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.
標(biāo)簽： A_Guide_to_Radio_Frequency_IDenti fication RFID
上傳時間： 2020-06-08
上傳用戶：shancjb
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.
標(biāo)簽： RFID-Enabled Sensors RFID and
上傳時間： 2020-06-08
上傳用戶：shancjb
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.
標(biāo)簽： Infrastructure RFID as an
上傳時間： 2020-06-08
上傳用戶：shancjb
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.
標(biāo)簽： Architecture Integrated RFID-WSN
上傳時間： 2020-06-08
上傳用戶：shancjb
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.
標(biāo)簽： Internet Things From The RFI of
上傳時間： 2020-06-08
上傳用戶：shancjb
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).
標(biāo)簽： Identification Wireless
上傳時間： 2020-06-08
上傳用戶：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.
標(biāo)簽： Embedded_Deep_Learning Algorithms
上傳時間： 2020-06-10
上傳用戶：shancjb
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.
標(biāo)簽： 高效率 射頻 微波 固態(tài) 功率放大器
上傳時間： 2021-10-30
上傳用戶：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
標(biāo)簽： XR2981
上傳時間： 2021-11-05
上傳用戶：
摘要：無線傳感器網(wǎng)絡(luò)（Wireless Sensor Networks，wSN是由許多具有低功率無線收發(fā)裝置的傳感器節(jié)點組成，它們監(jiān)測采集周邊環(huán)境信息并傳送到基站進(jìn)行處理在某一時刻通過wSN采集的數(shù)據(jù)量非常大，如何正確、高效地處理這些數(shù)據(jù)成為當(dāng)前WSN研究中的一個熱點。傳感器節(jié)點一般部署在惡劣環(huán)境中，一些偶然因素會使采集的數(shù)據(jù)中出現(xiàn)不準(zhǔn)確的數(shù)據(jù)，用戶依據(jù)這樣的數(shù)據(jù)很難準(zhǔn)確判斷出被測對象的真實狀態(tài)。基于模糊理論的決策級數(shù)據(jù)融合算法能夠很好的解決這個問題本文以國家863研究項目《基于無線傳感器網(wǎng)絡(luò)的鐵路危險貨物在途安全狀態(tài)監(jiān)測技術(shù)研究》為背景，結(jié)合鐵路運輸中棉花在途狀態(tài)監(jiān)測系統(tǒng)的開發(fā)，在分析了當(dāng)前有效的決策級數(shù)據(jù)融合技術(shù)基礎(chǔ)上，提出了基于模糊理論的決策級數(shù)據(jù)融合算法，該算法通過對采集數(shù)據(jù)進(jìn)行處理和分析，以獲得準(zhǔn)確的被測對象狀態(tài)的描述。本文的主要工作包括：（1）分析了WSN中傳統(tǒng)的決策級數(shù)據(jù)融合算法，如自適應(yīng)加權(quán)數(shù)據(jù)融合算法和算術(shù)平均數(shù)數(shù)據(jù)融合算法，總結(jié)這兩種算法的優(yōu)缺點和檢測系統(tǒng)的需求，進(jìn)步明確理想算法應(yīng)達(dá)到的目標(biāo)。（2）提出了基于模糊理論的兩階段數(shù)據(jù)融合算法：該算法第一階段利用基于貼近度的數(shù)據(jù)融合算法進(jìn)行同類數(shù)據(jù)的融合校準(zhǔn)，這一階段的目的是剔除錯誤的和可信度較差的數(shù)據(jù)，得到相對更加準(zhǔn)確的數(shù)據(jù)，第二階段利用模糊推理對第個階段得到的異類數(shù)據(jù)進(jìn)行融合推理，得到被測對象當(dāng)前狀態(tài)的描述，為決策提供支持（3）結(jié)合實測數(shù)據(jù)仿真本文所提出的算法，結(jié)果證明與傳統(tǒng)的融合算法相比，可以更加準(zhǔn)確的描述被測對象狀態(tài)
標(biāo)簽： 無線傳感器
上傳時間： 2022-03-17
上傳用戶：
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