官方离线安装包,亲测可用
2021-12-11 22:01:37 377KB rpm
官方离线安装包,亲测可用
2021-12-11 22:01:36 224KB rpm
官方离线安装包,亲测可用
2021-12-11 22:01:36 416KB rpm
官方离线安装包,亲测可用
2021-12-11 22:01:35 266KB rpm
Previous generations of mobile networks enabled voice, data, video, and other life-changing services. In comparison, 5G will change our society by opening up the telecom ecosystem to vertical industries. 5G will help vertical industries to achieve the “Internet of Everything” vision of ubiquitously connected, highly reliable, ultra-low latency services for massive number of devices. Service-guaranteed network slicing introduced in this white paper is one of the essential features for 5G to achieve this vision. Key players from operators, vendors, and vertical industries have come together to establish a common understanding on service-guaranteed network slicing in terms of the vision, end-to end (E2E) solution, key enabling technologies, and the impacts for vertical industries. This white paper describes the thinking on network slicing in 5G
2021-12-11 20:19:49 1.43MB 5G 白皮书
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适用于ns-2的Google BBR实施 这是要在上使用的ns-2网络模拟器的Google BBR TCP拥塞控制算法的实现 这是一个未完成的实现,欢迎贡献。 TL; DR 该文件夹必须位于ns-allinone-2.35旁边(与ns-allinone-2.35处于同一级别)。 make patch make all make plot 如何安装和使用 我们假设您的工作ns-allinone-2.35文件夹位于包含此存储库的文件夹旁边,即: |- bbr | |-Makefile | |-tcp.h | |- (...) | |- ns-allinone-2.35 |- ns-2.35 |- include |- bin |- (...) 如果这是您首次安装此软件,则必须修补三个重要文件: Makefile.in tcp/tcp.h lib/tcl/ns-defaults.tcl 您可以通过以下方式应用这些补丁: make patch 这样做了以后,你需要调用make all的代码和复制.tcl脚本到ns2.35。 此副本与修
2021-12-11 15:38:27 177KB google tcp simulation network
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用于HP网络打印机的适配器 hp usb network print adapter 驱动程序软件
2021-12-11 11:23:49 41.18MB hp usb network print
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https://github.com/devsisters/pointer-network-tensorflow 的 tsp_10_train.zip
2021-12-11 11:09:04 139.8MB 深度学习
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usb-over-network Linux Client端,版本为5.2.17,从官网下载,解压后doc目录下的README有安装说明
2021-12-10 16:43:55 4.24MB usb over network
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癫痫发作检测 该存储库包含“深度学习”项目的代码,用于识别癫痫诊断患者的异常脑电图。 参考 ChronoNet:用于异常EEG识别的深度递归神经网络 如果您发现存储库中的代码很有用,请使用以下命令将其引用: @misc{chitlangia2021epileptic, author = {Chitlangia, Sharad}, title = {Epileptic Seizure Detection using Deep Learning}, year = {2021}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/Sharad24/Epileptic-Seizure-Detection/}}, }
2021-12-10 16:28:51 3.42MB deep-learning neural-network eeg identification
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