Long-TermEvolution(LTE)isarguablyoneofthemostimportantstepsinthecurrentphaseof the development of modern mobile communications. It provides a suitable base for enhanced services due to increased data throughput and lower latency figures, and also gives extra impetus to the modernization of telecom Architectures. The decision to leave the circuit- switched domainoutofthescope ofLTE/SAEsystem standardization might soundradical but itindicatesthatthetelecomworldisgoingstronglyfortheall-IPconcept----andthedeployment of LTE/SAE is concrete evidence of this global trend.
標(biāo)簽： Deployment Handbook LTE-SAE The
上傳時(shí)間： 2020-06-01
上傳用戶：shancjb
Welcome to the world of wireless communications and the logical extension to the broadband Architectures that are emerging as the future of the industry. No aspect of communications will be untouched by the wireless interfaces;no part of our working environment will be left untouched either. As the world changes and the newer technologies emerge, we can expect to see more in the line of untethered communications than in the wired inter- faces.
標(biāo)簽： Broadband Wireless Handbook
上傳時(shí)間： 2020-06-01
上傳用戶：shancjb
The advent of modern wireless devices, such as smart phones and MID 1 terminals, has revolutionized the way people think of personal connectivity. Such devices encompass multiple applications ranging from voice and video to high-speed data transfer via wireless networks. The voracious appetite of twenty-first century users for supporting more wireless applications on a single device is ever increasing. These devices employ multiple radios and modems that cover multiple frequency bands and multiple standards with a manifold of wireless applications often running simultaneously.
標(biāo)簽： Architectures Wireless Receiver Design and
上傳時(shí)間： 2020-06-01
上傳用戶：shancjb
Over many years, RF-MEMS have been a hot topic in research at the technology and device level. In particular, various kinds of mechanical Si-MEMS resonators and piezoelectric BAW (bulk acoustic wave) resonators have been developed. The BAW technology has made its way to commercial products for passive RF filters, in particular for duplexers in RF transceiver front ends for cellular communica- tions. Beyond their use in filters, micromachined resonators can also be used in conjunction with active devices in innovative circuits and Architectures.
標(biāo)簽： MEMS-based Circuits Systems and
上傳時(shí)間： 2020-06-06
上傳用戶：shancjb
The author’s group has developed various chipless RFID tags and reader Architectures at 2.45, 4–8, 24, and 60 GHz. These results were published extensively in the form of books, book chapters, refereed conference and journal articles, and finally, as patent applications. However, there is still room for improvement of chipless RFID sys- tems. In this book, we proposed advanced techniques of chipless RFID systems that supersede their predecessors in signal processing, tag design, and reader architecture.
標(biāo)簽： Advanced Chipless RFID
上傳時(shí)間： 2020-06-08
上傳用戶：shancjb
Radio frequency identification (RFID) technology is witnessing a recent explosion of development in both industry and academia. A number of applications include supply chain management, electronic payments, RFID passports, environmental monitoring and control, office access control, intelligent labels, target detection and tracking, port management, food production control, animal identification, and so on. RFID is also an indispensable foundation to realize the pervasive computing paradigm—“Internet of things.” It is strongly believed that many more scenarios will be identified when the principles of RFID are thoroughly understood, cheap components available, and when RFID security is guaranteed.
標(biāo)簽： Architectures NETWORKS SENSOR RFID AND
上傳時(shí)間： 2020-06-08
上傳用戶：shancjb
The past decade has seen an explosion of machine learning research and appli- cations; especially, deep learning methods have enabled key advances in many applicationdomains,suchas computervision,speechprocessing,andgameplaying. However, the performance of many machine learning methods is very sensitive to a plethora of design decisions, which constitutes a considerable barrier for new users. This is particularly true in the booming field of deep learning, where human engineers need to select the right neural Architectures, training procedures, regularization methods, and hyperparameters of all of these components in order to make their networks do what they are supposed to do with sufficient performance. This process has to be repeated for every application. Even experts are often left with tedious episodes of trial and error until they identify a good set of choices for a particular dataset.
標(biāo)簽： Auto-Machine-Learning-Methods-Sys tems-Challenges
上傳時(shí)間： 2020-06-10
上傳用戶：shancjb
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