Future lighting will be LED for point light sources and Organic LED for surface light sources. This research proposes a system that achieves multiplex communications under wide illumination while maintaining the lighting performance by utilizing the three-color RGB lighting of Organic LEDs. Using red and blue lights modulated signals, which have small overlap in emission wavelengths, and separating and demodulating color signals through color filters. With the current panel performance, the high cutoff frequency of the emission was 5.51 kHz for red and 6.97 kHz for blue, which did not reach the 20 kHz required for the targeted low-frequency transmission. Therefore, peaking was applied to the driving circuit to increase the Organic LED emission to higher frequencies. The cutoff frequency of the luminescence is above 20 kHz for both red and blue light, and it is expected to transmit audio signal. The measured overall crosstalk from the blue signal to the red signal was -25.7 dB, and from the red signal to the blue signal was -31.9 dB. Because the crosstalk values reduced to these values, proposed system can be adapted to transmit stereo signal in the speaker environment.

Condensation on traffic mirrors in winter makes it difficult to check safety. As a solution, we proposed an anti-condensation system that heats the mirror surface using induction heating with high power efficiency, utilizing daytime charging energy. A design method was proposed, and the design error was less than 10.8%. Induction heating coils were selected, and Litz wire coils were found to be optimal. A prototype self-excited oscillation circuit was fabricated and its performance was evaluated.

We proposed exponentiation conversion circuit utilizing weak inversion operation by circuit design using practical voltage input and output with reducing circuit area. Because conventional circuit used input and output signal current and needed to convert signal current to signal voltage, it is not practical. Conventional circuit enabled exponentiation conversion by converting input signal current logarithmically, amplifying by power exponent value and converting signal to current exponentially. In order to enable convert small signal current in weak inversion region to 8bit 256 steps signal voltage with 10 mV per step, it is necessary to mount a few MΩ current voltage conversion resistance and difficult to mount on IC. Therefore, we reduced area of current voltage conversion resistance by using mult step current mirror circuit to amplify current. In addition, we proposed method to suppress MOSFET area increasing as side effect with decreasing of current density and completed the IC design. Feasibility of this IC will be confirmed by prototype IC soon.

Brain-inspired intelligence technology is always cutting-edge research in Artificial Intelligence (AI). These years, mimicking the properties of nerve impulses in the brain, a new type of deep learning network structure has been introduced-Spiking Neural Networks (SNNs). However, the properties of SNNs are still poorly understood, especially their potential biological plausibility. Here, we investigated Spiking Recurrent Neural Networks (SRNNs) obtained by parameters transformation. We investigated their performance and characteristics when achieving working memory tasks under biological constraints from the real brain. Finally, it was proved that the constraints introduced by us are biologically reasonable and can help to create SNNs with keeping both working memory capacity and biological plausibility.

Peripheral sensitization, decrease in pain threshold in sensory neurons, can cause chronic pain. Little is known about how peripheral sensitization led to chronic pain. Here, we aimed to develop a method for evaluating peripheral sensitization in cultured sensory neurons with electrical recording. Sensory neurons from rat dorsal root ganglion were cultured on high-density microelectrode arrays, and their activity was evoked by capsaicin stimulation with or without substance P. Fluorescent imaging-based electrode selection effectively selected five times as many capsaicin-sensitive sensory neurons as the existing method. Peripheral sensitization by substance P was detected from 31.9% of selected sensory neurons, and majority of these neurons co-expressed capsaicin and substance P receptors. These results indicate that our method is suitable for evaluating peripheral sensitization by substance P in cultured sensory neurons.

A mean-field game based on a static output feedback (SOF) strategy for delay stochastic systems is considered. First, after defining the stabilization problem for a single decision maker, the problem of minimizing the upper bound of the cost function is given via the cost guarantee cost control theory. For this problem, the KKT condition establishes a necessary condition that satisfies the suboptimality by using a stochastic large-scale matrix equations. Then, using the obtained preliminary results, we apply it to the Pareto optimal strategy, which is a cooperative game, for the mean-field stochastic system involving a large number of decision makers. The main contribution is to derive a design method for deriving a centralized strategy. To obtain the strategy set, the Newton's method is considered. Furthermore, a new decomposition algorithm is discussed to avoid the huge dimensional problem. Finally, a simple numerical example is performed to demonstrate the usefulness and effectiveness of the proposed set of strategies.

In embedded system industry in Japan, few young engineers have a comprehensive understanding the important embedded software skills that determine the product performance, especially interrupt processing skill. As a result, this is a factor that causes a failure. I propose a curriculum for the purpose to improve this problem and method to verify the training effect.

This paper proposes a PID tuning method based on model matching in the frequency domain. Using the proposed method, PID tuning can be performed considering the trade-off between control bandwidth and stability margin, even if the plant model is unknown, as long as the frequency characteristics of the plant are known. The effectiveness of the proposed method is shown through a numerical experiment where the plant model is given and an experiment of a position control of an actual mass-spring system whose transfer function model is unknown.

Recently, robots have been introduced into the painting process to save operator labor. However, since painting quality is greatly affected by the painting environment, a mechanism is required to adjust the operating condition of the robot according to the environmental condition. In this paper, based on the Just-in-Time method, a mechanism for determining the operating condition (bell cup rotation speed) of a painting robot based on various environmental condition, is newly proposed. Moreover, the effectiveness of the proposed method is verified by a numerical example simulating a painting process.

Speech training methods using speech presentation are difficult to adapt to learning foreign languages or supporting the hearing impaired. Nonetheless, this issue could be resolved by displaying articulation movements. Therefore, it is necessary to devise a method of displaying articulation movements for speech training situations. In this study, real-time MRI (rtMRI) movie images of actual articulation movements were analyzed. However, the articulation movements imaged in rtMRI movies are incredibly fine and fast and impossible to discern with the naked eye. Therefore, we investigated a method for detecting articulation movements captured from rtMRI movies using optical flow. Analysis was performed on an rtMRI movie during /ra/ single-mora vocalizations, and movements of the tongue contacting and separating from the posterior part of the gums were well observed. These are considered to constitute detected articulation movements for /ra/ verbalizations. Thus, the proposed method could be used to confirm the movements during any mora speech. Furthermore, the proposed method could be useful for selecting articulation movements to be presented during speech training.

Neural Network Collocation Method (NNCM) is a kind of model fitting method and is an effective imaging technique for application plasma tomographic imaging field that has limited to the number of views. The reconstructed image is represented as nonlinear combination of the basis functions by the activation functions of NNCM. NNCM could fit models based on few spatial location data because every basis function cover whole reconstruction region and are trained based on error back propagation algorithm. Therefore, the image could be effectively reconstructed even from few-view poor projection data. However, application of NNCM to practical imaging has an issue that NNCM requires many training epochs even to reconstruct a small-scale image. The main reason of the issue is attributed to that the small gradient value of sigmoid activation functions let the training speed slow.

The paper proposes an improvement of NNCM by replacing the activation functions of hidden layers of NNCM with deep learning type ones which have larger gradient values, ReLU and tanh, to increase training speed of NNCM. Numerical simulation results show the effectiveness of the proposed method.

There is a learning method called “problem posing” in which learners create their own tasks. Previous research show that the problem posing can make the learner initiative, which enables more effective review and helps to consolidate the understanding than in conventional learning. For this reason, the learning with problem posing is said to be more effective than general learning. There have been several studies on programming learning support focusing on the effects of problem posing. In this study, we focus on fill-in-blank problems, which are widely used in programming education, and design and develop a new system that provides a learning task of problem posing on fill-in-the-blank programming problem. Fill-in-blank is a programming learning task that has been generally recognized as effective for learning. Therefore, asking students to post a learning task of fill-in-blank would be appropriate, and the problem posing of fill-in-blank programming problems is expected to be a higher learning effectiveness than usual fill-in-blank task.

Drug delivery systems (DDS) that deliver drugs selectively and efficiently to target sites can be made more effective by using ultrasound. However, ultrasound DDS has a problem of low drug release rate. In this study, we propose liposomes encapsulating metal nanoparticles as a new drug carrier with enhanced responsiveness to ultrasound stimulation. We report the results of microscopic observations of changes in brightness values within liposomes to confirm the improvement in ultrasound response.

The morphology and mobility of cells would be altered depending on the elastic modulus of the scaffold. However, previous research has not been able to reproduce the partial gradient of elastic modulus and reversible changes. In this study, we propose a scaffold that can partially and reversibly change its elastic modulus by using a magnetic gel containing magnetic particles in a hydrogel. To confirm the cells' reactivity on the magnetic gel, we observe the cells on a magnetic gel under a microscope when applying magnetic field during incubation.

Active learning using virtual reality (VR), which allows participants to experience the same effects as the real thing using all five senses, is attracting attention as a new learning method to effectively develop ICCs. In this study, we aim to increase subjects’ motivation and interest in different cultures by constructing a VR environment where subjects can communicate with foreigners in the state of an avatar.

Laser imagers using KTN crystals require a large number of observations to obtain high-resolution images, resulting in long measurement times. In this paper, we propose a method to perform appropriate dot scanning by determining the probability density distribution of the next scan based on the results of previous scans and their restoration result. Compared to randomly scanned images with uniform weights, the proposed scanning method produces more accurate processing results.