Track Categories

The track category is the heading under which your abstract will be reviewed and later published in the conference printed matters if accepted. During the submission process, you will be asked to select one track category for your abstract.

A Programmable Logic Controller (or PLC) is a specialised digital controller that can control machines and processes. it monitors inputs, makes decisions, and controls outputs in order to automate machines and processes. A building automation system is a system that controls and monitors building services. These systems can be built up in several different ways. In this chapter a general building automation system for a building with complex requirements due to the activity, such as a hospital, will be described. Real systems usually have several of the features and components described here but not all of them. The Automation level includes all the advanced controllers that controls and regulates the Field level devices in real time.

 

  • Track 1-1Electronics Automation and Electrical Engineering
  • Track 1-2Automation Instrument and Device
  • Track 1-3Plc and Micro-Controllers
  • Track 1-4Automation in Chemical Engineering
  • Track 1-5Cloud Computing for Automation
  • Track 1-6Building Automation

SCADA systems are a type of Industrial Control System. They are used to gather information and exercise control from remote locations. In situations where integrated data procurement is as significant as control, SCADA systems are employed to monitor remote units. These systems find applications in distribution processes such as water supply and wastewater collection systems, oil and gas pipelines, electrical utility transmission, and rail and other public transportation systems. SCADA systems perform consolidated control for various process inputs and outputs by integrating Human Machine Interface (HMI) software and data transmission systems with data acquisition systems. The transfer of data between operator terminals, such as Remote Terminal Units (RTUs) and Programmable Logic Controllers (PLCs), and the central host computer is included in SCADA systems. A SCADA system collects relevant data, transfers the data back to a central site, then notifies the home station about the event, implementing the required analysis and control, and then displays the data in a logical and systematic manner using graphs or text, thus enabling the operator to control a whole process in real time.

 

  • Track 2-1ANN - Artificial neural network
  • Track 2-2DCS - Distributed Control System
  • Track 2-3HMI - Human Machine Interface
  • Track 2-4SCADA - Supervisory Control and Data Acquisition
  • Track 2-5PLC - Programmable Logic Controller
  • Track 2-6Instrumentation
  • Track 2-7Motion Control
  • Track 2-8Robotics

The concept of biomimetic control, i.e., control systems that mimic biological animals in the way they exercise control, rather than just humans, has led to the definition of a new class of biologically inspired robots that exhibit much greater robustness in performance in unstructured environments than the robots that are currently being built. A key feature of biomimetic robots is their capacity to adapt to the environment and ability to learn and react fast. However, a biomimetic robot is not just about learning and adaptation but also involves novel mechanisms and manipulator structures capable of meeting the enhanced performance requirements. Thus, biomimetic robots are being designed to be substantially more compliant and stable than conventionally controlled robots and will take advantage of new developments in materials, microsystems technology, as well as developments that have led to a deeper understanding of biological behaviour.

 

  • Track 3-1Robot Control
  • Track 3-2Mobile Robotics
  • Track 3-3Micro and Nano Robots
  • Track 3-4Rescue and Field Robotics
  • Track 3-5Medical Robots and Bio-robotics
  • Track 3-6Space and Underwater Robots
  • Track 3-7SLAM (Simultaneous Localization and Mapping)
  • Track 3-8Assistive Robotics
  • Track 3-9Autonomous Robots
  • Track 3-10Bio-inspired Robotics
  • Track 3-11Biomechanics
  • Track 3-12Biomedical Robots
  • Track 3-13Biomimetic Robotics
  • Track 3-14Humanoid Robots
  • Track 3-15Multi-Robots

Augmented Reality (AR) is a general-purpose term used for any view of reality where elements of that view are augmented with virtual imagery. Augmented Reality (AR) is a technology where the reality is augmented, enhanced with different types of virtual information. This information can be e.g. 3D models, text and images. With AR the user sees this information as an overlay on top of the real world. Unlike virtual reality where the user it totally immersed in the virtual world and cannot see anything but the virtual environment. To be able to place the overlay in the correct position the AR software can use different types of techniques. Some of these techniques are marker tracking, image recognition and the use of embedded sensors. Augmented reality (AR) creates an environment where computer generated information is superimposed onto the user’s view of a real-world scene.

 

  • Track 4-1Vision, Recognition and Reconstruction
  • Track 4-2Robot Design, Development and Control
  • Track 4-3Tele-robotics and Tele-operation
  • Track 4-4Industrial Networks and Automation
  • Track 4-5Modelling, Simulation and Architecture
  • Track 4-6Augmented Reality
  • Track 4-7Perception and Awareness
  • Track 4-8Surveillance, Fault detection and Diagnosis
  • Track 4-9Haptics
  • Track 4-10Modelling, Identification and Control
  • Track 4-11Signal and Image Processing

Usually, the procedure of the planning and development of a process of an assembly, inspection and measurement equipment using machine vision is split into precise determination of tasks and goals like detection, recognition, grasping, handling, measurement, fault detection, etc. and into machine vision component selection and working conditions determination like camera, computer, lenses and optics, illumination, position determination, etc.

 

  • Track 5-1Robotics Ethics and Policy
  • Track 5-2Social Robotics and Safety
  • Track 5-3Sensors for Robot Safety
  • Track 5-4Intelligent Autonomous Robots and Safety
  • Track 5-5Wearable Robots and Safety
  • Track 5-6Rehabilitation System, Transfer Machine and Safety
  • Track 5-7Interaction Control of Assistive Robots and Safety
  • Track 5-8Human-in-Loop and Safety
  • Track 5-9Multi-Agent Coordination for Human
  • Track 5-10Human-Robot Interaction and Interfaces
  • Track 5-11Machine Vision for Robot Safety

Vehicle control can be defined as the set of tasks involved in navigating, guiding, and manoeuvring a vehicle via control of the electrical, mechanical and other devices provided on the vehicle for these purposes. Vehicle control can in its broadest sense be either entirely manual, entirely automated, or on a point somewhere along the continuum between these two extremes. The application of telematics to Vehicle Control involves the development and deployment of Autonomous and Infrastructure linked systems whose aim is to assist drivers in controlling their vehicle. ‘Automated Highway Systems’ or ‘Automated Vehicle Guidance’ is where vehicle guidance and control inputs are derived from on-board sensors, which can be supplemented by equipment residing outside the vehicle. Vehicle control is affected without intervention by the driver although driver override is still possible.

 

  • Track 6-1Control and Supervision Systems
  • Track 6-2Intelligent Transportation Technologies and Systems
  • Track 6-3Engineering Applications
  • Track 6-4Industrial Automation and Robotics
  • Track 6-5Vehicle Control Applications

When a traditional feedback control system is closed via a communication channel, which may be shared with other nodes outside the control system, then the control system is called a Networked control system. An NCS can also be defined as a feedback control system wherein the control loops are closed through a real-time network. The defining feature of an NCS is that information (reference input, plant output, control input, etc.) is exchanged using a network among control system components (sensors, controllers, actuators, etc.,). Network controllers allow data to be shared efficiently. It is easy to fuse the global information to take intelligent decisions over a large physical space. They eliminate unnecessary wiring. It is easy to add more sensors, actuators and controllers with very little cost and without heavy structural changes to the whole system. Most importantly, they connect cyber space to physical space making task execution from a distance easily accessible.

 

  • Track 7-1Marine and Aerospace Guidance and Control
  • Track 7-2Space Control Systems
  • Track 7-3Linear and Nonlinear Systems Control
  • Track 7-4Fractional Order Systems
  • Track 7-5Chaotic Systems
  • Track 7-6Complex Systems
  • Track 7-7Automatic Control and Technology
  • Track 7-8Networked Control Systems
  • Track 7-9Signal Processing Systems for Control
  • Track 7-10Hybrid Systems and Control

Autonomous systems have the capability to independently (and successfully) perform complex tasks. Consumer and governmental demands for such systems are frequently forcing engineers to push many functions normally performed by humans into machines. s a functional architecture for an intelligent autonomous controller with an interface to the process involving sensing (e.g., via conventional sensing technology, vision, touch, smell, etc.), actuation (e.g., via hydraulics, robotics, motors, etc.), and an interface to humans (e.g., a driver, pilot, crew, etc.) and other systems. The “execution level” has low-level numeric signal processing and control algorithms (e.g., PID, optimal, adaptive, or intelligent control; parameter estimators, failure detection and identification (FDI) algorithms). The “coordination level” provides for tuning, scheduling, supervision, and redesign of the execution-level algorithms, crisis management, planning and learning capabilities for the coordination of execution-level tasks, and higher-level symbolic decision making for FDI and control algorithm management. The “management level” provides for the supervision of lower-level functions and for managing the interface to the human(s) and other systems.

 

  • Track 8-1Adaptive Control
  • Track 8-2Robust Control
  • Track 8-3Optimal Control
  • Track 8-4Process Control
  • Track 8-5Stochastic Systems Control and Remote Supervisory Control
  • Track 8-6Manufacturing Systems Control
  • Track 8-7Co-Operative Control
  • Track 8-8Predictive, Intelligent and Servo Control
  • Track 8-9Cooperative, Coordinated and Decentralized Control
  • Track 8-10Advanced Process Control

Big Data is the fact & figures obtained by any company and managed through new techniques to yield value in the finest way possible. Decades before anyone articulated the term “big data,” industries were using basic analytics to be informed about the trends.  This Big Data Analysis focuses on finding hidden threads, trends, or patterns which may be invisible to the naked eye. However, with the advancement of new ideas in Big Data Analysis the efficiency and speed has increased, which leads to smarter business models, more efficient operations and happier customers. This Big Data Analysis session will benefit specially organisations and industry people to mobilize knowledge on how to utilise their data and use it to earn new opportunities.

 

  • Track 9-1Data Fusion
  • Track 9-2Tensors
  • Track 9-3Machine Learning
  • Track 9-4Data Mining
  • Track 9-5LHC and Big Data
  • Track 9-6Data Journalism
  • Track 9-7Data Lineage
  • Track 9-8Data Philanthropy
  • Track 9-9Urban Informatics
  • Track 9-10Surveillance Capitalism

Industrial Automation use many control systems managing like computers or robots, and information technologies for handling different processes and machineries in an industry. Intelligent Automation can be used to enhance productivity and efficiency, reduce operational risks, and improve customer experiences. Industrial Automation plays a crucial role in any industry’s next-generation modernization. This Industrial Automation session will helps to evaluate how the widespread integration of Automation, Robotics and Artificial Intelligence is helping to create a difference in human lives.

 

  • Track 10-1Industrial Artificial Intelligence
  • Track 10-2Cyber-Physical Systems
  • Track 10-3Autonomous Vehicles and Robotics Leveraging AI
  • Track 10-4AI in Renewable Energy Forecasting
  • Track 10-5AI, Industry and Intellectual Property
  • Track 10-6Integrating AI with Human
  • Track 10-7Image Recognition Method
  • Track 10-8AI, Animation & Games

Robots are already at work in our day to day life and the Automation Process have transformed the way we used to live. We are now living in the smart technology era. What we used to see in science fiction is transformed into reality. Starting from home to office, from underwater to space they are now in every sector.  Robots are increasingly coming nearer to us as smart technology allows people to control the functions of their home. As technology becomes more forward-thinking, it's clear that the world is changing and there's a good possibility that robots will be functioning in ordinary people's homes within the next decade or so. The main discussion point of the session will be how Robots grew into an essential partner in our journey and how they are helping us to simplifying our life.

 

  • Track 11-1Time to Expand Our Definition of Robot
  • Track 11-2Robots And 3D Printing
  • Track 11-3Object Recognition and Motion Planning
  • Track 11-4Will Robots Rule Finance?
  • Track 11-5Social Robot
  • Track 11-6Human-Robot Interaction
  • Track 11-7Mechatronics & Robotics

Robots are also assimilation of many parts. All Robot consists of some mechanical, electrical components along with some computer programming. Nowadays as Robots are designed for specific tasks components are also designed likewise which will help to perform better tasks. Robots can be designed in many ways, using all manner of materials, however most Robots share a great deal in common. This session relating to the structure and design of Robot will help a lot to researchers, Industry peoples, students to analyse about the process of creation of Robot and what are the future trend in this area of Automation.

 

  • Track 12-1Actuator
  • Track 12-2Soft Robotics
  • Track 12-3Elastic Nanotubes
  • Track 12-4Robotic Sensing
  • Track 12-5Computer Vision
  • Track 12-6Sensor Fusion
  • Track 12-7Control Theory
  • Track 12-8Optical Sensors for Robotics Technology
  • Track 12-9Gyroscopes and Accelerometers
  • Track 12-10Power Supply and Energy Storage

Beside Earth the only other planet that humans have set foot on is the Moon. However, Robotic explorers have touched the Moon, Mars, Venus, and Jupiter, as well as a few comets and asteroids. In the space society, any unmanned spacecraft can be described as Robotic Spacecraft. For Robonauts, a big advantage is that they need neither food nor drink for survival and can function in very inhospitable conditions. Also Sending a robot to space is much economical than sending a human being.  This section will explore about the robotic journey in space and will highlight how Robonaut become a helping hand in space exploration.

 

  • Track 13-1History of Unmanned Space Robots
  • Track 13-2ROV and RMS
  • Track 13-3Robots and Manned Space Exploration
  • Track 13-4Aerobot
  • Track 13-5Robonaut
  • Track 13-6Orbiters, Rovers, Landers
  • Track 13-7Aerial Robotics
  • Track 13-8Role of Robots in Space Race
  • Track 13-9Mars and Beyond

Robots gradually becomes an integral part in almost every sector to lift productivity and work efficiently. The major sectors where Robots are now playing an important role are aerospace, agriculture, industry, food processing, automobile, construction, Medical, energy, etc. Robotics has open a plethora of opportunities for both entrepreneurs and students. This session about the various utilities of Robot will highlight the use of robots in various sectors and how they are helping to enhance the quality.

 

  • Track 14-1Nanorobots
  • Track 14-2Agricultural Robots
  • Track 14-3Military Robots
  • Track 14-4Ant Robotics
  • Track 14-5Mobile Robotics
  • Track 14-6Bio Robotics
  • Track 14-7Distributed Robotics
  • Track 14-8What Human can do, Cobots can too
  • Track 14-9Civilian use of Robotics
  • Track 14-10Disaster Management and Robots

People have imagined about Industrial Robots from a long time. The first patent for Industrial Robot was applied in 1954. Since then, Robots have taken away some work in factories but also opened new job opportunities in other areas. The addition of technology to the automation process has open many new The Standard function of Robots in industrial sector includes welding, painting, assembly, pick and place packaging, labeling etc. The session about the Industrial use of Robots has been specially designed for those who are part of various industries and for students who are going to join those industries in future. It will also be helpful to business groups to check out various latest Robotic Technologies evolving in various sectors.

 

  • Track 15-1Building Artificial Brains
  • Track 15-2Mobile industrial robots
  • Track 15-3Robotic arm
  • Track 15-4Robot’s working envelope
  • Track 15-5Mechatronics as the new language of the automobile
  • Track 15-6Safety standards applied to Robotics
  • Track 15-7Robots in labour surplus country
  • Track 15-8Robots getting job so people losing job
  • Track 15-9The whole market of robots
  • Track 15-10Robots and Mechatronics

Automation Technology’s ability to create wonderful things in the future stretches from fiction to fact: self-driving cars, Virtual Reality, smart devices. The applications for internet connected devices are widespread. A growing number of Internet of Things(IoT) devices are designed for human use. Starting from agriculture to defence and space, Internet of Things is affecting our life in many ways. This session about Internet of Things will discuss how the IoT has been now an inseparable part of our lifestyle and the concerns and technologies related to it.

 

  • Track 16-1Smart agriculture
  • Track 16-2Home automation
  • Track 16-3e-Health
  • Track 16-4Industrial automation
  • Track 16-5Internet of Things Devices
  • Track 16-6Efficient transportation system
  • Track 16-7Smart logistics
  • Track 16-8Environment Monitoring
  • Track 16-9IoT-Agent of change for defence sector
  • Track 16-10Digital Technologies Solving Social Issues

The process of globalisation has turn the whole world into a global village where each and every one is interconnected and interdependent. To a large extent the development of earth will not be possible without Internet of Things. The emergence of the Internet of Things (IoT) era brought new hope and promised a better future. The interconnection between the IoT and globalisation will be the focus of the session.

 

  • Track 17-1Hybrid Cloud
  • Track 17-2Globalization and governance
  • Track 17-3Cyber attack
  • Track 17-4Internet activism
  • Track 17-5Internet censorship
  • Track 17-6Net neutrality
  • Track 17-7Block chain & Bitcoin
  • Track 17-8Global Citizen have no Privacy
  • Track 17-9How IoT Helps To Feed The World

The world is changing, and with it so is the Cyber Threat. At one hand greater connectivity helps to provide new business and social opportunities but simultaneously demands for more responsibility. The next generation warfare will be fought by sitting in front of computer screens. As the risk is higher, demand for qualified professionals is also high. This session about the Cyber Security, Cyber Threat, its challenges and future will be helpful for every participant to discuss about challenges of Cyber Security and what can be the possible solution for a peaceful planet.

 

  • Track 18-1Cyber Ecosystem
  • Track 18-2Learning from the Past Attacks
  • Track 18-3Ransomware-The Deadly Weapon
  • Track 18-4Cyberwarfare & Cyberterrorism
  • Track 18-5Cyber Attacks - A war crime
  • Track 18-6Mobile & IoT Security
  • Track 18-7Digital Forensic
  • Track 18-8Security Architecture
  • Track 18-9Information Security Culture
  • Track 18-10Ways to Fight Cybercrime

The Internet of Things (IoT) refers to the network interconnection of objects equipped with ubiquitous intelligence, or simply “Smart Objects”. Several efforts have been made in the last decade to bring together standard modeling languages with generic simulation frameworks. Modeling allow description of aspects at a higher level of abstraction independent from the target platform and simulation helps to simulate such description in large-scale scenarios. This Internet of Things related session will help to learn about the nitty gritty of the importance of modelling and simulation in IoT.

 

  • Track 19-1IoT-Systems of Systems
  • Track 19-2Internet of Things and Smart-Territories
  • Track 19-3Simulation of the Internet of Things
  • Track 19-4Multi-Level Simulation
  • Track 19-5Cognitive Agent-based Computing
  • Track 19-6Simulation and market
  • Track 19-7Embedded software
  • Track 19-8Data sensing and analytics
  • Track 19-9Ferries based modelling
  • Track 19-10Geographical based modelling

Unlike other sector experts are unanimous in predicting that long-term sustainability of healthcare systems could be met by Automation driven by digital health technologies, such as Artificial Intelligence, 3D-printing or robotics. Robots can support, assist and extend the service health workers are offering. New and smart technology powered by Artificial Intelligence, Robotics& Internet of Things will be one of the driver that will change the traditional health care system. This session about Robots and Medical System will highlight the role played by these machines in our health care system and issues arising out of it.

 

  • Track 20-1Artificial Health Professionals
  • Track 20-2Remote Surgery
  • Track 20-3Robotic Surgery
  • Track 20-4Microbot
  • Track 20-5Bio-Inspired Robotics
  • Track 20-6Designing Treatment Practices
  • Track 20-7Elderly Care
  • Track 20-8Medical Research and Robotics
  • Track 20-9Putting the “Care” Back in Healthcare
  • Track 20-10AI for Disabled Person
  • Track 20-11Sky Chain & Watson in Health Care System

Evolutionary Robotics is a computer-simulated technique of creating intelligent and Autonomous Robots. The initial idea of encoding a robot control system into a genome and have artificial evolution dates to the late 80s. The prime goals of Evolutionary Robotics is to acquire automatic methods for creating intelligent autonomous robot controllers. The central advantage of Robot design methods is that they might one day be used to produce controllers or even whole robots that are skilled of functioning in environments that humans do not understand well. Neural Network is the most common type of controller used in Evolutionary Robotics. This Evolutionary Robotics session is designed to understand and discuss about how to create useful controller to use the robot in many constructive ways.

 

  • Track 21-1Virtual Agents
  • Track 21-2Cognitive Robotics
  • Track 21-3Artificial Metamorphosis
  • Track 21-4Simulated Robotic Agents
  • Track 21-5Universal Darwinism
  • Track 21-6Artificial Neural Networks
  • Track 21-7Evolutionary Computation

Like all quests, the Quest for Artificial Intelligence (AI) also begins with dreams. Artificial Intelligence (AI) is a domain that has a long history but is still constantly and actively growing and changing. Human-like systems are illustrated in many stories and are pictured in many ancient art. Starting from the time of ancient Greek philosopher Aristotle people visualized of automation. This Quest for Artificial Intelligence: Dreams and Dreamers session is specially planned for celebrating the vision of the dreamers which includes philosophers, Science Historian, writers, scientists, researchers etc.

 

  • Track 22-1Early mythology and AI
  • Track 22-2Aristotle and Syllogism
  • Track 22-3Symbolic reasoning
  • Track 22-4Alan Turing and his test
  • Track 22-51956: A turning point
  • Track 22-6AI Winter and its lessons
  • Track 22-7Brain Inspired AI Project

A knowledge Based System is a computer platform which uses Artificial Intelligence to solve complications within a specializes domain that involves human expertise. Knowledge-Based Systems were first developed by artificial intelligence researchers.  These preliminary knowledge-Based Systems were mostly expert systems .The most recent development of knowledge-Based Systems has been to adopt the skills for the development of systems that use the internet. As knowledge-Based Systems became more intricate the techniques used to denote the knowledge base became further sophisticated. This Knowledge Based Systems session will help researchers to understand how to use Knowledge Based System as a diagnostic tool.

 

  • Track 23-1Inference Engine
  • Track 23-2Semantic Web
  • Track 23-3Bayesian Network
  • Track 23-4Reasoning System
  • Track 23-5Object-oriented Programming
  • Track 23-6Knowledge Base
  • Track 23-7User Interface

Natural Language Processing, or NLP for short, is broadly defined as the automatic manipulation of natural language, like speech and text, by software. The study of Natural Language Processing has been around for more than 50 years and grew out of the field of linguistics with the rise of computers. The steps which are used in Natural Language Processing includes Lexical Analysis, Syntactic Analysis, Semantic Analysis, Discourse Integration, Pragmatic Analysis, etc. This Natural Language Processing session will help participants to understand how a computer can be utilised to perform useful job using various human languages.

 

  • Track 24-1Natural language Understanding
  • Track 24-2Natural Language Generation
  • Track 24-3The Georgetown Experiments
  • Track 24-4Major Evaluation and Task
  • Track 24-5Machine Translation
  • Track 24-6Computational Linguistics
  • Track 24-7NLG Application
  • Track 24-8Natural Language Programming
  • Track 24-9Concept Mining

Modern Artificial intelligence (AI), is the capability of a computer-controlled robot to perform assignments commonly associated with intelligent beings. Integration of various systems are necessary for a promising Artificial Intelligence. In modern era, Artificial Intelligence techniques have undergone a renaissance following parallel advances in computer power, Big amounts of data, and theoretical understanding. Modern Artificial  Intelligence uses tools and data from the fields like computer science, psychology, philosophy, neuroscience, cognitive science, linguistics, operations research, economics, control theory etc. The Modern Artificial Intelligence session will set a clear picture about various facets of Modern Artificial Intelligence.

 

  • Track 25-1Fuzzy Logic
  • Track 25-2Genetic Algorithms
  • Track 25-3AI in Shopping and Customer Service
  • Track 25-4AI and Emotions
  • Track 25-5Discovering New Drugs
  • Track 25-6AI: Educating the Future
  • Track 25-7AI based Aviation System
  • Track 25-8Artificial Intelligence in Healthcare
  • Track 25-9Virtual Reality and Image Processing
  • Track 25-10AI and Space

The ability of the Autonomous Systems to make decisions for themselves, with little to no input from humans greatly increases the utility of Artificial Intelligence, Robots and similar IoT devices. However due to this autonomous function Some experts and scholars have reservations about the use of Artificial Intelligence in various jobs. Writers like Isaac Asimov considered the issue in many of his works. It is very important that consumers trust AI systems, or else their recognition in society will be threatened. This Ethics related session of Artificial Intelligence aims to provide an agenda within which researchers and policy planner foresee the current and future ethical issues and to provide insights about concern related to ethical behaviour.

 

  • Track 26-1Ambient Intelligence Challenges
  • Track 26-2Machine Ethics
  • Track 26-3Artificial Consciousness
  • Track 26-4Robot Ethics and Rights
  • Track 26-5Artificial Moral Agents
  • Track 26-6Philosophy of Artificial Intelligence
  • Track 26-7Computer Power and Human Reason
  • Track 26-8AI: Threat to Human Dignity

In open loop control, it is assumed that the dynamical model of the system is well known, that there is little or no environmental noise and that the control signal can be applied with high precision. This approach is generally utilized when there is a target value, to achieve at a particular final time, T. The disadvantage of open-loop control is that the performance of the controller is highly susceptible to any unanticipated disturbances. In feedback control, continuous or discrete time measurements of the system output, y(t), are used to adjust the control signal in real time. At each instant, the observed process, y is compared to a tracking reference, r(t), and used to generate an error signal. Feedback therefore provides the backbone of most modern control applications. In learning control, a measurement of the system, y(t), is also used to design the optimal feedback signal; however, it is not done in real time. Instead, a large number of trial control signals are tested in advance.

  • Track 27-1Dynamic Programming in Continuous Time
  • Track 27-2Kalman Filter and Certainty Equivalence
  • Track 27-3Observability
  • Track 27-4Controllability
  • Track 27-5Continuous-Time Markov Decision Processes
  • Track 27-6Programming Average-Cost
  • Track 27-7Optimal Stopping Problems
  • Track 27-8Dynamic Programming over the Infinite Horizon
  • Track 27-9Markov Decision Problems
  • Track 27-10Dynamic Programming
  • Track 27-11Optimization Problems in Control Engineering
  • Track 27-12Automotive Control Systems and Autonomous Vehicles
  • Track 27-13Process Control and Automatic Control Theory
  • Track 27-14Control System Modeling
  • Track 27-15Control Theory and Application
  • Track 27-16Control Theory and Methodologies

Mechatronics is a multidisciplinary field of science. Mechatronics includes the combination of mechanics and electronics. As technical systems have become more and more complex the definition has been broadened to include more technical areas like telecommunications engineering, computer engineering, control engineering and systems engineering. Examples of Mechatronics System are robots, aircraft flight control, automated guided vehicles, navigation systems, digitally controlled combustion engines and self-adaptive tools etc.  The sensors in these systems receives signals from the surroundings, react to these signals using appropriate processing to generate acquired output signals. Mechatronics can be referred as Electromechanical systems.

 

  • Track 28-1Mechatronics Basics
  • Track 28-2Nano/Micro-Systems
  • Track 28-3Sensors and Signal Processing
  • Track 28-4Visual Sensing and Image Processing
  • Track 28-5Actuators and Motion Control
  • Track 28-6Modeling and Control
  • Track 28-7Simulations and Simulation Software
  • Track 28-8Transportation Systems

Deep learning allows the computer to build complex concepts out of simpler concepts. Deep learning system can represent the concept of an image of a person by combining simpler concepts, such as corners and contours, which are in turn defined in terms of edges. The idea of learning the right representation for the data provides one perspective on deep learning. Another perspective on deep learning is that depth allows the computer to learn a multi-step computer program. Each layer of the representation can be thought of as the state of the computer’s memory after executing another set of instructions in parallel. Networks with greater depth can execute more instructions in sequence. Deep learning is a specific kind of machine learning.

 

  • Track 29-1Ambient Intelligence
  • Track 29-2Artificial Intelligence
  • Track 29-3Brain Modeling and Simulation
  • Track 29-4Computational Intelligence
  • Track 29-5Deep Learning
  • Track 29-6Neural Networks and Neuro-Fuzzy Systems
  • Track 29-7Intelligent Control
  • Track 29-8Intelligent Medical Diagnostics
  • Track 29-9Intelligent Networks
  • Track 29-10Probabilistic Reasoning
  • Track 29-11Swarm Intelligence

Genetic Algorithms are heuristic search approaches that are applicable to a wide range of optimization problems. This flexibility makes them attractive for many optimization problems in practice. Evolution is the basis of Genetic Algorithms. It follows 3 rules and they are Selection rule, Cross over rule and Mutation Rule. Genetic operators change the solutions. Crossover operators combine the genomes of two or more solutions. Mutation adds randomness to solutions and should be scalable, drift-less, and reach each location in solution space. Genetic Algorithms are search based algorithms based on the concepts of natural selection and genetics. Genetic Algorithms are a subset of a much larger branch of computation known as Evolutionary Computation. Genetic algorithms optimize a given function by means of a random search. They are best suited for optimization and tuning problems in the cases where no prior information is available. As an optimization method genetic algorithm are much more effective than a random search. Genetic Algorithms are adaptive heuristic search algorithms that belong to the larger part of evolutionary algorithms. Genetic Algorithms  have demonstrated to be effective procedures for solving multicriterial optimization problems. It is a very popular meta-heuristic technique for solving optimization problems. These algorithms mimic models of natural evolution and can adaptively search large spaces in near-optimal ways. They are commonly used to generate high-quality solutions for optimisation problems and search problems.

 

  • Track 30-1Genetic Algorithms
  • Track 30-2Fuzzy Control
  • Track 30-3Decision Support Systems
  • Track 30-4Machine Learning in Control Applications
  • Track 30-5Knowledge-Based Systems Applications
  • Track 30-6Hybrid Learning Systems
  • Track 30-7Distributed Control Systems
  • Track 30-8Evolutionary Computation and Control
  • Track 30-9Optimization Algorithms
  • Track 30-10Soft Computing
  • Track 30-11Software Agents for Intelligent Control Systems
  • Track 30-12Neural Networks based Control Systems
  • Track 30-13Planning and Scheduling
  • Track 30-14Intelligent Fault Detection and Diagnosis
  • Track 30-15Engineering Applications