FIT2013( 第 12 回情報科学技術フォーラム ) I-032 Acceleration of Adaptive Bilateral Filter base on Spatial Decomposition and Symmetry of Weights 1. Taiki Makishi Ch

Size: px
Start display at page:

Download "FIT2013( 第 12 回情報科学技術フォーラム ) I-032 Acceleration of Adaptive Bilateral Filter base on Spatial Decomposition and Symmetry of Weights 1. Taiki Makishi Ch"

Transcription

1 I-032 Acceleration of Adaptive Bilateral Filter base on Spatial Decomposition and Symmetry of Weights 1. Taiki Makishi Chikatoshi Yamada Shuichi Ichikawa Gaussian Filter GF GF Bilateral Filter BF CG [1] BF GF [2] MRI Magnetic Resonance Imaging system CT Coputed Tomography BF BF PSNR BF Adaptive Bilateral Filter ABF ABF GF GF BF BF [3] ABF BF ABF [4] [5] 2 ABF ABF ABF GPU CPU GPU Graphics Processing Unit BF bi lateral f(i, j) b(i, j) BF (1) b(i, j) = f(i + x, j + y)w (i, j : x, y) W (i, j : x, y) W (i, j : x, y) = e α x2 +y 2 e β f(i,j)2 f(i+x,j+y) 2 (1) (1) exp( α(x 2 + y 2 )) GF BF GF BF [1] (1) (2) (3) α β 2 (4) (5) GF GF 311

2 2.2. ABF BF ABF b(i, j) f(i, j) BF b(i, j) BF GF GF g(i, j) 2 g(i, j) = f(i + x, j + y)e ˆα x2 +y 2 e ˆα x2 +y 2 (2) 3: BF 4: ABF [pixel] 5 8 GF ˆα q (W (i, j : x, y) e ˆα x2 +y 2 ) = 0 (3) x= q BF GF ˆα ABF b (i,j) 4 5: b (i,j) = { f(i, j) b(i, j) [b(i, j) f(i, j)][g(i, j) f(i, j)] < 0 otherwise (4) (4) BF GF BF b(i, j) f(i, j) [3] 512*512[pixel] 5 ABF BF 1 2 BF ABF 3 4 6: 1: 2: 7: BF [pixel] 5 BF ABF 312

3 ω α (x, y) = exp( α x 2 + y 2 ) ω α (x, y) = e αx2 e βy2 (5) 8: ABF ω β (f(i, j), f(i+x, j+ y)) = exp( β f(i, j) + f(i + x.j + y) 2 ) BF 3 Unsharp Masking: UM [pixel] 10 e β f(i,j) f(i+x,j+y) 2 = e β(d2 i,j(x) D 2 i+x,y(y) 2D i,j (x)d i+x,y (y)) D i,j (x) = f(i, j) f(i + x, j) D i+x,j (y) = f(i + x, y) f(i + x, j + y) (6) exp( 2βD i,j (x)d i+x,y (y)) 9: BF UM e β f(i,j) f(i+x,j+y) 2 = (7) e β(d2 i,j(x) D 2 i+x,y(y)) 2βD i,j (x)e βd2i,j(x) D i+x,y (y)e βd2 i+x,y(y) +2β 2 D 2 i,j(x)e βd2i,j(x) D 2 i+x,y(y)e βd2 i+x,y(y) + (8) x y (1) b i,j = s i,j t i,j (8) 10: BF [pixel] BF s i,j = t i,j = f(i + x, j + y) (9) W (i, j : x, y) W (i, j : x, y) (10) (8) y x 313

4 s i,j = q x= q e αx2 β(f(i,j) f(i+x,j)) 2 (11) (g i+x,j 2βD i,j (x)g i+x,j ) q t i,j = e αx2 β(f(i,j) f(i+x,j)) 2 (12) x= q (h i+x,j 2βD i,j (x)h i+x,j ) g i,j G i,j h i,j H i,j g i,j = e αy2 β(f(i,j) f(i,j+y)) 2 (13) f(i, j + y) G i,j = e αy2 β(f(i,j) f(i,j+y)) 2 (14) h i,j = H i,j = (f(i, j) f(i, j + y))f(i, j + y) e αy2 β(f(i,j) f(i,j+y)) 2 (15) e αy2 β(f(i,j) f(i,j+y)) 2 (16) (f(i, j) f(i, j + y)) ABF GF BF GF 17 Step 2) Step 1 s i,j t i,j S i,j T i,j [4] 3.2. (1) N f(i, j) f(i N, j N) f(i + N, j + N) f(i, j) f(i + x, j + y) f(i + 1 N, j + 1 N) f(i N, j N) f(i, j) f(i + 1 N, j + 1 N) f(i, j) [5] : C/C++ Visual Strudio 2010 OpenCV 2.4.3/CUDA 5.0 OS CPU GPU RAM Windows 7 64bit Core i7 2640M 2.8GHz Geforce GTX295 DDR3 8GB S i,j = T i,j = g i,j = S i,j T i,j (17) x= ω x= ω e αx2 P i+x,j (18) e αx2 Q i+x,j (19) ABF α β α = β = *16 512*512[pixel] lena ABF CPU 11 P i,j Q i,j P i,j = Q i,j = e αy2 f(i, j + y) (20) y= ω y= ω e αy2 (21) Step 1) g i,j G i,j h i,j H i,j P i,j Q i,j 11: 314

5 16*16 64*64 CPU GPU 512*512[pixel] 6.15[s] 0.48[s] 1/12 PSNR PSNR 30dB 16* *4096[pixel] ABF CPU GPU 12 13: CT 14: 12: CPU GPU 4096*4096[pixel] GPU 4.44[s] GPU 0.84[s] GPU CPU 1/5 16*16 512*512[pixel] GPU CPU GPU CPU CPU GPU CPU CT 13 CT 14 BF 15 ABF 16 15: BF 16: ABF

6 : BF 1/12 PSNR 30dB ABF GPU CPU 1/5 CPU GPU 1 CPU GPGPU FPGA JSPS CT 18: ABF GF GF BF CG BF GF [2] BF ABF ABF BF ABF ABF [1] 8 62(8) pp [2], 54, p [3] Vol J93-D No. 1 pp [4], Vol J89-A No. 7 pp [5],, Vol J93-D. No. 2 p

07-二村幸孝・出口大輔.indd

07-二村幸孝・出口大輔.indd GPU Graphics Processing Units HPC High Performance Computing GPU GPGPU General-Purpose computation on GPU CPU GPU GPU *1 Intel Quad-Core Xeon E5472 3.0 GHz 2 6 MB L2 cache 1600 MHz FSB 80 GFlops 1 nvidia

More information

26102 (1/2) LSISoC: (1) (*) (*) GPU SIMD MIMD FPGA DES, AES (2/2) (2) FPGA(8bit) (ISS: Instruction Set Simulator) (3) (4) LSI ECU110100ECU1 ECU ECU ECU ECU FPGA ECU main() { int i, j, k for { } 1 GP-GPU

More information

untitled

untitled A = QΛQ T A n n Λ Q A = XΛX 1 A n n Λ X GPGPU A 3 T Q T AQ = T (Q: ) T u i = λ i u i T {λ i } {u i } QR MR 3 v i = Q u i A {v i } A n = 9000 Quad Core Xeon 2 LAPACK (4/3) n 3 O(n 2 ) O(n 3 ) A {v i }

More information

1 911 9001030 9:00 A B C D E F G H I J K L M 1A0900 1B0900 1C0900 1D0900 1E0900 1F0900 1G0900 1H0900 1I0900 1J0900 1K0900 1L0900 1M0900 9:15 1A0915 1B0915 1C0915 1D0915 1E0915 1F0915 1G0915 1H0915 1I0915

More information

GPGPU

GPGPU GPGPU 2013 1008 2015 1 23 Abstract In recent years, with the advance of microscope technology, the alive cells have been able to observe. On the other hand, from the standpoint of image processing, the

More information

2012 M

2012 M 2012 M0109218 2012 : M0109218 36 1 1 1.1............................. 1 1.2................................. 5 2 6 2.1................... 6 2.2................ 8 2.3............ 12 3 15 3.1...................

More information

GPU GPU CPU CPU CPU GPU GPU N N CPU ( ) 1 GPU CPU GPU 2D 3D CPU GPU GPU GPGPU GPGPU 2 nvidia GPU CUDA 3 GPU 3.1 GPU Core 1

GPU GPU CPU CPU CPU GPU GPU N N CPU ( ) 1 GPU CPU GPU 2D 3D CPU GPU GPU GPGPU GPGPU 2 nvidia GPU CUDA 3 GPU 3.1 GPU Core 1 GPU 4 2010 8 28 1 GPU CPU CPU CPU GPU GPU N N CPU ( ) 1 GPU CPU GPU 2D 3D CPU GPU GPU GPGPU GPGPU 2 nvidia GPU CUDA 3 GPU 3.1 GPU Core 1 Register & Shared Memory ( ) CPU CPU(Intel Core i7 965) GPU(Tesla

More information

untitled

untitled A = QΛQ T A n n Λ Q A = XΛX 1 A n n Λ X GPGPU A 3 T Q T AQ = T (Q: ) T u i = λ i u i T {λ i } {u i } QR MR 3 v i = Q u i A {v i } A n = 9000 Quad Core Xeon 2 LAPACK (4/3) n 3 O(n 2 ) O(n 3 ) A {v i }

More information

-1-1 1 1 1 1 12 31 2 2 3 4

-1-1 1 1 1 1 12 31 2 2 3 4 2007 -1-1 1 1 1 1 12 31 2 2 3 4 -2-5 6 CPU 3 Windows98 1 -3-2. 3. -4-4 2 5 1 1 1 -5- 50000 50000 50000 50000 50000 50000 50000 50000 50000 50000-6- -7-1 Windows 2 -8-1 2 3 4 - - 100,000 200,000 500,000

More information

iphone GPGPU GPU OpenCL Mac OS X Snow LeopardOpenCL iphone OpenCL OpenCL NVIDIA GPU CUDA GPU GPU GPU 15 GPU GPU CPU GPU iii OpenMP MPI CPU OpenCL CUDA OpenCL CPU OpenCL GPU NVIDIA Fermi GPU Fermi GPU GPU

More information

main.dvi

main.dvi PC 1 1 [1][2] [3][4] ( ) GPU(Graphics Processing Unit) GPU PC GPU PC ( 2 GPU ) GPU Harris Corner Detector[5] CPU ( ) ( ) CPU GPU 2 3 GPU 4 5 6 7 1 toyohiro@isc.kyutech.ac.jp 45 2 ( ) CPU ( ) ( ) () 2.1

More information

23 Fig. 2: hwmodulev2 3. Reconfigurable HPC 3.1 hw/sw hw/sw hw/sw FPGA PC FPGA PC FPGA HPC FPGA FPGA hw/sw hw/sw hw- Module FPGA hwmodule hw/sw FPGA h

23 Fig. 2: hwmodulev2 3. Reconfigurable HPC 3.1 hw/sw hw/sw hw/sw FPGA PC FPGA PC FPGA HPC FPGA FPGA hw/sw hw/sw hw- Module FPGA hwmodule hw/sw FPGA h 23 FPGA CUDA Performance Comparison of FPGA Array with CUDA on Poisson Equation (lijiang@sekine-lab.ei.tuat.ac.jp), (kazuki@sekine-lab.ei.tuat.ac.jp), (takahashi@sekine-lab.ei.tuat.ac.jp), (tamukoh@cc.tuat.ac.jp),

More information

2010 : M0107189 3DCG 3 (3DCG) 3DCG 3DCG 3DCG S

2010 : M0107189 3DCG 3 (3DCG) 3DCG 3DCG 3DCG S 2010 M0107189 2010 : M0107189 3DCG 3 (3DCG) 3DCG 3DCG 3DCG S 1 1 1.1............................ 1 1.2.............................. 4 2 5 2.1............................ 5 2.2.............................

More information

研修コーナー

研修コーナー l l l l l l l l l l l α α β l µ l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l

More information

i IHE IHE-J HIS RIS PACS CT CT CT

i IHE IHE-J HIS RIS PACS CT CT CT 16 Marlporl CT(Computed Tomography: ) 4 CT CT PC CT PC CT CT PC 2 Ruby CT C 1 CT 170.4 153.6 37 6 102 5 PC i 1 1 2 3 2.1 IHE IHE-J.................. 3 2.2 HIS RIS PACS................ 3 3 2 CT 5 3.1 2

More information

2 4 8 13 18 24 29 34 39 44 46 48 1 2 3 4 5 6 7 18 11 11 15 10 16 10 8 9 10 1. 2. 3. 4. 5. 6. 7. 1. 2. 3. 4. 5. 6. 7. 11 1. 2. 3. 4. 5. 6. 7. 12 13 18 12 11 16 25 18 00 CPU Central Processing Unit 14 MUST-CAN-WILL

More information

GPU n Graphics Processing Unit CG CAD

GPU n Graphics Processing Unit CG CAD GPU 2016/06/27 第 20 回 GPU コンピューティング講習会 ( 東京工業大学 ) 1 GPU n Graphics Processing Unit CG CAD www.nvidia.co.jp www.autodesk.co.jp www.pixar.com GPU n GPU ü n NVIDIA CUDA ü NVIDIA GPU ü OS Linux, Windows, Mac

More information

10D16.dvi

10D16.dvi D IEEJ Transactions on Industry Applications Vol.136 No.10 pp.686 691 DOI: 10.1541/ieejias.136.686 NW Accelerating Techniques for Sequence Alignment based on an Extended NW Algorithm Jin Okaze, Non-member,

More information

Input image Initialize variables Loop for period of oscillation Update height map Make shade image Change property of image Output image Change time L

Input image Initialize variables Loop for period of oscillation Update height map Make shade image Change property of image Output image Change time L 1,a) 1,b) 1/f β Generation Method of Animation from Pictures with Natural Flicker Abstract: Some methods to create animation automatically from one picture have been proposed. There is a method that gives

More information

2008 : 80725872 1 2 2 3 2.1.......................................... 3 2.2....................................... 3 2.3......................................... 4 2.4 ()..................................

More information

1 GPU GPGPU GPU CPU 2 GPU 2007 NVIDIA GPGPU CUDA[3] GPGPU CUDA GPGPU CUDA GPGPU GPU GPU GPU Graphics Processing Unit LSI LSI CPU ( ) DRAM GPU LSI GPU

1 GPU GPGPU GPU CPU 2 GPU 2007 NVIDIA GPGPU CUDA[3] GPGPU CUDA GPGPU CUDA GPGPU GPU GPU GPU Graphics Processing Unit LSI LSI CPU ( ) DRAM GPU LSI GPU GPGPU (I) GPU GPGPU 1 GPU(Graphics Processing Unit) GPU GPGPU(General-Purpose computing on GPUs) GPU GPGPU GPU ( PC ) PC PC GPU PC PC GPU GPU 2008 TSUBAME NVIDIA GPU(Tesla S1070) TOP500 29 [1] 2009 AMD

More information

1 3DCG [2] 3DCG CG 3DCG [3] 3DCG 3 3 API 2 3DCG 3 (1) Saito [4] (a) 1920x1080 (b) 1280x720 (c) 640x360 (d) 320x G-Buffer Decaudin[5] G-Buffer D

1 3DCG [2] 3DCG CG 3DCG [3] 3DCG 3 3 API 2 3DCG 3 (1) Saito [4] (a) 1920x1080 (b) 1280x720 (c) 640x360 (d) 320x G-Buffer Decaudin[5] G-Buffer D 3DCG 1) ( ) 2) 2) 1) 2) Real-Time Line Drawing Using Image Processing and Deforming Process Together in 3DCG Takeshi Okuya 1) Katsuaki Tanaka 2) Shigekazu Sakai 2) 1) Department of Intermedia Art and Science,

More information

STEP1 STEP3 STEP2 STEP4 STEP6 STEP5 STEP7 10,000,000 2,060 38 0 0 0 1978 4 1 2015 9 30 15,000,000 2,060 38 0 0 0 197941 2016930 10,000,000 2,060 38 0 0 0 197941 2016930 3 000 000 0 0 0 600 15

More information

1

1 1 2 3 4 5 6 7 8 9 0 1 2 6 3 1 2 3 4 5 6 7 8 9 0 5 4 STEP 02 STEP 01 STEP 03 STEP 04 1F 1F 2F 2F 2F 1F 1 2 3 4 5 http://smarthouse-center.org/sdk/ http://smarthouse-center.org/inquiries/ http://sh-center.org/

More information

P072-076.indd

P072-076.indd 3 STEP0 STEP1 STEP2 STEP3 STEP4 072 3STEP4 STEP3 STEP2 STEP1 STEP0 073 3 STEP0 STEP1 STEP2 STEP3 STEP4 074 3STEP4 STEP3 STEP2 STEP1 STEP0 075 3 STEP0 STEP1 STEP2 STEP3 STEP4 076 3STEP4 STEP3 STEP2 STEP1

More information

1 4 1.1........................................... 4 1.2.................................. 4 1.3................................... 4 2 5 2.1 GPU.....

1 4 1.1........................................... 4 1.2.................................. 4 1.3................................... 4 2 5 2.1 GPU..... CPU GPU N Q07-065 2011 2 17 1 1 4 1.1........................................... 4 1.2.................................. 4 1.3................................... 4 2 5 2.1 GPU...........................................

More information

IPSJ SIG Technical Report Vol.2013-ARC-203 No /2/1 SMYLE OpenCL (NEDO) IT FPGA SMYLEref SMYLE OpenCL SMYLE OpenCL FPGA 1

IPSJ SIG Technical Report Vol.2013-ARC-203 No /2/1 SMYLE OpenCL (NEDO) IT FPGA SMYLEref SMYLE OpenCL SMYLE OpenCL FPGA 1 SMYLE OpenCL 128 1 1 1 1 1 2 2 3 3 3 (NEDO) IT FPGA SMYLEref SMYLE OpenCL SMYLE OpenCL FPGA 128 SMYLEref SMYLE OpenCL SMYLE OpenCL Implementation and Evaluations on 128 Cores Takuji Hieda 1 Noriko Etani

More information

( )

( ) ) ( ( ) 3 15m t / 1.9 3 m t / 0.64 3 m ( ) ( ) 3 15m 3 1.9m / t 0.64m 3 / t ) ( β1 β 2 β 3 y ( ) = αx1 X 2 X 3 ( ) ) ( ( ) 3 15m t / 1.9 3 m 3 90m t / 0.64 3 m ( ) : r : ) 30 ( 10 0.0164

More information

,., ping - RTT,., [2],RTT TCP [3] [4] Android.Android,.,,. LAN ACK. [5].. 3., 1.,. 3 AI.,,Amazon, (NN),, 1..NN,, (RNN) RNN

,., ping - RTT,., [2],RTT TCP [3] [4] Android.Android,.,,. LAN ACK. [5].. 3., 1.,. 3 AI.,,Amazon, (NN),, 1..NN,, (RNN) RNN DEIM Forum 2018 F1-1 LAN LSTM 112 8610 2-1-1 163-8677 1-24-2 E-mail: aoi@ogl.is.ocha.ac.jp, oguchi@is.ocha.ac.jp, sane@cc.kogakuin.ac.jp,,.,,., LAN,. Android LAN,. LSTM LAN., LSTM, Analysis of Packet of

More information

Microsoft PowerPoint - GPU_computing_2013_01.pptx

Microsoft PowerPoint - GPU_computing_2013_01.pptx GPU コンピューティン No.1 導入 東京工業大学 学術国際情報センター 青木尊之 1 GPU とは 2 GPGPU (General-purpose computing on graphics processing units) GPU を画像処理以外の一般的計算に使う GPU の魅力 高性能 : ハイエンド GPU はピーク 4 TFLOPS 超 手軽さ : 普通の PC にも装着できる 低価格

More information

Real AdaBoost HOG 2009 3 A Graduation Thesis of College of Engineering, Chubu University Efficient Reducing Method of HOG Features for Human Detection based on Real AdaBoost Chika Matsushima ITS Graphics

More information

3-2 PET ( : CYRIC ) ( 0 ) (3-1 ) PET PET [min] 11 C 13 N 15 O 18 F 68 Ga [MeV] [mm] [MeV]

3-2 PET ( : CYRIC ) ( 0 ) (3-1 ) PET PET [min] 11 C 13 N 15 O 18 F 68 Ga [MeV] [mm] [MeV] 3 PET 3-1 PET 3-1-1 PET PET 1-1 X CT MRI(Magnetic Resonance Imaging) X CT MRI PET 3-1 PET [1] H1 D2 11 C-doxepin 11 C-raclopride PET H1 D2 3-2 PET 0 0 H1 D2 3-1 PET 3-2 PET ( : CYRIC ) ( 0 ) 3-1-2 (3-1

More information

Emacs ML let start ::= exp (1) exp ::= (2) fn id exp (3) ::= (4) (5) ::= id (6) const (7) (exp) (8) let val id = exp in

Emacs ML let start ::= exp (1) exp ::= (2) fn id exp (3) ::= (4) (5) ::= id (6) const (7) (exp) (8) let val id = exp in Emacs, {l06050,sasano}@sic.shibaura-it.ac.jp Eclipse Visual Studio Standard ML Haskell Emacs 1 Eclipse Visual Studio variable not found LR(1) let Emacs Emacs Emacs Java Emacs JDEE [3] JDEE Emacs Java 2

More information

WebGL OpenGL GLSL Kageyama (Kobe Univ.) Visualization / 57

WebGL OpenGL GLSL Kageyama (Kobe Univ.) Visualization / 57 WebGL 2014.04.15 X021 2014 3 1F Kageyama (Kobe Univ.) Visualization 2014.04.15 1 / 57 WebGL OpenGL GLSL Kageyama (Kobe Univ.) Visualization 2014.04.15 2 / 57 WebGL Kageyama (Kobe Univ.) Visualization 2014.04.15

More information

July 28, H H 0 H int = H H 0 H int = H int (x)d 3 x Schrödinger Picture Ψ(t) S =e iht Ψ H O S Heisenberg Picture Ψ H O H (t) =e iht O S e i

July 28, H H 0 H int = H H 0 H int = H int (x)d 3 x Schrödinger Picture Ψ(t) S =e iht Ψ H O S Heisenberg Picture Ψ H O H (t) =e iht O S e i July 8, 4. H H H int H H H int H int (x)d 3 x Schrödinger Picture Ψ(t) S e iht Ψ H O S Heisenberg Picture Ψ H O H (t) e iht O S e iht Interaction Picture Ψ(t) D e iht Ψ(t) S O D (t) e iht O S e ih t (Dirac

More information

マルチコアPCクラスタ環境におけるBDD法のハイブリッド並列実装

マルチコアPCクラスタ環境におけるBDD法のハイブリッド並列実装 2010 GPGPU 2010 9 29 MPI/Pthread (DDM) DDM CPU CPU CPU CPU FEM GPU FEM CPU Mult - NUMA Multprocessng Cell GPU Accelerator, GPU CPU Heterogeneous computng L3 cache L3 cache CPU CPU + GPU GPU L3 cache 4

More information

() L () 20 1

() L () 20 1 () 25 1 10 1 0 0 0 1 2 3 4 5 6 2 3 4 9308510 4432193 L () 20 1 PP 200,000 P13P14 3 0123456 12345 1234561 2 4 5 6 25 1 10 7 1 8 10 / L 10 9 10 11 () ( ) TEL 23 12 7 38 13 14 15 16 17 18 L 19 20 1000123456

More information

308 ( ) p.121

308 ( ) p.121 307 1944 1 1920 1995 2 3 4 5 308 ( ) p.121 309 10 12 310 6 7 ( ) ( ) ( ) 50 311 p.120 p.142 ( ) ( ) p.117 p.124 p.118 312 8 p.125 313 p.121 p.122 p.126 p.128 p.156 p.119 p.122 314 p.153 9 315 p.142 p.153

More information

戦後の補欠選挙

戦後の補欠選挙 1 2 11 3 4, 1968, p.429., pp.140-141. 76 2005.12 20 14 5 2110 25 6 22 7 25 8 4919 9 22 10 11 12 13 58154 14 15 1447 79 2042 21 79 2243 25100 113 2211 71 113 113 29 p.85 2005.12 77 16 29 12 10 10 17 18

More information

日経テレコン料金表(2016年4月)

日経テレコン料金表(2016年4月) 1 2 3 4 8,000 15,000 22,000 29,000 5 6 7 8 36,000 42,000 48,000 54,000 9 10 20 30 60,000 66,000 126,000 166,000 50 100 246,000 396,000 1 25 8,000 7,000 620 2150 6,000 4,000 51100 101200 3,000 1,000 201

More information

73 p.1 22 16 2004p.152

73 p.1 22 16 2004p.152 1987 p.80 72 73 p.1 22 16 2004p.152 281895 1930 1931 12 28 1930 10 27 12 134 74 75 10 27 47.6 1910 1925 10 10 76 10 11 12 139 p.287 p.10 11 pp.3-4 1917 p.284 77 78 10 13 10 p.6 1936 79 15 15 30 80 pp.499-501

More information

122011pp.139174 18501933

122011pp.139174 18501933 122011pp.139174 18501933 122011 1850 3 187912 3 1850 8 1933 84 4 1871 12 1879 5 2 1 9 15 1 1 5 3 3 3 6 19 9 9 6 28 7 7 4 1140 9 4 3 5750 58 4 3 1 57 2 122011 3 4 134,500,000 4,020,000 11,600,000 5 2 678.00m

More information

2 2 3 4 5 5 2 7 3 4 6 1 3 4 7 4 2 2 2 4 2 3 3 4 5 1932 A p. 40. 1893 A p. 224, p. 226. 1893 B pp. 1 2. p. 3.

2 2 3 4 5 5 2 7 3 4 6 1 3 4 7 4 2 2 2 4 2 3 3 4 5 1932 A p. 40. 1893 A p. 224, p. 226. 1893 B pp. 1 2. p. 3. 1 73 72 1 1844 11 9 1844 12 18 5 1916 1 11 72 1 73 2 1862 3 1870 2 1862 6 1873 1 3 4 3 4 7 2 3 4 5 3 5 4 2007 p. 117. 2 2 3 4 5 5 2 7 3 4 6 1 3 4 7 4 2 2 2 4 2 3 3 4 5 1932 A p. 40. 1893 A p. 224, p. 226.

More information

29 2011 3 4 1 19 5 2 21 6 21 2 21 7 2 23 21 8 21 1 20 21 1 22 20 p.61 21 1 21 21 1 23

29 2011 3 4 1 19 5 2 21 6 21 2 21 7 2 23 21 8 21 1 20 21 1 22 20 p.61 21 1 21 21 1 23 29 2011 3 pp.55 86 19 1886 2 13 1 1 21 1888 1 13 2 3,500 3 5 5 50 4 1959 6 p.241 21 1 13 2 p.14 1988 p.2 21 1 15 29 2011 3 4 1 19 5 2 21 6 21 2 21 7 2 23 21 8 21 1 20 21 1 22 20 p.61 21 1 21 21 1 23 1

More information

Microsoft Word - 映画『東京裁判』を観て.doc

Microsoft Word - 映画『東京裁判』を観て.doc 1 2 3 4 5 6 7 1 2008. 2 2010, 3 2010. p.1 4 2008 p.202 5 2008. p.228 6 2011. 7 / 2008. pp.3-4 1 8 1 9 10 11 8 2008, p.7 9 2011. p.41 10.51 11 2009. p. 2 12 13 14 12 2008. p.4 13 2008, p.7-8 14 2008. p.126

More information

Int Int 29 print Int fmt tostring 2 2 [19] ML ML [19] ML Emacs Standard ML M M ::= x c λx.m M M let x = M in M end (M) x c λx.

Int Int 29 print Int fmt tostring 2 2 [19] ML ML [19] ML Emacs Standard ML M M ::= x c λx.m M M let x = M in M end (M) x c λx. 1, 2 1 m110057@shibaura-it.ac.jp 2 sasano@sic.shibaura-it.ac.jp Eclipse Visual Studio ML Standard ML Emacs 1 ( IDE ) IDE C C++ Java IDE IDE IDE IDE Eclipse Java IDE Java Standard ML 1 print (Int. 1 Int

More information

橡matufw

橡matufw 3 10 25 3 18 42 1 2 6 2001 8 22 3 03 36 3 4 A 2002 2001 1 1 2014 28 26 5 9 1990 2000 2000 12 2000 12 12 12 1999 88 5 2014 60 57 1996 30 25 205 0 4 120 1,5 A 1995 3 1990 30 6 2000 2004 2000 6 7 2001 5 2002

More information