无插件无捆绑绿色DD虚拟键盘

乐视 随身看 随身看1 T50 固件 dd命令 命令行 DD

DD Low Level Format Tool即硬盘低格工具，而系统自带的属于高级格式化，此工具为低级格式化，它可以在WIDNOWS平台上直接低格你的硬盘而不需要进入DOS环境下使用。硬盘低格需要一定的时间且会彻底抹除硬盘上的所有数据，所有数据将被完全和不可逆转地擦除！请谨慎操作以免造成不可挽回的损失。机器码完整低级格式 化，也可执行快速擦除，只删除分区和MBR分区表。 本版本为绿色版不用安装，解压之后直接运行主程序HDDLLF.4.40.exe就可以使用了。如果提示注册，使用压缩包内附的注册机可以生成注册码。点击Enter code按钮来输入注册码。 HDD Low Level Format Tool是一款可以用于HDD硬盘以及U盘、MMC/SD/CF闪存卡，支持使用读卡器进行低格。支持所有的硬盘。本软件可以运行在WINXP/VISTA/WIN7以及2003/2008/2012系统上。低格不同于高级格式化，低格可以彻底清除计算机中的病毒，屏蔽掉硬盘中的坏道，延长硬盘 的使用寿命，但低格不宜多次使用，会有损于硬盘的寿命。 注：执行低格操作后，磁盘中的所有数据将丢失，

NSK DD马达操作手册pdf,NSK DD马达操作手册

最近在工作中进行了NLP的内容，使用的还是Keras中embedding的词嵌入来做的。 Keras中embedding层做一下介绍。 中文文档地址：https://keras.io/zh/layers/embeddings/ 参数如下： 其中参数重点有input_dim,output_dim,非必选参数input_length. 初始化方法参数设置后面会单独总结一下。 demo使用预训练（使用百度百科（word2vec）的语料库）参考 embedding使用的demo参考： def create_embedding(word_index, num_words, word2vec_mode

tl-wr841nd-webflash（DD-WRT）

前言 某些场景下我们需要利用addRoutes动态添加路由，但是刷新后就会失效，前段时间项目里刚好遇到了这个应用场景，所以就花时间研究了一下，做下分享跟记录，说的不对的地方，请大家指正。 应用场景：用户a登录进系统，页面上有个按钮，点击之后会动态添加路由并且跳转，跳转过去之后，用户刷新后也会停留在当前页面。 不点这个按钮，浏览器输入地址，用户会跳到404页面 github:https://github.com/Mrblackant/keepRouter/tree/master 思路 1.用户点击按钮，用addRutes动态添加路由并跳转，并把路由保存; 2.用户在新跳转的页面，刷新

bios by 百度贴吧dsanke 本人闲鱼付费购买，不支持八代i5，仅支持八代奔腾i3 ; ; These settings are not part recommendations, nor are they an ; indication these parts are supported on Intel platforms. ; All parts on this list have NOT been validated, and it is the ; responsibility of the customer to validate the flash parts used ; on their platform. ; ; Flash parts may change opcodes and architectures so please refer ; to the respective flash datasheet and errata/application note and ; flash vendor to confirm. ; ;Description of fields: ; 1) Display name ; 2) Device ID (2 or 3 bytes) ; 3) Device Size (in bits) ; 4) Block Erase Size (in bytes - 256, 4K, 64K) ; 5) Block Erase Command ; 6) Write Granularity (1 or 64) ; 7) Enable Write Status Register Command (1- True, 0- False) ;winbond W25Q256FV, 0xEF4019, 0x10000000, 0x1000, 0x20, 64, 0 W25Q256JV, 0xEF4019, 0x10000000, 0x1000, 0x20, 64, 0 W25Q256JW,0xEF6019, 0x10000000, 0x1000, 0x20, 64, 0 W25Q128FV,0xEF4018, 0x8000000, 0x1000, 0x20, 64, 0 W25R128FV,0xEF4018, 0x8000000, 0x1000, 0x20, 64, 0 W25Q128JV, 0xEF4018, 0x8000000, 0x1000, 0x20, 64, 0 W25Q128FW,0xEF6018, 0x8000000, 0x1000, 0x20, 64, 0 W25Q64FV, 0xEF4017, 0x4000000, 0x1000, 0x20, 64, 0 W25R64FV, 0xEF4017, 0x4000000, 0x1000, 0x20, 64, 0 W25Q64JV, 0xEF4017, 0x4000000, 0x1000, 0x20, 64, 0 W25Q64FW, 0xEF6017, 0x4000000, 0x1000, 0x20, 64, 0 W25Q32FV, 0xEF4016, 0x2000000, 0x1000, 0x20, 64, 0 W25Q32JV, 0xEF4016, 0x2000000, 0x1000, 0x20, 64, 0 W25Q32FW, 0xEF6016, 0x2000000, 0x1000, 0x20, 64, 0 W25Q16DV, 0xEF4015, 0x1000000, 0x1000, 0x20, 64, 0 W25Q16JV, 0xEF4015, 0x1000000, 0x1000, 0x20, 64, 0 W25Q16CL, 0xEF4015, 0x1000000, 0x1000, 0x20, 64, 0 W25Q16DW, 0xEF6015, 0x1000000, 0x1000, 0x20, 64, 0 W25Q16FW, 0xEF6015, 0x1000000, 0x1000, 0x20, 64, 0 W25Q80DV, 0xEF4014, 0x800000, 0x1000, 0x20, 64, 0 W25Q80JV, 0xEF4014, 0x800000, 0x1000, 0x20, 64, 0 W25Q80BL, 0xEF4014, 0x800000, 0x1000, 0x20, 64, 0 W25Q80EW, 0xEF6014, 0x800000, 0x1000, 0x20, 64, 0 W25X40CL, 0xEF3013, 0x400000, 0x1000, 0x20, 64, 0 W25Q40CL, 0xEF4013, 0x400000, 0x1000, 0x20, 64, 0 W25Q40EW, 0xEF6013, 0x400000, 0x1000, 0x20, 64, 0 W25X20CL, 0xEF3012, 0x200000, 0x1000, 0x20, 64, 0 W25Q20CL, 0xEF4012, 0x200000, 0x1000, 0x20, 64, 0 W25Q20EW, 0xEF6012, 0x200000, 0x1000, 0x20, 64, 0 W25X10CL, 0xEF3011, 0x100000, 0x1000, 0x20, 64, 0 W25Q10EW, 0xEF6011, 0x100000, 0x1000, 0x20, 64, 0 W25X05CL, 0xEF3010, 0x8000, 0x1000, 0x20, 64, 0 W25Q128JVSIQ, Ef4018, 0x8000000, 0x1000, 0x20, 64, 0 W25Q80DL, EF4014, 0x800000, 0x1000, 0x20, 64, 0 W25Q16JL, EF4015, 0x1000000, 0x1000, 0x20, 64, 0 W25Q256FV, EF4019, 0x10000000, 0x1000, 0x20, 64, 0 W25R256FV, EF4019, 0x10000000, 0x1000, 0x20, 64, 0 ;macronix MX25L12875F, 0xC22018, 0x8000000, 0x1000, 0x20, 64, 0 MX25L12835F, 0xC22018, 0x8000000, 0x1000, 0x20, 64, 0 MX25L6473E, 0xC22017, 0x4000000, 0x1000, 0x20, 64, 0 MX25L3273E, 0xC22016, 0x2000000, 0x1000, 0x20, 64, 0 MX25L1673E, 0xC22415, 0x1000000, 0x1000, 0x20, 64, 0 MX25L8075E, 0xC22014, 0x800000, 0x1000, 0x20, 64, 0 MX25L12873F, 0xC22018, 0x8000000, 0x1000, 0x20, 64, 0 MX25L8073E, 0xC22014, 0x800000, 0x1000, 0x20, 64, 0 MX25L6450F, 0xC22017, 0x4000000, 0x1000, 0x20, 64, 0 MX25L12850F, 0xC22018, 0x8000000, 0x1000, 0x20, 64, 0 MX25L3273F, 0xC22016, 0x2000000, 0x1000, 0x20, 64, 0 MX25L6473F, 0xC22017, 0x4000000, 0x1000, 0x20, 64, 0 MX25L25735F, 0xC22019, 0x10000000, 0x1000, 0x20, 64, 0 MX25L6445E, 0xC22017, 0x4000000, 0x1000, 0x20, 64, 0 MX25L6455E, 0xC22617, 0x4000000, 0x1000, 0x20, 64, 0 MX25L12855E, 0xC22618, 0x8000000, 0x1000, 0x20, 64, 0 MX25L6436E, 0xC22017, 0x4000000, 0x1000, 0x20, 64, 0 MX25L12836E, 0xC22018, 0x8000000, 0x1000, 0x20, 64, 0 MX25L8036E, 0xC22014, 0x800000, 0x1000, 0x20, 64, 0 MX25L1636E, 0xC22515, 0x1000000, 0x1000, 0x20, 64, 0 MX25L12865E, 0xC22018, 0x8000000, 0x1000, 0x20, 64, 0 MX25L6495F, 0xC29517, 0x4000000, 0x1000, 0x20, 64, 0 MX25L12835E, 0xC22018, 0x8000000, 0x1000, 0x20, 64, 0 MX25L12845E, 0xC22018, 0x8000000, 0x1000, 0x20, 64, 0 MX25L25635E, 0xC22019, 0x10000000, 0x1000, 0x20, 64, 0 MX25U1635F, 0xC22535h, 0x1000000, 0x1000, 0x20, 64, 0 MX25U3235F, 0xC22536h, 0x2000000, 0x1000, 0x20, 64, 0 MX25U6473F, 0xC22537h, 0x4000000, 0x1000, 0x20, 64, 0 MX25U6435F, 0xC22537h, 0x4000000, 0x1000, 0x20, 64, 0 MX25U12835F, 0xC22538h, 0x8000000, 0x1000, 0x20, 64, 0 MX25U12873F, 0xC22538h, 0x8000000, 0x1000, 0x20, 64, 0 MX25U25635F, 0xC22539h, 0x10000000, 0x1000, 0x20, 64, 0 MX25L25673G, 0xC22019h, 0x10000000, 0x1000, 0x20, 64, 0 MX66L51235F, 0xC2201Ah, 0x8000000, 0x1000, 0x20, 64, 0 MX25L51245G, 0xC2201Ah, 0x8000000, 0x1000, 0x20, 64, 0 MX25L12873G, 0xC22018h, 0x8000000, 0x1000, 0x20, 64, 0 MX25U8033E, 0xC22534h, 0x800000, 0x1000, 0x20, 64, 0 MX25U1635F, 0xC22535h, 0x1000000, 0x1000, 0x20, 64, 0 MX25U3273F, 0xC22536h, 0x2000000, 0x1000, 0x20, 64, 0 MX25U6473F, 0xC22537h, 0x4000000, 0x1000, 0x20, 64, 0 MX25U12873F, 0xC22538h, 0x8000000, 0x1000, 0x20, 64, 0 MX25U25635F, 0xC22539h, 0x10000000, 0x1000, 0x20, 64, 0 MX25L8073E, 0xC22014h, 0x800000, 0x1000, 0x20, 64, 0 MX25L1673E, 0xC22415h, 0x1000000, 0x1000, 0x20, 64, 0 MX25L3273F, 0xC22016h, 0x2000000, 0x1000, 0x20, 64, 0 MX25L6473F, 0xC22017h, 0x4000000, 0x1000, 0x20, 64, 0 MX25L6450F, 0xC22017h, 0x4000000, 0x1000, 0x20, 64, 0 MX25L12850F, 0xC22018h, 0x8000000, 0x1000, 0x20, 64, 0 MX25L12873F, 0xC22018h, 0x8000000, 0x1000, 0x20, 64, 0 MX25L25673G, 0xC22019h, 0x10000000, 0x1000, 0x20, 64, 0 MX25L25673G, 0xC2201Ah, 0x20000000, 0x1000, 0x20, 64, 0 MX25L25635F, 0xC22019, 0x10000000, 0x1000, 0x20, 64, 0 MX25L25645G, 0xC22019, 0x10000000, 0x1000, 0x20, 64, 0 MX25U25645G, 0xC22539, 0x10000000, 0x1000, 0x20, 64, 0 MX25U51245G, 0xC2253A, 0x20000000, 0x1000, 0x20, 64, 0 ;micron N25Q128, 0x20BA18, 0x8000000, 0x1000, 0x20, 64, 0 N25Q032, 0x20BB16, 0x2000000, 0x1000, 0x20, 64, 0 N25Q064, 0x20BA17, 0x4000000, 0x1000, 0x20, 64, 0 N25Q016, 0x20BA15, 0x1000000, 0x1000, 0x20, 64, 0 N25Q128A, 0x20BB18, 0x8000000, 0x1000, 0x20, 64, 0 N25Q256A, 0x20BB19, 0x10000000, 0x1000, 0x20, 64, 0 N25Q064A11EF640E, 0x20BB17, 0x4000000, 0x1000, 0x20, 64, 0 MT25QL256ABA1EW9-0SIT, 0x20BA19, 0x10000000, 0x1000, 0x20, 64, 0 MT25QL512ABA,0x20BA20, 0x20000000, 0x1000, 0x20, 64, 0 MT25QU128ABA1ESE, 0x20BB18, 0x8000000, 0x1000, 0x20, 64, 0 N25Q064A11ESEA0F, 0x20BB17, 0x4000000, 0x1000, 0x20, 64, 0 MT25QL128ABA, 0x20BA18, 0x8000000, 0x1000, 0x20, 64, 0 MT25QU128ABA, 0x20BB18, 0x8000000, 0x1000, 0x20, 64, 0 MT25QU256AB, 0x20BB19, 0x10000000, 0x1000, 0x20, 64, 0 ;EON / ESMT EN25QH256, 0x1C7019, 0x10000000, 0x1000, 0x20, 64, 0 EN25QH128A, 0x1C7018, 0x8000000, 0x1000, 0x20, 64, 0 EN25QH64A, 0x1C7017, 0x4000000, 0x1000, 0x20, 64, 0 EN25QH80, 0x1C7014, 0x800000, 0x1000, 0x20, 64, 0 EN25QH16A, 0x1C7015, 0x1000000, 0x1000, 0x20, 64, 0 EN25QH32A, 0x1C7016, 0x2000000, 0x1000, 0x20, 64, 0 EN25QH64, 0x1C7017, 0x4000000, 0x1000, 0x20, 64, 0 EN25Q40A, 0x1C3013, 0x400000, 0x1000, 0x20, 64, 0 EN25Q80B, 0x1C3014, 0x800000, 0x1000, 0x20, 64, 0 EN25S64, 0x1C3817, 0x4000000, 0x1000, 0x20, 64, 0 EN25S64A, 0x1C3817, 0x4000000, 0x1000, 0x20, 64, 0 EN25S10A, 0x1C3811, 0x100000, 0x1000, 0x20, 64, 0 EN25S20A, 0x1C3812, 0x200000, 0x1000, 0x20, 64, 0 EN25S40A, 0x1C3813, 0x400000, 0x1000, 0x20, 64, 0 EN25S80A, 0x1C3814, 0x800000, 0x1000, 0x20, 64, 0 EN25S32, 0x1C3816, 0x2000000, 0x1000, 0x20, 64, 0 EN25QH16, 0x1C7015, 0x0x1000000, 0x1000, 0x20, 64, 0 EN25QH32, 0x1C7016, 0x0x2000000, 0x1000, 0x20, 64, 0 EN25QH128, 0x1C7018, 0x0x8000000, 0x1000, 0x20, 64, 0 ;gigadevice GD25B16B, 0xC84015, 0x1000000, 0x1000, 0x20, 64, 0 GD25B32B, 0xC84016, 0x2000000, 0x1000, 0x20, 64, 0 GD25B64B, 0xC84017, 0x4000000, 0x1000, 0x20, 64, 0 GD25B128C, 0xC84018, 0x8000000, 0x1000, 0x20, 64, 0 GD25LQ64C, 0xC86017, 0x4000000, 0x1000, 0x20, 64, 0 GD25R64B, 0xC84017, 0x4000000, 0x1000, 0x20, 64, 0 GD25B64C, 0xC84017, 0x4000000, 0x1000, 0x20, 64, 0 GD25R64C, 0xC84017, 0x4000000, 0x1000, 0x20, 64, 0 GD25R128C, 0xC84018, 0x8000000, 0x1000, 0x20, 64, 0 GD25LB64, 0xC86017, 0x4000000, 0x1000, 0x20, 64, 0 GD25LB128, 0xC86018, 0x8000000, 0x1000, 0x20, 64, 0 GD25Q256C, 0xC84019, 0x10000000, 0x1000, 0x20, 64, 0 GD25LQ256C, 0xC86019, 0x10000000, 0x1000, 0x20, 64, 0 GD25R127C, 0xC84018, 0x8000000, 0x1000, 0x20, 64, 0 GD25B256C, 0xC84019, 0x10000000, 0x1000, 0x20, 64, 0 GD25B127D, 0xC84018, 0x8000000, 0x1000, 0x20, 64, 0 GD25LB128D, 0xC86018, 0x8000000, 0x1000, 0x20, 64, 0 GD25LB256D, 0xC86019, 0x10000000, 0x1000, 0x20, 64, 0 GD25Q80C, 0xC84014, 0x800000, 0x1000, 0x20, 64, 0 GD25B16C, 0xC84015, 0x1000000, 0x1000, 0x20, 64, 0 GD25Q16C, 0xC84015, 0x1000000, 0x1000, 0x20, 64, 0 GD25B32C, 0xC84016, 0x2000000, 0x1000, 0x20, 64, 0 GD25Q32C, 0xC84016, 0x2000000, 0x1000, 0x20, 64, 0 GD25Q64C, 0xC84017, 0x4000000, 0x1000, 0x20, 64, 0 GD25LQ128D, 0xC86018, 0x8000000, 0x1000, 0x20, 64, 0 GD25Q128C, 0xC84018, 0x8000000, 0x1000, 0x20, 64, 0 GD25B256D, 0xC84019, 0x10000000, 0x1000, 0x20, 64, 0 GD25LQ256D, 0xC86019, 0x10000000, 0x1000, 0x20, 64, 0 ;Adesto AT25SF641, 0x1F3217, 0x4000000, 0x1000, 0x20, 64, 0 AT25SL128A, 0x1F4218, 0x8000000, 0x1000, 0x20, 64, 0 AT25SL641, 0x1F4317, 0x4000000, 0x1000, 0x20, 64, 0 AT25SL321, 0x1F4216, 0x2000000, 0x1000, 0x20, 64, 0 AT25SF128, 0x1F7018, 0x8000000, 0x1000, 0x20, 64, 0 AT25QF128, 0x1F7018, 0x8000000, 0x1000, 0x20, 64, 0 AT25QF641, 0x1F3217, 0x4000000, 0x1000, 0x20, 64, 0 AT25QL128A, 0x1F4218, 0x8000000, 0x1000, 0x20, 64, 0 AT25QL641, 0x1F4317, 0x4000000, 0x1000, 0x20, 64, 0 AT25QL321, 0x1F4216, 0x2000000, 0x1000, 0x20, 64, 0 ;ISSI IS25LP256A, 0x9D6019, 0x10000000, 0x1000, 0x20, 64, 0 IC25LP256A, 0x9D6019, 0x10000000, 0x1000, 0x20, 64, 0 IS25LP128, 0x9D6018, 0x8000000, 0x1000, 0x20, 64, 0 IC25LP128, 0x9D6018, 0x8000000, 0x1000, 0x20, 64, 0 IS25LP064A, 0x9D6017, 0x4000000, 0x1000, 0x20, 64, 0 IC25LP064A, 0x9D6017, 0x4000000, 0x1000, 0x20, 64, 0 IS25WP256A, 0x9D7019, 0x10000000, 0x1000, 0x20, 64, 0 IC25WP256A, 0x9D7019, 0x10000000, 0x1000, 0x20, 64, 0 IS25WP128, 0x9D7018, 0x8000000, 0x1000, 0x20, 64, 0 IC25WP128, 0x9D7018, 0x8000000, 0x1000, 0x20, 64, 0 IS25WP064, 0x9D7017, 0x4000000, 0x1000, 0x20, 64, 0 IC25WP064, 0x9D7017, 0x4000000, 0x1000, 0x20, 64, 0 IS25WP064A, 0x9D7017, 0x4000000, 0x1000, 0x20, 64, 0 IC25WP064A, 0x9D7017, 0x4000000, 0x1000, 0x20, 64, 0 IS25LP256D, 0x9D6019, 0x10000000, 0x1000, 0x20, 64, 0 IC25LP256D, 0x9D6019, 0x10000000, 0x1000, 0x20, 64, 0 IS25WP256D, 0x9D6019, 0x10000000, 0x1000, 0x20, 64, 0 IC25WP256D, 0x9D6019, 0x10000000, 0x1000, 0x20, 64, 0 IS25WP080D, 0x9D7014, 0x800000, 0x1000, 0x20, 64, 0 IS25LP080D, 0x9D6014, 0x800000, 0x1000, 0x20, 64, 0 IS25WP016D, 0x9D7015, 0x1000000, 0x1000, 0x20, 64, 0 IS25LP016D, 0x9D6015, 0x1000000, 0x1000, 0x20, 64, 0 IS25WP032D, 0x9D7016, 0x2000000, 0x1000, 0x20, 64, 0 IS25LP032D, 0x9D6016, 0x2000000, 0x1000, 0x20, 64, 0 IS25LP128F, 0x9D6018, 0x8000000, 0x1000, 0x20, 64, 0 IS25WP128F, 0x9D7018, 0x8000000, 0x1000, 0x20, 64, 0 ;XMC XM25QH64A, 0x207017, 0x4000000, 0x1000, 0x20, 64, 0 XM25QH128A, 0x207018, 0x8000000, 0x1000, 0x20, 64, 0 XM25QU64A, 0x203817, 0x4000000, 0x1000, 0x20, 64, 0 XM25QH32B, 0x204016, 0x2000000, 0x1000, 0x20, 64, 0

一点见解，不断学习，欢迎指正 1、自定义loss层作为网络一层加进model，同时该loss的输出作为网络优化的目标函数 from keras.models import Model import keras.layers as KL import keras.backend as K import numpy as np from keras.utils.vis_utils import plot_model x_train=np.random.normal(1,1,(100,784)) x_in = KL.Input(shape=(784,)) x = x_in x = KL.Dense

matlab除噪声代码DD-SRAD DD-SRAD代表距离驱动散斑减少各向异性扩散，用于消除SAR时间堆栈的噪声。 该代码是为与MATLAB一起使用而编写的。 用于对合成Kong径雷达数据的时间堆栈进行去噪，但可用于随时间推移而被噪声破坏的任何图像数据。 可以在下面列出的相关论文中找到更多详细信息。 如果使用此代码，请引用以下内容： N. Tabassum，A。Vaccari和S. Acton，“通过合成Kong径雷达时间堆栈的距离驱动各向异性扩散进行斑点去除和变化保留”，《数字信号处理》，第1卷。 74，第43-55页，2018年。 该代码提供了两个数据集，一个是合成生成的，另一个是一组实际SAR幅度数据。 原始合成数据也包括在内以进行比较。 要运行演示，请运行DD_SRAD.m。 如有任何疑问，请联系。 谢谢！