Author Affiliations
Abstract
1 Peking University, National Engineering Research Center of Visual Technology, Beijing, China
2 Hangzhou Dianzi University, School of Automation, Hangzhou, China
3 Medical School of Nanjing University, Nanjing, China
4 Hangzhou Dianzi University, School of Communication Engineering, Hangzhou, China
5 Lishui Institute of Hangzhou Dianzi University, Lishui, China
Light-field fluorescence microscopy (LFM) is a powerful elegant compact method for long-term high-speed imaging of complex biological systems, such as neuron activities and rapid movements of organelles. LFM experiments typically generate terabytes of image data and require a substantial amount of storage space. Some lossy compression algorithms have been proposed recently with good compression performance. However, since the specimen usually only tolerates low-power density illumination for long-term imaging with low phototoxicity, the image signal-to-noise ratio (SNR) is relatively low, which will cause the loss of some efficient position or intensity information using such lossy compression algorithms. Here, we propose a phase-space continuity-enhanced bzip2 (PC-bzip2) lossless compression method for LFM data as a high-efficiency and open-source tool that combines graphics processing unit-based fast entropy judgment and multicore-CPU-based high-speed lossless compression. Our proposed method achieves almost 10% compression ratio improvement while keeping the capability of high-speed compression, compared with the original bzip2. We evaluated our method on fluorescence beads data and fluorescence staining cells data with different SNRs. Moreover, by introducing temporal continuity, our method shows the superior compression ratio on time series data of zebrafish blood vessels.
light-field microscopy lossless compression phase space entropy judgment Advanced Photonics Nexus
2024, 3(3): 036005
强激光与粒子束
2023, 35(11): 114005
强激光与粒子束
2022, 34(6): 064007
强激光与粒子束
2021, 33(2): 024003
红外与激光工程
2020, 49(11): 20200053
强激光与粒子束
2019, 31(12): 125101
Author Affiliations
Abstract
1 State Key Laboratory of Nuclear Physics and Technology, and Key Laboratory of HEDP of the Ministry of Education, CAPT, Peking University, Beijing, 100871,China
2 Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, Shanxi, 030006, China
The radiation reaction effects on electron dynamics in counter-propagating circularly polarized laser beams are investigated through the linearization theorem and the results are in great agreement with numeric solutions. For the first time, the properties of fixed points in electron phase-space were analyzed with linear stability theory, showing that center nodes will become attractors if the classical radiation reaction is considered. Electron dynamics are significantly affected by the properties of the fixed points and the electron phase-space densities are found to be increasing exponentially near the attractors. The density growth rates are derived theoretically and further verified by particle-in-cell simulations, which can be detected in experiments to explore the effects of radiation reaction qualitatively. The attractor can also facilitate realizing a series of nanometer-scaled flying electron slices via adjusting the colliding laser frequencies.
Radiation reaction effect Radiation reaction effect Phase space dynamics Phase space dynamics PIC simulation PIC simulation Matter and Radiation at Extremes
2016, 1(6): 308
成都信息工程大学大气科学学院高原大气与环境四川省重点实验室, 四川 成都 610225
对不同湿度条件下消光系数序列演变特性的正确认知是构建大气颗粒物湿度订正模型的前提和基础。利用成都市人民南路4段环境监测站所提供的2013年6月到2014年5月逐时(降雨天除外)细颗粒物(PM2.5)浓度监测数据以及相应的地面能见度、相对湿度观测数据,反演得该区域相应时段单位质量消光系数时间序列。简要论述了消光系数吸湿过程中的复杂演变性及已有湿度订正模型的非普适性;基于相空间重构理论确定该时间序列的最佳延迟时间f和最佳嵌入维数m,据此计算出饱和关联维数、最大Lyapunov指数以及Kolmogorov熵特征量,其结果显示该序列具有低维混沌的特征;应用Cao方法排除其为非线性序列的可能性;结合替代数据法论证得成都市单位质量消光系数时间序列为随机序列。该研究结论不仅明晰了单位质量消光系数序列的特性,还为大气颗粒物湿度订正模型的改进奠定理论基础。
大气光学 随机特性 消光系数 相空间重构 替代数据法
1 河南师范大学物理与电子工程学院
2 河南师范大学新联学院 新乡 453007
3 新乡学院, 新乡 453007
借助于两模纠缠相干态,利用Wigner函数的定义和特性,通过严格的数学推导,首先获得了双阱势中玻色子正则规范粒子数差和位相差算符的Wigner函数,然后通过高斯光滑获得了它的Husimi函数。在准线性拉比规范下绘出了8个粒子9个本征态的相空间概率密度分布图;计算了随机变量-数差的均值和方差;从不同角度分析讨论了本征态的统计特性。发现数差的分布不同本征态有不同的结构,分别呈现单峰、双峰、三峰乃至多峰结构,峰的个数随着系统中粒子数的增加而增加;基态和最高激发态呈现单峰结构;且方差较小;分布于基态和最高激发态之间的态具有多峰结构;方差较大,最居中的态方差最大;而相位差的概率密度分布是周期函数,类似于光学里干涉条纹可见度分布,一个周期内总是呈现双峰结构,而且相位差的最可几概率仅有两种选择: 0、π和π/2、-π/2.
粒子数差和位相差正则共轭算符对 相空间 Husimi函数 双模哈密顿量 particle number difference-phase difference canoni phase space Husimi function two-mode Hamiltonian
针对平台误差系数建模预测问题,提出了基于多变量相空间重构的多输出高斯过程回归预测算法。通过多变量相空间重构将两个相关性较强的平台误差系数重构在一个相空间中,采用多输出高斯过程回归模型同时预测这两个平台误差系数。该算法充分利用了两个误差系数之间的相关性,提高了预测精度,而且可以得到任意置信度下的预测均值和置信区间,为解决平台误差系数建模预测提供一条新的途径。
多变量相空间重构 多输出高斯过程回归 平台误差系数 建模预测 multivariate phase space reconstruction multipleoutput Gaussian process regression platform error coefficient modeling and forecasting