diff --git a/articles/Daubechies wavelet - Wikipedia/Daubechies wavelet - Wikipedia.pdf.txt b/articles/Daubechies wavelet - Wikipedia/Daubechies wavelet - Wikipedia.pdf.txt new file mode 100644 index 0000000..c18909c --- /dev/null +++ b/articles/Daubechies wavelet - Wikipedia/Daubechies wavelet - Wikipedia.pdf.txt @@ -0,0 +1 @@ +https://en.wikipedia.org/w/index.php?title=Daubechies_wavelet&oldid=1193512728 diff --git a/articles/Daubechies wavelet - Wikipedia/Daubechies wavelet - Wikipedia.xopp.xml b/articles/Daubechies wavelet - Wikipedia/Daubechies wavelet - Wikipedia.xopp.xml new file mode 100644 index 0000000..a227438 --- /dev/null +++ b/articles/Daubechies wavelet - Wikipedia/Daubechies wavelet - Wikipedia.xopp.xml @@ -0,0 +1,44 @@ + + + Xournal++ document - see https://github.com/xournalpp/xournalpp + + + + 172.64824049 83.85844454 219.04610213 83.85844454 + ? + ? + ? + ? + ? + ? + 239.00191606 80.02337539 383.70920791 80.02337539 + 40.81922105 89.23095816 63.36417688 89.23095816 + 78.68624742 88.75165460 387.75007254 88.75165460 + 311.22816847 97.82036507 43.36981608 97.82036507 + 313.84784617 130.88690745 384.44627676 130.88690745 + 311.75502900 137.23707298 385.48496867 137.23707298 + 401.00956180 200.48319292 466.04243259 200.48319292 + 148.43777737 129.75364220 307.79961631 129.75364220 + 237.89073060 138.56947049 288.03588121 138.56947049 + 40.08387411 137.63048409 198.08614877 137.63048409 + 59.41514707 145.12947168 197.62468576 145.12947168 + 209.56967336 145.14474375 315.99866274 145.14474375 + 40.75312404 145.21740256 54.33912516 145.89522362 + + + + + + 165.23019488 352.81533520 171.86879545 352.81533520 + 207.35249235 339.86148777 383.99709849 339.86148777 + + + + + + + + + + + diff --git a/articles/Gradient - Wikipedia/Gradient - Wikipedia.pdf.txt b/articles/Gradient - Wikipedia/Gradient - Wikipedia.pdf.txt new file mode 100644 index 0000000..20d7ac9 --- /dev/null +++ b/articles/Gradient - Wikipedia/Gradient - Wikipedia.pdf.txt @@ -0,0 +1 @@ +https://en.wikipedia.org/w/index.php?title=Gradient&oldid=1206147282 diff --git a/articles/Gradient - Wikipedia/Gradient - Wikipedia.xopp.xml b/articles/Gradient - Wikipedia/Gradient - Wikipedia.xopp.xml new file mode 100644 index 0000000..8c13dca --- /dev/null +++ b/articles/Gradient - Wikipedia/Gradient - Wikipedia.xopp.xml @@ -0,0 +1,56 @@ + + + Xournal++ document - see https://github.com/xournalpp/xournalpp + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/articles/On the Sensor Pattern Noise Estimation in Image Forensics: A Systematic Empirical Evaluation/On the Sensor Pattern Noise Estimation in Image Forensics: A Systematic Empirical Evaluation.xopp.xml b/articles/On the Sensor Pattern Noise Estimation in Image Forensics: A Systematic Empirical Evaluation/On the Sensor Pattern Noise Estimation in Image Forensics: A Systematic Empirical Evaluation.xopp.xml index eabb0b9..519ce53 100644 --- a/articles/On the Sensor Pattern Noise Estimation in Image Forensics: A Systematic Empirical Evaluation/On the Sensor Pattern Noise Estimation in Image Forensics: A Systematic Empirical Evaluation.xopp.xml +++ b/articles/On the Sensor Pattern Noise Estimation in Image Forensics: A Systematic Empirical Evaluation/On the Sensor Pattern Noise Estimation in Image Forensics: A Systematic Empirical Evaluation.xopp.xml @@ -157,6 +157,8 @@ why face noise and strong enough to be visible? 315.27665980 693.20429626 403.73010304 693.20429626 395.68440216 724.37852029 553.24403230 724.37852029 344.38527255 735.14444484 561.98764426 735.14444484 + 105.94606163 313.10599570 109.57916489 316.18986176 + 109.38128549 316.08004360 115.80024582 304.96207818 @@ -577,6 +579,110 @@ unsure why a white Gaussian 315.29164619 160.49892605 558.25665019 160.49892605 316.52765882 171.36593027 376.86558928 171.36593027 ? + 566.77892269 161.75420100 569.07709368 165.08376688 + 569.07709368 164.86190837 572.76243593 156.29821112 + 512.34556881 170.91427543 558.29006497 170.91427543 + 314.70503417 183.66784106 366.34488515 183.66784106 + 447.35489181 182.87952759 561.59543788 182.87952759 + 316.49168511 194.46686748 387.06423329 194.46686748 + 404.56353712 205.91181413 481.35054688 205.91181413 + 344.20710013 217.96204401 391.51660804 217.96204401 + 427.05687922 219.16342878 564.01235703 219.16342878 + 316.09389361 230.07280737 337.91039406 230.07280737 + 366.27946403 229.94147347 407.42768521 229.94147347 + 440.23784850 230.61008085 561.28336583 230.61008085 + 316.21416818 242.55945028 351.89147695 241.08619882 + DC: dark current it seems + seems correct + 411.24616565 241.98231497 558.98526951 241.98231497 + 315.76755712 252.01905696 562.31230866 252.01905696 + 316.49685303 266.77258776 560.38557076 266.77258776 + 315.27653646 277.52300134 351.25923445 279.14545046 + 422.96041226 275.91057798 508.92439100 275.91057798 + 517.73298048 280.07250518 560.87384358 280.07250518 + 314.11782142 292.49768438 367.02370271 292.49768438 + 314.10111184 301.05395706 437.16061445 301.05395706 + 373.95491759 309.98576476 513.35272538 309.98576476 + zero-mean because sometimes too many electrons, sometimes +not enough + + 438.41143908 329.62436886 445.45967526 329.62436886 + 342.77820659 323.65494680 343.93106466 325.85680511 + 375.84540913 323.95601219 427.93247419 323.95601219 + 437.59149797 323.09119637 438.81109103 326.25356303 + 537.48443458 324.63657468 562.48603923 324.63657468 + 312.18312699 335.55814954 332.15536137 335.55814954 + 365.73293998 334.51006229 404.51895525 336.15304525 + 444.54788809 335.99591429 512.91923804 335.99591429 + 443.45466774 359.93626404 562.41656273 359.93626404 + 315.56507828 371.62749621 413.91176218 371.62749621 + 419.52950360 368.19496827 422.65777926 370.82698646 + 422.65777926 370.82698646 426.61815785 364.57460392 + 541.92608369 335.00197872 561.35840192 332.79012911 + 313.59693023 348.65922288 555.05375615 348.65922288 + 315.27529616 359.99848571 389.45402178 359.99848571 + 392.58266064 287.24148602 482.46420302 287.24148602 + 489.37367794 292.76035218 545.46521075 292.76035218 + 448.36094670 328.87610671 509.19273918 328.87610671 + we take into account +all kinds of noises +but are there all listed +and considered here? + Analog-to-Digital Converter + 392.38149100 304.85907430 305.94135591 277.24019591 + why? + 334.95371041 428.88280631 563.10993066 428.88280631 + 314.43161711 441.94470686 345.15892638 440.47808756 + 325.78455768 399.78997017 546.44201414 399.78997017 + 525.57211908 417.70175052 541.10027483 417.70175052 + + + + + 409.20315176 406.04808047 433.37581712 412.52512663 + 433.23918485 413.01507342 458.71554980 408.00806322 + 433.44131916 412.97531494 348.21071692 421.14273661 + why absolute? + 392.32423052 441.89519826 434.00749681 441.89519826 + 469.86277298 442.50537376 563.88633217 442.50537376 + 369.30128067 454.55804978 561.63803995 454.55804978 + 314.95960557 467.09545883 357.23897146 467.09545883 + 393.95460377 466.02580416 539.09559946 466.02580416 + 314.06118109 478.03002238 477.90846746 478.03002238 + 401.30409982 488.90208864 474.25615178 488.90208864 + + otherwise correct + otherwise correct + 371.89426389 500.32371078 555.95491422 500.32371078 + 315.83770293 512.65681499 527.17118216 512.65681499 + 316.22233348 524.81501980 463.20653527 524.81501980 + 525.65218728 524.04413942 561.60543491 524.04413942 + 314.97758991 535.82687810 344.78414246 535.82687810 + to check after above + 458.27789646 535.94739384 563.62297231 535.94739384 + 315.87063976 549.01484129 562.77609428 549.01484129 + 372.01229902 555.64403360 371.51586928 558.76924296 + 407.31672563 558.78941505 526.84672284 558.78941505 + 556.35345027 560.19329589 564.65814893 559.26520759 + 316.60613716 571.31362869 558.49224449 571.31362869 + 454.68974544 584.19208556 557.05023763 584.19208556 + 316.75597219 596.43919570 355.88222483 596.43919570 + 328.45564529 627.56751780 569.17541861 627.56751780 + 313.58866157 639.15756223 379.99348121 639.15756223 + 418.23635392 642.83074266 561.01663257 642.83074266 + 315.79329333 656.40362924 503.22847887 656.40362924 + 385.27481797 697.40216595 561.45755205 697.40216595 + 315.22523631 710.27252642 560.69244594 710.27252642 + 314.57028945 723.69326964 373.35500533 723.69326964 + 419.06945597 723.79785098 555.94213665 723.79785098 + 312.91183720 734.30056648 445.63594705 734.30056648 + 471.15082366 735.30524325 547.59591431 735.30524325 + i.e. D8 + 481.66235907 694.22884332 498.80405145 693.54845309 + 1 + 372.53639432 342.03797193 440.14635521 342.03797193 + 370.53286329 236.46567101 320.15005085 239.92584728 + fixed for a camera in all conditions? @@ -585,6 +691,265 @@ unsure why a white Gaussian 321.36116578 107.06404749 397.15094586 107.06404749 55.51631248 369.91599576 212.45550725 369.91599576 318.70068996 574.58677853 458.52920990 574.58677853 + 46.41166737 56.34579183 122.75468202 59.97049723 + + 136.85976030 56.77626177 169.74798343 56.77626177 + 210.21763326 56.96604477 213.84736876 56.96604477 + 234.01365124 56.90404704 298.47858791 56.90404704 + 48.39965160 68.06617103 292.04445111 68.06617103 + 48.80500548 78.49465807 63.32163914 79.14757222 + 94.00552071 80.22668418 213.89141267 80.22668418 + 240.42370680 80.50878333 294.98619459 80.50878333 + 49.91912143 91.68787530 109.00987130 91.68787530 + 124.66186592 101.93477910 124.66186592 117.71048868 234.42102061 117.71048868 234.42102061 101.93477910 124.66186592 101.93477910 + 68.47389354 129.24359993 187.07496247 129.24359993 + 200.54845048 129.63017707 204.79511423 126.67785163 + 239.35741128 127.06747783 270.20461967 127.06747783 + 47.90201170 138.03103337 65.97986726 139.76853746 + 105.20931531 138.45301071 162.92163797 138.45301071 + 181.47051328 139.65138087 238.59035193 139.65138087 + 47.45419049 151.33026173 73.89024898 151.33026173 + 142.72968280 151.18164977 297.64924626 151.18164977 + 48.52801825 161.95356187 265.31657741 161.95356187 + 117.99112359 174.92225050 291.80041063 174.92225050 + 47.90409179 185.51778172 106.48776344 185.51778172 + 135.37302059 186.22120361 289.07569919 186.22120361 + 79.84740241 197.12191732 180.26366418 197.12191732 + 91.25706084 205.78669895 91.25706084 233.33486307 257.24161221 233.33486307 257.24161221 205.78669895 91.25706084 205.78669895 + 74.52609696 243.52963985 94.17824960 241.75708008 + 124.26769196 243.44950274 264.89012007 243.44950274 + 45.89230932 257.64400315 82.97993299 254.67293541 + 122.83964251 257.92927196 169.60141510 257.92927196 + 208.12297469 255.97655874 294.58478097 255.97655874 + 48.58019264 267.18686491 136.32598696 267.18686491 + 63.14972851 280.26681871 274.64687888 280.26681871 + 67.39658175 293.29516229 128.48088600 293.29516229 + 148.26541359 292.88849920 296.22415449 292.88849920 + 48.11425790 303.89884158 233.53215973 303.89884158 + 123.16585841 315.23789770 293.78760696 315.23789770 + 48.56754848 327.77503112 231.13059305 327.77503112 + 259.66095354 338.70058528 292.29747942 340.18987450 + 82.50240058 352.50290998 155.03053924 348.11666090 + 56.64819658 388.22071797 85.16629114 388.22071797 + 188.53086459 383.66931961 292.32423913 383.66931961 + 50.33372588 395.90418179 248.77735553 395.90418179 + to deepen + to deepen + 281.40282939 273.35962713 283.64005345 276.63610131 + 283.81776076 276.63610131 290.42056226 266.88435250 + 53.39553786 274.86142270 54.81409909 277.31844282 + 55.01232381 277.40891105 58.68608562 272.84150938 + p + 184.19188468 409.75451641 272.92491002 409.75451641 + 47.55291055 422.79969016 186.52845985 422.79969016 + 267.05895906 416.46388028 298.08026483 416.46388028 + 49.93235129 430.86278888 292.68197454 430.86278888 + 174.64742087 439.47392757 220.83558918 439.47392757 + 246.17383708 439.85549183 293.78824942 439.85549183 + 47.20104888 451.82418926 85.56400216 451.82418926 + 99.30524152 450.59191759 293.11805745 450.59191759 + 49.51162717 463.19253022 293.47976345 463.19253022 + 47.33991071 474.46242243 64.66406488 476.17634360 + 86.57889356 475.29166577 125.30751759 475.29166577 + 175.48222834 475.98762864 293.46311640 475.98762864 + 48.89820018 487.97947832 136.48936899 487.97947832 + 165.93369911 487.02773821 279.78445729 487.02773821 + 50.19739633 498.97268046 145.27730813 498.97268046 + 165.63961039 499.80876268 208.18909350 499.80876268 + 228.06076650 498.85636796 280.51825042 498.85636796 + 73.82341580 509.57890926 196.13288781 509.57890926 + 124.22065533 518.13926459 126.36803580 520.28664506 + 126.33927579 520.31502495 128.94426688 515.80304804 + 216.73490580 516.21773111 219.46017036 518.14965210 + 219.46017036 518.14965210 221.61265787 513.83497861 + 5.15908035 515.50786656 41.39788026 515.50786656 27.69735837 507.59786656 41.39788026 515.50786656 27.69735837 523.41786656 + 5.81333385 515.58681505 5.81333385 548.10322737 13.72333385 534.40270549 5.81333385 548.10322737 -2.09666615 534.40270549 + 297.54239451 520.51421355 299.86082372 523.36299123 + 299.86082372 523.58588333 304.78345007 515.05964438 + 145.38748887 532.67271120 147.44597584 534.73119817 + 147.67928610 534.84180495 151.39909833 526.75712157 + 227.49923829 530.97456938 228.96372152 532.81908373 + 228.96372152 533.06678181 237.31463856 523.28142434 + 299.74637167 534.96926291 302.62889582 538.44032625 + 302.73015247 538.57228030 309.40478796 528.76340882 + n + 161.37134477 543.10557374 163.55572876 545.28995774 + 163.53247993 545.31296172 168.72247732 538.44230728 + 281.83200735 539.23761467 283.30103835 541.41075661 + 283.30103835 541.41075661 286.41114595 536.02389224 + 186.25028173 565.62916537 187.21835245 567.06884262 + 187.21835245 567.06884262 190.17593194 562.38216552 + ? + 220.36297034 558.23346817 261.15384189 558.23346817 + 47.04963764 568.89266808 78.78596473 567.18158926 + 63.10456095 585.85251884 94.27313444 585.85251884 + as depend on neighbor/local region? if so, which of both? + 93.22729328 398.97333652 210.18896228 398.97333652 + 159.16002917 397.79683525 135.61935027 374.25615635 + smooth + + could have a training and testing set to find optimal alternative 20 threshold + east-west +difference +far greater +than north +-south +difference + 58.29522773 612.10248594 64.47830424 611.11996228 + 31.64246917 587.89024423 31.64246917 606.06493756 + 42.16834984 588.35661394 42.16834984 605.98195644 + 21.86609526 595.23853443 53.41589705 595.23853443 + 21.39329580 602.19265066 53.41074610 602.19265066 + 20.40474722 595.74228538 20.40474722 602.51924555 29.42418551 602.51924555 29.42418551 595.74228538 20.40474722 595.74228538 + 43.88162503 595.80240544 43.88162503 602.08531311 55.21092241 602.08531311 55.21092241 595.80240544 43.88162503 595.80240544 + 30.98223812 586.56832649 30.98223812 594.60767004 41.63799706 594.60767004 41.63799706 586.56832649 30.98223812 586.56832649 + 31.55971796 604.45321647 31.55971796 609.55026034 41.82417975 609.55026034 41.82417975 604.45321647 31.55971796 604.45321647 + 186.32044476 446.16113832 193.24236490 589.56371787 + only other, no? Maybe just design instances + weakest direction mean +that way keep edge but smooth otherwise + vert. edge + no - s + hor. edge + 99.64615152 685.75885756 102.85700182 688.62208818 + 102.85700182 688.62208818 111.18901291 679.56025578 + 195.63645236 687.41000582 198.36798732 690.37345950 + 198.60217992 690.37345950 207.83403666 678.07013969 + 61.35368058 640.52502651 30.90881930 734.62626559 + no + s + for. diag. + backward diagonal + 50.34236060 693.00744149 174.10761615 693.00744149 + 48.80397620 705.16595637 263.30196034 705.16595637 + really eliminate and leak only in this case? + 47.91155081 717.57942164 47.91155081 717.57942164 + 67.50545245 717.31516901 291.81097963 717.31516901 + 48.91769183 727.13716698 296.64184777 727.13716698 + 316.62953058 57.32571430 334.13330054 57.32571430 + seem appropriate, hoping the resolution is not so good that we are not always smooth +what happens if apply many times the filter? Get a single color image or only edges? + ? + Independent and identically distributed random variables + 360.62404437 56.50215566 412.62421835 56.50215566 + 503.93888999 56.74246362 563.85102495 56.74246362 + 314.16312679 67.89352828 377.15982828 67.89352828 + 326.73579824 79.29706273 566.05358802 79.29706273 + 314.14872554 92.91554753 396.99601563 92.91554753 + 385.42358496 72.64296108 503.46352239 72.64296108 + why this value and what does it mean? + 331.12401113 122.99908986 558.38174858 122.99908986 + 315.90684961 135.29700058 338.35950301 137.08041297 + 396.62717285 134.53080577 565.25802294 134.53080577 + 317.17581346 146.79726114 487.24290093 146.79726114 + 548.86235340 152.79092071 564.85673461 152.15407877 + 315.02551369 163.93430240 546.29901042 163.93430240 + 557.46052063 158.65713954 561.32980613 158.16561932 + 315.04342912 169.37444402 562.98915459 169.37444402 + ? + 313.56495807 182.02036202 559.28678767 182.02036202 + 316.53089235 192.43854769 558.68283155 192.43854769 + 317.12699391 204.78283182 411.18959585 204.78283182 + unclear + 439.68470953 203.68699306 562.53207490 203.68699306 + 314.84570477 216.45583848 431.48563399 216.45583848 + 315.38871463 229.19762127 380.71589667 229.19762127 + 402.44169532 227.68716083 561.44238776 227.68716083 + 313.26594261 238.96533893 557.94773848 238.96533893 + 313.98300779 251.54789832 559.21175853 251.54789832 + 314.15730428 262.37092107 562.79947271 262.37092107 + 316.27559744 275.45661126 338.08315747 274.52221810 + 376.62814126 274.47200321 517.09067455 274.47200321 + 351.04714329 286.04785311 560.32406307 286.04785311 + 312.97922679 296.81250429 561.84358933 296.81250429 + 315.49124603 308.36361711 560.23396916 308.36361711 + 316.04624545 320.02204164 363.83179326 320.02204164 + 4x4, should not be 15 as do not count itself? or just ease comprehension here? + + i.e. PRNU signal + 3 + 2 + 4 + 567.97026449 248.20981400 572.87123772 252.48766610 + 572.87123772 252.48766610 580.53180769 237.68657327 + that way we get the sum of 2 possibly different PRNU +how do they manage this blurred PRNU, feels like +our issue with multiple sensors + really? + 563.49312904 320.13842305 563.49312904 320.13842305 + 314.39006709 333.73627065 532.75157963 333.73627065 + 315.73355225 345.23487748 425.45149941 345.23487748 + 435.36695252 343.20120028 563.22885822 343.20120028 + 317.33174663 355.66226203 377.80094677 355.66226203 + 384.19355272 354.44950784 565.30581008 354.44950784 + 316.14350558 367.35519961 365.22577724 367.35519961 + 394.98787633 367.54729094 456.70659841 367.54729094 + 489.62594101 367.71454184 508.45816947 367.71454184 + 315.94102674 377.72106877 366.60925581 377.72106877 + 316.48079804 388.13849647 316.48079804 404.73507738 422.67634930 404.73507738 422.67634930 388.13849647 316.48079804 388.13849647 + 423.53784042 270.63858303 416.51850512 381.84463282 + so get the mean of 2 possibly +different PRNU + 432.69434899 380.44435230 325.58694691 273.33695021 + 524.16630888 407.59222709 528.37640103 410.45196075 + 528.37640103 410.45196075 532.37784730 401.57107422 + 314.26844886 434.50087917 346.13238133 434.50087917 + 441.71294318 433.56376425 544.88126402 433.56376425 + 365.02576274 445.60176340 420.03471078 445.60176340 + + these are conditions, to the following + 390.41055227 445.60207347 313.69884147 421.49561895 + 442.26677121 439.48314369 562.28575784 439.48314369 + 387.30225921 452.65916899 401.10858044 451.73128741 + 313.38197949 415.80469573 538.66277663 415.80469573 + 412.63857372 451.38234991 561.04072662 451.38234991 + 315.52311483 463.90778663 502.39420509 463.90778663 + 433.58202421 456.78312925 312.09475568 401.79916868 + ? + 546.53214135 459.51595620 543.41681992 465.51890079 + For the threshold see the following. But how do we +figure out an alternative signed + 379.36217405 317.42390775 443.00946924 455.29135944 + 378.98002410 315.45608699 350.74047933 468.03746605 + 537.24719257 461.31426267 561.01478343 461.31426267 + 314.58706954 477.60916533 347.72873364 477.60916533 + 428.38520871 477.24139325 561.24097973 477.24139325 + 315.96023805 488.19927931 461.52338554 488.19927931 + the shot noise is not modelled as taking that wide +values, is it? + 336.92464484 500.46176306 462.81723593 500.46176306 + 503.04561925 501.70540517 564.04704274 501.70540517 + 315.83748449 513.19617240 562.78673277 513.19617240 + 314.76798193 526.41558127 364.70550101 526.41558127 + 373.80927459 529.03975441 546.83274814 529.03975441 + 441.28484507 534.86106293 566.00470703 534.86106293 + 313.32469260 547.27039269 563.90668162 547.27039269 + 353.62462075 558.59889811 514.53722134 558.59889811 + still unclezr how they precisely proceed + 339.92855204 596.66641656 358.20481865 596.66641656 + 483.94054301 590.47410117 563.45503483 590.47410117 + 312.63140289 600.90707510 542.73110135 600.90707510 + 314.07044656 612.81342865 560.34679136 612.81342865 + 312.63238007 625.30420099 368.72506285 625.30420099 + like? + for random noise, right? + 471.85007597 625.50706615 541.95760162 625.50706615 + 314.00783989 635.54209348 353.50132961 635.54209348 + 430.98294755 636.72296044 521.46786215 636.72296044 + 314.83328426 653.18373034 466.11791593 653.18373034 + 362.95995618 659.02625033 476.90970793 659.02625033 + 376.81217092 668.70179473 376.81217092 688.20301486 525.97509380 688.20301486 525.97509380 668.70179473 376.81217092 668.70179473 + 388.56750019 697.11892265 468.12000933 697.11892265 + 502.30043812 697.54736389 556.31950822 697.54736389 + 315.93668473 709.22785494 368.94647512 709.22785494 + 347.13883237 696.56573980 347.13883237 696.56573980 + 396.07238232 707.73024809 427.35793430 707.73024809 + 450.47606385 709.33396281 486.95242299 709.33396281 + 520.75107584 709.23681801 563.77038442 711.37750792 + 334.67188292 722.46367364 362.63472971 722.46367364 + 463.59755292 721.12433564 484.24087967 721.12433564 + 511.44765027 721.26582403 564.96356814 721.26582403 + 316.02800017 730.89063950 469.74763762 730.89063950 + 1 @@ -595,6 +960,47 @@ unsure why a white Gaussian 47.80585596 537.29194686 175.81070545 537.29194686 313.63286686 163.41478363 512.86929736 163.41478363 317.68667511 428.15766075 391.38114059 428.15766075 + 5 + 7 + 6 + 121.66477014 50.97736894 121.66477014 75.67376016 220.67795882 75.67376016 220.67795882 50.97736894 121.66477014 50.97736894 + 90.84086598 82.62351438 90.84086598 102.91016794 267.81457693 102.91016794 267.81457693 82.62351438 90.84086598 82.62351438 + 153.10080064 116.72213204 167.69047297 117.62874587 + 286.39209850 112.51034373 295.00157003 113.24905028 + 48.72295721 124.03293821 74.97465374 125.97654194 + 98.32741909 124.68418592 142.15897386 122.64132539 + 159.81100486 129.33725235 296.23010756 129.33725235 + 47.32696717 142.22935129 96.19222836 142.22935129 + 124.82475270 136.61827293 167.24450193 136.61827293 + 221.91645456 136.83940785 297.00942587 136.83940785 + 48.27315548 150.02589168 241.75767346 150.02589168 + 63.01620992 180.85585725 216.94396283 180.85585725 + 49.22095533 196.55122127 274.86172559 196.55122127 + 73.46150478 204.04908504 191.26757109 204.04908504 + 192.53252633 242.36309879 209.69835980 242.36309879 + 47.43529473 255.32974719 154.00430920 255.32974719 + 215.45631593 251.14099056 292.88840465 251.14099056 + 47.60937457 261.60602564 79.93946240 261.60602564 + 88.52537840 269.44782576 88.52537840 280.83859080 254.65052633 280.83859080 254.65052633 269.44782576 88.52537840 269.44782576 + `.` is not a typo + 108.28067893 290.14985321 108.28067893 290.14985321 + 78.07185916 289.84909058 78.07185916 289.84909058 + 152.53207030 290.30368843 292.00071385 290.30368843 + 135.19759266 307.40673785 186.50705342 307.40673785 + 228.67060192 302.25841333 294.88324682 302.25841333 + 47.26546386 313.69019767 196.96510695 313.69019767 + 234.07044539 320.64065292 298.93479679 320.64065292 + 48.21213948 335.41517715 111.22032561 335.41517715 + 147.59588688 324.65804837 147.59588688 332.09919641 293.76285201 332.09919641 293.76285201 324.65804837 147.59588688 324.65804837 + 48.56000421 343.59819496 106.70223961 343.59819496 + 143.27335157 343.23208185 293.77192262 343.23208185 + 48.86378046 353.84024849 164.12207883 353.84024849 + 177.02466448 352.81745034 241.45308792 352.81745034 + 82.82033523 365.39435550 212.38955017 365.39435550 + 218.24675713 372.11177528 247.77338381 372.11177528 + 237.84039133 358.89389861 206.71850860 242.74545101 + 46.58963278 382.50527023 202.56231204 382.50527023 + 136.51804152 392.15665604 210.57548036 387.27930856 @@ -603,6 +1009,8 @@ unsure why a white Gaussian 381.42528733 197.68428757 490.97943736 197.68428757 317.23552939 440.66761664 486.42751461 440.66761664 319.91997570 586.40761369 441.19501087 586.40761369 + 8 + 9 @@ -612,6 +1020,8 @@ unsure why a white Gaussian 87.00674914 574.74089845 258.24542230 574.74089845 319.39671002 95.85735748 498.25035105 95.85735748 319.32304762 517.35903459 421.01378198 517.35903459 + 10 + 11 @@ -622,6 +1032,9 @@ unsure why a white Gaussian 341.31909075 348.19610327 532.11972274 348.19610327 318.88132334 481.13929046 510.85312618 481.13929046 318.88628420 642.24731954 420.29010166 642.24731954 + 12 + 13 + 14 @@ -632,6 +1045,9 @@ unsure why a white Gaussian 318.41500324 322.14998295 398.92002418 322.14998295 331.24013999 638.08157945 542.82775915 638.08157945 315.02077988 709.10639112 474.01809374 709.10639112 + 15 + 16 + 17 @@ -639,6 +1055,7 @@ unsure why a white Gaussian 52.55896719 314.02027189 191.07654130 314.02027189 373.82000921 241.53100834 505.05535106 241.53100834 + 18 diff --git a/articles/Sécurité multimédia 1/Sécurité multimédia 1.pdf.txt b/articles/Sécurité multimédia 1/Sécurité multimédia 1.pdf.txt new file mode 100644 index 0000000..44c68da --- /dev/null +++ b/articles/Sécurité multimédia 1/Sécurité multimédia 1.pdf.txt @@ -0,0 +1 @@ +https://books.google.fr/books?id=2Ws3EAAAQBAJ diff --git a/articles/Sécurité multimédia 1/Sécurité multimédia 1.xopp.xml b/articles/Sécurité multimédia 1/Sécurité multimédia 1.xopp.xml new file mode 100644 index 0000000..a37da41 --- /dev/null +++ b/articles/Sécurité multimédia 1/Sécurité multimédia 1.xopp.xml @@ -0,0 +1,323 @@ + + + Xournal++ document - see https://github.com/xournalpp/xournalpp + + + + 56.33486938 235.34841919 383.90442724 235.34841919 + 83.70358276 280.80239868 364.15401885 280.80239868 + 126.78527832 309.83660889 326.41296851 309.83660889 + 133.67620850 465.43853760 306.92145184 465.43853760 + 156.61151123 497.09783936 288.53984953 497.09783936 + 35.66342163 111.55590820 404.00446554 111.55590820 + 176.59283447 620.62936401 258.17791321 620.62936401 + 181.69464111 638.43688965 262.71247060 638.43688965 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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- Wikipedia.xopp.xml @@ -0,0 +1,94 @@ + + + Xournal++ document - see https://github.com/xournalpp/xournalpp + + + + 62.38388062 117.45330811 110.18388434 117.45330811 + 50.95126343 85.99273682 141.33876388 85.99273682 + 126.34671021 132.85809326 226.32006749 132.85809326 + 46.90173340 149.93865967 226.37132498 149.93865967 + 49.91009521 166.18173218 131.44212303 166.18173218 + 183.15277100 165.64047241 224.72326575 165.64047241 + 45.22964478 178.92517090 119.46238422 178.92517090 + 146.28335571 181.98068237 228.00074457 181.98068237 + 46.11941528 196.51092529 177.41695603 196.51092529 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/articles/Wiener filter - Wikipedia/Wiener filter - Wikipedia.pdf.txt b/articles/Wiener filter - Wikipedia/Wiener filter - Wikipedia.pdf.txt new file mode 100644 index 0000000..8cb545b --- /dev/null +++ b/articles/Wiener filter - Wikipedia/Wiener filter - Wikipedia.pdf.txt @@ -0,0 +1 @@ +https://en.wikipedia.org/w/index.php?title=Wiener_filter&oldid=1178093606 diff --git a/articles/Wiener filter - Wikipedia/Wiener filter - Wikipedia.xopp.xml b/articles/Wiener filter - Wikipedia/Wiener filter - Wikipedia.xopp.xml new file mode 100644 index 0000000..3501e29 --- /dev/null +++ b/articles/Wiener filter - Wikipedia/Wiener filter - Wikipedia.xopp.xml @@ -0,0 +1,64 @@ + + + Xournal++ document - see https://github.com/xournalpp/xournalpp + + + + 48.93957520 85.40319824 192.34981557 85.40319824 + 61.62439084 124.85156213 137.34677309 124.85156213 + 342.48224799 124.38810078 554.42637490 124.38810078 + 44.52724442 139.51391712 458.26500586 139.51391712 + 469.32119001 138.42048903 551.81987263 138.42048903 + 44.88236953 154.11074299 308.99097872 154.11074299 + ? + ? + 414.37152774 153.83314997 549.41416911 153.83314997 + 45.80421362 169.06011454 370.53950825 169.06011454 + + + + + + + + + + + + + + + + + + 51.71200406 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