《信息技术》2期

Information TechVol.2，2014* New-Tech* Computer* Communication* InternetMCanxixun Information and News ServiceMcanxixun Information ContentsNew-Tech（科技前沿） (3)Robots learn to use kitchen tools by watching YouTube videos (3)机器人通过观看YouTube学会使用厨房用具 (4)Sensor-enabled nodes support the IoT for smart buildings and smart transport (5)传感器功能节点支持智能建筑与智能交通物联网 (9)New germanium-tin laser could replace copper wire for data transfers (13)新锗-锡激光可取代铜线传送数据 (14)Computer Software & Application（计算机应用与软件） (15)New signal amplification process set to transform communications, imaging, computing (15)新的信号放大过程或可转换通信、成像与计算 (17)Four Pillars for Choosing New SCADA Software (18)选择新SCADA软件的四大支柱 (21)Virtualization and the Cloud What‘s the Right Approach (23)选择虚拟化技术还是云服务？ (25)ARM‘s trifecta: New CPU, GPU, and interconnect hardware on the way (26)安谋国际科技三重彩：中央处理机、图形处理器及互连硬件 (28)Windows 10 preview: A glimpse of our desktop future (29)Windows 10 ——未来台式电脑预览 (31)Java 8 Auto-Update, Java 7 End of Public Update (33)Java 8开启自动更新，Java 7 将结束公共更新 (34)Communication（通信） (35)Wi-Fi growth set to drive sales of new Ethernet speeds (35)Wi-Fi的增长将带动新以太网的销售速度 (35)Many antennas, multiple benefits: Can handle cellular traffic more reliably (36)多条天线，多重效益：可靠处理移动通信 (37)Google wireless service could disrupt carriers (38)谷歌无线服务将影响运营商 (40)How attached to our smart phones are we? (41)我们对智能手机的依赖程度 (42)Maximizing access to mobile networks by seamlessly 'offloading' some traffic (42)无缝卸载通信率让移动网络通信力最大化 (43)Adobe Lightroom now available for Android, just as good as iOS version (44)Adobe后期制作软件现可兼容安卓系统和苹果系统 (45)Social Media Now Drives 31% Of All Referral Traffic (45)社交媒体推荐流量不容小觑，该如何利用 (48)Intenet（网络） (50)New search engine lets users look for relevant results faster (50)新搜索引擎使用户更快找到相关结果 (52)FCC will attempt to regulate ISPs as utilities, Chairman confirms (53)1Mcanxixun Information2联邦通信委员主席证实，试图将网络服务提供商归入公共事业 (54)US watchdog urges safeguards for 'Internet of Things' (54)美国监管部门督促加强物联网保护措施 (55)A Speedy Wireless Protocol Is Coming to Many Gadgets (56)很多小工具即将迎来一个快速的无线协议 (57)Mcanxixun Information New-Tech（科技前沿）Robots learn to use kitchen tools by watching YouTube videosImagine having a personal robot prepare your breakfast every morning. Now, imagine that this robot didn't need any help figuring out how to make the perfect omelet, because it learned all the necessary steps by watching videos on YouTube. It might sound like science fiction, but a team at the University of Maryland has just made a significant breakthrough that will bring this scenario one step closer to reality.Researchers at the University of Maryland Institute for Advanced Computer Studies (UMIACS) partnered with a scientist at the National Information Communications Technology Research Centre of Excellence in Australia (NICTA) to develop robotic systems that are able to teach themselves. Specifically, these robots are able to learn the intricate grasping and manipulation movements required for cooking by watching online cooking videos. The key breakthrough is that the robots can "think" for themselves, determining the best combination of observed motions that will allow them to efficiently accomplish a given task.The work will be presented on Jan. 29, 2015, at the Association for the Advancement of Artificial Intelligence Conference in Austin, Texas. The researchers achieved this milestone by combining approaches from three distinct research areas: artificial intelligence, or the design of computers that can make their own decisions; computer vision, or the engineering of systems that can accurately identify shapes and movements; and natural language processing, or the development of robust systems that can understand spoken commands. Although the underlying work is complex, the team wanted the results to reflect something practical and relatable to people's daily lives. "We chose cooking videos because everyone has done it and understands it," said Yiannis Aloimonos, UMD professor of computer science and director of the Computer Vision Lab, one of 16 labs and centers in UMIACS. "But cooking is complex in terms of manipulation, the steps involved and the tools you use. If you want to cut a cucumber, for example, you need to grab the knife, move it into place, make the cut and observe the results to make sure you did them properly."One key challenge was devising a way for the robots to parse individual steps appropriately, while gathering information from videos that varied in quality and consistency. The robots needed to be able to recognize each distinct step, assign it to a "rule" that dictates a certain behavior, and then string together these behaviors in the proper order."We are trying to create a technology so that robots eventually can interact with humans," said Cornelia Fermüller, an associate research scientist at UMIACS. "So they need to understand what humans are doing. For that, we need tools so that the robots can pick up a human's actions and track them in real time. We are interested in understanding all of these components. How is an action performed by humans? How is it perceived by humans? What are the cognitive processes behind it?"Aloimonos and Fermüller compare these individual actions to words in a sentence. Once a robot has learned a "vocabulary" of actions, they can then string them together in a way that achieves a given goal. In fact, this is precisely what distinguishes their work from previous efforts."Others have tried to copy the movements. Instead, we try to copy the goals. This is the breakthrough," Aloimonos explained. This approach allows the robots to decide for themselves how best to combine various actions, rather than reproducing a predetermined series of actions.The work also relies on a specialized software architecture known as deep-learning neural networks. While this3Mcanxixun Informationapproach is not new, it requires lots of processing power to work well, and it took a while for computing technology to catch up. Similar versions of neural networks are responsible for the voice recognition capabilities in smartphones and the facial recognition software used by Facebook and other websites.While robots have been used to carry out complicated tasks for decades -- think automobile assembly lines -- these must be carefully programmed and calibrated by human technicians. Self-learning robots could gather the necessary information by watching others, which is the same way humans learn. Aloimonos and Fermüller envision a future in which robots tend to the mundane chores of daily life while humans are freed to pursue more stimulating tasks."By having flexible robots, we're contributing to the next phase of automation. This will be the next industrial revolution," said Aloimonos. "We will have smart manufacturing environments and completely automated warehouses. It would be great to use autonomous robots for dangerous work -- to defuse bombs and clean up nuclear disasters such as the Fukushima event. We have demonstrated that it is possible for humanoid robots to do our human jobs."机器人通过观看YouTube学会使用厨房用具试想，一个私人机器人每天早上为你准备早餐，那将会使怎样的情形。