Drones, Signals, and the Techno-Colonisation of Landscape

This research project is a cross-disciplinary, creative practice-led investigation that interrogates increasing military interest in the electromagnetic spectrum (EMS). The project’s central argument is that painted visualisations of normally invisible aspects of contemporary EMS-enabled warfare can reveal useful, novel, and speculative but informed perspectives that contribute to debates about war and technology. It pays particular attention to how visualising normally invisible signals reveals an insidious techno-colonisation of our extended environment from Earth to orbiting satellites

Diagnostic assistance to improve acute burn referral and triage : assessment of routine clinical tools at specialised burn centres and potential for digital health development at point of care

Background: Inappropriate referral of patients for specialised care leads to overburdened health systems and improper treatment of patients who are denied transfer due to a scarcity of resources. Burn injuries are a global health problem where specialised care is particularly important for severe cases while minor burns can be treated at point of care. Whether several solutions, existing or in development, could be used to improve the diagnosis, referral and triage of acute burns at admission to specialised burn centres remains to be evaluated. Aim: The overarching aim of this thesis is to determine the potential of diagnostic support tools for referral and triage of acute burns injuries. More specifically, sub-aims include the assessment of routine and digital health tools utilised in South Africa and Sweden: referral criteria, mortality prediction scores, image-based remote consultation and automated diagnosis. Methods: Studies I and II were two retrospective studies of patients admitted to the paediatric (I) and the adult (II) specialised burn centres of the Western Cape province in South Africa. Study I examined adherence to referral criteria at admission of 1165 patients. Logistic regression was performed to assess the associations between adherence to the referral criteria and patient management at the centre. Study II assessed mortality prediction at admission of 372 patients. Logistic regression was performed to evaluate associations between patient, injury and admission-related characteristics with mortality. The performance of an existing mortality prediction model (the ABSI score) was measured. Study III and IV were related to two image-based digital-health tools for remote diagnosis. In Study III, 26 burns experts provided a diagnosis in terms of burn size and depth for 51 images of acute burn cases using their smartphone or tablet. Diagnostic accuracy was measured with intraclass correlation coefficient. In Study IV, two deep-learning algorithms were developed using 1105 annotated acute burn images of cases collected in South Africa and Sweden. The first algorithm identifies a burn area from healthy skin, and the second classifies burn depth. Differences in performances by patient Fitzpatrick skin types were also measured. Results: Study I revealed a 93.4% adherence to the referral criteria at admission. Children older than two years (not fulfilling the age criterion) as well as those fulfilling the severity criterion were more likely to undergo surgery or stay longer than seven days at the centre. At the adult burn centre (Study II), mortality affected one in five patients and was associated with gender, burn size, and referral status after adjustments for all other variables. The ABSI score was a good estimate of mortality prediction. In Study III experts were able to accurately diagnose burn size, and to a lesser extent depth, using handheld devices. A wound identifier and a depth classifier algorithm could be developed with assessments of relatively high accuracy (Study IV). Differences were observed in performances by skin types of the patients. Conclusions: Altogether the findings inform on the use in clinical practice of four different tools that could improve the accuracy of the diagnosis, referral and triage of patients with acute burns. This would reduce inequities in access to care by improving access for both paediatric and adult patient populations in settings that are resource scarce, geographically distant or under high clinical pressure

Karl E. Peace papers

This collection consists of the personal and research papers of Karl E. Peace, Professor of Biostatistics at Georgia Southern University and namesake of the Karl E. Peace Center for Biostatistics and Survey Research. Materials span 1941to 2018 and include, correspondence, teaching materials, published articles, and manuscripts. A small portion of 3 photographs and artists renderings are also included. This collection is still undergoing processing. Find this collection in the University Libraries\u27 catalog.https://digitalcommons.georgiasouthern.edu/finding-aids/1100/thumbnail.jp

Service Abstractions for Scalable Deep Learning Inference at the Edge

Deep learning driven intelligent edge has already become a reality, where millions of mobile, wearable, and IoT devices analyze real-time data and transform those into actionable insights on-device. Typical approaches for optimizing deep learning inference mostly focus on accelerating the execution of individual inference tasks, without considering the contextual correlation unique to edge environments and the statistical nature of learning-based computation. Specifically, they treat inference workloads as individual black boxes and apply canonical system optimization techniques, developed over the last few decades, to handle them as yet another type of computation-intensive applications. As a result, deep learning inference on edge devices still face the ever increasing challenges of customization to edge device heterogeneity, fuzzy computation redundancy between inference tasks, and end-to-end deployment at scale. In this thesis, we propose the first framework that automates and scales the end-to-end process of deploying efficient deep learning inference from the cloud to heterogeneous edge devices. The framework consists of a series of service abstractions that handle DNN model tailoring, model indexing and query, and computation reuse for runtime inference respectively. Together, these services bridge the gap between deep learning training and inference, eliminate computation redundancy during inference execution, and further lower the barrier for deep learning algorithm and system co-optimization. To build efficient and scalable services, we take a unique algorithmic approach of harnessing the semantic correlation between the learning-based computation. Rather than viewing individual tasks as isolated black boxes, we optimize them collectively in a white box approach, proposing primitives to formulate the semantics of the deep learning workloads, algorithms to assess their hidden correlation (in terms of the input data, the neural network models, and the deployment trials) and merge common processing steps to minimize redundancy

Interaction Design for Digital Musical Instruments

The thesis aims to elucidate the process of designing interactive systems for musical performance that combine software and hardware in an intuitive and elegant fashion. The original contribution to knowledge consists of: (1) a critical assessment of recent trends in digital musical instrument design, (2) a descriptive model of interaction design for the digital musician and (3) a highly customisable multi-touch performance system that was designed in accordance with the model. Digital musical instruments are composed of a separate control interface and a sound generation system that exchange information. When designing the way in which a digital musical instrument responds to the actions of a performer, we are creating a layer of interactive behaviour that is abstracted from the physical controls. Often, the structure of this layer depends heavily upon: 1. The accepted design conventions of the hardware in use 2. Established musical systems, acoustic or digital 3. The physical configuration of the hardware devices and the grouping of controls that such configuration suggests This thesis proposes an alternate way to approach the design of digital musical instrument behaviour – examining the implicit characteristics of its composite devices. When we separate the conversational ability of a particular sensor type from its hardware body, we can look in a new way at the actual communication tools at the heart of the device. We can subsequently combine these separate pieces using a series of generic interaction strategies in order to create rich interactive experiences that are not immediately obvious or directly inspired by the physical properties of the hardware. This research ultimately aims to enhance and clarify the existing toolkit of interaction design for the digital musician

Digital Art as ‘Monetised Graphics:’ Enforcing Intellectual Property on the Blockchain

In a global economic landscape of hyper-commodification and financialisation, efforts to assimilate digital art into the high-stakes commercial art market have so far been rather unsuccessful, presumably because digital art works cannot easily assume the status of precious object worthy of collection. This essay explores the use of blockchain technologies in attempts to create proprietary digital art markets in which uncommodifiable digital art works are financialised as artificially scarce commodities. Using the decentralisation techniques and distributed database protocols underlying current cryptocurrency technologies, such efforts, exemplified here by the platform Monegraph, tend to be presented as concerns with the interest of digital artists and with shifting ontologies of the contemporary work of art. I challenge this characterisation, and argue, in a discussion that combines aesthetic theory, legal and philosophical theories of intellectual property, rhetorical analysis, and research in the political economy of new media, that the formation of proprietary digital art markets by emerging commercial platforms such as Monegraph constitutes a worrisome amplification of long-established, on-going efforts to fence in creative expression as private property. As I argue, the combination of blockchain-based protocols with established ambitions of intellectual property policy yields hybrid conceptual-computational financial technologies (such as self-enforcing smart contracts attached to digital artefacts) that are unlikely to empower artists, but which serve to financialise digital creative practices as a whole, curtailing the critical potential of the digital as an inherently dynamic and potentially uncommodifiable mode of production and artistic expression