Results 221 to 230 of about 16,615 (264)

Updatable Closed‐Form Evaluation of Arbitrarily Complex Multiport Network Connections

Advanced Electronic Materials, EarlyView.
The inverse design of electrically large wave devices often uses reduced‐order multiport models with discrete optimization, requiring many evaluations of complex interconnections between subsystems that differ only in a few blocks. This paper introduces a closed‐form framework enabling efficient Woodbury low‐rank updates of related, previous ...
Hugo Prod'homme, Philipp del Hougne
wiley   +1 more source

Tensor Formats and Tensor Decompositions

Francisco Chinesta, Elías Cueto, Victor Champaney, Chady Ghnatios, Amine Ammar, Nicolas Hascoët, David González, Icíar Alfaro, Daniele Di Lorenzo, Angelo Pasquale, Dominique Baillargeat   +10 more
openaire   +1 more source

Terahertz Channel Modeling, Estimation and Localization in RIS‐Assisted Systems

Advanced Electronic Materials, EarlyView.
Reconfigurable intelligent surfaces have become a recent intensive research focus. Based on practical applications, channel strategies for RIS‐assisted terahertz wireless communication systems are categorized into three different types: channel modeling, channel estimation, and channel localization.
Hongjing Wang, Weipeng Wang, Xuan Cong, Shiyu Wang, Gao Li, Sen Gong, Lin Huang, Hongxin Zeng, Yaxin Zhang, Ziqiang Yang   +9 more
wiley   +1 more source

Fundamental Challenges, Physical Implementations, and Integration Strategies for Ising Machines in Large‐Scale Optimization Tasks

Advanced Electronic Materials, EarlyView.
Ising machines are emerging as specialized hardware solvers for computationally hard optimization problems. This review examines five major platforms—digital CMOS, analog CMOS, emerging devices, coherent optics, and quantum systems—highlighting physics‐rooted advantages and shared bottlenecks in scalability and connectivity.
Hyunjun Lee, Joon Pyo Kim, Sanghyeon Kim
wiley   +1 more source

People Counting and Positioning Using Low‐Resolution Infrared Images for FeFET‐Based In‐Memory Computing

Advanced Electronic Materials, EarlyView.
In this work, low‐resolution infrared imaging is combined with a 28 nm FeFET IMC architecture to enable compact, energy‐efficient edge inference. MLC FeFET devices are experimentally characterized, and controlled multi‐level current accumulation is validated at crossbar array level.
Alptekin Vardar, Li Zhang, Alexander Rehmer, Marc Lupp, Subhashree Baskaran, Osama Abdelaal, Franz Müller, Nandakishor Yadav, Bing Li, Thomas Kämpfe   +9 more
wiley   +1 more source

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Random Tensor Theory for Tensor Decomposition

Proceedings of the AAAI Conference on Artificial Intelligence, 2022
We propose a new framework for tensor decomposition based on trace invariants, which are particular cases of tensor networks. In general, tensor networks are diagrams/graphs that specify a way to "multiply" a collection of tensors together to produce another tensor, matrix or scalar.
Ouerfelli, Mohamed, Tamaazousti, Mohamed, Rivasseau, Vincent   +2 more
openaire   +2 more sources

Time-Aware Tensor Decomposition for Sparse Tensors

2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA), 2021
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Dawon Ahn, Jun-Gi Jang, U Kang
openaire   +2 more sources

Tensor Decompositions and Applications

SIAM Review, 2009
Summary: This survey provides an overview of higher-order tensor decompositions, their applications, and available software. A tensor is a multidimensional or \(N\)-way array. Decompositions of higher-order tensors (i.e., \(N\)-way arrays with \(N \geq 3\)) have applications in psycho-metrics, chemometrics, signal processing, numerical linear algebra ...
Tamara G. Kolda, Brett W. Bader
openaire   +2 more sources

Multiscale tensor decomposition

2016 50th Asilomar Conference on Signals, Systems and Computers, 2016
Large datasets usually contain redundant information and summarizing these datasets is important for better data interpretation. Higher-order data reduction is usually achieved through low-rank tensor approximation which assumes that the data lies near a linear subspace across each mode.
Alp Ozdemir, Mark A. Iwen, Selin Aviyente   +2 more
openaire   +1 more source

Sensitivity in Tensor Decomposition

IEEE Signal Processing Letters, 2019
Canonical polyadic (CP) tensor decomposition is an important task in many applications. Many times, the true tensor rank is not known, or noise is present, and in such situations, different existing CP decomposition algorithms provide very different results.
Petr Tichavský, Anh-Huy Phan 0001, Andrzej Cichocki   +2 more
openaire   +1 more source