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Total Size:
29.1 MB
Info Hash:
A99219B300A071245947E83E0BC45CDD7E217B8C
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Added:
April 20, 2026, 4:57 a.m.
Stats:
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(Last updated: April 20, 2026, 5:02 a.m.)
| File | Size |
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| Yu Y. Memristive Computing 2025.pdf | 29.1 MB |
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348.2 MB
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| Uploaded by darkdream787 | Size 348.2 MB | Health [ 17 /5 ] | Added 2023-06-01 |
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| Uploaded by darkdream787 | Size 10.0 GB | Health [ 18 /16 ] | Added 2023-06-01 |
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| Uploaded by darkdream787 | Size 91.0 GB | Health [ 12 /7 ] | Added 2023-06-01 |
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| Uploaded by darkdream787 | Size 81.2 GB | Health [ 25 /5 ] | Added 2023-06-01 |
NOTE
SOURCE: Yu Y. Memristive Computing 2025
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COVER
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MEDIAINFO
Textbook in PDF format This book delves into a wide array of topics, ranging from memristor and its emulator to chaotic circuits based on memristor, memristor-based en/decryption systems, filter design based on memristive family, memristive filter for signal processing, memristor network-based swarm intelligence, dynamic analysis of memristive neural networks, and the application of memristor-based neural networks. It provides a comprehensive and systematic exploration of how memristors empower and drive cutting-edge research in neuromorphic computing and Artificial Intelligence (AI). This book encourages fostering interdisciplinary information literacy and cultivating cross-disciplinary computational thinking. This book plays a pivotal role in embracing and advancing the development of neuromorphic computing. Through profound foundational theories and academic analysis methods, this book guides Artificial Intelligence graduate students and engineering professionals in constructing a comprehensive knowledge and technological framework for memristor research. Swarm intelligence, inspired by the collective behavior of natural systems such as ant colonies and fish schools, has emerged as a powerful paradigm for solving complex optimization problems. In recent years, the advent of memristor technology has opened up new avenues for the hardware implementation of swarm intelligence algorithms. Memristors, with their unique properties of non-volatility, nanoscale dimensions, and the ability to perform both storage and computation, provide an ideal platform for realizing swarm intelligence in a highly parallel and efficient manner. The chapter begins by introducing a memristor crossbar array-based implementation of ant colony optimization (ACO) for image edge detection. A nonlinear voltage-controlled memristor model with a relaxation term is proposed, along with an improved ACO algorithm incorporating a padding strategy. The memristor crossbar array, with its high device density and parallel computing capabilities, enables efficient deployment of ACO for edge detection tasks. Experimental results demonstrate the superiority of the proposed method compared to existing edge detection techniques. - Provides a comprehensive introduction to how memristors empower cutting-edge directions, such as brain computing and AI - Presents the latest development in diverse theories and applications of memristors - Explains the application of memristors, and promotes the integration of technologies
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