We examined whether we could identify the feeding behaviours of the trophic generalist fish Epinephelus ongus on different prey types (crabs and fish) using a data logger that incorporated a three-axis gyroscope and a three-axis accelerometer. Feeding behaviours and other burst behaviours, including escape responses, intraspecific interactions and routine movements, were recorded from six E. ongus individuals using data loggers sampling at 200 Hz, and were validated by simultaneously recorded video images. For each data-logger record, we extracted 5 s of data when any of the three-axis accelerations exceeded absolute 2.0 g, to capture all feeding behaviours and other burst behaviours. Each feeding behaviour was then identified using a combination of parameters that were derived from the extracted data. Using decision trees with the parameters, high true identification rates (87.5% for both feeding behaviours) with low false identification rates (5% for crab-eating and 6.3% for fisheating) were achieved for both feeding behaviours.

Development of an ITS (Intelligent Transport System) has drawn much attention from computer vision community in recent years. In particular, various techniques for detecting pedestrians automatically have been proposed by many researchers. Among them, the HOG feature proposed by Dalal & Triggs has gained much interest in the pedestrian detection. However, previous methods including the original HOG feature have not achieved satisfactory detection rates.In this paper, we propose an extension of the HOG feature, i.e., flexible choice of the number of bins and automatic definition of a cell size and a block size by parameterizing their scales. By comparative experiments, it was confirmed that the proposed method outperforms the previous methods in the performance of pedestrian detection.

Zinc sulfide nanoparticles are obtained as primary particles in a polymer matrix by a matrix-mediated synthesis. Two types of matrix polymer are synthesized via the copolymerization of hydrophobic, cation-exchange, and cross-linking monomers. The ZnS nanoparticles are affected by the composition of the matrix polymer, and especially by its hydrophobicity. In a low-hydrophobicity copolymer matrix, aggregates of ZnS nanoparticles are observed in the matrix using transmission electron microscopy (TEM) and X-ray diffraction (XRD). In a high-hydrophobicity cation-exchange copolymer matrix, primary particles of ZnS with 2-5 nm diameters are observed in the matrix by TEM. However, the ZnS pattern is not distinguishable in XRD measurements because the particle sizes are too small to diffract X-rays.

In this paper, we propose a FTOP (Feature Transform Optimization Problem) and its solution. We propose a method to optimize both parameters and processing order of feature transform simultaneously, not limited to convolution and pooling included in CNN (Convolutional Neural Network). In order to realize the optimization, we formulate it as a combinatorial optimization problem and solve it by meta-heuristics. The effectiveness of the proposed method is shown by applying the proposed method to pedestrian classification based on a benchmark data set.

Phosphorus (P) doped ultra thin n+-layer is formed on crystalline silicon (c-Si) at low substrate temperatures of 80 – 350 °C using radicals generated by the catalytic reaction of phosphine (PH3) with a tungsten catalyzer heated at 1300 °C. The sheet carrier concentration obtained by Hall effect is in the range between 3×1012cm-2 and 8×1012cm-2. The distribution of P atoms obtained by secondary ion mass spectrometry (SIMS) indicates that P atoms locate within the depth of 4 nm from surface and the profile has almost the same distribution independent of any doping conditions such as substrate temperature or radical exposure time. The sheet carrier concentration is 1.15 – 2.12% of the amount of P atoms incorporated through the radical doping. The ratio of activated donors increases with substrate temperature during the radical doping, suggesting that P-related species bonded on the c-Si surface require thermal energy for their activation. Using the n+-layer formed by radical doping, the reduction of surface recombination velocity for n-type c-Si wafer is attempted. The effective minority carrier lifetime of the n-type c-Si sample covered with 6-nm-thick intrinsic amorphous Si (i-a-Si) layers on both side increases from 32 μs to1576 μs by the radical doping of P atoms to n-type c-Si surface, suggesting that the radical doping can be utilized for the formation of passivation layers on a-Si/ n-c-Si hetero-interface.