Measurement of the time required for termites to pass each other in tunnels of different curvatures

Subterranean termites construct complex tunnel networks for foraging. During travel in the tunnels, termites often encounter one another when passing in opposite directions. Such encounters are likely to affect the ＂movement efficiency,＂ which is the time required for a termite to travel a certain distance in a tunnel. In this study, we explored how individual-individual encounters affect movement efficiency in tunnels by measuring the time （v） taken by two termites to pass one another in tunnels of different curvatures. Artificial tunnels of 5 cm in length and variable widths （W） of 2, 3, or 4 mm were made. Tunnel distance （D） was 2, 3, 4, or 5 cm. When D had a higher value, curvature was lower. When W = 2, T was significantly shorter in the tunnel with D = 5 than in tunnels ofD = 2, 3, or 4, whereas v was statistically the same for D = 2, 3 and 4. When W = 3, r was shorter in the tunnel with D = 5 than for D = 3 and 4, while ~ was longer in the tunnel with D = 2 than for D = 3 and 4. When W = 4, r was longer in the tunnels with D = 2 and 3 than for D = 4 and 5. Based on these observations, 3 types of termite behavior were identified： biased walking, backward walking, and zigzag walking. We considered these results in relation to foraging efficiency.

Steady-state probability density function in wave turbulence under large volume limit

We investigate the possibility for two-mode probability density function （PDF） to have a non-zero flux steady state solution. We take the large volume limit so that the space of modes becomes continuous. It is shown that in this limit all the steady-state twoor higher-mode PDFs are the product of one-mode PDFs. The flux of this steady-state solution turns out to be zero for any finite mode PDF.

Finger vein recognition using weighted local binary pattern code based on a support vector machine

Finger vein recognition is a biometric technique which identifies individuals using their unique finger vein patterns. It is reported to have a high accuracy and rapid processing speed. In addition, it is impossible to steal a vein pattern located inside the finger. We propose a new identification method of finger vascular patterns using a weighted local binary pattern (LBP) and support vector machine (SVM). This research is novel in the following three ways. First, holistic codes are extracted through the LBP method without using a vein detection procedure. This reduces the processing time and the complexities in detecting finger vein patterns. Second, we classify the local areas from which the LBP codes are extracted into three categories based on the SVM classifier: local areas that include a large amount (LA), a medium amount (MA), and a small amount (SA) of vein patterns. Third, different weights are assigned to the extracted LBP code according to the local area type (LA, MA, and SA) from which the LBP codes were extracted. The optimal weights are determined empirically in terms of the accuracy of the finger vein recognition. Experimental results show that our equal error rate (EER) is significantly lower compared to that without the proposed method or using a conventional method.