A multipurpose audio aggregation watermarking based on multistage vector quantization

Watermarking technology can achieve multipurpose such as copyright protection, copy protection, and integrity authentication. We propose a novel watermarking approach which involves robust watermark and fragile watermark in a two-stage quantization technique. Traditional watermarking algorithms mostly have poor performance in imperceptibility as the codeword selected from the modified codebook is not optimal. In our method, we select the codeword in the original codebook to ensure optimality. Furthermore, we use Huffman encoding to pick up property which is sensitive to many attacks in the entire aggregation. For copyright protection, the proposed scheme can resist attacks such as lossy compression, noise addition, and normalization. As for content authentication, the proposed scheme is sensitive to various attacks provided by Stirmark Benchmark for Audio. Experimental results show that the proposed method can be used respectively for protecting the copyright and authenticating the integrity of the audio aggregation.     

Using self-organizing maps with complex network topologies and coalitions for time series prediction

A Self-organizing Map (SOM) is a competitive learning neural network architecture that make available a certain amount of classificatory neurons, which self-organize spatially based on input patterns. In this paper we explore the use of complex network topologies, like small-world, scale-free or random networks; for connecting the neurons within a SOM, and apply them for Time Series Prediction. We follow the VQTAM model for function prediction, and consider several benchmarks to evaluate the quality of the predictions. Afterwards, we introduce the CASOM algorithm (Coalitions and SOM) that uses coalitions to temporarily extend the neighborhood of a neuron, and to provide more accuracy in prediction problems than classical SOM. The results presented in this work suggest that the most regular the network topology is, the better results it provides in prediction. Besides, we have found that not updating all the neurons at the same time provides much better results. Finally, we describe how the use of coalitions can enhance the capability of SOM for Time Series Prediction.     

基于A-VFSA粒子发生器的PSO算法的研究

对超快速模拟退火算法进行改进(A-VFSA),并以此为粒子发生器,提出了PSO-PG算法.该算法通过粒子发生器对搜索域内粒子进行改进,从而生成一个精英粒子池;并根据随机策略从粒子池中选择粒子,作为初始种群,采用PSO算法进行优化,得到全局最优解.相比于标准PSO算法和LDW算法,PSO-PG算法拥有更好的稳定性和优化精度,能够更加快速地收敛到全局最优解,在一定范围内几乎不依赖于初始参数的选择.     

快速鲁棒特征算法的CUDA加速优化

提出一种基于统一计算设备架构(Compute Unified Device Architecture,CUDA)的快速鲁棒特征(Speed-up Robust Feature,SURF)图像匹配算法。分析了SURF算法的并行性,在图像处理单元(Graphics Processing Unit,GPU)的线程映射和内存模型方面对算法的构建尺度空间、特征点提取、特征点主方向的确定、特征描述子的生成及特征匹配5个步骤进行CUDA加速优化。实验表明,相比适用于CPU的SURF算法,文中提出的适用于GPU的SURF算法在处理30MB的图片时性能提高了33倍。适用于GPU的SURF算法拓展了SURF算法在遥感等领域的快速应用,尤其是大影像的快速配准。     

改进的DV-Hop算法在节点定位中的应用研究

基于传统DV-Hop算法的定位技术,通过分析该算法误差产生的主要原因,提出了节点间跳数的修正方法。通过在广播通信中采用双通信频率,将节点间的跳数缩减为0.5 R,减少了平均跳距的误差。通过Matlab软件的实验验证,本算法可以提高节点定位精度和网络通信频率,可进一步提高定位精度,定位结果达到了预期的目的。     

改进的反向蛙跳算法求解函数优化问题

针对混洗蛙跳算法在求解连续函数优化问题中出现的收敛速度慢、求解精度低的缺点,提出了一种基于反向学习策略的改进算法,在种群初始化和进化过程中分别加入反向操作,产生更靠近优质解的种群,从而提高了算法的全局寻优能力,促进了算法收敛。实验仿真表明,新算法在寻优效率、计算精度等方面均优于原算法。     

浅析LTE规划优化的核心技术

LTE作为目前4G的主要标准,研究LTE规划优化的核心技术,对4G网络的建设和发展具有非常重要的促进作用,本文在LTE概念和架构的基础上,从用户需求、频谱使用效率、新技术规划和与2G、3G共存等方面,分析了LTE规划优化面临的挑战,最后深入的研究了天馈方案、时隙配比和连接状态转换等核心技术,希望能给实际的LTE规划优化,提供一定的参考。     

试论科技发展中的伦理向度

对于科技发展带来的一些消极影响,有人认为伦理的干预是无济于事的,甚至最终会影响科技本身的发展。但是,现实中科技与社会发展之间的密切关系表明,对科技的发展给予伦理导向是社会发展的必然走向。把科学发展观作为科技发展的伦理向度,能够实现科技发展和社会进步两者的双赢。     

鲍曼的知识分子角色危机论浅析

介绍了齐格蒙特·鲍曼时知识分子的立法者角色危机的论述.知识分子曾经的立法者角色为国家统治提供了合法化的依据,而其立法者地位的丧失是因为国家不再需要他们为统治提供合法化的依据.