This slide explains the deep learning model, DeepStereo. DeepStereo is proposed by J.Flynn, et al. This model solves the problem of new view synthesis.
This slide introduces the model which is one of the deep Q network. Dueling Network is the successor model of DQN or DDQN. You can easily understand the architecture of Dueling Network.
This document introduces the deep reinforcement learning model 'A3C' by Japanese.
Original literature is "Asynchronous Methods for Deep Reinforcement Learning" written by V. Mnih, et. al.
This document introduce the literature 'Deep Compression' written by S. Han, et al. You can easily understand that literature by reading this. Only Japanese.
This slide explains the deep learning model, DeepStereo. DeepStereo is proposed by J.Flynn, et al. This model solves the problem of new view synthesis.
This slide introduces the model which is one of the deep Q network. Dueling Network is the successor model of DQN or DDQN. You can easily understand the architecture of Dueling Network.
This document introduces the deep reinforcement learning model 'A3C' by Japanese.
Original literature is "Asynchronous Methods for Deep Reinforcement Learning" written by V. Mnih, et. al.
This document introduce the literature 'Deep Compression' written by S. Han, et al. You can easily understand that literature by reading this. Only Japanese.
1. RF-IDraw :
Virtual Touch Screen in
the Air Using RF Signals
Takahiro Hashizume
Asami & Kawahara Lab.,
The University of Tokyo
Jue Wang, Deepak Vasisht, and Dina Katabi
SIGCOMM 2014
2015/04/25 M1GP
11. ?参考文献?引用画像
? 参考文献
? [1] Wang, Jue, Deepak Vasisht, and Dina Katabi. "RF-IDraw: virtual touch
screen in the air using RF signals." Proceedings of the 2014 ACM
conference on SIGCOMM. ACM, 2014.
? 引用画像
? [2] Pu, Qifan, et al. "Whole-home gesture recognition using wireless
signals." Proceedings of the 19th annual international conference on
Mobile computing & networking. ACM, 2013.
? [3] http://ja.wikipedia.org/wiki/Kinect#/media/File:KinectSensor.png
M1GP11
13. ?参考(1) - アンテナ間距離と位相差
D cos
=
j,i
2
+ k
di,j = dS,i dS,j
di,j
=
j,i
2
+ k
j,i = j i
k Z M1GP13
14. ?参考(2) - アンテナ間距離と曖昧さ
? 例:D = 8λのとき
k = 8 cos
j,i
2
9 k 9, k Z
= arccos
D
j,i
2
+
k
D
kの個数分だけθがあるので、
曖昧さ(ビームの本数)が増える。
M1GP14
15. ?参考(3) - アンテナ間距離と解像度
cos =
D
j,i
2
+
k
D
? 全てのハードウェアには読み取り精度があるため、
位相の測定時にはある程度の量子化が行われる。
? ハードウェアが読み取れる最小の間隔をδとする
と、最良のcosθの量子化は以下。
cos =
D 2
Dが大きくなれば
δの影響が小さくなる。
M1GP15