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Author: David Macêdo Publisher: Editora Dialética ISBN: 6525230756 Category : Computers Languages : en Pages : 107
Book Description
Recently, deep learning has caused a significant impact on computer vision, speech recognition, and natural language understanding. In spite of the remarkable advances, deep learning recent performance gains have been modest and usually rely on increasing the depth of the models, which often requires more computational resources such as processing time and memory usage. To tackle this problem, we turned our attention to the interworking between the activation functions and the batch normalization, which is virtually mandatory currently. In this work, we propose the activation function Displaced Rectifier Linear Unit (DReLU) by conjecturing that extending the identity function of ReLU to the third quadrant enhances compatibility with batch normalization. Moreover, we used statistical tests to compare the impact of using distinct activation functions (ReLU, LReLU, PReLU, ELU, and DReLU) on the learning speed and test accuracy performance of VGG and Residual Networks state-of-the-art models. These convolutional neural networks were trained on CIFAR-10 and CIFAR-100, the most commonly used deep learning computer vision datasets. The results showed DReLU speeded up learning in all models and datasets. Besides, statistical significant performance assessments (p
Author: David Macêdo Publisher: Editora Dialética ISBN: 6525230756 Category : Computers Languages : en Pages : 107
Book Description
Recently, deep learning has caused a significant impact on computer vision, speech recognition, and natural language understanding. In spite of the remarkable advances, deep learning recent performance gains have been modest and usually rely on increasing the depth of the models, which often requires more computational resources such as processing time and memory usage. To tackle this problem, we turned our attention to the interworking between the activation functions and the batch normalization, which is virtually mandatory currently. In this work, we propose the activation function Displaced Rectifier Linear Unit (DReLU) by conjecturing that extending the identity function of ReLU to the third quadrant enhances compatibility with batch normalization. Moreover, we used statistical tests to compare the impact of using distinct activation functions (ReLU, LReLU, PReLU, ELU, and DReLU) on the learning speed and test accuracy performance of VGG and Residual Networks state-of-the-art models. These convolutional neural networks were trained on CIFAR-10 and CIFAR-100, the most commonly used deep learning computer vision datasets. The results showed DReLU speeded up learning in all models and datasets. Besides, statistical significant performance assessments (p
Author: Zhiqin Zhu Publisher: Frontiers Media SA ISBN: 2832552005 Category : Science Languages : en Pages : 208
Book Description
Multi-sensor image fusion focuses on processing images of the same object or scene acquired by multiple sensors, in which various sensors with multi-level and multi-spatial information are complemented and combined to ultimately yield a consistent interpretation of the observed environment. In recent years, multi-sensor image fusion has become a highly active topic, and various fusion methods have been proposed. Many effective processing methods, including multi-scale transformation, fuzzy inference, and deep learning, have been introduced to design fusion algorithms. Despite the great progress, there are still some noteworthy challenges in the field, such as the lack of unified fusion theories and methods for effective generalized fusion, the lack of fault tolerance and robustness, the lack of benchmarks for performance evaluation, the lack of work on specific applications of multi-sensor image fusion, and so on.
Author: Hui-Huang Hsu Publisher: Morgan Kaufmann ISBN: 012809625X Category : Computers Languages : en Pages : 328
Book Description
Big Data Analytics for Sensor-Network Collected Intelligence explores state-of-the-art methods for using advanced ICT technologies to perform intelligent analysis on sensor collected data. The book shows how to develop systems that automatically detect natural and human-made events, how to examine people's behaviors, and how to unobtrusively provide better services. It begins by exploring big data architecture and platforms, covering the cloud computing infrastructure and how data is stored and visualized. The book then explores how big data is processed and managed, the key security and privacy issues involved, and the approaches used to ensure data quality. In addition, readers will find a thorough examination of big data analytics, analyzing statistical methods for data analytics and data mining, along with a detailed look at big data intelligence, ubiquitous and mobile computing, and designing intelligence system based on context and situation. Indexing: The books of this series are submitted to EI-Compendex and SCOPUS - Contains contributions from noted scholars in computer science and electrical engineering from around the globe - Provides a broad overview of recent developments in sensor collected intelligence - Edited by a team comprised of leading thinkers in big data analytics
Author: Luca Pancioni Publisher: Springer ISBN: 3319999788 Category : Computers Languages : en Pages : 415
Book Description
This book constitutes the refereed proceedings of the 8th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2018, held in Siena, Italy, in September 2018. The 29 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 35 submissions. The papers present and discuss the latest research in all areas of neural network- and machine learning-based pattern recognition. They are organized in two sections: learning algorithms and architectures, and applications. Chapter "Bounded Rational Decision-Making with Adaptive Neural Network Priors" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Author: Arun Kumar Sangaiah Publisher: Academic Press ISBN: 0128172932 Category : Technology & Engineering Languages : en Pages : 282
Book Description
Deep Learning and Parallel Computing Environment for Bioengineering Systems delivers a significant forum for the technical advancement of deep learning in parallel computing environment across bio-engineering diversified domains and its applications. Pursuing an interdisciplinary approach, it focuses on methods used to identify and acquire valid, potentially useful knowledge sources. Managing the gathered knowledge and applying it to multiple domains including health care, social networks, mining, recommendation systems, image processing, pattern recognition and predictions using deep learning paradigms is the major strength of this book. This book integrates the core ideas of deep learning and its applications in bio engineering application domains, to be accessible to all scholars and academicians. The proposed techniques and concepts in this book can be extended in future to accommodate changing business organizations' needs as well as practitioners' innovative ideas. - Presents novel, in-depth research contributions from a methodological/application perspective in understanding the fusion of deep machine learning paradigms and their capabilities in solving a diverse range of problems - Illustrates the state-of-the-art and recent developments in the new theories and applications of deep learning approaches applied to parallel computing environment in bioengineering systems - Provides concepts and technologies that are successfully used in the implementation of today's intelligent data-centric critical systems and multi-media Cloud-Big data
Author: Alexander M. Wyglinski Publisher: Artech House ISBN: 1630814598 Category : Technology & Engineering Languages : en Pages : 375
Book Description
Based on the popular Artech House classic, Digital Communication Systems Engineering with Software-Defined Radio, this book provides a practical approach to quickly learning the software-defined radio (SDR) concepts needed for work in the field. This up-to-date volume guides readers on how to quickly prototype wireless designs using SDR for real-world testing and experimentation. This book explores advanced wireless communication techniques such as OFDM, LTE, WLA, and hardware targeting. Readers will gain an understanding of the core concepts behind wireless hardware, such as the radio frequency front-end, analog-to-digital and digital-to-analog converters, as well as various processing technologies. Moreover, this volume includes chapters on timing estimation, matched filtering, frame synchronization message decoding, and source coding. The orthogonal frequency division multiplexing is explained and details about HDL code generation and deployment are provided. The book concludes with coverage of the WLAN toolbox with OFDM beacon reception and the LTE toolbox with downlink reception. Multiple case studies are provided throughout the book. Both MATLAB and Simulink source code are included to assist readers with their projects in the field.
Author: James F. Manwell Publisher: John Wiley & Sons ISBN: 9780470686287 Category : Technology & Engineering Languages : en Pages : 704
Book Description
Wind energy’s bestselling textbook- fully revised. This must-have second edition includes up-to-date data, diagrams, illustrations and thorough new material on: the fundamentals of wind turbine aerodynamics; wind turbine testing and modelling; wind turbine design standards; offshore wind energy; special purpose applications, such as energy storage and fuel production. Fifty additional homework problems and a new appendix on data processing make this comprehensive edition perfect for engineering students. This book offers a complete examination of one of the most promising sources of renewable energy and is a great introduction to this cross-disciplinary field for practising engineers. “provides a wealth of information and is an excellent reference book for people interested in the subject of wind energy.” (IEEE Power & Energy Magazine, November/December 2003) “deserves a place in the library of every university and college where renewable energy is taught.” (The International Journal of Electrical Engineering Education, Vol.41, No.2 April 2004) “a very comprehensive and well-organized treatment of the current status of wind power.” (Choice, Vol. 40, No. 4, December 2002)
Author: David Macêdo
Author: David Macêdo
Author: Zhiqin Zhu
Author: Hui-Huang Hsu
Author: Luca Pancioni
Author: Arun Kumar Sangaiah
Author: Roger Bartlett
Author: Naval Sea Systems Command
Author: United States. Flight Standards Service
Author: Alexander M. Wyglinski
Author: James F. Manwell