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Membrane theory attributes the cause of action potential generation to the ion transport across the plasma membrane. It even states that the action potential generation is a unique phenomenon in the living cell. There have been some reports that the action potential-like potential spike is induced even in the nonliving system. Ling’s adsorption theory (LA theory) is a typical opposing concept to the membrane theory. The LA theory attributes the action potential generation mechanism to the ion adsorption-desorption process. The authors examined the LA theory and found that the membrane theory has a limitation. By contrast, the LA theory is in line with the experimental results. Therefore, it might be necessary to take into consideration the ion adsorption-desorption effect at least as a part of the action potential generation mechanism.

Road condition monitoring usually requires extremely expensive special vehicles, equipment, or many human resources. On the other hand, with the development of ICT and data science technologies in recent years, there are several research trials in which the heavy technical tasks of road asset condition monitoring are replaced by automatic inspection systems consisting of common devices such as smartphones and dashcam videos. As the system consists of low-price devices, it also suitable for developing countries. However, there are many differences in the situation and the inspection items on road condition monitoring between advanced countries and developing countries. There are few trials to develop such a road condition monitoring system in developing countries. Our project is developing an integrated road condition monitoring system focusing on developing countries like Timor-Leste. In developing countries, many parts of the road are still unpaved, and the “road width” is an important item to be inspected. In this paper, we discuss the road width and pothole size estimation as a part of the integrated system we are developing. We survey the road width of both paved and unpaved roads. We use a common dashcam to take video along the road. The estimated values are integrated into a database with GPS information and visualized in Google Map, QGIS, or the original visualization system which we developed. To estimate the real width of the road and pothole size, we need to transform the captured forward view image of dashcam video into bird’s-eye-view. For the transformation, we need to estimate the vanishing point in a captured image. However, unlike the advanced countries, it is difficult to detect the vanishing point in developing countries because there are usually no straight lines in the images in the unpaved road of the province. In this study, we propose to use the optical flow method to detect the vanishing point in the rural road. To identify the area of road and the existence of potholes in images, we apply state-of-the-art semantic segmentation using deep learning.

In developing countries like Timor-Leste, regular road condition monitoring is a significant subject not only for maintaining road quality but also for a national plan of road network construction. The sophisticated equipment for road surface inspection is so expensive that it is difficult to introduce them in developing countries, and the monitoring is usually achieved by manual operation. On the other hand, the utilization of ICT devices such as smartphones has gained much attention in recent years, especially in developing countries because the penetration rate of the smartphone is remarkably increasing even in developing countries. The smartphones equip various high precision sensors, i.e., accelerometers, gyroscopes, GPS, and so on, in the small body in low price. In this project, we are developing an integrated road condition monitoring system that consists of smartphones, dashcams, and a server. There are similar trials in advanced countries but not so many in developing countries. This system assumes to be used in developing countries. The system is very low cost and does not require trained specialists in the field side. The items that are automatically inspected in this system were carefully selected with the local ministry of public works and include paved and unpaved classification, road roughness, road width, detection and size estimation of potholes, bumps, etc., at present. All the inspected items are visualized in Google Maps, Open Street Map, or QGIS with GPS information. The survey results are collected on a server and updated to more accurate values by the repeated surveys. On the analysis, we use several state-of-the-art machine learning and deep learning techniques. In this paper, we summarize related works and introduce this project’s target and framework, which especially focused on the developing countries, and achievements of each of our tasks.

This study examines the effect of the discussion process on the creativity of output when students discuss and solve problems in groups using design thinking proposed by d.school Stanford University. According to the design thinking, students were instructed to develop novel and useful communication digital tools according to the design thinking five steps. Microsoft Teams chatbox was used to have voice-based discussions and to keep text data of their discussions. Nine components were extracted from the discussion process, i.e., ‘information gathering’, ‘problem analysis’, ‘needs’, ‘functions for realization’, ‘prototype’, ‘expectation’, ‘information sharing’, ‘confirmation’, and ‘agreement’. For each group’s output, the level of creativity was evaluated using subscales of ‘novelty’ and ‘usefulness’. Regression analysis was used to examine the effects of each component on ‘novelty’ and ‘usefulness’. The results showed that ‘problem analysis’, ‘prototype’, and ‘confirmation’ components influenced ‘novelty’ on creativity.

Research on road monitoring system has been actively conducted by using both machine learning and deep learning technique. One of our nearest goal in the framework of road condition monitoring system is to segment all road related object and provide a technical report regarding road condition. Our final objective is to develop a community participant-based system for road condition monitoring. As one of our task, in this research, we start with the segmentation of road and pothole. To conduct this task we proposed a semantic segmentation method for road and pothole image segmentation by using one of the famous deep learning technique U-Net. Various condition of road images were used for training and validating the model. The experiment result showed that U-Net model can achieve 97 % of accuracy and 0.86 of mean Intersection Over Union (mIOU).

The objective of our project is to develop an automatic survey system for road condition monitoring using smartphone devices. One of the main tasks of our project is the classification of paved and unpaved roads. Assuming recordings will be archived by using various types of vehicle suspension system and speeds in practice, hence, we use the multiple sensors found in smartphones and state-of-the-art machine learning techniques for signal processing. Despite usually not being paid much attention, the results of the classification are dependent on the feature extraction step. Therefore, we have to carefully choose not only the classification method but also the feature extraction method and their parameters. Simple statistics-based features are most commonly used to extract road surface information from acceleration data. In this study, we evaluated the mel-frequency cepstral coefficient (MFCC) and perceptual linear prediction coefficients (PLP) as a feature extraction step to improve the accuracy for paved and unpaved road classification. Although both MFCC and PLP have been developed in the human speech recognition field, we found that modified MFCC and PLP can be used to improve the commonly used statistical method.

Image processing techniques have been actively used for research on road condition inspection and achieving high detection accuracies. Many studies focus on the detection of cracks and potholes of the road. However, in some least developed countries, there are some distances of roads are still unpaved and it escaped the attention of the researchers. Inspired by penetration and success in applying deep learning technic to computer vision and to any other fields and by the existence of the various type of smartphone devices, we proposed a low - cost method for paved and unpaved road images classification using convolutional neural network (CNN). Our model is trained with 13.186 images and validate with 3.186 images which collected using smartphone device in various conditions of roads such as wet, muddy, dry, dusty and shady conditions and with different types of road surface such as ground, rocks and sands. The experiment using 500 new testing images showed that our model can achieve high Precision (98.0%), Recall (98.4%) and F1 -Score (98.2%) simultaneously.

For developing countries like Timor-Leste, regular road surface monitoring is a major challenge not only for maintaining road quality but also for national plan of road network construction. In Timor-Leste nearly 50% of roads are still unpaved. For this reason, an automated system is required to do a survey of paved and unpaved roads. In this study, we present a new approach for the use of smartphones sensor to classify paved and unpaved roads, and anomaly detection. Although, the most remarkable factor to differentiate paved and unpaved road is based on amplitude of the vertical acceleration, each vehicle has a different type of suspension system. Therefore, we used high-dimensional features and state-of-the-art machine learning techniques to make the system robust for differences of vehicle and also smartphone type. This study divided into two stages such as paved and unpaved road classification and road anomaly detection such as pothole and bump. For paved and unpaved road classification, we tried to use the SVM, HMM and ResNet and compared the performance of these models. Of all comparison, the ResNet was the best choice in this study, because it outperformed the SVM and HMM on the all performance evaluation criteria. Furthermore, the KNN and DTW are applied for anomaly detection on the paved road. The KNN-DTW are also compared to the other machine learning techniques like SVM and classical KNN using same criteria. As a result of the comparison, the KNN-DTW and SVM performed better than classical KNN.

This research proposes a low-cost solution for detecting road potholes image by using convolutional neural network (CNN). Our model is trained entirely on the image which collected from several different places and has variation such as in wet, dry and shady conditions. The experiment using the 500 testing images showed that our model can achieve (99.80 %) of Accuracy, Precision (100%), Recall (99.60%), and F-Measure (99.60%) simultaneously.

In Timor-Leste, the coffee is one of the most important product for the acquisition of foreign currency. However, there are almost no rationalizations, therefore, enhancing the value of the coffee efficiently at the local of production is desired. The final objective of our study is to develop the automatic coffee beans sorting system for the producers of coffee beans in Timor-Leste. As the first step, we developed an image processing system which classifies the images of green coffee beans into each type of defect. We employed deep convolutional neural networks, the state-of-the-art machine learning technique, for the image processing. As the results, we succeeded to sort defect beans from 72.4% to 98.7% of accuracies based on the types of defects.

Although humans perceive animacy in blinking fireflies, humans do not perceive animacy in flashing traffic lights. In the present study, we conducted a psychological experiment to investigate which characteristics of temporal changes of luminance affect animacy perception in humans. Unpredictability in temporal change might be one of the key factors that cause animacy perception. We focused on the periodicities and chaos intensities of time series because both characteristics are related to the subjective impression of predictability. Furthermore, we investigated the effects of top-down categorical knowledge on the animacy perception of an entity. The participants were asked to rate videos in which the luminance of a target entity (i.e., a squid, charcoal fire, or power lamp) changed according to pre-defined periodicities and chaos intensities. The experimental results indicated that the participants perceived animacy strongly based on luminance changes with periodic and high chaos intensities and that animacy was not affected by the chaos intensity of aperiodic change. These results imply that the periodicity of a time series contributes more strongly to the cause of human animacy perception than the chaos characteristics.

High-frequency oscillations (HFOs) in somatosensory evoked potentials (SEP) have been observed in both humans and animals. In magnetoencephalography (MEG), the HFOs are observed as somatosensory evoked field (SEF) with advantage of higher reliability of current source localization. On the other hand, to extract the HFOs from MEG data, the conventional method requires the averaging of 2000–10,000 of evoked responses which causes difficulty of measurements. In this study, we used blind source separation (BSS) method to extract waveform of HFOs with smaller number of averaging times. As a result, we could reduce the number of averaging times to about one-fifth to get HFOs. Scalp distributions of the magnetic field of the HFOs components can be also obtained by the method.

The Hodgkin-Huxley equations (Hodgkin & Huxley 1952) are a neuron model describing electrical excitation of the squid giant axon membrane. By examining the global bifurcation structure of these equations, we found a degenerate Hopf bifurcation point. Several stable periodic orbits coexist in the neighborhood of this point. We determined parameter ranges where such multistability occurs and delimited regions where either two stable periodic solutions, or two stable periodic solutions and a stable equilibrium point coexist. We argue that comparison between the global bifurcation structure experimental data provides insight into the domain of validity of the Hodgkin-Huxley equations.

Despite unique well characterized neuronal properties, such as extensive electrical coupling and low firing rates, the role of the inferior olive (IO), which is the source of the climbing fiber inputs to cerebellar Purkinje cells, is still controversial. We propose that the IO stochastically recodes the high-frequency information carried by its synaptic inputs into stochastic, low-rate spikes in its climbing fiber output. Computer simulations of realistic IO networks showed that moderate electrical coupling produced chaotic firing, which maximized the input-output mutual information. This “chaotic resonance” may allow rich error signals to reach individual Purkinje cells, even at low firing rates, allowing efficient cerebellar learning.

The Hodgkin-Huxley equations (HH) are parameterized by a number of parameters and shows a variety of qualitatively different behaviors depending on the parameter values. We explored the dynamics of the HH for a wide range of parameter values in the multiple-parameter space, that is, we examined the global structure of bifurcations of the HH. Results are summarized in various two-parameter bifurcation diagrams with I(ext) (externally applied DC current) as the abscissa and one of the other parameters as the ordinate. In each diagram, the parameter plane was divided into several regions according to the qualitative behavior of the equations. In particular, we focused on periodic solutions emerging via Hopf bifurcations and identified parameter regions in which either two stable periodic solutions with different amplitudes and periods and a stable equilibrium point or two stable periodic solutions coexist. Global analysis of the bifurcation structure suggested that generation of these regions is associated with degenerate Hopf bifurcations.