基于RS码的跳频信号发生器的设计

为了得到具有最佳跳频性能的跳频图案,采用RS码作为跳频序列,控制DDS产生各种频率正弦波。RS跳频序列具有非重复且汉明相关性能好的特性,能够使跳频信号产生最大的跳频增益。相比传统跳频序列,RS跳频序列不存在频率滞留问题。直接数字频率合成器DDS具有许多直接式频率合成技术DS和问接式频率合成技术IS所不具备的优点,如具有极高的频率分辨率和极短的频率转换时间等。文中给出了跳频信号发生器的设计方案,使用MATLAB仿真验证了该方案的可行性。     

神经元膜电位对信息的编码

神经元膜电位的放电活动是神经编码的基础。然而,目前对于神经元电活动对神经信息的编码方式,至今尚未形成一个完整的认识。传统的编码理论认为神经系统以离散的动作电位放电序列进行信息的表达和传递,主要研究动作电位的发放频率和放电活动的时间模式。基于该理论,对神经元放电序列所携带的信息已经出现了一些定量的计算方法,但这些方法还很难应用到大规模神经元网络的计算当中。本研究以神经元的膜电位为研究对象,展示了如何量化膜电位序列所携带的信息,并将该计算结果与传统放电序列方沣的计算结果进行了对比分析,其结果取得了很好的一致性。本研究为神经活动信息量的定量计算提供了一种新的思路和方法。     

一种基于机器学习的电力能耗异常检测与预测的方法

在预处理电力能耗数据时，现有的电力数据异常检测与预测方法没有补全由删除或修正数据导致的缺失项，致使时间序列中出现明显的错漏。为了提高电力能耗异常检测与预测的准确性，基于机器学习设计一种电力能耗异常检测与预测方法。在数据的预处理过程中，通过清洗、转换与提取电力能耗数据，补全时间序列中的缺失值，保证被删除与修正的数据不会影响到整体的数据处理，在此基础上设计基于机器学习的电力能耗异常检测算法以及电力能耗预测算法。选择春、夏、秋、冬4个季节的电力数据，比较机器学习方法与其他3种方法在电力能耗异常数据检测与预测的结果。由实验结果可知，所提方法的ROC值在不同的时间中均大于其他算法，且其预测结果的RMSE与MAE误差指标均小于其他算法，可见所提方法的预测结果准确性高于其他算法。     

方便快捷用“运行”

在Word２０００中要想实现文字（或符号）的快捷输入，就需要掌握一些利用快捷键输入的技巧，例如Ctrl＋C（复制）和Ctrl＋V（粘贴），用到了快捷键，输入和编辑文稿就省事多了。下面就是几则有关Word２０００中快捷键输入的小技巧：１、删除整个单词。在Word中要想删除整个单词需要按下很多次的Backspace（→）键，其实同时按下Ctrl和Backspace组合键就可以删除光标前一个单词。２、重复输入／删除字或词。如果一个字或词需要重复输入可以直接按下F４键，例如输入一个“星”字，按一下F４键就会出现“星星”了。同样，如果需要删除一…     

频率扫描信号的一种数字实现方法

介绍了利用直接数字合成技术产生频率扫描信号的新方法，利用计算器和相位累加器实现对波形存储器寻址，从而产生频率扫描信号序列，该序列通过数－模转换器和低通滤波器后，产生出频率扫描信号。被合成的频率扫描信号的起始频率，终止频率和扫描时间可根据需要随意设定，并且可以实现对三的精确控制。     

DnaReSM：一个基于多支持度的DNA重复序列挖掘算法

DNA序列分析研究是生物信息学的重要内容之一。基因组的基因相关区域和基因外区域中含有大量重复序列，尽管目前大多数重复序列的功能还没能肯定，但它们在遗传分析中已起重要作用。挖掘DNA重复序列成为DNA序列分析的关键。自底向上的挖掘算法中间过程产生很多短的、甚至单字符的模式，使得挖掘效率降低；另一方面，目前序列模式挖掘算法在多序列挖掘中表现出高效性，但由于单支持度定义的局限导致无法在挖掘过程中同时找到单条DNA序列中的重复序列，因此不能很好地适用于DNA重复序列挖掘。本文基于新的多支持度序列模式挖掘框架，提出了一种融合自底向上和自顶向下策略挖掘DNA重复序列的新算法DnaReSM，其结果为生物学相关实验提供基础。实验结果表明，DnaReSM探测算法能有效挖掘DNA重复序列。     

基于二级索引的重复数据删除系统中性能相关参数的量化分析与研究

随着重复数据删除技术应用的普及,性能已成为影响其应用效果的核心要素.已有研究提出了基于二级索引结构的重复数据删除模型以提升系统读写性能,但没有对模型中一些参数的选择进行量化分析.对基于二级索引结构的重复数据删除模型中块大小等一些性能相关的参数进行了分析研究,设计了相关实验,对这些参数与读写性能的关系进行了量化描述,对本类模型在实际环境中的应用有很好的指导意义,同时为下一步性能优化工作提供了重要的数据基础.     

调制信号波形任意的直接数字频率合成器的设计

本文提出了采用直接数字频率合成技术产生调制信号的新方法，用两个双口存储器分别存储频率调制信号序列和载波信号序列，频率调制信号序列与载波控制字在加法器相加，结果作为相位累加器的频率控制码，相位累加器输出信号对载波波形存储器寻址，经过数／模转换和低通滤波后，实现直接数字频率合成，该方法能产生载波波形和频率任意，调制波形任意的高精度调频信号，整个设计在一片可编程逻辑器件（CPLD）上实现，工作模式由计算机控制。     

重复数据删除技术的存储空间利用率测评研究

在大数据时代,为了提高存储空间利用率,重复数据删除技术被广泛应用.然而,重复数据删除技术的重删效果如何评价,它对存储系统的存储空间利用率影响程度如何等问题,目前还没有有效的测评指标和测评方法来解决.为此,对3种典型的重复数据删除技术进行了分析,研究了存储空间利用率的测评指标和测评方法,然后通过理论分析和真实数据实验对其可行性和有效性进行了验证.分析和实验结果表明:设计的测评指标和测评方法实际可行,能定量的评估存储空间利用率,有利于数据中心存储效率的提高,从而实现绿色存储和节能减排,并能减少企业的资源浪费和提高投资回报率.     

基于MD5和布鲁姆过滤器的重复数据删除算法

针对目前重复数据处理技术的低效性和不可靠性,本文提出了一种基于MD5算法和布鲁姆过滤器的重复数据删除算法。新算法采用两级布鲁姆过滤器并有效结合MDS算法的方式,在发挥布鲁姆过滤器空间效率的同时汲取了MD5算法的可靠性,使得文件级别和数据块级别的重复数据删除策略交替工作。测试分析表明,新算法性能稳定并且实现了高效且可靠的重复数据删除功能。     

一种具有精确边界的重复体识别算法

当前大部分重复体识别算法不是依靠于已经标识的重复体数据库就是定义重复体为两个最大长度的相似序列,而没有一个严格的定义来平衡重复体的长度和频率.针对这些问题文中提出了一种基于局部序列比对算法BLAST变型且支持空位的快速识别重复体的RepeatSearcher算法.算法通过定义重复体的精确边界运用逐步扩展调和序列来识别重复体.算法使用C.briggsae基因组序列作为测试对象,并与当前通用的重复体识别算法RECON以及新近的识别算法RepeatScout做了比较分析.结果表明RepeatSearcher使每一条重复体序列具有了精确的边界,而且相对其它算法在没有损失精度的情况下,缩短了算法的运行时间.     

The evolution and recognition of protein sequence repeats

Many proteins sequences contain motifs which display similarity. The similarities between the repeats are a result of gene duplication and/or gene fusion. The evolutionary role of repeats within protein sequences is considered and some repeat examples are given ranging from tandem repeats to multiple types of repeats which are sequentially interspersed. Existing computer methods to delineate repeats in individual protein sequences are discussed and a novel sensitive repeat recognition method is introduced.     

On the measurement of alliteration in poetry

This work is a preliminary study of methods to quantify alliteration. Ten pieces made up of poetry and prose (literary and non-literary) were used to create test sets. Three forms of each test set were examined: texts transcribed in IPA notational equivalent, in Chomsky and Halle features, and in Fromkin and Rodman features. Tests included the deletion of vowels, the weighting of the initial sounds, and the weighting of types according to their frequency in the population of the set. The various configurations were analyzed using a gap-recurrence method. Rankings were obtained by combining measures both of high frequency and of clustering properties. The resulting rankings compare not unfavorably with an intuitive ranking.     

Roles of repetitive sequences

Repetitive sequences are ubiquitous in the DNA of eukaryotes, some as tandem arrays and others interspersed widely in the genome. Repetitive sequences have special roles in genome evolution, which increasingly detailed sequence information is helping to elucidate. Processes, including meiotic crossing over (equal and unequal), unequal mitotic sister chromatid exchange, gene conversion and transposition, with or without multiplication, can foster homogeneity of the members of a repeat family (concerted evolution) and turnover of the whole genome. Some examples are considered. Tandem repeats, satellite and minisatellite sequences are considered as well as telomeric repeats. For a minisatellite locus, in which the frequency of length mutations has been measured, comparisons are made with expectations due to unequal sister chromatid exchange, and qualitative agreement is found. Interspersed repeats, of which Alu sequences are discussed as an important example, can through unequal recombination lead to a loss or duplication of the DNA between the recombination sites and hence to genetic disease or to gene duplication. It is argued that the rate of unequal Alu recombination may be quite high in the human genome.     

A short term memory for Neural Networds which allows recognition and reproduction of complex sequences of integers with the minimum number of weights

An artificial short term memory, the binary kernel function, is presented to facilitate the learning of complex sequences of integers by Neural Networks, requiring far fewer weights than are usually needed. This is achieved by using only a single weight to encode repeat occurrences of an integer in a sequence. The coding used allows a complex sequence to be learned in only one presentation. The kernel's exponential complexity growth is overcome with hierarchical architectures which chunk the sequences to be learnt. Architectures are introduced for recognition and reproduction of complex sequences.     

MapReduce paradigm: DNA sequence clustering based on repeats as features

Clustering is one of the major operations to analyse genome sequence data. Sophisticated sequencing technologies generate huge DNA sequence data; consequently, the complexity of analysing sequences is also increased. So, there is an enormous need for faster sequence analysis algorithms. Most of the existing tools focused on alignment-based approaches, which are slow-paced for sequence comparison. Alignment-free approaches are more successful for fast clustering. The state-of-the-art methods have been applied to cluster small genome sequences of various species; however, they are sensitive to large size sequences. To subdue this limitation, we propose a novel alignment-free method called DNA sequence clustering with map-reduce (DCMR). Initially, MapReduce paradigm is used to speed up the process of extracting eight different types of repeats. Then, the frequency of each type of repeat in a sequence is considered as a feature for clustering. Finally, K-means (DCMR-Kmeans) and K-median (DCMR-Kmedian) algorithms are used to cluster large DNA sequences by using extracted features. The two variants of proposed method are evaluated to cluster large genome sequences of 21 different species and the results show that sequences are very well clustered. Our method is tested for different benchmark data sets like viral genome, influenza A virus, mtDNA, and COXI data sets. Proposed method is compared with MeshClust, UCLUST, STARS, and ClustalW. DCMR-Kmeans outperforms MeshClust, UCLUST, and DCMR-Kmedian with respect to purity and NMI on virus data sets. The computational time of DCMR-Kmeans is less than STARS, DCMR-Kmedian, and much less than UCLUST on COXI data set.     

一种适应于IEEE 802.15.4k标准的高精度频偏估计算法

为实现基于IEEE 802.15.4k标准的长码字直接序列扩频系统(Direct sequency spread spectrum, DSSS)在低信噪比下的频偏估计,提出一种高精度、稳定的改进频偏估计算法。该算法在保证频偏估计范围的前提下同时利用相位和幅度信息对信号作有偏自相关和迭代,提高包含相位信息的序列的信噪比,然后对有偏自相关信号的各阶差分计算相位幅角并加权平均,克服了 Fitz方法在低信噪比下近似条件难以满足的缺点。实验结果表明该算法能在不缩小估计范围的同时实现极低信噪比下的精确频偏估计,估计精度满足IEEE 802.15.4k标准下接收系统对残余频偏的容忍度。     

基于FFT的直扩序列周期相关函数的计算

周期相关函数是评估直扩序列相关性能的重要指标。随着直扩序列周期的增加,该函数的运算量将迅速增加。为了解决大运算量问题,本文基于FFT给出了一种快速计算周期相关函数的方法。通过使用该方法,可以大大减少运算次数,从而为直扩序列的分析和构造提供了便利的工具。     

安全审计中频率敏感的异常检测算法

传统使用系统调用序列的异常检测算法主要关注切分的长度,忽略了各个系统调用序列的发生频率对整个检测结果的重要性。该文提出一种对序列发生频率敏感的基于支持向量描述异常检测算法,利用发生频率定义样本的重要性,使分类器更加倾向于这些重要的样本。采用国际标准数据集进行测试,讨论了核参数对分类结果的影响。实验结果表明,与传统检测模型相比,基于序列发生频率的检测模型具有较低的误警率。     

The BIOMASS mission: Mapping global forest biomass to better understand the terrestrial carbon cycle

In response to the urgent need for improved mapping of global biomass and the lack of any current space systems capable of addressing this need, the BIOMASS mission was proposed to the European Space Agency for the third cycle of Earth Explorer Core missions and was selected for Feasibility Study (Phase A) in March 2009. The objectives of the mission are 1) to quantify the magnitude and distribution of forest biomass globally to improve resource assessment, carbon accounting and carbon models, and 2) to monitor and quantify changes in terrestrial forest biomass globally, on an annual basis or better, leading to improved estimates of terrestrial carbon sources (primarily from deforestation); and terrestrial carbon sinks due to forest regrowth and afforestation. These science objectives require the mission to measure above-ground forest biomass from 70° N to 56° S at spatial scale of 100-200 m, with error not exceeding ± 20% or ± 10 t ha^− 1 and forest height with error of ± 4 m. To meet the measurement requirements, the mission will carry a P-Band polarimetric SAR (centre frequency 435 MHz with 6 MHz bandwidth) with interferometric capability, operating in a dawn-dusk orbit with a constant incidence angle (in the range of 25°-35°) and a 25-45 day repeat cycle. During its 5-year lifetime, the mission will be capable of providing both direct measurements of biomass derived from intensity data and measurements of forest height derived from polarimetric interferometry. The design of the BIOMASS mission spins together two main observational strands: (1) the long heritage of airborne observations in tropical, temperate and boreal forest that have demonstrated the capabilities of P-band SAR for measuring forest biomass; (2) new developments in recovery of forest structure including forest height from Pol-InSAR, and, crucially, the resistance of P-band to temporal decorrelation, which makes this frequency uniquely suitable for biomass measurements with a single repeat-pass satellite. These two complementary measurement approaches are combined in the single BIOMASS sensor, and have the satisfying property that increasing biomass reduces the sensitivity of the former approach while increasing the sensitivity of the latter. This paper surveys the body of evidence built up over the last decade, from a wide range of airborne experiments, which illustrates the ability of such a sensor to provide the required measurements.At present, the BIOMASS P-band radar appears to be the only sensor capable of providing the necessary global knowledge about the world's forest biomass and its changes. In addition, this first chance to explore the Earth's environment with a long wavelength satellite SAR is expected to make yield new information in a range of geoscience areas, including subsurface structure in arid lands and polar ice, and forest inundation dynamics.