In digital holographic microscopy, when the object is placed near the COD, the Fresnel approximation is no longer valid and the convolution approach has to be applied. With this approach,the sampling spacing of the re...In digital holographic microscopy, when the object is placed near the COD, the Fresnel approximation is no longer valid and the convolution approach has to be applied. With this approach,the sampling spacing of the reconstructed image plane is equal to the pixel size of the COD. If the lateral resolution of the reconstructed image is higher than that of the COD,Nyquist sampling criterion is violated and aliasing errors will be introduced. In this Letter,a new method is proposed to solve this problem by investigating convolution reconstruction of digital holograms. By appending enough zeros to the angular spectrum between the two FFT＇s in convolution reconstruction of digital holograms,the displayed resolution of the reconstructed image can be improved. Experimental results show a good agreement with theoretical analysis.展开更多
To address the issue of field size in random network coding, we propose an Improved Adaptive Random Convolutional Network Coding (IARCNC) algorithm to considerably reduce the amount of occupied memory. The operation o...To address the issue of field size in random network coding, we propose an Improved Adaptive Random Convolutional Network Coding (IARCNC) algorithm to considerably reduce the amount of occupied memory. The operation of IARCNC is similar to that of Adaptive Random Convolutional Network Coding (ARCNC), with the coefficients of local encoding kernels chosen uniformly at random over a small finite field. The difference is that the length of the local encoding kernels at the nodes used by IARCNC is constrained by the depth; meanwhile, increases until all the related sink nodes can be decoded. This restriction can make the code length distribution more reasonable. Therefore, IARCNC retains the advantages of ARCNC, such as a small decoding delay and partial adaptation to an unknown topology without an early estimation of the field size. In addition, it has its own advantage, that is, a higher reduction in memory use. The simulation and the example show the effectiveness of the proposed algorithm.展开更多
文摘In digital holographic microscopy, when the object is placed near the COD, the Fresnel approximation is no longer valid and the convolution approach has to be applied. With this approach,the sampling spacing of the reconstructed image plane is equal to the pixel size of the COD. If the lateral resolution of the reconstructed image is higher than that of the COD,Nyquist sampling criterion is violated and aliasing errors will be introduced. In this Letter,a new method is proposed to solve this problem by investigating convolution reconstruction of digital holograms. By appending enough zeros to the angular spectrum between the two FFT＇s in convolution reconstruction of digital holograms,the displayed resolution of the reconstructed image can be improved. Experimental results show a good agreement with theoretical analysis.
基金supported by the National Science Foundation （NSF） under Grants No.60832001,No.61271174 the National State Key Lab oratory of Integrated Service Network （ISN） under Grant No.ISN01080202
文摘To address the issue of field size in random network coding, we propose an Improved Adaptive Random Convolutional Network Coding (IARCNC) algorithm to considerably reduce the amount of occupied memory. The operation of IARCNC is similar to that of Adaptive Random Convolutional Network Coding (ARCNC), with the coefficients of local encoding kernels chosen uniformly at random over a small finite field. The difference is that the length of the local encoding kernels at the nodes used by IARCNC is constrained by the depth; meanwhile, increases until all the related sink nodes can be decoded. This restriction can make the code length distribution more reasonable. Therefore, IARCNC retains the advantages of ARCNC, such as a small decoding delay and partial adaptation to an unknown topology without an early estimation of the field size. In addition, it has its own advantage, that is, a higher reduction in memory use. The simulation and the example show the effectiveness of the proposed algorithm.
