Shape-Controlled Synthesis of Surface-Clean Ultrathin Palladium Nanosheets by Simply Mixing a Dinuclear Pd-I Carbonyl Chloride Complex with H2O

通讯作者地址: Zheng, NF (通讯作者) Xiamen Univ, State Key Lab Phys Chem Solid Surfaces, Collaborat Innovat Ctr Chem Energy Mat, Xiamen 361005, Peoples R China.MOST of China 2011CB932403 ,2009CB930703 ,NSFC 21131005 ,21021061 ,20925103 ,2092300

基于多态蚁群算法的高光谱遥感影像最优波段选择

由于传统蚁群算法搜索空间大,算法时间复杂度高等,导致基于传统蚁群算法的高光谱数据波段选择算法(ACA-BS)耗时长,算法效率低下,且易陷入局部最优。而多态蚁群算法能大大缩小算法的搜索空间,降低算法时间复杂度。因此,研究设计了基于多态蚁群算法的高光谱数据波段选择算法(PACA-BS)。从算法运行时间、波段子集的类别可分性及信息量、总体分类精度等方面对算法进行对比分析。用于实验的数据为Hyperion和AVIRIS高光谱影像。实验结果表明: PACA-BS的运行时间较ACA-BS大大减少;对Hyperion影像进行降维时,基于PACA-BS的运行时间约为ACA-BS的一半。两种算法获得的波段子集的类别可分性大小较为接近,但PACA-BS获得的波段子集的信息量和总体分类精度优于ACA-BS。研究表明PACA-BS是一种效率较高的高光谱波段选择算法

OFDM系统中一种新的联合信道估计和信号检测算法

提出一种新的基于迭代的联合信道估计和信号检测算法,利用ZF（迫零）算法检测出的信号进行下一个OFDM（正交频分复用）符号的信道估计,充分利用了信道估计值和检测符号之间的互信息量,有效地消除了符号间干扰和载波间干扰,提高了信道估计和符号检测的精确度。仿真结果表明,在不同的信道条件下,该算法的性能比传统信号检测方法有明显的提升

A novel unsupervised Levy flight particle swarm optimization (ULPSO) method for multispectral remote-sensing image classification

The rapid development of earth observation technology has produced large quantities of remote-sensing data. Unsupervised classification (i.e. clustering) of remote-sensing images, an important means to acquire land-use/cover information, has become increasingly in demand due to its simplicity and ease of application. Traditional methods, such as k-means, struggle to solve this NP-hard (Non-deterministic Polynomial hard) image classification problem. Particle swarm optimization (PSO), always achieving better result than k-means, has recently been applied to unsupervised image classification. However, PSO was also found to be easily trapped on local optima. This article proposes a novel unsupervised Levy flight particle swarm optimization (ULPSO) method for image classification with balanced exploitation and exploration capabilities. It benefits from a new searching strategy: the worst particle in the swarm is targeted and its position is updated with Levy flight at each iteration. The effectiveness of the proposed method was tested with three types of remote-sensing imagery (Landsat Thematic Mapper (TM), Flightline C1 (FLC), and QuickBird) that are distinct in terms of spatial and spectral resolution and landscape. Our results showed that ULPSO is able to achieve significantly better and more stable classification results than k-means and the other two intelligent methods based on genetic algorithm (GA) and particle swarm optimization (PSO) over all of the experiments. ULPSO is, therefore, recommended as an effective alternative for unsupervised remote-sensing image classification