On Fast Channel Polarization of Double-layer Binary Discrete Memoryless Channels

Polar codes are linear codes which split input channels to increase its transition performance and provably achieve the capacity of symmetric binary discrete memoryless channels (B-DMC). The idea of Polar codes is related to the recursive construction of Reed-Muller codes on the basis of 2-order square matrix G2, can achieve the symmetric capacity of arbitrary binary-input discrete memoryless channels and to create from N independent copies of a B-DMC W, N different channels through a linear transformation. It has already been mentioned that in principle larger matrices can be employed to construct polar codes with better performances. In this paper we consider a problem of systematic constructions of polar codes based on fast channel polarization of binary discrete memoryless channel, which is an idea approach to construct code sequences as splitting input channels to increase the cutoff rate. We analyzes a novel polar channel coding and decoding approach by using the 4×4 matrix G4 = G⊗2 2 as a core on dual binary discrete memoryless channels (D-BDMC). In this paper, we characterize its parameters for a given core square standard matrix G4 and derive upper and lower bounds on achievable exponents of derived polar codes based on G4n = G⊗n 4 with block-length 4n, through which the performance can be improved with lower encoding and decoding complexity and achieve explicit construction. We investigate polarization schemes whose salient features may be decoded with a maximize likelihood (ML) decoder, which render the schemes analytically tractable and provide powerful low-complexity coding algorithms. Moreover, we give a general family of polar codes based on Reed-Mull codes with fast channel polarization

Video Shot Boundary Recognition Based on Adaptive Locality Preserving Projections

A novel video shot boundary recognition method is proposed, which includes two stages of video feature extraction and shot boundary recognition. Firstly, we use adaptive locality preserving projections (ALPP) to extract video feature. Unlike locality preserving projections, we define the discriminating similarity with mode prior probabilities and adaptive neighborhood selection strategy which make ALPP more suitable to preserve the local structure and label information of the original data. Secondly, we use an optimized multiple kernel support vector machine to classify video frames into boundary and nonboundary frames, in which the weights of different types of kernels are optimized with an ant colony optimization method. Experimental results show the effectiveness of our method