Geometric Methods In Artificial Intelligence And Wireless Networks

Communication and network technology has witnessed recent rapid development and numerous information services and applications have been developed globally. These technologies have high impact on society and the way people are leading their lives. The advancement in technology has undoubtedly improved the quality of service and user experience yet a lot needs to be still done. Some areas that still need improvement include seamless wide-area coverage, high-capacity hot-spots, low-power massive-connections, low-latency and high-reliability and so on. Thus, it is highly desirable to develop smart technologies for communication to improve the overall services and management of wireless communication. Machine learning and cognitive computing have converged to give some groundbreaking solutions for smart machines. With these two technologies coming together, the machines can acquire the ability to reason similar to the human brain. The research area of machine learning and cognitive computing cover many fields like psychology, biology, signal processing, physics, information theory, mathematics, and statistics that can be used effectively for topology management. Therefore, the utilization of machine learning techniques like data analytics and cognitive power will lead to better performance of communication and wireless systems.

What is Geometric Feature Learning Geometric feature learning is a technique combining machine learning and computer vision to solve visual tasks. The main goal of this method is to find a set of representative features of geometric form to represent an object by collecting geometric features from images and learning them using efficient machine learning methods. Humans solve visual tasks and can give fast response to the environment by extracting perceptual information from what they see. Researchers simulate humans' ability of recognizing objects to solve computer vision problems. For example, M. Mata et al.(2002) applied feature learning techniques to the mobile robot navigation tasks in order to avoid obstacles. They used genetic algorithms for learning features and recognizing objects (figures). Geometric feature learning methods can not only solve recognition problems but also predict subsequent actions by analyzing a set of sequential input sensory images, usually some extracting features of images. Through learning, some hypothesis of the next action are given and according to the probability of each hypothesis give a most probable action. This technique is widely used in the area of artificial intelligence. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Geometric Feature Learning Chapter 2: Pattern Recognition Chapter 3: Hough Transform Chapter 4: Loss Function Chapter 5: Expectation-Maximization Algorithm Chapter 6: Rejection Sampling Chapter 7: Array Processing Chapter 8: Autoencoder Chapter 9: Stochastic Approximation Chapter 10: Chessboard Detection (II) Answering the public top questions about geometric feature learning. (III) Real world examples for the usage of geometric feature learning in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Geometric Feature Learning.

This book focuses on artifi cial intelligence in the field of digital signal processing and wireless communication. The implementation of machine learning and deep learning in audio, image, and video processing is presented, while adaptive signal processing and biomedical signal processing are also explored through DL algorithms, as well as 5G and green communication. Finally, metaheuristic algorithms of related mathematical problems are explored.

This is an open access book. Scope of Conference 2023 International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI2023), which will be held from August 11 to August 13 in Singapore provides a forum for researchers and experts in different but related fields to discuss research findings. The scope of ICIAAI 2023 covers research areas such as imaging, algorithms and artificial intelligence. Related fields of research include computer software, programming languages, software engineering, computer science applications, artificial intelligence, Intelligent data analysis, deep learning, high-performance computing, signal processing, information systems, computer graphics, computer-aided design, Computer vision, etc. The objectives of the conference are: The conference aims to provide a platform for experts, scholars, engineers and technicians engaged in the research of image, algorithm and artificial intelligence to share scientific research results and cutting-edge technologies. The conference will discuss the academic trends and development trends of the related research fields of image, algorithm and artificial intelligence together, carry out discussions on current hot issues, and broaden research ideas. It will be a perfect gathering to strengthen academic research and discussion, promote the development and progress of relevant research and application, and promote the development of disciplines and promote talent training.

The text provides a comprehensive study of the application of advanced artificial intelligence (AI) in next-generation wireless communications with a focus on theory, standardization, and core development. It further highlights AI-enabled intelligent architecture for sixth-generation (6G) networks to realize smart resource management, automatic network adjustment, and intelligent service layers. The book covers artificially assisted non-orthogonal multiple access schemes for 6G communication. This book: Discusses the use of AI in various aspects of wireless communications, including channel modeling, signal detection, channel coding design, and resource management Explores technical challenges in the ubiquitous fifth-generation (5G) wireless networks and the prospects of introducing artificial intelligence-based techniques in the envisioned 6G wireless networks Presents potential issues in AI-enabled approaches in wireless communications Covers AI-enabled energy efficiency optimization and cross-layer optimization in the next-generation wireless networks Explains artificially empowered security and privacy schemes in next-generation wireless networks and next-generation mobile management It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, and computer engineering.

The two-volume set LNCS 10350 and 10351 constitutes the thoroughly refereed proceedings of the 30th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2017, held in Arras, France, in June 2017. The 70 revised full papers presented together with 45 short papers and 3 invited talks were carefully reviewed and selected from 180 submissions. They are organized in topical sections: constraints, planning, and optimization; data mining and machine learning; sensors, signal processing, and data fusion; recommender systems; decision support systems; knowledge representation and reasoning; navigation, control, and autonome agents; sentiment analysis and social media; games, computer vision; and animation; uncertainty management; graphical models: from theory to applications; anomaly detection; agronomy and artificial intelligence; applications of argumentation; intelligent systems in healthcare and mhealth for health outcomes; and innovative applications of textual analysis based on AI.

This book is a collection of best selected research papers presented at the Conference on Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication (MDCWC 2020) held during October 22nd to 24th 2020, at the Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, India. The presented papers are grouped under the following topics (a) Machine Learning, Deep learning and Computational intelligence algorithms (b)Wireless communication systems and (c) Mobile data applications and are included in the book. The topics include the latest research and results in the areas of network prediction, traffic classification, call detail record mining, mobile health care, mobile pattern recognition, natural language processing, automatic speech processing, mobility analysis, indoor localization, wireless sensor networks (WSN), energy minimization, routing, scheduling, resource allocation, multiple access, power control, malware detection, cyber security, flooding attacks detection, mobile apps sniffing, MIMO detection, signal detection in MIMO-OFDM, modulation recognition, channel estimation, MIMO nonlinear equalization, super-resolution channel and direction-of-arrival estimation. The book is a rich reference material for academia and industry.