Synthesis of Compositionally Homogeneous Sr(CuxZn1−x)1/2W1/2O3

A complex perovskite of Sr(Cu _x Zn_{1- x} )_1/2 W_1/2O₃ (SCZW) is synthesized by a new combination of wet and dry processess. Mixed oxides containing Cu²⁺ and Zn²⁺ (CZ) are prepared by the wet process (coprecipitate method). SCZW is obtained by the dry process (mixed-oxide method) from a mixture of CZ, SrCO₃, and WO₃. SCZW has practically no compositional, unlike solid solutions prepared by the conventional dry method. The wet–dry process method is useful because the wet process is applied to only B-site cations having the same valence.     

Cost-Efficient Traffic Aggregation Employing Cross-Layer Shared Protection

Shared protection/restoration is a promising solution for reducing protection resources and is supported at each layer of the current multi-layer networks. Software-defined networking is expected to reduce equipment cost as well as operational cost by orchestrating these shared protection functionalities. However, although protection resource sharing improves link utilization, it sometimes increases the required equipment. Meanwhile, traffic re-aggregation at each layer is an important technique for low volume traffic to utilize the underlying link capacity more efficiently, but re-aggregation also makes it difficult to share protection resources with traffic at lower layers. In this paper, we present multi-layer network design strategy and method that reduce equipment cost by means of both traffic re-aggregation at each layer and protection resource sharing among multiple service traffic at different layers. The strategy first prioritizes traffic re-aggregation at each layer, and then maximally delegates shared protection to lower layers as long as it does not increase the required capacity at the lower layer. Evaluation results from the example three-layer networks confirm that the proposed method can effectively reduce equipment cost compared to the conventional design method. Cost reduction is achieved by leveraging shared protection functions at multiple layers.     

Development of a flexible autonomous robot without sensors or controllers

Recently, various robots with many degrees of freedom, such as rescue robots and domestic robots, have been developed and used in practical applications. It is difficult to control such robots autonomously in real environments, because in order to control the many degrees of freedom, we have to observe many states, calculate huge amounts of information, and operate many actuators. In this study, we consider a flexible robot without sensors or controllers that can determine the inclination of a slope and climb up the slope. In order to demonstrate the effectiveness of the proposed framework, we have developed a prototype robot and conducted experiments. The result indicates that the robot could determine the inclination and climb up a gentle slope autonomously. Thus, we have realized an autonomous robot that has no explicit sensors or controllers.     

Integrative Bayesian model on two opposite types of sensory adaptation

Adaptation is a fundamental property of human perception. Recently, it was found that there are two opposite types of adaptation to repetitive stimuli with a temporal difference. In this article, we construct an integrative model of adaptation. We model the perception as a Bayesian inference, and represent the two types of adaptation as changes in the likelihood function and the prior distribution in the Bayesian inference. We examine our model analytically and show how the types of adaptation depend on model parameters.     

Survey of actual viewing conditions at home and appropriate luminance of LCD‐TV screens

Abstract— To understand actual viewing conditions at home is important for TV design. And the preferred luminance level of LCD TVs under actual viewing conditions is also important in order to obtain both good picture quality and low power consumption. The actual viewing conditions of households and the preferred luminance levels was investigated. In a field test of 83 households, the display luminance, screen illuminance, and viewing locations were measured on site. In laboratory experiments, young and elderly subjects adjusted the luminance of an LCD‐TV screen to their preferred levels under different screen illuminance levels, angular screen sizes, and average luminance levels (ALL) of the images. As a result, two equations, which represent the preferred luminance level of LCD‐TV screens corresponding to different viewing conditions for young and elderly subjects were obtained. When the ALL of the images was 25% and the screen illuminance and angular screen size were set at 100 lx and 20°, respectively, the preferred luminance was 1 60 cd/m² for the young subjects and 248 cd/m² for the elderly subjects. By using the setting of the preferred luminance of an LCD TV under actual viewing conditions, it is possible to conserve energy consumption.     

Greedy versus social: resource-competing oscillator network as a model of amoeba-based neurocomputer

A single-celled amoeboid organism, the true slime mold Physarum polycephalum, exhibits rich spatiotemporal oscillatory behavior and sophisticated computational capabilities. The authors previously created a biocomputer that incorporates the organism as a computing substrate to search for solutions to combinatorial optimization problems. With the assistance of optical feedback to implement a recurrent neural network model, the organism changes its shape by alternately growing and withdrawing its photosensitive branches so that its body area can be maximized and the risk of being illuminated can be minimized. In this way, the organism succeeded in finding the optimal solution to the four-city traveling salesman problem with a high probability. However, it remains unclear how the organism collects, stores, and compares information on light stimuli using the oscillatory dynamics. To study these points, we formulate an ordinary differential equation model of the amoeba-based neurocomputer, considering the organism as a network of oscillators that compete for a fixed amount of intracellular resource. The model, called the “Resource-Competing Oscillator Network (RCON) model,” reproduces well the organism’s experimentally observed behavior, as it generates a number of spatiotemporal oscillation modes by keeping the total sum of the resource constant. Designing the feedback rule properly, the RCON model comes to face a problem of optimizing the allocation of the resource to its nodes. In the problem-solving process, “greedy” nodes having the highest competitiveness are supposed to take more resource out of other nodes. However, the resource allocation pattern attained by the greedy nodes cannot always achieve a “socially optimal” state in terms of the public cost. We prepare four test problems including a tricky one in which the greedy pattern becomes “socially unfavorable” and investigate how the RCON model copes with these problems. Comparing problem-solving performances of the oscillation modes, we show that there exist some modes often attain socially favorable patterns without being trapped in the greedy one.     

Amoeba-based Chaotic Neurocomputing: Combinatorial Optimization by Coupled Biological Oscillators

We demonstrate a neurocomputing system incorporating an amoeboid unicellular organism, the true slime mold Physarum, known to exhibit rich spatiotemporal oscillatory behavior and sophisticated computational capabilities. Introducing optical feedback applied according to a recurrent neural network model, we induce that the amoeba’s photosensitive branches grow or degenerate in a network-patterned chamber in search of an optimal solution to the traveling salesman problem (TSP), where the solution corresponds to the amoeba’s stably relaxed configuration (shape), in which its body area is maximized while the risk of being illuminated is minimized.Our system is capable of reaching the optimal solution of the four-city TSP with a high probability. Moreover, our system can find more than one solution, because the amoeba can coordinate its branches’ oscillatory movements to perform transitional behavior among multiple stable configurations by spontaneously switching between the stabilizing and destabilizing modes. We show that the optimization capability is attributable to the amoeba’s fluctuating oscillatory movements. Applying several surrogate data analyses, we present results suggesting that the amoeba can be characterized as a set of coupled chaotic oscillators.

Semi-autonomous serially connected multi-crawler robot for search and rescue

In this paper, we develop a semi-autonomous serially connected multi-crawler robot for search and rescue. In large-scale disasters, such as earthquakes and tornadoes, the application of rescue robots to search for survivors under rubble would be beneficial. Snake-like robots (robots composed of serially connected units) are an effective candidate for such robots. Their long body enables them to overcome obstacles, and they can move into narrow spaces because of their thin shape. However, conventional snake-like robots have significant problems with operability. The numerous degrees of freedom of their bodies require complex operation to overcome obstacles, and training is required for the operators. Thus, survivors or community members cannot operate conventional robots to search for victims, despite the availability of such rescue robots. Here, we address this problem and develop a semi-autonomous serially connected multi-crawler robot designed for non-trained operators, such as community members or rescued survivors. It can be controlled easily by a conventional two-channel user interface with levers for turning and straight line motion. To demonstrate the effectiveness of our proposed mechanism, a prototype robot was developed and experiments were conducted. The results confirm that the proposed robot had both higher operability and higher mobility than conventional robots.     

Region with velocity constraints: map information and its usage for safe motion planning of a mobile robot in a public environment

Recently, various autonomous mobile robots have been developed for practical use. To support the coexistence of robots and humans in real environments, we propose a concept named ‘Region with Velocity Constraints (RVC),’ which is set around hazardous areas. RVCs are regions where the velocities of the robot are constrained to predefined values. Inside the RVCs, the robot has to reduce its translational velocity to avoid predicted hazards such as collisions with obstacles, and to reduce its rotational velocity to prevent undesirable motions such as sharp turns. We also propose a motion planning method for navigating the mobile robot in an environment with RVCs based on the Navigation Function and Global Dynamic Window Approach. Our method generates a trajectory satisfying both translational and rotational velocity constraints to be compatible with the surroundings. Moreover, to demonstrate the validity of our method, we performed numerical simulations and experiments.