Search Results for author: Satoshi Sunada

Found 8 papers, 0 papers with code

Optical hyperdimensional soft sensing: Speckle-based touch interface and tactile sensor

no code implementations6 Jan 2024 Kei Kitagawa, Kohei Tsuji, Koyo Sagehashi, Tomoaki Niiyama, Satoshi Sunada

Hyperdimensional computing (HDC) is an emerging computing paradigm that exploits the distributed representation of input data in a hyperdimensional space, the dimensions of which are typically between 1, 000--10, 000.

Ultrafast single-channel machine vision based on neuro-inspired photonic computing

no code implementations15 Feb 2023 Tomoya Yamaguchi, Kohei Arai, Tomoaki Niiyama, Atsushi Uchida, Satoshi Sunada

This approach allows for compressive acquisitions of visual information with a single channel at gigahertz rates, outperforming conventional approaches, and enables its direct photonic processing using a photonic reservoir computer in a time domain.

Anomaly Detection

Controlling chaotic itinerancy in laser dynamics for reinforcement learning

no code implementations12 May 2022 Ryugo Iwami, Takatomo Mihana, Kazutaka Kanno, Satoshi Sunada, Makoto Naruse, Atsushi Uchida

In this paper, we propose a method for controlling the chaotic itinerancy in a multi-mode semiconductor laser to solve a machine learning task, known as the multi-armed bandit problem, which is fundamental to reinforcement learning.

BIG-bench Machine Learning reinforcement-learning +1

Optical skin: Sensor-integration-free multimodal flexible sensing

no code implementations3 Feb 2022 Sho Shimadera, Kei Kitagawa, Koyo Sagehashi, Tomoaki Niiyama, Satoshi Sunada

The proposed approach is based on an optical interference technique, which can encode the information of various stimuli as a spatial pattern.

Photonic neural field on a silicon chip: large-scale, high-speed neuro-inspired computing and sensing

no code implementations22 May 2021 Satoshi Sunada, Atsushi Uchida

In contrast to existing photonic neural networks, the photonic neural field is a spatially continuous field that nonlinearly responds to optical inputs, and its high spatial degrees of freedom allow for large-scale and high-density neural processing on a millimeter-scale chip.

Time Series Prediction

Physical deep learning based on optimal control of dynamical systems

no code implementations16 Dec 2020 Genki Furuhata, Tomoaki Niiyama, Satoshi Sunada

In this study, we perform pattern recognition based on the optimal control of continuous-time dynamical systems, which is suitable for physical hardware implementation.

Lotka-Volterra competition mechanism embedded in a decision-making method

no code implementations29 Jul 2019 Tomoaki Niiyama, Genki Furuhata, Atsushi Uchida, Makoto Naruse, Satoshi Sunada

Decision making is a fundamental capability of living organisms, and has recently been gaining increasing importance in many engineering applications.

Decision Making

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