Recent advances in artificial intelligence (AI) have inspired researchers to explore machine learning (ML)based optimization and reverse design techniques for photonic crystal fibers (PCFs). These studies often seek t...
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Recent advances in artificial intelligence (AI) have inspired researchers to explore machine learning (ML)based optimization and reverse design techniques for photonic crystal fibers (PCFs). These studies often seek to improve model generalization, particularly for data that the model has not previously encountered. Traditional centralized training methods are challenging for devices with limited resources, as they rely on aggregating expansive datasets, which is hindered by constraints in storage capacity and communication efficiency. This paper introduces an innovative distributed framework for optimizing PCF parameters, utilizing decentralized training to amalgamate knowledge across various institutions while maintaining data privacy. Each institution develops a lightweight neural network using a small subset of local data, contributing to the construction of a collective and robust global model. This approach is advantageous for both internal and external applications in PCF engineering. Rigorous empirical experiments conducted with real-world PCF optimization data substantiate the efficacy and benefits of the proposed framework. This framework shows promise in achieving an equilibrium between data protection and resource efficiency, offering a novel platform for the reverse design of microstructured optical fibers.
Today it has become extremely critical for the business to utilize and safeguard the data(Enterprise data) in a proper way in order to achieve the organizational success which is in accordance with the digital environ...
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With the construction of modern (intelligent) supply chain and green modern digital intelligence supply chain, the digital capability of contract management business has been comprehensively improved, but the traditio...
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This research paper addresses the enhancement of data security and privacy in cloud storage through diverse encryption methods, such as one-to-many encryption, data integrity, resilient data deletion, and privacy-pres...
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We enhance our prior all-optical reconfigurable network to accelerate the data loading with minimized communication distance between compute and storage nodes. A novel corresponding data loading scheme is proposed to ...
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The rise of artificial intelligence-generated content (AIGC) has fueled a growing demand for data uploads. Massive data are transferred from clients and aggregated on the cloud for the AIGC model update. However, traf...
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ISBN:
(纸本)9798350378412
The rise of artificial intelligence-generated content (AIGC) has fueled a growing demand for data uploads. Massive data are transferred from clients and aggregated on the cloud for the AIGC model update. However, traffic fluctuation makes it difficult to carry AIGC uploads over conventional end-to-end (E2E) connections. In this paper, we present a storage-assisted uploading method for hierarchical federated learning (SU-HFL) over optical AIGC networks. SU-HFL not only reduces uploading traffic via edge aggregation enabled by HFL, but also relaxes the E2E constraint via temporary storage on intermediate nodes. Simulations show that SU-HFL outperforms conventional methods in terms of network performance and training accuracy.
Femtosecond laser direct writing is a powerful technique for fabricating micro-nano devices as it can modify the interior of transparent optical materials in a spatially selective manner through nonlinear multi-photon...
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Femtosecond laser direct writing is a powerful technique for fabricating micro-nano devices as it can modify the interior of transparent optical materials in a spatially selective manner through nonlinear multi-photon absorption. In this context, laser-induced nanogratings, i.e., a sub-wavelength assembly of nanolayers (approximate to 20 nm in width, approximate to 200 nm period), are ultrashort self-organized structures created by light in the bulk of transparent materials. These have been intensively explored over the last two decades opening a novel era of micro photonic devices due to their unique physicochemical properties, like orientable form birefringence, anisotropic light scattering, highly selective chemical etching, optical chirality, and extraordinary thermal stability. This review provides a throughout overview of the advances in this field, specifically focused on the formation of nanogratings, optical properties that can be exploited in various transparent solids, and the related main applications. Also, the fundamental characteristics, formation mechanism, tuning methods of nanogratings are reviewed. In this review, an overview of nanogratings (light-forced replicated nanostructures inside transparent materials) has been conducted, including their discovery, characteristics, parameter dependencies, and formation mechanisms. Furthermore, the targeted optical applications (like 5D opticaldatastorage) including recent achievements have been demonstrated and discussed, due to the unique characteristics, such as orientable form birefringence, highly selective chemical etching, optical chirality, or extraordinary thermal stability. image
Digital images are captured by various fixed and mobile cameras, compressed with traditional and novel techniques, transmitted through different communication channels, and stored in various storage devices. Distortio...
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This paper addresses the growing need for scalable and flexible data acquisition systems in modern power grids. It proposes a new generation system utilizing distributed technologies, big data analytics, and microserv...
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Distributed Acoustic Sensing (DAS) has emerged as a groundbreaking technology in seismology, transforming fiber-optic cables into dense, cost-effective seismic monitoring arrays. DAS makes use of Rayleigh backscatteri...
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Distributed Acoustic Sensing (DAS) has emerged as a groundbreaking technology in seismology, transforming fiber-optic cables into dense, cost-effective seismic monitoring arrays. DAS makes use of Rayleigh backscattering to detect and measure dynamic strain and vibrations over extended distances. It can operate using both pre-existing telecommunication networks and specially designed fibers. This review explores the principles of DAS, including Coherent optical Time Domain Reflectometry (COTDR) and Phase-Sensitive OTDR (phi-OTDR), and discusses the role of optoelectronic interrogators in data acquisition. It examines recent advancements in fiber design, such as helically wound and engineered fibers, which improve DAS sensitivity, spatial resolution, and the signal-to-noise ratio (SNR). Additionally, innovations in deployment techniques include cemented borehole cables, flexible liners, and weighted surface coupling to further enhance mechanical coupling and data accuracy. This review also demonstrated the applications of DAS across earthquake detection, microseismic monitoring, reservoir characterization and monitoring, carbon storage sites, geothermal reservoirs, marine environments, and urban infrastructure surveillance. The study highlighted several challenges of DAS, including directional sensitivity limitations, vast data volumes, and calibration inconsistencies. It also addressed solutions to these problems, such as advances in signal processing, noise suppression techniques, and machine learning integration, which have improved real-time analysis and data interpretability, enabling DAS to compete with traditional seismic networks. Additionally, modeling approaches such as full waveform inversion and forward simulations provide valuable insights into subsurface dynamics and fracture monitoring. This review highlights DAS's potential to revolutionize seismic monitoring through its scalability, cost-efficiency, and adaptability to diverse applications while ident
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