The conventional optical systems have the limitation for using the multi-wavelengths due to a large systems, in which the dichroic mirrors and cameras are employed. We have been developing a multi-wavelength evanescent optical systems by using a prism to spectralize wavelengths. Moreover, not only the input wavelengths, but also the other wavelengths were able to be observed such as fluorescence light. In this paper, we estimated the wavelength of fluorescence by fluorescent particles and verified measured the wavelength by the developed optical system.

The phenomenon in which the focus of light attracts objects has been utilized as a method for trapping small particles, among other applications. This focus can be generated by localized surface plasmon polaritons (LSPPs), which confine light to the nano-gaps in metal structures. By utilizing LSPPs, multiple focuses can be created, increasing the number of trapping points. In this study, we aim to induce particle motion through LSPPs that are excited when the direction of the gap and the polarization of the light are perpendicular to each other. This report presents experimental results that demonstrate how particle motion changes in response to variations in polarization.

We are focusing on Goos-Hänschen shift (GH shift) measurement, which is a method for measuring the position of reflected light from a resonant grating on a metal surface, as a highly sensitive biosensor. This method allows for high-speed measurements because it does not require a mechanical drive unit in the measurement device. In previous studies, the concentration of ethanol solutions was measured. In this study, we have succeeded in effectively removing noise from GH shift measurements by using Noise2Noise deep learning, which does not require a clean signal.

In recent years, nano-bubbles have been utilized across various fields, with their effectiveness actively explored. However, there are few examples of microscopic studies, and the effects of nano-bubbles are unknown. Distinguishing between nano-bubbles and nano-particles is essential for understanding nano-bubble behavior when they mixed. Therefore, we observed the behavior of nano-bubbles and nano-particles in liquids using dark-field microscopy, focusing on their identification. In this paper, we verify that the rapid fluctuations in scattered light intensity of nano-bubbles are caused by their deformation.

This study aims to establish a high-precision, non-contact measurement method for determining the Q value corresponding to the shear modulus of metallic materials. The Q value is a critical parameter in vibration analysis using finite element methods, which is essential for accurately evaluating vibrational characteristics. However, a reliable database of Q values is currently unavailable. Conventional measurement approaches are hindered by the influence of support loss. To overcome this limitation, this research proposes a novel measurement technique that combines near-field ultrasonic non-contact support using a Langevin transducer with non-contact excitation method through an EMAT (Electromagnetic Acoustic Transducer).

Acoustophoresis, known for its non-contact mechanism and energy efficiency, has shown great promise in manipulating biological samples, with broad potential in bioengineering techniques. However, its low throughput remains a major challenge, limiting its clinical applications. We have already proposed an Elliptical Reflector Focusing Transducer and successfully utilized it to drive microfluidic chip to focus 5-µm particles at a high flow rate of 5 mL/min. Despite this success, the resonance frequency mismatch between the chip and the transducer's peak velocities limited further throughput enhancement. In this study, we optimized the bulk-wave acoustophoresis device by matching the resonance frequencies of the chip and transducer. Simulations demonstrated an improved efficiency using frequency-matched chips compared to the original device, providing a clear optimization strategy for bulk-wave acoustophoresis systems. We are currently conducting experiments to validate the improved particle focusing performance of the optimized prototype. This work offers a promising solution to improve the throughput of acoustofluidic devices, paving the way for broader clinical and industrial applications.

Surface acoustic wave (SAW) devices are essential in various fields, such as sensors and actuators. To solve the problems of conventional SAW devices (interdigital transducers and wedge transducers), such as material limitation, output power limitation, and driving frequency limitation, we have proposed a novel Rayleigh wave device called 'ELIPS SAW device.' It can strongly excite the Rayleigh waves on any solid materials in the megahertz range by the elliptical reflector focusing structure (ELIPS).This study verified that the ELIPS SAW device can achieve multi-frequency strong SAW excitation in the 1-10 MHz range in a single device, which was impossible with conventional SAW devices. For example, in this study, we tried to measure the liquid sample's bulk modulus by measuring the frequency dependence of leaky SAW's attenuation coefficient.

Ultrasonic motors offer unique advantages including miniaturization, low noise, high holding torque, and rapid response, making them ideal for various applications. Torque control is crucial for their operation, particularly in robotics and gripping mechanisms. While traditional torque control methods rely on physical sensors to obtain real-time torque values, this approach hinders miniaturization in experimental environments and introduces additional noise and costs. By collecting relevant characteristic variables during motor operation and inputting them into time series prediction networks, reasonably accurate sensorless control of ultrasonic motors can be achieved compared to sensor-based methods. This alternative approach provides new possibilities for miniaturized applications of ultrasonic motors.

Next generation ultrasound cleaning technology is effective in removing fine stains, and in recent years, its application to cleaning silicon wafers has attracted significant attention. To achieve high-efficiency cleaning without causing defects on the silicon wafer surface, it is necessary to drive at a high frequency band with a high cavitation threshold. However, current ultrasonic cleaning devices face challenges, such as the difficulty of performing cleaning at frequencies above the MHz range. In this study, high-power operation in the MHz range was achieved by focusing ultrasound with parabolic reflection surface.

We proposed a system capable of real-time observation of cellular responses to ultrasound irradiation, aiming to optimize ultrasound irradiation conditions in the field of biology.A prototype transducer was developed, featuring an ultrasonic focusing mechanism with two elliptical reflective surfaces and a cylindrical design that can enclose the objective lens of a microscope. Using this transducer, we successfully observed calcium influx into cells in real-time during ultrasound irradiation.

Piezoelectric actuator possess fast response and high force generation; however, hysteresis and creep limit its precision control. In many cases, precise position control using displacement sensors is impractical due to increased spatial and economic costs. To overcome these problems, our previous research developed a self-sensing piezoelectric actuator control system using a real-time permittivity detection method. This approach is based on the assumed one-to-one relationship between permittivity change and piezoelectric displacement. However, experimental results revealed that the permittivity change information is insufficient to fully eliminate the hysteresis. In this study, to address this limitation, we introduced the complex permittivity, which incorporates leakage current (resistance change) and employed a deep learning method to capture complex and subtle nonlinear relationships. As a result, the piezoelectric displacement could be predicted precisely compared to the previous system.

Rehabilitation is performed by occupational therapists and physical therapists under the guidance of physicians to prevent or improve contractures. However, these are time constraints and physical strain on the patient and surgeon. This study focuses on the finger movements and aims to develop a finger movement rehabilitation system using image processing. The proposed system analyzes the finger position using MediaPipe and controls the motor of the rehabilitation assistive device using the motion of the healthy finger to promote the flexion and extension motion of the finger on the affected side.

The myoelectric data used to control myoelectric prosthetic hands varies depending on the position of the sensor's attachment, individual differences among users, physical condition and muscle fatigue. Therefore, the development of myoelectric prosthetic hands using deep learning requires both the quantity and quality of training data. This study proposes a myoelectric data augmentation method using CGAN and aims to realize high-performance and multifunctional myoelectric prosthetic hands by generating a variety of myoelectric data based on a small amount of actual measurement data.

Postural feedback training during standing has the potential to improve mental health, but its underlying mechanisms remain unclear. This study investigated the possibility that enhanced body ownership contributes to mental health improvement. Twelve healthy young adults were divided into three groups: a training group, a perturbation group where feedback information was disturbed, and a control group. Over two weeks, participants underwent four experimental sessions. The impact on mental health was evaluated using the General Health Questionnaire-28 and heart rate variability indices, while the influence on body ownership was assessed through the proprioceptive drift observed in the rubber hand illusion test.

Long-term ultrasound (US) monitoring holds promise for facilitating early diagnosis; however, it remains challenging with conventional hand-held US probes. Although flexible, textile-based US probes with air permeability and low mechanical stress have been developed, their limited element density compromises US image quality. In this study, we aimed to enhance the image quality of flexible US probes by developing a narrow-pitch flexible US probe. A probe with a 0.6 mm element pitch was fabricated by cutting and attaching copper foil onto a cloth substrate for electrode wiring. The amplitude and noise level of US signals from each element were evaluated using an agar gel phantom. The results demonstrated the feasibility of a flexible US probe incorporating copper foil wiring on a cloth substrate.

Aortic dissection tends to have a very poor prognosis, and there is a need to establish a technique for rapid evaluation of vessel wall pressure and stenting effect based on analysis of CT data in order to optimize treatment. The purpose of this study is to develop an efficient method for generating aortic mesh for fluid-structure interaction analysis from CT data of patients with aortic dissection. In this paper, we propose a method for rapid generation of aortic mesh for analysis of the aortic ductal structure and its three branches, which is an extension of the model-based method based on tracking of the vessel centerline.

To miniaturize electronic devices, it is essential to make the mechanical components, such as housings and hinges, smaller. This research aims to apply Zr-based metallic glass, which has excellent mechanical properties, to these parts. In this report, we used end milling tools made of different materials to machine grooves on Zn-based metallic glass surfaces and considered tool wear, cutting resistance, and machining accuracy. As a result, we found that CBN tools gave better results regarding wear resistance and machining accuracy than other tools, such as DLC-coated tools.

Although there has been much research on anomaly detection systems using deep learning in cutting, there has not been enough research on machined surface defect detection systems for CFRP. In this study, cutting noise was monitored and analyzed to construct a real-time defect detection system for CFRP cutting. As a result, it was found that an impulsive noise, which is very different from the normal cutting noise, is generated when a defect occurs.