25 11M n O(n 2 ) O(n) O(n) O(n)
|
|
- くにひと いなくら
- 7 years ago
- Views:
Transcription
1 M15133
2 25 11M n O(n 2 ) O(n) O(n) O(n)
3
4 1 (Compressed Sensing) x x y x ϵ y x m n A (m < n) y = Ax y A x ϵ p e Candes Tao [1] L 1 RIP (Restricted Isometry Property) [2] L p (0 p 1) Donoho Tanner [3, 4] L 1 [2] [5] Krzakala [6] (Belief Propagation) [7] 2 RIP Luby (Verification-based Decoding Algorithm) [8] LDPC (Low-Density Parity-Check) Zhang [9] (Nodebased Verification-based Decoding Algorithm) Kudekar Pfister [10] (Massage-based Verification-based Decoding Algorithm) (Spatially Coupled) [11] 2
5 LDPC BP Chandar [12] n O(n)
6 2 2.1 α x = t (x 1,..., x n ) R n A R m n (m < n) y = Ax = t (y 1,..., y m ) R m α := m n A y ˆx m n A ranka = n ˆx = x ˆx A α < 1 m < n x ˆx x x j ( j [1, n]) ϵ ϵ := Pr[X j 0] ( j [1, n]) x j x R Pr[X j = x] = 0 ( j [1, n], x 0, x R) 4
7 p(x) := d Pr[X x] (x 0) dx X p(x) p(x) = (1 ϵ)δ(x) + ϵ p(x) δ(x) x j Pr[X j = x j ] = ˆx p e p e := 1 n j [1,n] { 1 ϵ (xj = 0) 0 (x j 0) Pr[ ˆX j X j ] {0, 1} d l 2 d r = d k d l (d k 2) m = d l M n = d r M (d l, d r, M)- α α = m n = d l d r = 1 d k
8 Kudekar [11] (d l, d r, L, M)- (d l, d r, L, M)- d l d r = d k d l 1 (L 1 + d l ) d k L H(d l, d r, L) H(5, 10, 7) H(5, 10, 7) = M M 0 M M (L 1 + d l )M d k LM (d l, d r, L, M)- M n = d k LM m = (L + d l 1)M α α = m n = L + d l 1 d k L = 1 d k + d l 1 d k L (2.1) (d l, d r, M)- 1 d k L 1 d k 2.3 (Belief Propagation) x\{x j } x \ x j y = (x 1,..., x n ) A y = (y 1,..., y m ) y = (y 1,..., y m ) ˆx = (ˆx 1,..., ˆx n ) p(x j y) = d dx j Pr[X j x j Y = y] x j p e p(x j y) ˆx j = arg max x j R = arg max x j R p(x j y) R p(x y) dx \ x j
9 p(y x)p(x) p(x y) = p(y) x p(y) n p(y x) = 1[Ax = y] x j p(x) = p(x j ) ˆx j = arg max x j R p(x y) 1[Ax = y] R ( 1[Ax = y] n p(x j ) j=1 n k=1 j=1 ) p(x k ) dx \ x j (2.2) (2.2) sum-product sum-product sum-product A 2 n m A i,j = 1 j i [ j x j i ] 1 x j = y i j c(i) i i j j i c (i) c (i) := {j [1, n] A i,j = 1} j v (j) v (j) := {i [1, m] A i,j = 1} (2.2) ˆx j = arg max x j R R ( m i=1 [ 1 k c (i) ]) n x k = y i p(x k ) dx \ x j (2.3) k=1
10 p(x 1 ). 1 x 1 1. [ 1 j c (1) x j = y 1 ] p(x j ) p(x n ). j x j n x n A i. m [ 1 j c (i) [ 1 j c (m) x j = y i ] x j = y m ] 2.1: A A (2.3) (2.3) l (l 1) j i µ (l) j i (x j) 1 j arg max p(x j y) k x j R j (j k) j p(x j ) µ (1) j i (x j) l 2 l 1 l (l 1) i j M (l) i j (x j) l l (l 1) j p (l) (x j y)
11 l j arg max p (l) (x j y) A t (arg max x j R ) p (l) (x j y) = y ˆx = t (arg max x j R ) p (l) (x j y) 1 x j R
12 : Input: y A Output: ˆx loop // l (l 1) foreach j [1, n] do if l = 1 then j µ (1) j i (x j) := (1 ϵ)δ(x j ) + ϵ p(x j ) else 2 j µ (l) j i (x j) := p(x j ) k v(j)\i M (l 1) k j (x j) foreach i [1, m] do i ( [ M (l) i j (x ] j) := 1 x h = y i R h c(i) foreach j [1, n] do j if j # arg max x j R p (l) (x j y) := i v (j) k c(i)\j M (l) i j (x j) ) µ (l) k i (x k) dx \ x j ) p (l) (x j y) = y then p (l) (x j y) = 1, A t (arg max x j R ˆx Aˆx = y ˆx output ˆx := t (arg max x j R ) p (l) (x j y)
13 [8,9,12 15] d X 1,..., X d ξ 0 X X d = ξ 0 d = 2 X 1 + X 2 q(x) p(x) (1 ϵ)δ(x) + ϵ p(x) ˆ q(x) = p(ξ)p(x ξ)dξ = (1 ϵ) 2 δ(x) + 2ϵ(1 ϵ) p(x) + ϵ 2 p 2 (x) ˆ ξ+η ξ 0 Pr[X = ξ] = lim p(x)dx = 0 η +0 ξ ˆ { ξ+η 0 (ξ 0) Pr[X 1 + X 2 = ξ] = lim q(x)dx = η +0 ξ ϵ 2 (ξ 0) d 3 2. d X 1,..., X d X X d = 0 X 1 = = X d = 0 [ ] J := {1,..., d} Pr X j = 0 j 0 J, X j0 = x j0 0 = 0 j J j J [ ] Pr X j = 0 j 0 J, X j0 = x j0 0 [ ] = Pr X j = X j0 j 0 J, X j0 = x j0 0 j J\j 0 [ ] = Pr X j = x j0 j 0 J, X j0 = x j0 0 j J\j 0 [ (a) ] = Pr X j = x j0 j J\j 0 (b) = 0 (a) 1 j J\j 0 X j X j0 (b)
14 d X 1,..., X d j 0 J 1, J 2 {1,..., d} ] Pr [X j0 = j J1 X j = j J2 X j j J1 X j = j J2 X j = 1 [ ] Pr X j = X j j J 1 J 2 \ j 0, X j 0 = 0 j J 1 j J 2 j J 1 [ ] Pr X j = X j j J 1 J 2 \ j 0, X j 0 j J 1 j J 2 [ ] = Pr X j = X j j J 1 J 2 \ j 0, X j 0 j J 1 \j 0 j J 2 \j 0 [ = Pr X j ] X j = X j1 j J 1 J 2 \ j 0, X j 0 j J 1 \{j 0,j } j J 2 \j 0 [ = Pr X j ] X j = x j 0 j J 1 \{j 0,j } j J 2 \j 0 = 0 4. J j 0 x j0 [ Pr X j0 = x 0 x ] X j = x 0, = 1 j J j J\j 0 X j = x 5. X 1,..., X d J := {1,..., d} j 0 x j0 [ Pr X j0 [λ, υ] j J \ j 0, X j [λ j, υ j ], ] X j = x = 1 j J λ := max (0, x ) υ j j J\j 0 υ := x λ j j J\j 0
15 X j0 = x j J\j 0 X j x j J\j 0 υ j x j J\j 0 X j x j J\j 0 λ j 6. x j0 x j0 Pr [ X j = λ j X j [λ j, υ j ], λ j = υ j ] = j i ˆx j j ˆx j s j λ j υ j 4 i σ i, d i, Λ i, Υ i 4 σ i y i ˆx j d i (2.7) (2.8) (2.9) i σ i d i ˆx j s j = 1 5 (2.5) ˆx j 6 (2.6) ˆx j s j = 1 2 (2.5) (2.6)
16 : - Input: y A Output: ˆx foreach j [1, n] do // j (ˆx j := 0, s j := 0, λ (0) j := 0, υ (0) j := max (y i)) i v (j) loop // l (l 1) foreach i [1, m] do // i ( (l) σ i := y i s j ˆx j, d (l) i := # c (i) Λ (l) i j c (i) := y i j c (i) j c (i) s j, υ (l 1) j, Υ (l) i := y i λ (l 1) ) j j c (i) (2.4) foreach j [1, n] s j = 0 do // j j (ˆxj := 0, s j := 0, λ (l) j := max(0, υ (l 1) j + max i v(j) (Λ (l) i )), υ (l) j := λ (l 1) j if λ (l) j := υ (l) j then + min (Υ (l) i ) ) (2.5) i v(j) (ˆx j := λ (l) j, s j := 1, λ (l+) j := ˆx j, υ (l+) j := ˆx j ) (2.6) else if i v (j), σ i = 0 then 0 (ˆx j := 0, s j := 1, λ (l+) j := ˆx j, υ (l+) j := ˆx j ) (2.7) else if i v (j), d i = 1 then 1 (ˆx j := σ i, s j := 1, λ (l+) j := ˆx j, υ (l+) j := ˆx j ) (2.8) else if h, i v (j), σ h = σ i then (ˆx j := σ i, s j := 1, λ (l+) j := ˆx j, υ (l+) j := ˆx j ) (2.9) if j [1,n] s j = n then ˆx output ˆx := t (ˆx j )
17 X j 0 < x min < x max x min, x max Pr [ X j [x min, x max ] X j 0 ] = 1 (3.1) x min x max 3.2 (3.1) Pr[X j x max X j x 1 ] = 1 (x max x 1 ) (3.2) Pr[X j x min X j x 2 ] = 1 (0 < x 2 x min ) (3.3) Pr[X j = 0 0 X j x 3 ] = 1 (0 < x 3 < x min ) (3.4) (3.2) (3.3) (3.4) 3 (3.5) (3.6) (3.7) 15
18 : - Input: y A x min x max Output: ˆx foreach j [1, n] do // j (ˆx j := 0, s j := 0, λ (0) j := 0, υ (0) j := x max ) loop // l (l 1) foreach i [1, m] do // i (2.4) foreach j [1, n] s j = 0 do // j j (2.5) if 0 < λ j < x min then 0 λ (l) j := x min (3.5) if υ j > x max then υ (l) j := x max (3.6) if λ (l) j = υ (l) j then (2.6) else if λ j = 0, υ j < 1 then (ˆx j := 0, s j := 1, λ (l+) j := ˆx j, υ (l+) j := ˆx j ) (3.7) if else if i v (j), σ i = 0 then 0 (2.7) else if i v (j), d i = 1 then 1 (2.8) else if h, i v (j), σ h = σ i then (2.9) s j = n then j [1,n] ˆx output ˆx := t (ˆx j )
19 [x min, x max ] p e r = x max x min { Xj U[x min, x max ] w.p. ϵ X j = 0 w.p. 1 ϵ (3.8) x j = x j x min f : x j x j { X j U[1, r] w.p. ϵ X j = 0 w.p. 1 ϵ (3.9) (3.9) x A y = Ax ˆx = (ˆx j) 3 4
20 : 3 Input: y A r Output: ˆx foreach j [1, n] do // j (ˆx j := 0, s j := 0, λ (0) j := 0, υ (0) j := r) loop // l (l 1) foreach i [1, m] do // i (2.4) foreach j [1, n] s j = 0 do // j j (2.5) if 0 < λ j < 1 then 0 1 λ (l) j := 1 if υ j > r then r υ (l) j := r if if λ (l) j = υ (l) j then (2.6) else if λ j = 0, υ j < 1 then 1 (3.7) else if i v (j), σ i = 0 then 0 (2.7) else if i v (j), d i = 1 then 1 (2.8) else if h, i v (j), σ h = σ i then (2.9) s j = n then j [1,n] ˆx output ˆx := t (ˆx j )
21 x min x max (3.8) X 3 (x min, x max ) X 3 (x min, x max ) A 3 p e 3(A, x min, x max ) 1 r (3.9) X 4 (r) X 4 (r) A 4 p e 4(A, r) x min A r > 1 x > 0 p e 3(A, x, rx) = p e 4(A, r) p e 3 r
22 G G = 4 i, h # ( c (i) c (h) ) 2 3 (2.9) # ( c (i) c (h) ) 1 G 6 α 0.5 d k d k = 2 ϵ ϵ := sup { ϵ lim M p e = 0 } ϵ M p e < 10 1 ϵ ϵ 4.1 r ϵ d l = 3, 4, r ϵ 2 r 3 r 2 ϵ n n = d l = 3 n n = d l = 3, 4, 5 (2.1) α = 0.51, 0.515, 0.52 d l α =
23 ϵ (3,6,100,10000) (4,8,100,10000) (5,10,100,10000) r 4.1: (d l, d r, L, M)- r-ϵ d l = 3, 4, (3,6,333334) (4,8,250000) (5,10,200000) ϵ r 4.2: (d l, d r, M)- r-ϵ d l = 3, 4, 5
24 d l = 5 ϵ d l 6 d l = d l = 3 ϵ d l = 5 d l = 3 ϵ d l = 5 ϵ (5,10,200,10000) (5,10,100,10000) (5,10,50,10000) (5,10,25,10000) (5,10,100,20000) (5,10,100,10000) (5,10,100,5000) (5,10,100,2500) G=6 G=8 G=10 pe ϵ ϵ ϵ 4.3: (5, 10, L, M)- ϵ-p e L = 25, 50, 100 M = G = 6 M = 2 500, 5 000, L = 100 G = 6 G = 6, 8, 10 L = 100 M = ϵ p e 0 p e < 0.3
25 L M G ϵ p e 4.3 L = 25, 50, 100, 200 (5, 10, L, )- (2.1) L 0.5 ϵ L 4.3 M = 2 500, 5 000, , (5, 10, 100, M)- n n = 5 000, , , n ϵ M ,8,10 (5, 10, 100, )- G G L = 100 M = G = (5, 10, 100, )- α = n O(n 2 ) O(n) 4.1 [6]
26 [6] x j x j R x j [0, + ) x j [0, x max ] α = 0.44 α = 0.52 α = 0.52 x j {0} [x min, + ) ϵ = 0.40 ϵ = 0.33 α = 0.52 ϵ = 0.36 ϵ = 0.36 x j {0} [x min, x max ] α = 0.52 r = 4 ϵ = 0.41 x j {0} [x min, x max ] α = 0.52 r = 2 ϵ = : α ϵ x max x min = r
27 [16] 25
28 26
29 [1] E. Candes and T. Tao, Decoding by linear programming, IEEE Trans. Inf. Theory, vol. 51, no. 12, pp , [2] Y. Kabashima, T. Wadayama, and T. Tanaka, A typical reconstruction limit for compressed sensing based on L p -norm minimization, Journal of Statistical Mechanics: Theory and Experiment, vol. 2009, no. 09, p. L09003, [3] D. L. Donoho, A. Maleki, and A. Montanari, Message-passing algorithms for compressed sensing, Proceedings of the National Academy of Sciences, vol. 106, no. 45, pp , abstract [4] D. L. Donoho and J. Tanner, Neighborliness of randomly projected simplices in high dimensions, Proceedings of the National Academy of Sciences, vol. 102, no. 27, pp , abstract [5] T. Tanaka, Mathematics of compressed sensing, IEICE ESS Fundamentals Review, vol. 4, no. 1, pp , [6] F. Krzakala, M. Mézard, F. Sausset, Y. F. Sun, and L. Zdeborová, Statisticalphysics-based reconstruction in compressed sensing, Phys. Rev. X, vol. 2, p , May [7] T. Wadayama, On random construction of a bipolar sensing matrix with compact representation, in Proc. IEEE Information Theory Workshop (ITW), 2009, pp [8] M. G. Luby and M. Mitzenmacher, Verification-based decoding for packetbased low-density parity-check codes, IEEE Trans. Inf. Theory, vol. 51, no. 1, pp , [9] F. Zhang and H. D. Pfister, List-message passing achieves capacity on the q- ary symmetric channel for large q, in Proc. IEEE Global Telecommunications Conference (GLOBECOM), 2007, pp
30 28 [10] S. Kudekar and H. D. Pfister, The effect of spatial coupling on compressive sensing, in Proc. Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2010, pp [11] S. Kudekar, T. Richardson, and R. Urbanke, Threshold saturation via spatial coupling: Why convolutional LDPC ensembles perform so well over the BEC, IEEE Trans. Inf. Theory, vol. 57, no. 2, pp , [12] V. Chandar, D. Shah, and G. W. Wornell, A simple message-passing algorithm for compressed sensing, in Proc. IEEE International Symposium on Information Theory (ISIT), 2010, pp [13] F. Zhang and H. D. Pfister, Verification decoding of high-rate LDPC codes with applications in compressed sensing, IEEE Trans. Inf. Theory, vol. 58, no. 8, pp , [14] S. Sarvotham, D. Baron, and R. G. Baraniuk, Sudocodes - fast measurement and reconstruction of sparse signals, in Proc. IEEE International Symposium on Information Theory (ISIT), 2006, pp [15] X. Wu and Z. Yang, Verification-based interval-passing algorithm for compressed sensing, IEEE Signal Process. Lett., vol. 20, no. 10, pp , [16] Y. Eftekhari, A. Heidarzadeh, A. Banihashemi, and I. Lambadaris, Density evolution analysis of node-based verification-based algorithms in compressed sensing, IEEE Trans. Inf. Theory, vol. 58, no. 10, pp , 2012.
31 ,,,, 34 (SITA2011), pp , , MacKay-Neal,,
.1.1.1 S H(S) T canonical distribution P (S) = e βh(s) Z(β) (1) β = (k B T ) 1 k B Z(β) = Tr S e βh(s) partition function free energy F = β 1 ln Z(β)
58 1 HAL9000 Google Amazon SF 1 [1, ] 1 E-mail: kaba@dis.titech.ac.jp .1.1.1 S H(S) T canonical distribution P (S) = e βh(s) Z(β) (1) β = (k B T ) 1 k B Z(β) = Tr S e βh(s) partition function free energy
More informationturbo 1993code Berrou 1) 2[dB] SNR 05[dB] 1) interleaver parallel concatenated convolutional code ch
1 -- 2 6 LDPC 2012 3 1993 1960 30 LDPC 2 LDPC LDPC LDPC 6-1 LDPC 6-2 6-3 c 2013 1/(13) 1 -- 2 -- 6 6--1 2012 3 turbo 1993code Berrou 1) 2[dB] SNR 05[dB] 1) 6 1 2 1 1 interleaver 2 2 2 parallel concatenated
More information& 3 3 ' ' (., (Pixel), (Light Intensity) (Random Variable). (Joint Probability). V., V = {,,, V }. i x i x = (x, x,, x V ) T. x i i (State Variable),
.... Deeping and Expansion of Large-Scale Random Fields and Probabilistic Image Processing Kazuyuki Tanaka The mathematical frameworks of probabilistic image processing are formulated by means of Markov
More informationN M kb 1 1% 1 kb N M N M N + M ez43-rf2 N M M N/( N) 2 3 WSN Donoho Candès [6], [7] N x x N s x N N Ψ (1) x = Ψs (1) s x s K x
Vol.212-HCI-1 No.2 Vol.212-UBI-36 No.2 212/11/2 1 1,2 1 N M N + M ez43-rf2 N M M N/(1 + 2.82 1 3 N) 1. (WSN) Sink WSN WSN WSN 1 Graduate School of Information Science and Technology, The University of
More informationmain.dvi
CDMA 1 CDMA ( ) CDMA CDMA CDMA 1 ( ) Hopfield [1] Hopfield 1 E-mail: okada@brain.riken.go.jp 1 1: 1 [] Hopfield Sourlas Hopfield [3] Sourlas 1? CDMA.1 DS/BPSK CDMA (Direct Sequence; DS) (Binary Phase-Shift-Keying;
More informationohgane
Signal Detection Based on Belief Propagation in a Massive MIMO System Takeo Ohgane Hokkaido University, Japan 28 October 2013 Background (1) 2 Massive MIMO An order of 100 antenna elements channel capacity
More information0 1-4. 1-5. (1) + b = b +, (2) b = b, (3) + 0 =, (4) 1 =, (5) ( + b) + c = + (b + c), (6) ( b) c = (b c), (7) (b + c) = b + c, (8) ( + b)c = c + bc (9
1-1. 1, 2, 3, 4, 5, 6, 7,, 100,, 1000, n, m m m n n 0 n, m m n 1-2. 0 m n m n 0 2 = 1.41421356 π = 3.141516 1-3. 1 0 1-4. 1-5. (1) + b = b +, (2) b = b, (3) + 0 =, (4) 1 =, (5) ( + b) + c = + (b + c),
More information4 4 2 RAW 4 4 4 (PCA) 4 4 4 4 RAW RAW [5] 4 RAW 4 Park [12] Park 2 RAW RAW 2 RAW y = Mx + n. (1) y RAW x RGB M CFA n.. R G B σr 2, σ2 G, σ2 B D n ( )
RAW 4 E-mail: hakiyama@ok.ctrl.titech.ac.jp Abstract RAW RAW RAW RAW RAW 4 RAW RAW RAW 1 (CFA) CFA Bayer CFA [1] RAW CFA 1 2 [2, 3, 4, 5]. RAW RAW RAW RAW 3 [2, 3, 4, 5] (AWGN) [13, 14] RAW 2 RAW RAW RAW
More information三石貴志.indd
流通科学大学論集 - 経済 情報 政策編 - 第 21 巻第 1 号,23-33(2012) SIRMs SIRMs Fuzzy fuzzyapproximate approximatereasoning reasoningusing using Lukasiewicz Łukasiewicz logical Logical operations Operations Takashi Mitsuishi
More informationMicrosoft PowerPoint - 若手研究者のための2017_for_dist.pptx
スパース重ね合わせ符号の状況と課題 竹内純一 三村和史 九州大学大学院システム情報科学研究院 広島市立大学大学院情報科学研究科 27//28 若手研究者のための講演会 at 新潟県月岡温泉 Thanks to: 川喜田雅則, 高木美里, 武石啓成, 波多江優和, 宮本耕平 Special thanks to: 竹内啓悟 スパース重ね合わせ符号 (Sparse superposition codes;
More information<4D6963726F736F667420576F7264202D204850835483938376838B8379815B83578B6594BB2D834A836F815B82D082C88C60202E646F63>
誤 り 訂 正 技 術 の 基 礎 サンプルページ この 本 の 定 価 判 型 などは, 以 下 の URL からご 覧 いただけます http://wwwmorikitacojp/books/mid/081731 このサンプルページの 内 容 は, 第 1 版 発 行 時 のものです http://wwwmorikitacojp/support/ e mail editor@morikitacojp
More informationClaude E. Shannon Award SITA 6 28 7 3 2009 IEEE International Symposium on Information Theory (ISIT2009) (7 2 ) 2010 Shannon Award (Te Sun Han) SITA A
No.71 2009 7 17 Claude E. Shannon Award...................................................................................................................................................................
More informationRun-Based Trieから構成される 決定木の枝刈り法
Run-Based Trie 2 2 25 6 Run-Based Trie Simple Search Run-Based Trie Network A Network B Packet Router Packet Filtering Policy Rule Network A, K Network B Network C, D Action Permit Deny Permit Network
More information,,, 2 ( ), $[2, 4]$, $[21, 25]$, $V$,, 31, 2, $V$, $V$ $V$, 2, (b) $-$,,, (1) : (2) : (3) : $r$ $R$ $r/r$, (4) : 3
1084 1999 124-134 124 3 1 (SUGIHARA Kokichi),,,,, 1, [5, 11, 12, 13], (2, 3 ), -,,,, 2 [5], 3,, 3, 2 2, -, 3,, 1,, 3 2,,, 3 $R$ ( ), $R$ $R$ $V$, $V$ $R$,,,, 3 2 125 1 3,,, 2 ( ), $[2, 4]$, $[21, 25]$,
More informationEndoPaper.pdf
Research on Nonlinear Oscillation in the Field of Electrical, Electronics, and Communication Engineering Tetsuro ENDO.,.,, (NLP), 1. 3. (1973 ),. (, ),..., 191, 1970,. 191 1967,,, 196 1967,,. 1967 1. 1988
More information4. C i k = 2 k-means C 1 i, C 2 i 5. C i x i p [ f(θ i ; x) = (2π) p 2 Vi 1 2 exp (x µ ] i) t V 1 i (x µ i ) 2 BIC BIC = 2 log L( ˆθ i ; x i C i ) + q
x-means 1 2 2 x-means, x-means k-means Bayesian Information Criterion BIC Watershed x-means Moving Object Extraction Using the Number of Clusters Determined by X-means Clustering Naoki Kubo, 1 Kousuke
More information14 2 5
14 2 5 i ii Surface Reconstruction from Point Cloud of Human Body in Arbitrary Postures Isao MORO Abstract We propose a method for surface reconstruction from point cloud of human body in arbitrary postures.
More informationŠéŒØ‘÷†u…x…C…W…A…fi…l…b…g…‘†[…NfiüŒå†v(fl|ŁŠ−Ù) 4. −mŠ¦fiI’—Ÿ_ 4.1 −mŠ¦ŁªŁz‡Ì„v”Z
( ) 4. 4.1 2009 1 14 ( ) ( ) 4. 2009 14.1 14 ( ) 1 / 41 1 2 3 4 5 4.1 ( ) 4. 2009 14.1 14 ( ) 2 / 41 X i (Ω)
More informationuntitled
3,,, 2 3.1 3.1.1,, A4 1mm 10 1, 21.06cm, 21.06cm?, 10 1,,,, i),, ),, ),, x best ± δx 1) ii), x best ), δx, e,, e =1.602176462 ± 0.000000063) 10 19 [C] 2) i) ii), 1) x best δx
More information(4) ω t(x) = 1 ω min Ω ( (I C (y))) min 0 < ω < C A C = 1 (5) ω (5) t transmission map tmap 1 4(a) 2. 3 2. 2 t 4(a) t tmap RGB 2 (a) RGB (A), (B), (C)
(MIRU2011) 2011 7 890 0065 1 21 40 105-6691 1 1 1 731 3194 3 4 1 338 8570 255 346 8524 1836 1 E-mail: {fukumoto,kawasaki}@ibe.kagoshima-u.ac.jp, ryo-f@hiroshima-cu.ac.jp, fukuda@cv.ics.saitama-u.ac.jp,
More information(a) 1 (b) 3. Gilbert Pernicka[2] Treibitz Schechner[3] Narasimhan [4] Kim [5] Nayar [6] [7][8][9] 2. X X X [10] [11] L L t L s L = L t + L s
1 1 1, Extraction of Transmitted Light using Parallel High-frequency Illumination Kenichiro Tanaka 1 Yasuhiro Mukaigawa 1 Yasushi Yagi 1 Abstract: We propose a new sharpening method of transmitted scene
More informationit-ken_open.key
深層学習技術の進展 ImageNet Classification 画像認識 音声認識 自然言語処理 機械翻訳 深層学習技術は これらの分野において 特に圧倒的な強みを見せている Figure (Left) Eight ILSVRC-2010 test Deep images and the cited4: from: ``ImageNet Classification with Networks et
More information¿¸µLDPCÉä¹æ¤È¤½¤Î±þÍÑ
LDPC 2010 9 21 1 / 51 LDPC LDPC LDPC 2 LDPC 2 / 51 Irregular LDPC Codes (λ(x), ρ(x)) Polar Codes Spatially Coupled Regular LDPC Codes 2 3 / 51 [ 08 Polyanskiy and Poor] log M (n, P B ) = nc(ɛ) nv (ɛ)q
More information(Basic of Proability Theory). (Probability Spacees ad Radom Variables , (Expectatios, Meas) (Weak Law
I (Radom Walks ad Percolatios) 3 4 7 ( -2 ) (Preface),.,,,...,,.,,,,.,.,,.,,. (,.) (Basic of Proability Theory). (Probability Spacees ad Radom Variables...............2, (Expectatios, Meas).............................
More informationRCS 5 5G 28 10 19 1 1 2 3 2.1........................................ 3 2.2............................... 3 2.3.............................. 10 3 17 3.1...................................... 17 3.2
More informationばらつき抑制のための確率最適制御
( ) http://wwwhayanuemnagoya-uacjp/ fujimoto/ 2011 3 9 11 ( ) 2011/03/09-11 1 / 46 Outline 1 2 3 4 5 ( ) 2011/03/09-11 2 / 46 Outline 1 2 3 4 5 ( ) 2011/03/09-11 3 / 46 (1/2) r + Controller - u Plant y
More informationuntitled
K-Means 1 5 2 K-Means 7 2.1 K-Means.............................. 7 2.2 K-Means.......................... 8 2.3................... 9 3 K-Means 11 3.1.................................. 11 3.2..................................
More information2 1,384,000 2,000,000 1,296,211 1,793,925 38,000 54,500 27,804 43,187 41,000 60,000 31,776 49,017 8,781 18,663 25,000 35,300 3 4 5 6 1,296,211 1,793,925 27,804 43,187 1,275,648 1,753,306 29,387 43,025
More information2007/8 Vol. J90 D No. 8 Stauffer [7] 2 2 I 1 I 2 2 (I 1(x),I 2(x)) 2 [13] I 2 = CI 1 (C >0) (I 1,I 2) (I 1,I 2) Field Monitoring Server
a) Change Detection Using Joint Intensity Histogram Yasuyo KITA a) 2 (0 255) (I 1 (x),i 2 (x)) I 2 = CI 1 (C>0) (I 1,I 2 ) (I 1,I 2 ) 2 1. [1] 2 [2] [3] [5] [6] [8] Intelligent Systems Research Institute,
More information1 -- 9 -- 3 3--1 LMS NLMS 2009 2 LMS Least Mean Square LMS Normalized LMS NLMS 3--1--1 3 1 AD 3 1 h(n) y(n) d(n) FIR w(n) n = 0, 1,, N 1 N N = 2 3--1-
1 -- 9 3 2009 2 LMS NLMS RLS FIR IIR 3-1 3-2 3-3 3-4 c 2011 1/(13) 1 -- 9 -- 3 3--1 LMS NLMS 2009 2 LMS Least Mean Square LMS Normalized LMS NLMS 3--1--1 3 1 AD 3 1 h(n) y(n) d(n) FIR w(n) n = 0, 1,, N
More information3. ( 1 ) Linear Congruential Generator:LCG 6) (Mersenne Twister:MT ), L 1 ( 2 ) 4 4 G (i,j) < G > < G 2 > < G > 2 g (ij) i= L j= N
RMT 1 1 1 N L Q=L/N (RMT), RMT,,,., Box-Muller, 3.,. Testing Randomness by Means of RMT Formula Xin Yang, 1 Ryota Itoi 1 and Mieko Tanaka-Yamawaki 1 Random matrix theory derives, at the limit of both dimension
More informationThe Empirical Study on New Product Concept of the Dish Washer Abstract
The Empirical Study on New Product Concept of the Dish Washer Abstract t t Cluster Analysis For Applications International Conference on Quality 96 in Yokohama Clustering Algorithms
More information2 (March 13, 2010) N Λ a = i,j=1 x i ( d (a) i,j x j ), Λ h = N i,j=1 x i ( d (h) i,j x j ) B a B h B a = N i,j=1 ν i d (a) i,j, B h = x j N i,j=1 ν i
1. A. M. Turing [18] 60 Turing A. Gierer H. Meinhardt [1] : (GM) ) a t = D a a xx µa + ρ (c a2 h + ρ 0 (0 < x < l, t > 0) h t = D h h xx νh + c ρ a 2 (0 < x < l, t > 0) a x = h x = 0 (x = 0, l) a = a(x,
More informationSignal Processing for Low Complexity Terminals and Pilot Signal Design in Multiple-Input Multiple-Output Systems ( ) ( ) LAN 3.9 Multiple-Input Multip
No.75 2010 7 5 Signal Processing for Low Complexity Terminals and Pilot Signal Design in Multiple-Input Multiple-Output Systems..................................... ( )........... ( ) Design of Power Efficient
More informationii
I05-010 : 19 1 ii k + 1 2 DS 198 20 32 1 1 iii ii iv v vi 1 1 2 2 3 3 3.1.................................... 3 3.2............................. 4 3.3.............................. 6 3.4.......................................
More information1 Abstract 2 3 n a ax 2 + bx + c = 0 (a 0) (1) ( x + b ) 2 = b2 4ac 2a 4a 2 D = b 2 4ac > 0 (1) 2 D = 0 D < 0 x + b 2a = ± b2 4ac 2a b ± b 2
1 Abstract n 1 1.1 a ax + bx + c = 0 (a 0) (1) ( x + b ) = b 4ac a 4a D = b 4ac > 0 (1) D = 0 D < 0 x + b a = ± b 4ac a b ± b 4ac a b a b ± 4ac b i a D (1) ax + bx + c D 0 () () (015 8 1 ) 1. D = b 4ac
More information106 4 4.1 1 25.1 25.4 20.4 17.9 21.2 23.1 26.2 1 24 12 14 18 36 42 24 10 5 15 120 30 15 20 10 25 35 20 18 30 12 4.1 7 min. z = 602.5x 1 + 305.0x 2 + 2
105 4 0 1? 1 LP 0 1 4.1 4.1.1 (intger programming problem) 1 0.5 x 1 = 447.7 448 / / 2 1.1.2 1. 2. 1000 3. 40 4. 20 106 4 4.1 1 25.1 25.4 20.4 17.9 21.2 23.1 26.2 1 24 12 14 18 36 42 24 10 5 15 120 30
More information1 1 ( ) ( 1.1 1.1.1 60% mm 100 100 60 60% 1.1.2 A B A B A 1
1 21 10 5 1 E-mail: qliu@res.otaru-uc.ac.jp 1 1 ( ) ( 1.1 1.1.1 60% mm 100 100 60 60% 1.1.2 A B A B A 1 B 1.1.3 boy W ID 1 2 3 DI DII DIII OL OL 1.1.4 2 1.1.5 1.1.6 1.1.7 1.1.8 1.2 1.2.1 1. 2. 3 1.2.2
More informationuntitled
c 645 2 1. GM 1959 Lindsey [1] 1960 Howard [2] Howard 1 25 (Markov Decision Process) 3 3 2 3 +1=25 9 Bellman [3] 1 Bellman 1 k 980 8576 27 1 015 0055 84 4 1977 D Esopo and Lefkowitz [4] 1 (SI) Cover and
More information[6] DoN DoN DDoN(Donuts DoN) DoN 4(2) DoN DDoN 3.2 RDoN(Ring DoN) 4(1) DoN 4(3) DoN RDoN 2 DoN 2.2 DoN PCA DoN DoN 2 DoN PCA 0 DoN 3. DoN
3 1,a) 1,b) 3D 3 3 Difference of Normals (DoN)[1] DoN, 1. 2010 Kinect[2] 3D 3 [3] 3 [4] 3 [5] 3 [6] [7] [1] [8] [9] [10] Difference of Normals (DoN) 48 8 [1] [6] DoN DoN 1 National Defense Academy a) em53035@nda.ac.jp
More informationWeb 1 q q 2 1 2 Step1) Twitter Step2) (w i, w j ) S(w i, w j ) Step3) q 2 2 2.1 I Twitter MeCab[6] URL http:// @ 2.2 (w i, w j ) S(w i, w j ) I w i w
ARG WI2 No.6, 2015 a b b 565-0871 2-1 a) yoshitake@nanase.comm.eng.osaka-u.ac.jp b) {naoko, babaguchi}@comm.eng.osaka-u.ac.jp 1 Citizen Sensor [1] Twitter 140 Twitter Sakaki [2] [3] Massoudi [4] [5] Copyright
More informationH(ω) = ( G H (ω)g(ω) ) 1 G H (ω) (6) 2 H 11 (ω) H 1N (ω) H(ω)= (2) H M1 (ω) H MN (ω) [ X(ω)= X 1 (ω) X 2 (ω) X N (ω) ] T (3)
72 12 2016 pp. 777 782 777 * 43.60.Pt; 43.38.Md; 43.60.Sx 1. 1 2 [1 8] Flexible acoustic interface based on 3D sound reproduction. Yosuke Tatekura (Shizuoka University, Hamamatsu, 432 8561) 2. 2.1 3 M
More information(MIRU2008) HOG Histograms of Oriented Gradients (HOG)
(MIRU2008) 2008 7 HOG - - E-mail: katsu0920@me.cs.scitec.kobe-u.ac.jp, {takigu,ariki}@kobe-u.ac.jp Histograms of Oriented Gradients (HOG) HOG Shape Contexts HOG 5.5 Histograms of Oriented Gradients D Human
More information36 581/2 2012
4 Development of Optical Ground Station System 4-1 Overview of Optical Ground Station with 1.5 m Diameter KUNIMORI Hiroo, TOYOSHMA Morio, and TAKAYAMA Yoshihisa The OICETS experiment, LEO Satellite-Ground
More information2008 : 80725872 1 2 2 3 2.1.......................................... 3 2.2....................................... 3 2.3......................................... 4 2.4 ()..................................
More information2). 3) 4) 1.2 NICTNICT DCRA Dihedral Corner Reflector micro-arraysdcra DCRA DCRA DCRA 3D DCRA PC USB PC PC ON / OFF Velleman K8055 K8055 K8055
1 1 1 2 DCRA 1. 1.1 1) 1 Tactile Interface with Air Jets for Floating Images Aya Higuchi, 1 Nomin, 1 Sandor Markon 1 and Satoshi Maekawa 2 The new optical device DCRA can display floating images in free
More informationf(x) = e x2 25 d f(x) 0 x d2 dx f(x) 0 x dx2 f(x) (1 + ax 2 ) 2 lim x 0 x 4 a 3 2 a g(x) = 1 + ax 2 f(x) g(x) 1/2 f(x)dx n n A f(x) = Ax (x R
29 ( ) 90 1 2 2 2 1 3 4 1 5 1 4 3 3 4 2 1 4 5 6 3 7 8 9 f(x) = e x2 25 d f(x) 0 x d2 dx f(x) 0 x dx2 f(x) (1 + ax 2 ) 2 lim x 0 x 4 a 3 2 a g(x) = 1 + ax 2 f(x) g(x) 1/2 f(x)dx 11 0 24 n n A f(x) = Ax
More information自動残差修正機能付き GBiCGSTAB$(s,L)$法 (科学技術計算アルゴリズムの数理的基盤と展開)
1733 2011 149-159 149 GBiCGSTAB $(s,l)$ GBiCGSTAB(s,L) with Auto-Correction of Residuals (Takeshi TSUKADA) NS Solutions Corporation (Kouki FUKAHORI) Graduate School of Information Science and Technology
More information2.2 (a) = 1, M = 9, p i 1 = p i = p i+1 = 0 (b) = 1, M = 9, p i 1 = 0, p i = 1, p i+1 = 1 1: M 2 M 2 w i [j] w i [j] = 1 j= w i w i = (w i [ ],, w i [
RI-002 Encoding-oriented video generation algorithm based on control with high temporal resolution Yukihiro BANDOH, Seishi TAKAMURA, Atsushi SHIMIZU 1 1T / CMOS [1] 4K (4096 2160 /) 900 Hz 50Hz,60Hz 240Hz
More information(Basics of Proability Theory). (Probability Spacees ad Radom Variables,, (Ω, F, P ),, X,. (Ω, F, P ) (probability space) Ω ( ω Ω ) F ( 2 Ω ) Ω σ (σ-fi
I (Basics of Probability Theory ad Radom Walks) 25 4 5 ( 4 ) (Preface),.,,,.,,,...,,.,.,,.,,. (,.) (Basics of Proability Theory). (Probability Spacees ad Radom Variables...............2, (Expectatios,
More information1
5-3 Photonic Antennas and its Application to Radio-over-Fiber Wireless Communication Systems LI Keren, MATSUI Toshiaki, and IZUTSU Masayuki In this paper, we presented our recent works on development of
More informationα = 2 2 α 2 = ( 2) 2 = 2 x = α, y = 2 x, y X 0, X 1.X 2,... x 0 X 0, x 1 X 1, x 2 X 2.. Zorn A, B A B A B A B A B B A A B N 2
1. 2. 3. 4. 5. 6. 7. 8. N Z 9. Z Q 10. Q R 2 1. 2. 3. 4. Zorn 5. 6. 7. 8. 9. x x x y x, y α = 2 2 α x = y = 2 1 α = 2 2 α 2 = ( 2) 2 = 2 x = α, y = 2 x, y X 0, X 1.X 2,... x 0 X 0, x 1 X 1, x 2 X 2.. Zorn
More information形状変形による古文書画像のシームレス合成
Use of Shape Deformation to Seamlessly Stitch Historical Document Images Wei Liu Wei Fan Li Chen Sun Jun あらまし 1 2 Abstract In China, efforts are being made to preserve historical documents in the form
More informationTHE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. TV A310
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. TV 367 0035 1011 A310 E-mail kawamura@suou.waseda.jp Total Variation Total Variation Total Variation Abstract
More informationSICE東北支部研究集会資料(2017年)
307 (2017.2.27) 307-8 Deep Convolutional Neural Network X Detecting Masses in Mammograms Based on Transfer Learning of A Deep Convolutional Neural Network Shintaro Suzuki, Xiaoyong Zhang, Noriyasu Homma,
More informationIPSJ SIG Technical Report Vol.2014-DBS-159 No.6 Vol.2014-IFAT-115 No /8/1 1,a) 1 1 1,, 1. ([1]) ([2], [3]) A B 1 ([4]) 1 Graduate School of Info
1,a) 1 1 1,, 1. ([1]) ([2], [3]) A B 1 ([4]) 1 Graduate School of Information Science and Technology, Osaka University a) kawasumi.ryo@ist.osaka-u.ac.jp 1 1 Bucket R*-tree[5] [4] 2 3 4 5 6 2. 2.1 2.2 2.3
More information., White-Box, White-Box. White-Box.,, White-Box., Maple [11], 2. 1, QE, QE, 1 Redlog [7], QEPCAD [9], SyNRAC [8] 3 QE., 2 Brown White-Box. 3 White-Box
White-Box Takayuki Kunihiro Graduate School of Pure and Applied Sciences, University of Tsukuba Hidenao Iwane ( ) / Fujitsu Laboratories Ltd. / National Institute of Informatics. Yumi Wada Graduate School
More information数値計算:有限要素法
( ) 1 / 61 1 2 3 4 ( ) 2 / 61 ( ) 3 / 61 P(0) P(x) u(x) P(L) f P(0) P(x) P(L) ( ) 4 / 61 L P(x) E(x) A(x) x P(x) P(x) u(x) P(x) u(x) (0 x L) ( ) 5 / 61 u(x) 0 L x ( ) 6 / 61 P(0) P(L) f d dx ( EA du dx
More informationohpmain.dvi
fujisawa@ism.ac.jp 1 Contents 1. 2. 3. 4. γ- 2 1. 3 10 5.6, 5.7, 5.4, 5.5, 5.8, 5.5, 5.3, 5.6, 5.4, 5.2. 5.5 5.6 +5.7 +5.4 +5.5 +5.8 +5.5 +5.3 +5.6 +5.4 +5.2 =5.5. 10 outlier 5 5.6, 5.7, 5.4, 5.5, 5.8,
More information149 (Newell [5]) Newell [5], [1], [1], [11] Li,Ryu, and Song [2], [11] Li,Ryu, and Song [2], [1] 1) 2) ( ) ( ) 3) T : 2 a : 3 a 1 :
Transactions of the Operations Research Society of Japan Vol. 58, 215, pp. 148 165 c ( 215 1 2 ; 215 9 3 ) 1) 2) :,,,,, 1. [9] 3 12 Darroch,Newell, and Morris [1] Mcneil [3] Miller [4] Newell [5, 6], [1]
More information3 3 i
00D8102021I 2004 3 3 3 i 1 ------------------------------------------------------------------------------------------------1 2 ---------------------------------------------------------------------------------------2
More information橡固有値セミナー2_棚橋改.PDF
1 II. 2003 5 14 2... Arnoldi. Lanczos. Jacobi-Davidson . 3 4 Ax = x A A Ax = Mx M: M 5 Householder ln ln-1 0 l3 0 l2 l1 6 Lanczos Lanczos, 1950 Arnoldi Arnoldi, 1951 Hessenberg Jacobi-Davidson Sleijpen
More information格子QCD実践入門
-- nakamura at riise.hiroshima-u.ac.jp or nakamura at an-pan.org 2013.6.26-27 1. vs. 2. (1) 3. QCD QCD QCD 4. (2) 5. QCD 2 QCD 1981 QCD Parisi, Stamatescu, Hasenfratz, etc 2 3 (Cut-Off) = +Cut-Off a p
More information(Basics of Proability Theory). (Probability Spacees ad Radom Variables,, (Ω, F, P ),, X,. (Ω, F, P ) (probability space) Ω ( ω Ω ) F ( 2 Ω ) Ω σ (σ-fi
II (Basics of Probability Theory ad Radom Walks) (Preface),.,,,.,,,...,,.,.,,.,,. (Basics of Proability Theory). (Probability Spacees ad Radom Variables...............2, (Expectatios, Meas).............................
More informationexample2_time.eps
Google (20/08/2 ) ( ) Random Walk & Google Page Rank Agora on Aug. 20 / 67 Introduction ( ) Random Walk & Google Page Rank Agora on Aug. 20 2 / 67 Introduction Google ( ) Random Walk & Google Page Rank
More information,.,. NP,., ,.,,.,.,,, (PCA)...,,. Tipping and Bishop (1999) PCA. (PPCA)., (Ilin and Raiko, 2010). PPCA EM., , tatsukaw
,.,. NP,.,. 1 1.1.,.,,.,.,,,. 2. 1.1.1 (PCA)...,,. Tipping and Bishop (1999) PCA. (PPCA)., (Ilin and Raiko, 2010). PPCA EM., 152-8552 2-12-1, tatsukawa.m.aa@m.titech.ac.jp, 190-8562 10-3, mirai@ism.ac.jp
More informationGray [6] cross tabulation CUBE, ROLL UP Johnson [7] pivoting SQL 3. SuperSQL SuperSQL SuperSQL SQL [1] [2] SQL SELECT GENERATE <media> <TFE> GENER- AT
DEIM Forum 2017 E3-1 SuperSQL 223 8522 3 14 1 E-mail: {tabata,goto}@db.ics.keio.ac.jp, toyama@ics.keio.ac.jp,,,, SuperSQL SuperSQL, SuperSQL. SuperSQL 1. SuperSQL, Cross table, SQL,. 1 1 2 4. 1 SuperSQL
More information(a) (b) (c) Canny (d) 1 ( x α, y α ) 3 (x α, y α ) (a) A 2 + B 2 + C 2 + D 2 + E 2 + F 2 = 1 (3) u ξ α u (A, B, C, D, E, F ) (4) ξ α (x 2 α, 2x α y α,
[II] Optimization Computation for 3-D Understanding of Images [II]: Ellipse Fitting 1. (1) 2. (2) (edge detection) (edge) (zero-crossing) Canny (Canny operator) (3) 1(a) [I] [II] [III] [IV ] E-mail sugaya@iim.ics.tut.ac.jp
More informationIPSJ SIG Technical Report Vol.2017-MUS-116 No /8/24 MachineDancing: 1,a) 1,b) 3 MachineDancing MachineDancing MachineDancing 1 MachineDan
MachineDancing: 1,a) 1,b) 3 MachineDancing 2 1. 3 MachineDancing MachineDancing 1 MachineDancing MachineDancing [1] 1 305 0058 1-1-1 a) s.fukayama@aist.go.jp b) m.goto@aist.go.jp 1 MachineDancing 3 CG
More information特別寄稿.indd
特別寄稿 ソフトインフラとしてのデジタル地図を活用した自動運転システム Autonomous vehicle using digital map as a soft infrastructure 菅沼直樹 Naoki SUGANUMA 1. はじめに 1) 2008 2012 ITS 2) CO 2 3) 4) Door to door Door to door Door to door DARPA(
More informationReal 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 informationuntitled
IT E- IT http://www.ipa.go.jp/security/ CERT/CC http://www.cert.org/stats/#alerts IPA IPA 2004 52,151 IT 2003 12 Yahoo 451 40 2002 4 18 IT 1/14 2.1 DoS(Denial of Access) IDS(Intrusion Detection System)
More informationER Eröds-Rényi ER p ER 1 2.3BA Balabasi 9 1 f (k) k 3 1 BA KN KN 8,10 KN 2 2 p 1 Rich-club 11 ( f (k) = 1 +
Vol.4, No.2, pp.33-40, 2012 33 * * Relation between network structure and cascade phenomena Takanori Komatsu* and Akira Namatame* Abstract Which social network structures are suitable for diffusion of
More informationIII 1 (X, d) d U d X (X, d). 1. (X, d).. (i) d(x, y) d(z, y) d(x, z) (ii) d(x, y) d(z, w) d(x, z) + d(y, w) 2. (X, d). F X.. (1), X F, (2) F 1, F 2 F
III 1 (X, d) d U d X (X, d). 1. (X, d).. (i) d(x, y) d(z, y) d(x, z) (ii) d(x, y) d(z, w) d(x, z) + d(y, w) 2. (X, d). F X.. (1), X F, (2) F 1, F 2 F F 1 F 2 F, (3) F λ F λ F λ F. 3., A λ λ A λ. B λ λ
More informationJAPAN MARKETING JOURNAL 110 Vol.28 No.22008
JAPAN MARKETING JOURNAL 110 Vol.28 No.22008 JAPAN MARKETING JOURNAL 110 Vol.28 No.22008 JAPAN MARKETING JOURNAL 110 Vol.28 No.22008 JAPAN MARKETING JOURNAL 110 Vol.28 No.22008 JAPAN MARKETING JOURNAL 110
More informationJAPAN MARKETING JOURNAL 123 Vol.31 No.32012
Japan Marketing Academy JAPAN MARKETING JOURNAL 123 Vol.31 No.32012 JAPAN MARKETING JOURNAL 123 Vol.31 No.32012 JAPAN MARKETING JOURNAL 123 Vol.31 No.32012 JAPAN MARKETING JOURNAL 123 Vol.31 No.32012 JAPAN
More informationJAPAN MARKETING JOURNAL 115 Vol.29 No.32010
Japan Marketing Academy JAPAN MARKETING JOURNAL 115 Vol.29 No.32010 JAPAN MARKETING JOURNAL 115 Vol.29 No.32010 JAPAN MARKETING JOURNAL 115 Vol.29 No.32010 JAPAN MARKETING JOURNAL 115 Vol.29 No.32010 JAPAN
More informationone way two way (talk back) (... ) C.E.Shannon 1948 A Mathematical theory of communication. 1 ( ) 0 ( ) 1
1 1.1 1.2 one way two way (talk back) (... ) 1.3 0 C.E.Shannon 1948 A Mathematical theory of communication. 1 ( ) 0 ( ) 1 ( (coding theory)) 2 2.1 (convolution code) (block code), 3 3.1 Q q Q n Q n 1 Q
More informationIPSJ SIG Technical Report Vol.2009-BIO-17 No /5/26 DNA 1 1 DNA DNA DNA DNA Correcting read errors on DNA sequences determined by Pyrosequencing
DNA 1 1 DNA DNA DNA DNA Correcting read errors on DNA sequences determined by Pyrosequencing Youhei Namiki 1 and Yutaka Akiyama 1 Pyrosequencing, one of the DNA sequencing technologies, allows us to determine
More information2 HI LO ZDD 2 ZDD 2 HI LO 2 ( ) HI (Zero-suppress ) Zero-suppress ZDD ZDD Zero-suppress 1 ZDD abc a HI b c b Zero-suppress b ZDD ZDD 5) ZDD F 1 F = a
ZDD 1, 2 1, 2 1, 2 2 2, 1 #P- Knuth ZDD (Zero-suppressed Binary Decision Diagram) 2 ZDD ZDD ZDD Knuth Knuth ZDD ZDD Path Enumeration Algorithms Using ZDD and Their Performance Evaluations Toshiki Saitoh,
More informationLMC6022 Low Power CMOS Dual Operational Amplifier (jp)
Low Power CMOS Dual Operational Amplifier Literature Number: JAJS754 CMOS CMOS (100k 5k ) 0.5mW CMOS CMOS LMC6024 100k 5k 120dB 2.5 V/ 40fA Low Power CMOS Dual Operational Amplifier 19910530 33020 23900
More information