M-ary distributed detection in the presence of channel estimation error

Hajibabaei, Zahra; Sani, Alireza; Vosoughi, Azadeh

doi:10.1109/acssc.2014.7094855

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…These advancements include two categories: neural networks (NNs) [220] and kernel-based methods using Gaussian approximation potentials (GAP), [221] Bayesian linear regression (BLR) variants, [222] Gaussian process regression (GPR), [221,223] and sparse GPR (SGPP). [224] NNs are suitable for large datasets on-the-fly learning, while kernel-based methods excel with smaller ones. [222b,223a,224b,225] To enhance scalability, low-rank reduction of the covariance matrix in kernel [224a,224b] can be exploited.…”

Section: Ml-based First Principles Level Potentials and Simulationsmentioning

confidence: 99%

“…Cumulative learning allows to building of a covariance matrix library, facilitating reliable simulations for large complex materials systems. SGPR-based universal first principles level potentials [224] have been employed across various material studies of macromolecular systems, [226] metal clusters, [226] Li diffusion in batteries and solid electrolytes, [224b,224c,228] and solar conversion. [229] Moreover, these ML approaches have been employed in investigating diverse electro/photo-catalytic reactions including the stability of embedded SACs on 2D materials templates, such as chlorine evolution reaction by Pt SA embedded N-doped graphene.…”

Section: Ml-based First Principles Level Potentials and Simulationsmentioning

confidence: 99%

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Progress in Single/Multi Atoms and 2D‐Nanomaterials for Electro/Photocatalytic Nitrogen Reduction: Experimental, Computational and Machine Leaning Developments

Singh,

Anand,

Zafari

et al. 2024

Advanced Energy Materials

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The conversion of atmospheric nitrogen (N2) into ammonia (NH3), known as nitrogen fixation, plays a crucial role in sustaining life on Earth, facing innovation with electrocatalytic and photocatalytic methods. These approaches promise gentler conversions from atmospheric nitrogen to ammonia, diverging from the energy‐intensive Haber‐Bosch process, which requires complex plant infrastructure. Vitality lies in eco‐friendly, cost‐effective, and energy‐efficient pathways. The challenge is that electrocatalysts and photocatalysts for nitrogen reduction have shown low Faraday efficiency, hampered by hydrogen evolution. This work delves into recent strides in electro/photo‐catalytic nitrogen fixation/reduction, deciphering mechanisms, catalysts, and prospects. By unveiling the core principles steering these processes, it dissects efficiency drivers. Experimental and theoretical studies, ranging from density functional calculations/simulations to machine learning‐based catalyst screening, mark the path toward highly efficient catalysts, including single/multi‐atom catalysts embedded in 2D materials. The journey explores diverse catalysts, assessing their performance, spotlighting emerging nanomaterials, heterostructures, and co‐catalyst techniques. Perspectives on future directions and potential applications of electro/photo‐catalytic nitrogen fixation/reduction are offered, by emphasizing their role in sustainable nitrogen management and their implications for global agriculture and environmental sustainability.

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Section: Ml-based First Principles Level Potentials and Simulationsmentioning

confidence: 99%

Section: Ml-based First Principles Level Potentials and Simulationsmentioning

confidence: 99%

Progress in Single/Multi Atoms and 2D‐Nanomaterials for Electro/Photocatalytic Nitrogen Reduction: Experimental, Computational and Machine Leaning Developments

Singh,

Anand,

Zafari

et al. 2024

Advanced Energy Materials

View full text Add to dashboard Cite

show abstract

On Bandwidth Constrained Distributed Detection of a Known Signal in Correlated Gaussian Noise

Maleki

Vosoughi

2020

IEEE Trans. Veh. Technol.

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M-ary distributed detection in the presence of channel estimation error

Cited by 2 publications

References 8 publications

Progress in Single/Multi Atoms and 2D‐Nanomaterials for Electro/Photocatalytic Nitrogen Reduction: Experimental, Computational and Machine Leaning Developments

Progress in Single/Multi Atoms and 2D‐Nanomaterials for Electro/Photocatalytic Nitrogen Reduction: Experimental, Computational and Machine Leaning Developments

On Bandwidth Constrained Distributed Detection of a Known Signal in Correlated Gaussian Noise

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