Robotics is an interdisciplinary field that integrates computer science and engineering. Robotics involves design, construction, operation, and use of robots. The goal of robotics is to design machines that can help and assist humans. Robotics integrates fields of mechanical engineering, electrical engineering, information engineering, mechatronics, electronics, bioengineering, computer engineering, control engineering, software engineering, mathematics, among others. Robots can be used in many situations for many purposes, but today many are used in dangerous environments (including inspection of radioactive materials, bomb detection and deactivation), manufacturing processes, or where humans cannot survive (e.g. in space, underwater, in high heat, and clean up and containment of hazardous materials and radiation). Industry 4.0 needs robotic applications, which involve issues such as non-linear control.
Natural Language Processing:
Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. The goal is a computer capable of "understanding" the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves. In this digital era, sentiment analysis can check users' reactions towards the products they use and feedback to affect the design and production of these products.
Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. It combines computer science and robust datasets, to enable problem-solving. It also encompasses sub-fields of machine learning and deep learning, which are frequently mentioned in conjunction with artificial intelligence. These methods are comprised of AI algorithms which seek to create expert systems which make predictions or classifications based on input data.
Future wireless communication system should effectively support a universal and ubiquitous
cyber physical infrastructure for a huge variety of applications with novel network structure,
spectrum access schemes, and resource allocation solutions, while taking into account the energy efficiency and security/privacy considerations. To effectively deliver ultra-high data rate,massive connectivity, and seamless coverage, while accommodating dramatically different quality of service requirements, highly innovative technological solutions are required to address various challenges facing wireless communications.
The basic concept of edge computing is to bring the functional capabilities of cloud computing close to the source of the data generations, i.e., to the network edge. By extending
the computational and the storage resources to the resource-constrained end-users, edge computing greatly removes the bottleneck of the transmission delay between the end-users
and the remote cloud data centers.
Automated Machine Learning:
Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. AutoML covers the complete pipeline from the raw dataset to the deployable machine learning model. AutoML was proposed as an artificial intelligence-based solution to the ever-growing challenge of applying machine learning. The high degree of automation in AutoML allows non-experts to make use of machine learning models and techniques without requiring them to become experts in machine learning. Automating the process of applying machine learning end-to-end additionally offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform hand-designed models. AutoML has been used to compare the relative importance of each factor in a prediction model.