基于算数编码的概率星座整形技术的研究

针对超100G光网络中信道受限问题,提出了一种基于算数编码的速率可调正交幅度调制(QAM)概率星座整形(PCS)编码调制方案,使光网络传输容量更接近香农极限,并且提高信号的噪声恢复能力。发送端由分布匹配器和FEC编码器组成,分别负责对信号进行概率整形和信道编码的任务,系统通过改变分布匹配器的概率分布来调节速率。在接收端,FEC解码器和反向分布匹配器解码噪声信号恢复数据。实验结果表明,16QAM采用概率星座整形方案后,OSNR的增益为0.382dB、互信息增益为0.09bit/symbol。证明该PCS方案能够优化光信噪比(OSNR),提升传输容量,同时误码率明显低于标准16QAM,整形信号对噪声有更好的恢复能力。

差分动态二进制化的二进制算数编码

JCT-VC组织公布的HEVC协议草案沿用了H264的CABAC,改进了二进制化过程。在阐述高性能压缩算法CABAC的同时,创新性地提出了动态二进制化算数编码,并预先对数据进行差分。最后,通过压缩Java文件实验证实差分动态二进制化算数编码在压缩率方面有较大的提高,高于PAQ和CABAC。

基于混沌理论的自适应算术编码算法

该算法是基于混沌变换的自适应算数编码的加密压缩联合算法,仔细分析了这种加密压缩联合算法中的密钥空间、随机性、信息熵、压缩率、编码速率等多项指标,对算法进行了评估.经过大量实验进行对照测试,实验结果说明,这种算法同样大幅度提高了加密压缩联合算法输出流中的概率分布和信息熵.算法的压缩率不受影响,但安全性有所提高.

基于同态加密的电力计量数据抗泄露加密系统设计

为保障电力计量数据安全,避免数据流失导致用户隐私泄露,设计了基于同态加密的电力计量数据抗泄露加密系统。系统通过传输层供电站、配电中心、变电站以及输电中心为用户提供优质电能,用户使用电能后产生电力计量数据;利用采集层智能电表实时采集用户电力计量数据,并每间隔小时通过网络层传输所采集数据至数据层;数据层采用数据聚合器合并所接收数据,同时由其内置密码编码器对数据进行初步加密处理,加密后数据暂存于数据聚合器内,而后每月定时传输至处理层;处理层利用密钥生成器对所接收电力计量数据生成专属密钥,并采用同态加密技术实现电力计量数据加密控制。系统测试结果表明,该系统可实现电力计量数据的加密处理,防止非系统用户查询,避免电力计量数据泄露,系统抗攻击能力为0.98以上,具备较强的电力计量数据抗泄露加密性能。

Inverse procedure for determining model parameter of soils using real-coded genetic algorithm

The hybrid genetic algorithm is utilized to facilitate model parameter estimation.The tri-dimensional compression tests of soil are performed to supply experimental data for identifying nonlinear constitutive model of soil.In order to save computing time during parameter inversion,a new procedure to compute the calculated strains is presented by multi-linear simplification approach instead of finite element method(FEM).The real-coded hybrid genetic algorithm is developed by combining normal genetic algorithm with gradient-based optimization algorithm.The numerical and experimental results for conditioned soil are compared.The forecast strains based on identified nonlinear constitutive model of soil agree well with observed ones.The effectiveness and accuracy of proposed parameter estimation approach are validated.

A Novel High Spectral Efficiency Waveform Coding-OVFDM

Based on the Overlapped Multiplexing Principle[12],a frequency domain OVFDM(Overlapped Frequency Domain Multiplexing) Coding is proposed.By the data weighted shift overlapped version of any band-limited Multiplexing Transfer Function H(f) the coding gain and spectral efficiency are both achieved.The heavier the overlap of the data weighted Multiplexing Transfer Function H(f),the higher the coding gain and spectral efficiency as well as the closer the output to the optimum complex Gaussian distribution.The bit error probability performance is estimated.The time domain OVTDM(Overlapped Time Domain Multiplexing) Coding,the dual of OVFDM in time domain is incidentally proposed as well.Both theoretical analysis and testified simulations show that OVFDM(OVTDM) is suitable for high spectral efficiency application and its spectral efficiency is only roughly linear to SNR rather than the well-known logarithm to SNR.

More about Bernoulli Numbers

Bernoulli Numbers are coded with Deterministic Redundancy of Arithmetic Operations, adding and multiplying or exponent, in Natural Number System. And based on the redundancy, a process for obtaining the Bernoulli Numbers is elaborated.

Research on Data Compression of WSN Based on Compressed Sensing

For Wireless Sensor Networks （WSN） is responsible for sensing, collecting, processing and monitoring of environmental data, but it might be limited in resources. This paper describes in detail the compressed sensing theory, study the wireless sensor network data conventional compression and network coding method. The linear network coding scheme based on sparse random projection theory of compressed sensing. Simulation results show that this system satisfies the requirements of the reconstruction error of packets needed to reduce the number of nodes to the total number of 30%, improves the efficiency of data communications in wireless sensor network, reduce the energy consumption of the system. With other wireless sensor network data compression algorithm, the proposed algorithm has the advantages of simple realization, the compression effect is good, especially suitable for resource limited, and the accuracy requirements are not particularly stringent in wireless sensor networks.

多重随机序列在算术编码中的应用

算术编码凭借其高效的压缩比以及适度的编码效率,在图像压缩技术标准(比如JPEG等)中有着重要的地位。该算法仅仅依赖于一个参数:信源符号出现的概率。该概率决定了压缩编码的效率,同时也决定了编码过程中信源符号的间隔。然而,经典的算术编码都没有考虑信源符号输入序列的内在结构,仅仅是考虑单个的符号。这些连续的输入组合中的某些组合若大量出现在信源符号中,就有必要考虑这些组合的出现概率了。而最终需要给哪些特定的组合分配编码区间,以及是否有行之有效的分配算法,都需要考虑。