Question

悬赏英语翻译 在线等 不要机器翻译，好的追加分（30*）！非常感谢！

Y是有待改善的。在正确率较高的前提下，如果需要提高I，需要扩大选择项或减少单次闪烁刺激的时间，前者的改进需要进一步研究FPGA上生成M程序结构，尽可能减少程序占用逻辑单元的数目；后者的改进则需进一步展开实验，寻找人脑能够被诱发出X的最小闪烁刺激时间。
在线系统选用N作为特征向量，由于N数据较大，使得在线进行SVM分类器的训练的时间不够，最终在线系统使用的SVM分类器是通过离线数据训练好之后嵌入到在线系统中的。之后可以尝试进一步挖掘时频信息，降低特征向量的数据量，使得在线训练分类器称为可能。

Answer 1

Y is remained to be improved. On the premise of high accuracy, if need to improve I, need to expand options or reduce single twinkling exciting time, former improvement requires further study on FPGA generation M program structure so as to reduce the number of units occupied program logic; The latter will need to improve further experiment, looking for the human mind can be induced out the minimum time flashing stimulate X. Online system chooses N as the characteristic vector, due to the large N data, make online SVM classifier.experimental training time is not enough, eventually online systems use the SVM classifier.experimental is through training and after data embedded in the online system. Then can try to further mining time-frequency information, reduce the amount of characteristic vector data, make online training classifier known as possible.

Answer 2

Y is for improvement.
Under the premise of higher accuracy, if you need to improve I, select an item needs to be expanded or reduced single flashing stimulation of time, which requires further improvement of study on FPGA generation m program structure, as far as possible reduce the number of program uses logical unit; the latter will require further improvement of experiment, looking for the human brain can be induced by x the smallest Flash stimulation time. Online selection system n eigenvectors as, due to the larger n data, making online SVM classifiers trained for lack of time, the ultimate online system using SVM classifiers by training after well embedded in the offline data online system.
You can try after further time-frequency information, reducing the data volume of eigenvectors, makes online training a classifier is known as possible.

Answer 3

Y is to be improved.In a high correct rate under the premise, if need to increase the I, the need to expand the options or reduce single flicker stimulation time, the former improved further research is needed to generate FPGA M program structure, as far as possible to reduce the program takes the logical unit number; this latter improvement needs further unfolding experiments, looking for a human to be the induced X minimum flicker stimulation time.
Online system selected N as a feature vector, because the N data is bigger, make the online SVM classifier training time is not enough, the ultimate online system using SVM classifier is trained by off-line data after embedded in the on-line system.After further excavation can try the time-frequency information, reduce the feature vector data, which enables online classifier training known as possible.