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Dance performance recognition methods have been investigated and shown various applications such as picture-pose evaluation and synchronizing foot timing and direction. However, detailed analysis and feedback are still missing. To provide them, understanding the performance by component level is necessary. Specifically, we formulate it as a dance-figure classification problem using three-dimensional body joints and wearable sensors. Our model is based on long short-term memory (LSTM) and includes the temporal and trajectory-wise structure that uses the trajectory information in a timestep and the temporal masking module. As a result, we achieved 93% accuracy with our proposed method, which is highly overwhelming the baseline result (84.7%) and very close to the accuracy of the experienced dancers (93.6%). We have made the dataset of ballroom dance performance dataset open to researchers to develop the activity recognition field further.

Large-scale events attracting many participants might have a strong negative impact on productivity, mobility, comfort, and safety in a city. Within the few years, serious accidents led by congestion have occurred, especially during sports events, religious ceremonies, festivals, and so on. To alleviate these serious accidents, predicting the occurrence of a large-scale event is much significant. When we know an event occurrence in advance, some of those who are not interested in the event might change their plans and/or might take a detour to avoid to get involved in a heavy congestion. In this paper, we present an early event detection technique named BusBeat . BusBeat uses GPS trajectory data collected from periodic-cars that are vehicles periodically traveling on a pre-scheduled route with a pre-determined departure time, such as a transit bus, shuttle, garbage truck, or municipal patrol car. BusBeat interpolates the missing GPS data by using the features of the periodic-cars. In addition, BusBeat uses the network-based analysis with a Time-dependent Congestion Network (TCN) in order to detect geo-spatial events. BusBeat achieves early event detection without incurring any privacy invasion, by using the continuous trajectories of the periodic-cars that provide the real-time traffic flow and speed. Since traffic towards an event venue would be slow before the event starts, BusBeat detects the geo-spatial events before the attendees gather. We evaluate our BusBeat using over 7,000-bus data collected in Beijing for 5 months and compare with the check-in data collected from a social network service.

Human mobility analysis is a key method for understanding urban dynamics and mobility optimization. Novel last-mile mobility, called micromo-bilities, that includes shared bicycles, electric bicycles (e-bikes), and electric scooters are seeing rapid widespread acceptance in major cities. Compared with existing mobility data such as cars, buses, and trains, the majority trip distance of micromobilities is short, typically less than a few miles. The riders use them for commuting, sightseeing, shopping, and/or fun. By using the mobility data of micromobilities, we can observe more fine-grained human mobility in urban areas than existing data sources. In this paper, we present an unsupervised learning-based technique to analyze human mobility in urban areas and to study user clusters for such micromobility services. In our approach, we cluster user mobility patterns by using non-negative tensor factorization (NTF) from area-based trip data (which only included locations of origin and destination). Our approach was applied to micromobility data collected from Chicago and Washington, D.C., and we observed characteristic patterns.

The use of micro-mobility (e.g., bicycle and scooter) and their data for urban sensing and rider assessment is becoming increasingly popular in research. However, different research topics require different sensor setups; no general data collecting tools for the micro-mobility makes the researcher who wishes to collect data has to build their own collecting system from scratch. To this end, we present MiMoSense, an open crowdsensing platform for micro-mobility. MiMoSense consists of two components: (1) MiMoSense server, which is set up on the cloud, and used to manage sensing studies and the collected data for research and sharing. (2) MiMoSense client, uses micro-mobility carrying various sensors and IoT devices to collect multiple kinds of data during traveling. As a reusable open-source software, MiMoSense shifts the researcher's focus from software development to sensing data analysis; it can help researchers quickly develop an extensible platform for collecting micro-mobility's raw sensing data and inferring traveling context. We have evaluated MiMoSense's battery consumption, message latency and discuss its use.

Intersection management is one of the main challenging issues in road safety because intersections are a leading cause of traffic congestion and accidents. In fact, more than 44% of all reported crashes in the U.S. occur around intersection areas, which, in turn, has led to 8,500 fatalities and approximately 1 million injuries every year. With vehicles expected to become self-driving, the question is whether high throughput can be obtained through intersections while keeping them safe. A spatio-temporal intersection protocol named the Ballroom Intersection Protocol (BRIP) [8] was recently proposed in the literature to address this situation. Under this protocol, automated and connected vehicles arrive at and go through an intersection in a cooperative fashion with no vehicle needing to stop, while maximizing the intersection throughput. Though no vehicles run into one another under ideal environments with BRIP, vehicle accidents can occur when the self-driving vehicles have location errors and/or control system failure. In this article, we present a safe and practical intersection protocol named the Configurable Synchronous Intersection Protocol (CSIP). CSIP is a more general and resilient version of BRIP. CSIP utilizes a certain inter-vehicular distance to meet safety requirements in the presence of GPS inaccuracies and control failure. The inter-vehicular distances under CSIP are much more acceptable and comfortable to human passengers due to longer inter-vehicular distances that do not cause fear. With CSIP, the inter-vehicular distances can also be changed at each intersection to account for different traffic volumes, GPS accuracy levels, and geographical layout of intersections. Our simulation results show that CSIP never leads to traffic accidents even when the system has typical location errors, and that CSIP increases the traffic throughput of the intersections compared to common signalized intersections.

Human-robot collaboration and cooperation are critical for Autonomous Mobile Robots (AMRs) in order to use them in indoor environments, such as offices, hospitals, libraries, schools, factories, and warehouses. Since a long transition period might be required to fully automate such facilities, we have to deploy AMRs while improving safety in the mixed environments of human and mobile robots. In addition, human behaviors in such environments might be difficult to predict. In this paper, we present a Digital Twin for Autonomous Mobile Robots system named DigiMobot to support, manage, monitor, and validate AMRs in indoor environments. First, DigiMobot can simulate human behaviors and robot movements to verify and validate AMRs to improve safety in a virtual world. Secondly, DigiMobot can monitor and manage AMRs in the physical world by collecting sensor data from each robot in real-time. Since DigiMobot enables us to test the robot systems in the virtual world, we can deploy and implement AMRs in each facility without any modifications. To show the feasibility of DigiMobot, we develop a software framework and two different types of autonomous mobile robots. Finally, we conduct real-world experiments in a warehouse located in Saitama, Japan, in which more than 400, 000 items are stored for commercial purposes.

Connected and Automated Vehicles (CAVs) have real-time information from the surrounding environment by using local on-board sensors, V2X (Vehicle-to-Everything) communications, pre-loaded vehicle-specific lookup tables, and map database. CAVs are capable of improving energy efficiency by incorporating these information. In particular, Eco-Cruise and Eco-Lane Selection on highways and/or motorways have immense potential to save energy, because there are generally fewer traffic controllers and the vehicles keep moving in general. In this paper, we present a cooperative and energy-efficient lane-selection strategy named MultiCruise, where each CAV selects one among multiple candidate lanes that allows the most energy-efficient travel. MultiCruise incorporates an Eco-Cruise component to select the most energy-efficient lane. The Eco-Cruise component calculates the driving parameters and prospective energy consumption of the ego vehicle for each candidate lane, and the Eco-Lane Selection component uses these values. As a result, MultiCruise can account for multiple data sources, such as the road curvature and the surrounding vehicles' velocities and accelerations. The eco-autonomous driving strategy, MultiCruise, is tested, designed and verified by using a co-simulation test platform that includes autonomous driving software and realistic road networks to study the performance under realistic driving conditions. Our experimental evaluations show that our eco-autonomous Mul-tiCruise saves up to 8.5% fuel consumption.

Inbound tourism business has grown all over the world in recent years, and the competition to attract tourists is becoming more intense. Each country is putting a lot of effort into tourism promotion. Videos, in particular, are often used for these tourism marketing campaigns. Promotional videos from these countries are delivered on the internet. While some of these countries have succeeded in attracting tourists from abroad with effective promotional videos showing their countries’ charms, others are not so successful in drawing foreign tourists to countries due to poor video promotion despite a huge amount of money spent on the production. In this thesis, we analyze and compare two types of promotional videos: the ones from countries successfully promoting tourism with them, and the others from countries with poor promotional results.

With advances in sensing, computing and communication technologies, Connected and Automated Vehicles (CAVs) are becoming feasible. The advent of CAVs presents new opportunities to improve the energy efficiency of individual vehicles. However, testing and verifying energy-efficient autonomous driving systems are difficult due to safety considerations and repeatability. In this paper, we present a co-simulation platform to develop and test novel vehicle eco-autonomous driving technologies named InfoRich, which incorporates the information from on-board sensors, V2X communications, and map database. The co-simulation platform includes eco-autonomous driving software, vehicle dynamics and powertrain (VD&PT) model, and a traffic environment simulator. Also, we utilize synthetic drive cycles derived from real-world driving data to test the strategies under realistic driving scenarios. To build road networks from the real-world driving data, we develop an Automated Parser and Calculator for Map/Scenario named AutoPASCAL. Overall, the simulation platform provides a realistic vehicle model, powertrain model, sensor model, traffic model, and road-network model to enable the evaluation of the energy efficiency of eco-autonomous driving.

Self-driving vehicles are expected to be at the core of future transportation systems. Over time, these vehicles might enhance traffic efficiency and safety, especially at road intersections. However, there will likely be a long transition period before human-driven vehicles will be completely replaced by automated vehicles. Intersection safety and efficiency might be barely improved if automated vehicles just follow the traffic light signals. In this paper, we present a decentralized intersection protocol named the Distributed Synchronous Intersection Protocol (DSIP) that is for mixed traffic environments, where human-driven and self-driving vehicles cooperate with one another in order to avoid vehicle collisions and possible deadlocks while improving traffic efficiency. In DSIP, all automated vehicles use both Vehicle-to-Vehicle (V2V) communications and traffic lights to traverse the intersection safely. On the other hand, human-driven vehicles simply follow the traffic lights just like they do today. Under the protocol, the automated vehicles synchronize when there are no human-driven vehicles around the intersection. In addition, Cooperative Perception is used to detect the presence of human-driven vehicles, where all automated and connected vehicles sense and share the presence of the human-driven vehicles at the intersection. Our simulation results show that DSIP increases the traffic throughput of the intersections compared to common signalized intersections and other V2V-based intersection protocols.

We have investigated the intrinsic thermoelectric properties of epitaxial β-FeSi2 thin films and the impact of phosphorus (P) doping. Epitaxial β-FeSi2 thin films with single phase were grown on Si(111) substrates by two different techniques in an ultrahigh-vacuum molecular beam epitaxy (MBE) system: solid-phase epitaxy (SPE), where iron silicide films formed by codeposition of Fe and Si at room temperature were recrystallized by annealing at 530°C to form epitaxial β-FeSi2 thin films on Si(111) substrates, and MBE of β-FeSi2 thin films on epitaxial β-FeSi2 templates formed on Si(111) by reactive deposition epitaxy (RDE) at 530°C (RDE + MBE). Epitaxial SPE thin films based on codeposition had a flatter surface and more abrupt β-FeSi2/Si(111) interface than epitaxial RDE + MBE thin films. We investigated the intrinsic thermoelectric properties of the epitaxial β-FeSi2 thin films on Si(111), revealing lower thermal conductivity and higher electrical conductivity compared with bulk β-FeSi2. We also investigated the impact of doping on the Seebeck coefficient of bulk and thin-film β-FeSi2. A route to enhance the thermoelectric performance of β-FeSi2 is proposed, based on (1) fabrication of thin-film structures for high electrical conductivity and low thermal conductivity, and (2) proper choice of doping for high Seebeck coefficient.

Connected and automated vehicles are expected to be at the core of future intelligent transportation systems. One of the main practical challenges for self-driving vehicles on public roads is safe cooperation and collaboration among multiple vehicles when conflicts arise on shared road segments. Intersections controlled by traffic lights and stop signs are common examples of such potential conflicts, and cooperative protocols for such intersections have been studied. On the other hand, there are many different types of shared road segments. In this paper, we study Dynamic Intersections that might appear almost anytime and anywhere on public roads and that might lead to automobile accidents. We consider how a self-driving vehicle can safely navigate these dynamic intersections by using sensor-based perception and inter-vehicle communications. We present a cooperative protocol for dynamic intersections which can be used by self-driving vehicles for safely coordinating with other vehicles. Under our protocol, self-driving vehicles can also create a vehicular communication-based traffic manager named Cyber Traffic Light when the area is congested. A cyber traffic light functions as a self-optimizing traffic light by estimating the traffic volumes and by wirelessly coordinating among multiple self-driving vehicles. Our simulation results show that our protocol has higher traffic throughput, compared to simple traffic protocols while ensuring safety.

This paper studies hardware implementation of a simple dynamic binary neural network that can generate various periodic orbits. The network is characterized by local binary connection and signum activation function. First, using a simple feature quantity, stability of a target periodic orbit is considered. Second, using a FPGA board, a test circuit is implemented. The signum activation function is realized by a majority decision circuit and the binary connection is realized by switches and inverters. The circuit operation is confirmed experimentally.

The analysis and study of the relationship between a geo-spatial event and human mobility in an urban area is very significant for improving productivity, mobility, and safety. In particular, in order to alleviate serious road congestions, traffic jams, and stampedes, it is essential to predict and be informed about the occurrence of an event as soon as possible. When we know an event occurrence in advance, some of those who are not interested in the event might change their plans and/or might take a detour to avoid to get involved in a heavy congestion. In this context, this paper presents an early event detection technique using GPS trajectories collected from periodic-cars, which are vehicles periodically traveling on a pre-scheduled route with a pre-determined departure time, such as a transit bus, shuttle, garbage truck, or municipal patrol car. Using these trajectories, which provide the real-time and continuous traffic flow and speed, our technique detects large-scale events in advance, without incurring any privacy invasion. The behavior of periodic-cars shows a certain sign of a large-scale event before attendees gather around a venue because traffic can be slowed around the venue before the event occurrence. We evaluated our method using over 7,000-bus data from January to May in 2015 in Beijing, which we compared with the check-in data collected from a social network service.

Road intersections represent a leading cause of traffic congestion and accidents. In fact, more than 44% of all reported crashes in the U.S. occur within intersection areas, which, in turn, lead to 8,500 fatalities and approximately 1 million injuries every year. The expected advent of self-driving vehicles raises the question of how automated and connected vehicles can be used to improve throughput at intersections while keeping them safe. A spatio-temporal intersection protocol named the Ballroom Intersection Protocol (BRIP) [5] was recently proposed in the literature to address this situation. Under this protocol, automated vehicles arrive at and go through an intersection in a cooperative fashion with no vehicle needing to stop, while maximizing the intersection throughput. Though no vehicles run into one another under ideal environments with BRIP, vehicle accidents can occur when the self-driving vehicles have location errors. In this paper, we present a safe and practical intersection protocol named the Configurable Synchronous Intersection Protocol (CSIP) that is a more general and resilient version of BRIP. CSIP utilizes a certain inter-vehicle distance to meet safety requirements against GPS inaccuracy and control failure. In addition, the inter-vehicle distances under CSIP are much more acceptable to human passengers due to longer inter-vehicle distances that do not cause fear. With CSIP, the inter-vehicle distances can also be changed at each intersection to account for different traffic volumes and GPS inaccuracies. Our simulation results show that CSIP never leads to traffic accidents even when the system has typical location errors, and that CSIP increases the traffic throughput of the intersections compared to common signalized intersections.

Self-driving vehicle technologies are progressing rapidly and are expected to play a significant role in the future of transportation. One of the main challenges for self-driving vehicles on public roads is the safe cooperation and collaboration among multiple vehicles using sensor-based perception and inter-vehicle communications. When self-driving vehicles try to occupy the same spatial area simultaneously, they might collide with one another, might become deadlocked, or might slam on the brakes making it uncomfortable or unsafe for passengers in a self-driving vehicle. In this paper, we study how a self-driving vehicle can safely navigate merge points, where two lanes with different priorities meet. We present a safe protocol for merge points named Autonomous Vehicle Protocol for Merge Points, where self-driving vehicles use both vehicular communications and their own perception systems for cooperating with other self-driving and/or human-driven vehicles. Our simulation results show that our traffic protocol has higher traffic throughput, compared to simple traffic protocols, while ensuring safety.

Privacy issue has become serious in the area of mobile participatory sensing, smart grids, location-based services, and intelligent transportation systems. In response, many researchers are tackling the issue with technical approaches. In particular, data perturbation schemes, where all sensor data are processed on the user-side, are prospective techniques for utilizing personal data collected by embedded systems and sensor-equipped devices. However, existing data perturbation schemes require sharing the protocol between the user-side and the server-side, even though this can allow malicious attackers to estimate the original sensor data from the perturbed data. In this context, the paper presents a protocol-hidden perturbation framework, called Democratic Privacy, with which a service provider can acquire the original data probability without sharing the perturbation protocol with each user. In Democratic Privacy, the perturbation protocol is selected dramatically by each user, and the original data are reconstructed based on the selection of the crowd. Consequently, malicious attackers cannot distinguish the individual settings by eavesdropping. In addition, this paper presents Three-level Perturbation as one of the methods for Democratic Privacy, and evaluates it by means of simulations. The results of our evaluation demonstrate that Democratic Privacy provides a secure platform for pervasive computing environments.

A two-dimensional microstrip gas chamber (MSGC) has been developed with a 10 cm-square detection area and an ultrafast read-out system. The MSGC was made using multi-chip module (MCM) technology, and has a very thin substrate of 17 µm and many anodes and back strips, both with 200 µm pitches. The new read-out system, in which the hit addresses of the electrodes were sequentially encoded to the hit positions by a synchronous clock, handles data rates of up to 107 events s−1 from MSGCs. This enables the acquisition of fast and sequential digital images. Furthermore, since the MSGC is a real photon-counting detector, the timing of the photons, to an accuracy of a few tens of nanoseconds, and energy can be recorded. Here, the performance of the MSGC system as a real-time area detector is reported, and the abilities of this system are discussed.

A high-performance data-acquisition system has been developed in order to obtain time-resolved sequential images from a two-dimensional microstrip gas chamber (MSGC). This was achieved using fully digital processing with a synchronized pipeline method. Complex logical circuits for processing large numbers of signals are mounted on a small number of complex programmable logic devices. The system is operated with a 10 MHz synchronous clock, and has the capability of handling more than 3 × 106 counts s−1 for asynchronous events. The system was examined using a 5 × 5 cm MSGC and the recently developed 10 × 10 cm MSGC (1024 outputs); the anticipated performances were achieved.

Community sensing is an emerging system which allows the increasing number of mobile phone users to share effectively minute statistical information collected by themselves. This system relies on participants' active contribution including intentional input data through mobile phone's applications, e.g. Facebook, Twitter and Linkdin. However, a number of privacy concerns will hinder the spread of community sensing applications. It is difficult for resource-constrained mobile phones to rely on complicated encryption scheme. We should prepare a privacy-preserving community sensing scheme with less computational-complexity. Moreover, an environment that is reassuring for participants to conduct community sensing is strongly required because the quality of the statistical data is depending on general users' active contribution. In this article, we suggest a privacy-preserving community sensing scheme for human-centric data such as profile information by using the combination of negative surveys and randomized response techniques. By using our method described in this paper, the server can reconstruct the probability distributions of the original distributions of sensed values without violating the privacy of users. Especially, we can protect sensitive information from malicious tracking attacks. We evaluated how this scheme can preserve the privacy while keeping the integrity of aggregated information.

Although it is known that a moving stimulus appears to dilate in duration compared to a stationary stimulus, whether subjective motion devoid of stimulus motion is sufficient remains unknown. To elucidate this, we used a motion illusion in which an actually static stimulus clearly appears to move, a useful dissociation between actual and subjective motions. We used the jitter aftereffect resulting from adaptation to dynamic noise as such a tool and measured subjective durations of a static random-dot pattern in which illusory jitter was seen, an actually oscillating pattern mimicking the illusory jitter, and a static pattern without illusory jitter. Pattern oscillation as tiny as fixational eye movements robustly evoked time dilation, and time dilation to a similar extent was also induced by an actually static but subjectively jittering pattern. Taken together with the previous knowledge that this subjective jitter is related to a visually based compensation of spurious retinal image motions due to fixational eye movements, these findings demonstrate that visual duration computation is influenced by a representation at a high-level motion processing stage at which a stable visual world despite jittery retinal inputs has been established.

Participatory sensing is a crowd-sourcing technique that relies on participants' active contribution. By aggregating data sets from each participant, we can collect statistical information of the environment or phenomenona in the cloud. To promote use of participatory sensing in healthcare, research, and other useful applications, the protection of privacy is important to consider. The invasion of privacy in participatory sensing would have dire consequences because mobile phone used by these applications have sensitive data about users daily life. In this paper, we suggest a privacy-preserving participatory sensing scheme for real-world data sets with large numbers of categories by using Limited Negative Surveys. By using our method described in this paper, the server can reconstruct the probability distributions of the original distributions of sensed values without knowing the personal information of citizens. Furthermore, our research has the capablity to change, according to the features of data, especially the number of categories. We evaluate how this scheme of aggregate information can be still useful while protecting the privacy of users' original data.

In order to develop tissue engineering applications for oral and maxillofacial surgery, the surgical anatomy of the miniature pig's face was investigated in three miniature pig cadavers and three anaesthetized miniature pigs using identical procedures that were previously described for humans. A preauricular incision with a retromandibular and a submandibular extension was initially made through the facial skin and subcutaneous tissues. The underlying tissues were then carefully dissected in order to progressively expose the platysma muscle, the superficial layer of deep cervical fascia, the marginal mandibular branch and buccal branch of the facial nerve, the mental nerve and the mandibular skeleton. The marginal mandibular branch of the facial nerve has an upper and lower division. Stimulation of the facial nerve and its branches showed that the upper division of the marginal mandibular branch innervates muscles and tissues in the upper lip and nose region, and the lower division innervates muscles and tissues in the lower lip region. The gross anatomy of the maxillofacial region in the pigs was found to be similar to that of humans. Although the distributions of the marginal mandibular branch of the facial nerve and the mental nerve are different from that of humans, we concluded that miniature pigs are a suitable experimental model for the preclinical development of tissue engineering applications in oral and maxillofacial surgery.

Hierarchical structure of deterministic chaos in a chaotic neural network model (CNN) is investigated in the view of application in robotics. The result shows a rich capacity of CNN in selectively controlling the synchronization of neuron outputs, and sensitively responding to external sensory inputs, both being based on the intrinsic mechanism of the dynamics called chaotic itinerancy. Choosing appropriate parameters, the simple designed robot realized a chaotic search to the hierarchically selected directions. The macroscopic drift preserving chaotic fluctuation was also derived by simply adding weak external inputs to an intended direction. Obstacle avoidance was simulated with the use of these properties.

We have developed a two-dimensional MicroStrip Gas Chamber (MSGC) with a 5cm × 5cm detection area based on Multi-Chip Module (MCM) technology. We report on the capability of the MSGC as an imaging detector operated in an intense X-ray beam, and as an X-ray polarization detector. The anisotropy between the number of hit anodes and back strips was measured, and more than 10% difference was obtained for polarized X-rays at 8–16 keV.

A two-dimensional microstrip gas chamber (MSGC) with a 5 cm × 5 cm detection area has been developed. It has 254 anodes and 255 back strips, both with 200 µm pitches. Using this MSGC, linear polarization of X-rays was successfully measured in the energy range 6–14 keV. In addition, the performance of the MSGC as an X-ray imaging polarimeter has been simulated using the EGS4 program (Electron Gamma Shower, Version 4) modified for this purpose. In this article, the results of both the polarization measurement and the simulation are reported.