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"Physical implementation of the memristor at industrial scale sparked the interest from various disciplines, ranging from physics, nanotechnology, electrical engineering, neuroscience, to intelligent robotics. As any promising new technology, it has raised hopes and questions; it is an extremely challenging task to live up to the high expectations and to devise revolutionary and feasible future applications for memristive devices. "

"The two-volume set LNCS 7367 and 7368 constitutes the refereed proceedings of the 9th International Symposium on Neural Networks, ISNN 2012, held in Shenyang, China, in July 2012. The 147 revised full papers presented were carefully reviewed and selected from numerous submissions. The contributions are structured in topical sections on mathematical modeling, neurodynamics, cognitive neuroscience, learning algorithms, optimization, pattern recognition, vision, image processing, information processing, neurocontrol, and novel applications."

"The two-volume set LNCS 7367 and 7368 constitutes the refereed proceedings of the 9th International Symposium on Neural Networks, ISNN 2012, held in Shenyang, China, in July 2012. The 147 revised full papers presented were carefully reviewed and selected from numerous submissions. The contributions are structured in topical sections on mathematical modeling, neurodynamics, cognitive neuroscience, learning algorithms, optimization, pattern recognition, vision, image processing, information processing, neurocontrol, and novel applications."

"ABSTRAKStudi yang dilakukan dalam penelitian ini untuk menganalisis multiatribut dari data seismik dan data log dalam mengidentifikasi litologi bawah permukaan, terutama dalam mengidentifikasi penyebaran sourcerockpada suatu daerah target tertentu. Metode multiatribut dalam penelitian ini dilakukan untuk memprediksi log vshaleyang diturunkan dari log resisitivity. Untuk mendapatkan gambaran distribusi sifat fisik batuan di bawah permukaan, maka hubungan antara data log dengan data seismik dapat membantu menghasilkan volume sebaran sifat fisik batuan yang tadinya hanya didapat dari tempat yang memiliki data log hingga menjadi sebaran pada seluruh area yang terdapat data seismik. Kemampuan neural network untuk mengenali pola dan menemukan hubungan non linear antara data seismik, data multiatribut dan sifat fisik batuan dari data log dimanfaatkan untuk mendapatkan sebaran sifat fisik batuan pada volume seismik dengan mengacu pada data log. Aplikasi geostatistik digunakan untuk mendapatkan model distribusi spasial di mana hasil dari prediksi volume shale dipakai sebagai data sekunder sedangkan data dari lubang sumur digunakan sebagai data primer, sehingga diharapkan mendapatkan kemenerusan secara lateral.

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ABSTRACTThe Study conducted in this research to analyze the multi attributes of seismic data and log data in identifying subsurface lithology, especially in identifying sourcerock deployment in a given target area. Multi attributes method in this research is done to predict log vshale derived from log resistivity. To obtain a description of the distribution of the physical properties of rocks below the surface, the relationship between log data and seismic data can help to generate volume distribution of physical properties of rocks that were only obtained from places that have log data to be distributed in all areas of seismic data. The ability of neural networks to recognize patterns and find nonlinear relationships between multi attribute data seismic data and the physical properties of rocks from log data are utilized to obtain the distribution of physical properties of rocks at seismic volumes with reference to log data. The geostatistical application is used to obtain spatial distribution model where the result of the shale volume prediction is used as secondary data while the data from the wellbore is used as the primary data, so it is expected to obtain lateral generation."

"Increasing miniaturization of devices, components, and integrated systems requires developments in the capacity to measure, organize, and manipulate matter at the nonoscale. This textbook is a comprehensive, interdisciplinary account of the technology and science that underpin nanoelectronics, covering the underlying physics, nanostructures, nanomaterials, and nanodevices. Without assuming prior knowledge of quantum physics, this book provides a unifying framework for the basic ideas needed to understand the recent developments in the field." "Numerous illustrations, homework problems, and interactive Java applets help the student to appreciate the basic principles of nanotechnology, and to apply them to real problems. Written in a clear yet rigorous and interdisciplinary manner, this textbook is suitable for advanced undergraduate and graduate students in electrical and electronic engineering, nanoscience, materials, bioengineering, and chemical engineering.Contents"