National Chung Hsing University

About: National Chung Hsing University is a education organization based out in Taichung, Taiwan. It is known for research contribution in the topics: Catalysis & Thin film. The organization has 19443 authors who have published 24060 publications receiving 540154 citations. The organization is also known as: NCHU.

Multiple-wavelength optical orthogonal codes under prime-sequence permutations for optical CDMA

Abstract: A new family of two-dimensional (2-D) wavelength-hopping time-spreading codes, which employs wavelength hopping algebraically under prime-sequence permutations on top of time-spreading optical orthogonal codes, is studied and analyzed. Different from other 2-D codes, our new codes allow the number of wavelengths and code length to be chosen independently and, at the same time, the code cardinality is a quadratic function of the number of wavelengths without sacrificing the maximum cross-correlation value (i.e., still at most one). They are particularly suitable for high bit-rate optical code-division multiple-access systems with broadband mode-locked lasers, in which the number of time slots is very limited, and system capacity can only be grown by increasing the number of wavelengths, rather than code length. Finally, a novel wavelength-aware detector for wavelength-hopping time-spreading codes is discussed and shown to provide improved code performance.

Neural network modeling for estimation of scour depth around bridge piers

Abstract: It is essential to predict the scour depth around bridge piers for hydraulic engineers involved in the economical design of bridge pier foundation. Conventional investigations have long been of the opinion that empirical scour prediction equations based on laboratory data over predict scour depths. In this article, the Back-Propagation Neural Network (BPN) was applied to predict the scour depth in order to overcome the problem of exclusive and the nonlinear relationships. The observations obtained from thirteen states in USA was verified by the present model. From the comparison with conventional experimental methods, it can be found that the scour depth around bridge piers can be efficiently predicted using the BPN.