Radio frequency identifi cation (RFID) technology is a WIRELESS communication technology that enables users to uniquely identify tagged objects or people. RFID is rapidly becoming a cost-effective technology. This is in large part due to the efforts of Wal-Mart and the Department of Defense (DoD) to incorporate RFID technology into their supply chains. In 2003, with the aim of enabling pallet-level tracking of inventory, Wal-Mart issued an RFID mandate requiring its top suppliers to begin tagging pallets and cases, with Electronic Product Code (EPC) labels. The DoD quickly followed suit and issued the same mandate to its top 100 suppliers. This drive to incorporate RFID technology into their supply chains is motivated by the increased ship- ping, receiving and stocking effi ciency and the decreased costs of labor, storage, and product loss that pallet-level visibility of inventory can offer.
标签： A_Guide_to_Radio_Frequency_IDenti fication RFID
上传时间： 2020-06-08
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adio Frequency Identification (RFID) is a rapidly developing automatic WIRELESS data-collection technology with a long history.The first multi-bit functional passive RFID systems,with a range of several meters, appeared in the early 1970s, and continued to evolve through the 1980s. Recently, RFID has experienced a tremendous growth,due to developments in integrated circuits and radios, and due to increased interest from the retail industrial and government.
标签： RFID-Enabled Sensors RFID and
上传时间： 2020-06-08
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RFID (radio-frequency identification) is the use of a WIRELESS non-contact system that uses radio-frequencyelectromagnetic fields to transfer datafrom a tag attached to an object, for the purposes of automatic identification and tracking [38]. The basic technologies for RFID have been around for a long time. Its root can be traced back to an espionage device designed in 1945 by Leon Theremin of the Soviet Union,whichretransmittedincidentradiowaves modulatedwith audioinformation. After decades of development, RFID systems have gain more and more attention from both the research community and the industry.
标签： Infrastructure RFID as an
上传时间： 2020-06-08
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Radio frequency identification (RFID) and WIRELESS sensor networks (WSN) are the two key WIRELESS technologies that have diversified applications in the present and the upcoming systems in this area. RFID is a WIRELESS automated recognition technology which is primarily used to recognize objects or to follow their posi- tion without providing any sign about the physical form of the substance. On the other hand, WSN not only offers information about the state of the substance and environment but also enables multi-hop WIRELESS communications.
标签： Architecture Integrated RFID-WSN
上传时间： 2020-06-08
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With more than two billion terminals in commercial operation world-wide, wire- less and mobile technologies have enabled a first wave of pervasive communication systems and applications. Still, this is only the beginning as WIRELESS technologies such as RFID are currently contemplated with a deployment potential of tens of billions of tags and a virtually unlimited application potential. A recent ITU report depicts a scenario of “Internet of things” — a world in which billions of objects will report their location, identity, and history over WIRELESS connections.
标签： Internet Things From The RFI of
上传时间： 2020-06-08
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We are in the era of ubiquitous computing in which the use and development of Radio Frequency Iden- tification (RFID) is becoming more widespread. RFID systems have three main components: readers, tags, and database. An RFID tag is composed of a small microchip, limited logical functionality, and an antenna. Most common tags are passive and harvest energy from a nearby RFID reader. This energy is used both to energize the chip and send the answer back to the reader request. The tag provides a unique identifier (or an anonymized version of that), which allows the unequivocal identification of the tag holder (i.e. person, animal, or items).
上传时间： 2020-06-08
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
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Research on microwave power amplififiers has gained a growing importance demanded by the many continuously developing applications which require such subsystem performance. A broad set of commercial and strategic systems in fact have their overall performance boosted by the power amplififier, the latter becoming an enabling component wherever its effificiency and output power actually allows functionalities and operating modes previously not possible. This is the case for the many WIRELESS systems and battery-operated systems that form the substrate of everyday life, but also of high-performance satellite and dual-use systems.
上传时间： 2021-10-30
上传用户：得之我幸78
2.7V to 5.5V input voltage Range Efficiency up to 96% 24V Boost converter with 12A switch current Limit 600KHz fixed Switching Frequency Integrated soft-start Thermal Shutdown Under voltage Lockout Support external LDO auxiliary power supply 8-Pin SOP-PP PackageAPPLICATIONSPortable Audio Amplifier Power SupplyPower BankQC 2.0/Type CWIRELESS ChargerPOS Printer Power SupplySmall Motor Power Supply
标签： XR2981
上传时间： 2021-11-05
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摘要：无线传感器网络（WIRELESS Sensor Networks，wSN是由许多具有低功率无线收发装置的传感器节点组成，它们监测采集周边环境信息并传送到基站进行处理在某一时刻通过wSN采集的数据量非常大，如何正确、高效地处理这些数据成为当前WSN研究中的一个热点。传感器节点一般部署在恶劣环境中，一些偶然因素会使采集的数据中出现不准确的数据，用户依据这样的数据很难准确判断出被测对象的真实状态。基于模糊理论的决策级数据融合算法能够很好的解决这个问题本文以国家863研究项目《基于无线传感器网络的铁路危险货物在途安全状态监测技术研究》为背景，结合铁路运输中棉花在途状态监测系统的开发，在分析了当前有效的决策级数据融合技术基础上，提出了基于模糊理论的决策级数据融合算法，该算法通过对采集数据进行处理和分析，以获得准确的被测对象状态的描述。本文的主要工作包括：（1）分析了WSN中传统的决策级数据融合算法，如自适应加权数据融合算法和算术平均数数据融合算法，总结这两种算法的优缺点和检测系统的需求，进步明确理想算法应达到的目标。（2）提出了基于模糊理论的两阶段数据融合算法：该算法第一阶段利用基于贴近度的数据融合算法进行同类数据的融合校准，这一阶段的目的是剔除错误的和可信度较差的数据，得到相对更加准确的数据，第二阶段利用模糊推理对第个阶段得到的异类数据进行融合推理，得到被测对象当前状态的描述，为决策提供支持（3）结合实测数据仿真本文所提出的算法，结果证明与传统的融合算法相比，可以更加准确的描述被测对象状态
标签： 无线传感器
上传时间： 2022-03-17
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