Optoelectronic Devices Fusion in Machine Vision Applications

2021 ◽  
pp. 1-36
Author(s):  
Wendy Flores-Fuentes ◽  
Moises Rivas-Lopez ◽  
Daniel Hernandez-Balbuena ◽  
Oleg Sergiyenko ◽  
Julio Cesar Rodriguez-Quiñonez ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Pattern Recognition ◽  
Optoelectronic Devices ◽  
Industrial Applications ◽  
Linear Behavior ◽  
Intelligent Decision ◽  
Collection Data ◽  
Deep Survey ◽  
Software And Hardware ◽  
And Control

This chapter presents the application of optoelectronic devices fusion as the base for those systems with non-linear behavior supported by artificial intelligence techniques, which require the use of information from various sensors for pattern recognition to produce an enhanced output. It also included a deep survey to define the state of the art in industrial applications following this tendency to identify and recognize the most used optoelectronic sensors, interconnectivity, raw data collection, data processing and interpretation, data fusion, intelligent decision algorithms, software and hardware instrumentation and control. Finally, it exemplifies how these technologies implemented in the industry can also be useful for other kinds of sector applications.

AI-based anomaly detection for cyberattacks on Windows systems - Creation of a prototype for automated monitoring of the process environment

2020 ◽  
Vol 2020 (3) ◽  
pp. 331-1-331-13
Author(s):  
Benjamin Yüksel ◽  
Klaus Schwarz ◽  
Reiner Creutzburg
Keyword(s):  
Artificial Intelligence ◽  
Anomaly Detection ◽  
Cyber Security ◽  
Secret Data ◽  
Security Vulnerabilities ◽  
Modern Computer ◽  
Wide Range ◽  
Software And Hardware ◽  
Attack Surface ◽  
And Control

Cyber security has become an increasingly important topic in recent years. The increasing popularity of systems and devices such as computers, servers, smartphones, tablets and smart home devices is causing a rapidly increasing attack surface. In addition, there are a variety of security vulnerabilities in software and hardware that make the security situation more complex and unclear. Many of these systems and devices also process personal or secret data and control critical processes in the industry. The need for security is tremendously high. The owners and administrators of modern computer systems are often overwhelmed with the task of securing their systems as the systems become more complex and the attack methods increasingly intelligent. In these days a there are a lot of encryption and hiding techniques available. They are used to make the detection of malicious software with signature based scanning methods very difficult. Therefore, novel methods for the detection of such threats are necessary. This paper examines whether cyber threats can be detected using modern artificial intelligence methods. We develop, describe and test a prototype for windows systems based on neural networks. In particular, an anomaly detection based on autoencoders is used. As this approach has shown, it is possible to detect a wide range of threats using artificial intelligence. Based on the approach in this work, this research topic should be continued to be investigated. Especially cloud-based solutions based on this principle seem to be very promising to protect against modern threats in the world of cyber security.

Work and Technological Change

2020 ◽  
Author(s):  
Stephen R. Barley
Keyword(s):  
Artificial Intelligence ◽  
Historical Context ◽  
Energy Transformation ◽  
History Of ◽  
Work And Organizations ◽  
Role Based ◽  
Control Technologies ◽  
And Control ◽  
Work And Employment

The four chapters of this book summarize the results of thirty-five years dedicated to studying how technologies change work and organizations. The first chapter places current developments in artificial intelligence into the historical context of previous technological revolutions by drawing on William Faunce’s argument that the history of technology is one of progressive automation of the four components of any production system: energy, transformation, and transfer and control technologies. The second chapter lays out a role-based theory of how technologies occasion changes in organizations. The third chapter tackles the issue of how to conceptualize a more thorough approach to assessing how intelligent technologies, such as artificial intelligence, can shape work and employment. The fourth chapter discusses what has been learned over the years about the fears that arise when one sets out to study technical work and technical workers and methods for controlling those fears.

Autonomous Vehicles in the Mobility System

2020 ◽  
pp. 500-514
Author(s):  
Thilo von Pape
Keyword(s):  
Artificial Intelligence ◽  
Mobile Phones ◽  
Mobile Communication ◽  
Autonomous Vehicles ◽  
Communication Research ◽  
Collective Control ◽  
System P ◽  
Mobility System ◽  
And Control ◽  
Transportation Technologies

This chapter discusses how autonomous vehicles (AVs) may interact with our evolving mobility system and what they mean for mobile communication research. It juxtaposes a conceptualization of AVs as manifestations of automation and artificial intelligence with an analysis of our mobility system as a historically grown hybrid of communication and transportation technologies. Since the emergence of railroad and telegraph, this system has evolved on two layers: an underlying infrastructure to power and coordinate the movements of objects, people, and ideas in industrially scaled speeds, volumes, and complexity and an interface to seamlessly access this infrastructure and control it. AVs are poised to further enhance the seamlessness which mobile phones and cars already lent to mobility. But in assuming increasingly sophisticated control tasks, AVs also disrupt an established shift toward individual control, demanding new interfaces to enable higher levels of individual and collective control over the mobility infrastructure.

scholarly journals Artificial Intelligence-Based Wearable Robotic Exoskeletons for Upper Limb Rehabilitation: A Review

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2146
Author(s):  
Manuel Andrés Vélez-Guerrero ◽  
Mauro Callejas-Cuervo ◽  
Stefano Mazzoleni
Keyword(s):  
Artificial Intelligence ◽  
Upper Limb ◽  
Main Trend ◽  
Motor Rehabilitation ◽  
Rehabilitation Process ◽  
Upper Limb Rehabilitation ◽  
Multiple Sensors ◽  
Meta Analyses ◽  
Limb Rehabilitation ◽  
And Control

Processing and control systems based on artificial intelligence (AI) have progressively improved mobile robotic exoskeletons used in upper-limb motor rehabilitation. This systematic review presents the advances and trends of those technologies. A literature search was performed in Scopus, IEEE Xplore, Web of Science, and PubMed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology with three main inclusion criteria: (a) motor or neuromotor rehabilitation for upper limbs, (b) mobile robotic exoskeletons, and (c) AI. The period under investigation spanned from 2016 to 2020, resulting in 30 articles that met the criteria. The literature showed the use of artificial neural networks (40%), adaptive algorithms (20%), and other mixed AI techniques (40%). Additionally, it was found that in only 16% of the articles, developments focused on neuromotor rehabilitation. The main trend in the research is the development of wearable robotic exoskeletons (53%) and the fusion of data collected from multiple sensors that enrich the training of intelligent algorithms. There is a latent need to develop more reliable systems through clinical validation and improvement of technical characteristics, such as weight/dimensions of devices, in order to have positive impacts on the rehabilitation process and improve the interactions among patients, teams of health professionals, and technology.

Principles of induction and approaches to attribute based induction

1991 ◽  
Vol 6 (4) ◽  
pp. 307-333 ◽  
Author(s):  
G. Kalkanis ◽  
G. V. Conroy
Keyword(s):  
Artificial Intelligence ◽  
Pattern Recognition ◽  
Cognitive Psychology ◽  
Expert Systems ◽  
Basic Principles ◽  
Inductive Approach ◽  
Induction Process

AbstractThis paper presents a survey of machine induction, studied mainly from the field of artificial intelligence, but also from the fields of pattern recognition and cognitive psychology. The paper consists of two parts: Part I discusses the basic principles and features of the machine induction process; Part II uses these principles and features to review and criticize the major supervised attribute-based induction methods. Attribute-based induction has been chosen because it is the most commonly used inductive approach in the development of expert systems and pattern recognition models.

scholarly journals A Survey of Encoding Techniques for Signal Processing in Spiking Neural Networks

2021 ◽  
Author(s):  
Daniel Auge ◽  
Julian Hille ◽  
Etienne Mueller ◽  
Alois Knoll
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Industrial Applications ◽  
Spiking Neural Networks ◽  
Biologically Inspired ◽  
Power Efficient ◽  
Signal Encoding ◽  
Uniform Nomenclature ◽  
Theoretical Foundations ◽  
Do So

AbstractBiologically inspired spiking neural networks are increasingly popular in the field of artificial intelligence due to their ability to solve complex problems while being power efficient. They do so by leveraging the timing of discrete spikes as main information carrier. Though, industrial applications are still lacking, partially because the question of how to encode incoming data into discrete spike events cannot be uniformly answered. In this paper, we summarise the signal encoding schemes presented in the literature and propose a uniform nomenclature to prevent the vague usage of ambiguous definitions. Therefore we survey both, the theoretical foundations as well as applications of the encoding schemes. This work provides a foundation in spiking signal encoding and gives an overview over different application-oriented implementations which utilise the schemes.

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