arXiv subjects

Tomoaki Niiyama

Publications and source records attributed to Tomoaki Niiyama.

At least 19 recordsLinked to original sources

arXiv2026

Negative Strain-Rate Sensitivity in Metallic Glasses Driven by Rejuvenation-Relaxation Competition: Kinetic Monte Carlo Simulations and a Minimal Effective Model

When strain-rate sensitivity (SRS) is negative in metallic glasses, the material becomes weaker as the deformation rate increases, leading to accelerated plastic deformation and, eventually, catastrophic fracture. In this study, we elucidate the mechanism underlying the negative SRS using micromechanics-based kinetic Monte Carlo simulations that couple heterogeneous randomized shear transformation zone (STZ) models for metallic glasses. The model accounted for both the thermomechanical structural rejuvenation and relaxation of the energy barrier for thermal activation of STZs, incorporating a Kohlrausch-Williams-Watts (KWW)-type relaxation function. The present simulations systematically reproduce the dependence of flow stresses on strain rate, temperature, and the form of the relaxation function. The SRS tends to decrease at high strain rates and low temperatures in the simulations, and negative SRS appears when a compressed-exponential relaxation function is employed. Shear localization also appears; however, the conditions under which the observed localization emerges do not fully coincide with those leading to the negative SRS, leaving the dominant factor unclear. To clarify the dominant factor, we introduce a simplified theoretical model that reproduces flow stresses consistent with the simulation results. An analytical expression derived from the theoretical model reveals that negative SRS originates primarily from the temporal evolution of the activation barrier. Specifically, negative SRS arises when the timescale of external loading exceeds that of STZ relaxation.

cond-mat.mtrl-sci↗

arXiv2026

A thin and soft optical tactile sensor for highly sensitive object perception

Tactile sensing is crucial in robotics and wearable devices for safe perception and interaction with the environment. Optical tactile sensors have emerged as promising solutions, as they are immune to electromagnetic interference and have high spatial resolution. However, existing optical approaches, particularly vision-based tactile sensors, rely on complex optical assemblies that involve lenses and cameras, resulting in bulky, rigid, and alignment-sensitive designs. In this study, we present a thin, compact, and soft optical tactile sensor featuring an alignment-free configuration. The soft optical sensor operates by capturing deformation-induced changes in speckle patterns generated within a soft silicone material, thereby enabling precise force measurements and texture recognition via machine learning. The experimental results show a root-mean-square error of 40 mN in the force measurement and a classification accuracy of 93.33% over nine classes of textured surfaces, including Mahjong tiles. The proposed speckle-based approach provides a compact, easily fabricated, and mechanically compliant platform that bridges optical sensing with flexible shape-adaptive architectures, thereby demonstrating its potential as a novel tactile-sensing paradigm for soft robotics and wearable haptic interfaces.

cs.RO↗

arXiv2025

100 Mfps ghost imaging with wavelength division multiplexing

Ghost imaging (GI) and single-pixel imaging (SPI) techniques enable image reconstruction without spatially resolved detectors, offering unique access to wide spectral ranges and challenging imaging environments. Yet, their adoption has been limited by the slow generation of mask patterns, which constrains achievable frame rates. Here, we demonstrate ultrafast GI that achieves a spatial-temporal information flux of 78.4 gigapixels per second across five wavelengths, which is at least two orders of magnitude larger than that reported for previous training-data-free GI approaches. This breakthrough is enabled by 25 GHz speckle pattern switching and allows parallelizing the pattern illumination using a wavelength-division multiplexing (WDM) technique. We show that the proposed approach is capable of reconstructing 28$\times$28-pixel images at the exposure time of 10 ns, achieving 100 megaframes per second (Mfps), and demonstrate the GI of a microsecond-scale dynamic event. This approach opens avenues for studying rapid processes in physics, chemistry, and biology, where conventional cameras are limited by detector bandwidth, readout speed, or cost.

physics.optics↗

arXiv2025

Expanding detection bandwidth via a photonic reservoir for ultrafast optical sensing

The detection of ultrafast optical and radio-frequency (RF) signals is crucial for applications ranging from high-speed communications to advanced sensing. However, conventional detectors are fundamentally constrained by their intrinsic bandwidth, limiting accurate broadband signal measurement. Here, we show that a neuromorphic photonic processing approach can overcome this limitation, enabling accurate broadband signal detection beyond the detector bandwidth. The key idea lies in the spatiotemporal encoding of input waveforms within a photonic reservoir network, which reconstructs high-frequency components otherwise inaccessible to individual detectors. We experimentally demonstrate the detection of high-speed optical phase signals with more than an eightfold effective bandwidth expansion using an on-chip silicon photonic reservoir. This approach provides a scalable and integrable platform for high-speed optical and RF signal processing, opening new opportunities in ultrafast photonics and next-generation communication systems.

physics.optics↗

arXiv2025

Blending Optimal Control and Biologically Plausible Learning for Noise-Robust Physical Neural Networks

The rapidly increasing computational demands for artificial intelligence (AI) have spurred the exploration of computing principles beyond conventional digital computers. Physical neural networks (PNNs) offer efficient neuromorphic information processing by harnessing the innate computational power of physical processes; however, training their weight parameters is computationally expensive. We propose a training approach for substantially reducing this training cost. Our training approach merges an optimal control method for continuous-time dynamical systems with a biologically plausible training method--direct feedback alignment. In addition to the reduction of training time, this approach achieves robust processing even under measurement errors and noise without requiring detailed system information. The effectiveness was numerically and experimentally verified in an optoelectronic delay system. Our approach significantly extends the range of physical systems practically usable as PNNs.

physics.app-ph↗

arXiv2025

Power-law fluctuations near critical point in semiconductor lasers with delayed feedback

Since the analogy between laser oscillation and second-order phase transition was indicated in the 1970s, dynamical fluctuations on lasing threshold inherent in critical phenomena have gained significant interest. Here, we numerically and experimentally demonstrate that a semiconductor laser subject to delayed optical feedback can exhibit unusual large intensity fluctuations characterized by power-law distributions. Such an intensity fluctuation consists of distinct intermittent bursts of light intensity, whose peak values attain tens of times the intensity of the maximum gain mode. This burst behavior emerges when a laser with a long time delay (over 100 ns) and an optimal feedback strength operates around the lasing threshold. The intensity and waiting time statistics follow power-law-like distributions. This implies the emergence of nonequilibrium critical phenomena, namely self-organized criticality. In addition to numerical results, we report experimental results that suggest the power-law intensity dynamics in a semiconductor laser with delayed feedback.

physics.optics↗

arXiv2024

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

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. The hyperdimensional distributed representation enables energy-efficient, low-latency, and noise-robust computations with low-precision and basic arithmetic operations. In this study, we propose optical hyperdimensional distributed representations based on laser speckles for adaptive, efficient, and low-latency optical sensor processing. In the proposed approach, sensory information is optically mapped into a hyperdimensional space with >250,000 dimensions, enabling HDC-based cognitive processing. We use this approach for the processing of a soft-touch interface and a tactile sensor and demonstrate to achieve high accuracy of touch or tactile recognition while significantly reducing training data amount and computational burdens, compared with previous machine-learning-based sensing approaches. Furthermore, we show that this approach enables adaptive recalibration to keep high accuracy even under different conditions.

eess.SP↗

arXiv2023

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

High-speed machine vision is increasing its importance in both scientific and technological applications. Neuro-inspired photonic computing is a promising approach to speed-up machine vision processing with ultralow latency. However, the processing rate is fundamentally limited by the low frame rate of image sensors, typically operating at tens of hertz. Here, we propose an image-sensor-free machine vision framework, which optically processes real-world visual information with only a single input channel, based on a random temporal encoding technique. 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. We experimentally demonstrate that the proposed approach is capable of high-speed image recognition and anomaly detection, and furthermore, it can be used for high-speed imaging. The proposed approach is multipurpose and can be extended for a wide range of applications, including tracking, controlling, and capturing sub-nanosecond phenomena.

physics.optics↗

arXiv2023

Analysis of temporal structure of laser chaos by Allan variance

Allan variance has been widely utilized in evaluating the stability of the time series generated by atomic clocks and lasers, in time regimes ranging from short to extremely long. This multi-scale examination capability of the Allan variance may also be beneficial in evaluating the chaotic oscillating dynamics of semiconductor lasers, not just for conventional phase stability analysis purposes. Here we demonstrate Allan variance analysis of the complex time series generated by a semiconductor laser with delayed feedback, including low-frequency fluctuations (LFFs), which exhibit both fast and slow dynamics. Whereas the detection of LFFs is not easy with the conventional power spectrum analysis method in the low-frequency regime, we show that the Allan variance approach clearly captured the appearance of multiple time-scale dynamics, like LFFs. This study demonstrates that Allan variance can help in understanding and characterizing versatile laser dynamics, including LFFs, which involve dynamics spanning a wide range of time scales.

physics.optics↗

arXiv2022

Gigahertz-rate random speckle projection for high-speed single-pixel image classification

Imaging techniques based on single-pixel detection, such as ghost imaging, can reconstruct or recognize a target scene from multiple measurements using a sequence of random mask patterns. However, the processing speed is limited by the low rate of the pattern generation. In this study, we propose an ultrafast method for random speckle pattern generation, which has the potential to overcome the limited processing speed. The proposed approach is based on multimode fiber speckles induced by fast optical phase modulation. We experimentally demonstrate dynamic speckle projection with phase modulation at 10 GHz rates, which is five to six orders of magnitude higher than conventional modulation approaches using spatial light modulators. Moreover, we combine the proposed generation approach with a wavelength-division multiplexing technique and apply it for image classification. As a proof-of-concept demonstration, we show that 28x28-pixel images of digits acquired at GHz rates can be accurately classified using a simple neural network. The proposed approach opens a novel pathway for an all-optical image processor.

physics.optics↗

arXiv2022

Optical skin: Sensor-integration-free multimodal flexible sensing

The biological skin enables animals to sense various stimuli. Extensive efforts have been made recently to develop smart skin-like sensors to extend the capabilities of biological skins; however, simultaneous sensing of several types of stimuli in a large area remains challenging because this requires large-scale sensor integration with numerous wire connections. We propose a simple, highly sensitive, and multimodal sensing approach, which does not require integrating multiple sensors. The proposed approach is based on an optical interference technique, which can encode the information of various stimuli as a spatial pattern. In contrast to the existing approach, the proposed approach, combined with a deep neural network, enables us to freely select the sensing mode according to our purpose. As a key example, we demonstrate simultaneous sensing mode of three different physical quantities, contact force, contact location, and temperature, using a single soft material without requiring complex integration. Another unique property of the proposed approach is spatially continuous sensing with ultrahigh resolution of few tens of micrometers, which enables identifying the shape of the object in contact. Furthermore, we present a haptic soft device for a human-machine interface. The proposed approach encourages the development of high-performance optical skins.

cs.CV↗

arXiv2021

Physical deep learning based on optimal control of dynamical systems

Deep learning is the backbone of artificial intelligence technologies, and it can be regarded as a kind of multilayer feedforward neural network. An essence of deep learning is information propagation through layers. This suggests that there is a connection between deep neural networks and dynamical systems in the sense that information propagation is explicitly modeled by the time-evolution of dynamical systems. In this study, we perform pattern recognition based on the optimal control of continuous-time dynamical systems, which is suitable for physical hardware implementation. The learning is based on the adjoint method to optimally control dynamical systems, and the deep (virtual) network structures based on the time evolution of the systems are used for processing input information. As a key example, we apply the dynamics-based recognition approach to an optoelectronic delay system and demonstrate that the use of the delay system allows for image recognition and nonlinear classifications using only a few control signals. This is in contrast to conventional multilayer neural networks, which require a large number of weight parameters to be trained. The proposed approach provides insight into the mechanisms of deep network processing in the framework of an optimal control problem and presents a pathway for realizing physical computing hardware.

cs.NE↗

arXiv2020

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

Decision making is a fundamental capability of living organisms, and has recently been gaining increasing importance in many engineering applications. Here, we consider a simple decision-making principle to identify an optimal choice in multi-armed bandit (MAB) problems, which is fundamental in the context of reinforcement learning. We demonstrate that the identification mechanism of the method is well described by using a competitive ecosystem model, i.e., the competitive Lotka--Volterra (LV) model. Based on the "winner-take-all" mechanism in the competitive LV model, we demonstrate that non-best choices are eliminated and only the best choice survives; the failure of the non-best choices exponentially decreases while repeating the choice trials. Furthermore, we apply a mean-field approximation to the proposed decision-making method and show that the method has an excellent scalability of $O(\log N)$ with respect to the number of choices $N$. These results allow for a new perspective on optimal search capabilities in competitive systems.

physics.app-ph↗

arXiv2020

Structural relaxation affecting shear transformation avalanches in metallic glasses

Avalanche behaviors, characterized by power-law statistics and structural relaxation that induces shear localization in amorphous plasticity, play an essential role in deciding the mechanical properties of amorphous metallic solids (i.e., metallic glasses). However, their interdependence is still not fully understood. To investigate the influence of structural relaxation on elementary avalanche behavior, we perform molecular-dynamics simulations for the shear deformation test of metallic glasses using two typical metallic-glass models comprising a less-relaxed (as-quenched) glass and a well-relaxed (well-aged) glass exhibiting a relatively homogeneous deformation and a shear-band-like heterogeneous deformation, respectively. The data on elementary avalanches obtained from both glass models follow the same power-law statistics with different maximum event sizes, and the well-relaxed glass shows shear localization. Evaluating the spatial correlation functions of the nonaffine squared displacements of atoms during each elementary avalanche event, we observe that the shapes of the elementary avalanche regions in the well-relaxed glasses tend to be anisotropic, whereas those in the less-relaxed glasses are relatively isotropic. Furthermore, we demonstrate that a temporal clustering in the direction of the avalanche propagation emerges, and a considerable correlation between the anisotropy and avalanche size exists in the well-relaxed glass model.

cond-mat.mtrl-sci↗

arXiv2019

Mean first passage times reconstruct the slowest relaxations in potential energy landscapes of nanoclusters

Relaxation modes are the collective modes in which all probability deviations from equilibrium states decay with the same relaxation rates. In contrast, a first passage time is the required time for arriving for the first time from one state to another. In this paper, we discuss how and why the slowest relaxation rates of relaxation modes are reconstructed from the first passage times. As an illustrative model, we use a continuous-time Markov state model of vacancy diffusion in KCl nanoclusters. Using this model, we reveal that all characteristics of the relaxations in KCl nanoclusters come from the fact that they are hybrids of two kinetically different regions of the fast surface and slow bulk diffusions. The origin of the different diffusivities turns out to come from the heterogeneity of the activation energies on the potential energy landscapes. We also develop a stationary population method to compute the mean first passage times as mean times required for pair annihilations of particle-hole pairs, which enables us to obtain the symmetric results of relaxation rates under the exchange of the sinks and the sources. With this symmetric method, we finally show why the slowest relaxation times can be reconstructed from the mean first passage times.

physics.atm-clus↗

arXiv2018

Changes of graph structure of transition probability matrices indicate the slowest kinetic relaxations

Graphs of the most probable transitions for a transition probability matrix, $e^{τK}$, i.e., the time evolution matrix of the transition rate matrix $K$ over a finite time interval $τ$, are considered. We study how the graph structures of the most probable transitions change as functions of $τ$, thereby elucidating that a kinetic threshold $τ_g$ for the graph structures exists. Namely, for $τ<τ_g$, the number of connected graph components are constant. In contrast, for $τ\geq τ_g$, recombinations of most probable transitions over the connected graph components occur multiple times, which introduce drastic changes into the graph structures. Using an illustrative multi-funnel model, we show that the recombination patterns indicate the existence of the eigenvalues and eigenvectors of slowest relaxation modes quite precisely. We also devise an evaluation formula that enables us to correct the values of eigenvalues with high accuracy from the data of merging processes. We show that the graph-based method is valid for a wide range of kinetic systems with degenerate, as well as non-degenerate, relaxation rates.

cond-mat.dis-nn↗

arXiv2018

Slowest kinetic modes revealed by metabasin renormalization

Understanding the slowest relaxations of complex systems, such as relaxation of glass-forming materials, diffusion in nanoclusters, and folding of biomolecules, is important for physics, chemistry, and biology. For a kinetic system, the relaxation modes are determined by diagonalizing its transition rate matrix. However, for realistic systems of interest, numerical diagonalization, as well as extracting physical understanding from the diagonalization results, is difficult due to the high dimensionality. Here, we develop an alternative and generally applicable method of extracting the long-time scale relaxation dynamics by combining the metabasin analysis of Okushima et al. [Phys. Rev. E 80, 036112 (2009)] and a Jacobi method. We test the method on a illustrative model of a four-funnel model, for which we obtain a renormalized kinematic equation of much lower dimension sufficient for determining slow relaxation modes precisely. The method is successfully applied to the vacancy transport problem in ionic nanoparticles [Niiyama et al. Chem. Phys. Lett. 654, 52 (2016)], allowing a clear physical interpretation that the final relaxation consists of two successive, characteristic processes.

cond-mat.mes-hall↗

arXiv2017

Barrier effect of grain boundaries on the avalanche propagation of polycrystalline plasticity

To investigate the barrier effect of grain boundaries on the propagation of avalanche-like plasticity at the atomic-scale, we perform three-dimensional molecular dynamics simulations by using simplified polycrystal models including symmetric-tilt grain boundaries. The cut-offs of stress-drop distributions following power-law distributions decrease as the size of the crystal grains decreases. We show that some deformation avalanches are confined by grain boundaries; on the other hand, unignorable avalanches penetrate all the grain boundaries included in the models. The blocking probability that one grain boundary hinders this system-spanning avalanche is evaluated by using an elemental probabilistic model.

cond-mat.mtrl-sci↗