The author of this textbook intends to consider all stages of the life cycle of the energy resources: extraction of mineral energy resources and mastering for power engineering renewable energy, transportation of mineral energy raw materials to the place of consumption, the conversion of primary energy sources into electrical and/or thermal energy, transportation and distribution among the customers, and energy storage (if necessary).
标签： Engineering Electrical Current Power State
上传时间： 2020-06-07
上传用户：shancjb
Although state of the art in many typical machine learning tasks, deep learning algorithmsareverycostly interms ofenergyconsumption,duetotheirlargeamount of required computations and huge model sizes. Because of this, deep learning applications on battery-constrained wearables have only been possible through wireless connections with a resourceful cloud. This setup has several drawbacks. First, there are privacy concerns. Cloud computing requires users to share their raw data—images, video, locations, speech—with a remote system. Most users are not willing to do this. Second, the cloud-setup requires users to be connected all the time, which is unfeasible given current cellular coverage. Furthermore, real-time applications require low latency connections, which cannot be guaranteed using the current communication infrastructure. Finally, wireless connections are very inefficient—requiringtoo much energyper transferredbit for real-time data transfer on energy-constrained platforms.
标签： Embedded_Deep_Learning Algorithms
上传时间： 2020-06-10
上传用户：shancjb
General paradigm in solving a computer vision problem is to represent a raw image using a more informative vector called feature vector and train a classifier on top of feature vectors collected from training set. From classification perspective, there are several off-the-shelf methods such as gradient boosting, random forest and support vector machines that are able to accurately model nonlinear decision boundaries. Hence, solving a computer vision problem mainly depends on the feature extraction algorithm
标签： Convolutional Networks Neural Guide to
上传时间： 2020-06-10
上传用户：shancjb
CV2880是一颗数字信号转模拟信号和数字信号的芯片。 类比于ADI，TI等外国芯片，此芯片性能不输于以上，价格上却实惠很多。 CV2880支持各类标准和非标的数字信号。 例如内嵌同步的BT656，外置同步的BT1120，RGB，YUV等等。 输出也可输出以上数字信号外加VGA,CVBS,YPBPR等模拟信号。 分辨率可以支持到1920x1080P 60Hz。
上传时间： 2022-05-25
上传用户：1208020161
CVBS+AHD+TVI+CVI四合一转换方案(模拟高清4合1转换)此方案支持CVBS, AHD, TVI，CVI信号输入，对信号进行任意处理比如添加OSD,增加图像效果后转为AV/VGA/YPBPR/HDMI或者数字656/601/1120/YUV/RGB等任意信号模式输出。此方案支持摄像头规格如下：AHD1.0,2.0,3MP,4MP, 5MP.TVI1.0,2.0,3.0,4MP,5MP.CVI1.0,2MP.CVBS PAL,NTSC.CVBS 960H.当输入信号为AHD/TVI/CVI时，理论传输距离为500米，实际测试传输200米以上。
上传时间： 2022-05-25
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1、原始套接字透析之前言大多数程序员所接触到的套接字（Socket）为两类服务应用：（1）流式套接字（SOCK-STREAM）：一种面向连接的Socket，针对于面向连接的TCP（2）数据报式套接字（SOCK-DGRAM）：一种无连接的Socket，对应于无连接的UDP服务应用.从用户的角度来看，SOCK-STREAM，SOCK-DGRAM这两类套接字似乎的确涵盖了TCP/IP应用的全部，因为基于TCPIP的应用，从协议栈的层次上讲，在传输层的确只可能建立于TCP或UDP协议之上（图1），而SOCK STREAM，SOCK DGRAM又分别对应于TCP和UDP，所以几乎所有的应用都可以用这两类套接字实现。但是，当我们面对如下问题时，SOCK_STREAM，SOCK DGRAM将显得这样无助：（1）.怎样发送一个自定义的IP包？（2）怎样发送一个ICMP协议包？（3）怎样使本机进入杂糅模式，从而能够进行网络sniffer？（4）怎样分析所有经过网络的包，而不管这样包是否是发给自己的？（5）怎样伪装本地的IP地址？这使得我们必须面对另外一个深刻的主题--原始套字（Raw Socket），Raw Socket广泛应用于高级网络编程，也是一种广泛的黑客手段。著名的网络sniffer、拒绝服务攻击（DOS），IP欺骗等都可以以Raw Socket实现。Raw Socket与标准套接字（SOCK STREAM，SOCK DGRAM）的区别在于前者直接置"根"于操作系统网络核心（Network Core），而SOCK STREAM.SOCK DGRAM则"悬浮“于TCP和UDP协议的外围，如图2所示：
上传时间： 2022-06-19
上传用户：得之我幸78
第一章 LWIP 无操作系统移植第二章 LWIP 带操作系统移植第三章 RAW 编程接口 UDP 实验第四章 RAW 编程接口 TCP 客户端实验第五章 RAW 编程接口 TCP 服务器实验第六章 RAW 编程接口 Web Server 实验第七章 NETCONN 编程接口简介第八章 NETOCNN 编程接口 UDP 实验第九章 NETCONN 编程接口 TCP 客户端实验第十章 NETCONN 编程接口 TCP 服务器实验
上传时间： 2022-07-25
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