Vol.5, No.1, 25 April 2015 Monthly Lecture Meeting Published by the Medical Information System Laboratory of Doshisha University, Kyotanabe, Japan

Medical Information System Laboratory Monthly Lecture Meeting Contents... 1... 8... 12 The wireless capsule endoscopy... 17... 23... 32 IT... 37... 45 Deep Learning... 50 DPC NDB... 57 3... 62

... 69... 76 Pepper... 80... 84... 90 Windows 10... 95 AppleWatch... 100

44 2015 04 25 Tomonori ISHIHARA Izumi ISHIDA Abstract ICT ICT 2045 1 ICT ( Information and Communication Technology ) ICT (Artificial Intelligence) AI ICT AI AI ( ) 2045 1.1 ICT OCR(Optical Character Recoqnition) IC Fig1 ICT IT 1) 2 CPU AI 1

Fig. 1 IC ( 2) ) 3 AI AI 3.1 AI AI AI Amazon Facebook Fig. 2 ( ) 3.2 AI 1950 AI AI AI 1980 AI AI 2

1980 2 IBM 2 Deep Blue 1997 Deep Blue IBM Watoson 2011 2 IBM Facebook IT 2015 2 10 Watson 3.3 32 IBM RS/6000SP 512 1 DeepBlue 35 5 35 5 1997 DeepBlue DeepBlue 8 8 9 8 DeepBlue AI Fig. 3 Deep Blue( 3) ) 3.3.1 DeepBlue Fig4 4) 3

Fig. 4 ( 4) ) 3.4 IBM Watson Watson AI Watson Watson Fig. 5 Watson( 3) ) Watson Watson 80 Watson Watson AI Fig6 Watson Watson Watson Watson Watson 3) 4

Fig. 6 Watson ( ) 4 4.1 Google Google 4.2 1 1 1 1 4 5) 5 2) 5

5.1 ALS 5.2 5.3 10 2) 6 6) 6.1 2020 6.2 AI 6

7 google 1) Ray Kurzweil,,, NHK, 2007 2) 21 ( ), http://www.ikedahayato.com/index.php/archives/27729, 2015 4 2 3) IBMJapan, Watson news, http://www.ibm.com/smarterplanet/jp/ja/ibmwatson/?lnk=jphpcs2, 2015 4 2. 4),, https://www.google.co.jp/#q= 2015 4 17 5), 1, http://www.tel.co.jp/museum/magazine/news/015.html, 2015 4 17 6), http://user.keio.ac.jp/ũa805580/reading/20121012 significance.html, 2015 4 2 7

44 2015 04 25 Masataka YABUUCHI Hayato TANAKA Abstract 1 1) 2) 2 Fig. 1 注 視 点 環 境 カメラ アイカメラ 被 験 者 Fig. 1 3) 8

3 3) Fig. 2 注 視 点 環 境 カメラ 被 験 者 眼 球 計 測 カメラ Fig. 2 3) 3.1 GRP Gaze Reflection Point GRP 2 GRP GRP GRP GRP 3.2 9

HF-PIP High Frame-rate Programmable Illumination Projector HF-PIP LED Hz LED (LED-AP 3.2.1 LED-AP LED-AP LED LED 0.05ms LED 2 LED LED ID ID LED LED GRP 3 LED GRP LED GRP ID ID LED LED-AP LED LED 3.2.2 LED-AP LED Aruduino GRP 4 ALS Fig. 3 Tobbi Technology C15Eye 4) Fig. 4 Tobii X2-30 10

Fig. 3 C15Eye 4) Fig. 4 Tobii X2 60 4) 5 6 1), http://itpro.nikkeibp.co.jp/ article/keyword/20100901/351671/, 2015 4 5 2), http://bizmakoto. jp/makoto/articles/1403/26/news045.html, 2015 4 5 3),,, Vol.18, No.19, pp.1 8, 2013 4) Tobii, http://www.medtecjapan.com/ja/news/2013/08/ 20/907, 2015 4 6 11

44 2015 04 25 Rina HAGIWARA Tatsuya OKAMURA Abstract LAN LED(Light Emitting Diode: ) 1 LAN LED LED 2 380[nm] 780[nm] LED Fig. 1 Fig. 1 1) 12

2) 3 3.1 LED LED LED LED LED 2 LED 3 3) LED LED 3 LED 3 LED 1 2 Table. 1 Table. 1 4) [bps] LED[bps] LED[bps] LED[bps] LED[bps] 11M 54M 614M 520M 662M 614M 3.2 pn pin pn pin 2 2) LED Fig. 2 13

Fig. 2 5) 3.3 Fig. 3 ASK (Amplitude Shift Keying) ASK Fig. 3(a) 2 0/1 ASK 0 1 OOK (On-Off Keying) FSK (Frequency shift Keying) FSK Fig. 3(b) 2 0/1 PSK (Pulse Shift Keying) PSK Fig. 3(c) 2 0/1 4PPM (4 Pulse Position Modulation) 4PPM Fig. 3(d) 4 1 I-4PPM (Inverted 4 Pulse Position Modulation) I-4PPM Fig. 3(e) PPM 4PPM I-4PPM 3 JEITA 2013 5 CP-1223 I-4PPM JEITA 4PPM I-4PPM SC-I-4PPM 4 0.416[ms] SC-I-4PPM (Subcarrier Inverted 4 Pulse Position Modulation) SC-I-4PPM Fig. 3(f) 4PPM I-4PPM SC-I-4PPM 14

(a) ASK (b) FSK (c) PSK (d) 4PPM (e) I-4PPM Fig. 3 6) (f) SC-I-4PPM 4 7) 1. 2. 3. 1 (VLCC) 2008 10 2[km] 1022[bps] 1[km] 1200[bps] 8) 2 3 5 LED ITS(Intelligent Transport Systems: ) 2) LED LED LED LED 9) 15

6 LED LED 1), http://www.naka-lab.jp/product/datatransmit.html, 2015 4 1 2), LED,, Vol.31, No.10, pp.799 801, 2011 3),, LED,, Vol.31, No.10, pp.799 801, 2011 4), http://www.kindai.ac.jp/, 2015 4 2 5) CMOS, http://www.sony.co.jp/products/sc-hp/tech/isensor/ cmos/, 2015 4 17 6),, -IC RF UWB ZigBee, 1 1,, 2005 7) LED, http://www.kendenkyo.or.jp/pdf/technology/159_basic.pdf, 2015 4 2 8), http://www.vlcc. net/pr/090323.pdf, 2015 4 2 9),,,, C (, Vol.133, No.5, pp.922 929, 2013 16

44 2015 04 25 The wireless capsule endoscopy Tomoka KATAYAMA Kenichi TAKI Abstract 1 (The wireless capsule endoscopy) 2000 2001 2014 200 1) 2 11mm 26mm 2) 2.1 LED(Light Emitting Diode) Fig. 1 17

CMOS(Complementary Metal Oxide Semiconductor) CCD(Charge Coupled Device) CMOS CCD DSP Digital Signal Processor) CMOS 19 27 CCD 41 CMOS CCD CCD CCD CMOS CMOS LED LED LED 3) 8 8 315MHz 4, 5) Fig. 2 LED 8 2, 6) Fig. 1 ( 7) ) Fig. 2 ( 8) ) 18

2.2 3 26mm 11mm 30 2) Fig. 3 30 30 X 100 200 1 6 2 5 8 13 2012 0 9, 10) Fig. 3 ( 9) ) 19

4 4.1 2010 7) Fig. 4 500ml MRI(magnetic resonance imaging) 1/100 11) XYZ 3 7) (a) ( 11) ) (b) ( 12) ) Fig. 4 20

4.2 11mm 24mm Fig. 5 1 5mm 10mm 1 28 13, 14). Fig. 5 ( 13) ) 5 21

6 2000 1),, http://techon. nikkeibp.co.jp/article/news/20140528/354700/?st=ndh, 2015 4 19 2),,,, 2010 3), LED,,, 2006 4),,,, Vol.5, No.1, pp.26 30, 2011 5),,, Vol.62, No.4, pp.475 478, 2008 6),,, Vol.120, No.3, pp.351 352, 2008 7) Olympas,, http://www.onaka-kenko.com/ endoscope-closeup/endoscope-technology/et_06.html, 2015 4 17 8),, http://www8.cao.go.jp/ cstp/5minutes/012/index3.html, 2015 4 17 9) COVIDIEN,, http://www.nomudake.com/ cd/capsule/03.html, 2015 4 17 10), 3d, http://www.innervision. co.jp/041products/2012/p20120824.html, 2015 4 17 11), ( ), http://www. mu-frontier.com/1106.html, 2015 4 17 12) W. Japan,, http://web-japan.org/kidsweb/ja/hitech/ capsule_endoscopes/002.html, 2015 4 17 13),, http://www.micro.mse.kyutech.ac.jp/ Research/Research.html, 2015 4 17 14),, http://www.sankei.com/region/ news/150303/rgn1503030076-n1.html, 2015 4 17 22

44 2015 04 25 Yoshimi SAKAGUCHI Satoshi SHIGARAKI Abstract 1 10 µw 1 (Energy Harvesting) ( ) 1) IC Wireless Sensor Networks WSN 2) QOL (Quality of Life) ID 2 ( ) 1. 2. 3. 4. 23

1 10 µw Table. 1 Table. 1 [µw/cm 2 ] 1 10 10 10 1980 1. 2. 3. 2.1 2.1.1 Fig. 1 ( ) Fig. 2 (Rectifying antenna; Rectenna) Fig. 1 ( 1) ) 24

Fig. 2 ( )( 1) ) 90 % RFID 2.1.2 Fig. 3 p Si n Si pn pn p n n p n p Fig. 3 ( 1) ) 25

Fig. 4 p n p Si n Si n Si p Si I SC Fig. 4 ( 1) ) n p V OC 0 Fig. 5 I max V max Fig. 5 (I) V) ( 1) ) 2.1.3 Fig. 6 A B T H T L V AB (1) S AB A B (2) V AB = S AB (T H T L ) (1) 26

Fig. 6 ( 1) ) S AB = S A S B (2) 1 K µv/k µv/k Fig. 7 T 0 I (3) π Fig. 7 ( 1) ) Q = S AB T 0 I = πi (3) 0.1 % 3 % 2.1.4 68kg 67 W Fig. 8(a) Fig. 8(b) 27

Fig. 8 ( 1) ) (electret) 1 2 3 100 Hz 0.1 µw 0.1 V 100 Hz. 2 cm 30 Hz 100 µw 1) 2.2 (Structural Health Monitoring; SHM) 28

2.2.1 ( ) Mu ( ) 3). Table. 2 4). Table. 2 WSN ( ) CEMS(Cluster/Community Energy Management System) GPS POS RF HAN(Home Area Network) H(Home)EMS ( ) (HVAC(Heating Ventilaton and Air Conditioning)) B(Building)EMS FA F(Factory)EMS RF 3 5) 2 1 ( ) 2 ( ) GPS/ / / 29

3.1 UHF (Ultra High Frequency) 860 960 MHz RFID (Radio Frequency Identification) RF RFID JIS RFID RF 100 m / RF UHF RFID ISO/IEC18000-6 Type C 30 dbm / / (MS) / UHF RFID RFID / / 3.1.1 ( / ) RFID Fig. 9 ( 1) ) Fig. 9 950 MHz 950 MHz RFID ( ) 30

2011 ( 23 ) 950 MHz 920 MHz 2018 4 950 MHz 950 MHz Fig. 10 950 MHz ( 1) ) 950 MHZ Fig. 10 950 MHz 958 MHz 200 khz 33 (30 dbm) 4.200 khz 29 (10 dbm) 4 29 ( / ) 4 IC 31

5 1 10 µw QOL 1),,,, 2012 2) (1), http://www.nikkei. com/article/dgxnasfk2901s\_z20c10a9000000/?df=2, 2015 04 21 3), http://www. den-gyo.com/solution/solution10.html, :2015 04 21 4), http://tocos-wireless.com/jp/tech/wsn.html, 2015 04 21 5), 3 LAN,,, 2004 32

44 2015 04 25 Naoya ISHIDA Akiho MURAKAMI Abstract SNS SNS 1-2013 11.2 1) - 1) SNS 2 2.1 PayPal PayPal PayPal Fig. 1 PayPal Fig. 1 PayPal 33

PayPal PayPal PayPal PayPal PayPal PayPal PayPal SNS 2.2 6 2) 3 3 3). 2.3 IC IC ICOCA Suica IC 3 SNS Fig. 2 Fig. 2 3.1 SNS SNS 34

3.2 2 PayPal SNS 3 SNS 3 IC 4 LINE Pay Google Wallet 2 4.1 LINE Pay LINE Pay LINE LINE 4) Fig. 3 2014 LINE Fig. 3 LINE 5) Fig. 3 LINE 4.1.1 LINE Pay LINE Pay Fig. 4 LINE Pay LINE Pay LINEPay (LINE STORE) LINE STORE LINE Pay PIN LINE LINE LINE 4.1.2 LINE Pay LINE Pay QR 35

Fig. 4 LINE Pay 4.2 Google Wallet Google Wallet Google Wallet Google Wallet LINE Pay Google Wallet PIN Fig. 5 Google Wallet 4.2.1 Google Wallet NFC (Near Field Communication: NFC) Google Wallet NFC NFC Google Wallet 36

5 ID SNS 6 SNS SNS 1), 25 ( ), 2014 2), 25, 2014 3) WebPay, webpay, http://webpay.jp, 2015/4/13. 4) LINE, Line pay, http://line.me/ja/pay, 2015/4/13 5) line, http://iphone-mania.jp/news-42874/, 2015/4/20 37

44 2015 04 25 IT Saki YOSHITAKE Shogo OBUCHI Abstract 1 IT IT 1 66.8 1) IT IT IT 2 IT IT 3 IT IT IT IT 38

3 IT IT 3.1 IT ph 3.1.1 Table. 1 2). Table. 1 [ ] [ ] [ppm] -20 +50 10 95 0 5000 0.3 2 50 0.1 0.5 1 2km 18 3.1.2 Panasonic Table. 2 3) 4) Table. 2 [V] [Hz] [W] 100 50/60 170/190 100 50/60 160/185 200 50 490 200 50/60 49/39 3.2 Ubiquitous Environment Control System:UECS 39

Fig. 1 IEEE802.3 Web Web Fig. 2 5) Fig. 3 40

3.3 IT Good Agricultual Practice GAP GAP 6) Fig. 4 IT 7) Fig. 5 Panasonic 41

TC FUJITSU Technical Computing Solution TC HPC High Performance Computing HPC Web GUI HPC PC 8). 4 IT IT 2 4.1 2 1 LED Fig. 6 2 42

Fig. 7 4.2 5 IT IT IT 9) 10) 6 IT 2 1 43

ICT 11) 2 IT 561.8 159.7 1 721.6 1 265.3 7 IT IT IT IT IT 1), http://www.maff.go.jp/j/tokei/sihyo/data/08.html, 2015 4 19 2) OPUS CO2, http://senecom.co.jp/opus20.html, 2015 4 19 3), http://panasonic.biz/es/air/air.html, 2015 4 19 4), http://www.nepon.co.jp/nk/, 2015 4 19 5) UECS, http://uecs.jp/uecs/uecs-1.html, 2015 4 20 6), AI(Agri-Informatics),, Vol.30, No.2, pp.174 181, 2015 7),,, Vol.30, No.2, pp.167 173, 2015 8) TC, http://www.fujitsu.com/jp/solutions/businesstechnology/tc/sol/tccloud/platform/, 2015 4 20 9), http://www.logistics.or.jp/data/survey/cost.html, 2015 4 21 10),,, Vol.30, No.2, pp.193 198, 2015 11),,, Vol.30, No.2, pp.188 192, 2015 44

44 2015 04 25 Shuhei YOKOYAMA Ryota TAMURA Abstract 1 1) Online Public Access Catalog: OPAC 2 1960 70 2) 2) 1. 2. 3. 4. 5. 6. 1 4 3.1 20 2) 45

3 2000 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 10 3) Fig. 1 4) 4 4.1 1950 46

Fig. 2 4.2 2) 4.2.1 4.2.2 5 1 9 2) 1. 2. 3. 4. 5. 6. 7. 47

8. 1 9. OPAC 1 1 OPAC 1) 6 OPAC OPAC Fig. 3 3) 1 6.1 OPAC OPAC OPAC 5) 6.2 CiNii Books CiNii Books (NII) 1200 1000 150 48

6) 7 OPAC OPAC OPAC 2010 1968 90 Optical Character Reader:OCR) 8 1),,,, 2013 2),,,, 2013 3),,,, 2013 4), http://www.slis.doshisha.ac.jp/grad/course/index.html, :2015 4 14 5) opac, https://www.library.toyama.toyama.jp/file/img/tayori/t57.pdf, :2015 4 19 6) Cinii, http://support.nii.ac.jp/ja/cinii/cinii.outline, :2015 4 19 49

44 2015 04 25 Deep Learning Takaya TAMAKI Kenya HANAWA Abstract Deep Learning 1 1 Deep Learning Deep Learning 2006 Hinton 2011 10% 2012 ILSVRC(ImageNet Large Scale Visual Recognition Competition) 84% 2 10% 1) 2) Deep Learning Deep Learning Deep Learning 2 ( ) ( ) 2.1 ( ) (Fig. 1(a)) Fig. 1(b) W b. 50

b i k + j W i1 b 1 W 13 W j1 b 3 W j2 W b 2 3 2 W k2 (a) (b) ( ) Fig. 1 ( ) ( ) (Fig. 2) h k-1 h k h k+1 0.02 0 0.04 0 i 0.80 1 j w ij 0.05 0 Fig. 2 ( ) k i k k 1 [h 1 k 1 h 2 k 1 ] w ij k h j k 1 (1) b k i i w k ij k i k 1 j w k i = [w k i1 w k i2 ] T f h k i h k i = f(b k i w kt i k ) (2) ( (3) Fig. 3) f(x) = 1 + exp( x) (3) 51

Fig. 3 ( ) (4) p(x) = exp(b i k w i kt k ) exp(bi k w i kt k ) (4) 2.2 ( ) ( ) C C w ij w ij w ij w ij b k b k + b k w ij b k w ij ϵ C w ij (5) b k ϵ C b k (6) ϵ ( ) 3) 3 Deep Learning Deep Learning Deep Learning Deep Learning Convolutional Neural Network Deep Autoencoder Deep Belief Network Deep Autoencoder Convolutional Neural Network 3.1 Deep Autoencoder Deep Autoencoder Fig. 4 pretraining (finetuning) 52 4)

4) W W 4 copy W 3 W 3 W 3 copy W 2 W 2 W 2 W 1 W 1 W 1 Fig. 4 Deep Autoencoder ( 4) ) 3.1.1 Autoencoder Deep Autoencoder pretraining Autoencoder (x) (h) 2 h = f(wx + b) (7) x h encoder y = f (W h + b ) (8) decoder Autoencoder x encoder h decoder h y y x W b W b 4) 3.1.2 Pretraining 3.1 pretraining Fig. 4 Autoencoder pretraining 4). step.1 2 Autoencoder step.2 2 Autoencoder 2 step.3 step.2 3.1.3 Finetuning finetuning pretraining finetuning Fig. 4 2 1 1 1 Autoencoder. x h k x y 4) 53

3.2 Convolutional Neural Network(CNN) (V1) (simple cells) (complex cells) CNN Fig. 5 CNN A 0.94(A 0.02(B 0.01(Z ) 1 0 0 Fig. 5 Convolutional Neural Network(CNN) ( ) 3.2.1 n x n x x n w n w w h = x w h n h (9) n h = n x n h n x n w + 1 (9) A 32 32 5 5 Fig. 6 4) Fig. 6 ( 4) ) 54

3.2.2 CNN max pooling max pooling h j P i (i = 1 2 ) h i (10) h i = max j P i h j (10) Fig. 6 4 4 maxpooling 4 4 1/4 8 8 (Fig. 7) 4) Fig. 7 ( 4) ) 3.2.3 CNN CNN 4) 4 Deep Learning Deep Learning Deep Learning 2014 Facebook Deep Face 4030 440 97.53 5) 2015 1 Audi Nvidia Jack Deep Learning Deep Learning 6) 7) 5 Deep Learning Deep Learning.. 55

Deep Learning 6 Deep Learning Deep Learning Deep Autoencoder Convolutional Neural Network Deep Learning Deep Learning 1) F. Seide, G. Li and D. Yu, Conversational speech transcription using context-dependent deep neural networks, INTERSPEECH2011, pp.437 440, 2011. 2) ILSVRC2012, Image net large scale visual recognition challenge 2012, http://www. image-net.org/challenges/lsvrc/2012/results.html, 2015 4 18. 3),,, 2007 4),, 6,, 2013 5) Y. Taigman, M. Yang, M. Ranzato and L. Wolf, Deepface: Closing the gap to humanlevel performance in face verification, Computer Vison Papers, pp.1 8, 2014. 6) Nvidia, How nvidia drive px will help automakers slim down self-driving cars nvidia blog, http://blogs.nvidia.com/blog/2015/03/17/nvidia-drive-px, 2015 4 18. 7) Audi, Audi piloted driving, http://www.audi.com/com/brand/en/vorsprung_ durch_technik/content/2014/10/piloted-driving.html, 2015 4 18. 56

44 2015 04 25 DPC,NDB, Hiroshi WADA Kohei MISHIMA Abstract,,., DPC(Diagnosis Procedure Combination).DPC,,.,,,,. 1,,,.,.,,., 1).,.,,,,,,.,.,,,.,,DPC Diagnosis Procedure Combination.DPC,,,,.DPC DPC. DPC,,. 2,,.,,,,.,..,,.,,,,.,., Table. 1,,,,., 57

,., NDB(National Database),.,. Table. 1 2.1 NDB NDB,,.,. NDB,. DPC.,,. NDB,. 2.2,,,,,,.,,,.,,. 3 DPC DPC, 1,, 500 1500,. DPC. DPC. DPC..,,. 2).,., DPC. 3.1 DPC Fig. 1 DPC,14., 14,. 6. 58

. JCS JCS(Japan Coma Scale).,,..,...,,. Fig. 1 2) 3.2 DPC DPC DPC..DPC. DPC DPC. 1. 1.,,,,.,,. 2. E. 3. F. E,F,,.,. 4. D DPC. 5. 3,. 6. 4 59

.,. 3.3 DPC DPC. 1.,. 2.,,,,.,, 3.,,. 4.,,,. 3.4 DPC,DPC.,,,,,.,.,.,,,.,,,.,DPC,.DPC,.,,,.,,,.,DPC,.,,,,. 4 2016.., 1..,,.,.,,,.,,.,., ID.,. 5,DPC,NDB.DPC,.,,,.,,.,,,, 60

,. 1),,, Vol.16, No.5, pp.386 392, 1975 2), DPC-, 3,, 2012 61

44 2015 04 25 3 Yuto OKADA Katsutoshi HAYASHINUMA Abstract NAND 3 3 HDD SSD 1 Electrically Erasable Programmable Read-Only Memory EEPROM 1 2 1 EEPROM HDD 1) 1980 EEPROM HDD 3 3 2 NAND 2.1 NOR NOR Fig. 1 1 B 2 W 1 3 1 Fig. 2 EEPROM Metal Oxide Semiconductor MOS Floating Gate : FG FG FG FG NOR EEPROM NOR NAND 1) 1 2 2 3 62

Fig. 1 NOR 1) Fig. 2 1) 2.2 NAND NAND Fig. 3 MOS String MOS Fig. 3 MOS ON FG MOS non volatile MOS NV-MOS MOS1 ON MOS1 MOS2 MOS ON MOS2 MOS 2) NAND FG NV-MOS 15nm 1 NAND 16GB 3) 15nm 3 NAND 1 30µm 1) 3 3 3.1 3 3 Fig. 4 a NAND Fig. 4 b 4) 1 4 1 4 63

Fig. 3 NAND 2) Table. 1 3 Bit-Cost Scalable Technology BiCS pipe-shaped BiCS p-bics 3D Vertical NAND 3D V-NAND Samsung 2) 3 Samsung Table. 1 p-bics BiCS BiCS 3D V-NAND 3.2 BiCS 3.2.1 BiCS 3 Fig. 5 1 3 BiCS BiCS Fig. 6 Fig. 5 Fig. 6 1 NANDString NANDString 3 NANDString 1 NANDString NANDString 1 1 5, 6) 2 NAND BiCS NANDString 64

Fig. 4 3 NAND 5) Fig. 5 BiCS 5) p-bics 3.2.2 p-bics p-bics NANDString NANDString Fig. 7 BiCS 6) 3.2.3 p-bics p-bics NANDString BiCS 1 2 BiCS 6) Fig. 8 5 6) 5 65

Fig. 6 BiCS 6) Fig. 7 p-bics 6) 3.3 3D V-NAND 3.3.1 3D V-NAND 3D V-NAND Fig. 9 Channel Hole Channel Hole Fig. 10 a Fig. 10 b Fig. 10 c Fig. 10 c 32 1 NAND 16GB 7) 3.3.2 BiCS V-NAND Fig. 6 Fig. 9 BiCS 2015 3 48 16GB 8) V-NAND Samsung 3.4 2 3 BiCS V-NAND 2 16GB 3 2 2 3 2 7) 4 HDD SSD NAND HDD SDD HDD HDD SSD 66

Fig. 8 6) Fig. 9 3D V-NAND 7) HDD 3 Samsung 3D V-NAND 3 128GB 256GB 512GB 1TB SSD 2018 1 NAND 100 128GB 9) 1TB HDD 1 1TB SSD 9 3 NAND BiCS 48 2015 3 V-NAND BiCS 2 16GB 8, 9) 2 BiCS V-NAND 7, 10) 5 2 NAND 3 BiCS p-bics Samsung V-NAND 2015 BiCS Intel-Micron SK Hynix 3 11, 12) 3 SD SSD USB 67

Fig. 10 Channel Hole 7) HDD 1), [ ], 5,, 2003 2), 3 NAND,, Vol.30, No.1, pp.42 48, 2014 3),, 3 NAND,, Vol.1, No.2, pp.ss14 SS17, 2014 4) S.-M. Jung, Three Dimensionally Stacked NAND Flash Memory Technology Using Stacking Single Crystal Si Layers on ILD and TANOS Structure for Beyond 30nm Node, Electron Devices Meeting, 2006, Vol.1, No.1, pp.1 4, 2006. 5),,, 3 NAND,, Vol.63, No.2, pp.28 31, 2008 6), 3 BiCS,, Vol.66, No.9, pp.16 19, 2011 7) J. Elliott, Ushering in the 3D Memory Era with V- NAND, Flash Memory Summit 2013, Vol.1, No.1, pp.1 32, 2013. 8) 48 3 BiCS, http://www.toshiba.co.jp/about/press/2015_03/pr_j2601.htm, :2015 4 12 9) SSD, http://www.samsung.com/global/business/semiconductor/minisite/ SSD/jp/html/ssd850pro/overview.html, :2015 4 12. 10), 3 BiCS,, Vol.64, No.12, pp.56 57, 2009 11) Micron and Intel Unveil New 3D NAND Flash Memory, http: //newsroom.intel.com/community/intel_newsroom/blog/2015/03/26/ micron-and-intel-unveil-new-3d-nand-flash-memory, :2015 5 4. 12), 3 NAND 2015,, Vol.1117, pp.81 90, 2013 68

44 2015 04 25 Seiya KATSURADA Shogo OBUCHI Abstract 1 High Performance Computing (HPC) 1 3 1 1, 24 9 1),.,.1 19 19 361 361 10 760. HPC PC 2 PC 0 1 1 1 0 1 3 0 1 2) 3.1.Fig. 1 2 1 1/2. Fig. 2 2 1/2 A 1/2 69

1/2.2 100 A 100 B 0 100 1 2 Fig. 3 A 1.B 0 2 3) Fig. 1 Fig. 2 Fig. 3 3.2 0 1 2 2 70

3 3.2.1 3 Fig. 4 0 1 0 1 Fig. 4 x 2 x 4) Fig. 4 ( ) Fig. 5 0 0 1 1 a 0 (1) a = 0 (1) Fig. 6 0 1 (2) a = 1 2 0 + 1 2 1 (2) i exp(i ) Fig. 7 (3) a = cos θ 2 0 + exp(i ) sin θ 0 (3) 2 Fig. 5 a = 0 Fig. 6 a = 1 2 0 + 1 2 1 71

Fig. 7 a = cos θ 2 0 + exp(i ) sin θ 2 0 3.3 PC 3.3.1 Fig. 8 0 y 180 1 y 180 NOT Fig. 8 3.3.2 0 0 1 Fig. 9 x z 2 180 0 0 1 Fig. 9 3.3.3 2 2 1. 2 Fig. 10 NOR 72

Fig. 10 4-0 1 Fig. 11 0 1 A 0 1 4 a2, a1, a0, b (4) ( 0, 0, 0, 0 + 0, 0, 1, 0 + 0, 1, 0, 0 + 0, 1, 1, 0 + 1, 0, 0, 0 + 1, 0, 1, 0 + 1, 1, 0, 0 + 1, 1, 1, 0 B 0, 0, 0 2 2 1 b 0 (5) ( 0, 0, 0, 1 + 0, 0, 1, 0 + 0, 1, 0, 1 + 0, 1, 1, 1 + 1, 0, 0, 0 + 1, 0, 1, 1 + 1, 1, 0, 0 + 1, 1, 1, 0 10110100 1 C C 1 2 2 (6) ( 0, 0, 0, 1 + 0, 0, 1, 0 0, 1, 0, 1 0, 1, 1, 1 + 1, 0, 0, 0 1, 0, 1, 1 + 1, 1, 0, 0 + 1, 1, 1, 0 D 1 (7) 2 2 ( 0, 0, 0, 0 + 0, 0, 1, 0 0, 1, 0, 0 0, 1, 1, 0 + 1, 0, 0, 0 1, 0, 1, 0 + 1, 1, 0, 0 + 1, 1, 1, 0 b 0 E 0 1 2 0 + 1 2 1 1 1 2 0 1 2 1 (8) 2 2 (4) (5) (6) (7) ( 0, 0, 1 + 0, 1, 1 1, 0, 0 + 1, 1, 0 2 (8) 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0 0 0 1 2 n 2N+3 PC 2 n 1 73

Fig. 11 5 N N N x x r N 1 r r x r 2 + 1,x r 2 1 N z z z N z PC r r r Fig. 12 6 1 7 74

5) 8 1), Pc watch 2, http: //pc.watch.impress.co.jp/docs/news/20141118_676509.html, 2015 4 10 2), 5, http://www. ieice-hbkb.org/portal/doc_s2_05.html, 2015 4 5 3),, 1,, 2005 4),, http://www.appi.keio.ac.jp/itoh_ group/coursework/pdf/qcn1.pdf, 2015 4 5 5),, http://kincha.kek.jp/kincha032_ takeuchi.pdf#search= %E3%83%9F%E3%83%AB%E3%83%90%E3%83%BC%E3%83%B3+%E5% 85%89%E5%AD%90+%E9%87%8F%E5%AD%90, 2015 4 20 75

44 2015 04 25 Chinami KINOSHITA Kenta TANAKA Abstract 1 2013 2013 10 65 10 2 67% 1) 2 ACC Adaptive Cruise Control 2.1 1 10mm 30G 300GHz 100 200m 2) 2.1.1 FM-CW FM-CW Frequency Modulated-Continuous Wave FM-CW Fig. 1(a) Voltage Controlled Oscillator: VCO FM Fig. 1(b) FFT 3) 76

高 周 波 周 波 数 受 信 波 FM 変 調 された 送 信 波 と 受 信 波 VCO 周 波 数 送 信 波 時 間 時 間 FM 変 調 ビート 信 号 出 力 ミクサ 送 信 アンテナ 受 信 アンテナ 反 射 波 送 信 波 ターゲット ビート 周 波 数 生 成 された ビート 信 号 時 間 (a) (b) Fig. 1 FM-CW ( 3) ) 2.1.2 Fig. 2(a) Fig. 2(b) FFT 3) 強 さ[dB] FFT 強 さ[dB] FFT - 0 + 周 波 数 位 相 差 - 0 + 周 波 数 位 相 差 (a) (b) Fig. 2 ( 3) ) 2.2 890nm 1T 80m 2.2.1 77

2.3 2 80m 2.3.1 Fig. 3 d B f Z 4 Z 左 画 像 右 画 像 d f B 自 車 バックミラー Fig. 3 ( 2) ) 2.3.2 4) 5) 3 Fig. 4 LDM Local Dynamic Map 6) GPS LDM Google LIDAR Light Detection and Ranging 360 3D LDM 78

Signal Phase Type4: Highly dynamic data (vehicles, signal phase) Type3: Transient dynamic data (congestion, road works) Type2: Transient static data (roads infrastructure) Type1: Permanent static data (map data) Vehicles Ego Vehicle Slippy Road Accident Trees Landscape Map Fig. 4 LDM 6) 4 2020 1),, http://www.keishicho.metro.tokyo.jp/kotu/ kourei/koureijiko.htm, : 2015 4 20 2),, http://www. nikkei.com/article/dgxmzo78095290x01c14a0000000/, 2014 3),,,,,, Vol.9, No.2, pp.83 87, 2004 4),, 10, pp.1 4, 2013 5) SUBARU OFFICIAL WEBSITE,, http://www. subaru.jp/eyesight/function/, : 2015 4 19 6),,, LDM,, pp.63 69, 2015 79

44 2015 04 25 Pepper Junichi TANI Nachi TANAKA Abstract ALDEBARAN Robotics 2014 Pepper Pepper Pepper Pepper 1 2014 ALDEBARAN Robotics Pepper 20 Honda ASIMO Pepper Pepper Pepper 2 Pepper NAO AI 12 3 Pepper Pepper 1) Pepper Pepper Pepper ( ) ( ) 80

Pepper 360 12 Pepper 19 8 4 Pepper 4.1 NAO Pepper 3 360 2cm Fig. 1 4.2 Fig. 1 2) Pepper 4 3 3D 1 RGB 2 2 2 2 2 6 2 Fig. 2 Pepper 頭 部 タッチセンサー 3 RGBカメラ 2 a マイク 4 3Dセンサー 1 赤 外 線 センサー 2 ジャイロセンサー 2 手 部 タッチセンサー 2 レーザーセンサー 6 ソナーセンサー 2 バンパーセンサー 3 Fig. 2 1) 4.3 Pepper 81

4.4 Pepper 3) Pepper Pepper! Pepper! Pepper Fig. 3. Fig. 3 4) 4.5 AI Pepper Pepper AI AI 5) ( ) AI AI 2 1 Pepper Pepper Pepper Pepper 2 Pepper Pepper Pepper Pepper AI Pepper Pepper 82

Fig. 4 AI Fig. 4 AI 1) 5 Pepper LTE Pepper Pepper Pepper Pepper AI Pepper 6 Pepper 7 Pepper AI 1) Softbank pepper, http://www.softbank.jp/robot/products/, 2015 4 6 2) Dances with pepper, http://pepper.ohwada.jp/archives/13, 2015 4 7. 3) pepper pepper ai, http://robot-times.jp/, 2015 4 7 4),, 1,, 2011 5), 2, 1,, 2002 83

44 2015 04 25 Ryota OBANA Takuma SATO Abstract GPS Global Positioning System GIS Geographic Information System 1 UAV(Unmanned Aerial Vehicle) GPS GIS DJI IS1SRC Parrot PF725140 1916 1950 1991 D dangerous dirty dull 2011 3 RQ-4 Global Hawk Amazon GPS GIS 2 GPS 1) 84

飛 行 モニタ 飛 行 計 画 グランドステーション 移 動 指 示 無 人 航 空 機 内 部 機 能 飛 行 計 画 目 標 位 置 方 向 GPS INS 高 度 計 対 流 速 度 計 自 機 位 置 姿 勢 指 定 目 標 位 置 姿 勢 指 定 駆 動 系 航 法 センサ 外 乱 Fig. 1 3 4 GPS GIS 3.1 GPS GPS GPS GIS 2) 3.2 GIS Fig. 3 GIS A B AB state.1 A state.2 GIS B state.3 B 85

Fig. 2 A B AB GIS GIS Point A Point A Point B 1m Fig. 3 A B GPS 3 GIS 2 3) 4 4.1 GIS GIS 86

GIS Fig. 4 GIS 4.2 GIS SIFT Scale Invariant Feature Transform Fig. 5 4) 87

Fig. 5 4.3 Amazon 2013 Amazon Prime Air 30 2015 30 5 ( 2.3kg) 8 Amazon 86 5) 5 AED AED 6 GPS GIS GIS 1),,, pp.56 59, 2012 88

2),, http://www.kawada.co.jp/technology/ gihou/pdf/vol32/3201_04_05.pdf, 2015 4 7 3),,,, Vol.26, No.8, pp.905 912, 2008 4),,,, 2003 5) Amazon.com, Amazon prime air, http://www.amazon.com/b?node=8037720011, 2015 4 20. 89

44 2015 04 25 Hiroki KIMPARA Yudai GOTO Abstract,.1985 NTT 1)., 1 Modulation AM Amplitude Modulation FM Frequency Modulation AM FM FM FM 1 0. 2 Fig.1 FM BPSK QPSK QAM modulating signal modulated signal Fig 2 90

Fig. 1 Fig. 2 91

2.1 FM FM FM 50Hz 15000Hz, FM FM. 2.2 BPSK (Binary Phase Shift Keying) BPSK BPSK 1 1 BPSK Fig 3 BPSK. Fig. 3 BPSK 2). 2.3 QPSK Quadrature Phase Shift Keying QPSK 2 BPSK 2 BPSK QPSK 1 2bit BPSK 2 QPSK Fig 4 SP 2 BPSK QPSK 92

Fig. 4 QPSK 2). 2.4 QAM QAM 16QAM 2 SP 2 SP DAC digital to analog converter 1 4 2 2 64QAM 2 8 (2 3 ) 256QAM 4 1 16 (2 4 ) 3 1 FM 2 BPSK, 3 BPSK QPSK 3.5 QPSK 16QAM 3.9 (3.9 ) 4 QPSK 16QAM 64QAM. Table. 1 3) 4). Table. 1 3). 4). 1 FM, 2 BPSK kbit/s 3 BPSK,QPSK 384kbit/s 2G 3.5 QPSK,16QAM 14Mbit/s 3 5. 3.9 QPSK,16QAM,64QAM 100Mbit/s LTE 4 5). QPSK,16QAM,64QAM 1Gbit/s 1960 1.. 2 3 2 3 9, PC 93

4 1 3. 4 IMT-Advanced 3.9.. 5. 1),,, pp.245 251, April 2002 2),, http://www.dengyo.com/labo/kouza/radio03.html, 2015.4.14 3), 104, http://www.arib.or.jp/osirase/seminar/no104konwakai.pdf, 2015.4.14 4),, http://kogures.com/hitoshi/history/keitai-denwa/index.html, 2015.4.14 5),, http://www.circuitdesign.jp/jp/technical/modulation/modulation m ain.asp, 2015.4.14 94

44 2015 04 25 Windows 10 Naoya YAMAGUCHI Tomoyuki HIROYASU Jun NISHIDA Abstract Windows 10 Cortana Windows Hello PC Aero Snap 1 Windows (OS) OS PC Windows 1985 Windows 1.01 1995 Windows 95 Windows 98 Me NT 2000 XP Vista 7 8 8 1 Windows 2015 Windows 10( :Threshold) 2 Windows 10 Windows 10 :1GB (PC32 ) 2GB (PC64 ) 2MB ( ) :DirectX 9.0 GPU :16GB (PC32 ) 20GB (PC64 ) 4GB ( ) :800 x 600 (PC) 800 x 480 2560 x 2048( ) :8 (PC) 3 7.99 ( ) Windows 10 PC OS OS Windows 10 1 PC OS One Windows OS 1 OS Windows 7 8.1 Windows 10 1 Windows 10 PC ( ) Windows 8 3 Windows 10 3.1 Windows 8 and Windows 7 PC Windows 8.1 Windows 10 Windows 10 Fig. 2 Windows7 Windows 8 95

(a) Windows 7 (b) Windows 8 Fig. 1 Windows 7 Windows 8 ( 1) 2) ) Fig. 2 Windows 10 ( 3) ) 3.2 Cortana OS Windows Phone Windows Phone 8.1 Cortana Windows 10 Cortana 4) 3.3 Project Spartan Internet Explorer Trident Edge Project Spartan 5) Windows 10 Project Spartan 96

Trident Edge 6) Project Spartan Internet Explorer 2 1 Web 2 web Fig. 3 (a) (b) Fig. 3 ( 7) ) 3.4 Aero Snap Windows 7 Aero Snap Windows 7 and 8) 2 Fig. 4 Windows 10 4 Fig. 4 Windows 7 Aero Snap ( 8) ) 4 4.1 Windows 10 1 1 97

1 Windows 10 Fig. 5 6) Aero Snap (a) (b) Fig. 5 ( 9) ) 4.2 Windows Hello Windows 10 Windows Hello Windows Hello 10) 5 Windows 10 OS Windows Phone 8.1 Cortana Aero Snap Windows 7 Project Spartan Windows Hello One Windows OS 1) http://image.search.yahoo.co.jp/search?tt=c&ei=utf-8&fr=sfp_as&aq=-1&oq= &p=windows7+%e3%82%b9%e3%82%bf%e3%83%bc%e3%83%88%e3%83%a1%e3%83%8b%e3% 83%A5%E3%83%BC&meta=vc%3D#mode%3Ddetail%26index%3D4%26st%3D0/ 2015. 2) http://image.search.yahoo.co.jp/search?tt=c&ei=utf-8&fr=sfp_as&aq=6&oq= window&p=windows+8&meta=vc%3d#mode%3ddetail%26index%3d0%26st%3d0/ 2015. 3) http://image.search.yahoo.co.jp/search?p=windows+10&aq=1&oq=windows&ei= UTF-8#mode%3Ddetail%26index%3D0%26st%3D0/ 2015. 4) Windows 10 cortana, http://gigazine.net/news/ 20141208-windows-10-cortana/, 2015 5) J. Niino, edge project spartan ie, http://www.publickey1.jp/blog/ 15/edgeproject_spartanie11.html/, 2015 6), project spartan windows weekly report, http://news.mynavi.jp/articles/2015/02/02/ windows10report/, 2015 7), Windows 10 spartan, http://www. buildinsider.net/web/spartan/01/, 2015 98

8) Microsoft atlife, http://www.microsoft.com/ja-jp/atlife/tips/archive/ windows/tips/250.aspx/, 2015. 9), Ascii.jp, http://ascii.jp/elem/000/000/982/982103//, 2015. 10) SECURE, Secure, http://www.secureinc.co.jp/securityqa/answer02-01-06. html/, 2015. 99

44 2015 04 25 AppleWatch Souhei Arita Tomo Hiroyasu Yudai goto Abstract, Hand Free. Apple AppleWatch AppleWatch 1,. 122,.,,,. AppleWatch 2015 4 24.. 2 AppleWatch 2.1 AppleWatch Apple. iphone ipad. 42mm,,,.,. AppleWatch 4 LED GPS,. 1) 2.2 iphone.,.,,. iphone iphone iphone.applewatch.... 100

,,.,.,,siri,,,,. iphone AppleWatch. 2.3 Apple (Project Capacitive),,, IC. (ITO) 2. 2.. 2) 2.4 AppleWatch AppleWatch,. Apple.,.,,.,1 4 0.1., (PET, 1,., ( ).. 8 AD. 3, 4) xyz 2.5 AppleWatch 4,. (photoplethysmography,.,. AppleWatch. (,,. 5). 2.6 Taptic Engine 2.7? taptic Engine iphone,applewatch. AppleWatch. 101

. AppleWatch.AppleWatch. 3 AppleWatch,,.AppleWatch iphone GPS iphone.,,. 4, AppleWatch AppleWatch. iphone AppleWatch,. 1) 2) 3) 4) 5) 1) Apple, Apple watch, https://www.apple.com/jp/watch/, 2015/04/07. 2) EDNjapan,, http://ednjapan.com/edn/articles/1206/20/news087.html, 2015/04/13 3),, http://www.kamata.co.jp/html/pressure/, 2015/04/13 4),, http://www.nissha.com/products/dev/input/force.html, 2015/04/13 5),, www.jcangiology.org/journal/pdf/20054505/329.pdf, 2015/04/17 102