A fast improved fat tree encoder for wave union TDC in an FPGA

Up to now, the wave union method can achieve the best timing performance in FPGA-based TDC designs. However, it should be guaranteed in such a structure that the non-thermometer code to binary code (NTH2B) encoding process should be finished within just one system clock cycle. So the implementation of the NTH2B encoder is quite challenging considering the high speed requirement. Besides, the high resolution wave union TDC also demands that the encoder convert an ultra-wide input code to a binary code. We present a fast improved fat tree encoder (IFTE) to fulfill such requirements, in which bubble error suppression is also integrated. With this encoder scheme, a wave union TDC with 7.7 ps RMS and 3.8 ps effective bin size was implemented in an FPGA from Xilinx Virtex 5 family. An encoding time of 8.33 ns was achieved for a 276-bit non-thermometer code to a 9-bit binary code conversion. We conducted a series of tests on the oscillating period of the wave union launcher, as well as the overall performance of the TDC; test results indicate that the IFTE works well. In fact, in the implementation of this encoder, no manual routing or special constraints were required; therefore, this IFTE structure could also be further applied in other delay-chain-based FPGA TDCs.     

紫苏种子品质的近红外光谱分析

为加快紫苏优质育种进程,采用近红外光谱(NIRS)技术,结合线性偏最小二乘法(PLS),以250份全国范围内收集的紫苏资源为研究材料,分别较好的建立其种子中含油量,棕榈酸(C16∶0),硬脂酸(C18∶0),油酸(C18∶1),亚油酸(C18∶2),a-亚麻酸(C18∶3)含量的六个近红外光谱校正模型。结果显示,六个模型的校正决定系数(RSQ1)分别为：0.98,0.91,0.92,0.92,0.85,0.93;交叉验证决定系数(1-VR)分别为：0.97,0.89,0.89,0.91,0.85和0.91;外部验证相关系数(RSQ)分别为：0.98,0.91,0.89,0.90,0.80和0.89,且定标标准误差(SEC)分别为0.99,0.21,0.1,0.94,0.81,0.92;交叉验证标准误差(SECV)分别为1.16,0.23,0.11,1.05,0.92,1.02和预测标准误差(SEP)分别为0.97,0.21,0.11,1.12,0.99,1.14。结果表明,此六个校正模型质量均较高。这些首次建立的快速无损的近红外分析模型,可为紫苏资源开发提供指导,对紫苏油分品质育种具有重要意义。     

直链化合物中α-手性碳对羰基亲核加成立体选择性的影响*

羰基亲核加成的立体选择性常用Cram模型和Felkin-Ahn模型来判断,对于这2种反应模型的原理及它们的适用情况鲜见文章详细介绍。本文从羰基化合物的结构特点出发,分3种不同情况介绍α-手性碳对反应立体选择性的影响--没有杂原子;有杂原子无螯合效应;有杂原子且有螯合配位或氢键。将每个判断产物构型的过程清晰地分成5步,更有利于相关专业的学生进行理解记忆。