Dmitrii Kaplun – författare

Signal Processing Roadmap: Technologies, Applications, and Future Directions explores cutting-edge and emerging signal-processing techniques across various measurement and monitoring applications, serving as an authoritative reference for engineers, researchers, and technologists. The book critically analyzes key signal processing considerations such as uncertainty modeling that enable more intelligent and reliable next-generation measurement systems, all of which are backed by real-world implementation examples in areas ranging from Internet of Things devices to complex biomedical equipment. In addition, sections provide an overview of the latest research in the hybrid information system modeling field, with a particular emphasis on practical applications in various fields. The book includes case studies and examples of how these models have been used to solve problems in finance, healthcare, engineering, and other related fields. Finally, the book reviews the theories and concepts related to non-linear optimization, fuzzy sets, and rough sets.Provides a comprehensive reference for signal processing techniques in modern measurement systemsHighlights the latest innovations and future directions that drive transformative capabilitiesOffers a roadmap for signal processing advances across application domains like 6G networks, pervasive health monitoring, and industry 4.0Discusses emerging trends in areas like photonic signal processing, virtual/augmented reality, additive manufacturing, and autonomous robotsBrings critical analysis of signal processing and uncertainty modeling for enabling next-generation smart measurement systems

Signal Processing Roadmap: Technologies, Applications, and Future Directions explores cutting-edge and emerging signal-processing techniques across various measurement and monitoring applications, serving as an authoritative reference for engineers, researchers, and technologists. The book critically analyzes key signal processing considerations such as uncertainty modeling that enable more intelligent and reliable next-generation measurement systems, all of which are backed by real-world implementation examples in areas ranging from Internet of Things devices to complex biomedical equipment. In addition, sections provide an overview of the latest research in the hybrid information system modeling field, with a particular emphasis on practical applications in various fields. The book includes case studies and examples of how these models have been used to solve problems in finance, healthcare, engineering, and other related fields. Finally, the book reviews the theories and concepts related to non-linear optimization, fuzzy sets, and rough sets. - Provides a comprehensive reference for signal processing techniques in modern measurement systems- Highlights the latest innovations and future directions that drive transformative capabilities- Offers a roadmap for signal processing advances across application domains like 6G networks, pervasive health monitoring, and industry 4.0- Discusses emerging trends in areas like photonic signal processing, virtual/augmented reality, additive manufacturing, and autonomous robots- Brings critical analysis of signal processing and uncertainty modeling for enabling next-generation smart measurement systems

The development of novel techniques that can easily extract information from the joint observations of measurements coming from many modalities is necessary to realise the full potential of futuristic information processing applications in communication industries. Communication is an essential process in the design, development, and operation of systems and is expanded upon by exploring the essential information available at every stage of the system lifetime, including identifying the needs and constraints, conveying that knowledge, and confirming and validating that the right actions have been taken. Multimodal communication describes the integration of multiple communication technologies in a single interoperation system. Multimodal and cross modal applications differ from traditional interaction methods in several ways. They can use any combination of input and output modalities, including but not limited to: video, audio, radio, fax, telephone, electronic, mechanical creating a more holistic user experience. Artificial intelligence systems, like Chabot's and virtual assistants, may comprehend and react to users more instinctively and organically when they use multimodal artificial intelligence. It contributes to improving user experience as well as the efficacy and efficiency of interactions across a range of industries. Nevertheless, in recent times, deep learning and machine learning techniques are playing a pivotal role in multimodal signal processing for biomedical, fraud prevention, autonomous vehicles, Facial recognition, Hiring, Gaming, Social media, Travel and so on. But this domain suffers in exploiting its full potent due to absence of frameworks and tools for creating cross-modal and multimodal applications, as well as the lack of a common data structure that supports several modalities. This provides a common platform to a diverse and distinguished group of domain experts worldwide, all sharing a common passion for the ever-evolving information in the form of big data and forging new frontiers and building bridges to a brighter and sustainable future in the domain of communication.

The development of novel techniques that can easily extract information from the joint observations of measurements coming from many modalities is necessary to realise the full potential of futuristic information processing applications in communication industries. Communication is an essential process in the design, development, and operation of systems and is expanded upon by exploring the essential information available at every stage of the system lifetime, including identifying the needs and constraints, conveying that knowledge, and confirming and validating that the right actions have been taken. Multimodal communication describes the integration of multiple communication technologies in a single interoperation system. Multimodal and cross modal applications differ from traditional interaction methods in several ways. They can use any combination of input and output modalities, including but not limited to: video, audio, radio, fax, telephone, electronic, mechanical creating a more holistic user experience. Artificial intelligence systems, like Chabot's and virtual assistants, may comprehend and react to users more instinctively and organically when they use multimodal artificial intelligence. It contributes to improving user experience as well as the efficacy and efficiency of interactions across a range of industries. Nevertheless, in recent times, deep learning and machine learning techniques are playing a pivotal role in multimodal signal processing for biomedical, fraud prevention, autonomous vehicles, Facial recognition, Hiring, Gaming, Social media, Travel and so on. But this domain suffers in exploiting its full potent due to absence of frameworks and tools for creating cross-modal and multimodal applications, as well as the lack of a common data structure that supports several modalities. This provides a common platform to a diverse and distinguished group of domain experts worldwide, all sharing a common passion for the ever-evolving information in the form of big data and forging new frontiers and building bridges to a brighter and sustainable future in the domain of communication.

The development of novel techniques that can easily extract information from the joint observations of measurements coming from many modalities is necessary to realise the full potential of futuristic information processing applications in communication industries. Communication is an essential process in the design, development, and operation of systems and is expanded upon by exploring the essential information available at every stage of the system lifetime, including identifying the needs and constraints, conveying that knowledge, and confirming and validating that the right actions have been taken.  Multimodal communication describes the integration of multiple communication technologies in a single interoperation system. Multimodal and cross modal applications differ from traditional interaction methods in several ways. They can use any combination of input and output modalities, including but not limited to: video, audio, radio, fax, telephone, electronic, mechanical creating a more holistic user experience. Artificial intelligence systems, like Chabot’s and virtual assistants, may comprehend and react to users more instinctively and organically when they use multimodal artificial intelligence. It contributes to improving user experience as well as the efficacy and efficiency of interactions across a range of industries. Nevertheless, in recent times, deep learning and machine learning techniques are playing a pivotal role in multimodal signal processing for biomedical, fraud prevention, autonomous vehicles, Facial recognition, Hiring, Gaming, Social media, Travel and so on. But this domain suffers in exploiting its full potent due to absence of frameworks and tools for creating cross-modal and multimodal applications, as well as the lack of a common data structure that supports several modalities. This provides a common platform to a diverse and distinguished group of domain experts worldwide, all sharing a common passion for the ever-evolving information in the form of big data and forging new frontiers and building bridges to a brighter and sustainable future in the domain of communication.

The development of novel techniques that can easily extract information from the joint observations of measurements coming from many modalities is necessary to realise the full potential of futuristic information processing applications in communication industries. Communication is an essential process in the design, development, and operation of systems and is expanded upon by exploring the essential information available at every stage of the system lifetime, including identifying the needs and constraints, conveying that knowledge, and confirming and validating that the right actions have been taken.  Multimodal communication describes the integration of multiple communication technologies in a single interoperation system. Multimodal and cross modal applications differ from traditional interaction methods in several ways. They can use any combination of input and output modalities, including but not limited to: video, audio, radio, fax, telephone, electronic, mechanical creating a more holistic user experience. Artificial intelligence systems, like Chabot’s and virtual assistants, may comprehend and react to users more instinctively and organically when they use multimodal artificial intelligence. It contributes to improving user experience as well as the efficacy and efficiency of interactions across a range of industries. Nevertheless, in recent times, deep learning and machine learning techniques are playing a pivotal role in multimodal signal processing for biomedical, fraud prevention, autonomous vehicles, Facial recognition, Hiring, Gaming, Social media, Travel and so on. But this domain suffers in exploiting its full potent due to absence of frameworks and tools for creating cross-modal and multimodal applications, as well as the lack of a common data structure that supports several modalities. This provides a common platform to a diverse and distinguished group of domain experts worldwide, all sharing a common passion for the ever-evolving information in the form of big data and forging new frontiers and building bridges to a brighter and sustainable future in the domain of communication.

This book based on the best papers accepted for presentation during the International Conference on Current Problems of Applied Mathematics and Computer Systems (CPAMCS-2024), Russia. This book includes research focused on contemporary mathematical challenges and their resolutions within scientific computing, data analysis and modular computing. This book presents original studies on numerical methods in scientific computing, optimization problem-solving, function approximation techniques, among other topics. Furthermore, it encompasses research contributions in data analysis and modular computing, highlighting advancements in deep learning, neural networks, mathematical statistics, machine learning techniques, residue number systems and artificial intelligence. Additionally, this book addresses critical issues in mathematical education. This book intends for professionals engaged in scientific computing, parallel computing, computer technology, machine learning, information security, and mathematics education.