| |
|
|
|
|
|
|
|
|
1. |
Record Nr. |
UNINA9910139054403321 |
|
|
Autore |
Sanei Saeid |
|
|
Titolo |
Adaptive processing of brain signals [[electronic resource] /] / Saeid Sanei |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Chichester, West Sussex, : John Wiley & Sons Inc., c2013 |
|
|
|
|
|
|
|
ISBN |
|
1-118-62216-2 |
1-118-62214-6 |
1-118-62215-4 |
|
|
|
|
|
|
|
|
Descrizione fisica |
|
1 online resource (1039 p.) |
|
|
|
|
|
|
Classificazione |
|
|
|
|
|
|
Disciplina |
|
|
|
|
|
|
Soggetti |
|
Brain - Physiology |
Neural networks (Neurobiology) |
Signal processing - Digital techniques |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Note generali |
|
Description based upon print version of record. |
|
|
|
|
|
|
Nota di bibliografia |
|
Includes bibliographical references and index. |
|
|
|
|
|
|
Nota di contenuto |
|
Cover; Title Page; Copyright; Preface; Chapter 1 Brain Signals, Their Generation, Acquisition and Properties; 1.1 Introduction; 1.2 Historical Review of the Brain; 1.3 Neural Activities; 1.4 Action Potentials; 1.5 EEG Generation; 1.6 Brain Rhythms; 1.7 EEG Recording and Measurement; 1.8 Abnormal EEG Patterns; 1.9 Aging; 1.10 Mental Disorders; 1.11 Memory and Content Retrieval; 1.12 MEG Signals and Their Generation; 1.13 Conclusions; References; Chapter 2 Fundamentals of EEG Signal Processing; 2.1 Introduction; 2.2 Nonlinearity of the Medium; 2.3 Nonstationarity; 2.4 Signal Segmentation |
2.5 Other Properties of Brain Signals2.6 Conclusions; References; Chapter 3 EEG Signal Modelling; 3.1 Physiological Modelling of EEG Generation; 3.2 Mathematical Models; 3.3 Generating EEG Signals Based on Modelling the Neuronal Activities; 3.4 Electronic Models; 3.5 Dynamic Modelling of the Neuron Action Potential Threshold; 3.6 Conclusions; References; Chapter 4 Signal Transforms and Joint Time-Frequency Analysis; 4.1 Introduction; 4.2 Parametric Spectrum Estimation and Z-Transform; 4.3 Time-Frequency Domain Transforms; 4.4 Ambiguity Function and the Wigner-Ville Distribution |
4.5 Hermite Transform4.6 Conclusions; References; Chapter 5 Chaos |
|
|
|
|
|
|
|
|
|
|
|
and Dynamical Analysis; 5.1 Entropy; 5.2 Kolmogorov Entropy; 5.3 Lyapunov Exponents; 5.4 Plotting the Attractor Dimensions from Time Series; 5.5 Estimation of Lyapunov Exponents from Time Series; 5.6 Approximate Entropy; 5.7 Using Prediction Order; 5.8 Conclusions; References; Chapter 6 Classification and Clustering of Brain Signals; 6.1 Introduction; 6.2 Linear Discriminant Analysis; 6.3 Support Vector Machines; 6.4 k-Means Algorithm; 6.5 Common Spatial Patterns; 6.6 Conclusions; References |
Chapter 7 Blind and Semi-Blind Source Separation7.1 Introduction; 7.2 Singular Spectrum Analysis; 7.3 Independent Component Analysis; 7.4 Instantaneous BSS; 7.5 Convolutive BSS; 7.6 Sparse Component Analysis; 7.7 Nonlinear BSS; 7.8 Constrained BSS; 7.9 Application of Constrained BSS; Example; 7.10 Nonstationary BSS; 7.11 Tensor Factorization for Underdetermined Source Separation; 7.12 Tensor Factorization for Separation of Convolutive Mixtures in the Time Domain; 7.13 Separation of Correlated Sources via Tensor Factorization; 7.14 Conclusions; References |
Chapter 8 Connectivity of Brain Regions8.1 Introduction; 8.2 Connectivity Through Coherency; 8.3 Phase-Slope Index; 8.4 Multivariate Directionality Estimation; 8.5 Modelling the Connectivity by Structural Equation Modelling; 8.6 EEG Hyper-Scanning and Inter-Subject Connectivity; 8.7 State-Space Model for Estimation of Cortical Interactions; 8.8 Application of Adaptive Filters; 8.9 Tensor Factorization Approach; 8.10 Conclusions; References; Chapter 9 Detection and Tracking of Event-Related Potentials; 9.1 ERP Generation and Types; 9.2 Detection, Separation, and Classification of P300 Signals |
9.3 Brain Activity Assessment Using ERP |
|
|
|
|
|
|
Sommario/riassunto |
|
"Brain signal processing spans a broad range of knowledge across engineering, science and medicine, and this book brings together the disparate theory and application to create a comprehensive resource on this growing topic. It will provide advanced tools for the detection, monitoring, separation, localizing and understanding of brain functional, anatomical, and physiological abnormalities. The focus will be on advanced and adaptive signal processing techniques for the processing of electroencephalography and magneto-encephalography signals, and their correlation to the corresponding functional magnetic resonance imaging (fMRI). Multimodal processing of brain signals, the new focus for brain signal research, will also be explored. The book covers the broad remit of neuro-imaging, ensuring comprehensive coverage of all issues related to brain signal processing. Topics such as mental fatigue, brain connectivity and new recording techniques will also be covered.This book will be a progression/follow on from Dr Sanei's first book with Wiley, EEG Signal Processing"-- |
"Covers the fundamentals of brain signal processing, before developing the subject at advanced level"-- |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
2. |
Record Nr. |
UNINA9910731487803321 |
|
|
Autore |
Bhushan Bharat |
|
|
Titolo |
AI Models for Blockchain-Based Intelligent Networks in IoT Systems : Concepts, Methodologies, Tools, and Applications / / edited by Bharat Bhushan, Arun Kumar Sangaiah, Tu N. Nguyen |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
|
|
|
|
|
|
|
|
|
ISBN |
|
|
|
|
|
|
Edizione |
[1st ed. 2023.] |
|
|
|
|
|
Descrizione fisica |
|
1 online resource (396 pages) |
|
|
|
|
|
|
Collana |
|
Engineering Cyber-Physical Systems and Critical Infrastructures, , 2731-5010 ; ; 6 |
|
|
|
|
|
|
|
|
Altri autori (Persone) |
|
SangaiahArun Kumar |
NguyenTu N |
|
|
|
|
|
|
|
|
Disciplina |
|
|
|
|
|
|
Soggetti |
|
Engineering - Data processing |
Computational intelligence |
Artificial intelligence |
Data Engineering |
Computational Intelligence |
Artificial Intelligence |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Nota di contenuto |
|
From Smart Devices to Smarter Systems: The Evolution of Artificial Intelligence of Things (AIoT) with Characteristics, Architecture, Use cases and Challenges -- AI enabled human and machine activity monitoring in Industrial IoT systems -- AI Model for Block-chain Based Industrial IoT and Big Data Analytics -- SIMDPS: Smart Industry Monitoring And Disaster Prediction System -- An Artificial Intelligence Based Sustainable Approaches - IoT Systems for Smart Cities -- Empowering Artificial Intelligence of Things (AIoT) toward Smart Healthcare Systems -- AI Enabled Internet of Medical Things In Smart Healthcare -- AI Model for Blockchain based Industrial Application in Healthcare IoT. |
|
|
|
|
|
|
|
|
Sommario/riassunto |
|
The goal of this book is to explore various security paradigms such as Machine Learning, Big data, Cyber Physical Systems, and Blockchain to address both intelligence and reconfigurability in various IoT devices. |
|
|
|
|
|
|
|
|
|
|
The book further aims to address and analyze the state of the art of blockchain-based intelligent networks in IoT systems and related technologies including healthcare sector. AI can ease, optimize, and automate the blockchain-based decision-making process for better governance and higher performance in IoT systems. Considering the incredible progress made by AI models, a blockchain system powered by intelligent AI algorithms can detect the existence of any kind of attack and automatically invoke the required defense mechanisms. In case of unavoidable damage, AI models can help to isolate the compromised component from the blockchain platform and safeguard the overall system from crashing. Furthermore, AI models can also contribute toward the robustness and scalability of blockchain-based intelligent IoT networks. The book is designed to be the first-choice reference at university libraries, academic institutions, research and development centers, information technology centers, and any institutions interested in integration of AI and IoT. The intended audience of this book include UG/PG students, Ph.D. scholars of this fields, industry technologists, young entrepreneurs, professionals, network designers, data scientists, technology specialists, practitioners, and people who are interested in exploring the role of AI and blockchain technology in IoT systems. |
|
|
|
|
|
| |