Titlebook: Big Data Computing and Communications; First International Yu Wang,Hui Xiong,JianZhong Li Conference proceedings 2015 Springer Internation

BID

書目名稱Big Data Computing and Communications影響因子(影響力)




書目名稱Big Data Computing and Communications影響因子(影響力)學(xué)科排名




書目名稱Big Data Computing and Communications網(wǎng)絡(luò)公開度




書目名稱Big Data Computing and Communications網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Big Data Computing and Communications被引頻次




書目名稱Big Data Computing and Communications被引頻次學(xué)科排名




書目名稱Big Data Computing and Communications年度引用




書目名稱Big Data Computing and Communications年度引用學(xué)科排名




書目名稱Big Data Computing and Communications讀者反饋




書目名稱Big Data Computing and Communications讀者反饋學(xué)科排名

館長(zhǎng)

命令變成大炮

歹徒

Autopilots and Flight Management Systems,c signals to emulate a virtual antenna array. In this case, we leverage smoothed MUSIC (Multiple Signal Classification) algorithm to estimate the DOA (direction of arrival) of the target signals. We design and deploy our system under various circumstances in an underground parking lot. By getting ri

天文臺(tái)

鍵琴

思考

Dynamic Sparse Channel Estimation Using ,-constrained Kalman Filter in OFDM Systemss which are used to approximate to the discontinuous .-norm. Therefore, DSCE is performed by employing KF in a stand alone manner. Numerical evaluations are presented to demonstrate the effectiveness of the proposed algorithm in practice.

膽小鬼

FOAM: Frequency-Offset Aware Multiple Client Selection for Cooperative Packet Recovery Systemet recovery among clients. We show the performance of PDR and SINR in various carrier frequency offset levels. Our conclusion is, when multiple clients’ transmissions are involved for cooperative packet recovery, the frequency offset should be fully considered to enhance the network performance. Suc

虛弱的神經(jīng)

Gender Prediction Based on Data Streams of Smartphone Applications In the experiments, the dataset is real world data collected from 25 volunteers. The prediction results can achieve 86.50% in Accuracy and 86.43% in F1-score, respectively. To the best of our knowledge, this is the first time that the gender information was predicted by leveraging users’ response t

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