Protein nanowires harvested from microbes G. sulfurreducens are a renewable, green electronic material. Electronic devices made from protein nanowires show novel or improved functions in energy harvesting, computing a...
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For a given polygonal region P, the Lawn Mowing Problem (LMP) asks for a shortest tour T that gets within Euclidean distance 1/2 of every point in P;this is equivalent to computing a shortest tour for a unit-diameter ...
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Exploiting the 0.1-10 THz spectrum range in sixth-generation (6G) wireless networks promises significant advancements;however, it presents challenges in resource allocation (RA) and security to meet diverse user deman...
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ISBN:
(纸本)9798350370249
Exploiting the 0.1-10 THz spectrum range in sixth-generation (6G) wireless networks promises significant advancements;however, it presents challenges in resource allocation (RA) and security to meet diverse user demands. This paper introduces a novel approach integrating blockchain technology (BCT) and deep learning (DL) for small base station (SBS) coalition formation (CF) and RA. We address critical challenges in dynamic RA and CF, presenting a comprehensive framework grounded in both theoretical and simulation-based analyses. By leveraging BCT, secure retrieval of reputation data is ensured, thereby establishing trust within the network. Our proposed algorithm dynamically optimizes the reputation-to-demand (R/D) ratios, prioritizing SBSs with higher R/D ratios for RA, thus enhancing network efficiency. Further, notable improvements in network performance metrics, validated through extensive simulations and theoretical calculations. The adaptability of the algorithm allows SBSs to enhance their reputation via increased upload capacity and coalition participation. Utilizing Shapley values and network monitoring, the algorithm optimizes CF and the operational status of SBSs. The stability and reliability of the network are assured through Nash equilibrium checks and reputation integrity verification. This comprehensive implementation illustrates that our proposed methodology offers an efficient solution to dynamic wireless challenges, optimizing RA and CF in evolving environments. The integration of DL and BCT provides a robust framework for efficient wireless network management. Our simulations lay a strong foundation for future research in 6G network optimization, emphasizing the proposed solutions’ practical applicability, scalability, and resilience. Key innovations include dynamic R/D ratio optimization, blockchain-based secure reputation data retrieval, adaptable reputation improvement algorithms, efficient coalition formation using Shapley values, Nash equili
We fabricated GaN metasurfaces doped with InGaN quantum dots by templated molecular beam epitaxy (MBE) that support tunable high Q-factor quasi-bound states in the continuum (q-BICs) and demonstrated efficient optical...
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This paper deals with the exploration of a system which acts on demand in case of emergency matter. To verify functionality, each component of the system must be regularly inspected with a specified inspection interva...
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Customer churn is a most crucial challenge faced by various industries, especially for the telecommunications sector since it causes over 5 - 6 times cost to keep customers. Thus, customer churn prediction has experie...
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Modern optical technologies encompass classical light phenomena and non-linear effects, crucial for biomedical imaging and therapies. Despite substantial interest and many experimental studies, non-linear optical effe...
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This paper presents the development of a secure data platform designed to enhance operational efficiency and to facilitate cross-company collaboration within the manufacturing supply chain. The platform is designed to...
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We present the Coloured Petri Nets (CPNs) modelling of the SmartOcean platform currently under development and aimed at providing cloud-based services for data-driven software systems and applications relying on marin...
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The dominant noise in an “erasure qubit” is an erasure—a type of error whose occurrence and location can be detected. Erasure qubits have potential to reduce the overhead associated with fault tolerance. To date, r...
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The dominant noise in an “erasure qubit” is an erasure—a type of error whose occurrence and location can be detected. Erasure qubits have potential to reduce the overhead associated with fault tolerance. To date, research on erasure qubits has primarily focused on quantum computing and quantum networking applications. Here, we consider the applicability of erasure qubits to quantum sensing and metrology. We show theoretically that, for the same level of noise, an erasure qubit acts as a more precise sensor or clock compared to its nonerasure counterpart. We experimentally demonstrate this by artificially injecting either erasure errors (in the form of atom loss) or dephasing errors into a differential optical lattice clock comparison, and observe enhanced precision in the case of erasure errors for the same injected error rate. In the context of a clock with repeated measurement cycles, erasure can improve the stability by a factor of 2. Similar benefits of erasure qubits to sensing can be realized in other quantum platforms like Rydberg atoms and superconducting qubits.
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