For fixed-size formats like FNT or PCF, each image is a bitmap. 对于固定尺寸字体格式,如FNT或者PCF,每一个图像都是一个位图。
Because of FNT's inherent restrictions, it is hard to implement cyclic convolution using FNT. 由于FNT变换本身的局限,不能完成大点数循环卷积。
These works are presented in this thesis: 1, The multiplication-free Fermat number transform ( FNT) with the cyclic convolution property is used for calculating the multiplication of two polynomials to improve speed and accuracy. 本文有特色的主要工作如下:1、利用Fermat数变换(FNT)的循环卷积性质进行整系数多项式相乘,提高了运算的精度和速度。
Its advantages include not only zero-error brought by FNT, but also convenience for hardware manipulation, no saving for intermediate data and less area-consume. 不仅具有FNT本身零误差的优点,而且有适合于硬件实现,具有不用保存中间结果,节省面积的优点。
On the basis of FPT, in this paper we introduce an iteration algorithm for computing hyper-large scale 2-D cyclic convolutions by combining FPT with FNT. 本文将FPT和FNT相结合提出了一种计算超大型二维循环卷积的迭代算法,它的基础是应用了FPT。
As compared with the FPT algorithm for 2-D cyclic convolutions, the amount of multiplications of the algorithm introduced here decreases by one order of magnitude, and also it relieves the strict restriction of word length about cyclic convolution scale in FNT algorithm of 2-D cyclic convolutions. 与二维循环卷积的FPT算法相比,乘法量减少了一个数量级,同时实际取消了FNT算法中卷积规模所受到的字长的限制。
A Convolver Implemented by Using FNT 一个用FNT实现的卷积器
This paper presents a method of building expert systems with fuzzy-neural technology '( FNT method). 本文提出了一种用模糊-神经技术建造专家系统的方法(FNT方法)。
A Parameterized FPGA Realization of High-Speed FNT Processor 一种可参数化快速FNT的FPGA实现
In order to find a better network structure, this article first uses a flexible neural tree ( FNT) in protein tertiary structure prediction, PSO optimizes the network parameters, probabilistic incremental program evolution ( PIPE) optimizes the network structure. 为了寻找一种更优的网络结构,本文首次将灵活神经树(FNT)应用在蛋白质三级结构预测中,PSO对网络参数进行优化,概率增强式程序进化(PIPE)对网络结构进行优化。