveforms together. The PSCR, although very useful, unfortunately is not portable enough to be used by public safety officials. The power requirement, processing requirement and equipment is respectively large, hungry and bulky. In this thesis, a possible solution to the portability problem is addressed by installing the PSCR in a public safety vehicle and using a Personal Digital Assistant (PDA) for remote access. The PDA allows the user to remotely scan, classify, talk, and bridge waveforms similar in operation to the PSCR. An ergonomically designed interface masks the channel and modulation selection procedure. This architecture can be extended to offer service to any remotely connected device. In the second part of this thesis, the concept of remote access is extended to a wide-area wireless public safety network. A public safety network consisting of heterogeneous devices is proposed utilizing a small number of backbone nodes. The major research focus of this section is the algorithm for distributing services across the network. Service discovery is optimized to reduce the overhead of service messages and multiple service distribution techniques are utilized depending on the location of the services. Simulation is performed to evaluate the performance of the service discovery protocol in terms of overhead, dissemination time and scalability. The proposed protocol is determined to be superior to the competition in the overhead and scalability tests. / Master of Science

Causal discovery is the problem of learning causal relationships between variables from a set of data. One interesting area of use for causal discovery is the health care domain, where application could help facilitate a better understanding of disease and treatment mechanisms. The health care domain has recently undergone a major digitization, making available a large amount of data for use in learning algorithms, available in formats such as medical images or electronic health records. This thesis aims to explore the application of causal discovery on electronic health record data. We provide an overview of the field of causal discovery and identify 3 contemporary methods for causal discovery on time-series data which we apply on a preprocessed version of the MIMIC-IV data set. Each causal discovery method is run on time-series comprising of electronic health record data related to hospital stays for patients with sepsis. We provide an empiric report of the overlap between the learned graphs from different hospital stays as a heuristic evaluation measure. We find that it is possible to identify common themes in the learned graphs between different causal discovery methods, indicating potential practical value of causal discovery on electronic health record data. We also identify important considerations for future application and evaluation, such as incorporating extensive domain knowledge, and provide suggestions for future work. / Kausal upptäckt är problemet med att lära sig orsakssamband mellan variabler från en uppsättning data. Ett intressant användningsområde för kausal upptäckt är hälso- och sjukvårdsdomänen, där tillämpning kan bidra till en bättre förståelse av sjukdomar och behandlingsmekanismer. Sjukvårdsdomänen har nyligen genomgått en stor digitalisering vilket gör en stor mängd data tillgänglig för användning i inlärningsalgoritmer, tillgänglig i format som medicinska bilder eller elektroniska patientjournaler. Denna avhandling syftar till att utforska tillämpningen av kausal upptäckt på elektroniska patientjournaler. Vi ger en översikt över området för kausal upptäckt och identifierar 3 samtida metoder för kausal upptäckt på tidsseriedata som vi tillämpar på en förbearbetad version av MIMIC-IV-datauppsättningen. Varje identifierad metod för kausal upptäckt körs på tidsserier som består av elektroniska patientjournaler relaterade till sjukhusvistelser för patienter med sepsis. Vi tillhandahåller en empirisk rapport över överlappningen mellan de inlärda graferna från olika sjukhusvistelser som ett heuristiskt utvärderingsmått. Vi finner att det är möjligt att identifiera gemensamma teman i de inlärda graferna mellan olika kausala upptäcktsmetoder, vilket indikerar potentiellt praktiskt värde av kausal upptäckt på elektroniska patientjournaler. Vi identifierar också viktiga överväganden för framtida tillämpning och utvärdering, såsom att integrera omfattande domänkunskap, och ger förslag för framtida arbete.

This research study is conducted to test Workman, Fiszman, Rindflesch and Nahl's information flow-serendipitous knowledge discovery (IF-SKD) model of information behavior, in a clinical care context. To date, there have been few attempts to model the serendipitous knowledge discovery of physicians. Due to the growth and complexity of the biomedical literature, as well as the increasingly specialized nature of medicine, there is a need for advanced systems that can quickly present information and assist physicians to discover new knowledge. The National Library of Medicine's (NLM) Lister Hill Center for Biocommunication's Semantic MEDLINE project is focused on identifying and visualizing semantic relationships in the biomedical literature to support knowledge discovery. This project led to the development of a new information discovery system, Spark. The aim of Spark is to promote serendipitous knowledge discovery by assisting users in maximizing the use of their conceptual short-term memory to iteratively search for, engage, clarify and evaluate information presented from the biomedical literature. Using Spark, this study analyzes the IF- SKD model by capturing and analyzing physician feedback. The McCay-Peet, Toms and Kelloway's Perception of Serendipity and Serendipitous Digital Environment (SDE) questionnaires are used. Results are evaluated to determine whether Spark contributes to physicians' serendipitous knowledge discovery and the ability of the IF-SKD ability to capture physicians' information behavior in a clinical setting.

As an artist, my work reflects my own life experiences, allowing me to reinterpret and process difficult events in a new light. Creating art is a therapeutic process for me, enabling me to explore and understand my past and my own Self. In line with James Baldwin's views, I believe that the duty of an artist is to provide their audience with an opportunity to rediscover themselves; to help them explore their inner selves. In my experience, to achieve that goal, the first and most important hurdle the artist needs to cross is exploring themselves. In the process of answering questions about their own selves, they can touch many other souls. In Fragmented Self, I employ composited 3D animations of my own body parts juxtaposed over still and moving images. Each body part and piece in Fragmented Self is a metaphorical representation of a specific experience I have lived through. The resulting pieces are meditative, surreal, and abstracted spaces that speak to the complexities of life experiences. I believe each body holds messages from the past, and in Fragmented Self, I disembody and fragment my own body to study and explore my own Self. Drawing inspiration from Walt Whitman's Leaves of Grass in which he proclaims, "I contain multitudes", I see my Self as a composite of various selves shaped by different life experiences coming together to form one Self. I believe that I am a constantly evolving individual, influencing my everyday encounters and choices. As in the Japanese tradition of Kintsugi, in Fragmented Self, I trace the gold lined cracks that unite my multitudinous selves into one in hopes of answering the question "What makes me who I am today?" / Master of Fine Arts / Fragmented Self is a body of artwork created in an effort to learn about my own Self. The work explores how I see my Self as containing multiple selves. The project utilizes video and 3D rendering to create digital composites with semi-realistic aesthetics. The finished work includes 3 pieces focusing on ideas of time, perception, and fragmentation.

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