Titlebook: Crowd-Powered Mobile Computing and Smart Things; Seng W. Loke Book 2017 The Author(s) 2017 mobile computing.pervasive computing.crowd comp

clannish

書目名稱Crowd-Powered Mobile Computing and Smart Things影響因子(影響力)




書目名稱Crowd-Powered Mobile Computing and Smart Things影響因子(影響力)學(xué)科排名




書目名稱Crowd-Powered Mobile Computing and Smart Things網(wǎng)絡(luò)公開(kāi)度




書目名稱Crowd-Powered Mobile Computing and Smart Things網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書目名稱Crowd-Powered Mobile Computing and Smart Things被引頻次




書目名稱Crowd-Powered Mobile Computing and Smart Things被引頻次學(xué)科排名




書目名稱Crowd-Powered Mobile Computing and Smart Things年度引用




書目名稱Crowd-Powered Mobile Computing and Smart Things年度引用學(xué)科排名




書目名稱Crowd-Powered Mobile Computing and Smart Things讀者反饋




書目名稱Crowd-Powered Mobile Computing and Smart Things讀者反饋學(xué)科排名

天空

Osteoarthritis

Scalable Context-Awareness,ware mobile computing?[1, 7], a mobile device is made aware of the current context of the user, including the circumstances or the surroundings as well as the user’s activity on the phone, or the phone’s current state (e.g., battery level, device properties and so on), and can take action based on s

詢問(wèn)

Drone Services for Mobile Crowds, drones, so that while regulations and drone protection measures (protecting drones and protecting people from drones) are being investigated, practical applications are being developed, from product delivery, disaster assessment and recovery, art and exergaming?[7] to self-driving cars guided by dr

危機(jī)

傷心

Conclusion and Future Work,ve computing, and swarm dynamics, this book has attempted to focus on five ideas, namely, crowd+cloud machines, extreme cooperation with smart things, scalable context-awareness, drone services for mobile crowds and social links in (mobile) crowds. We have not been exhaustive in our exploration of t

傷心

形容詞

Exploring Terabyte Archives in Astronomy data analysis techniques has been employed to process sensor data in order to learn to recognise activities—recently, Deep Convolutional Neural Networks have been employed achieving accuracy in recognition of up to 97–99%?[5].

Paraplegia

虛假

The Gemini Multi-Object Spectrographs, between all types of mobile devices, including smart vehicles and smart drones, e.g., vehicle-to-vehicle, vehicle-to-pedestrian, pedestrian-to-pedestrian, vehicle-to-bicycle, bicycle-to-bicycle, drone-to-vehicle, drone-to-drone, drone-to-pedestrian, and so on. Over such a networked mesh of devices