Cognitive radio (CR) technology introduces a revolutionary wireless communication mechanism in terminals and network segments, so that they are able to learn their environment and adapt intelligently to the most appropriate way of providing the service for the user's exact need. By supporting multi-band, mode-mode cognitive applications, the cognitive radio addresses an interactive way of managing the spectrum that harmonizes technology, market and regulation. This dissertation gives a complete story of building a cognitive radio. It goes through concept clarification, architecture definition, functional block building, system integration, and finally to the implementation of a fully-functional cognitive radio node prototype that can be directly packaged for application use. This dissertation starts with a comprehensive review of CR research from its origin to today. Several fundamental research issues are then addressed to let the reader know what makes CR a challenging and interesting research area. Then the CR system solution is introduced with the details of its hierarchical functional architecture called the Egg Model, modular software system called the cognitive engine, and the kernel machine learning mechanism called the cognition cycle. Next, this dissertation discusses the design of specific functional building blocks which incorporate environment awareness, solution making, and adaptation. These building blocks are designed to focus on the radio domain that mainly concerns the radio environment and the radio platform. Awareness of the radio environment is achieved by extracting the key environmental features and applying statistical pattern recognition methods including artificial neural networks and k-nearest neighbor clustering. Solutions for the radio behavior are made according to the recognized environment and the previous knowledge through case based reasoning, and further adapted or optimized through genetic algorithm solution search. New experiences are gained through the practice of the new solution, and thus the CR's knowledge evolves for future use; therefore, the CR's performance continues improving with this reinforcement learning approach. To deploy the solved solution in terms of the radio's parameters, a platform independent radio interface is designed. With this general radio interface, the algorithms in the cognitive engine software system can be applied to various radio hardware platforms. To support and verify designed cognitive algorithms and cognitive functionalities, a complete reconfigurable SDR platform, called the CWT2 waveform framework, is designed in this dissertation. In this waveform framework, a hierarchical configuration and control system is constructed to support flexible, real-time waveform reconfigurability. Integrating all the building blocks described above allows a complete CR node system. Based on this general CR node structure, a fully-functional Public Safety Cognitive Radio (PSCR) node is prototyped to provide the universal interoperability for public safety communications. Although the complete PSCR node software system has been packaged to an official release including installation guide and user/developer manuals, the process of building a cognitive radio from concept to a functional prototype is not the end of the CR research; on-going and future research issues are addressed in the last chapter of the dissertation. / Ph. D.

Background:The dynamic field of computer vision and artificial intelligence has continually evolved, pushing the boundaries in areas like semantic segmentation andenvironmental recognition, pivotal for indoor scene analysis. This research investigates the integration of these two technologies, examining their synergy and implicayions for enhancing indoor scene understanding. The application of this integrationspans across various domains, including smart home systems for enhanced ambientliving, navigation assistance for Cleaning robots, and advanced surveillance for security. Objectives: The primary goal is to assess the impact of integrating semantic segmentation data on the accuracy of environmental recognition algorithms in indoor environments. Additionally, the study explores how environmental context can enhance the precision and accuracy of contour-aware semantic segmentation. Methods: The research employed an extensive methodology, utilizing various machine learning models, including standard algorithms, Long Short-Term Memorynetworks, and ensemble methods. Transfer learning with models like EfficientNet B3, MobileNetV3 and Vision Tranformer was a key aspect of the experimentation. The experiments were designed to measure the effect of semantic segmentation on environmental recognition and its reciprocal influence. Results: The findings indicated that the integration of semantic segmentation data significantly enhanced the accuracy of environmental recognition algorithms. Conversely, incorporating environmental context into contour-aware semantic segmentation led to notable improvements in precision and accuracy, reflected in metrics such as Mean Intersection over Union(MIoU). Conclusion: This research underscores the mutual enhancement between semantic segmentation and environmental recognition, demonstrating how each technology significantly boosts the effectiveness of the other in indoor scene analysis. The integration of semantic segmentation data notably elevates the accuracy of environmental recognition algorithms, while the incorporation of environmental context into contour-aware semantic segmentation substantially improves its precision and accuracy.The results also open avenues for advancements in automated annotation processes, paving the way for smarter environmental interaction.