pcmanx-gtk2-0.3.4.tar

该代码文件全称为卷积神经网络，这是深度学习神经网络里面处理图片比较好的网络。(The code file is called convolutional neural network, which is a better network for processing pictures in deep learning neural network.)

模拟路由器功能，简单的c++程序代码，可以实现。(Simulation router function a simple c++ code, can be achieved.)

一个简单留言本源代码(附数据库)-a simple message origin code (database)

开放的IVR系统平台，可以直接基于DIALOGIC卡上使用-open IVR platform can be directly based on the use of cards DIALOGIC

网页下载-website

通过子带脉压技术，对大宽带信号进行频带分割，分别进行子带匹配滤波，最终实现大宽带的AD采样。(Through the subband pulse pressure technology, divide the big broadband signal ,through matched filter respectively, and finally achieve big broadband AD sampling.)

TS的模糊神经网络程序，包括数据对比输出，网络均方差输出对比，隶属度函数对比(TS fuzzy neural network program, including the comparison of the data output, the network mean square output contrast, comparison of membership functions)

说明：  思科路由器仿真器,用来仿7200系列得,可以在电脑上模拟路由器(Cisco router simulator, used to fake a 7200 series can be simulated on a computer router)

量子粒子群算法，测试函数，人工智能，多目标优化(Quantum particle swarm ，function test，Artificial intelligence, multi-objective optimization)

我们提供一类称为MobileNets的高效模型，用于移动和嵌入式视觉应用。?MobileNets是基于一个流线型的架构，它使用深度可分离的卷积来构建轻量级的深层神经网络。我们引入两个简单的全局超参数，在延迟度和准确度之间有效地进行平衡。这两个超参数允许模型构建者根据问题的约束条件，为其应用选择合适大小的模型。我们进行了资源和精度权衡的广泛实验，与ImageNet分类上的其他流行的网络模型相比，MobileNets表现出很强的性能。最后，我们展示了MobileNets在广泛的应用场景中的有效性，包括物体检测，细粒度分类，人脸属性和大规模地理定位。(We provide an efficient model called MobileNets for mobile and embedded vision applications. MobileNets is based on a streamlined architecture that USES deep separable convolution to build a lightweight deep neural network. We introduce two simple global hyperparameters to effectively balance the delay and accuracy. These two hyperparameters allow the model builder to select an appropriate size model for its application based on the constraints of the problem. We conducted extensive experiments on resource and precision tradeoffs, and MobileNets showed strong performance compared with other popular network models on the ImageNet classification. Finally, we demonstrate the effectiveness of MobileNets in a wide range of application scenarios, including object detection, fine-grained classification, face attributes, and large-scale geographic localization.)