With the development of digital TV system, how to display the NTSC signal in digital TV system is a problem. De-interlacing is an algorithm to solve it. In previous papers, using motion compensation (MC) method for de-interlacing needs lots of computation complexity and it is not easy to implement in hardware. In this paper, a content adaptive de-interlacing algorithm is proposed. Our algorithm is based on the motion adaptive (MA) method which combines the advantages of intra-field and inter-field method. We propose a block type decision mechanism to predict the video content instead of a blind processing with MC method throughout the entire frame. Additionally, in intra-field method, we propose the edge-base adaptive weight average (EAWA) method to achieve a better performance and smooth the edge and stripe. In order to demonstrate our algorithm, we implement the de-interlacing system on the DSP platform with thorough complexity analysis. Compared to MC method, we not only achieve higher video quality in objective and subjective view, but also consume lower computation power. From the profiling on CPU run-time analysis, the proposed algorithm is only one-fifth of MC method. At the DSP demonstration board, the saving ratio is about 54% to 96%.

The finite-difference time-domain (FDTD) method incorporating Berenger's PML absorbing boundary condition is developed to model three-dimensional dielectric resonators. The fast Fourier transform (FFT) coupled with the Pade interpolation technique is employed to obtain frequency domain results with satisfactory resolution and accuracy, and to reduce the computation time significantly compared with that needed when the conventional FFT algorithm is used. Computed resonant frequencies of two types of cylindrical dielectric resonators are compared with theoretical and measured results. A good agreement is observed.

A new postprocessing method using interpolated n-gram model for Japanese documents is proposed. The method has the advantages over conventional approaches in enabling high-speed, knowledge-free processing. In parameter estimation of an n-gram model for a large size of vocabulary, it is difficult to obtain sufficient training samples. To overcome poverty of samples, two smoothing methods for Japanese character trigram model are evaluated, and the superiority of deleted interpolation method is shown by using perplexity. A document recognition system based on the trigram model is constructed, which finds maximum likelihood solutions through Viterbi algorithm. Experimental results for three kinds of documents show that the performance is high when using deleted interpolation method for smoothing. 90% of OCR errors are corrected for the documents similar to training text data, and 75% of errors are corrected for the documents not so similar to training text data.