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A Supervised Learning Algorithm for Multilayer Spiking Neural Networks Based on Temporal Coding Toward Energy-Efficient VLSI Processor Design
A Supervised Learning Algorithm for Multilayer Spiking Neural Networks Based on Temporal Coding Toward Energy-Efficient VLSI Processor Design
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A Supervised Learning Algorithm for Multilayer Spiking Neural Networks Based on Temporal Coding Toward Energy-Efficient VLSI Processor Design

Sakemi, Yusuke ; Morino, Kai ; Morie, Takashi ; Aihara, Kazuyuki

IEEE transaction on neural networks and learning systems, 2023-01, Vol.34 (1), p.394-408

United States: IEEE

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Impact of time-history terms on reservoir dynamics and prediction accuracy in echo state networks
Impact of time-history terms on reservoir dynamics and prediction accuracy in echo state networks
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Impact of time-history terms on reservoir dynamics and prediction accuracy in echo state networks

Ebato, Yudai ; Nobukawa, Sou ; Sakemi, Yusuke ; Nishimura, Haruhiko ; Kanamaru, Takashi ; Sviridova, Nina ; Aihara, Kazuyuki

Scientific reports, 2024-04, Vol.14 (1), p.8631-8631, Article 8631 [Periódico revisado por pares]

England: Nature Publishing Group

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A 18.7 TOPS/W Mixed-Signal Spiking Neural Network Processor With 8-bit Synaptic Weight On-Chip Learning That Operates in the Continuous-Time Domain
A 18.7 TOPS/W Mixed-Signal Spiking Neural Network Processor With 8-bit Synaptic Weight On-Chip Learning That Operates in the Continuous-Time Domain
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A 18.7 TOPS/W Mixed-Signal Spiking Neural Network Processor With 8-bit Synaptic Weight On-Chip Learning That Operates in the Continuous-Time Domain

Uenohara, Seiji ; Aihara, Kazuyuki

IEEE access, 2022, Vol.10, p.48338-48348 [Periódico revisado por pares]

Piscataway: IEEE

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4
Feasibility of Plasmonic Circuits in Nanophotonics
Feasibility of Plasmonic Circuits in Nanophotonics
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Feasibility of Plasmonic Circuits in Nanophotonics

Fukuda, Mitsuo ; Okahisa, Shinya ; Tonooka, Yuta ; Ota, Masashi ; Aihara, Takuma ; Ishikawa, Yasuhiko

IEEE access, 2020-01, Vol.8, p.1-1 [Periódico revisado por pares]

Piscataway: IEEE

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5
Cooperation Method Between CPUs in Large-Scale Cell-Free Massive MIMO for User-Centric RAN
Cooperation Method Between CPUs in Large-Scale Cell-Free Massive MIMO for User-Centric RAN
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Cooperation Method Between CPUs in Large-Scale Cell-Free Massive MIMO for User-Centric RAN

Ikami, Akio ; Aihara, Naoki ; Tsukamoto, Yu ; Murakami, Takahide ; Shinbo, Hiroyuki

IEEE access, 2023, Vol.11, p.95267-95277 [Periódico revisado por pares]

Piscataway: IEEE

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Compressed Sensing Framework Applying Independent Component Analysis after Undersampling for Reconstructing Electroencephalogram Signals
Compressed Sensing Framework Applying Independent Component Analysis after Undersampling for Reconstructing Electroencephalogram Signals
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Compressed Sensing Framework Applying Independent Component Analysis after Undersampling for Reconstructing Electroencephalogram Signals

KANEMOTO, Daisuke ; KATSUMATA, Shun ; AIHARA, Masao ; OHKI, Makoto

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 2020/12/01, Vol.E103.A(12), pp.1647-1654 [Periódico revisado por pares]

Tokyo: The Institute of Electronics, Information and Communication Engineers

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7
Model-free Dominant Pole Placement for Restabilizing High-Dimensional Network Systems via Small-Sample-Size Data
Model-free Dominant Pole Placement for Restabilizing High-Dimensional Network Systems via Small-Sample-Size Data
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Model-free Dominant Pole Placement for Restabilizing High-Dimensional Network Systems via Small-Sample-Size Data

Shen, Xun ; Sasahara, Hampei ; Morishita, Masahide ; Imura, Jun-ichi ; Oku, Makito ; Aihara, Kazuyuki

IEEE access, 2023-01, Vol.11, p.1-1 [Periódico revisado por pares]

Piscataway: IEEE

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8
Dysarthric Speech Recognition Using Pseudo-Labeling, Self-Supervised Feature Learning, and a Joint Multi-Task Learning Approach
Dysarthric Speech Recognition Using Pseudo-Labeling, Self-Supervised Feature Learning, and a Joint Multi-Task Learning Approach
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Dysarthric Speech Recognition Using Pseudo-Labeling, Self-Supervised Feature Learning, and a Joint Multi-Task Learning Approach

Takashima, Ryoichi ; Sawa, Yuya ; Aihara, Ryo ; Takiguchi, Tetsuya ; Imai, Yoshie

IEEE access, 2024-01, Vol.12, p.36990-36999 [Periódico revisado por pares]

Piscataway: IEEE

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9
Multiple Non-Negative Matrix Factorization for Many-to-Many Voice Conversion
Multiple Non-Negative Matrix Factorization for Many-to-Many Voice Conversion
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Multiple Non-Negative Matrix Factorization for Many-to-Many Voice Conversion

Aihara, Ryo ; Takiguchi, Tetsuya ; Ariki, Yasuo

IEEE/ACM transactions on audio, speech, and language processing, 2016-07, Vol.24 (7), p.1175-1184 [Periódico revisado por pares]

Piscataway: IEEE

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10
Motion dense sampling and component clustering for action recognition
Motion dense sampling and component clustering for action recognition
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Motion dense sampling and component clustering for action recognition

Aihara, Kazuaki ; Aoki, Terumasa

Multimedia tools and applications, 2015-08, Vol.74 (16), p.6303-6321 [Periódico revisado por pares]

New York: Springer US

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