A Quantitative Genetic Study of Sclerotinia Head Rot Resistance Introgressed from the Wild Perennial Helianthus maximiliani into Cultivated Sunflower (Helianthus annuus L.)

Sclerotinia head rot (HR), caused by Sclerotinia sclerotiorum, is an economically important disease of sunflower with known detrimental effects on yield and quality in humid climates worldwide. The objective of this study was to gain insight into the genetic architecture of HR resistance from a sunflower line HR21 harboring HR resistance introgressed from the wild perennial Helianthus maximiliani. An F₂ population derived from the cross of HA 234 (susceptible-line)/HR21 (resistant-line) was evaluated for HR resistance at two locations during 2019–2020. Highly significant genetic variations (p < 0.001) were observed for HR disease incidence (DI) and disease severity (DS) in both individual and combined analyses. Broad sense heritability (H²) estimates across environments for DI and DS were 0.51 and 0.62, respectively. A high-density genetic map of 1420.287 cM was constructed with 6315 SNP/InDel markers developed using genotype-by-sequencing technology. A total of 16 genomic regions on eight sunflower chromosomes, 1, 2, 10, 12, 13, 14, 16 and 17 were associated with HR resistance, each explaining between 3.97 to 16.67% of the phenotypic variance for HR resistance. Eleven of these QTL had resistance alleles from the HR21 parent. Molecular markers flanking the QTL will facilitate marker-assisted selection breeding for HR resistance in sunflower.     

实现工艺映射的新方法

逻辑综合中,工艺映射是关键的一步。过去,基于树的映射由于其速度快、算法简洁而被广泛使用。但是,将电路结构划分为树,破坏了电路结构,缩小了映射的解空间。双态覆盖(Binate Covering)能充分利用门级网表的结构特性,使工艺映射后的电路具有更好的性能。     

基于自适应重采样的同步定位与地图构建

就移动机器人同步定位与地图构建展开研究,针对FastSLAM算法产生的粒子退化及粒子集重采样问题,提出了基于自适应重采样的FastSLAM算法。该算法首先计算粒子集的有效样本数,确定粒子退化程度。然后设定有效样本阈值,当有效样本数小于阈值时则进行重采样。仿真表明:与EKF-SLAM相比,基于自适应重采样FastSLAM重采样效率更高,鲁棒性更好,在机器人路径和陆标位置的估计上,也具有更高的精度。     

基于深度学习的采摘机器人目标识别定位算法

为了提升采摘机器人对果实的识别准确率以及定位定精度,提出一种基于深度学习Faster-RCNN框架的采摘机器人目标识别和定位算法.首先采用卷积神经网络VGG16模型提取输入图像的特性信息,并利用区域提议网络RPN生成含有目标的候选框,通过引入自适应候选框数的方法有效提升了算法性能,然后利用多任务损失函数对目标进行分类识...     

基于函数映射的FFT谐波分析的研究

因为免除了等待和倒序操作,从而使递推实现的FFT在谐波分析中更具有实时性,已呈现出取代常规FFT的趋势。本介绍了一种递推实现的FFT算法——基于函数映射的FFT算法的原理和程序过程,进行了其应用于谐波分析的全面性能评估。通过与基于离散Hartely变换的谐波分析的性能的比较,发现尽管两的系统误差相同,但它的频谱噪声底线稍低于后,频谱泄漏优于后,且更易于硬件实现。     

A novel energy-aware multi-task dynamic mapping heuristic of NoC-based MPSoCs

Task mapping is an important issue in network-on-chip (NoC)-based multiprocessor systems-on-chips (MPSoCs) design. The dynamic characteristic of application execution enforces the use of dynamic task mapping. In this article, a hybrid energy-aware dynamic mapping strategy is proposed. The strategy consists of an off-line part and an on-line part. In the off-line part, optimisation tools are used to extract information that helps to reduce the energy consumption in the on-line mapping, while the on-line mapping heuristic makes use of the information. Experimental result shows that the energy consumption is reduced by 21%, on average, compared to the best neighbour heuristic.     

基于叠加调制策略的改进型协作分集方案

在无线传感器网络(WSN)中,协作分集是一种至关重要的技术。采用该技术,可利用WSN中冗余节点多的特性,用户间共享彼此的天线;从而,不仅可获得空间分集增益,而且可提高系统的吞吐量和抗衰落特性。但是,WSN中节点间如何进行协作,尤其是接收端如何译码以达到更高效的数据传输,仍然是一个值得进一步研究的问题。本文在传统传输方案的基础上引入了叠加调制策略。利用该策略,首先从节点间的链路状态和能量匹配的角度设计新型的符号映射算法,然后从信噪比和误码率方面考虑接收端的译码问题。与无协作方式、译码转发协作方式相比,这一优化算法不仅解决了传统协作方式的时隙利用率只有直通方式二分之一的问题,而且减少了误码率。此外,文中还证实了新算法可比传统传输方式最多获得3dB的信噪比增益。仿真结果表明,良好的链路状态在合适能量匹配参数时不仅可以同时传输自己和伙伴的数据,而且可以权衡系统的性能。      

混沌噪声源在噪声雷达的应用

混沌序列具有伪随机性,可作为噪声源。但是混沌序列在某些调制方式下,显现出其内部结构性,使得相关特性变的很差,难以应用。文中提出的具有MSPL混沌序列克服了这个问题,其相关特性和真正噪声的相同,并且兼具混沌序列易于使用的特点,适合作为噪声源运用于噪声雷达。因此,混沌序列也能运用于噪声雷达。     

基于蛙跳算法的模糊图像复原

为了提高模糊图像复原后的清晰度,提出蛙跳算法。将蛙群体分成若干个族群,每个族群包含若干只青蛙,每次进化只更新最差青蛙的位置。为防止解空间收缩,对最优个体进行高斯变异算子操作,同时设定阈值策略对蛙跳各维变量进行指导性更新。建立模糊图像复原模型,用蛙跳算法非线性映射特性建立模糊图像与复原的函数关系,使模糊图像复原的解最终收敛于泊松统计的最大似然解。建立了复原评价指标。仿真实验结果表明,与其他算法相比,蛙跳算法复原较清晰,改善百分比最大,同时耗时最少。