Threshold for Millimeter-Wave (60 GHz)-Induced Ocular Injury

Journal of Infrared, Millimeter, and Terahertz Waves - Millimeter waves (MMW) absorbed by skin or cornea may induce damage by heat. We have developed a 60 GHz MMW exposure-induced eye...     

Delignification kinetics of empty fruit bunch (EFB): a sustainable and green pretreatment approach using malic acid-based solvents

Recently, the development of efficient and environmentally benign solvents has received great attention to replace current harsh organic solvents. In this context, low-transition-temperature mixtures (LTTMs) have emerged as favorable green solvents for biomass delignification. Palm oil biomass, empty fruit bunch (EFB) was pretreated with commercial l-malic acid and microwave hydrothermally extracted cactus malic acid-derived LTTMs at 60, 80, and 100 °C. The LTTMs applied in this study were derived from malic acid–choline chloride–water and malic acid–monosodium glutamate–water with a molar ratio of 2:4:2 and 3:1:5, respectively. Three first-order reactions were used to express the delignification kinetic model of EFB. The first term was based on the initial stage and assigned as infinite due to the fast rate of delignification which could not be detected. The second and third terms were proportional to bulk and residual delignification stages. A good agreement was obtained between the kinetic model and the experimental data obtained in this study with R²?≥?0.91. The activation energies for the delignification reactions using l-malic acid and cactus malic acid-based LTTMs in the bulk and residual stages were approximated as 36–56 and 19–26 kJ/mol and 34–90 and 47–87 kJ/mol, respectively.     

Single-mode optical waveguides fabricated from fluorinated polyimides

Buried channel optical waveguides were fabricated from fluorinated polyimides. They operated in single mode and showed an optical loss of less than 0.3 and 0.7 dB/cm for TE and TM polarizations, respectively, at a wavelength of 1.3 mum. Moreover, these waveguides had high heat and moisture resistance; the optical loss did not significantly change after heating at 380 degrees C for 1 h or after exposure to 85% relative humidity at 85 degrees C for over 200 h.     

Automated 360° profilometry of a three-dimensional diffuse object and its reconstruction by use of the shading model

The 360° profilometry of a three-dimensional (3-D) diffuse object by use of the light intersection and its image reconstruction by surface shading are presented. The lack of data in one direction, which was due to occlusion, was compensated by the projection of two lines of light from different directions. Some experiments to profile objects and their reconstruction by computer are shown. The entire surface model was constructed, and a real shading image was obtained by means of computer graphics.     

Dynamic digital photorefractive memory for optoelectronic neural network learning modules

Neural network modules based on page-oriented dynamic digital photorefractive memory are described. The modules can implement two different interconnection organizations, fan-out and fan-in, depending on their target network applications. Neural network learning is realized by the real-time memory update of dynamic digital photorefractive memory. Physical separation of subvolumes in the page-oriented photorefractive memory architecture contributes to the low cross talk and high diffraction efficiency of the stored interconnection weights. Digitally encoded interconnection weights ensure high accuracy, providing superior neural network system scalability. Module scalability and feedforward throughput have been investigated based on photorefractive memory geometry and the photodetector power requirements. The following four approaches to extend module scalability are discussed: partial optical summation, semiparallel feedforward operation, time partitioning, and interconnection matrix partitioning. Learning capabilities of the system are investigated in terms of required interconnection primitives for implementing learning processes and three memory-update schemes. The experimental results of Perceptron learning network implementation with 900 input neurons with digital 6-bit accuracy are reported.     

In situ measurements of ion-exchange processes in single polymer particles: laser trapping microspectroscopy and confocal fluorescence microspectroscopy

Ion-exchange processes of a cationic dye (Rhodamine B; RhB) were studied for individual polymer particles (diameter of 16-20 μm) by laser trapping microspectroscopy and confocal fluorescence laser microspectroscopy. The absorbance of RhB at 565 nm adsorbed on a cation-exchange particle increased linearly with the concentration of RhB in the aqueous phase, while it was independent of the particle diameter. Fluorescence intensity profile measurements of RhB along the particle diameter by confocal fluorescence microspectroscopy directly proved that ion exchange took place in the surface layer (～2-μm thickness) of the particle in the initial stage (1 h). Diffusion of RhB in the particle was very slow, and ion exchange proceeded gradually to the inner volume in the order of days. The ion-exchange processes were analyzed on the basis of simulation of the time course of the concentration profile of RhB in the particle, and the diffusion coefficient of RhB was determined to be (2-4) × 10(-11) cm(2)·s(-1).     

A biped static balance control and torque pattern learning under unknown periodic external forces

This paper addresses a biped balancing task in which an unknown external force is exerted, using the so-called ‘ankle strategy’ model. When an external force is periodic, a human adaptively maintains the balance, next learns how much force should be produced at the ankle joint from its repeatability, and finally memorized it as a motion pattern. To acquire motion patterns with balancing, we propose a control and learning method: as the control method, we adopt ground reaction force feedback to cope with an uncertain external force, while, as the learning method, we introduce a motion pattern generator that memorizes the torque pattern of the ankle joint by use of Fourier series expansion. In this learning process, the period estimation of the external force is crucial; this estimation is achieved based on local autocorrelation of joint trajectories. Computer simulations and robot experiments show effective control and learning results with respect to unknown periodic external forces.     

Damage to seagrass and seaweed beds in Matsushima Bay,Japan, caused by the huge tsunami of the Great East Japan Earthquake on 11 March 2011

Damage to seagrass and seaweed beds caused by the huge tsunami of the Great East Japan Earthquake on 11 March 2011 was investigated in Matsushima Bay, Miyagi Prefecture, Japan, by comparing satellite images taken before the tsunami in November 2009 and after the tsunami in May 2011. The results showed that the tsunami destroyed 220 ha of seagrass and seaweed beds, from 320 ha in 2009 to 100 ha just after the tsunami. Zostera marina beds on the sandy and muddy bottom were rather severely damaged compared with brown seaweed beds of Sargassum horneri on the rocky substrate. Substrates of the beds and directions of the tsunami seem to be responsible for the magnitude of the damage to the beds. Maps of the spatial distributions of the beds before and after the tsunami can provide information for the restoration of seagrass and seaweed beds as natural infrastructures of coastal fisheries resources.     

Probabilistic nearest neighbor query processing on distributed uncertain data

A nearest neighbor (NN) query, which returns the most similar object to a user-specified query object, plays an important role in a wide range of applications and hence has received considerable attention. In many such applications, e.g., sensor data collection and location-based services, objects are inherently uncertain. Furthermore, due to the ever increasing generation of massive datasets, the importance of distributed databases, which deal with such data objects, has been growing. One emerging challenge is to efficiently process probabilistic NN queries over distributed uncertain databases. The straightforward approach, that each local site forwards its own database to the central server, is communication-expensive, so we have to minimize communication cost for the NN object retrieval. In this paper, we focus on two important queries, namely top-k probable NN queries and probabilistic star queries, and propose efficient algorithms to process them over distributed uncertain databases. Extensive experiments on both real and synthetic data have demonstrated that our algorithms significantly reduce communication cost.