Identities and recurrence relations of special numbers and polynomials of higher order by analysis of their generating functions.

The aim of this is to give generating functions for new families of special numbers and polynomials of higher order. By using these generating functions and their functional equations, we derive identities and relations for these numbers and polynomials. Relations between these new families of special numbers and polynomials and Bernoulli numbers and polynomials are given. Finally, recurrence relations and derivative formulas, which are related to these numbers and polynomials, are given.

Classical analysis of time behavior of radiation fields associated with biophoton signals.

BACKGROUND: Propagation of photon signals in biological systems, such as neurons, accompanies the production of biophotons. The role of biophotons in a cell deserves special attention because it can be applied to diverse optical systems. OBJECTIVE: This work has been aimed to investigate the time behavior of biophoton signals emitted from living systems in detail, by introducing a Hamiltonian that describes the process. The ratio of the energy loss during signal dissipation will also be investigated. METHOD: To see the adiabatic properties of the biophoton signal, we introduced an adiabatic invariant of the system according to the method of its basic formulation. RESULTS: The energy of the released biophoton dissipates over time in a somewhat intricate way when t is small. However, after a sufficient long time, it dissipates in proportion (1+λ_0t)^2 to where λ_0 is a constant that is relevant to the degree of dissipation. We have confirmed that the energy of the biophoton signal oscillates in a particular way while it dissipates. CONCLUSION: This research clarifies the characteristics of radiation fields associated with biophotons on the basis of Hamiltonian dynamics which describes phenomenological aspects of biophotons signals.

New finger biometric method using near infrared imaging.

In this paper, we propose a new finger biometric method. Infrared finger images are first captured, and then feature extraction is performed using a modified gaussian high-pass filter through binarization, local binary pattern (LBP), and local derivative pattern (LDP) methods. Infrared finger images include the multimodal features of finger veins and finger geometries. Instead of extracting each feature using different methods, the modified gaussian high-pass filter is fully convolved. Therefore, the extracted binary patterns of finger images include the multimodal features of veins and finger geometries. Experimental results show that the proposed method has an error rate of 0.13%.