It is common that a pinhole camera captures object images through a transparent plate with two parallel planar faces. In this case, the refraction leads to a non-linear error using the simple pinhole model. To address this issue, a modified pinhole camera model, which is called flat refractive camera model, is established based on the analysis of light refraction in the transparent plate placed between the scene and the camera. Then, an effective backward-projection algorithm is proposed to realise plane dimension detection and eliminate the plate refraction effect. Unlike other models, this proposed model has a form similar to the pinhole camera model. Therefore, it is easy to employ conventional vision techniques in the plate imaging system. At the same time, experiments are designed to demonstrate the plane dimension detection based on the proposed method. Experimental results show that the proposed method can obtain higher accuracy than the traditional method without considering the plate refraction effect.

The conventional DFT-based channel estimation method can improve the performance of the least square (LS) or minimum mean square error (MMSE) channel estimation by removing the noise outside the maximum channel delay while the noise inside channel delay still remain. Here, a novel DFT-based pilot-aided channel estimation method for filter bank multicarrier with offset quadrature amplitude modulation (FBMC-OQAM) is proposed. Using clustering and discriminant analysis, this method can separate automatically the noise inside channel delay from channel impulse response (CIR) coefficient of the conventional DFT-based channel estimation. Theoretical analysis and simulation results show that the accuracy of channel estimation can be improved remarkably. Compared with conventional DFT-based channel estimation method and DFT-based channel estimation method with threshold, the proposed DFT-based channel estimation method with clustering has a better bit error ratio (BER) performance in additive white Gaussian noise (AWGN) channel and flat fading channel.

In this study, the authors stress the problem of sampling and recovering sparse wideband signals. A new methodology for sparse multiband signal acquisition using sinusoidal frequency modulation (SMF) multiplier-based modulated wideband converter (MWC) is presented. The harmonic property of the SMF signals enables sparse multiband signal to be precisely recovered from the sub-Nyquist measurements without any high-speed devices. The proposed architecture offers very close recovery accuracy to the existing pseudorandom binary sequences (PRBS)-based MWC in the absence of the prior knowledge of sparse wideband signals, while it provides more accurate recovery performance than the PRBS-MWC in the presence of prior information concerning sparsity. Besides, the proposed architecture is easier for hardware implementation than the PRBS-MWC. The experimental results demonstrate the effectiveness of the proposed scheme.