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.

Novel missense variants cause intermediate phenotypes in the phenotypic spectrum of SLC5A6-related disorders.

SLC5A6 encodes the sodium-dependent multivitamin transporter, a transmembrane protein that uptakes biotin, pantothenic acid, and lipoic acid. Biallelic SLC5A6 variants cause sodium-dependent multivitamin transporter deficiency (SMVTD) and childhood-onset biotin-responsive peripheral motor neuropathy (COMNB), which both respond well to replacement therapy with the above three nutrients. SMVTD usually presents with various symptoms in multiple organs, such as gastrointestinal hemorrhage, brain atrophy, and global developmental delay, at birth or in infancy. Without nutrient replacement therapy, SMVTD can be lethal in early childhood. COMNB is clinically milder and has a later onset than SMVTD, at approximately 10 years of age. COMNB symptoms are mostly limited to peripheral motor neuropathy. Here we report three patients from one Japanese family harboring novel compound heterozygous missense variants in SLC5A6, namely NM_021095.4:c.[221C>T];[642G>C] p.[(Ser74Phe)];[(Gln214His)]. Both variants were predicted to be deleterious through multiple lines of evidence, including amino acid conservation, in silico predictions of pathogenicity, and protein structure considerations. Drosophila analysis also showed c.221C>T to be pathogenic. All three patients had congenital brain cysts on neonatal cranial imaging, but no other morphological abnormalities. They also had a mild motor developmental delay that almost completely resolved despite no treatment. In terms of severity, their phenotypes were intermediate between SMVTD and COMNB. From these findings we propose a new SLC5A6-related disorder, spontaneously remitting developmental delay with brain cysts (SRDDBC) whose phenotypic severity is between that of SMVTD and COMNB. Further clinical and genetic evidence is needed to support our suggestion.

Analysis of temporal structure of laser chaos by Allan variance.

Allan variance has been widely utilized for evaluating the stability of the time series generated by atomic clocks and lasers, in time regimes ranging from short to extremely long. This multiscale 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. In the present study, we demonstrated 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. While the detection of LFFs is difficult with the conventional power spectrum analysis method in the low-frequency regime, the Allan variance approach clearly captured the appearance of multiple time-scale dynamics, such as LFFs. This study demonstrates that Allan variance can help in understanding and characterizing diverse laser dynamics, including LFFs, spanning a wide range of timescales.

Controlling chaotic itinerancy in laser dynamics for reinforcement learning.

Photonic artificial intelligence has attracted considerable interest in accelerating machine learning; however, the unique optical properties have not been fully used for achieving higher-order functionalities. Chaotic itinerancy, with its spontaneous transient dynamics among multiple quasi-attractors, can be used to realize brain-like functionalities. In this study, we numerically and experimentally investigate a method for controlling the chaotic itinerancy in a multimode semiconductor laser to solve a machine learning task, namely, the multiarmed bandit problem, which is fundamental to reinforcement learning. The proposed method uses chaotic itinerant motion in mode competition dynamics controlled via optical injection. We found that the exploration mechanism is completely different from a conventional searching algorithm and is highly scalable, outperforming the conventional approaches for large-scale bandit problems. This study paves the way to use chaotic itinerancy for effectively solving complex machine learning tasks as photonic hardware accelerators.

Universal Single-Mode Lasing in Fully Chaotic Billiard Lasers.

By numerical simulations and experiments of fully chaotic billiard lasers, we show that single-mode lasing states are stable, whereas multi-mode lasing states are unstable when the size of the billiard is much larger than the wavelength and the external pumping power is sufficiently large. On the other hand, for integrable billiard lasers, it is shown that multi-mode lasing states are stable, whereas single-mode lasing states are unstable. These phenomena arise from the combination of two different nonlinear effects of mode-interaction due to the active lasing medium and deformation of the billiard shape. Investigations of billiard lasers with various shapes revealed that single-mode lasing is a universal phenomenon for fully chaotic billiard lasers.

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 28×28-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.

Speckle-based high-resolution multimodal soft sensing.

Skin-like soft sensors are key components for human-machine interfaces; however, the simultaneous sensing of several types of stimuli remains challenging because large-scale sensor integration is required with numerous wire connections. We propose an optical high-resolution multimodal sensing approach, which does not require integrating multiple sensors. This approach is based on the combination of an optical scattering phenomenon, which can encode the information of various stimuli as a speckle pattern, and a decoding technique using deep learning. We demonstrate the simultaneous sensing of three different physical quantities-contact force, contact location, and temperature-with a single soft material. Another unique capability of the proposed approach is spatially continuous sensing with an ultrahigh resolution of few tens of micrometers, in contrast to previous multimodal sensing approaches. Furthermore, a haptic soft device is presented for a human-machine interface. Our approach encourages the development of high-performance smart skin-like sensors.

Using multidimensional speckle dynamics for high-speed, large-scale, parallel photonic computing.

The recent rapid increase in demand for data processing has resulted in the need for novel machine learning concepts and hardware. Physical reservoir computing and an extreme learning machine are novel computing paradigms based on physical systems themselves, where the high dimensionality and nonlinearity play a crucial role in the information processing. Herein, we propose the use of multidimensional speckle dynamics in multimode fibers for information processing, where input information is mapped into the space, frequency, and time domains by an optical phase modulation technique. The speckle-based mapping of the input information is high-dimensional and nonlinear and can be realized at the speed of light; thus, nonlinear time-dependent information processing can successfully be achieved at fast rates when applying a reservoir-computing-like-approach. As a proof-of-concept, we experimentally demonstrate chaotic time-series prediction at input rates of 12.5 Gigasamples per second. Moreover, we show that owing to the passivity of multimode fibers, multiple tasks can be simultaneously processed within a single system, i.e., multitasking. These results offer a novel approach toward realizing parallel, high-speed, and large-scale photonic computing.

Photonic reservoir computing based on nonlinear wave dynamics at microscale.

High-dimensional nonlinear dynamical systems, including neural networks, can be utilized as computational resources for information processing. In this sense, nonlinear wave systems are good candidates for such computational resources. Here, we propose and numerically demonstrate information processing based on nonlinear wave dynamics in microcavity lasers, i.e., optical spatiotemporal systems at microscale. A remarkable feature is its ability of high-dimensional and nonlinear mapping of input information to the wave states, enabling efficient and fast information processing at microscale. We show that the computational capability for nonlinear/memory tasks is maximized at the edge of dynamical stability. Moreover, we show that computational capability can be enhanced by applying a time-division multiplexing technique to the wave dynamics. Thus, the computational potential of the wave dynamics can sufficiently be extracted even when the number of detectors to monitor the wave states is limited. In addition, we discuss the merging of optical information processing with optical sensing, revealing a novel method for model-free sensing by using a microcavity reservoir as a sensing element. These results pave a way for on-chip photonic computing with high-dimensional dynamics and a model-free sensing method.

Decision making for the multi-armed bandit problem using lag synchronization of chaos in mutually coupled semiconductor lasers.

We numerically and experimentally demonstrate the utilization of the synchronization of chaotic lasers for decision making. We perform decision making to solve the multi-armed bandit problem using lag synchronization of chaos in mutually coupled semiconductor lasers. We observe the spontaneous exchanges of the leader-laggard relationship under lag synchronization of chaos, and we find that the leader laser can be controlled by changing the coupling strengths between the two lasers. To solve the multi-armed bandit problem, we select one of the slot machines with unknown hit probabilities based only on the identity of the leader laser while reconfiguring the coupling strength to determine the correct decision. We successfully perform an on-line experimental demonstration of the decision making based on the two-laser coupled architecture. This is the first time that synchronization in chaotic lasers is utilized for decision making, and this study paves the way for novel resources for future photonic intelligence.

On-chip photonic decision maker using spontaneous mode switching in a ring laser.

Efficient and accurate decision making is gaining increased importance with the rapid expansion of information communication technologies including artificial intelligence. Here, we propose and experimentally demonstrate an on-chip, integrated photonic decision maker based on a ring laser. The ring laser exhibits spontaneous switching between clockwise and counter-clockwise oscillatory dynamics; we utilize such nature to solve a multi-armed bandit problem. The spontaneous switching dynamics provides efficient exploration to find the accurate decision. On-line decision making is experimentally demonstrated including autonomous adaptation to an uncertain environment. This study paves the way for directly utilizing the fluctuating physics inherent in ring lasers, or integrated photonics technologies in general, for achieving or accelerating intelligent functionality.

Compact reservoir computing with a photonic integrated circuit.

Photonic reservoir computing is a new paradigm for performing high-speed prediction and classification tasks in an efficient manner. The major challenge for the miniaturization of photonic reservoir computing is the need for the use of photonic integrated circuits. Herein, we experimentally demonstrate reservoir computing using a photonic integrated circuit with a semiconductor laser and a short external cavity. We propose a method to increase the number of virtual nodes in delayed feedback using short node intervals and outputs from multiple delay times. We perform time-series prediction and nonlinear channel equalization tasks using reservoir computing with the photonic integrated circuit. We show that the photonic integrated circuit with optical feedback outperforms the photonic integrated circuit without optical feedback for prediction tasks. To enhance the memory effect we feed past input signals in the current input data and demonstrate successful performance in an n-step-ahead prediction task.

Common-signal-induced synchronization in photonic integrated circuits and its application to secure key distribution.

We experimentally achieve common-signal-induced synchronization in two photonic integrated circuits with short external cavities driven by a constant-amplitude random-phase light. The degree of synchronization can be controlled by changing the optical feedback phase of the two photonic integrated circuits. The change in the optical feedback phase leads to a significant redistribution of the spectral energy of optical and RF spectra, which is a unique characteristic of PICs with the short external cavity. The matching of the RF and optical spectra is necessary to achieve synchronization between the two PICs, and stable synchronization can be obtained over an hour in the presence of optical feedback. We succeed in generating information-theoretic secure keys and achieving the final key generation rate of 184 kb/s using the PICs.

Chaotic laser based physical random bit streaming system with a computer application interface.

We demonstrate a random bit streaming system that uses a chaotic laser as its physical entropy source. By performing real-time bit manipulation for bias reduction, we were able to provide the memory of a personal computer with a constant supply of ready-to-use physical random bits at a throughput of up to 4 Gbps. We pay special attention to the end-to-end entropy source model describing how the entropy from physical sources is converted into bit entropy. We confirmed the statistical quality of the generated random bits by revealing the pass rate of the NIST SP800-22 test suite to be 65 % to 75 %, which is commonly considered acceptable for a reliable random bit generator. We also confirmed the stable operation of our random bit steaming system with long-term bias monitoring.

Signature of Wave Chaos in Spectral Characteristics of Microcavity Lasers.

We report an experimental investigation on the spectra of fully chaotic and nonchaotic microcavity lasers under continuous-wave operating conditions. It is found that fully chaotic microcavity lasers operate in single mode, whereas nonchaotic microcavity lasers operate in multimode. The suppression of multimode lasing for fully chaotic microcavity lasers is explained by large spatial overlaps of the resonance wave functions that spread throughout the two-dimensional cavity due to the ergodicity of chaotic ray orbits.

Fast physical random bit generation with photonic integrated circuits with different external cavity lengths for chaos generation.

We generate random bit sequences from chaotic temporal waveforms by using photonic integrated circuits (PICs) with different external cavity lengths. We investigate the condition for generating random bits at different sampling rates of single-bit generation method with the PICs. We succeed in generating certified random bit sequences by using the PIC with 3, 4, 5, or 10-mm-long external cavity, whereas random bits cannot pass all the statistical tests of randomness when the PIC with 1 or 2 mm-long external cavity is used.

Lasing of TM modes in a two-dimensional GaAs microlaser.

We fabricated and tested an unstrained GaAs single-quantum-well microlaser which has a two-dimensional cavity shape known as the Penrose unilluminable room. The cavity exhibits quasi-one-dimensional modes, namely axial, diamond-shaped, and V-shaped modes. In contrast to previous observations of TE-polarized emission in GaAs microlasers, we observed TM-polarized emission. We explain this observation as being the result of lasing of the diamond-shaped modes whose incident angle at the cavity interface is very close to the Brewster angle.

Selective excitation of lowest-order transverse ring modes in a quasi-stadium laser diode.

For a two-dimensional quasi-stadium laser diode, we demonstrate stable excitation of the lowest-order transverse ring modes by optimally designing the confocal end mirrors of the laser cavity based on extended Fox-Li mode calculations. We observe kink-free light output versus injection current characteristics and highly directional single-peak emissions corresponding to the diamond-shaped trajectory in the cavity. These results provide convincing evidence for selective excitation of the lowest-order transverse modes.

Randomness in a Galton board from the viewpoint of predictability: sensitivity and statistical bias of output states.

The Galton board is a classic example of the appearance of randomness and stochasticity. In the dynamical model of the Galton board, the macroscopic motion is governed by deterministic equations of motion, and predictability depends on uncertainty in the initial conditions and its evolution by the dynamics. In this sense the Galton board is similar to coin tossing. In this paper, we analyze a simple dynamical model which is inspired by the Galton board. Especially, we focus on the predictability, considering the relation between the uncertainty of initial states and the structure of basins of initial states that result in the same exit state. The model has basins with fractal basin structure, unlike the basins in coin tossing models which have only finite structure. Arbitrarily small uncertainty of initial conditions can cause unpredictability of final states if the initial conditions are chosen in fractal regions. In this sense, our model is in a different category from the coin tossing model. We examine the predictability of a small Galton board model from the viewpoint of the sensitivity and the statistical bias of final states. We show that it is possible to determine the radii of scatterers corresponding to a given predictability criterion, specified as a statistical bias, and a given uncertainty of initial conditions.