18 research outputs found
A Coupled Spintronics Neuromorphic Approach for High-Performance Reservoir Computing
The rapid development in the field of artificial intelligence has increased the demand for neuromorphic computing hardware and its information-processing capability. A spintronics device is a promising candidate for neuromorphic computing hardware and can be used in extreme environments due to its high resistance to radiation. Improving the information-processing capability of neuromorphic computing is an important challenge for implementation. Herein, a novel neuromorphic computing framework using spintronics devices is proposed. This framework is called coupled spintronics reservoir (CSR) computing and exploits the high-dimensional dynamics of coupled spin-torque oscillators as a computational resource. The relationships among various bifurcations of the CSR and its information-processing capabilities through numerical experiments are analyzed and it is found that certain configurations of the CSR boost the information-processing capability of the spintronics reservoir toward or even beyond the standard level of machine learning networks. The effectiveness of our approach is demonstrated through conventional machine learning benchmarks and edge computing in real physical experiments using pneumatic artificial muscle-based wearables, which assist human operations in various environments. This study significantly advances the availability of neuromorphic computing for practical uses
Sudden change in fractality of basin boundary in passive dynamic walking
The 11th International Symposium on Adaptive Motion of Animals and Machines. Kobe University, Japan. 2023-06-06/09. Adaptive Motion of Animals and Machines Organizing Committee.Poster Session P7
A mechanical true random number generator
Random number generation has become an indispensable part of information processing: it is essential for many numerical algorithms, security applications, and in securing fairness in everyday life. Random number generators (RNGs) find application in many devices, ranging from dice and roulette wheels, via computer algorithms, lasers to quantum systems, which inevitably capitalize on their physical dynamics at respective spatio-temporal scales. Herein, to the best of our knowledge, we propose the first mathematically proven true RNG (TRNG) based on a mechanical system, particularly the triple linkage of Thurston and Weeks. By using certain parameters, its free motion has been proven to be an Anosov flow, from which we can show that it has an exponential mixing property and structural stability. We contend that this mechanical Anosov flow can be used as a TRNG, which requires that the random number should be unpredictable, irreproducible, robust against the inevitable noise seen in physical implementations, and the resulting distribution’s controllability (an important consideration in practice). We investigate the proposed system’s properties both theoretically and numerically based on the above four perspectives. Further, we confirm that the random bits numerically generated pass the standard statistical tests for random bits
Embedding bifurcations into pneumatic artificial muscle
Harnessing complex body dynamics has been a long-standing challenge in
robotics. Soft body dynamics is a typical example of high complexity in
interacting with the environment. An increasing number of studies have reported
that these dynamics can be used as a computational resource. This includes the
McKibben pneumatic artificial muscle, which is a typical soft actuator. This
study demonstrated that various dynamics, including periodic and chaotic
dynamics, could be embedded into the pneumatic artificial muscle, with the
entire bifurcation structure using the framework of physical reservoir
computing. These results suggest that dynamics that are not presented in
training data could be embedded by using this capability of bifurcation
embeddment. This implies that it is possible to embed various qualitatively
different patterns into pneumatic artificial muscle by learning specific
patterns, without the need to design and learn all patterns required for the
purpose. Thus, this study sheds new light on a novel pathway to simplify the
robotic devices and training of the control by reducing the external pattern
generators and the amount and types of training data for the control
Delayed Acute Perimyocarditis and Bilateral Facial Nerve Palsy in a Patient with COVID-19
A 41-year-old Japanese man was admitted to our hospital with acute perimyocarditis 4 weeks after coronavirus disease 2019 (COVID-19) infection. Ten days after admission, the patient showed bilateral facialnerve palsy in the course of improvement of perimyocarditis under treatment with aspirin and colchicine. After prednisolone therapy, perimyocarditis completely improved, and the facial nerve palsy gradually improved. Acute perimyocarditis and facial nerve palsy can occur even 4 weeks after contracting COVID-19