181 research outputs found

    Deep Learning Techniques for Multi-Dimensional Medical Image Analysis

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    Optimization of scientific algorithms in heterogeneous systems and accelerators for high performance computing

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    Actualmente, la computaciรณn de propรณsito general en GPU es uno de los pilares bรกsicos de la computaciรณn de alto rendimiento. Aunque existen cientos de aplicaciones aceleradas en GPU, aรบn hay algoritmos cientรญficos poco estudiados. Por ello, la motivaciรณn de esta tesis ha sido investigar la posibilidad de acelerar significativamente en GPU un conjunto de algoritmos pertenecientes a este grupo. En primer lugar, se ha obtenido una implementaciรณn optimizada del algoritmo de compresiรณn de vรญdeo e imagen CAVLC (Context-Adaptive Variable Length Encoding), que es el mรฉtodo entrรณpico mรกs usado en el estรกndar de codificaciรณn de vรญdeo H.264. La aceleraciรณn respecto a la mejor implementaciรณn anterior estรก entre 2.5x y 5.4x. Esta soluciรณn puede aprovecharse como el componente entrรณpico de codificadores H.264 software, y utilizarse en sistemas de compresiรณn de vรญdeo e imagen en formatos distintos a H.264, como imรกgenes mรฉdicas. En segundo lugar, se ha desarrollado GUD-Canny, un detector de bordes de Canny no supervisado y distribuido. El sistema resuelve las principales limitaciones de las implementaciones del algoritmo de Canny, que son el cuello de botella causado por el proceso de histรฉresis y el uso de umbrales de histรฉresis fijos. Dada una imagen, esta se divide en un conjunto de sub-imรกgenes, y, para cada una de ellas, se calcula de forma no supervisada un par de umbrales de histรฉresis utilizando el mรฉtodo de MedinaCarnicer. El detector satisface el requisito de tiempo real, al ser 0.35 ms el tiempo promedio en detectar los bordes de una imagen 512x512. En tercer lugar, se ha realizado una implementaciรณn optimizada del mรฉtodo de compresiรณn de datos VLE (Variable-Length Encoding), que es 2.6x mรกs rรกpida en promedio que la mejor implementaciรณn anterior. Ademรกs, esta soluciรณn incluye un nuevo mรฉtodo scan inter-bloque, que se puede usar para acelerar la propia operaciรณn scan y otros algoritmos, como el de compactaciรณn. En el caso de la operaciรณn scan, se logra una aceleraciรณn de 1.62x si se usa el mรฉtodo propuesto en lugar del utilizado en la mejor implementaciรณn anterior de VLE. Esta tesis doctoral concluye con un capรญtulo sobre futuros trabajos de investigaciรณn que se pueden plantear a partir de sus contribuciones

    ์‹œ๊ณต๊ฐ„ ํ•ด์ƒ๋„ ํ–ฅ์ƒ์„ ํ†ตํ•œ ์‹์ƒ ๋ณ€ํ™” ๋ชจ๋‹ˆํ„ฐ๋ง

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ํ™˜๊ฒฝ๋Œ€ํ•™์› ํ˜‘๋™๊ณผ์ • ์กฐ๊ฒฝํ•™, 2023. 2. ๋ฅ˜์˜๋ ฌ.์œก์ƒ ์ƒํƒœ๊ณ„์—์„œ ๋Œ€๊ธฐ๊ถŒ๊ณผ ์ƒ๋ฌผ๊ถŒ์˜ ์ƒํ˜ธ ์ž‘์šฉ์„ ์ดํ•ดํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์‹์ƒ ๋ณ€ํ™”์˜ ๋ชจ๋‹ˆํ„ฐ๋ง์ด ํ•„์š”ํ•˜๋‹ค. ์ด ๋•Œ, ์œ„์„ฑ์˜์ƒ์€ ์ง€ํ‘œ๋ฉด์„ ๊ด€์ธกํ•˜์—ฌ ์‹์ƒ์ง€๋„๋ฅผ ์ œ๊ณตํ•  ์ˆ˜ ์žˆ์ง€๋งŒ, ์ง€ํ‘œ๋ณ€ํ™”์˜ ์ƒ์„ธํ•œ ์ •๋ณด๋Š” ๊ตฌ๋ฆ„์ด๋‚˜ ์œ„์„ฑ ์ด๋ฏธ์ง€์˜ ๊ณต๊ฐ„ ํ•ด์ƒ๋„์— ์˜ํ•ด ์ œํ•œ๋˜์—ˆ๋‹ค. ๋˜ํ•œ ์œ„์„ฑ์˜์ƒ์˜ ์‹œ๊ณต๊ฐ„ ํ•ด์ƒ๋„๊ฐ€ ์‹์ƒ์ง€๋„๋ฅผ ํ†ตํ•œ ๊ด‘ํ•ฉ์„ฑ ๋ชจ๋‹ˆํ„ฐ๋ง์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์€ ์™„์ „ํžˆ ๋ฐํ˜€์ง€์ง€ ์•Š์•˜๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ณ ํ•ด์ƒ๋„ ์‹์ƒ ์ง€๋„๋ฅผ ์ผ๋‹จ์œ„๋กœ ์ƒ์„ฑํ•˜๊ธฐ ์œ„์„ฑ ์˜์ƒ์˜ ์‹œ๊ณต๊ฐ„ ํ•ด์ƒ๋„๋ฅผ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•˜์˜€๋‹ค. ๊ณ ํ•ด์ƒ๋„ ์œ„์„ฑ์˜์ƒ์„ ํ™œ์šฉํ•œ ์‹์ƒ ๋ณ€ํ™” ๋ชจ๋‹ˆํ„ฐ๋ง์„ ์‹œ๊ณต๊ฐ„์ ์œผ๋กœ ํ™•์žฅํ•˜๊ธฐ ์œ„ํ•ด 1) ์ •์ง€๊ถค๋„ ์œ„์„ฑ์„ ํ™œ์šฉํ•œ ์˜์ƒ์œตํ•ฉ์„ ํ†ตํ•ด ์‹œ๊ฐ„ํ•ด์ƒ๋„ ํ–ฅ์ƒ, 2) ์ ๋Œ€์ ์ƒ์„ฑ๋„คํŠธ์›Œํฌ๋ฅผ ํ™œ์šฉํ•œ ๊ณต๊ฐ„ํ•ด์ƒ๋„ ํ–ฅ์ƒ, 3) ์‹œ๊ณต๊ฐ„ํ•ด์ƒ๋„๊ฐ€ ๋†’์€ ์œ„์„ฑ์˜์ƒ์„ ํ† ์ง€ํ”ผ๋ณต์ด ๊ท ์งˆํ•˜์ง€ ์•Š์€ ๊ณต๊ฐ„์—์„œ ์‹๋ฌผ ๊ด‘ํ•ฉ์„ฑ ๋ชจ๋‹ˆํ„ฐ๋ง์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์ด์ฒ˜๋Ÿผ, ์œ„์„ฑ๊ธฐ๋ฐ˜ ์›๊ฒฉํƒ์ง€์—์„œ ์ƒˆ๋กœ์šด ๊ธฐ์ˆ ์ด ๋“ฑ์žฅํ•จ์— ๋”ฐ๋ผ ํ˜„์žฌ ๋ฐ ๊ณผ๊ฑฐ์˜ ์œ„์„ฑ์˜์ƒ์€ ์‹œ๊ณต๊ฐ„ ํ•ด์ƒ๋„ ์ธก๋ฉด์—์„œ ํ–ฅ์ƒ๋˜์–ด ์‹์ƒ ๋ณ€ํ™”์˜ ๋ชจ๋‹ˆํ„ฐ๋ง ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ œ2์žฅ์—์„œ๋Š” ์ •์ง€๊ถค๋„์œ„์„ฑ์˜์ƒ์„ ํ™œ์šฉํ•˜๋Š” ์‹œ๊ณต๊ฐ„ ์˜์ƒ์œตํ•ฉ์œผ๋กœ ์‹๋ฌผ์˜ ๊ด‘ํ•ฉ์„ฑ์„ ๋ชจ๋‹ˆํ„ฐ๋ง ํ–ˆ์„ ๋•Œ, ์‹œ๊ฐ„ํ•ด์ƒ๋„๊ฐ€ ํ–ฅ์ƒ๋จ์„ ๋ณด์˜€๋‹ค. ์‹œ๊ณต๊ฐ„ ์˜์ƒ์œตํ•ฉ ์‹œ, ๊ตฌ๋ฆ„ํƒ์ง€, ์–‘๋ฐฉํ–ฅ ๋ฐ˜์‚ฌ ํ•จ์ˆ˜ ์กฐ์ •, ๊ณต๊ฐ„ ๋“ฑ๋ก, ์‹œ๊ณต๊ฐ„ ์œตํ•ฉ, ์‹œ๊ณต๊ฐ„ ๊ฒฐ์ธก์น˜ ๋ณด์™„ ๋“ฑ์˜ ๊ณผ์ •์„ ๊ฑฐ์นœ๋‹ค. ์ด ์˜์ƒ์œตํ•ฉ ์‚ฐ์ถœ๋ฌผ์€ ๊ฒฝ์ž‘๊ด€๋ฆฌ ๋“ฑ์œผ๋กœ ์‹์ƒ ์ง€์ˆ˜์˜ ์—ฐ๊ฐ„ ๋ณ€๋™์ด ํฐ ๋‘ ์žฅ์†Œ(๋†๊ฒฝ์ง€์™€ ๋‚™์—ฝ์ˆ˜๋ฆผ)์—์„œ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, ์‹œ๊ณต๊ฐ„ ์˜์ƒ์œตํ•ฉ ์‚ฐ์ถœ๋ฌผ์€ ๊ฒฐ์ธก์น˜ ์—†์ด ํ˜„์žฅ๊ด€์ธก์„ ์˜ˆ์ธกํ•˜์˜€๋‹ค (R2 = 0.71, ์ƒ๋Œ€ ํŽธํ–ฅ = 5.64% ๋†๊ฒฝ์ง€; R2 = 0.79, ์ƒ๋Œ€ ํŽธํ–ฅ = -13.8%, ํ™œ์—ฝ์ˆ˜๋ฆผ). ์‹œ๊ณต๊ฐ„ ์˜์ƒ์œตํ•ฉ์€ ์‹์ƒ ์ง€๋„์˜ ์‹œ๊ณต๊ฐ„ ํ•ด์ƒ๋„๋ฅผ ์ ์ง„์ ์œผ๋กœ ๊ฐœ์„ ํ•˜์—ฌ, ์‹๋ฌผ ์ƒ์žฅ๊ธฐ๋™์•ˆ ์œ„์„ฑ์˜์ƒ์ด ํ˜„์žฅ ๊ด€์ธก์„ ๊ณผ์†Œ ํ‰๊ฐ€๋ฅผ ์ค„์˜€๋‹ค. ์˜์ƒ์œตํ•ฉ์€ ๋†’์€ ์‹œ๊ณต๊ฐ„ ํ•ด์ƒ๋„๋กœ ๊ด‘ํ•ฉ์„ฑ ์ง€๋„๋ฅผ ์ผ๊ฐ„๊ฒฉ์œผ๋กœ ์ƒ์„ฑํ•˜๊ธฐ์— ์ด๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์œ„์„ฑ ์˜์ƒ์˜ ์ œํ•œ๋œ ์‹œ๊ณต๊ฐ„ ํ•ด์ƒ๋„๋กœ ๋ฐํ˜€์ง€์ง€ ์•Š์€ ์‹๋ฌผ๋ณ€ํ™”์˜ ๊ณผ์ •์„ ๋ฐœ๊ฒฌํ•˜๊ธธ ๊ธฐ๋Œ€ํ•œ๋‹ค. ์‹์ƒ์˜ ๊ณต๊ฐ„๋ถ„ํฌ์€ ์ •๋ฐ€๋†์—…๊ณผ ํ† ์ง€ ํ”ผ๋ณต ๋ณ€ํ™” ๋ชจ๋‹ˆํ„ฐ๋ง์„ ์œ„ํ•ด ํ•„์ˆ˜์ ์ด๋‹ค. ๊ณ ํ•ด์ƒ๋„ ์œ„์„ฑ์˜์ƒ์œผ๋กœ ์ง€๊ตฌ ํ‘œ๋ฉด์„ ๊ด€์ธกํ•˜๋Š” ๊ฒƒ์„ ์šฉ์ดํ•˜๊ฒŒ ํ•ด์กŒ๋‹ค. ํŠนํžˆ Planet Fusion์€ ์ดˆ์†Œํ˜•์œ„์„ฑ๊ตฐ ๋ฐ์ดํ„ฐ๋ฅผ ์ตœ๋Œ€ํ•œ ํ™œ์šฉํ•ด ๋ฐ์ดํ„ฐ ๊ฒฐ์ธก์ด ์—†๋Š” 3m ๊ณต๊ฐ„ ํ•ด์ƒ๋„์˜ ์ง€ํ‘œ ํ‘œ๋ฉด ๋ฐ˜์‚ฌ๋„์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ณผ๊ฑฐ ์œ„์„ฑ ์„ผ์„œ(Landsat์˜ ๊ฒฝ์šฐ 30~60m)์˜ ๊ณต๊ฐ„ ํ•ด์ƒ๋„๋Š” ์‹์ƒ์˜ ๊ณต๊ฐ„์  ๋ณ€ํ™”๋ฅผ ์ƒ์„ธ ๋ถ„์„ํ•˜๋Š” ๊ฒƒ์„ ์ œํ•œํ–ˆ๋‹ค. ์ œ3์žฅ์—์„œ๋Š” Landsat ๋ฐ์ดํ„ฐ์˜ ๊ณต๊ฐ„ ํ•ด์ƒ๋„๋ฅผ ํ–ฅ์ƒํ•˜๊ธฐ ์œ„ํ•ด Planet Fusion ๋ฐ Landsat 8 ๋ฐ์ดํ„ฐ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์ด์ค‘ ์ ๋Œ€์  ์ƒ์„ฑ ๋„คํŠธ์›Œํฌ(the dual RSS-GAN)๋ฅผ ํ•™์Šต์‹œ์ผœ, ๊ณ ํ•ด์ƒ๋„ ์ •๊ทœํ™” ์‹์ƒ ์ง€์ˆ˜(NDVI)์™€ ์‹๋ฌผ ๊ทผ์ ์™ธ์„  ๋ฐ˜์‚ฌ(NIRv)๋„๋ฅผ ์ƒ์„ฑํ•˜๋Š” ํ•œ๋‹ค. ํƒ€์›Œ๊ธฐ๋ฐ˜ ํ˜„์žฅ ์‹์ƒ์ง€์ˆ˜(์ตœ๋Œ€ 8๋…„)์™€ ๋“œ๋ก ๊ธฐ๋ฐ˜ ์ดˆ๋ถ„๊ด‘์ง€๋„๋กœ the dual RSS-GAN์˜ ์„ฑ๋Šฅ์„ ๋Œ€ํ•œ๋ฏผ๊ตญ ๋‚ด ๋‘ ๋Œ€์ƒ์ง€(๋†๊ฒฝ์ง€์™€ ํ™œ์—ฝ์ˆ˜๋ฆผ)์—์„œ ํ‰๊ฐ€ํ–ˆ๋‹ค. The dual RSS-GAN์€ Landsat 8 ์˜์ƒ์˜ ๊ณต๊ฐ„ํ•ด์ƒ๋„๋ฅผ ํ–ฅ์ƒ์‹œ์ผœ ๊ณต๊ฐ„ ํ‘œํ˜„์„ ๋ณด์™„ํ•˜๊ณ  ์‹์ƒ ์ง€์ˆ˜์˜ ๊ณ„์ ˆ์  ๋ณ€ํ™”๋ฅผ ํฌ์ฐฉํ–ˆ๋‹ค(R2> 0.96). ๊ทธ๋ฆฌ๊ณ  the dual RSS-GAN์€ Landsat 8 ์‹์ƒ ์ง€์ˆ˜๊ฐ€ ํ˜„์žฅ์— ๋น„ํ•ด ๊ณผ์†Œ ํ‰๊ฐ€๋˜๋Š” ๊ฒƒ์„ ์™„ํ™”ํ–ˆ๋‹ค. ํ˜„์žฅ ๊ด€์ธก์— ๋น„ํ•ด ์ด์ค‘ RSS-GAN๊ณผ Landsat 8์˜ ์ƒ๋Œ€ ํŽธํ–ฅ ๊ฐ’ ๊ฐ๊ฐ -0.8% ์—์„œ -1.5%, -10.3% ์—์„œ -4.6% ์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๊ฐœ์„ ์€ Planet Fusion์˜ ๊ณต๊ฐ„์ •๋ณด๋ฅผ ์ด์ค‘ RSS-GAN๋กœ ํ•™์Šตํ•˜์˜€๊ธฐ์— ๊ฐ€๋Šฅํ–ˆ๋‹ค. ํ—ค๋‹น ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” Landsat ์˜์ƒ์˜ ๊ณต๊ฐ„ ํ•ด์ƒ๋„๋ฅผ ํ–ฅ์ƒ์‹œ์ผœ ์ˆจ๊ฒจ์ง„ ๊ณต๊ฐ„ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•˜๋Š” ์ƒˆ๋กœ์šด ์ ‘๊ทผ ๋ฐฉ์‹์ด๋‹ค. ๊ณ ํ•ด์ƒ๋„์—์„œ ์‹๋ฌผ ๊ด‘ํ•ฉ์„ฑ ์ง€๋„๋Š” ํ† ์ง€ํ”ผ๋ณต์ด ๋ณต์žกํ•œ ๊ณต๊ฐ„์—์„œ ํƒ„์†Œ ์ˆœํ™˜ ๋ชจ๋‹ˆํ„ฐ๋ง์‹œ ํ•„์ˆ˜์ ์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ Sentinel-2, Landsat ๋ฐ MODIS์™€ ๊ฐ™์ด ํƒœ์–‘ ๋™์กฐ ๊ถค๋„์— ์žˆ๋Š” ์œ„์„ฑ์€ ๊ณต๊ฐ„ ํ•ด์ƒ๋„๊ฐ€ ๋†’๊ฑฐ๋‚˜ ์‹œ๊ฐ„ ํ•ด์ƒ๋„ ๋†’์€ ์œ„์„ฑ์˜์ƒ๋งŒ ์ œ๊ณตํ•  ์ˆ˜ ์žˆ๋‹ค. ์ตœ๊ทผ ๋ฐœ์‚ฌ๋œ ์ดˆ์†Œํ˜•์œ„์„ฑ๊ตฐ์€ ์ด๋Ÿฌํ•œ ํ•ด์ƒ๋„ ํ•œ๊ณ„์„ ๊ทน๋ณตํ•  ์ˆ˜ ์žˆ๋‹ค. ํŠนํžˆ Planet Fusion์€ ์ดˆ์†Œํ˜•์œ„์„ฑ ์ž๋ฃŒ์˜ ์‹œ๊ณต๊ฐ„ ํ•ด์ƒ๋„๋กœ ์ง€ํ‘œ๋ฉด์„ ๊ด€์ธกํ•  ์ˆ˜ ์žˆ๋‹ค. 4์žฅ์—์„œ, Planet Fusion ์ง€ํ‘œ๋ฐ˜์‚ฌ๋„๋ฅผ ์ด์šฉํ•˜์—ฌ ์‹์ƒ์—์„œ ๋ฐ˜์‚ฌ๋œ ๊ทผ์ ์™ธ์„  ๋ณต์‚ฌ(NIRvP)๋ฅผ 3m ํ•ด์ƒ๋„ ์ง€๋„๋ฅผ ์ผ๊ฐ„๊ฒฉ์œผ๋กœ ์ƒ์„ฑํ–ˆ๋‹ค. ๊ทธ๋Ÿฐ ๋‹ค์Œ ๋ฏธ๊ตญ ์บ˜๋ฆฌํฌ๋‹ˆ์•„์ฃผ ์ƒˆํฌ๋ผ๋ฉ˜ํ† -์ƒŒ ํ˜ธ์•„ํ‚จ ๋ธํƒ€์˜ ํ”Œ๋Ÿญ์Šค ํƒ€์›Œ ๋„คํŠธ์›Œํฌ ๋ฐ์ดํ„ฐ์™€ ๋น„๊ตํ•˜์—ฌ ์‹๋ฌผ ๊ด‘ํ•ฉ์„ฑ์„ ์ถ”์ •ํ•˜๊ธฐ ์œ„ํ•œ NIRvP ์ง€๋„์˜ ์„ฑ๋Šฅ์„ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ์ „์ฒด์ ์œผ๋กœ NIRvP ์ง€๋„๋Š” ์Šต์ง€์˜ ์žฆ์€ ์ˆ˜์œ„ ๋ณ€ํ™”์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ๊ฐœ๋ณ„ ๋Œ€์ƒ์ง€์˜ ์‹๋ฌผ ๊ด‘ํ•ฉ์„ฑ์˜ ์‹œ๊ฐ„์  ๋ณ€ํ™”๋ฅผ ํฌ์ฐฉํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋Œ€์ƒ์ง€ ์ „์ฒด์— ๋Œ€ํ•œ NIRvP ์ง€๋„์™€ ์‹๋ฌผ ๊ด‘ํ•ฉ์„ฑ ์‚ฌ์ด์˜ ๊ด€๊ณ„๋Š” NIRvP ์ง€๋„๋ฅผ ํ”Œ๋Ÿญ์Šค ํƒ€์›Œ ๊ด€์ธก๋ฒ”์œ„์™€ ์ผ์น˜์‹œํ‚ฌ ๋•Œ๋งŒ ๋†’์€ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ณด์˜€๋‹ค. ๊ด€์ธก๋ฒ”์œ„๋ฅผ ์ผ์น˜์‹œํ‚ฌ ๊ฒฝ์šฐ, NIRvP ์ง€๋„๋Š” ์‹๋ฌผ ๊ด‘ํ•ฉ์„ฑ์„ ์ถ”์ •ํ•˜๋Š” ๋ฐ ์žˆ์–ด ํ˜„์žฅ NIRvP๋ณด๋‹ค ์šฐ์ˆ˜ํ•œ ์„ฑ๋Šฅ์„ ๋ณด์˜€๋‹ค. ์ด๋Ÿฌํ•œ ์„ฑ๋Šฅ ์ฐจ์ด๋Š” ํ”Œ๋Ÿญ์Šค ํƒ€์›Œ ๊ด€์ธก๋ฒ”์œ„๋ฅผ ์ผ์น˜์‹œํ‚ฌ ๋•Œ, ์—ฐ๊ตฌ ๋Œ€์ƒ์ง€ ๊ฐ„์˜ NIRvP-์‹๋ฌผ ๊ด‘ํ•ฉ์„ฑ ๊ด€๊ณ„์˜ ๊ธฐ์šธ๊ธฐ๊ฐ€ ์ผ๊ด€์„ฑ์„ ๋ณด์˜€๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” ์œ„์„ฑ ๊ด€์ธก์„ ํ”Œ๋Ÿญ์Šค ํƒ€์›Œ ๊ด€์ธก๋ฒ”์œ„์™€ ์ผ์น˜์‹œํ‚ค๋Š” ๊ฒƒ์˜ ์ค‘์š”์„ฑ์„ ๋ณด์—ฌ์ฃผ๊ณ  ๋†’์€ ์‹œ๊ณต๊ฐ„ ํ•ด์ƒ๋„๋กœ ์‹๋ฌผ ๊ด‘ํ•ฉ์„ฑ์„ ์›๊ฒฉ์œผ๋กœ ๋ชจ๋‹ˆํ„ฐ๋งํ•˜๋Š” ์ดˆ์†Œํ˜•์œ„์„ฑ๊ตฐ ์ž๋ฃŒ์˜ ์ž ์žฌ๋ ฅ์„ ๋ณด์—ฌ์ค€๋‹ค.Monitoring changes in terrestrial vegetation is essential to understanding interactions between atmosphere and biosphere, especially terrestrial ecosystem. To this end, satellite remote sensing offer maps for examining land surface in different scales. However, the detailed information was hindered under the clouds or limited by the spatial resolution of satellite imagery. Moreover, the impacts of spatial and temporal resolution in photosynthesis monitoring were not fully revealed. In this dissertation, I aimed to enhance the spatial and temporal resolution of satellite imagery towards daily gap-free vegetation maps with high spatial resolution. In order to expand vegetation change monitoring in time and space using high-resolution satellite images, I 1) improved temporal resolution of satellite dataset through image fusion using geostationary satellites, 2) improved spatial resolution of satellite dataset using generative adversarial networks, and 3) showed the use of high spatiotemporal resolution maps for monitoring plant photosynthesis especially over heterogeneous landscapes. With the advent of new techniques in satellite remote sensing, current and past datasets can be fully utilized for monitoring vegetation changes in the respect of spatial and temporal resolution. In Chapter 2, I developed the integrated system that implemented geostationary satellite products in the spatiotemporal image fusion method for monitoring canopy photosynthesis. The integrated system contains the series of process (i.e., cloud masking, nadir bidirectional reflectance function adjustment, spatial registration, spatiotemporal image fusion, spatial gap-filling, temporal-gap-filling). I conducted the evaluation of the integrated system over heterogeneous rice paddy landscape where the drastic land cover changes were caused by cultivation management and deciduous forest where consecutive changes occurred in time. The results showed that the integrated system well predict in situ measurements without data gaps (R2 = 0.71, relative bias = 5.64% at rice paddy site; R2 = 0.79, relative bias = -13.8% at deciduous forest site). The integrated system gradually improved the spatiotemporal resolution of vegetation maps, reducing the underestimation of in situ measurements, especially during peak growing season. Since the integrated system generates daily canopy photosynthesis maps for monitoring dynamics among regions of interest worldwide with high spatial resolution. I anticipate future efforts to reveal the hindered information by the limited spatial and temporal resolution of satellite imagery. Detailed spatial representations of terrestrial vegetation are essential for precision agricultural applications and the monitoring of land cover changes in heterogeneous landscapes. The advent of satellite-based remote sensing has facilitated daily observations of the Earths surface with high spatial resolution. In particular, a data fusion product such as Planet Fusion has realized the delivery of daily, gap-free surface reflectance data with 3-m pixel resolution through full utilization of relatively recent (i.e., 2018-) CubeSat constellation data. However, the spatial resolution of past satellite sensors (i.e., 30โ€“60 m for Landsat) has restricted the detailed spatial analysis of past changes in vegetation. In Chapter 3, to overcome the spatial resolution constraint of Landsat data for long-term vegetation monitoring, we propose a dual remote-sensing super-resolution generative adversarial network (dual RSS-GAN) combining Planet Fusion and Landsat 8 data to simulate spatially enhanced long-term time-series of the normalized difference vegetation index (NDVI) and near-infrared reflectance from vegetation (NIRv). We evaluated the performance of the dual RSS-GAN against in situ tower-based continuous measurements (up to 8 years) and remotely piloted aerial system-based maps of cropland and deciduous forest in the Republic of Korea. The dual RSS-GAN enhanced spatial representations in Landsat 8 images and captured seasonal variation in vegetation indices (R2 > 0.95, for the dual RSS-GAN maps vs. in situ data from all sites). Overall, the dual RSS-GAN reduced Landsat 8 vegetation index underestimations compared with in situ measurements; relative bias values of NDVI ranged from โˆ’3.2% to 1.2% and โˆ’12.4% to โˆ’3.7% for the dual RSS-GAN and Landsat 8, respectively. This improvement was caused by spatial enhancement through the dual RSS-GAN, which captured fine-scale information from Planet Fusion. This study presents a new approach for the restoration of hidden sub-pixel spatial information in Landsat images. Mapping canopy photosynthesis in both high spatial and temporal resolution is essential for carbon cycle monitoring in heterogeneous areas. However, well established satellites in sun-synchronous orbits such as Sentinel-2, Landsat and MODIS can only provide either high spatial or high temporal resolution but not both. Recently established CubeSat satellite constellations have created an opportunity to overcome this resolution trade-off. In particular, Planet Fusion allows full utilization of the CubeSat data resolution and coverage while maintaining high radiometric quality. In Chapter 4, I used the Planet Fusion surface reflectance product to calculate daily, 3-m resolution, gap-free maps of the near-infrared radiation reflected from vegetation (NIRvP). I then evaluated the performance of these NIRvP maps for estimating canopy photosynthesis by comparing with data from a flux tower network in Sacramento-San Joaquin Delta, California, USA. Overall, NIRvP maps captured temporal variations in canopy photosynthesis of individual sites, despite changes in water extent in the wetlands and frequent mowing in the crop fields. When combining data from all sites, however, I found that robust agreement between NIRvP maps and canopy photosynthesis could only be achieved when matching NIRvP maps to the flux tower footprints. In this case of matched footprints, NIRvP maps showed considerably better performance than in situ NIRvP in estimating canopy photosynthesis both for daily sum and data around the time of satellite overpass (R2 = 0.78 vs. 0.60, for maps vs. in situ for the satellite overpass time case). This difference in performance was mostly due to the higher degree of consistency in slopes of NIRvP-canopy photosynthesis relationships across the study sites for flux tower footprint-matched maps. Our results show the importance of matching satellite observations to the flux tower footprint and demonstrate the potential of CubeSat constellation imagery to monitor canopy photosynthesis remotely at high spatio-temporal resolution.Chapter 1. Introduction 2 1. Background 2 1.1 Daily gap-free surface reflectance using geostationary satellite products 2 1.2 Monitoring past vegetation changes with high-spatial-resolution 3 1.3 High spatiotemporal resolution vegetation photosynthesis maps 4 2. Purpose of Research 4 Chapter 2. Generating daily gap-filled BRDF adjusted surface reflectance product at 10 m resolution using geostationary satellite product for monitoring daily canopy photosynthesis 6 1. Introduction 6 2. Methods 11 2.1 Study sites 11 2.2 In situ measurements 13 2.3 Satellite products 14 2.4 Integrated system 17 2.5 Canopy photosynthesis 21 2.6 Evaluation 23 3. Results and discussion 24 3.1 Comparison of STIF NDVI and NIRv with in situ NDVI and NIRv 24 3.2 Comparison of STIF NIRvP with in situ NIRvP 28 4. Conclusion 31 Chapter 3. Super-resolution of historic Landsat imagery using a dual Generative Adversarial Network (GAN) model with CubeSat constellation imagery for monitoring vegetation changes 32 1. Introduction 32 2. Methods 38 2.1 Real-ESRGAN model 38 2.2 Study sites 40 2.3 In situ measurements 42 2.4 Vegetation index 44 2.5 Satellite data 45 2.6 Planet Fusion 48 2.7 Dual RSS-GAN via fine-tuned Real-ESRGAN 49 2.8 Evaluation 54 3. Results 57 3.1 Comparison of NDVI and NIRv maps from Planet Fusion, Sentinel 2 NBAR, and Landsat 8 NBAR data with in situ NDVI and NIRv 57 3.2 Comparison of dual RSS-SRGAN model results with Landsat 8 NDVI and NIRv 60 3.3 Comparison of dual RSS-GAN model results with respect to in situ time-series NDVI and NIRv 63 3.4 Comparison of the dual RSS-GAN model with NDVI and NIRv maps derived from RPAS 66 4. Discussion 70 4.1 Monitoring changes in terrestrial vegetation using the dual RSS-GAN model 70 4.2 CubeSat data in the dual RSS-GAN model 72 4.3 Perspectives and limitations 73 5. Conclusion 78 Appendices 79 Supplementary material 82 Chapter 4. Matching high resolution satellite data and flux tower footprints improves their agreement in photosynthesis estimates 85 1. Introduction 85 2. Methods 89 2.1 Study sites 89 2.2 In situ measurements 92 2.3 Planet Fusion NIRvP 94 2.4 Flux footprint model 98 2.5 Evaluation 98 3. Results 105 3.1 Comparison of Planet Fusion NIRv and NIRvP with in situ NIRv and NIRvP 105 3.2 Comparison of instantaneous Planet Fusion NIRv and NIRvP with against tower GPP estimates 108 3.3 Daily GPP estimation from Planet Fusion -derived NIRvP 114 4. Discussion 118 4.1 Flux tower footprint matching and effects of spatial and temporal resolution on GPP estimation 118 4.2 Roles of radiation component in GPP mapping 123 4.3 Limitations and perspectives 126 5. Conclusion 133 Appendix 135 Supplementary Materials 144 Chapter 5. Conclusion 153 Bibliography 155 Abstract in Korea 199 Acknowledgements 202๋ฐ•

    Using Statistics, Computational Modelling and Artificial Intelligence Methods to Study and Strengthen the Link between Kinematic Impacts and mTBIs

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    Mild traumatic brain injuries (mTBIs) are frequently occurring, yet poorly understood, injuries in sports (e.g., ice hockey) and other physical recreation activities where head impacts occur. Helmets are essential pieces of equipment used to protect participantsโ€™ heads from mTBIs. Evaluating the performance of helmets to prevent mTBIs using simulations on anatomically accurate computational head finite element models is critically important for advancing the development of safer helmets. Advancing the level of detail in, and access to, such models, and their continued validation through state-of-the-art brain imaging methods and traditional head injury assessment procedures, is also essential to improve safety. The significant research contributions in this thesis involve evaluating the decrease in blunt impact-induced brain axon fiber tract strains that various helmets provide by studying outputs of existing finite element brain models and implementing open-source artificial intelligence technology to create a novel pipeline for predicting such strains

    Systematic Approaches for Telemedicine and Data Coordination for COVID-19 in Baja California, Mexico

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    Conference proceedings info: ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologies Raleigh, HI, United States, March 24-26, 2023 Pages 529-542We provide a model for systematic implementation of telemedicine within a large evaluation center for COVID-19 in the area of Baja California, Mexico. Our model is based on human-centric design factors and cross disciplinary collaborations for scalable data-driven enablement of smartphone, cellular, and video Teleconsul-tation technologies to link hospitals, clinics, and emergency medical services for point-of-care assessments of COVID testing, and for subsequent treatment and quar-antine decisions. A multidisciplinary team was rapidly created, in cooperation with different institutions, including: the Autonomous University of Baja California, the Ministry of Health, the Command, Communication and Computer Control Center of the Ministry of the State of Baja California (C4), Colleges of Medicine, and the College of Psychologists. Our objective is to provide information to the public and to evaluate COVID-19 in real time and to track, regional, municipal, and state-wide data in real time that informs supply chains and resource allocation with the anticipation of a surge in COVID-19 cases. RESUMEN Proporcionamos un modelo para la implementaciรณn sistemรกtica de la telemedicina dentro de un gran centro de evaluaciรณn de COVID-19 en el รกrea de Baja California, Mรฉxico. Nuestro modelo se basa en factores de diseรฑo centrados en el ser humano y colaboraciones interdisciplinarias para la habilitaciรณn escalable basada en datos de tecnologรญas de teleconsulta de telรฉfonos inteligentes, celulares y video para vincular hospitales, clรญnicas y servicios mรฉdicos de emergencia para evaluaciones de COVID en el punto de atenciรณn. pruebas, y para el tratamiento posterior y decisiones de cuarentena. Rรกpidamente se creรณ un equipo multidisciplinario, en cooperaciรณn con diferentes instituciones, entre ellas: la Universidad Autรณnoma de Baja California, la Secretarรญa de Salud, el Centro de Comando, Comunicaciones y Control Informรกtico. de la Secretarรญa del Estado de Baja California (C4), Facultades de Medicina y Colegio de Psicรณlogos. Nuestro objetivo es proporcionar informaciรณn al pรบblico y evaluar COVID-19 en tiempo real y rastrear datos regionales, municipales y estatales en tiempo real que informan las cadenas de suministro y la asignaciรณn de recursos con la anticipaciรณn de un aumento de COVID-19. 19 casos.ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologieshttps://doi.org/10.1007/978-981-99-3236-

    Acoustic modelling, data augmentation and feature extraction for in-pipe machine learning applications

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    Gathering measurements from infrastructure, private premises, and harsh environments can be difficult and expensive. From this perspective, the development of new machine learning algorithms is strongly affected by the availability of training and test data. We focus on audio archives for in-pipe events. Although several examples of pipe-related applications can be found in the literature, datasets of audio/vibration recordings are much scarcer, and the only references found relate to leakage detection and characterisation. Therefore, this work proposes a methodology to relieve the burden of data collection for acoustic events in deployed pipes. The aim is to maximise the yield of small sets of real recordings and demonstrate how to extract effective features for machine learning. The methodology developed requires the preliminary creation of a soundbank of audio samples gathered with simple weak annotations. For practical reasons, the case study is given by a range of appliances, fittings, and fixtures connected to pipes in domestic environments. The source recordings are low-reverberated audio signals enhanced through a bespoke spectral filter and containing the desired audio fingerprints. The soundbank is then processed to create an arbitrary number of synthetic augmented observations. The data augmentation improves the quality and the quantity of the metadata and automatically creates strong and accurate annotations that are both machine and human-readable. Besides, the implemented processing chain allows precise control of properties such as signal-to-noise ratio, duration of the events, and the number of overlapping events. The inter-class variability is expanded by recombining source audio blocks and adding simulated artificial reverberation obtained through an acoustic model developed for the purpose. Finally, the dataset is synthesised to guarantee separability and balance. A few signal representations are optimised to maximise the classification performance, and the results are reported as a benchmark for future developments. The contribution to the existing knowledge concerns several aspects of the processing chain implemented. A novel quasi-analytic acoustic model is introduced to simulate in-pipe reverberations, adopting a three-layer architecture particularly convenient for batch processing. The first layer includes two algorithms: one for the numerical calculation of the axial wavenumbers and one for the separation of the modes. The latter, in particular, provides a workaround for a problem not explicitly treated in the literature and related to the modal non-orthogonality given by the solid-liquid interface in the analysed domain. A set of results for different waveguides is reported to compare the dispersive behaviour against different mechanical configurations. Two more novel solutions are also included in the second layer of the model and concern the integration of the acoustic sources. Specifically, the amplitudes of the non-orthogonal modal potentials are obtained using either a distance minimisation objective function or by solving an analytical decoupling problem. In both cases, results show that sources sufficiently smooth can be approximated with a limited number of modes keeping the error below 1%. The last layer proposes a bespoke approach for the integration of the acoustic model into the synthesiser as a reverberation simulator. Additional elements of novelty relate to the other blocks of the audio synthesiser. The statistical spectral filter, for instance, is a batch-processing solution for the attenuation of the background noise of the source recordings. The signal-to-noise ratio analysis for both moderate and high noise levels indicates a clear improvement of several decibels against the closest filter example in the literature. The recombination of the audio blocks and the system of fully tracked annotations are also novel extensions of similar approaches recently adopted in other contexts. Moreover, a bespoke synthesis strategy is proposed to guarantee separable and balanced datasets. The last contribution concerns the extraction of convenient sets of audio features. Elements of novelty are introduced for the optimisation of the filter banks of the mel-frequency cepstral coefficients and the scattering wavelet transform. In particular, compared to the respective standard definitions, the average F-score performance of the optimised features is roughly 6% higher in the first case and 2.5% higher for the latter. Finally, the soundbank, the synthetic dataset, and the fundamental blocks of the software library developed are publicly available for further research

    Deep Learning Techniques for Multi-Dimensional Medical Image Analysis

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    Technology, Science and Culture: A Global Vision, Volume IV

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    Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges

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    Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and execute tasks with human cognitive abilities, engenders significant anticipation and intrigue across scientific, commercial, and societal arenas. This fascination extends particularly to the Internet of Things (IoT), a landscape characterized by the interconnection of countless devices, sensors, and systems, collectively gathering and sharing data to enable intelligent decision-making and automation. This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the IoT. Specifically, it starts by outlining the fundamental principles of IoT and the critical role of Artificial Intelligence (AI) in IoT systems. Subsequently, it delves into AGI fundamentals, culminating in the formulation of a conceptual framework for AGI's seamless integration within IoT. The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education. However, adapting AGI to resource-constrained IoT settings necessitates dedicated research efforts. Furthermore, the paper addresses constraints imposed by limited computing resources, intricacies associated with large-scale IoT communication, as well as the critical concerns pertaining to security and privacy
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