688 research outputs found

    Southern Adventist University Undergraduate Catalog 2023-2024

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    Southern Adventist University\u27s undergraduate catalog for the academic year 2023-2024.https://knowledge.e.southern.edu/undergrad_catalog/1123/thumbnail.jp

    2023-2024 Catalog

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    The 2023-2024 Governors State University Undergraduate and Graduate Catalog is a comprehensive listing of current information regarding:Degree RequirementsCourse OfferingsUndergraduate and Graduate Rules and Regulation

    Southern Adventist University Undergraduate Catalog 2022-2023

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    Southern Adventist University\u27s undergraduate catalog for the academic year 2022-2023.https://knowledge.e.southern.edu/undergrad_catalog/1121/thumbnail.jp

    Synchronization of data in heterogeneous decentralized systems

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    Data synchronization is the problem of reconciling the differences between large data stores that differ in a small number of records. It is a common thread among disparate distributed systems ranging from fleets of Internet of Things (IoT) devices to clusters of distributed databases in the cloud. Most recently, data synchronization has arisen in globally distributed public blockchains that build the basis for the envisioned decentralized Internet of the future. Moreover, the parallel development of edge computing has significantly increased the heterogeneity of networks and computing devices. The merger of highly heterogeneous system resources and the decentralized nature of future Internet applications calls for a new approach to data synchronization. In this dissertation, we look at the problem of data synchronization through the prism of set reconciliation and introduce novel tools and protocols that improve the performance of data synchronization in heterogeneous decentralized systems. First, we compare the analytical properties of the state-of-the-art set reconciliation protocols, and investigate the impact of theoretical assumptions and implementation decisions on the synchronization performance. Second, we introduce GenSync, the first unified set reconciliation middleware. Using GenSync's distinctive benchmarking layer, we find that the best protocol choice is highly sensitive to the system conditions, and a bad protocol choice causes a severe hit in performance. We showcase the evaluative power of GenSync in one of the world's largest wireless network emulators, and demonstrate choosing the best GenSync protocol under a high and low user mobility in an emulated cellular network. Finally, we introduce SREP (Set Reconciliation-Enhanced Propagation), a novel blockchain transaction pool synchronization protocol with quantifiable guarantees. Through simulations, we show that SREP incurs significantly smaller bandwidth overhead than a similar approach from the literature, especially in the networks of realistic sizes (tens of thousands of participants)

    COCAM: a cooperative video edge caching and multicasting approach based on multi-agent deep reinforcement learning in multi-clouds environment

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    The evolution of the Internet of Things technology (IoT) has boosted the drastic increase in network traffic demand. Caching and multicasting in the multi-clouds scenario are effective approaches to alleviate the backhaul burden of networks and reduce service latency. However, existing works do not jointly exploit the advantages of these two approaches. In this paper, we propose COCAM, a cooperative video edge caching and multicasting approach based on multi-agent deep reinforcement learning to minimize the transmission number in the multi-clouds scenario with limited storage capacity in each edge cloud. Specifically, by integrating a cooperative transmission model with the caching model, we provide a concrete formulation of the joint problem. Then, we cast this decision-making problem as a multi-agent extension of the Markov decision process and propose a multi-agent actor-critic algorithm in which each agent learns a local caching strategy and further encompasses the observations of neighboring agents as constituents of the overall state. Finally, to validate the COCAM algorithm, we conduct extensive experiments on a real-world dataset. The results show that our proposed algorithm outperforms other baseline algorithms in terms of the number of video transmissions

    Security and Privacy for Modern Wireless Communication Systems

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    The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

    Predictive QoS for cellular connected UAV payload communication

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    Unmanned aerial vehicles (UAVs), or drones, are revolutionizing industries due to their versatility, affordability and applicability. Reliable communication links are essential for UAV operations, especially for beyond visual line of sight scenarios where drones are flown beyond the operator’s line of sight. Cellular networks, particularly in the context of 5G and beyond, offer potential solutions to meet the data-intensive demands of UAV applications. This study explores the feasibility of predictive quality of service for forecasting uplink (UL) throughput quality of service (QoS) parameter in UAV payload communication links. Comprehensive field tests were conducted to ensure accurate real-world results, as simulations may not fully capture real-world complexities. Field trial measurements were conducted in a sub-urban area to evaluate drone performance at various altitudes and bands. This sheds light on potential challenges and trade-offs for cellular-connected drones and their coexistence with terrestrial users. Drones flying at high altitudes often experience line of sight propagation, causing them to undergo frequent handovers between multiple base stations. Field trials demonstrated that drones connected to a 700 MHz signal encountered minimal interference and no handovers. Conversely, drones connected to the 3500 MHz frequency band faced multiple handovers, highlighting the complexities of UAV-cellular integration and emphasizing the significance of frequency band selection in drone applications. By harnessing machine learning (ML) models and comparative analysis of centralized and federated learning methods, this research investigates ML model performances in forecasting UL throughput based on prediction accuracy. The findings emphasize the importance of diverse training data and highlight the impact of frequency bands on UAV communication. These insights lay the groundwork for addressing UAV communication complexities and advancing the integration of machine learning and network dynamics for improving UAV operations

    Understanding building and urban environment interactions: An integrated framework for building occupancy modelling

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    Improving building energy efficiency requires accurate modelling and a comprehensive understanding of how occupants use building space. This thesis focuses on modelling building occupancy to enhance the predictive accuracy of occupancy patterns and gain a better understanding of the causal reasons for occupancy behaviour. A conceptual framework is proposed to relax the restriction of isolated building analysis, which accounts for interactions between buildings, its occupants, and other urban systems, such as the effects of transport incidents on occupancy and circulation in buildings. This thesis also presents a counterpart mapping of the framework that elaborates the links between modelling of transport and building systems. To operationalise the proposed framework, a novel modelling approach which has not been used in the current context, called the hazard-based model, is applied to model occupancy from a single building up to a district area. The proposed framework is further adapted to integrate more readily with transport models, to ensure that arrivals and departures to and from the building are consistent with the situation of the surrounding transport systems. The proposed framework and occupancy models are calibrated and validated using Wi-Fi data and other variables, such as transport and weather parameters, harvested from the South Kensington campus of Imperial College London. In addition to calibrating the occupancy model, integrating a travel simulator produces synthetic arrivals into or around the campus, which are further distributed over campus buildings via an adapted technique and feed the occupancy simulations. The model estimation results reveal the causal reasons for or exogenous effects on individual occupancy states. The validation results confirm the ability of the proposed models to predict building occupancy accurately both on average and day by day across the future dataset. Finally, evaluating occupancy simulations for various hypothetical scenarios provides valuable suggestions for efficient building design and facility operation.Open Acces

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    Undergraduate and Graduate Course Descriptions, 2023 Spring

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    Wright State University undergraduate and graduate course descriptions from Spring 2023
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