In recent years, many kinds of sign sounds have come into use in our daily life. The purpose of sign sounds is to communicate a message. However the ease of message understanding and listening comfort have rarely been taken into consideration. Because people have different tastes and attach a different "image" to sign sounds according to their individual sensibility, it is important for each person to be able to make melodies freely and easily, and customize sign sounds. We propose a sign sounds design system using an Interactive genetic algorithm (IGA). The proposed system produces a melody of two bars based on this IGA. A melody is considered to be a sort of "organism," whose chromosome consists of genes indicating the length, pitch, and velocity of the notes of the melody. Experimental results showed that this system is effective for generating sign sounds. In creating auditory signals, it was found that the method of evolving part-by-part is better than the method of evolving the whole two bars.

Functional near-infrared spectroscopy (fNIRS) is suitable for noninvasive mapping of relative changes in regional cortical activity but is limited for quantitative comparisons among cortical sites, subjects, and populations. We have developed a convolutional neural network (CNN) analysis method that learns feature vectors for accurate identification of group differences in fNIRS responses. In this study, subject gender was classified using CNN analysis of fNIRS data. fNIRS data were acquired from male and female subjects during a visual number memory task performed in a white noise environment because previous studies had revealed that the pattern of cortical blood flow during the task differed between males and females. A learned classifier accurately distinguished males from females based on distinct fNIRS signals from regions of interest (ROI) including the inferior frontal gyrus and premotor areas that were identified by the learning algorithm. These cortical regions are associated with memory storage, attention, and task motor response. The accuracy of the classifier suggests stable gender-based differences in cerebral blood flow during this task. The proposed CNN analysis method can objectively identify ROIs using fNIRS time series data for machine learning to distinguish features between groups.

In recent years, various types of equipment have become more intelligent. In this research, we propose an intelligent lighting system using visible-light communication technology for direct communication between lighting fixtures and illuminance sensors in order to provide the necessary illuminance to a desired location. We actually constructed an experiment simulator based on this concept and verified the effectiveness of the newly developed control method. Verification tests were conducted using an optimization algorithm specialized for lighting control, and the results showed that the various illuminance sensors converged to the preset target illuminance in a very short time. We also confirmed that the system can respond adaptively to the movement of illuminance sensors

Introduction: Many people spend a considerable amount of time performing intellectual activities within auditory environments that affect work efficiency. To investigate auditory environments that improve working efficiency, we investigated the relationship between brain activity and performance of the number memory task in environments with and without white noise using functional near‐infrared spectroscopy (fNIRS).Methods: Twenty‐nine healthy subjects (aged 21.9 ± 1.4 years) performed the number memory task in both the white noise and silent environments. Cerebral blood flow changes during the task were measured using an ETG‐7100 fNIRS system (Hitachi, Ltd., Tokyo, Japan). The psychological states of the subjects were also estimated by subjective ratings of the pleasantness of the auditory environment. Then, they were divided into three groups based on their task scores. The differences in the cerebral blood flow (CBF) changes, functional connection strength, and the subjects’ feelings of pleasantness to the noise between the subject groups were analyzed and discussed.Results: The first group felt that the white noise was pleasant, which strengthened the bilateral functional connections between the brain regions related to the memory task. Therefore, the subjects’ task performance improved in the white noise environment. Although the second group felt that the white noise was uncomfortable, the frontal regions related to attention control were more activated in the white noise environment to sustain the task performance in the noisy environment. The third group felt that the white noise was unpleasant, and their CBF decreased in that environment, which was associated with deteriorated task performance.Conclusions: Task performance was closely related to the subjects’ feelings of pleasantness to the noise. The results of the analysis of the CBF changes and functional connectivity suggested that the effects of the white noise on brain activity differed among the three groups.

MOEA/D is one of the multiobjective optimization methods in which an optimization problem is decomposed into subproblems and searches are performed. When the search difficulty of each subproblem is equal, the obtained Pareto solutions are evenly distributed. In contrast, in real problems, the search complexity of each subproblem is often not equal. In that case, a Pareto solution set to a difficult subproblem cannot be found by an easy search. To solve this problem, a method that adaptively assigns the weight vectors of MOEA/D according to the search situation is proposed. In the proposed method, subproblems that are difficult to search are divided into more subproblems, the search speeds of subproblems with different search difficulties are hence equalized, and solutions over a wider range should be found. The proposed method was used to a real-world problem, and its effectiveness is discussed. The target problem is to identify important brain regions using real data from a noninvasive functional brain imaging device. Identifying important brain regions is expected to promote elucidation of brain functions and contribute to effective training and therapeutic methods to improve human cognitive function. Compared to conventional MOEA/D, good solutions were obtained by the proposed method in the objective function space that is difficult to search. In addition, the influence of the proposed adaptive weight vector assignment was investigated, and it was confirmed that the proposed method adaptively allocates many weight vectors to the difficult search areas. Hence, the proposed method in this paper will extend the range of real problems that can be addressed using MOEA/D.

The human brain automatically processes information at multiple sites when recognizing various types of information at the same time, such as color, shape, etc. Cognitive conflict may occur when conflicting information is recognized at the same time. Functional near-infrared spectroscopy (fNIRS) is often used to examine the brain activity associated with this phenomenon. To examine activation of the visual system, we measured brain activity in the right / left hemispheres during cognitive conflict in the Stroop test. Consistent with the results reported previously by Ehlis, the brain activity in the near inferior-frontal gyrus of the left hemisphere was increased during the incongruent task. The brain activity also increased in the near inferior-frontal gyrus of the right hemisphere during the incongruent task. These results indicated that fNIRS can be used to detect brain activity in the inferior-frontal gyrus of the right / left hemispheres during the Stroop test.