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    ๋””์Šคํ”Œ๋ ˆ์ด ์žฅ์น˜๋ฅผ ์œ„ํ•œ ๊ณ ์ • ๋น„์œจ ์••์ถ• ํ•˜๋“œ์›จ์–ด ์„ค๊ณ„

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2016. 2. ์ดํ˜์žฌ.๋””์Šคํ”Œ๋ ˆ์ด ์žฅ์น˜์—์„œ์˜ ์••์ถ• ๋ฐฉ์‹์€ ์ผ๋ฐ˜์ ์ธ ๋น„๋””์˜ค ์••์ถ• ํ‘œ์ค€๊ณผ๋Š” ๋‹ค๋ฅธ ๋ช‡ ๊ฐ€์ง€ ํŠน์ง•์ด ์žˆ๋‹ค. ์ฒซ์งธ, ํŠน์ˆ˜ํ•œ ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค. ๋‘˜์งธ, ์••์ถ• ์ด๋“, ์†Œ๋น„ ์ „๋ ฅ, ์‹ค์‹œ๊ฐ„ ์ฒ˜๋ฆฌ ๋“ฑ์„ ์œ„ํ•ด ํ•˜๋“œ์›จ์–ด ํฌ๊ธฐ๊ฐ€ ์ž‘๊ณ , ๋ชฉํ‘œ๋กœ ํ•˜๋Š” ์••์ถ•๋ฅ ์ด ๋‚ฎ๋‹ค. ์…‹์งธ, ๋ž˜์Šคํ„ฐ ์ฃผ์‚ฌ ์ˆœ์„œ์— ์ ํ•ฉํ•ด์•ผ ํ•œ๋‹ค. ๋„ท์งธ, ํ”„๋ ˆ์ž„ ๋ฉ”๋ชจ๋ฆฌ ํฌ๊ธฐ๋ฅผ ์ œํ•œ์‹œํ‚ค๊ฑฐ๋‚˜ ์ž„์˜ ์ ‘๊ทผ์„ ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์••์ถ• ๋‹จ์œ„๋‹น ๋ชฉํ‘œ ์••์ถ•๋ฅ ์„ ์‹ค์‹œ๊ฐ„์œผ๋กœ ์ •ํ™•ํžˆ ๋งž์ถœ ์ˆ˜ ์žˆ์–ด์•ผ ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ด์™€ ๊ฐ™์€ ํŠน์ง•์„ ๋งŒ์กฑ์‹œํ‚ค๋Š” ์„ธ ๊ฐ€์ง€ ์••์ถ• ์•Œ๊ณ ๋ฆฌ์ฆ˜๊ณผ ํ•˜๋“œ์›จ์–ด ๊ตฌ์กฐ๋ฅผ ์ œ์•ˆํ•˜๋„๋ก ํ•œ๋‹ค. LCD ์˜ค๋ฒ„๋“œ๋ผ์ด๋ธŒ๋ฅผ ์œ„ํ•œ ์••์ถ• ๋ฐฉ์‹์œผ๋กœ๋Š” BTC(block truncation coding) ๊ธฐ๋ฐ˜์˜ ์••์ถ• ๋ฐฉ์‹์„ ์ œ์•ˆํ•˜๋„๋ก ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ์••์ถ• ์ด๋“์„ ์ฆ๊ฐ€์‹œํ‚ค๊ธฐ ์œ„ํ•˜์—ฌ ๋ชฉํ‘œ ์••์ถ•๋ฅ  12์— ๋Œ€ํ•œ ์••์ถ• ๋ฐฉ์‹์„ ์ œ์•ˆํ•˜๋Š”๋ฐ, ์••์ถ• ํšจ์œจ์„ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•˜์—ฌ ํฌ๊ฒŒ ๋‘ ๊ฐ€์ง€ ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ๋Š” ์ด์›ƒํ•˜๋Š” ๋ธ”๋ก๊ณผ์˜ ๊ณต๊ฐ„์  ์—ฐ๊ด€์„ฑ์„ ์ด์šฉํ•˜์—ฌ ๋น„ํŠธ๋ฅผ ์ ˆ์•ฝํ•˜๋Š” ๋ฐฉ๋ฒ•์ด๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋‘ ๋ฒˆ์งธ๋Š” ๋‹จ์ˆœํ•œ ์˜์—ญ์€ 2ร—16 ์ฝ”๋”ฉ ๋ธ”๋ก, ๋ณต์žกํ•œ ์˜์—ญ์€ 2ร—8 ์ฝ”๋”ฉ ๋ธ”๋ก์„ ์ด์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•์ด๋‹ค. 2ร—8 ์ฝ”๋”ฉ ๋ธ”๋ก์„ ์ด์šฉํ•˜๋Š” ๊ฒฝ์šฐ ๋ชฉํ‘œ ์••์ถ•๋ฅ ์„ ๋งž์ถ”๊ธฐ ์œ„ํ•˜์—ฌ ์ฒซ ๋ฒˆ์งธ ๋ฐฉ๋ฒ•์œผ๋กœ ์ ˆ์•ฝ๋œ ๋น„ํŠธ๋ฅผ ์ด์šฉํ•œ๋‹ค. ์ €๋น„์šฉ ๊ทผ์ ‘-๋ฌด์†์‹ค ํ”„๋ ˆ์ž„ ๋ฉ”๋ชจ๋ฆฌ ์••์ถ•์„ ์œ„ํ•œ ๋ฐฉ์‹์œผ๋กœ๋Š” 1D SPIHT(set partitioning in hierarchical trees) ๊ธฐ๋ฐ˜์˜ ์••์ถ• ๋ฐฉ์‹์„ ์ œ์•ˆํ•˜๋„๋ก ํ•œ๋‹ค. SPIHT์€ ๊ณ ์ • ๋ชฉํ‘œ ์••์ถ•๋ฅ ์„ ๋งž์ถ”๋Š”๋ฐ ๋งค์šฐ ํšจ๊ณผ์ ์ธ ์••์ถ• ๋ฐฉ์‹์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ 1D ํ˜•ํƒœ์ธ 1D SPIHT์€ ๋ž˜์Šคํ„ฐ ์ฃผ์‚ฌ ์ˆœ์„œ์— ์ ํ•ฉํ•จ์—๋„ ๊ด€๋ จ ์—ฐ๊ตฌ๊ฐ€ ๋งŽ์ด ์ง„ํ–‰๋˜์ง€ ์•Š์•˜๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ 1D SPIHT์˜ ๊ฐ€์žฅ ํฐ ๋ฌธ์ œ์ ์ธ ์†๋„ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•  ์ˆ˜ ์žˆ๋Š” ํ•˜๋“œ์›จ์–ด ๊ตฌ์กฐ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด 1D SPIHT ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ๋ณ‘๋ ฌ์„ฑ์„ ์ด์šฉํ•  ์ˆ˜ ์žˆ๋Š” ํ˜•ํƒœ๋กœ ์ˆ˜์ •๋œ๋‹ค. ์ธ์ฝ”๋”์˜ ๊ฒฝ์šฐ ๋ณ‘๋ ฌ ์ฒ˜๋ฆฌ๋ฅผ ๋ฐฉํ•ดํ•˜๋Š” ์˜์กด ๊ด€๊ณ„๊ฐ€ ํ•ด๊ฒฐ๋˜๊ณ , ํŒŒ์ดํ”„๋ผ์ธ ์Šค์ผ€์ฅด๋ง์ด ๊ฐ€๋Šฅํ•˜๊ฒŒ ๋œ๋‹ค. ๋””์ฝ”๋”์˜ ๊ฒฝ์šฐ ๋ณ‘๋ ฌ๋กœ ๋™์ž‘ํ•˜๋Š” ๊ฐ ํŒจ์Šค๊ฐ€ ๋””์ฝ”๋”ฉํ•  ๋น„ํŠธ์ŠคํŠธ๋ฆผ์˜ ๊ธธ์ด๋ฅผ ๋ฏธ๋ฆฌ ์˜ˆ์ธกํ•  ์ˆ˜ ์žˆ๋„๋ก ์•Œ๊ณ ๋ฆฌ์ฆ˜์ด ์ˆ˜์ •๋œ๋‹ค. ๊ณ ์ถฉ์‹ค๋„(high-fidelity) RGBW ์ปฌ๋Ÿฌ ์ด๋ฏธ์ง€ ์••์ถ•์„ ์œ„ํ•œ ๋ฐฉ์‹์œผ๋กœ๋Š” ์˜ˆ์ธก ๊ธฐ๋ฐ˜์˜ ์••์ถ• ๋ฐฉ์‹์„ ์ œ์•ˆํ•˜๋„๋ก ํ•œ๋‹ค. ์ œ์•ˆ ์˜ˆ์ธก ๋ฐฉ์‹์€ ๋‘ ๋‹จ๊ณ„์˜ ์ฐจ๋ถ„ ๊ณผ์ •์œผ๋กœ ๊ตฌ์„ฑ๋œ๋‹ค. ์ฒซ ๋ฒˆ์งธ๋Š” ๊ณต๊ฐ„์  ์—ฐ๊ด€์„ฑ์„ ์ด์šฉํ•˜๋Š” ๋‹จ๊ณ„์ด๊ณ , ๋‘ ๋ฒˆ์งธ๋Š” ์ธํ„ฐ-์ปฌ๋Ÿฌ ์—ฐ๊ด€์„ฑ์„ ์ด์šฉํ•˜๋Š” ๋‹จ๊ณ„์ด๋‹ค. ์ฝ”๋”ฉ์˜ ๊ฒฝ์šฐ ์••์ถ• ํšจ์œจ์ด ๋†’์€ VLC(variable length coding) ๋ฐฉ์‹์„ ์ด์šฉํ•˜๋„๋ก ํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ธฐ์กด์˜ VLC ๋ฐฉ์‹์€ ๋ชฉํ‘œ ์••์ถ•๋ฅ ์„ ์ •ํ™•ํžˆ ๋งž์ถ”๋Š”๋ฐ ์–ด๋ ค์›€์ด ์žˆ์—ˆ์œผ๋ฏ€๋กœ ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” Golomb-Rice ์ฝ”๋”ฉ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ๊ณ ์ • ๊ธธ์ด ์••์ถ• ๋ฐฉ์‹์„ ์ œ์•ˆํ•˜๋„๋ก ํ•œ๋‹ค. ์ œ์•ˆ ์ธ์ฝ”๋”๋Š” ํ”„๋ฆฌ-์ฝ”๋”์™€ ํฌ์Šคํ„ฐ-์ฝ”๋”๋กœ ๊ตฌ์„ฑ๋˜์–ด ์žˆ๋‹ค. ํ”„๋ฆฌ-์ฝ”๋”๋Š” ํŠน์ • ์ƒํ™ฉ์— ๋Œ€ํ•˜์—ฌ ์‹ค์ œ ์ธ์ฝ”๋”ฉ์„ ์ˆ˜ํ–‰ํ•˜๊ณ , ๋‹ค๋ฅธ ๋ชจ๋“  ์ƒํ™ฉ์— ๋Œ€ํ•œ ์˜ˆ์ธก ์ธ์ฝ”๋”ฉ ์ •๋ณด๋ฅผ ๊ณ„์‚ฐํ•˜์—ฌ ํฌ์Šคํ„ฐ-์ฝ”๋”์— ์ „๋‹ฌํ•œ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ํฌ์ŠคํŠธ-์ฝ”๋”๋Š” ์ „๋‹ฌ๋ฐ›์€ ์ •๋ณด๋ฅผ ์ด์šฉํ•˜์—ฌ ์‹ค์ œ ๋น„ํŠธ์ŠคํŠธ๋ฆผ์„ ์ƒ์„ฑํ•œ๋‹ค.์ œ 1 ์žฅ ์„œ๋ก  1 1.1 ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ 1 1.2 ์—ฐ๊ตฌ ๋‚ด์šฉ 4 1.3 ๋…ผ๋ฌธ ๊ตฌ์„ฑ 8 ์ œ 2 ์žฅ ์ด์ „ ์—ฐ๊ตฌ 9 2.1 BTC 9 2.1.1 ๊ธฐ๋ณธ BTC ์•Œ๊ณ ๋ฆฌ์ฆ˜ 9 2.1.2 ์ปฌ๋Ÿฌ ์ด๋ฏธ์ง€ ์••์ถ•์„ ์œ„ํ•œ BTC ์•Œ๊ณ ๋ฆฌ์ฆ˜ 10 2.2 SPIHT 13 2.2.1 1D SPIHT ์•Œ๊ณ ๋ฆฌ์ฆ˜ 13 2.2.2 SPIHT ํ•˜๋“œ์›จ์–ด 17 2.3 ์˜ˆ์ธก ๊ธฐ๋ฐ˜ ์ฝ”๋”ฉ 19 2.3.1 ์˜ˆ์ธก ๋ฐฉ๋ฒ• 19 2.3.2 VLC 20 2.3.3 ์˜ˆ์ธก ๊ธฐ๋ฐ˜ ์ฝ”๋”ฉ ํ•˜๋“œ์›จ์–ด 22 ์ œ 3 ์žฅ LCD ์˜ค๋ฒ„๋“œ๋ผ์ด๋ธŒ๋ฅผ ์œ„ํ•œ BTC 24 3.1 ์ œ์•ˆ ์•Œ๊ณ ๋ฆฌ์ฆ˜ 24 3.1.1 ๋น„ํŠธ-์ ˆ์•ฝ ๋ฐฉ๋ฒ• 25 3.1.2 ๋ธ”๋ก ํฌ๊ธฐ ์„ ํƒ ๋ฐฉ๋ฒ• 29 3.1.3 ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์š”์•ฝ 31 3.2 ํ•˜๋“œ์›จ์–ด ๊ตฌ์กฐ 33 3.2.1 ํ”„๋ ˆ์ž„ ๋ฉ”๋ชจ๋ฆฌ ์ธํ„ฐํŽ˜์ด์Šค 34 3.2.2 ์ธ์ฝ”๋”์™€ ๋””์ฝ”๋”์˜ ๊ตฌ์กฐ 37 3.3 ์‹คํ—˜ ๊ฒฐ๊ณผ 44 3.3.1 ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์„ฑ๋Šฅ 44 3.3.2 ํ•˜๋“œ์›จ์–ด ๊ตฌํ˜„ ๊ฒฐ๊ณผ 49 ์ œ 4 ์žฅ ์ €๋น„์šฉ ๊ทผ์ ‘-๋ฌด์†์‹ค ํ”„๋ ˆ์ž„ ๋ฉ”๋ชจ๋ฆฌ ์••์ถ•์„ ์œ„ํ•œ ๊ณ ์† 1D SPIHT 54 4.1 ์ธ์ฝ”๋” ํ•˜๋“œ์›จ์–ด ๊ตฌ์กฐ 54 4.1.1 ์˜์กด ๊ด€๊ณ„ ๋ถ„์„ ๋ฐ ์ œ์•ˆํ•˜๋Š” ํŒŒ์ดํ”„๋ผ์ธ ์Šค์ผ€์ฅด 54 4.1.2 ๋ถ„๋ฅ˜ ๋น„ํŠธ ์žฌ๋ฐฐ์น˜ 57 4.2 ๋””์ฝ”๋” ํ•˜๋“œ์›จ์–ด ๊ตฌ์กฐ 59 4.2.1 ๋น„ํŠธ์ŠคํŠธ๋ฆผ์˜ ์‹œ์ž‘ ์ฃผ์†Œ ๊ณ„์‚ฐ 59 4.2.2 ์ ˆ๋ฐ˜-ํŒจ์Šค ์ฒ˜๋ฆฌ ๋ฐฉ๋ฒ• 63 4.3 ํ•˜๋“œ์›จ์–ด ๊ตฌํ˜„ 65 4.4 ์‹คํ—˜ ๊ฒฐ๊ณผ 73 ์ œ 5 ์žฅ ๊ณ ์ถฉ์‹ค๋„ RGBW ์ปฌ๋Ÿฌ ์ด๋ฏธ์ง€ ์••์ถ•์„ ์œ„ํ•œ ๊ณ ์ • ์••์ถ•๋น„ VLC 81 5.1 ์ œ์•ˆ ์•Œ๊ณ ๋ฆฌ์ฆ˜ 81 5.1.1 RGBW ์ธํ„ฐ-์ปฌ๋Ÿฌ ์—ฐ๊ด€์„ฑ์„ ์ด์šฉํ•œ ์˜ˆ์ธก ๋ฐฉ์‹ 82 5.1.2 ๊ณ ์ • ์••์ถ•๋น„๋ฅผ ์œ„ํ•œ Golomb-Rice ์ฝ”๋”ฉ 85 5.1.3 ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์š”์•ฝ 89 5.2 ํ•˜๋“œ์›จ์–ด ๊ตฌ์กฐ 90 5.2.1 ์ธ์ฝ”๋” ๊ตฌ์กฐ 91 5.2.2 ๋””์ฝ”๋” ๊ตฌ์กฐ 95 5.3 ์‹คํ—˜ ๊ฒฐ๊ณผ 101 5.3.1 ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์‹คํ—˜ ๊ฒฐ๊ณผ 101 5.3.2 ํ•˜๋“œ์›จ์–ด ๊ตฌํ˜„ ๊ฒฐ๊ณผ 107 ์ œ 6 ์žฅ ์••์ถ• ์„ฑ๋Šฅ ๋ฐ ํ•˜๋“œ์›จ์–ด ํฌ๊ธฐ ๋น„๊ต ๋ถ„์„ 113 6.1 ์••์ถ• ์„ฑ๋Šฅ ๋น„๊ต 113 6.2 ํ•˜๋“œ์›จ์–ด ํฌ๊ธฐ ๋น„๊ต 120 ์ œ 7 ์žฅ ๊ฒฐ๋ก  125 ์ฐธ๊ณ ๋ฌธํ—Œ 128 ABSTRACT 135Docto

    Efficient architectures for multidimensional discrete transforms in image and video processing applications

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    PhD ThesisThis thesis introduces new image compression algorithms, their related architectures and data transforms architectures. The proposed architectures consider the current hardware architectures concerns, such as power consumption, hardware usage, memory requirement, computation time and output accuracy. These concerns and problems are crucial in multidimensional image and video processing applications. This research is divided into three image and video processing related topics: low complexity non-transform-based image compression algorithms and their architectures, architectures for multidimensional Discrete Cosine Transform (DCT); and architectures for multidimensional Discrete Wavelet Transform (DWT). The proposed architectures are parameterised in terms of wordlength, pipelining and input data size. Taking such parameterisation into account, efficient non-transform based and low complexity image compression algorithms for better rate distortion performance are proposed. The proposed algorithms are based on the Adaptive Quantisation Coding (AQC) algorithm, and they achieve a controllable output bit rate and accuracy by considering the intensity variation of each image block. Their high speed, low hardware usage and low power consumption architectures are also introduced and implemented on Xilinx devices. Furthermore, efficient hardware architectures for multidimensional DCT based on the 1-D DCT Radix-2 and 3-D DCT Vector Radix (3-D DCT VR) fast algorithms have been proposed. These architectures attain fast and accurate 3-D DCT computation and provide high processing speed and power consumption reduction. In addition, this research also introduces two low hardware usage 3-D DCT VR architectures. Such architectures perform the computation of butterfly and post addition stages without using block memory for data transposition, which in turn reduces the hardware usage and improves the performance of the proposed architectures. Moreover, parallel and multiplierless lifting-based architectures for the 1-D, 2-D and 3-D Cohen-Daubechies-Feauveau 9/7 (CDF 9/7) DWT computation are also introduced. The presented architectures represent an efficient multiplierless and low memory requirement CDF 9/7 DWT computation scheme using the separable approach. Furthermore, the proposed architectures have been implemented and tested using Xilinx FPGA devices. The evaluation results have revealed that a speed of up to 315 MHz can be achieved in the proposed AQC-based architectures. Further, a speed of up to 330 MHz and low utilisation rate of 722 to 1235 can be achieved in the proposed 3-D DCT VR architectures. In addition, in the proposed 3-D DWT architecture, the computation time of 3-D DWT for data size of 144ร—176ร—8-pixel is less than 0.33 ms. Also, a power consumption of 102 mW at 50 MHz clock frequency using 256ร—256-pixel frame size is achieved. The accuracy tests for all architectures have revealed that a PSNR of infinite can be attained

    Liquid Crystal on Silicon Devices: Modeling and Advanced Spatial Light Modulation Applications

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    Liquid Crystal on Silicon (LCoS) has become one of the most widespread technologies for spatial light modulation in optics and photonics applications. These reflective microdisplays are composed of a high-performance silicon complementary metal oxide semiconductor (CMOS) backplane, which controls the light-modulating properties of the liquid crystal layer. State-of-the-art LCoS microdisplays may exhibit a very small pixel pitch (below 4 ?m), a very large number of pixels (resolutions larger than 4K), and high fill factors (larger than 90%). They modulate illumination sources covering the UV, visible, and far IR. LCoS are used not only as displays but also as polarization, amplitude, and phase-only spatial light modulators, where they achieve full phase modulation. Due to their excellent modulating properties and high degree of flexibility, they are found in all sorts of spatial light modulation applications, such as in LCOS-based display systems for augmented and virtual reality, true holographic displays, digital holography, diffractive optical elements, superresolution optical systems, beam-steering devices, holographic optical traps, and quantum optical computing. In order to fulfil the requirements in this extensive range of applications, specific models and characterization techniques are proposed. These devices may exhibit a number of degradation effects such as interpixel cross-talk and fringing field, and time flicker, which may also depend on the analog or digital backplane of the corresponding LCoS device. The use of appropriate characterization and compensation techniques is then necessary

    Proceedings of the Second International Mobile Satellite Conference (IMSC 1990)

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    Presented here are the proceedings of the Second International Mobile Satellite Conference (IMSC), held June 17-20, 1990 in Ottawa, Canada. Topics covered include future mobile satellite communications concepts, aeronautical applications, modulation and coding, propagation and experimental systems, mobile terminal equipment, network architecture and control, regulatory and policy considerations, vehicle antennas, and speech compression

    NASA Tech Briefs, March 2002

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    Topics include: a special section on data acquisition, software, electronic components and systems, materials, computer programs, mechanics, machinery/automation, manufacturing, biomedical, physical sciences, book and reports, and a special section of Photonics Tech Briefs

    Intelligent Circuits and Systems

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    ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.ใ€€ This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

    Advanced Trends in Wireless Communications

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    Physical limitations on wireless communication channels impose huge challenges to reliable communication. Bandwidth limitations, propagation loss, noise and interference make the wireless channel a narrow pipe that does not readily accommodate rapid flow of data. Thus, researches aim to design systems that are suitable to operate in such channels, in order to have high performance quality of service. Also, the mobility of the communication systems requires further investigations to reduce the complexity and the power consumption of the receiver. This book aims to provide highlights of the current research in the field of wireless communications. The subjects discussed are very valuable to communication researchers rather than researchers in the wireless related areas. The book chapters cover a wide range of wireless communication topics
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