The center frequency of Brillouin scattering spectrum is easily influenced by the noise and the measurement accuracy of optical fiber strain is reduced. So a novel denoising method which can be applied in the Brillouin scattering spectrum is developed in this article. The Brillouin scattering spectrum is decomposed into multi-scale detail coefficients and approximation coefficients by using the wavelet transform. The wavelet decomposition detail coefficients are threshold quantified by utilizing the threshold algorithm. At the same time, the wavelet decomposition approximation coefficients are trained and simulated by using the BP neural network in order to remove noise hided in the approximation coefficients. So the novel method can reduce the wavelet decomposition scales. The Brillouin scattering spectrum which has a better denoising effect can be gained by using the inverse wavelet transform, and the measurement accuracy of optical fiber strain is enhanced also. The results of simulation and experiment demonstrate that the proposed method can suppress noise better; accordingly, the new method can gain more precision optical fiber strain and reduce the wavelet decomposition scales effectively than the conventional wavelet denoising method. Theory analysis and experiment show that the method is reasonable and efficient.

Two compact and low loss dual-mode filters are proposed by using degenerate modes of slotted triangular microstrip patch resonators. The geometrical size and radiation loss of the triangular patch are reduced simultaneously by loading both horizontal and vertical slots. The resonant frequencies of two degenerate modes can be easily controlled by varying the dimensions and positions of the slots. A two-pole dualmode filter operating at 3.94 GHz with a fractional bandwidth of 4.3% is designed, fabricated, and measured. The measured results verify well the theoretical predictions.

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One-dimensional slot resonator-based photonic bandgap for coplanar waveguide (SR-PBG-CPW) is proposed in this paper. The SR-PBG-CPW unit is realized by replacing the conventional rectangular holes on ground plane with a slot resonator. The proposed SR-PBG-CPW unit exhibits more excellent bandgap and slow-wave characteristics and better selectivity at cutoff frequency than the conventional PBG-CPW unit. Furthermore, the SR-PBG-CPW is applied effectively to design a bandstop filter. Comparison between simulations and measurement confirms the validity of the proposed filter configuration and design procedure. Figure 7 Comparison of the simulated and measured transmission characteristics for the CPW bandstop filters shown in Fig. 6: (a) conventional; (b) slot resonator ABSTRACT: A new method based on the adaptive neuro-fuzzy inference system (ANFIS) is presented to calculate accurately the patch radius of circular microstrip antennas (MSAs). ANFIS combines the benefits of artificial neural networks (ANNs) and fuzzy inference systems (FISs) in a single model. A hybrid learning algorithm based on the least-squares method (LSM) and the backpropagation algorithm is usedto identify the parameters of ANFIS. The results of ANFIS are in very good agreement with the experimental results reported elsewhere.

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