一种改进的LPDC译码算法

Mackay-Neal算法是基于LDPC码的BP译码简化算法,但仍存在大量乘法运算。为了降低译码算法的运算量,基于Mackay-Neal算法提出一种改进的对数和积译码算法。最后通过计算量复杂度分析结果表明,改进后的对数和积译码算法更简单,运算量大大降低,易于硬件的实现。

Cryptanalysis of Cryptosystems Based on General Linear Group

Advances in quantum computers threaten to break public key cryptosystems such as RSA, ECC, and EIGamal on the hardness of factoring or taking a discrete logarithm, while no quantum algorithms are found to solve certain mathematical problems on non-commutative algebraic structures until now. In this background, Majid Khan et al.proposed two novel public-key encryption schemes based on large abelian subgroup of general linear group over a residue ring. In this paper we show that the two schemes are not secure. We present that they are vulnerable to a structural attack and that, it only requires polynomial time complexity to retrieve the message from associated public keys respectively. Then we conduct a detailed analysis on attack methods and show corresponding algorithmic description and efficiency analysis respectively. After that, we propose an improvement assisted to enhance Majid Khan's scheme. In addition, we discuss possible lines of future work.

Enhanced PSO Based Energy-Efficient Resource Allocation and CQI Based MCS Selection in LTE-A Heterogeneous System

In order to maximize system energy efficiency(EE) under user quality of service(Qo S) restraints in Long Term Evolution-Advanced(LTE-A) networks,a constrained joint resource optimization allocation scheme is presented,which is NP-hard. Hence,we divide it into three sub-problems to reduce computation complexity,i.e.,the resource block(RB) allocation,the power distribution,and the modulation and coding scheme(MCS) assignment for user codewords. Then an enhanced heuristic approach GAPSO is proposed and is adopted in the RB and power allocation respectively to reduce computational complexity further on. Moreover,a novel MCS allocation scheme is put forward,which could make a good balance between the system reliability and availability under different channel conditions. Simulation results show that the proposed GAPSO could achieve better performance in convergence speed and global optimum searching,and that the joint resource allocation scheme could improve energy efficiency effectively under user Qo S requirements.

Research on variable block size motion estimation algorithm for airborne image

A fast motion estimation algorithm for variable block-size using the ＂line scan and block merge procedure＂ is proposed for airborne image compression modules.Full hardware implementation via FPGA is discussed in detail.The proposed pipelined architecture based on the line scan algorithm is capable of calculating the required 41 motion vectors of various size blocks supported by H.264 within a 16 × 16 block in parallel.An adaptive rate distortion cost function is used for various size block decision.The motion vectors of adjacent small blocks are merged to predict the motion vectors of larger blocks for reducing computation.Experimental results show that our proposed method has lower computational complexity than full search algorithm with slight quality decrease and little bit rate increase.Due to the high real-time processing speed it can be easily realized in hardware.

Fast adaptive flat-histogram ensemble to enhance the sampling in large systems

An efficient novel algorithm was developed to estimate the Density of States(DOS) for large systems by calculating the ensemble means of an extensive physical variable, such as the potential energy, U, in generalized canonical ensembles to interpolate the interior reverse temperature curve β_s(U)=SU/U, where S(U) is the logarithm of the DOS. This curve is computed with different accuracies in different energy regions to capture the dependence of the reverse temperature on U without setting prior grid in the U space. By combining with a U-compression transformation, we decrease the computational complexity from O(N3/2) in the normal Wang Landau type method to O(N1/2) in the current algorithm, as the degrees of freedom of system N. The efficiency of the algorithm is demonstrated by applying to Lennard Jones fluids with various N, along with its ability to find different macroscopic states, including metastable states.

Greedy Algorithm Computing Minkowski Reduced Lattice Bases with Quadratic Bit Complexity of Input Vectors

The authors present an algorithm which is a modilication of the Nguyen-Stenle greedy reduction algorithm due to Nguyen and Stehle in 2009. This algorithm can be used to compute the Minkowski reduced lattice bases for arbitrary rank lattices with quadratic bit complexity on the size of the input vectors. The total bit complexity of the algorithm is O（n^2·（4n!）^n·（n!/2^n）^n/2·（4/3）^n（n-1）/2）.log^2 A）where n is the rank of the lattice and A is maximal norm of the input base vectors. This is an O（log^2 A） algorithm which can be used to compute Minkowski reduced bases for the fixed rank lattices. A time complexity n!. 3n（log A）^O（1） algorithm which can be used to compute the successive minima with the help of the dual Hermite-Korkin-Zolotarev base was given by Blomer in 2000 and improved to the time complexity n!- （log A）^O（1） by Micciancio in 2008. The algorithm in this paper is more suitable for computing the Minkowski reduced bases of low rank lattices with very large base vector sizes.