nils-herrmann/clean_scirepeval huggingface dataset:
Periodicity detection for BCI based on periodic code modulation visual evoked potentials

In this paper, we studied the brain computer interface (BCI) based on periodic code modulation visual evoked potential (VEP). The code modulation VEP (c-VEP) is one of electroencephalogram (EEG)-based BCI methods, and can achieve high speed communication. In this method, by identifying a pseudorandom binary code (PRBC) that modulates visual stimulus from measured EEG, we can transfer the command related with the PRBC into external devices. However, the communication speed becomes slow inversely with increased number of commands. In order to solve this problem, we proposed extended c-VEP method using periodic pseudorandom binary codes. In this method, we identify the periodicity from the EEG by using autocorrelation, and the command related with periodicity of the EEG is transferred. As a result of computer simulation, we were able to detect the periodicity of the EEG. Therefore, we verified the feasibility of the periodic pseudorandom binary codes for VEP-based BCI.
The proposal of model-based initial frame alignment using real-coded GA for mixed reality

In this paper, we propose a model-based alignment system using real-coded genetic algorithm (RGA) for mixed reality (MR) or augmented reality (AR). MR/AR is a technique for superimposing useful virtual information on the real world to provide users more effective views. To realize the MR/AR, the alignment between real space and virtual space is a serious problem. In particular, an initial frame alignment for real-time tracking is needed in the previous knowledge based MR/AR. Therefore, we propose an initial frame alignment method using RGA. In this paper, we use the multiple RGA generation alternation models, crossovers, and mutations to design an optimum GA for the alignment system. This paper shows the expanding version of the pre-proposed method by our system. In order to show the effectiveness of proposed method, we perform some matching simulations when we applied to the real space.
The calibration between range sensor and mobile robot, and construction of a obstacle avoidance robot

Recently, robot that assists a human is desired, because the aging society will come. For example, if there is a robot which avoid a obstacle automatically, it is possible to assist humans. Therefore, we construct a obstacle avoidance robot. In order to achieve a autonomous robot, obstacle avoidance, path planing, and mobile robot control are required. In this study, TOF type range sensor that is a possible to measure the distance is used. Moreover, an obstacle is detected using the distance information acquired by TOF camera. Optimal path is decided by the potential method. When TOF camera is put on a mobile robot, it is a problem that a robot coordinate system differs from the coordinate system of TOF camera. Then, the calibration of a robot and TOF camera is performed by odometry information and estimating the pose between TOF camera and actual environment. In order to verify the practicality of this proposal system, we experiment at room environment with flat floor.
The EEG feature extraction method of listening to music using the genetic algorithms and the latency structure model

It is known that an electroencephalogram (EEG) is characterized by the unique and personal features of an individual. The EEG frequency components are contained the significant and immaterial information, and then each importance of these frequency components is different. These combinations are often unique like individual human beings and yet they have underlying basic characteristics. We think that these combinations and/or the importance of the frequency components show the personal features. Therefore we propose the two techniques for estimating the personal features. A simple genetic algorithm is used for specifying these frequency combinations. Other technique, a real-coded genetic algorithm is used for estimating the importance of EEG frequency components. Then a latency structure model based on the personal features is used for extracted the feature vector of the EEG. Furthermore, the visualization map is used for evaluating the extracted feature vector of the EEG. In order to show the effectiveness of the proposed methods, the performance of the proposed method is evaluated using real EEG data.
A design of robust face identification system for environmental changing

We propose the face identification method, which is robust for lighting based on the feature points method. First of all, the proposed method extracts an edge of facial feature. Then, by the Hough transform, it determines ellipse parameters of each facial feature from the extracted edge. Finally, proposed method performs the face identification by using parameters. Even if face image is taken under various lighting condition, it is easy to extract the facial feature edge. Moreover, it is possible to extract a subject even if the object has not appeared enough because this method extracts approximately the parameters by the Hough transformation. Therefore, proposed method is robust for the lighting condition compared with conventional method. In order to show the effectiveness of the proposed method, using the real images does computer simulations.
A design of face detection system by using lip detection neural network and skin distinction neural network

The authors propose a method to examine whether or not human faces are included in color images by using a lip detection neural network (LDNN) and a skin distinction neural network (SDNN). In conventional methods, if there are colors the same as skin color in scenes, the domain which is accepted as not only the skin color but any other color can be searched. However, first, the lips are detected by LDNN in the proposed method. Next, SDNN is utilized to distinguish skin color from other colors. The proposed method can obtain relatively high recognition accuracy, since it has the double recognition structure of LDNN and SDNN. Finally, in, order to demonstrate the effectiveness of the proposed scheme, computer simulations were performed. First, 100 lip color, 100 skin color and 100 background pictures, which were changed into 10/spl times/10 pixels, were prepared for training. The validity was verified by testing images containing several faces.
Feature analysis for the EMG signals based on the class distance

In this paper, a feature vector is extracted from an electromyography (EMG) signal at a wrist, and the EMG signals based on 7 motions are recognized. In order to perform good pattern recognition, it is desirable that the distance in feature vector between classes is far, and that the variance in a class is small. In consideration of these, important frequency bands of EMG signals are selected by using a genetic algorithm. We use the selected frequency band to perform the recognition experiment of EMG signal by a neural network. Finally, the effectiveness of this method is demonstrated by means of computer simulations.
Pattern Recognition of EMG Signals by the Evolutionary Algorithms

In this paper, we propose a method of function derivation for performing recognition of wrist operations by the electromyographic (EMG) signals extracted from 4-channel EMG sensor. In designing a recognition device of operations, the important fewer amount of information is needed for reduction of cost and accuracy improvement in practical systems. Then, date mining is performed by specifying important frequency bands using genetic algorithm (GA) and neural network (NN). The derivation of function for generating a feature vector is performed only using the important frequency bands obtained by GA and NN. In this case, the feature vector which consists of frequency spectrum to be used is mapped to another space. We use the generated function as an input feature to perform recognition experiments of EMG signal by NN. Finally, the effectiveness of this method is demonstrated by means of computer simulations
An EEG-based robot arm control to express human emotions

Prosthetic limbs are designed to move naturally like human limbs. To reduce the feeling of abnormality when moving prosthetic limbs, previous studies have altered the appearance of robotic limbs along with the speed of the motors inside these limbs. The use of the wearer's emotions to control prosthetic limbs would allow robotic arms and legs to move more naturally. Recently, electroencephalograms (EEGs) have been used to estimate human emotions. In this study, we developed an emotion analyzer and applied it in a robotic arm system that changes its movement based on the user's emotions. In the proposed system, EEG data are measured by a simplified EEG headset and transferred to an iPad. An iPad application then transforms the signal to a value of "wakuwaku" feeling using the band-pass filtering, fast Fourier transform, and support vector machine methods. The accuracy of the algorithm estimating the wakuwaku feeling was 75.10%, and the robot arm moved accurately based on the user emotion estimated from the EEG data. Our findings are expected to lead to a new field of study focused on controlling robot arms using human emotions.
A design of self-tuning PID controllers using a genetic algorithm

A self-tuning PID controller design scheme is proposed, which is able to deal with systems with unknown or time-varying parameters. The proposed scheme is derived based on the relationship between PID control and generalized minimum variance control (GMVC) laws. Furthermore, a suitable set of some user-specified parameters included in the GMVC criterion is sought by using a genetic algorithm recursively. A numerical simulation example shows the effectiveness of the newly proposed control scheme.
Amplitude and frequency feature extraction of neural activity in mouse ventrolateral striatum under different motivational states using fiber photometric system

In this paper, we focused on the motivation of mice and aimed to extract neural activity features of D2 medium spiny neurons (D2-MSNs) in the ventrolateral striatum of mice under different motivational states. Motivated behavior is defined as the activation of goal-directed behavior, and this enables actions such as ingestion, sleeping and reproduction, which are essential for living. In human society, motivation allows us to participate in society, improving our quality of life. Loss of motivation has been causing problems such as withdrawal from society. If the symptom is heavy, it can even threaten our lives, and treatment is necessary. The mechanisms which lead to loss of motivation are yet to be understood, and an effective treatment does not exist. To solve these problems, we decided to find features in the neural activity which affect motivation, because understanding the mechanism may contribute to the establishment of treatment. In the experiment, the neural activity was recorded using gene-encoded ratio metric calcium ion (Ca2+) indicator and by constructing a fiber photometric system, which enabled recording of neural activity at specific brain region for specific type of neurons. While the recording took place, mice performed food-incentive leverpressing tasks, which were used to define motivational states. The experimental results show that both the amplitude and frequency components of Ca2+ fluctuation have features which are good predictors of motivational states.
An age estimation system on the AIBO

In this paper, we propose an age estimation system on the AIBO. AIBO is an entertainment robot produced by SONY co., Ltd.. AIBO has many sensors to get information around itself and moves according to its instinct. This autonomous action is considerably natural. However, it is inadequate to communicate with people. If AIBO can estimate the human age from a face image, it becomes more excellent entertainment robot. Then, we propose the age estimation method on the AIBO by using face image. In this paper, the apparent age feature is extracted by the fast Fourier transform (FFT), and it is selected by the GA. Moreover, the age is estimated by the 1-dimensional SOM. In order to show the effectiveness of the proposed method, we show the simulation examples.
Method for detecting music to match the user’s mood in prefrontal cortex electroencephalogram activity based on individual characteristics

In this paper, we propose a method for detecting the music to match a user's mood in prefrontal cortex electroencephalogram (EEG) activity. The EEG frequencies analyzed are the components that contain significant and immaterial information. We focused on the combinations of the significant frequency. These frequency combinations are thought to express individual characteristics of EEG activity. In the proposed method, we calculate the percentage of the spectrum of these frequency combinations that does not include the noise frequency components and evaluates whether the music matches the user's mood through a simple threshold processing. Then, a genetic algorithm (GA) is used to specify the frequency of individual characteristics on the EEG. Threshold values that used the threshold processing is determined in the GA. Finally, the performance of the proposed method is evaluated using real EEG data.
A human tracking mobile-robot with face detection

In this paper, we propose the face detection method for tracking a human by a mobile-robot. We obtain images from a web camera, and detect faces by focusing on skin colors and eyes as facial features. If we detect faces from images, we trace the detected human, take a picture of him/her, and print it automatically by using the mobile-robot. In order to show the effectiveness of the proposed method, we show the experimental results. Firstly, in the face detection, we show the face detection accuracy. Then, in the human tracking with mobile-robot by using face detection, we show the tracking performance.
The room design system of individual preference with IGA

In this paper, we illustrate a decision support system of modeling the room designs suited for personal preference. The preference to colors is differed to individual personality. Nevertheless, modeling the room design is depended on the designer's sensitivity until now. Therefore, the technique of modeling the various room designs people satisfy is needed. In this paper, we propose automatic modeling system of the room design using interactive genetic algorithm (IGA). IGA has the advantage of learning the user's interactive evaluation, which is applied to the production of new room designs suited for the personal preference. The proposed system can help a decision support of modeling the various room designs, and reduce the burdens of designers significantly. In proposed system, the design evolution of IGA can reflect the user preference, and we present the effectiveness of the system by simulations.
Classification of hand postures based on 3D vision model for human-robot interaction

In this paper, a method for hand posture recognition, which is robust for hand posture changing in an actual environment, is proposed. Conventionally, a data glove device and a 3D scanner have been used for the feature extraction of hand shape. However, the performance of each approach is affected by hand posture changing. Therefore, this paper proposes the posture fluctuation model for efficient hand posture recognition, based on 3D hand shape and color feature obtained from a stereo camera. A large set of dictionary for posture recognition is built by various leaned hand images which were auto-created from one scanned hand image, based on plural proposed models. In order to show the effectiveness of proposed method, performance and processing times for posture recognition are compared to conventional method. In addition, we perform the evaluation experiment by using the Japanese sign language.
Parallelizing Hilbert-Huang Transform on a GPU

In this paper, we show parallel implementation of Hilbert-Huang Transform on GPU. This implementation focused on the reducing the computation complexity from O(N) on a single CPU to O(N/P log (N)) on GPU, as well as the use of 'shared-global' switching method to increase performance. Evaluation results show our single GPU implementation using Tesla C1060 achieves 29.0x speedup in best case, and a total of 7.1x speedup for all results when compared to a single Intel dual core CPU.
Stress and drowsiness evaluation BBased on earbud-type photoplethysmographic sensor

This paper proposes an evaluation method of human stress and drowsiness using photoplethysmography (PPG) measured from ears through the use of earbud-type photoplethysmographic sensor. For improving robustness with respect to body motion, velocity pulse waves have been calculated to detect motion artifact. In addition, to increase the temporal resolution and cope with misalignments in sensor position, 4 simple features have been derived from the velocity pulse waves. These 4 features are selectively related to stress and drowsiness scores using multiple regression analysis and a stepwise method. The stress and drowsiness evaluation models were implemented and tested with the validation dataset collected in our experiments. In the simulation results, human stress and drowsiness were evaluated successfully from PPG measured from ears. As the results show, the proposed method enabled human stress and drowsiness evaluation using PPG measured through the earbud-type photoplethysmographic sensor.
Activity recognition for children using self-organizing map

Based on a new concept of Kinder-GuaRdian which aims to realize a support system for kindergarten's staffs and parents by watching over children's activity and their growing process, activity recognition (AR) for children is treated in this paper. In case of children, smaller amount of sensors is better since easy fitting of the sensor device to child's body is strongly preferable. In this study, using one acceleration sensor attached to the upper arm, AR in cases of adults and children were evaluated using several orthodox classifiers and self-organizing map (SOM). Through the several types of validation, effectiveness, issues, and benefits of their methods are discussed.
A proposal of feature extraction for impression analysis

The concern of image processing has increased, in recent year. However, the evaluation of facial impression that we are naturally doing from ones face in our daily life is not treated in this area. The automatic facial impression evaluation applies for wide area. Then we analyzed important facial featres for impression analysis, in order to achieve automatic evaluation of facial impression. In order to evaluate the proposed method, we performed computer simulation. As the result of this simulation, we confirmed that important facial features for facial impression are differece while gender and age.
Riding condition extraction system by the EEG analysis

In this paper, we propose the electroencephalography (EEG) feature extraction method of the change in riding comfort according to the difference of the tire. It is a proposal of the objective evaluation technique. The feature of the EEG during the driving is extracted by the factor analysis. Moreover, the correlation between the subjective evaluation and the detected features of EEG is calculated. Then, the effectiveness of the proposed method as a objective evaluation technique was shown from the result.
Neural net based image retrieval by using color and location information

A neural net-based image retrieval method is presented, in which color and location features are extracted from images. This method can retrieve similar images to a selected one from a large data set of color images. In particular, the location features in the color distribution of an image are important in the image retrieval. This image retrieval method extracts color features and their location information included in an image. A neural network tries to find images with similar features from a data set. First, images are translated into gray-scale ones and then are divided into eight regions based on gray-scale values. The color and location features are extracted from these regions after integration of regions. The RGB and HSV color values in each region, area, and the X- and Y-values in the orthogonal coordinates are learned by a multi-layered neural network. After learning, the neural network evaluates the similarity between a selected image and the other ones in the data set. The similar images found by the neural network are the retrieval results.
Respiratory sound analysis for continuous positive airway pressure machines

Continuous positive airway pressure (CPAP) is a typical medical treatment for obstructive sleep apnea syndrome. Without using a small CPAP machine which are patented, we aim to design a new technique using microphone array for measuring respiratory sounds. Hence, it is necessary to obtain effective features of respiratory sounds such as, inspiration, expiration, and snoring in order to supply appropriate pressure depending on the respiratory conditions of patients. Furthermore, it is often difficult to measure only respiratory sounds separately from other environmental sounds by the use of CPAP machine operation, for example, blowing sound of subjects should be eliminated for CPAP machine. As the first step, short-time Fourier transform is applied then we can compare the sound features of inspiration and expiration observed by the microphones. Some different features can be observed in the time-frequency magnitude spectrogram. From these results, the feasibility of using inspiration and expiration features independently in a prospective detection technique.
Color Categorization Properties and Color Sensation on Monitor for Color-Vision Deficient Subjects and Normal Trichromat Subjects

This study aimed to compare color-naming characteristics of color-vision deficient (CVD) observers with those of normal trichromat (NT) observers and investigate color sensation. This experiment used 68 color stimuli at three different luminance levels. In the color-naming task, five CVD and five NT subjects selected one color category among 11 basic color terms, and an unknown category. In the evaluation task for color sensation, they evaluated color impression using six adjectives. We used the multidimensional scaling method to construct color categorization space, and applied the factor analysis to compare color sensation of the CVD subjects with those of the NT subjects. As the results, we found that the green and blue categories of the CVD subjects were expanded more than those of NT the subjects at all luminance levels. Moreover, with the activity evaluation for the pink or purple categories, there was difference between the NT and CVD subjects, although the color categorization behaviors of the CVD subjects were similar to those of the NT subjects.
Emotional speech classification with prosodic prameters by using neural networks

Interestingly, in order to achieve a new Human Interface such that digital computers can deal with the KASEI information, the study of the KANSEI information processing recently has been approached. In this paper, we propose a new classification method of emotional speech by analyzing feature parameters obtained from the emotional speech and by learning them using neural networks, which is regarded as a KANSEI information processing. In the present research, KANSEI information is usually human emotion. The emotion is classified broadly into four patterns such as neutral, anger, sad and joy. The pitch as one of feature parameters governs voice modulation, and can be sensitive to change of emotion. The pitch is extracted from each emotional speech by the cepstrum method. Input values of neural networks (NNs) are then emotional pitch patterns, which are time-varying. It is shown that NNs can achieve classification of emotion by learning each emotional pitch pattern by means of computer simulations.