17,156 research outputs found
The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions
The Metaverse offers a second world beyond reality, where boundaries are
non-existent, and possibilities are endless through engagement and immersive
experiences using the virtual reality (VR) technology. Many disciplines can
benefit from the advancement of the Metaverse when accurately developed,
including the fields of technology, gaming, education, art, and culture.
Nevertheless, developing the Metaverse environment to its full potential is an
ambiguous task that needs proper guidance and directions. Existing surveys on
the Metaverse focus only on a specific aspect and discipline of the Metaverse
and lack a holistic view of the entire process. To this end, a more holistic,
multi-disciplinary, in-depth, and academic and industry-oriented review is
required to provide a thorough study of the Metaverse development pipeline. To
address these issues, we present in this survey a novel multi-layered pipeline
ecosystem composed of (1) the Metaverse computing, networking, communications
and hardware infrastructure, (2) environment digitization, and (3) user
interactions. For every layer, we discuss the components that detail the steps
of its development. Also, for each of these components, we examine the impact
of a set of enabling technologies and empowering domains (e.g., Artificial
Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on
its advancement. In addition, we explain the importance of these technologies
to support decentralization, interoperability, user experiences, interactions,
and monetization. Our presented study highlights the existing challenges for
each component, followed by research directions and potential solutions. To the
best of our knowledge, this survey is the most comprehensive and allows users,
scholars, and entrepreneurs to get an in-depth understanding of the Metaverse
ecosystem to find their opportunities and potentials for contribution
Annual report of the officers of the town of Jackson, New Hampshire for the fiscal year ending December 31, 2022.
This is an annual report containing vital statistics for a town/city in the state of New Hampshire
A Design Science Research Approach to Smart and Collaborative Urban Supply Networks
Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness.
A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense.
Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice
The Artist as Surveillant: The Use of Surveillance Technology in Contemporary Art
Artists have long been called observers, voyeurs, and watchers, and with a particular interest in human behavior and society, they frequently use unknowing passersby as their subjects for works. Curators and scholars explored how artists put citizens under surveillance with photography and videography, which dates back to the early 1900s, years before governments deployed surveillance systems. Since the 1980s, artists have explicitly explored surveillance technology and theory to alert viewers to the rise of surveillance. Today, this genre is called artveillance, a term coined by Andrea Mubi Brighenti in 2010 to categorize art that explicitly deals with surveillance. This genre developed parallel to the rise of mass surveillance which created the current-day surveillance state. Since artveillance dominates the contemporary art scene, I was interested in the history of surveillance technology and themes in art. Although that history is brief, there is a wealth of artworks and studies on the topic. This thesis explores artists who use surveillance technology, specifically close-circuit video, in their practice and how this work has changed over time compared to the rise of government surveillance systems. To properly examine the artwork, each artwork’s technological history and broader cultural context is considered, with careful attention to the artists’ intentions. The thesis starts in the 1970s with Bruce Nauman and Peter Campus’s closed-circuit video installations. The artists did not aim to create a surveillance area but wanted to explore the viewer’s identity with their moving image. In Chapter 2, Julia Scher and Lynn Hershman Leeson’s work from the 1980s and early 1990s is discussed. Created when state surveillance was on the rise, the artists’ work used surveillance technology to critique the systems. The third chapter explores surveillance in a post-9/11 state through Jill Magid and Laura Poitras’s work. The artists exploited and exposed government systems to show how the public’s privacy is invaded. Finally, the paper concludes with an investigation into the public’s relationship with video surveillance, which resembles an apathetic acceptance
Body-UAV Near-Ground LoRa Links through a Mediterranean Forest
LoRa low-power wide-area network protocol has recently gained attention for
deploying ad-hoc search and rescue (SaR) systems. They could be empowered by
exploiting body-UAV links that enable communications between a body-worn radio
and a UAV-mounted one. However, to employ UAVs effectively, knowledge of the
signal's propagation in the environment is required. Otherwise, communications
and localization could be hindered. The radio range, the packet delivery ratio
(PDR), and the large- and small-scale fading of body-UAV LoRa links at 868 MHz
when the radio wearer is in a Mediterranean forest are here characterized for
the first time with a near-ground UAV having a maximum flying height of 30 m. A
log-distance model accounting for the body shadowing and the wearer's movements
is derived. Over the full LoRa radio range of about 600 m, the new model
predicts the path loss (PL) better than the state-of-the-art ones, with a
reduction of the median error even by 10 dB. The observed small-scale fading is
severe and follows a Nakagami-m distribution. Extensions of the model for
similar scenarios can be drawn through appropriate corrective factors
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A novel approach for communicating with patients suffering from completely locked-in-syndrome (CLIS) via thoughts: brain computer interface system using EEG signals and artificial intelligence
This paper investigates the development of an intelligent system method to address completely locked-in-syndrome (CLIS) that is caused by some illnesses such as Amyotrophic Lateral Sclerosis (ALS) as the most predominant type of Motor Neuron Disease (MND). In the last stages of ALS and despite the limitations in body movements, patients however will have a fully functional brain and cognitive capabilities and able to feel pain but fail to communicate. This paper aims to address the CLIS problem by utilizing EEG signals that human brain generates when thinking about a specific feeling or imagination as a way to communicate. The aim is to develop a low-cost and affordable system for patients to use to communicate with carers and family members. In this paper, the novel implementation of the ASPS (Automated Sensor and Signal Processing Selection) approach for feature extraction of EEG is presented to select the most suitable Sensory Characteristic Features (SCFs) to detect human thoughts and imaginations. Artificial Neural Networks (ANN) are used to verify the results. The findings show that EEG signals are able to capture imagination information that can be used as a means of communication; and the ASPS approach allows the selection of the most important features for reliable communication. This paper explains the implementation and validation of ASPS approach in brain signal classification for bespoke arrangement. Hence, future work will present the results of relatively high number of volunteers, sensors and signal processing methods
Solutions in Breast Reconstruction
Breast reconstruction, after cancer surgery, is not only a reconstructive surgery but also an esthetic surgery. No woman should be expected to give up the breast tissue, which is the symbol of female identity, easily. The reconstruction stage after breast cancer is difficult enough in the early and late stages. It is generally not possible to cover the defect and to equalize the two breasts in a single step. General surgery and plastic surgery should work together. Recently, innovative solutions have been offered in breast reconstruction. Starting from skin grafts and local flaps, various flap options, dermal equivalents, fat transfer, and tissue expansion operations are among the options. Breast reconstruction is difficult enough in breasts that have undergone radiotherapy, and reconstruction with autologous tissue is preferred
neuroAIx-Framework: design of future neuroscience simulation systems exhibiting execution of the cortical microcircuit model 20Ă— faster than biological real-time
IntroductionResearch in the field of computational neuroscience relies on highly capable simulation platforms. With real-time capabilities surpassed for established models like the cortical microcircuit, it is time to conceive next-generation systems: neuroscience simulators providing significant acceleration, even for larger networks with natural density, biologically plausible multi-compartment models and the modeling of long-term and structural plasticity.MethodsStressing the need for agility to adapt to new concepts or findings in the domain of neuroscience, we have developed the neuroAIx-Framework consisting of an empirical modeling tool, a virtual prototype, and a cluster of FPGA boards. This framework is designed to support and accelerate the continuous development of such platforms driven by new insights in neuroscience.ResultsBased on design space explorations using this framework, we devised and realized an FPGA cluster consisting of 35 NetFPGA SUME boards.DiscussionThis system functions as an evaluation platform for our framework. At the same time, it resulted in a fully deterministic neuroscience simulation system surpassing the state of the art in both performance and energy efficiency. It is capable of simulating the microcircuit with 20Ă— acceleration compared to biological real-time and achieves an energy efficiency of 48nJ per synaptic event
Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review
In this paper, a critical bibliometric analysis study is conducted, coupled
with an extensive literature survey on recent developments and associated
applications in machine learning research with a perspective on Africa. The
presented bibliometric analysis study consists of 2761 machine learning-related
documents, of which 98% were articles with at least 482 citations published in
903 journals during the past 30 years. Furthermore, the collated documents were
retrieved from the Science Citation Index EXPANDED, comprising research
publications from 54 African countries between 1993 and 2021. The bibliometric
study shows the visualization of the current landscape and future trends in
machine learning research and its application to facilitate future
collaborative research and knowledge exchange among authors from different
research institutions scattered across the African continent
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