Artificial neural networks (ANNs) are one of the most recently explored advanced technologies which show promise in the factory monitoring area. This paper focuses on two particular network models, back-propagation network (BPN) and general regression neural network (GRNN). The prediction accuracy of these two models is evaluated using a practical application situation in a monitor factory. GRNN emerged as a variant of the artificial neural network. Its principal advantages are that it can quickly learn and rapidly converge to the optimal regression surface with large number of data sets. According the simulation results we can show that GRNN is an effective way to considerably improve the predictive ability of BPN.

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In this letter, we present a blind carrier frequency offset (CFO) estimator by exploiting the polynomial rooting technique for multicarrier-code division multiple access (MC-CDMA) systems. Relative high accuracy and low-complexity to the CFO estimation can be achieved by rooting a polynomial. Simulation results are provided for illustrating the effectiveness of the proposed blind polynomial rooting estimator.

An X‐Band eight‐subarray smart antenna system has been developed in this article to demonstrate its capability of direction of arrival estimation for correlated arriving signals. The antenna array is composed of an eight‐subarray antenna and eight‐track of transceiver for quadrature baseband to RF up/down conversion. Multiple classification of signal (MUSIC) and modified MUSIC algorithm with spatial smoothing are used for high resolution angle of arrival (AoA) estimation. Measured results showed that the modified MUSIC algorithm with spatial smoothing is capable of estimating target AoA with average angle error of 1.14° when target arriving angles are varying from −30° to 30°. Accurate results of AoA estimation by simulation and measurement for stationary or dynamic targets has also been demonstrated to validate the feasibility of the proposed X‐Band smart antenna system. © 2014 Wiley Periodicals, Inc. Microwave Opt Technol Lett 56:2582–2587, 2014