AI Applications to Communications and Information Technologies E-Book

Daniel Minoli

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 782

Veröffentlichungsjahr: 2023

Beschreibung

AI Applications to Communications and Information Technologies Apply the technology of the future to networking and communications. Artificial intelligence, which enables computers or computer-controlled systems to perform tasks which ordinarily require human-like intelligence and decision-making, has revolutionized computing and digital industries like few other developments in recent history. Tools like artificial neural networks, large language models, and deep learning have quickly become integral aspects of modern life. With research and development into AI technologies proceeding at lightning speeds, the potential applications of these new technologies are all but limitless. AI Applications to Communications and Information Technologies offers a cutting-edge introduction to AI applications in one particular set of disciplines. Beginning with an overview of foundational concepts in AI, it then moves through numerous possible extensions of this technology into networking and telecommunications. The result is an essential introduction for researchers and for technology undergrad/grad student alike. AI Applications to Communications and Information Technologies readers will also find: * In-depth analysis of both current and evolving applications * Detailed discussion of topics including generative AI, chatbots, automatic speech recognition, image classification and recognition, IoT, smart buildings, network management, network security, and more * An authorial team with immense experience in both research and industry AI Applications to Communications and Information Technologies is ideal for researchers, industry observers, investors, and advanced students of network communications and related fields.

Leseprobe

Table of Contents

Cover

Table of Contents

Series Page

Title Page

Copyright Page

About the Authors

Preface

1 Overview

1.1 Introduction and Basic Concepts

1.2 Learning Methods

1.3 Areas of Applicability

1.4 Scope of this Text

A. Basic Glossary of Key AI Terms and Concepts

References

2 Current and Evolving Applications to Natural Language Processing

2.1 Scope

2.2 Introduction

2.3 Overview of Natural Language Processing and Speech Processing

2.4 Natural Language Processing/Natural Language Understanding Basics

2.5 Natural Language Generation Basics

2.6 Chatbots

2.7 Generative AI

A. Basic Glossary of Key AI Terms and Concepts Related to Natural Language Processing

References

3 Current and Evolving Applications to Speech Processing

3.1 Scope

3.2 Overview

3.3 Noise Cancellation

3.4 Training

3.5 Applications to Voice Interfaces Used to Control Home Devices and Digital Assistant Applications

3.6 Attention‐based Models

3.7 Sentiment Extraction

3.8 End‐to‐End Learning

3.9 Speech Synthesis

3.10 Zero‐shot TTS

3.11 VALL‐E: Unseen Speaker as an Acoustic Prompt

A. Basic Glossary of Key AI Terms and Concepts

References

4 Current and Evolving Applications to Video and Imaging

4.1 Overview and Background

4.2 Convolution Process

4.3 CNNs

4.4 Imaging Applications

4.5 Specific Application Examples

4.6 Other Models: Diffusion and Consistency Models

A. Basic Glossary of Key AI Terms and Concepts

B. Examples of Convolutions

References

5 Current and Evolving Applications to IoT and Applications to Smart Buildings and Energy Management

5.1 Introduction

5.2 Smart Building ML Applications

5.3 Example of a Commercial Product – BrainBox AI

A. Basic Glossary of Key IoT (Smart Building) Terms and Concepts

References

6 Current and Evolving Applications to Network Cybersecurity

6.1 Overview

6.2 General Security Requirements

6.3 Corporate Resources/Intranet Security Requirements

6.4 IoT Security (IoTSec)

6.5 Blockchains

6.6 Zero Trust Environments

6.7 Areas of ML Applicability

A. Basic Glossary of Key Security Terms and Concepts

References

7 Current and Evolving Applications to Network Management

7.1 Overview

7.2 Examples of Neural Network‐Assisted Network Management

A. Short Glossary of Network Management Concepts

References

Super Glossary

Index

End User License Agreement

List of Tables

Chapter 1

Table 1.1 Artificial intelligence in medicine and healthcare (short list)....

Table 1.2 Learning philosophies.

Table 1.3 Types of NNs (partial list).

Table 1.4 Types of learning processes.

Table 1.5 Decision algorithms mapped to decision method classes.

Table 1.6 Examples of general AI application in telecommunications.

Chapter 2

Table 2.1 Basic terminology.

Table 2.2 Basic NLP/NLU/NLG methods.

Chapter 3

Table 3.1 Cascaded TTS operation.

Table 3.2 A comparison between VALL‐E and current cascaded TTS systems.

Chapter 4

Table 4.1 Basic Nomenclature of CNNs.

Table 4.2 Well‐known press time CNN architectures.

Chapter 5

Table 5.1 A view to the Scope of IoT.

Table 5.2 Partial list of Smart City Applications.

Table 5.3 List of energy efficiency control measures.

Chapter 6

Table 6.1 Enterprise security mechanism typically utilized by CISOs.

Table 6.2 Sample of ML applications for cybersecurity.

Table 6.3 Recent US Patents and Patent Applications dealing with ML solutio...

Chapter 7

Table 7.1 Recent US Patents and Patent Applications dealing with ML solutio...

Table 7.2 MLMs to perform function of the system.

Table 7.3 ML algorithms used for CATV RF monitoring.

List of Illustrations

Chapter 1

Figure 1.1 AI in complex natural language processing.

Figure 1.2 Positioning of various AI systems.

Figure 1.3 Comparing ML to DL. Note: the nodes in the hidden layers are also...

Figure 1.4 Example of DNN.

Figure 1.5 Feature usage approach in DL (example).

Figure 1.6 Activations and weights.

Figure 1.7 Example of a multi‐layer machine‐trained feed‐forward NN that has...

Figure 1.8 Simplified neural network.

Figure 1.9 Typical activation functions.

Figure 1.10 Backpropagation.

Figure 1.11 Example of input, hidden, and output layers in an NN used for ge...

Figure 1.12 A mathematical definition of a DNN.

Figure 1.13 A looping constraint on the hidden layers of MLP makes it an RNN...

Figure 1.14 Example of RNN neural architecture.

Figure 1.15 Basic architecture of a CNN.

Figure 1.16 Pixel blocks of a picture, here two characters.

Figure 1.17 Comparison among various ANNs.

Figure 1.18 Generic concept of learning/training.

Figure 1.19 Comparison and interrelation of induction, deduction, and transd...

Figure 1.20 Commonly used machine learning methods and algorithms (figure is...

Figure 1.21 Summary pictorial for machine learning methods.

Figure 1.22 Decision boundary of logistic regression.

Figure 1.23 Comparison of some ML methods.

Figure 1.24 Very common network/cloud‐based AI applications.

Chapter 2

Figure 2.1 Positioning of the subdisciplines.

Figure 2.2 Basic generic example of NLP/NLG environment.

Figure 2.3 Illustrative networked environment with a spoken language process...

Figure 2.4 Example of speech synthesizer with STT and NLP mechanisms.

Figure 2.5 LSTM memory cell.

Figure 2.6 The transformer model.

Figure 2.7 Another view of the Transformer model.

Figure 2.8 Early Reiter/Dale pipeline architecture of an NLG system.

Figure 2.9 NLP processor adapted for context‐specific processing of natural ...

Figure 2.10 Tokenization example.

Figure 2.11 Language‐agnostic ML in NLP using feature extraction.

Figure 2.12 Typical modern NLP/NLG pipeline.

Figure 2.13 GAN‐based NLP system: The GAN learns word co‐occurrences and the...

Figure 2.14 Example of a process flow for NLP‐based training of an NLG syste...

Figure 2.15 Typical elements of a template‐based systems NLG.

Figure 2.16 Typical NLG tasks for converting inputs.

Figure 2.17 Typical Chatbot‐based system.

Figure 2.18 Example of chatbot.

Chapter 3

Figure 3.1 Basic ASR system. MFCC, Mel Frequency Cepstral Coefficients; FFT,...

Figure 3.2 Voice processing system example.

Figure 3.3 Wakeword detection using a neural network.

Figure 3.4 ASR elements: feature extraction and inference.

Figure 3.5 Distant‐talking speech recognition environment.

Figure 3.6 MVDR beampatterns. Patterns are strongly dependent on the number ...

Figure 3.7 Example of ASR system that makes heavy use of NN mechanisms.

Figure 3.8 Neural beamforming use in ASR where one encoder configured for cl...

Figure 3.9 Example of NAB model (simplified).

Figure 3.10 Example of ASR system with noise cancellation.

Figure 3.11 Diagram of the likelihood ratio (LR) calculation in an ASR syste...

Figure 3.12 BLSTM/Attention example.

Figure 3.13 ASR with sentiment analysis.

Figure 3.14 Modifying parameters for parametric speech synthesis responsive ...

Figure 3.15 RNN model for speech synthesis.

Figure 3.16 General view of Zero‐Shot TTS.

Figure 3.17 YourTSS.

Figure 3.18 The overview of VALL‐E.

Chapter 4

Figure 4.1 Calculating the Feature map via convolutions.

Figure 4.2 Sliding window sx × sy.

Figure 4.3 A neuron supporting an inner product.

Figure 4.4 An example of a fully connected and convolutional layers.

Figure 4.5 Example of a CNN.

Figure 4.6 Basic operation of a convolutional operation at the functional le...

Figure 4.7 Autoencoders. Top: Regular AE. Bottom CAE.

Figure 4.8 R‐CNN object detection system.

Figure 4.9 Fast R‐CNN architecture.

Figure 4.10 Macroblocks (simplified example).

Figure 4.11 Tiles. The ROI pooling layer segments each region proposal into ...

Figure 4.12 Encoding process example.

Figure 4.13

I

,

P

,

B

frame encoding.

Figure 4.14 Semantic image segmentation.

Figure 4.15 Basic CNN architecture for image classification and image segmen...

Figure 4.16 Encoder/decoder structure.

Figure 4.17 Examples of static upsampling.

Figure 4.18 Illustrative example of a CNN with depthwise convolution.

Figure 4.19 AlexNet CNN.

Figure 4.20 VGG16 CNN (13 CONV and 3 FC layers).

Figure 4.21 Inception (GoogLeNet) architecture summary.

Figure 4.22 ResNet: Top Original Residual Unit; Bottom: Improved Residual Un...

Figure 4.23 ResNeXt. Left: A block of ResNet. Right: A block of ResNeXt with...

Figure 4.24 Example of DenseNet and elements.

Figure 4.25 Example of CV system.

Figure 4.26 LeakyReLU.

Figure 4.27 Use of CNNs on reconstruct a block of video to reduce artifacts....

Figure 4.28 Image fusion model.

Figure 4.29 ML system for object classification from unmanned underwater veh...

Figure 4.30 CIFAR‐10 Object recognition NN.

Figure 4.31 Single‐shot detector. (a) Image with GT boxes. (b) 8 × 8 feature...

Figure 4.32 YOLO. (a) Object(s). (b) Predicted bounding boxes. (c) Final pre...

Figure 4.33 Video understanding model.

Figure 4.34 Training and

L

1 loss function.

Figure 4.35 Neural Network System with Temporal Feedback for Denoising of Re...

Figure 4.36 Facial expression recognition.

Figure 4.37 A situational awareness ecosystem.

Figure 4.38 Plethora of input devices.

Figure 4.39 Federated queries.

Figure 4.B1 Example of convolution operation with 2 × 2 kernel and stride of...

Figure 4.B2 Padding.

Figure 4.B3 Example of convolution operation with two 2 × 2 kernels and stri...

Figure 4.B4 Example of convolution operation with three channels.

Figure 4.B5 Example of convolution operation with three channels and multipl...

Figure 4.B6 Max pooling operation.

Chapter 5

Figure 5.1 Nonexhaustive overall taxonomy of IoT applications.

Figure 5.2 Typical distributed environment comprised of IoT‐enabled devices....

Figure 5.3 An exemplary IoT‐based smart home.

Figure 5.4 Another exemplary IoT‐based smart home.

Figure 5.5 Advanced metering infrastructure.

Figure 5.6 Medical wireless body area network.

Figure 5.7 Vehicular IoT asset tracking on a national scale.

Figure 5.8 Vehicle‐to‐infrastructure and vehicle‐to‐vehicle IoT applications...

Figure 5.9 Geolocation and tracking IoT applications.

Figure 5.10 Distributed IoT environment.

Figure 5.11 Commercial Buildings Energy Consumption Survey (CBECS).

Figure 5.12 BMS complemented by an ML/DNN system.

Figure 5.13 Block diagram of an exemplary commercial building structure.

Figure 5.14 Calibration method by which the neural network may be trained.

Figure 5.15 Block diagram illustrating optimization control system with an e...

Figure 5.16 DNN used to implement the Qin optimization control system.

Figure 5.17 NN training using simulation output data.

Figure 5.18 Trained NN performing the automated energy audit.

Figure 5.19 System block diagram of an economizer device.

Figure 5.20 Topological representation of the solution space covering a set ...

Figure 5.21 Performance optimization diagram.

Figure 5.22 Data flow diagram illustrating how the output of the particle sw...

Figure 5.23 Data flow diagram illustrating how deep reinforcement learning o...

Figure 5.24 Networked implementation of the optimizer control system integra...

Figure 5.25 VAV HVAC system.

Figure 5.26 LSTM‐MIMO architecture with multi‐input multi‐output to predict ...

Figure 5.27 BrainBox AI system.

Figure 5.28 Technical advantages of AI‐based system.

Figure 5.29 Business benefits of AI‐based system.

Figure 5.30 Fully autonomous self‐adaptive AI allows a stakeholder to move f...

Chapter 6

Figure 6.1 2022 headlines related to security breaches.

Figure 6.2 Typical computing ecosystem.

Figure 6.3 Security mechanisms.

Figure 6.4 Best practices: policy‐based approaches and defense in depth.

Figure 6.5 Enhanced classical NIST (National Institute of Standards and Tech...

Figure 6.6 Generic process for enterprise security.

Figure 6.7 Basic IDS environment.

Figure 6.8 Example of end‐point tools under the protection of an integrated ...

Figure 6.9 Evolution of security mechanism towards zero trust environments. ...

Figure 6.10 Schematic flow chart showing an intrusion detection method confi...

Figure 6.11 Block diagram of a system including a network computing platform...

Figure 6.12 Flow diagram of an interoperative process executed by a protecte...

Figure 6.13 Block diagram of an adaptive bot detector.

Figure 6.14 Method for on‐demand detection and scanning of email.

Figure 6.15 Illustrative classifier engine to identify potential phishing we...

Figure 6.16 Example of platform for data protection and resiliency.

Figure 6.17 Flow diagram for integrated cybersecurity threat management.

Figure 6.18 A flow diagram for threat response.

Figure 6.19 Vulnerability lifecycle management system.

Chapter 7

Figure 7.1 General view of a contemporary corporate network: Network and Net...

Figure 7.2 Communications and processing architecture to identify sequences ...

Figure 7.3 Telecommunication network machine learning data source fault dete...

Figure 7.4 Cloud based system for prioritizing network monitoring alerts....

Figure 7.5 DFRE architecture for prioritizing network monitoring alerts.

Figure 7.6 Procedure for prioritizing network alerts.

Figure 7.7 Exemplary layered architectural diagram of the system for recogni...

Figure 7.8 Learning technique used by the trained data model. Top: supervise...

Figure 7.9 Basic configuration for a 4G LTE or 5G NR network.

Figure 7.10 Block diagram of an implementation of an enterprise network that...

Figure 7.11 ML development workflow.

Figure 7.12 Network data collection infrastructure.

Figure 7.13 Compression of data and reconstruction for the system.

Figure 7.14 Network management ecosystem.

Figure 7.15 DNN configured for learning the contextual meanings of words.

Figure 7.16 Exemplary HFC network from a head end to a node that serves a gr...

Figure 7.17 System implementing ML techniques for the automated detection of...

Guide

Cover Page

Series Page

Title Page

Copyright Page

Table of Contents

About the Authors

Preface

Begin Reading

Super Glossary

Index

WILEY END USER LICENSE AGREEMENT

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IEEE Press445 Hoes LanePiscataway, NJ 08854

IEEE Press Editorial BoardSarah Spurgeon, Editor in Chief

Jón Atli Benediktsson

Behzad Razavi

Jeffrey Reed

Anjan Bose

Jim Lyke

Diomidis Spinellis

James DuncanAmin MoenessDesineni Subbaram Naidu

Hai LiBrian Johnson

Adam DrobotTom RobertazziAhmet Murat Tekalp

AI Applications to Communications and Information Technologies

The Role of Ultra Deep Neural Networks

First Edition

Daniel Minoli

DVI Communications, New York, NY, USA

Benedict Occhiogrosso

DVI Communications, New York, NY, USA

Copyright © 2024 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.

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Library of Congress Cataloging‐in‐Publication DataNames: Minoli, Daniel, 1952‐ author. | Occhiogrosso, Benedict, author.Title: AI applications to communications and information technologies: the role of ultra deep neural networks / Daniel Minoli, Benedict Occhiogrosso.Description: First edition. | Hoboken, New Jersey: Wiley, [2024] | Includes index.Identifiers: LCCN 2023025361 (print) | LCCN 2023025362 (ebook) | ISBN 9781394189991 (hardback) | ISBN 9781394190027 (adobe pdf) | ISBN 9781394190010 (epub)Subjects: LCSH: Artificial intelligence. | Neural networks (Computer science)Classification: LCC Q335 .M544 2024 (print) | LCC Q335 (ebook) | DDC 006.3/2–dc23/eng/20231011LC record available at https://lccn.loc.gov/2023025361LC ebook record available at https://lccn.loc.gov/2023025362

Cover Design: WileyCover Image: © Yuichiro Chino/Getty Images

About the Authors

Daniel Minoli, Principal Consultant, DVI Communications, New York, graduate of New York University, has published 60 technical books, 350 papers, and made 90 conference presentations. He started working on AI/expert systems in the late 1980s, when he co‐authored the book Expert Systems Applications in Integrated Network Management (Artech House, 1989). Over 7,500 academic researchers cite his work in their own peer‐reviewed publications, according to Google Scholar, and over 250 US patents and 40 US patent applications cite his published work. He has many years of technical and managerial experience in planning, designing, deploying, and operating secure IP/IPv6, VoIP, telecom, wireless, satellite, and video networks for carriers and financial companies. He has published and lectured in the area of M2M/IoT, network security, satellite systems, wireless networks, and IP/IPv6/Metro Ethernet, and has taught as adjunct for many years at New York University, Stevens Institute of Technology, and Rutgers University. He also has served as a testifying expert witness for over 25 technology‐related litigation cases pertaining to patent matters, equipment forensics, and breach of contract. He also has served as a testifying expert witness for technology‐related litigation pertaining to patent matters, equipment forensics, and breach of contract.

Benedict Occhiogrosso is a co‐founder of DVI Communications, New York. He is a graduate of New York University Polytechnic School of Engineering. For over 25 years, he has served as the CEO of a multidisciplinary consulting firm and advised both technology producers and consumers on adoption and deployment of technologies. He is also a technology expert in various aspects of telecommunications, security, and information technology with concentration in speech recognition, video surveillance, and more recently IoT. He has supported high‐tech litigation encompassing intellectual property (patent and trade secrets) as a testifying expert witness and advised inventors and acquirers on patent valuation. He has also served as both a technology and business advisor to several start‐up and operating companies with respect to product planning, company organization, and capital formation.

Preface

Following decades of research, artificial intelligence (AI) technologies, particularly machine learning (ML) and deep learning (DL), are becoming ubiquitous in nearly all aspects of modern life. R&D and productization are proceeding at a fast pace at this juncture. For example, as a reference year, in 2021 IBM received over 2,300 patents dealing with AI (just about a quarter of all patents granted for the year); many other research organizations and firms are doing the same. In addition, and for example, ChatGPT, the popular generative AI chatbot from OpenAI, is estimated to have reached 100 million monthly active users just two months after launch (in late 2022), making it the fastest‐growing consumer application in history.

With the widespread deployment of smart connected sensors in the IoT ecosystem in the smart city, smart building, smart institution, and smart home; data collection; and associated analysis, nearly all major industries have been impacted by and benefitted from AI. This is particularly true of systems operating as “narrow AI” performing objective functions utilizing data‐trained models and algorithms in the context of deep learning and/or machine learning. AI is entering an advanced stage that encompasses perception, reasoning, and generalization.

AI applications include, but are certainly not limited to, autonomous driving and navigation, speech recognition, language processing, robotics, computer vision, pattern recognition, face recognition, predictive text systems, generative systems/chatbots, social networks and advertisement placing, behavior‐predictive systems, data mining systems, financial systems, medical sciences, military systems, and telecommunications‐related systems, to name just a few.

Technology heavyweights such as Google, Apple, Microsoft, and Amazon are currently investing billions of dollars to develop AI‐based products and services. The US Department of Defense continues to sponsor extensive AI research and universities started offering AI‐based programs their curricula.

This text focuses on AI and Neural Network applications to Information and Communications Technology (ICT), for example applications to voice recognition, video/situational awareness/face recognition, smart buildings and energy management, wireless systems, cybersecurity, and network management – it should be noted that each of these topics could, in fact, benefit by a dedicated text. We explore basic principles and applications in these disciplines, emphasizing recent developments and near‐term commercial and deployment opportunities. This treatise, however, is not intended to be a basic tutorial on Neural Networks, or a review of the current applicable academic research, which as indicated is quite extensive; nonetheless, sufficient background is provided on the fundamental underlying topics to make the reading of this text reasonably self‐contained. AI is an extremely well‐documented science, and this text does not claim pure originality per se (or research innovation) but only a synthesis, an organization, a presentation, and a focused treatment of the field as applied to information technology and communications‐related applications – other basic references can and should be used by the interested reader. The hundreds of researchers cited in the references deserve the credit of the original, but widely scattered, conceptualizations.

It is the authors’ intent that this text can be used by industry observers, planners, investors, vendors, and students. It can serve as an ancillary text to an undergraduate or graduate course on AI, networking, or software development.

May 15, 2023

Daniel MinoliBenedict Occhiogrosso