For the problem that the input and output of real systems is a continuous process relative to time, this paper proposed a process neural network model for continuous function approximation.
针对实际系统的输入输出是与时间有关的连续过程，提出了一类用于连续过程逼近的过程神经元网络模型。
Classical saliency-based visual attention models are adapted for embedding real-time systems with less time and space costs based on approximate Gaussian pyramids of the input image.
利用输入图像的近似高斯金字塔，将经典的基于显著性的视觉注意模型改造为时空开销更小的版本，从而使其更加适合在嵌入式实时系统中实现。
Real Time: Responds to input instantly. General-purpose operating systems, such as DOS and UNIX, are not real-time.
实时：及时地对输入进行反应，通用操作系统如DOS和UNIX不是实时的。
应用推荐