Showing results for "pascal wallisch"
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Neural Data Science
A Primer with MATLAB® and Python™
2017
EN
A Primer with MATLAB® and Python™ present important information on the emergence of the use of Python, a more general purpose option to MATLAB, the preferred computation language for scientific computing and analysis in neuroscience. This book addresses the snake in the room by providing a beginner's introduction to the principles of computation and data analysis in neuroscience, using both Python and MATLAB, giving readers the ability to transcend platform tribalism and enable coding vers...
MATLAB for Neuroscientists
An Introduction to Scientific Computing in MATLAB
2014
EN
Accessible
MATLAB for Neuroscientists serves as the only complete study manual and teaching resource for MATLAB, the globally accepted standard for scientific computing, in the neurosciences and psychology. This unique introduction can be used to learn the entire empirical and experimental process (including stimulus generation, experimental control, data collection, data analysis, modeling, and more), and the 2nd Edition continues to ensure that a wide variety of computational problems can be addres...
MATLAB for Neuroscientists
An Introduction to Scientific Computing in MATLAB
2010
EN
Accessible
MATLAB for Neuroscientists: An Introduction to Scientific Computing in MATLAB is the first comprehensive teaching resource and textbook for the teaching of MATLAB in the Neurosciences and in Psychology. MATLAB is unique in that it can be used to learn the entire empirical and experimental process, including stimulus generation, experimental control, data collection, data analysis and modeling. Thus a wide variety of computational problems can be addressed in a single programming environmen...
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An Introduction to Object Recognition
Selected Algorithms for a Wide Variety of Applications
- Series -
- Computer Science (R0)
2010
EN
Rapid development of computer hardware has enabled usage of automatic object recognition in an increasing number of applications, ranging from industrial image processing to medical applications, as well as tasks triggered by the widespread use of the internet. Each area of application has its specific requirements, and consequently these cannot all be tackled appropriately by a single, general-purpose algorithm. This easy-to-read text/reference provides a comprehensive introduction to the...
Academic Press Library in Signal Processing, Volume 6
Image and Video Processing and Analysis and Computer Vision
2017
EN
Accessible
Academic Press Library in Signal Processing, Volume 6: Image and Video Processing and Analysis and Computer Vision is aimed at university researchers, post graduate students and R&D engineers in the industry, providing a tutorial-based, comprehensive review of key topics and technologies of research in both image and video processing and analysis and computer vision. The book provides an invaluable starting point to the area through the insight and understanding that it provides. With this...
2011
EN
Major strides have been made in face processing in the last ten years due to the fast growing need for security in various locations around the globe. A human eye can discern the details of a specific face with relative ease. It is this level of detail that researchers are striving to create with ever evolving computer technologies that will become our perfect mechanical eyes. The difficulty that confronts researchers stems from turning a 3D object into a 2D image. That subject is covered ...
Guide to Convolutional Neural Networks
A Practical Application to Traffic-Sign Detection and Classification
2017
EN
This must-read text/reference introduces the fundamental concepts of convolutional neural networks (ConvNets), offering practical guidance on using libraries to implement ConvNets in applications of traffic sign detection and classification. The work presents techniques for optimizing the computational efficiency of ConvNets, as well as visualization techniques to better understand the underlying processes. The proposed models are also thoroughly evaluated from different perspectives, usin...
- Book 31 -
- Handbook of Statistics
2013
EN
Statistical learning and analysis techniques have become extremely important today, given the tremendous growth in the size of heterogeneous data collections and the ability to process it even from physically distant locations. Recent advances made in the field of machine learning provide a strong framework for robust learning from the diverse corpora and continue to impact a variety of research problems across multiple scientific disciplines. The aim of this handbook is to familiarize beg...
2016
EN
Accessible
Introduction to EEG- and Speech-Based Emotion Recognition Methods examines the background, methods, and utility of using electroencephalograms (EEGs) to detect and recognize different emotions. By incorporating these methods in brain-computer interface (BCI), we can achieve more natural, efficient communication between humans and computers. This book discusses how emotional states can be recognized in EEG images, and how this is useful for BCI applications. EEG and speech processing method...
Psychophysics
A Practical Introduction
2016
EN
Psychophysics: A Practical Introduction, Second Edition, is the primary scientific tool for understanding how the physical world of colors, sounds, odors, movements, and shapes translates into the sensory world of sight, hearing, touch, taste, and smell; in other words, how matter translates into mind. This timely revision provides a unique introduction to the techniques for researching and understanding how the brain translates the external physical world to the internal world of sensatio...
2013
EN
This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data,...
2013
EN
Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences. Many real-world problems modeled by MDPs have huge state and/or action spaces, giving an opening to the curse of dimensionality and so making practical solution of the resulting models intractable. In other cases, the system of interest is too complex to allow explicit specification of some of the MDP model pa...











