4,793 research outputs found
Securing NextG networks with physical-layer key generation: A survey
As the development of next-generation (NextG) communication networks continues, tremendous devices are accessing the network and the amount of information is exploding. However, with the increase of sensitive data that requires confidentiality to be transmitted and stored in the network, wireless network security risks are further amplified. Physical-layer key generation (PKG) has received extensive attention in security research due to its solid information-theoretic security proof, ease of implementation, and low cost. Nevertheless, the applications of PKG in the NextG networks are still in the preliminary exploration stage. Therefore, we survey existing research and discuss (1) the performance advantages of PKG compared to cryptography schemes, (2) the principles and processes of PKG, as well as research progresses in previous network environments, and (3) new application scenarios and development potential for PKG in NextG communication networks, particularly analyzing the effect and prospects of PKG in massive multiple-input multiple-output (MIMO), reconfigurable intelligent surfaces (RISs), artificial intelligence (AI) enabled networks, integrated space-air-ground network, and quantum communication. Moreover, we summarize open issues and provide new insights into the development trends of PKG in NextG networks
An empirical investigation of the relationship between integration, dynamic capabilities and performance in supply chains
This research aimed to develop an empirical understanding of the relationships between integration,
dynamic capabilities and performance in the supply chain domain, based on which, two conceptual
frameworks were constructed to advance the field. The core motivation for the research was that, at
the stage of writing the thesis, the combined relationship between the three concepts had not yet
been examined, although their interrelationships have been studied individually.
To achieve this aim, deductive and inductive reasoning logics were utilised to guide the qualitative
study, which was undertaken via multiple case studies to investigate lines of enquiry that would
address the research questions formulated. This is consistent with the author’s philosophical
adoption of the ontology of relativism and the epistemology of constructionism, which was considered
appropriate to address the research questions. Empirical data and evidence were collected, and
various triangulation techniques were employed to ensure their credibility. Some key features of
grounded theory coding techniques were drawn upon for data coding and analysis, generating two
levels of findings. These revealed that whilst integration and dynamic capabilities were crucial in
improving performance, the performance also informed the former. This reflects a cyclical and
iterative approach rather than one purely based on linearity. Adopting a holistic approach towards
the relationship was key in producing complementary strategies that can deliver sustainable supply
chain performance.
The research makes theoretical, methodological and practical contributions to the field of supply
chain management. The theoretical contribution includes the development of two emerging
conceptual frameworks at the micro and macro levels. The former provides greater specificity, as it
allows meta-analytic evaluation of the three concepts and their dimensions, providing a detailed
insight into their correlations. The latter gives a holistic view of their relationships and how they are
connected, reflecting a middle-range theory that bridges theory and practice. The methodological
contribution lies in presenting models that address gaps associated with the inconsistent use of
terminologies in philosophical assumptions, and lack of rigor in deploying case study research
methods. In terms of its practical contribution, this research offers insights that practitioners could
adopt to enhance their performance. They can do so without necessarily having to forgo certain
desired outcomes using targeted integrative strategies and drawing on their dynamic capabilities
LATEST ADVANCES ON SECURITY ARCHITECTURE FOR 5G TECHNOLOGY AND SERVICES
The roll out of the deployment of the 5G technology has been ongoing globally. The
deployment of the technologies associated with 5G has seen mixed reaction as regards its
prospects to improve communication services in all spares of life amid its security concerns. The
security concerns of 5G network lies in its architecture and other technologies that optimize the
performance of its architecture. There are many fractions of 5G security architecture in the
literature, a holistic security architectural structure will go a long way in tackling the security
challenges. In this paper, the review of the security challenges of the 5G technology based on its
architecture is presented along with their proposed solutions. This review was carried out with
some keywords relating to 5G securities and architecture; this was used to retrieve appropriate
literature for fitness of purpose. The 5G security architectures are mojorly centered around the
seven network security layers; thereby making each of the layers a source of security concern on
the 5G network. Many of the 5G security challenges are related to authentication and authorization
such as denial-of-service attacks, man in the middle attack and eavesdropping. Different methods
both hardware (Unmanned Aerial Vehicles, field programmable logic arrays) and software (Artificial
intelligence, Machine learning, Blockchain, Statistical Process Control) has been proposed for
mitigating the threats. Other technologies applicable to 5G security concerns includes: Multi-radio
access technology, smart-grid network and light fidelity. The implementation of these solutions
should be reviewed on a timely basis because of the dynamic nature of threats which will greatly
reduce the occurrence of security attacks on the 5G network
Crisis for Whom?
Children feature centrally in the ubiquitous narratives of ‘migration crises’. They are often depicted as essentially vulnerable and in need of special protections, or suspiciously adult-like and a threat to national borders. At the same time, many voices, experiences, and stories are rarely heard, especially about children on the move within the global South. This bilingual book, written in English and Spanish, challenges simplistic narratives to enrich perspectives and understanding. Drawing on collaborations between young (im)migrants, researchers, artists and activists, this collection asks new questions about how crises are produced, mobility is controlled, and childhood is conceptualised. Answers to these questions have profound implications for resources, infrastructures, and relationships of care. Authors offer insights from diverse global contexts, painting a rich and insightful tapestry about childhood (im)mobility. They stress that children are more than recipients of care and that the crises they face are multiple and stratifying, with long historical roots. Readers are invited to understand migration as an act of concern and love, and to attend to how the solidarities between citizens and ‘others’, adults and children, and between children, are understood and forged.La niñez ocupa un lugar central en las narrativas omnipresentes de las ""crisis migratorias"". A menudo ésta es representada como esencialmente vulnerable y necesitada de protección especial, como sospechosamente parecida a los adultos, o como una amenaza para las fronteras nacionales. Al mismo tiempo, existen muchas voces, experiencias e historias que rara vez son escuchadas, especialmente aquellas que hablan sobre las infancias en movimiento dentro del Sur global. 'Este libro bilingüe, escrito en inglés y español, desafía las narrativas simplistas para enriquecer nuestra perspectivas y comprensión. Basada en colaboraciones entre jóvenes (in)migrantes, investigadores, artistas y activistas, esta colección plantea nuevas preguntas sobre cómo se producen las crisis, cómo se controla la movilidad y cómo se conceptualiza a la infancia y la niñez. Las respuestas a estas preguntas tienen profundas implicaciones para la distribución de recursos, la infraestructura y las prácticas de cuidado. Las y los autores ofrecen perspectivas que surgen de diversos contextos globales, construyendo un rico y detallado tapiz sobre la (in)movilidad infantil. Destacan que niñas y niños son mucho más que simples receptores de cuidados y que las crisis que enfrentan son múltiples y estratificadas, con profundas raíces históricas. Se invita a las/os lectoras/es a entender la migración como un acto de concientización y amor, y a poner atención en cómo se entienden y forjan las solidaridades entre ciudadanos y aquellos que son percibidos como “otros”; entre adultos y niñas/os, y entre las/os niñas/os mismas/os
Synthetic Aperture Radar (SAR) Meets Deep Learning
This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports
Tradition and Innovation in Construction Project Management
This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings
Constructive Incremental Learning for Fault Diagnosis of Rolling Bearings with Ensemble Domain Adaptation
Given the prevalence of rolling bearing fault diagnosis as a practical issue
across various working conditions, the limited availability of samples
compounds the challenge. Additionally, the complexity of the external
environment and the structure of rolling bearings often manifests faults
characterized by randomness and fuzziness, hindering the effective extraction
of fault characteristics and restricting the accuracy of fault diagnosis. To
overcome these problems, this paper presents a novel approach termed
constructive Incremental learning-based ensemble domain adaptation (CIL-EDA)
approach. Specifically, it is implemented on stochastic configuration networks
(SCN) to constructively improve its adaptive performance in multi-domains.
Concretely, a cloud feature extraction method is employed in conjunction with
wavelet packet decomposition (WPD) to capture the uncertainty of fault
information from multiple resolution aspects. Subsequently, constructive
Incremental learning-based domain adaptation (CIL-DA) is firstly developed to
enhance the cross-domain learning capability of each hidden node through domain
matching and construct a robust fault classifier by leveraging limited labeled
data from both target and source domains. Finally, fault diagnosis results are
obtained by a majority voting of CIL-EDA which integrates CIL-DA and parallel
ensemble learning. Experimental results demonstrate that our CIL-DA outperforms
several domain adaptation methods and CIL-EDA consistently outperforms
state-of-art fault diagnosis methods in few-shot scenarios
Application of Deep Learning Methods in Monitoring and Optimization of Electric Power Systems
This PhD thesis thoroughly examines the utilization of deep learning
techniques as a means to advance the algorithms employed in the monitoring and
optimization of electric power systems. The first major contribution of this
thesis involves the application of graph neural networks to enhance power
system state estimation. The second key aspect of this thesis focuses on
utilizing reinforcement learning for dynamic distribution network
reconfiguration. The effectiveness of the proposed methods is affirmed through
extensive experimentation and simulations.Comment: PhD thesi
The University of Montana: A History Through the Lens of Physical Culture, PE, Health, Athletics, and Recreation 1897-2019: The Evolution of a Department
https://scholarworks.umt.edu/burns/1000/thumbnail.jp
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