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- ゆみか きちや
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1 Vol.5, No.1, 25 April 2015 Monthly Lecture Meeting Published by the Medical Information System Laboratory of Doshisha University, Kyotanabe, Japan
2
3 Medical Information System Laboratory Monthly Lecture Meeting Contents The wireless capsule endoscopy IT Deep Learning DPC NDB
4 Pepper Windows AppleWatch
5
6 Tomonori ISHIHARA Izumi ISHIDA Abstract ICT ICT ICT ( Information and Communication Technology ) ICT (Artificial Intelligence) AI ICT AI AI ( ) ICT OCR(Optical Character Recoqnition) IC Fig1 ICT IT 1) 2 CPU AI 1
7 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
8 IBM 2 Deep Blue 1997 Deep Blue IBM Watoson IBM Facebook IT Watson IBM RS/6000SP DeepBlue DeepBlue DeepBlue DeepBlue AI Fig. 3 Deep Blue( 3) ) DeepBlue Fig4 4) 3
9 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
10 Fig. 6 Watson ( ) Google Google ) 5 2) 5
11 5.1 ALS ) 6 6) AI 6
12 7 google 1) Ray Kurzweil,,, NHK, ) 21 ( ), ) IBMJapan, Watson news, ),, ), 1, ), significance.html,
13 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
14 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
15 HF-PIP High Frame-rate Programmable Illumination Projector HF-PIP LED Hz LED (LED-AP 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 LED-AP LED Aruduino GRP 4 ALS Fig. 3 Tobbi Technology C15Eye 4) Fig. 4 Tobii X
16 Fig. 3 C15Eye 4) Fig. 4 Tobii X2 60 4) 5 6 1), article/keyword/ /351671/, ), jp/makoto/articles/1403/26/news045.html, ),,, Vol.18, No.19, pp.1 8, ) Tobii, 20/907,
17 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
18 2) 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
19 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 CP-1223 I-4PPM JEITA 4PPM I-4PPM SC-I-4PPM [ms] SC-I-4PPM (Subcarrier Inverted 4 Pulse Position Modulation) SC-I-4PPM Fig. 3(f) 4PPM I-4PPM SC-I-4PPM 14
20 (a) ASK (b) FSK (c) PSK (d) 4PPM (e) I-4PPM Fig. 3 6) (f) SC-I-4PPM 4 7) (VLCC) [km] 1022[bps] 1[km] 1200[bps] 8) LED ITS(Intelligent Transport Systems: ) 2) LED LED LED LED 9) 15
![1 (VLCC) 2008 10 2[km] 1022[bps] 1[km] 1200[bps] 8)](/docs-images/41/22631023/images/page_20.jpg "2 3 5 LED ITS(Intelligent Transport Systems: ) 2)")
21 6 LED LED 1), ), LED,, Vol.31, No.10, pp , ),, LED,, Vol.31, No.10, pp , ), ) CMOS, cmos/, ),, -IC RF UWB ZigBee, 1 1,, ) LED, ), net/pr/ pdf, ),,,, C (, Vol.133, No.5, pp ,
22 The wireless capsule endoscopy Tomoka KATAYAMA Kenichi TAKI Abstract 1 (The wireless capsule endoscopy) ) 2 11mm 26mm 2) 2.1 LED(Light Emitting Diode) Fig. 1 17
23 CMOS(Complementary Metal Oxide Semiconductor) CCD(Charge Coupled Device) CMOS CCD DSP Digital Signal Processor) CMOS CCD 41 CMOS CCD CCD CCD CMOS CMOS LED LED LED 3) MHz 4, 5) Fig. 2 LED 8 2, 6) Fig. 1 ( 7) ) Fig. 2 ( 8) ) 18
24 mm 11mm 30 2) Fig X , 10) Fig. 3 ( 9) ) 19
25 ) Fig ml MRI(magnetic resonance imaging) 1/100 11) XYZ 3 7) (a) ( 11) ) (b) ( 12) ) Fig. 4 20
26 4.2 11mm 24mm Fig mm 10mm , 14). Fig. 5 ( 13) ) 5 21
27 ),, nikkeibp.co.jp/article/news/ /354700/?st=ndh, ),,,, ), LED,,, ),,,, Vol.5, No.1, pp.26 30, ),,, Vol.62, No.4, pp , ),,, Vol.120, No.3, pp , ) Olympas,, endoscope-closeup/endoscope-technology/et_06.html, ),, cstp/5minutes/012/index3.html, ) COVIDIEN,, cd/capsule/03.html, ), 3d, co.jp/041products/2012/p html, ), ( ), mu-frontier.com/1106.html, ) W. Japan,, capsule_endoscopes/002.html, ),, Research/Research.html, ),, news/150303/rgn n1.html,
28 Yoshimi SAKAGUCHI Satoshi SHIGARAKI Abstract 1 10 µw 1 (Energy Harvesting) ( ) 1) IC Wireless Sensor Networks WSN 2) QOL (Quality of Life) ID 2 ( )
29 1 10 µw Table. 1 Table. 1 [µw/cm 2 ] Fig. 1 ( ) Fig. 2 (Rectifying antenna; Rectenna) Fig. 1 ( 1) ) 24
30 Fig. 2 ( )( 1) ) 90 % RFID Fig. 3 p Si n Si pn pn p n n p n p Fig. 3 ( 1) ) 25
31 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) ) 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
32 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 % kg 67 W Fig. 8(a) Fig. 8(b) 27
33 Fig. 8 ( 1) ) (electret) Hz 0.1 µw 0.1 V 100 Hz. 2 cm 30 Hz 100 µw 1) 2.2 (Structural Health Monitoring; SHM) 28
34 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
35 3.1 UHF (Ultra High Frequency) MHz RFID (Radio Frequency Identification) RF RFID JIS RFID RF 100 m / RF UHF RFID ISO/IEC Type C 30 dbm / / (MS) / UHF RFID RFID / / ( / ) RFID Fig. 9 ( 1) ) Fig MHz 950 MHz RFID ( ) 30
36 2011 ( 23 ) 950 MHz 920 MHz MHz 950 MHz Fig MHz ( 1) ) 950 MHZ Fig MHz 958 MHz 200 khz 33 (30 dbm) khz 29 (10 dbm) 4 29 ( / ) 4 IC 31
37 µw QOL 1),,,, ) (1), com/article/dgxnasfk2901s\_z20c10a /?df=2, ), den-gyo.com/solution/solution10.html, : ), ), 3 LAN,,,
38 Naoya ISHIDA Akiho MURAKAMI Abstract SNS SNS ) - 1) SNS PayPal PayPal PayPal Fig. 1 PayPal Fig. 1 PayPal 33
39 PayPal PayPal PayPal PayPal PayPal PayPal PayPal SNS ) 3 3 3). 2.3 IC IC ICOCA Suica IC 3 SNS Fig. 2 Fig SNS SNS 34
40 3.2 2 PayPal SNS 3 SNS 3 IC 4 LINE Pay Google Wallet LINE Pay LINE Pay LINE LINE 4) Fig LINE Fig. 3 LINE 5) Fig. 3 LINE LINE Pay LINE Pay Fig. 4 LINE Pay LINE Pay LINEPay (LINE STORE) LINE STORE LINE Pay PIN LINE LINE LINE LINE Pay LINE Pay QR 35
41 Fig. 4 LINE Pay 4.2 Google Wallet Google Wallet Google Wallet Google Wallet LINE Pay Google Wallet PIN Fig. 5 Google Wallet Google Wallet NFC (Near Field Communication: NFC) Google Wallet NFC NFC Google Wallet 36
42 5 ID SNS 6 SNS SNS 1), 25 ( ), ), 25, ) WebPay, webpay, /4/13. 4) LINE, Line pay, /4/13 5) line, /4/20 37
43 IT Saki YOSHITAKE Shogo OBUCHI Abstract 1 IT IT ) IT IT IT 2 IT IT 3 IT IT IT IT 38
44 3 IT IT 3.1 IT ph Table. 1 2). Table. 1 [ ] [ ] [ppm] km Panasonic Table. 2 3) 4) Table. 2 [V] [Hz] [W] /60 170/ /60 160/ /60 49/ Ubiquitous Environment Control System:UECS 39
45 Fig. 1 IEEE802.3 Web Web Fig. 2 5) Fig. 3 40
46 3.3 IT Good Agricultual Practice GAP GAP 6) Fig. 4 IT 7) Fig. 5 Panasonic 41
47 TC FUJITSU Technical Computing Solution TC HPC High Performance Computing HPC Web GUI HPC PC 8). 4 IT IT LED Fig
48 Fig IT IT IT 9) 10) 6 IT
49 ICT 11) 2 IT IT IT IT IT IT 1), ) OPUS CO2, ), ), ) UECS, ), AI(Agri-Informatics),, Vol.30, No.2, pp , ),,, Vol.30, No.2, pp , ) TC, ), ),,, Vol.30, No.2, pp , ),,, Vol.30, No.2, pp ,
50 Shuhei YOKOYAMA Ryota TAMURA Abstract 1 1) Online Public Access Catalog: OPAC ) 2) ) 45
51 ) Fig. 1 4)
52 Fig ) )
53 OPAC 1 1 OPAC 1) 6 OPAC OPAC Fig. 3 3) OPAC OPAC OPAC 5) 6.2 CiNii Books CiNii Books (NII)
54 6) 7 OPAC OPAC OPAC Optical Character Reader:OCR) 8 1),,,, ),,,, ),,,, ), : ) opac, : ) Cinii, :
55 Deep Learning Takaya TAMAKI Kenya HANAWA Abstract Deep Learning 1 1 Deep Learning Deep Learning 2006 Hinton % 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
56 b i k + j W i1 b 1 W 13 W j1 b 3 W j2 W b W k2 (a) (b) ( ) Fig. 1 ( ) ( ) (Fig. 2) h k-1 h k h k i j w ij 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
57 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)
58 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) ) 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) Pretraining 3.1 pretraining Fig. 4 Autoencoder pretraining 4). step.1 2 Autoencoder step.2 2 Autoencoder 2 step.3 step Finetuning finetuning pretraining finetuning Fig Autoencoder. x h k x y 4) 53
59 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 ) Fig. 5 Convolutional Neural Network(CNN) ( ) 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 Fig. 6 4) Fig. 6 ( 4) ) 54
60 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 maxpooling 4 4 1/4 8 8 (Fig. 7) 4) Fig. 7 ( 4) ) CNN CNN 4) 4 Deep Learning Deep Learning Deep Learning 2014 Facebook Deep Face ) Audi Nvidia Jack Deep Learning Deep Learning 6) 7) 5 Deep Learning Deep Learning.. 55
61 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 , ) ILSVRC2012, Image net large scale visual recognition challenge 2012, image-net.org/challenges/lsvrc/2012/results.html, ),,, ),, 6,, ) 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, ) Nvidia, How nvidia drive px will help automakers slim down self-driving cars nvidia blog, ) Audi, Audi piloted driving, durch_technik/content/2014/10/piloted-driving.html,
62 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
63 ,., NDB(National Database),.,. Table NDB NDB,,.,. NDB,. DPC.,,. NDB,. 2.2,,,,,,.,,,.,,. 3 DPC DPC, 1,, ,. DPC. DPC. DPC..,,. 2).,., DPC. 3.1 DPC Fig. 1 DPC,14., 14,
64 . JCS JCS(Japan Coma Scale).,,..,...,,. Fig. 1 2) 3.2 DPC DPC DPC..DPC. DPC DPC ,,,,.,,. 2. E. 3. F. E,F,,.,. 4. D DPC. 5. 3,
65 .,. 3.3 DPC DPC. 1.,. 2.,,,,.,, 3.,,. 4.,,,. 3.4 DPC,DPC.,,,,,.,.,.,,,.,,,.,DPC,.DPC,.,,,.,,,.,DPC,.,,,, , 1..,,.,.,,,.,,.,., ID.,. 5,DPC,NDB.DPC,.,,,.,,.,,,, 60
66 ,. 1),,, Vol.16, No.5, pp , ), DPC-, 3,,
67 Yuto OKADA Katsutoshi HAYASHINUMA Abstract NAND 3 3 HDD SSD 1 Electrically Erasable Programmable Read-Only Memory EEPROM EEPROM HDD 1) 1980 EEPROM HDD NAND 2.1 NOR NOR Fig. 1 1 B 2 W Fig. 2 EEPROM Metal Oxide Semiconductor MOS Floating Gate : FG FG FG FG NOR EEPROM NOR NAND 1)
68 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) Fig. 4 a NAND Fig. 4 b 4)
69 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 BiCS 3 Fig 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
70 Fig. 4 3 NAND 5) Fig. 5 BiCS 5) p-bics p-bics p-bics NANDString NANDString Fig. 7 BiCS 6) p-bics p-bics NANDString BiCS 1 2 BiCS 6) Fig ) 5 65
71 Fig. 6 BiCS 6) Fig. 7 p-bics 6) 3.3 3D V-NAND D 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) BiCS V-NAND Fig. 6 Fig. 9 BiCS GB 8) V-NAND Samsung BiCS V-NAND 2 16GB ) 4 HDD SSD NAND HDD SDD HDD HDD SSD 66
72 Fig. 8 6) Fig. 9 3D V-NAND 7) HDD 3 Samsung 3D V-NAND 3 128GB 256GB 512GB 1TB SSD NAND GB 9) 1TB HDD 1 1TB SSD 9 3 NAND BiCS 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
73 Fig. 10 Channel Hole 7) HDD 1), [ ], 5,, ), 3 NAND,, Vol.30, No.1, pp.42 48, ),, 3 NAND,, Vol.1, No.2, pp.ss14 SS17, ) 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, ),,, 3 NAND,, Vol.63, No.2, pp.28 31, ), 3 BiCS,, Vol.66, No.9, pp.16 19, ) J. Elliott, Ushering in the 3D Memory Era with V- NAND, Flash Memory Summit 2013, Vol.1, No.1, pp.1 32, ) 48 3 BiCS, : ) SSD, SSD/jp/html/ssd850pro/overview.html, : ), 3 BiCS,, Vol.64, No.12, pp.56 57, ) 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, : ), 3 NAND 2015,, Vol.1117, pp.81 90,
74 Seiya KATSURADA Shogo OBUCHI Abstract 1 High Performance Computing (HPC) , ),., HPC PC 2 PC ) 3.1.Fig /2. Fig /2 A 1/2 69
75 1/ A 100 B Fig. 3 A 1.B 0 2 3) Fig. 1 Fig. 2 Fig
76 Fig Fig. 4 x 2 x 4) Fig. 4 ( ) Fig a 0 (1) a = 0 (1) Fig (2) a = (2) i exp(i ) Fig. 7 (3) a = cos θ exp(i ) sin θ 0 (3) 2 Fig. 5 a = 0 Fig. 6 a =
77 Fig. 7 a = cos θ exp(i ) sin θ PC Fig. 8 0 y y 180 NOT Fig Fig. 9 x z Fig Fig. 10 NOR 72
78 Fig Fig A 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, 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, C C (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 (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, n 2N+3 PC 2 n 1 73
79 Fig 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
80 5) 8 1), Pc watch 2, http: //pc.watch.impress.co.jp/docs/news/ _ html, ), 5, ieice-hbkb.org/portal/doc_s2_05.html, ),, 1,, ),, group/coursework/pdf/qcn1.pdf, ),, 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,
81 Chinami KINOSHITA Kenta TANAKA Abstract % 1) 2 ACC Adaptive Cruise Control mm 30G 300GHz m 2) FM-CW FM-CW Frequency Modulated-Continuous Wave FM-CW Fig. 1(a) Voltage Controlled Oscillator: VCO FM Fig. 1(b) FFT 3) 76
82 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) ) Fig. 2(a) Fig. 2(b) FFT 3) 強 さ[dB] FFT 強 さ[dB] FFT 周 波 数 位 相 差 周 波 数 位 相 差 (a) (b) Fig. 2 ( 3) ) nm 1T 80m
83 m 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) ) ) 5) 3 Fig. 4 LDM Local Dynamic Map 6) GPS LDM Google LIDAR Light Detection and Ranging 360 3D LDM 78
84 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) ),, kourei/koureijiko.htm, : ),, nikkei.com/article/dgxmzo x01c14a /, ),,,,,, Vol.9, No.2, pp.83 87, ),, 10, pp.1 4, ) SUBARU OFFICIAL WEBSITE,, subaru.jp/eyesight/function/, : ),,, LDM,, pp.63 69,
85 Pepper Junichi TANI Nachi TANAKA Abstract ALDEBARAN Robotics 2014 Pepper Pepper Pepper Pepper ALDEBARAN Robotics Pepper 20 Honda ASIMO Pepper Pepper Pepper 2 Pepper NAO AI 12 3 Pepper Pepper 1) Pepper Pepper Pepper ( ) ( ) 80
86 Pepper Pepper Pepper 4.1 NAO Pepper cm Fig Fig. 1 2) Pepper 4 3 3D 1 RGB 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
87 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
88 Fig. 4 AI Fig. 4 AI 1) 5 Pepper LTE Pepper Pepper Pepper Pepper AI Pepper 6 Pepper 7 Pepper AI 1) Softbank pepper, ) Dances with pepper, ) pepper pepper ai, ),, 1,, ), 2, 1,,
89 Ryota OBANA Takuma SATO Abstract GPS Global Positioning System GIS Geographic Information System 1 UAV(Unmanned Aerial Vehicle) GPS GIS DJI IS1SRC Parrot PF D dangerous dirty dull RQ-4 Global Hawk Amazon GPS GIS 2 GPS 1) 84
90 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 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
91 Fig. 2 A B AB GIS GIS Point A Point A Point B 1m Fig. 3 A B GPS 3 GIS 2 3) GIS GIS 86
92 GIS Fig. 4 GIS 4.2 GIS SIFT Scale Invariant Feature Transform Fig. 5 4) 87
93 Fig Amazon 2013 Amazon Prime Air ( 2.3kg) 8 Amazon 86 5) 5 AED AED 6 GPS GIS GIS 1),,, pp.56 59,
94 2),, gihou/pdf/vol32/3201_04_05.pdf, ),,,, Vol.26, No.8, pp , ),,,, ) Amazon.com, Amazon prime air,
95 Hiroki KIMPARA Yudai GOTO Abstract,.1985 NTT 1)., 1 Modulation AM Amplitude Modulation FM Frequency Modulation AM FM FM FM Fig.1 FM BPSK QPSK QAM modulating signal modulated signal Fig 2 90
96 Fig. 1 Fig. 2 91
97 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
98 Fig. 4 QPSK 2). 2.4 QAM QAM 16QAM 2 SP 2 SP DAC digital to analog converter QAM 2 8 (2 3 ) 256QAM (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 QPSK,16QAM,64QAM 100Mbit/s LTE 4 5). QPSK,16QAM,64QAM 1Gbit/s , PC 93
99 IMT-Advanced ),,, pp , April ),, ), 104, ),, ),, m ain.asp,
100 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 Windows 95 Windows 98 Me NT 2000 XP Vista 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 x 2048( ) :8 (PC) ( ) Windows 10 PC OS OS Windows 10 1 PC OS One Windows OS 1 OS Windows Windows 10 1 Windows 10 PC ( ) Windows 8 3 Windows Windows 8 and Windows 7 PC Windows 8.1 Windows 10 Windows 10 Fig. 2 Windows7 Windows 8 95
101 (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
102 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) ) Windows
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