In the face of growing urban problems such as overcrowding and pollution, we urgently need innovative ideas to build smarter and greener cities. Current urban development strategies often fail to address these challenges, revealing a significant research gap in integrating advanced technologies. This study addresses these gaps by integrating green technologies and artificial intelligence (AI), studying its impact on achieving smart and sustainable habitats and identifying barriers to effective use of these technologies, considering local variations in infrastructural, cultural, and economic contexts. By analyzing how AI and green technologies can be combined, this study aims to provide a vision that can be used to improve urban development planning. The results emphasize the significance of environmental responsibility and technological innovation in the development of sustainable urban environments and provide practical recommendations for improving the overall quality of life in cities through planning and urban planning.

Retinal disorders, such as diabetic retinopathy, glaucoma, macular edema, and vein occlusions, are significant contributors to global vision impairment. These conditions frequently remain symptomless until patients suffer severe vision deterioration, underscoring the critical importance of early diagnosis. Fundus images serve as a valuable resource for identifying the initial indicators of these ailments, particularly by examining various characteristics of retinal blood vessels, such as their length, width, tortuosity, and branching patterns. Traditionally, healthcare practitioners often rely on manual retinal vessel segmentation, a process that is both time-consuming and intricate, demanding specialized expertise. However, this approach poses a notable challenge since its precision and consistency heavily rely on the availability of highly skilled professionals. To surmount these challenges, there is an urgent demand for an automatic and efficient method for retinal vessel segmentation and classification employing computer vision techniques, which form the foundation of biomedical imaging. Numerous researchers have put forth techniques for blood vessel segmentation, broadly categorized into machine learning, filtering-based, and model-based methods. Machine learning methods categorize pixels as either vessels or non-vessels, employing classifiers trained on hand-annotated images. Subsequently, these techniques extract features using 7D feature vectors and apply neural network classification. Additional post-processing steps are used to bridge gaps and eliminate isolated pixels. On the other hand, filtering-based approaches employ morphological operators within morphological image processing, capitalizing on predefined shapes to filter out objects from the background. However, this technique often treats larger blood vessels as cohesive structures. Model-based methods leverage vessel models to identify retinal blood vessels, but they are sensitive to parameter selection, necessitating careful choices to simultaneously detect thin and large vessels effectively. Our proposed research endeavors to conduct a thorough and empirical evaluation of the effectiveness of automated segmentation and classification techniques for identifying eye-related diseases, particularly diabetic retinopathy and glaucoma. This evaluation will involve various retinal image datasets, including DRIVE, REVIEW, STARE, HRF, and DRION. The methodologies under consideration encompass machine learning, filtering-based, and model-based approaches, with performance assessment based on a range of metrics, including true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), negative predictive value (NPV), false discovery rate (FDR), Matthews's correlation coefficient (MCC), and accuracy (ACC). The primary objective of this research is to scrutinize, assess, and compare the design and performance of different segmentation and classification techniques, encompassing both supervised and unsupervised learning methods. To attain this objective, we will refine existing techniques and develop new ones, ensuring a more streamlined and computationally efficient approach.

Spectrum map is the foundation of spectrum resource management, security governance and spectrum warfare. Aiming at the problem that the traditional spectrum mapping is limited to two-dimensional space, a three-dimensional spectrum data acquisition and mapping system architecture for the integration of space, sky and earth is presented, and a spectrum map reconstruction scheme driven by propagation model is proposed, which can achieve high-precision three-dimensional spectrum map rendering under the condition of sparse sampling. The spectrum map reconstructed by this method in the case of single radiation source and multiple radiation sources is in good agreement with the theoretical results based on ray tracing method. In addition, the measured results of typical scenes further verify the feasibility of this method.

Increasingly, U.S. cities are focusing on transit-oriented development (TOD) policies to expand the stock of higher-density, mixed-use development near public transit stations within the context of a transit corridor and, in most cases, a regional metropolis. A TOD zone relies on a regulatory and institutional environment, public and private participation and investment, and development incentives to create vibrant, people-oriented communities and mobility options and to support business development. TODs provide local governments with more tax revenues due to increased property values (and, as applicable, income and sales tax revenues), but most planning for TODs ignores the non-transit infrastructure costs of increasing development density. This study focused on determining the water and sewer infrastructure costs for TOD zones along a rail line in southeast Florida. The finding was that millions of dollars in funds are needed to meet those water and sewer needs and that few are currently planned as a part of community capital improvement programs.