nils-herrmann/clean_scirepeval huggingface dataset:
Detection of circadian rhythms using simple EEG device

Circadian rhythms (CRs) of human have a role to maintain our mind and body together with our health. Thus, a disorder of CRs is a serious problem because it evokes critical disease. In this society, we have a lot of factor that causes a disorder of CRs, such as a social stress and a light of smartphone in the night. Hence, observing our CRs ourselves is an efficient method to prevent it. We are supposed to realize it with a simple electroencephalographic (EEG) device which allows us to measure an EEG signal easily in our daily life. In this research, we investigate the possibility of detecting CRs by a simple EEG device. Sleeping is one of the functions of CRs but it would be thrown into disorder by watching smartphone at the night. Thus, we examine the time required to falling asleep as a behavior of CRs which could be influenced by watching smartphone. Our goal is to detect and identify the CRs of 24 hours by a simple EEG device.
A practical BCG measuring system with bed sensors and algorithm for heartbeat detection

Ballistocardiogram (BCG) is useful and informative signal result from the cardiac activity. In this paper, we present a practical BCG measurement system with bed sensors. Moreover, the simple algorithm to detect all individual heartbeats and estimate beat-to-beat interval lengths is presented. These are effective for all night continuous monitoring of cardiovascular system and enable us to detect sleep stage. Heart beats is detected by cross-correlation method. This method has three steps, pre­processing, typical wave form creation and cross-correlation calculation. Output of this process were validated with 10 subjects. As the results, the average 71.5% of the beat-to-beat intervals were detected. Moreover this study revealed that the individual difference is significant factor to detect heart beats precisely.
A simple algorithm of pitch detection by using fast direct transform

There are some fundamental frequency detection methods for speech processing such as cepstrum analysis. However, the traditional methods need a lot of computing time and arithmetic processing. Moreover, a speech signal is converted into frequency domain by using an analysis section. In this paper, we propose a fast direct transformation (FDT). FDT extracts an amplitude feature of a signal by a simple computation. We perform the fundamental frequency detection of speech signal by using FDT. We compare the FDT algorithm with the conventional fundamental frequency detection methods by using teacher data (fundamental frequency) detected by the inspection. We perform an improvement as compared with an autocorrelation method by using FDT algorithm.
Recognition of drift ice using synthetic aperture radar images

In recent years, observation of a wide variety in the Earth surface can be done by improvement of the remote sensing technology. The purpose in this paper is to recognize a drift ice using synthetic aperture radar (SAR) images. The recognition of the drift ice is achieved by using neural networks. The neural networks used include: a BP trained neural network and a self-organizing map. The training data are image features extracted from SAR images. The two methods used of extracting the features are: Fourier transform and high-order autocorrelation function. Furthermore, false colors are given to the SAR image. Features are extracted from that image and are recognized by the neural networks.
A proposal of model-based alignment using swarm intelligence and condensation

In this paper, we propose a model-based alignment system using swarm intelligence and condensation for mixed reality (MR) and augmented reality (AR). MR and AR are techniques to overlay useful virtual information on the real world and display them to provide users more effective views. To realize the MR and AR, 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 alignment. Therefore, we propose an alignment method using swarm intelligence for the initial frame alignment. In this paper, we use the multiple swarm intelligence methods for designing an effective method of alignment system. This paper shows the expanding version of the pre-proposed method by our system. Moreover, we conduct tracking of the alignment target using condensation based on the initial frame alignment results. By using this system, a model-based alignment can be conducted in a room-type environment such as being hard to acquire many feature points. In order to show the effectiveness of a proposed method, we perform some alignment experiments when we applied the alignment system to a real space.
Automatic alignment method for projection mapping on planes with depth

Projection of computer graphics images on walls or architecture, known as projection mapping, has been used widely and applied in many fields. In projection mapping, the relative alignment between the projected images and the target is important because the efficiency of projection mapping may be reduced due to misregistration. However, the process of alignment is manual and time-consuming. To solve this problem, we propose an automatic alignment method that uses depth information and geometrical characteristics of a plane. This method enables the correction of a distorted image projected from an arbitrary angle. As a result, distortion is reduced by applying our proposed method, which is evaluated by the error rate defined. Performing additional experiments and evaluating the effect of our method will be a part of our future works.
Furniture layout AR application using floor plans based on planar object tracking

In this paper, we propose a new approach of Augmented Reality (AR) system for the furniture layout based on a planar object tracking. The planar object tracking methods using natural features are effective methods to estimate the object's pose and position in the AR applications because we are able to use the natural images. However, most of the feature descriptors have a lot of matching procedure. Therefore, by using an efficient feature point descriptor which is very fast both to build and to match, we track the planar objects. Especially, we use floor plans as the planar objects, and then furniture CG models are overlaid on the floor plans. This is because the floor plans are presented in the selection of rooms for rent or buy. Therefore, this system helps borrowers and buyers to select some mansion or apartment rooms. In this system, we propose to use human whistle sounds and color rectangles recognition to operate the furniture layout. In order to show the effectiveness of our proposed system, we perform some planar object tracking experiments when we applied the proposed system to some floor plans.
Age and gender estimation system based on human perception

In this paper, we propose age and gender estimation system by vairous features. Age and gender has a lot of characteristics. These characteristics are one of the difficult cognitive process in human interaction. If we can extract the important feature of this cognitive process, it is considered that the age and gender estimation by the machine becomes possible. Therefore, we propose a method of age and gender feature extraction and estimation using the face image. In this paper, the age of the face means apparent-age that is based on the human perception of age. Moreover, person's aging and gender difference appear in the faces. For example, the pigmented spot, the wrinkle, sagging skin, shape, color of skin, and so on. Thus, we extract these several features for age and gender estimation. Furthermore, we estimate a continuous age and gender using a neural network (NN).
Eye blink artifact rejection in single-channel electroencephalographic signals by complete ensemble empirical mode decomposition and independent component analysis

To study an eye blink artifact rejection scheme from single-channel electroencephalographic (EEG) signals has been now a major challenge in the field of EEG signal processing. High removal performance is still needed to more strictly investigate pattern of EEG features. This paper proposes a new eye blink artifact rejection scheme from single-channel EEG signals by combining complete ensemble empirical mode decomposition (CEEMD) and independent component analysis (ICA). We compare the separation performance of our proposed scheme with existing schemes (wavelet-ICA, EMD-ICA, and EEMD-ICA) though real-life data by using signal-to-noise ratio. As a result, CEEMD-ICA showed high performance (11.86 dB) than all other schemes (10.78, 10.59, and 11.30 dB) in the ability of eye blink artifact removal.
Extraction of unconscious emotions while watching TV commercials

Our aim of this study is to examine whether there are emotional differences in TV commercials that consumers watch favorably/unfavorably, using an electroencephalogram (EEG) and a questionnaire. With the advance of marketing research, neuromarketing has attracted rising attention, which allows us to know consumers' unconscious emotions. Among a great number of neuromarketing researches, we focus on TV commercials, which are pervasive in our daily lives and which have great influence on society. Therefore, experiments are carried out with actual TV commercials created in Japan. We identify what kinds of emotions are correlated while consumers are watching their favorable/unfavorable TV commercials. Furthermore, as a long-term follow-up, we survey how many these TV commercials consumers keep in their memories even a month later from the experiment day. As a result, we confirmed that there were strong significant correlations between concentration and stress while watching favorable TV commercials. This tendency was not seen in the case of unfavorable TV commercials. Hence, different emotional states could interact one another depending on consumers' preferences of TV commercials. In addition, favorable TV commercials were more retained in consumers' memories. In this way, we investigate how TV commercials actually have effects on not only consumers' unconscious emotions but also their memories.
Head pose tracking system using a mobile device

In this paper, we propose a new head pose tracking method for mobile devices. Recently, head pose tracking technologies are expected to be incorporated into various situations such as robot vision. Especially, we focus on the applications using a mobile system. However, head pose estimation methods usually require a high performance hardware although the mobile systems have limited computational resource. Therefore, we propose a low cost computing algorithm of head pose tracking for the purpose of developing mobile system applications. The proposed method has two stages: (1) fast rough tracking and (2) iterative sampling stage. The proposed method has a merit of low computing cost, and is able to be applied to mobile systems. In order to confirm the effectiveness of the proposed method, we show the comparison results of two types of iterative sampling algorithms. The experimental results demonstrate that our method can be incorporated into actual mobile system applications.
Thresholds decision method for fast object detection systems

In this paper, we propose a new determination method of color thresholds for extracting objects. In order to obtain the optimal color threshold, it is necessary to determine RGB values all together because one color consists of a combination of RGB values. The two optimal thresholds, an upper and a lower, are determined for object extraction by applying genetic algorithm. Moreover, in the proposed method, individuals are evaluated using two fitness functions for obtaining the optimal color thresholds, and initial population is generated with color histograms for fast convergence. Finally, computer simulations are performed, and the result images are shown.
Recognition system for EMG signals by using non-negative matrix factorization

IN this paper, the feature vector of a few dimensions for the electromyograph (EMG) recognition systems is extracted. We aim at the construction of the comprehensive operation equipment to which the operation used frequently was summarized. Important frequency bands of EMG signals are selected by using a genetic algorithm. The EMG signals are a kind of the living organism signal. The EMG signals based on 7 operations at a wrist are measured and recognized. We perform a recognition experiment of EMG signals by neural network using the selected frequency band. We show the effectiveness of this method by means of computer simulations.
Analysis of color categorization for color vision deficient subjects at different luminance levels

This study aimed to compare color categorization characteristics of color vision deficient (CVD) observers with those of normal trichromat (NT) observers. In order to investigate the effects of color luminace and the use of perceptual judgements on color categorization properties, the experiment carried out at three luminance levels by using two experimental tasks, a color-naming task (CNT) and a color-sorting task (CST). We used 68 color stimuli at three luminance levels. In the CNT, four CVD and four NT subjects selected one color category among 12 categories including 11 basic color terms and an unknown category. In the CST, which is a color categorization performed using perceptual judgements, they sorted colors which were simultaneously presented according to 12 categories. As the results, we found that the number of responding the CVD subjects for the green category in both tasks was higher than that of the NT subjects at all luminance levels. For both NT and CVD subjects, each color categorization space derived by the multidimensional scaling method was described in two dimensions. We also explored the relationships between each dimension axis and a chromatic system. Moreover, consensus analysis revealed that the consensus among CVD subjects was low at lowest and highest luminance levels. We demonstrated that luminance affects color categorization properties, as evidenced by the differences between CVD and NT observers at lowest and highest luminance level.
Wrist EMG signals identification using neural network

Recently, researches of artificial arms and pointing devices using ElectroMyoGram(EMG) have been actively done. However, EMG is usually measured from a part with comparatively big muscular fibers such as arms and shoulders. Therefore, if we can recognize wrist motions using EMG which was measured from the wrist, the range of application will extend furthermore. Moreover, it is predicted that convenience in putting on and taking off the electrode improves. Therefore, we focus on EMG measured from the wrist. In this paper, we aim the construction of wrist EMG recognition system by using fast statistical method and neural network.
Construction of predictive models for bicycle riding comfort evaluation using electromyogram and electroencephalogram

In the bicycle manufacturing industries, manufacturers attempt to reflect a user's preference, namely riding comfort, on their products. Surface electromyogram (EMG)-based approaches have been researched for evaluation of riding comfort. However, the EMG does not capture user preferences, because it focuses on muscle fatigue, not riding comfort. To solve this problem, we propose an approach that combines an electroencephalogram (EEG) generated from the brain, which controls modulation of feelings and thoughts. Two bicycles that have different parameter settings and two types of tracks (straight and slalom) were selected to determine the riding comfort, especially riding difference, for the first time by using an EMG and EEG. Elastic net logistic regression analysis was used to construct predictive models. The classification accuracy of the bicycles was determined to be 81.9±7.0% for the slalom course. Furthermore, it was demonstrated that the rectus muscle and frontal lobe are important points for evaluation of the riding comfort of bicycles.
Analysis and recognition of wrist motions by using multidimensional directed information and EMG signal

We propose a recognition system of wrist motions using EMG signals. The objective in this paper is to recognize 7 wrist patterns using EMG signals measured in wrist. Many EMG signal researches have been presented in humanitarian use for handicapped persons. However, almost all the researches were based on measurement from comparatively large muscular region such as arm and shoulder in data measurement location. The EMG signals are measured with 4-channel sensor in wrist and are recorded as 4 channel's time series data. Multi-dimensional directed information (MDI) are applied to the 4 channel's frequency components. The information capacity, which is the amount of information propagation to flow out of a specific signal to other ones, is computed. The information flow is then used as the feature of wrist motions in a preprocessing, followed by a neural network and motion recognition by the EMG signal is carried out. Finally, in order to demonstrate the effectiveness of the proposed method, computer simulations are carried out for the wrist motions recognition.
License plate detection system in rainy days

License plate recognition is very important in an automobile society. However, it is very difficult to do it, because a background and a body color of cars are similar to that of the license plate. Furthermore, the detection of cars in a moving at a very high-speed is difficult to be done. In this paper, we propose a new robust thresholds determination method in the various background by using the real coded genetic algorithm (RGA). By using RGA, the most likely plate colors are decided under various light conditions. First, the average brightness Y values of images are calculated. Next, relationship between the Y value and the most likely plate color thresholds (upper and lower bounds) are obtained by GA to estimate threshold equations by using the recursive least squares algorithm. Finally, in order to show the effectiveness of the proposed method, we show simulation examples by using real images.
A method of obtaining sense of touch by using EEG

In this paper, we propose a method of obtaining the sense of touch by using the EEG. Multimodal device based on human sensory systems have obtained a lot of attention in the fields of human interface. Among the human sensory systems, especially, the sense of touch is important form of social interaction and it can have powerful emotional consequences. Therefore, it is important to improve tactile sensation device technique and evaluate the device objectively. By analyzing the EEG, we attempt to make the evaluation method clear how users feel when he/she touches objects. We measure the EEG while subjects touch objects by using portable electroencephalograph. The proposed method consists of feature extraction and EEG pattern classification by using fast fourier transform(FFT) and artificial neural network(ANN). Moreover, we apply particle swarm optimization(PSO) for deciding the weight and bias of the ANN fastly.
An estimation of heart rate variability from ballistocardiogram measured with bed leg sensors

Ballistocardiogram (BCG) is the repetitive body movements related to cardiac cycle. All night continuous measuring of BCG by using bed sensors enable us to detect sleep stage. However conventional methods are seemed to have unreliability caused by position of lying down. In this paper, we propose a reliable method of all night continuous measuring of BCG. Heart rate variability (HRV) is estimated from BCG measured with new bed sensors by template-matching approach. This approach has three steps, pre-processing, template-creation and template matching. Output of this process were validated with 4 subjects. The mean beat-to-beat interval error was 9.8[ms] and on an average 87.1% of the beat-to-beat intervals were detected.
Hybrid EMG Recognition System by MDA and PCA

In this paper, we propose a recognition system of wrist operation by focusing on ElectroMyoGram (EMG), that is, the living body signal generated with movement of a subject. In previous research, we only performed pattern recognition by Neural Network (NN) and Fast Fourier Transform (FFT). In contrast, in proposal research, we try to improve recognition accuracy and reduce learning-time of system by combining Multi Discriminant Analysis (MDA) and gradual Principal Component Analysis (PCA) based on the PCA result of EMG data. From results of computer simulation, it is shown that our approach is effective for improvement in recognition accuracy and speed.
Lip reading system using novel Japanese visemes classification and hierarchical weighted discrimination

In recent years, automatic lip reading based on `visemes' have been studied by researchers for realizing human-machine interactive communication system in many applications. However there are a lot of problems such as the definition of the number of viseme classes, discrimination method of visemes, speech recognition method based on visemes, and so on. In this paper, a novel classification of Japanese visemes and hierarchical weighted discrimination method for speech recognition are proposed to address these problems. We augmented the classification number of visemes from 6(conventional) to 9 to represent the words in more detailed by visemes. In addition, considering the difficulty in discriminating with increase of the number of visemes, the hierarchical weighted discrimination method is proposed. For the purpose of comparing with the conventional method, the ATR phonetically balanced word group, which is large vocabulary and includes various visemes, was used and applied to word recognition experiments. From these results, we confirmed the proposed method worked well.
An EEG feature detection system using the neural networks based on genetic algorithms

It is often known that an EEG has a personal characteristic. However, there are no researches to achieve the consideration of the personal characteristic. Then, the analyzed frequency components of the EEG have that the frequency components in which characteristics are contained significantly, and that not. Moreover, these combinations have the human equation. We think that these combinations are the personal characteristics frequency components of the EEG. In this paper, the EEG analysis method by using the GA, the FA and the NN is proposed. The GA is used for selecting the personal characteristics frequency components. The FA is used for extracting the characteristic data of the EEG. The NN is used for estimating extracted the characteristics data of the EEG. Finally, in order to show the effectiveness of the proposed method, EEG pattern was classified using computer simulations. The EEG pattern has 4 conditions, which are listening to rock music, Schmaltzy Japanese ballad music, healing music and classical music. The result, in the case of not using the personal characteristics frequency components, gave over 95% accuracy. This result of our experiment shows the effectiveness of the proposed method.
A genetic tuning algorithm of PID parameters

PID control schemes have been widely used in most of process control systems represented by chemical process for a long time. However, it is still a very important problem how to determine or tune the PID parameters, because these parameters have a great influence on the stability and the performance of the control system. On the other hand, in the last twenty years, a genetic algorithm is attracted as one method which gives us optimal answers for search, optimization and machine learning problems. In this paper, we propose a new genetic tuning algorithm of PID parameters, in which the search area of PID parameters is reduced sharply by considering an effective parameters' area from the viewpoint of the control performance.