Search

For autonomous mobile robots to work safely in human-coexistent environments, human-velocity estimation is essential. However, the human body periodically fluctuates to the front, rear, right, and left while walking. Also, a significant estimation error occurs due to the vibration of sensors installed in the robot. Quick trajectory adjustment requires high-accuracy and low-latency estimation, but these are in a trade-off relationship. We thus propose a human velocity estimation system (VES) using the Kalman filter (KF) and least squares (LS) with adjustable window size (AWS) to control the accuracy and latency. The VES adjusts two window sizes to calculate a system noise distribution for KF and a velocity vector for LS using a newly proposed cost function, including accuracy (direction and magnitude) and latency (time delay) costs. To select window sizes suitable for walking trajectories and individual gaits, we collected human walking data, calculated the three costs, and selected the window sizes with the minimum cost. The results of experiments using a laser range finder installed on a mobile robot indicate that the cost function could reveal window sizes to increase accuracy or reduce latency depending on walking trajectories and individual gaits, and the VES with AWS could enhance the performance of estimating human walking velocity for mobile robots.

Text-to-Image (T2I) generation is the task of synthesizing images corresponding to a given text input. The recent innovations in artificial intelligence have enhanced the capacity of conventional T2I generation, yielding more and more powerful models day by day. However, their behavior is known to become unstable in the face of text inputs containing nonwords that have no definition within a language. This behavior not only results in situations where image generation does not match human expectations but also hinders these models from being utilized in psycholinguistic applications and simulations. This paper exploits the human nature of associating nonwords with their phonetically and phonologically similar words and uses it to propose a T2I generation framework robust against nonword inputs. The framework comprises a phonetics-aware language model as well as an adjusted T2I generation model. Our evaluations confirm that the proposed nonword-to-image generation synthesizes images that depict visual concepts of phonetically similar words more stably than comparative methods. We also assess how the image generation results match human expectations, showing a better agreement than the phonetics-blind baseline.

Delays (wait times) often occur in computer use, and an illusion changing the subjective elapsed time can occur unintentionally due to perceptual stimuli from computer interfaces. Since this can adversely affect users’ experience and state of mind, investigating and manipulating such an illusion with computer interfaces has been an important topic for many years. While many studies have focused on visual interfaces, few studies examine these two research directions: 1) the possibility that illusion-manipulation methods verified under specific conditions may cause illusions to have completely different trends due to certain factors and 2) the feasibility of illusion-manipulation methods for changing the subjective elapsed time by using only visual stimuli in a small space at the edge of the field of view/screen. Therefore, this study verified these two directions. We designed two methods for changing the frequency and duration of visual stimuli. We also designed two visualization styles: the blinking visualization style and vibration visualization style. Three evaluations were conducted. First, the results showed the feasibility of illusion-manipulation methods using visual stimuli in a small space at the edge of the field of view/screen. The subjective elapsed time changed with a constant trend from −8.9 to 0.6% in 10 seconds. Second, the results showed that the trend in elapsed time of illusions caused by the same visual stimulus pattern could change when different visualization styles were used. Specifically, when using the method of changing the duration of visual stimuli, the increase or decrease in the subjective elapsed time was reversed depending on whether the blinking or visualization style was used. Third, the results showed that the trend in elapsed time of illusions caused by visual stimuli could change when there is visual information other than illusion-inducing visual stimuli (i.e., when some of the user’s attention is diverted to something other than illusion-inducing visual stimuli). Specifically, when using the method of changing the frequency of visual stimuli, the increase or decrease in subjective elapsed time was reversed depending on whether there was visual information other than illusion-inducing visual stimuli (e.g., an advertisement video, a first-person perspective movie of walking outdoors). These results could provide new insights for designers, researchers, and users to explore and consider the illusion of changing the subjective elapsed time with visual interfaces.

In order to meet the quality requirements of various communication services in the advanced 5G era around 2025, the authors have proposed an Adaptive radio access network (RAN) that changes the placement of virtualized base station functions according to the services. In Adaptive RAN, each base station function has its own scheduler, and multiple schedulers share common radio resources. Therefore, in order to guarantee communication quality for more users, it is necessary to control the allocation of the radio resources required for scheduling to each base station function with neither excess nor deficiency. This allocation control is performed based on the information collected from the base station functions by the RAN intelligent controller (RIC). If a large amount of information is used for control, allocation can be performed with higher accuracy, but the collection of information overuses the limited bandwidth of the network. Therefore, it is necessary to reduce the information volume required for control while at the same time guaranteeing communication quality. In this paper, we propose a method that guarantees communication quality while reducing the information volume required for control by combining a required resource estimation control in a long cycle and a resource allocation modification control based on feedback information related to communication quality in a short cycle. We evaluated the impact of the proposed method on communication quality compared to conventional methods through simulation, and verified that the proposed method can guarantee better communication quality than conventional methods while suppressing the increase in information volume.

Path planning in dynamic environments is still a challenging issue with autonomous mobile robots. Current methods lack adaptability to various passing scenarios, a variety of passing trajectories including an acceleration path, or immediacy in planning time, which require human-aware navigation. In this study, we propose Dynamic Waypoint Navigation (DWN), which is a model-based adaptive real-time trajectory planning method. DWN first predicts human-robot path interference and the time and position of the interference on the basis of the measured velocity of humans. It then dynamically designates several waypoints considering the time delay of both calculation time and robot travel time. Then, DWN generates several trajectories by combining different speeds (default, acceleration, and deceleration) and paths (default, right, and left) and selects the best trajectory in terms of an interference-avoidance energy cost based on the degree of velocity-vector change. DWN can also output a trajectory within 0.5 s to immediately adapt to changes in human behavior and adopt a simple mathematical model and algorithm to enable easy expansion. Simulation and experimental results reveal that the DWN can adequately select a time-efficient trajectory in real-time and adaptively change a trajectory depending on human movement.

Herein, we investigate the influence of an AlGaN metasurface on AlGaN-based deep-ultraviolet light-emitting diodes' light-extraction efficiency by utilizing the 3D finite-difference time-domain method. As the first step in this study, we optimize an AlGaN metasurface to maximize the transmittance from LED to air. Then, six different flip-chip structures' light-extraction efficiencies are compared with each other to judge the usefulness of the metasurface's adoption on AlGaN-based LED. Considered structures are classified into two categories, with and without inclined sidewall, respectively. Each classified structure is subdivided again by three factors: flat interface, roughed cones, and metasurface on n-AlGaN. Extracted results show that the combination of inclined sidewall and the AlGaN metasurface shows synergetic results positively increasing light-extraction efficiency.

Significant efforts have been devoted to improve the external quantum efficiency (EQE) of AlGaN-based top-emitting deep-UV light-emitting diodes (DUV LEDs). However, issues such as ohmic contact and challenges related to p-AlGaN doping and growth have hampered advancement. In this paper, a record-high EQE of 3.2% is reported for AlGaN-based lateral-type top-emitting DUV LEDs in which Ni-doped AlN (Ni:AlN) DUV-transparent ohmic electrodes are used. The ohmic electrode exhibits a transmittance of more than 90% at 280 nm and a reasonably good ohmic behavior with the p-Al0.64Ga0.36N contact layers. The Ni:AlN-based DUV LED demonstrates outstanding performance (i.e., operating voltage of 8.3 V at 20 mA, light output power of 11.6 mW at 100 mA) relative to the conventional thin ITO- and Ni/Au-based DUV LEDs. Furthermore, the proposed device is highly reliable, as evidenced by the fact that it maintained more than 80% of its light output power after 500 h of operation, and the operating voltage increased by only 2.7% over the operating time of 1 $\times \,\,10^{5}\text{s}$ .

Experimental demonstrations of quantum annealing with “native” implementation of Boolean logic Hamiltonians are reported. As a superconducting integrated circuit, a problem Hamiltonian whose set of ground states is consistent with a given truth table is implemented for quantum annealing with no redundant qubits. As examples of the truth table, nand and nor are successfully fabricated as an identical circuit. Similarly, a native implementation of a multiplier comprising six superconducting flux qubits is also demonstrated. These native implementations of Hamiltonians consistent with Boolean logic provide an efficient and scalable way of applying annealing computation to so-called circuit satisfiability problems that aim to find a set of inputs consistent with a given output over any Boolean logic functions, especially those like factorization through a multiplier Hamiltonian. A proof-of-concept demonstration of a hybrid computing architecture for domain-specific quantum computing is described.

Image captioning can show great performance for generating captions for general purposes, but it remains difficult to adjust the generated captions for different applications. In this paper, we propose an image captioning method which can generate both imageability- and length-controllable captions. The imageability parameter adjusts the level of visual descriptiveness of the caption, making it either more abstract or more concrete. In contrast, the length parameter only adjusts the length of the caption while keeping the visual descriptiveness on a similar degree. Based on a transformer architecture, our model is trained using an augmented dataset with diversified captions across different degrees of descriptiveness. The resulting model can control both imageability and length, making it possible to tailor output towards various applications. Experiments show that we can maintain a captioning performance similar to comparison methods, while being able to control the visual descriptiveness and the length of the generated captions. A subjective evaluation with human participants also shows a significant correlation of the target imageability in terms of human expectations. Thus, we confirmed that the proposed method provides a promising step towards tailoring image captions closer to certain applications.

Highly developed drone technology enables the use of drones in a wide variety of areas. However, those drones are mainly used in the unmanned aerial vehicles. We believe that underwater drones will become a big research topic and find a market in the near future. We developed an underwater drone with a 360° panoramic camera acting as the “eye”of the drone. The designs are based on the open-source hardware and will be shared as an open-source for contributing to the innovation of manufacturing including drone. The function of the 360° panoramic camera is generated by correcting the images taken by two fisheye lenses. The underwater drone was designed by extending the Raspberry Pi compute module, the frame was designed by OpenSCAD, and the printed circuit board was designed by MakePro. As for the application of the underwater drone, we focused on fish recognition for investigating fish species in a natural lake to help protect the original environment. Fish recognition is based on deep learning, which is the biggest topic in the artificial intelligence research field today. Experimental results show that the function of the underwater drone achieved at diving in the leak automatically. The 360° panoramic images were generated correctly. Fish recognition achieved 87% accuracy by deep learning.

Near-field magnetic wireless systems have distinct advantages over their conventional farfield counterparts in water-rich environments, such as underwater, underground, and in biological tissues, due to lower power absorption. This paper presents a comprehensive review of near-field magnetic wireless power transfer (WPT) and communication technologies in a variety of applications from general free-space systems, to implantable biomedical devices we find of particular interest. To implement a fully wirelessly-powered implantable system, both high-efficiency power transfer and high-rate data communication are essential. This paper first presents the history and the fundamentals of near-field WPT and communication in free-space systems, followed by technical details for their specific use in implantable biomedical devices. Finally, this paper reviews recent advances in simultaneous wireless information and power transfer and highlights their applications in implantable biomedical systems. The knowledge reviewed in the paper could provide intuition in the design of various wireless and mobile systems such as wireless body area networks, small-cell 5G cellular, as well as in-body biomedical applications, especially for efficient power and data management and higher security.