Cell-free massive multiple-input multiple-output (MIMO) provides more uniform spectral efficiency (SE) for users (UEs) than cellular technology. The main challenge to achieve the benefits of cell-free massive MIMO is ...
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In federated learning (FL), model training is distributed over clients and local models are aggregated by a central server. The performance of uploaded models in such situations can vary widely due to imbalanced data ...
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We present a simple approach based on photonic reservoir computing(P-RC)for modulation format identification(MFI)in optical fiber *** an optically injected semiconductor laser with self-delay feedback is trained with ...
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We present a simple approach based on photonic reservoir computing(P-RC)for modulation format identification(MFI)in optical fiber *** an optically injected semiconductor laser with self-delay feedback is trained with the representative features from the asynchronous amplitude histograms of modulation *** simulations are conducted for three widely used modulation formats(on–off keying,differential phase-shift keying,and quadrature amplitude modulation)for various transmission situations where the optical signal-to-noise ratio varies from 12 to 26 d B,the chromatic dispersion varies from-500 to 500 ps/nm,and the differential group delay varies from 0 to 20 *** these situations,final simulation results demonstrate that this technique can efficiently identify all those modulation formats with an accuracy of>95%after optimizing the control parameters of the P-RC layer such as the injection strength,feedback strength,bias current,and frequency *** proposed technique utilizes very simple devices and thus offers a resource-efficient alternative approach to MFI.
The Industrial Internet of Things or Industry 4.0 efficiently enhances the manufacturing process in terms of raising productivity, system performance, cost reduction, and building large-scale systems. It enables the c...
The Industrial Internet of Things or Industry 4.0 efficiently enhances the manufacturing process in terms of raising productivity, system performance, cost reduction, and building large-scale systems. It enables the connection of numerous heterogeneous devices and sensors into the internet network through the utilization of revolutionary techniques that transfer the manufacturing paradigm to intelligent processes. The traditional industrial internet of things adopts centralized architectures and complex encryption algorithms that suffer from several cyber-security threats and lead to data leakage, privacy disclosure, and high computational and communicational costs. In our work, we proposed a decentralized, efficient, low-power, scalable, secure, privacy-preserving, and trusting architecture based on a private blockchain network/smart contract and interplanetary file system technology for the Al-NajafyIraq oil refinery factory. That enables autonomous working and provides a high level of security authentication and privacy preservation, P2P communication, remote access, immutability and information backtracking. Traditional blockchain technology has storage limitation issues, most recent works adopted external servers or the cloud for storage purposes. Our proposed scheme addresses the limitations of blockchain data storage by adopting an interplanetary file system (IPFS) network; provides an efficient data encryption mechanism at the perceptual layer by adopting a lightweight encryption algorithm, high-performance, and ultra-low-power consumption ARM Cortex-M microcontroller. In addition, our architecture succeeds in merging blockchain technology, IoTs, and an IPFS with the oil refinery as an industrial application area. Moreover, it provides an efficient framework that resists common cyber-security attacks and realizes cyber-security requirements represented by data availability, integrity, and confidentiality.
Although rice cultivation is one of the most important agricultural sources of methane (CH4) and contributes ∼8% of total global anthropogenic emissions, large discrepancies remain among estimates of global CH4 emiss...
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Although rice cultivation is one of the most important agricultural sources of methane (CH4) and contributes ∼8% of total global anthropogenic emissions, large discrepancies remain among estimates of global CH4 emissions from rice cultivation (ranging from 18 to 115 Tg CH4 yr−1) due to a lack of observational constraints. The spatial distribution of paddy-rice emissions has been assessed at regional-to-global scales by bottom-up inventories and land surface models over coarse spatial resolution (e.g., > 0.5°) or spatial units (e.g., agro-ecological zones). However, high-resolution CH4 flux estimates capable of capturing the effects of local climate and management practices on emissions, as well as replicating in situ data, remain challenging to produce because of the scarcity of high-resolution maps of paddy-rice and insufficient understanding of CH4 predictors. Here, we combine paddy-rice methane-flux data from 23 global eddy covariance sites and MODIS remote sensing data with machine learning to 1) evaluate data-driven model performance and variable importance for predicting rice CH4 fluxes;and 2) produce gridded up-scaling estimates of rice CH4 emissions at 5000-m resolution across Monsoon Asia, where ∼87% of global rice area is cultivated and ∼ 90% of global rice production occurs. Our random-forest model achieved Nash-Sutcliffe Efficiency values of 0.59 and 0.69 for 8-day CH4 fluxes and site mean CH4 fluxes respectively, with land surface temperature, biomass and water-availability-related indices as the most important predictors. We estimate the average annual (winter fallow season excluded) paddy rice CH4 emissions throughout Monsoon Asia to be 20.6 ± 1.1 Tg yr−1 for 2001–2015, which is at the lower range of previous inventory-based estimates (20–32 CH4 Tg yr−1). Our estimates also suggest that CH4 emissions from paddy rice in this region have been declining from 2007 through 2015 following declines in both paddy-rice growing area and emission rates per unit
A microgrid is an effective solution to enhance the integration of distributed renewable energy resources, which can operate both in grid connected mode and islanded mode. In order to reduce the jumps of the system va...
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In this paper, we present CAROL (unConventionAl suRvey methOdoLogy) to collect users' opinions, to analyze them effectively, and to graphically provide interesting summaries in near-real-time. CAROL has been desig...
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ISBN:
(数字)9781728142661
ISBN:
(纸本)9781728142678
In this paper, we present CAROL (unConventionAl suRvey methOdoLogy) to collect users' opinions, to analyze them effectively, and to graphically provide interesting summaries in near-real-time. CAROL has been designed to collect citizens' opinions, needs, and a perception that can be very useful to redesign cities and drive the innovation process. To this aim, particular importance is given to the architecture implemented to collect the data, which involves sending voice messages instead of text, allowing users greater freedom of expression. Moreover, to make the survey more friendly, an initial analysis is done in real-time thanks to an intuitive dashboard, which shows the first results in the form of graphs and charts, updated after each message. Finally, a more in-depth analysis can be carried out at the end of the survey, thanks to text-mining techniques adapted to the context. As use case, the proposed methodology has been tested in a public event organized by Politecnico di Torino and addressed to female students, with the aim to collect their ideas about new services/opportunities that can be offered to support women in STEM.
This paper discusses model order reduction of large sparse second-order index-3 differential algebraic equations (DAEs) by applying Iterative Rational Krylov Algorithm (IRKA). In general, such DAEs arise in constraint...
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Wind power has received extensive attention due to its superiorities of clean and pollution-free. However, because of the randomness and volatility of wind power, accurate power prediction is needed to help its consum...
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The interaction between matter and squeezed light has mostly been treated within the approximation that the field-correlation time is small. Methods for treating squeezed light with more general correlations currently...
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The interaction between matter and squeezed light has mostly been treated within the approximation that the field-correlation time is small. Methods for treating squeezed light with more general correlations currently involve explicitly modeling the systems producing the light. We develop a general purpose input-output theory for a particular form of narrowband squeezed light, a squeezed wave-packet mode, that only concerns the statistics of the squeezed field and the shape of the wave packet. This formalism allows us to derive the input-output relations and the master equation. We also consider detecting the scattered field using photon counting and homodyne measurements, which necessitates the derivation of the stochastic master equation. The non-Markovian nature of the field manifests itself in the master equation as a coupled hierarchy of equations. We illustrate these with consequences for the decay and resonance fluorescence of two-level atoms in the presence of such fields.
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