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Libros sobre el tema "Embedded Systems, Algorithms, Optimization Techniques"

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Autor: Grafiati
Publicado: 14 de marzo de 2023

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1

Leupers, Rainer. Code Optimization Techniques for Embedded Processors: Methods, Algorithms, and Tools. Boston, MA: Springer US, 2000.

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2

Bhuvaneswari, M. C., ed. Application of Evolutionary Algorithms for Multi-objective Optimization in VLSI and Embedded Systems. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-1958-3.

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3

Malepati, Hazarathaiah. Digital media processing: DSP algorithms using C. Burlington, MA: Newnes/Elsevier, 2010.

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4

Digital media processing: DSP algorithms using C. Burlington, MA: Newnes, 2010.

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5

Gogniat, Guy. Algorithm-Architecture Matching for Signal and Image Processing: Best papers from Design and Architectures for Signal and Image Processing 2007 & 2008 & 2009. Dordrecht: Springer Science+Business Media B.V., 2011.

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6

Code Optimization Techniques for Embedded Processors - Methods, Algorithms, and Tools. Springer, 2000.

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7

Marwedel, Peter y Manish Verma. Advanced Memory Optimization Techniques for Low-Power Embedded Processors. Springer, 2010.

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8

Marwedel, Peter y Manish Verma. Advanced Memory Optimization Techniques for Low-Power Embedded Processors. Springer London, Limited, 2007.

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9

Marwedel, Peter y Manish Verma. Advanced Memory Optimization Techniques for Low-Power Embedded Processors. Springer, 2007.

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10

Bhuvaneswari, M. C. Application of Evolutionary Algorithms for Multi-objective Optimization in VLSI and Embedded Systems. Springer, 2016.

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11

Bhuvaneswari, M. C. Application of Evolutionary Algorithms for Multi-Objective Optimization in VLSI and Embedded Systems. Springer, 2014.

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12

Bhuvaneswari, M. C. Application of Evolutionary Algorithms for Multi-Objective Optimization in VLSI and Embedded Systems. Springer (India) Private Limited, 2014.

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13

Falk, Heiko. Source Code Optimization Techniques For Data Flow Dominated Embedded Software. Springer, 2011.

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14

Source Code Optimization Techniques for Data Flow Dominated Embedded Software. Springer, 2004.

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15

Marwedel, Peter y Heiko Falk. Source Code Optimization Techniques for Data Flow Dominated Embedded Software. Springer London, Limited, 2013.

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16

Balamurugan, S., Akash Kumar Bhoi, Sanjeevikumar Padmanaban, Neeraj Priyadarshi y Jens Bo Holm-Nielson. Intelligent Renewable Energy Systems: Integrating Artificial Intelligence Techniques and Optimization Algorithms. Wiley & Sons, Incorporated, John, 2021.

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17

Balamurugan, S., Akash Kumar Bhoi, Sanjeevikumar Padmanaban, Neeraj Priyadarshi y Jens Bo Holm-Nielson. Intelligent Renewable Energy Systems: Integrating Artificial Intelligence Techniques and Optimization Algorithms. Wiley & Sons, Incorporated, John, 2021.

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18

Balamurugan, S., Akash Kumar Bhoi, Sanjeevikumar Padmanaban, Neeraj Priyadarshi y Jens Bo Holm-Nielson. Intelligent Renewable Energy Systems: Integrating Artificial Intelligence Techniques and Optimization Algorithms. Wiley & Sons, Incorporated, John, 2021.

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19

Intelligent Renewable Energy Systems: Integrating Artificial Intelligence Techniques and Optimization Algorithms. Wiley & Sons, Limited, John, 2022.

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20

Malepati, Hazarathaiah. Digital Media Processing: DSP Algorithms Using C. Elsevier Science & Technology Books, 2010.

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21

Morawiec, Adam, Guy Gogniat y Dragomir Milojevic. Algorithm-Architecture Matching for Signal and Image Processing: Best papers from Design and Architectures for Signal and Image Processing 2007 & 2008 & 2009. Springer, 2010.

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22

Morawiec, Adam, Ahmet Erdogan, Guy Gogniat y Dragomir Milojevic. Algorithm-Architecture Matching for Signal and Image Processing: Best papers from Design and Architectures for Signal and Image Processing 2007 & 2008 ... Notes in Electrical Engineering ). Springer, 2012.

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23

Oulasvirta, Antti, Per Ola Kristensson, Xiaojun Bi y Andrew Howes, eds. Computational Interaction. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799603.001.0001.

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Resumen
This book presents computational interaction as an approach to explaining and enhancing the interaction between humans and information technology. Computational interaction applies abstraction, automation, and analysis to inform our understanding of the structure of interaction and also to inform the design of the software that drives new and exciting human-computer interfaces. The methods of computational interaction allow, for example, designers to identify user interfaces that are optimal against some objective criteria. They also allow software engineers to build interactive systems that adapt their behaviour to better suit individual capacities and preferences. Embedded in an iterative design process, computational interaction has the potential to complement human strengths and provide methods for generating inspiring and elegant designs. Computational interaction does not exclude the messy and complicated behaviour of humans, rather it embraces it by, for example, using models that are sensitive to uncertainty and that capture subtle variations between individual users. It also promotes the idea that there are many aspects of interaction that can be augmented by algorithms. This book introduces computational interaction design to the reader by exploring a wide range of computational interaction techniques, strategies and methods. It explains how techniques such as optimisation, economic modelling, machine learning, control theory, formal methods, cognitive models and statistical language processing can be used to model interaction and design more expressive, efficient and versatile interaction.
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24

Zaheer Ul-Haq y Angela K. Wilson, eds. Frontiers in Computational Chemistry: Volume 6. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/97898150368481220601.

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Frontiers in Computational Chemistry presents contemporary research on molecular modeling techniques used in drug discovery and the drug development process: computer aided molecular design, drug discovery and development, lead generation, lead optimization, database management, computer and molecular graphics, and the development of new computational methods or efficient algorithms for the simulation of chemical phenomena including analyses of biological activity. The sixth volume of this series features these six different perspectives on the application of computational chemistry in rational drug design: 1. Computer-aided molecular design in computational chemistry 2. The role of ensemble conformational sampling using molecular docking & dynamics in drug discovery 3. Molecular dynamics applied to discover antiviral agents 4. Pharmacophore modeling approach in drug discovery against the tropical infectious disease malaria 5. Advances in computational network pharmacology for Traditional Chinese Medicine (TCM) research 6. Progress in electronic-structure based computational methods: from small molecules to large molecular systems of biological significance
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25

Mehta, Vaishali, Dolly Sharma, Monika Mangla, Anita Gehlot, Rajesh Singh y Sergio Márquez Sánchez, eds. Challenges and Opportunities for Deep Learning Applications in Industry 4.0. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/97898150360601220101.

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The competence of deep learning for the automation and manufacturing sector has received astonishing attention in recent times. The manufacturing industry has recently experienced a revolutionary advancement despite several issues. One of the limitations for technical progress is the bottleneck encountered due to the enormous increase in data volume for processing, comprising various formats, semantics, qualities and features. Deep learning enables detection of meaningful features that are difficult to perform using traditional methods. The book takes the reader on a technological voyage of the industry 4.0 space. Chapters highlight recent applications of deep learning and the associated challenges and opportunities it presents for automating industrial processes and smart applications. Chapters introduce the reader to a broad range of topics in deep learning and machine learning. Several deep learning techniques used by industrial professionals are covered, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical project methodology. Readers will find information on the value of deep learning in applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. The book also discusses prospective research directions that focus on the theory and practical applications of deep learning in industrial automation. Therefore, the book aims to serve as a comprehensive reference guide for industrial consultants interested in industry 4.0, and as a handbook for beginners in data science and advanced computer science courses.
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