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Titlebook: OOIS 2001; 7th International Co Xingxu Wang,Ronald Johnston,Shushma Patel Conference proceedings 2001 Springer-Verlag London Limited 2001 H

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樓主: 贊美
21#
發(fā)表于 2025-3-25 05:05:29 | 只看該作者
Ying Lianguch in technischer Hinsicht bedeutet dies eine neue Form des Einflusses auf den Zugang der User zur Fotografie selbst. Wie sieht sich der einzelne User im Vergleich zu den anderen Benutzern. von Flickr? Welche neuen Erwartungen werden von der Technik geweckt oder durch die Communities angeregt? Welc
22#
發(fā)表于 2025-3-25 07:43:20 | 只看該作者
23#
發(fā)表于 2025-3-25 12:15:31 | 只看該作者
Xin Wang,Christine W. Chan materials with high precision and minimal thermal and mechanical influence of the surrounding. Due to the high intensities obtained in the focal volume even at moderate pulse energies not only surface structuring becomes possible but also the precise three-dimensional structuring within the bulk of
24#
發(fā)表于 2025-3-25 18:34:34 | 只看該作者
25#
發(fā)表于 2025-3-25 22:58:53 | 只看該作者
Rebécca Deneckère,Carine Souveyete first section of the chapter begins with the design of the frequency synthesizer, in particular, the evaluation of the different topologies for low-power quadrature generation. Then, the selected topology, namely a quadrature voltage controlled oscillator (QVCO), is refined for improved accuracy a
26#
發(fā)表于 2025-3-26 01:04:53 | 只看該作者
27#
發(fā)表于 2025-3-26 04:44:17 | 只看該作者
Gerard Meszaros,Jennitta Andrea,Shaun Smithas it has also been described in the previous chapters. UTMFs have cost and processability advantage over the TCOs, such as the widely used ITO. The peak transmittance of UTMFs, which also provide sufficiently large electrical conduction, is usually as high as 80?% at the plasma frequency and it rol
28#
發(fā)表于 2025-3-26 09:52:29 | 只看該作者
29#
發(fā)表于 2025-3-26 14:07:16 | 只看該作者
30#
發(fā)表于 2025-3-26 19:35:49 | 只看該作者
Eleni Stroulia,Rohit Kapoore system and recognize the sloshing pattern in the fuel tank.Accurate fluid level measurement in dynamic environments can be assessed using a Support Vector Machine (SVM) approach. SVM is a supervised learning model that analyzes and recognizes patterns. It is a signal classification technique which
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