4,852 research outputs found
Harbingers of A New Age: Irish and Scots Irish Indian Fighters on the Colonial American Frontier
Through the examination of various points of Irish and Scots Irish settlement in the New World, a previously underrepresented portion of American history emerges to tell the story of a hearty and industrious people who literally went out into the wilderness and settled their own communities. Through their hard work and enterprising nature, they were able to not only survive in the face of extreme adversity on the frontier, but they preserved their culture for generations and contributed to the cultural, political, military, religious, and environmental influences that shaped the New World and the American nation. Their martial prowess and military ingenuity enabled them to survive through frontier warfare, and to emerge as highly valued soldiers in North America. In doing so, they created an identity that has come to be known as uniquely American. Through an understanding of the history of the Irish history and the evolution of Irish Indian Fighters in the New World, a unique perspective of American history comes to light
Flood dynamics derived from video remote sensing
Flooding is by far the most pervasive natural hazard, with the human impacts of floods expected to worsen in the coming decades due to climate change. Hydraulic models are a key tool for understanding flood dynamics and play a pivotal role in unravelling the processes that occur during a flood event, including inundation flow patterns and velocities. In the realm of river basin dynamics, video remote sensing is emerging as a transformative tool that can offer insights into flow dynamics and thus, together with other remotely sensed data, has the potential to be deployed to estimate discharge. Moreover, the integration of video remote sensing data with hydraulic models offers a pivotal opportunity to enhance the predictive capacity of these models.
Hydraulic models are traditionally built with accurate terrain, flow and bathymetric data and are often calibrated and validated using observed data to obtain meaningful and actionable model predictions. Data for accurately calibrating and validating hydraulic models are not always available, leaving the assessment of the predictive capabilities of some models deployed in flood risk management in question. Recent advances in remote sensing have heralded the availability of vast video datasets of high resolution. The parallel evolution of computing capabilities, coupled with advancements in artificial intelligence are enabling the processing of data at unprecedented scales and complexities, allowing us to glean meaningful insights into datasets that can be integrated with hydraulic models. The aims of the research presented in this thesis were twofold. The first aim was to evaluate and explore the potential applications of video from air- and space-borne platforms to comprehensively calibrate and validate two-dimensional hydraulic models. The second aim was to estimate river discharge using satellite video combined with high resolution topographic data. In the first of three empirical chapters, non-intrusive image velocimetry techniques were employed to estimate river surface velocities in a rural catchment. For the first time, a 2D hydraulicvmodel was fully calibrated and validated using velocities derived from Unpiloted Aerial Vehicle (UAV) image velocimetry approaches. This highlighted the value of these data in mitigating the limitations associated with traditional data sources used in parameterizing two-dimensional hydraulic models. This finding inspired the subsequent chapter where river surface velocities, derived using Large Scale Particle Image Velocimetry (LSPIV), and flood extents, derived using deep neural network-based segmentation, were extracted from satellite video and used to rigorously assess the skill of a two-dimensional hydraulic model. Harnessing the ability of deep neural networks to learn complex features and deliver accurate and contextually informed flood segmentation, the potential value of satellite video for validating two dimensional hydraulic model simulations is exhibited. In the final empirical chapter, the convergence of satellite video imagery and high-resolution topographical data bridges the gap between visual observations and quantitative measurements by enabling the direct extraction of velocities from video imagery, which is used to estimate river discharge. Overall, this thesis demonstrates the significant potential of emerging video-based remote sensing datasets and offers approaches for integrating these data into hydraulic modelling and discharge estimation practice. The incorporation of LSPIV techniques into flood modelling workflows signifies a methodological progression, especially in areas lacking robust data collection infrastructure. Satellite video remote sensing heralds a major step forward in our ability to observe river dynamics in real time, with potentially significant implications in the domain of flood modelling science
Seeing is Believing: Detecting Sybil Attack in FANET by Matching Visual and Auditory Domains
The flying ad hoc network (FANET) will play a crucial role in the B5G/6G era
since it provides wide coverage and on-demand deployment services in a
distributed manner. The detection of Sybil attacks is essential to ensure
trusted communication in FANET. Nevertheless, the conventional methods only
utilize the untrusted information that UAV nodes passively ``heard'' from the
``auditory" domain (AD), resulting in severe communication disruptions and even
collision accidents. In this paper, we present a novel VA-matching solution
that matches the neighbors observed from both the AD and the ``visual'' domain
(VD), which is the first solution that enables UAVs to accurately correlate
what they ``see'' from VD and ``hear'' from AD to detect the Sybil attacks.
Relative entropy is utilized to describe the similarity of observed
characteristics from dual domains. The dynamic weight algorithm is proposed to
distinguish neighbors according to the characteristics' popularity. The
matching model of neighbors observed from AD and VD is established and solved
by the vampire bat optimizer. Experiment results show that the proposed
VA-matching solution removes the unreliability of individual characteristics
and single domains. It significantly outperforms the conventional RSSI-based
method in detecting Sybil attacks. Furthermore, it has strong robustness and
achieves high precision and recall rates.Comment: 7 pages, 9 figures, 1 tabl
Review of Path Selection Algorithms with Link Quality and Critical Switch Aware for Heterogeneous Traffic in SDN
Software Defined Networking (SDN) introduced network management flexibility that eludes traditional network architecture. Nevertheless, the pervasive demand for various cloud computing services with different levels of Quality of Service requirements in our contemporary world made network service provisioning challenging. One of these challenges is path selection (PS) for routing heterogeneous traffic with end-to-end quality of service support specific to each traffic class. The challenge had gotten the research community\u27s attention to the extent that many PSAs were proposed. However, a gap still exists that calls for further study. This paper reviews the existing PSA and the Baseline Shortest Path Algorithms (BSPA) upon which many relevant PSA(s) are built to help identify these gaps. The paper categorizes the PSAs into four, based on their path selection criteria, (1) PSAs that use static or dynamic link quality to guide PSD, (2) PSAs that consider the criticality of switch in terms of an update operation, FlowTable limitation or port capacity to guide PSD, (3) PSAs that consider flow variabilities to guide PSD and (4) The PSAs that use ML optimization in their PSD. We then reviewed and compared the techniques\u27 design in each category against the identified SDN PSA design objectives, solution approach, BSPA, and validation approaches. Finally, the paper recommends directions for further research
Challenges and Limitation Analysis of an IoT-Dependent System for Deployment in Smart Healthcare Using Communication Standards Features
The use of IoT technology is rapidly increasing in healthcare development and smart
healthcare system for fitness programs, monitoring, data analysis, etc. To improve the efficiency
of monitoring, various studies have been conducted in this field to achieve improved precision.
The architecture proposed herein is based on IoT integrated with a cloud system in which power
absorption and accuracy are major concerns. We discuss and analyze development in this domain
to improve the performance of IoT systems related to health care. Standards of communication for
IoT data transmission and reception can help to understand the exact power absorption in different
devices to achieve improved performance for healthcare development. We also systematically analyze
the use of IoT in healthcare systems using cloud features, as well as the performance and limitations
of IoT in this field. Furthermore, we discuss the design of an IoT system for efficient monitoring of
various healthcare issues in elderly people and limitations of an existing system in terms of resources,
power absorption and security when implemented in different devices as per requirements. Blood
pressure and heartbeat monitoring in pregnant women are examples of high-intensity applications
of NB-IoT (narrowband IoT), technology that supports widespread communication with a very
low data cost and minimum processing complexity and battery lifespan. This article also focuses
on analysis of the performance of narrowband IoT in terms of delay and throughput using singleand
multinode approaches. We performed analysis using the message queuing telemetry transport
protocol (MQTTP), which was found to be efficient compared to the limited application protocol
(LAP) in sending information from sensors.Ministerio Español de Ciencia e Innovación under project
number PID2020-115570GB-C22 (DemocratAI::UGR)Cátedra de Empresa Tecnología para
las Personas (UGR-Fujitsu
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