The integration of LiDAR and Artificial Intelligence (AI) has revolutionized feature detection in urban environments. LiDAR systems, which utilize pulsed laser emissions and reflection measurements, produce detailed 3D maps of urban landscapes. When combined with AI, this data enables accurate identification of urban features such as buildings, green spaces, and infrastructure. This synergy is crucial for enhancing urban development, environmental monitoring, and advancing smart city governance. LiDAR, known for its high-resolution 3D data capture capabilities, paired with AI, particularly deep learning algorithms, facilitates advanced analysis and interpretation of urban areas. This combination supports precise mapping, real-time monitoring, and predictive modeling of urban growth and infrastructure. For instance, AI can process LiDAR data to identify patterns and anomalies, aiding in traffic management, environmental oversight, and infrastructure maintenance. These advancements not only improve urban living conditions but also contribute to sustainable development by optimizing resource use and reducing environmental impacts. Furthermore, AI-enhanced LiDAR is pivotal in advancing autonomous navigation and sophisticated spatial analysis, marking a significant step forward in urban management and evaluation. The reviewed paper highlights the geometric properties of LiDAR data, derived from spatial point positioning, and underscores the effectiveness of machine learning algorithms in object extraction from point clouds. The study also covers concepts related to LiDAR imaging, feature selection methods, and the identification of outliers in LiDAR point clouds. Findings demonstrate that AI algorithms, especially deep learning models, excel in analyzing high-resolution 3D LiDAR data for accurate urban feature identification and classification. These models leverage extensive datasets to detect patterns and anomalies, improving the detection of buildings, roads, vegetation, and other elements. Automating feature extraction with AI minimizes the need for manual analysis, thereby enhancing urban planning and management efficiency. Additionally, AI methods continually improve with more data, leading to increasingly precise feature detection. The results indicate that the pulse emitted by continuous wave LiDAR sensors changes when encountering obstacles, causing discrepancies in measured physical parameters.

This paper investigates the constructability of angles multiples of 3 within the framework of Euclidean geometry. It makes a significant contribution by presenting the first geometric construction for all such angles, offering a rigorous solution to a longstanding geometric problem. The paper reaffirms the efficacy of Euclidean geometry in providing precise constructions and robust proofs for these angles, demonstrating the enduring strength of Euclidean principles from classical to modern times. The presented workflow goes beyond Euclidean geometry to examine non-Euclidean methods, particularly analytical approaches, revealing misconceptions that compromise the genetic and geometric rigor of Euclidean principles. The paper exposes incongruities when algebraic proofs related to angle constructability are applied to the Euclidean system, emphasizing the misalignment of fundamental geometric concepts. A notable result in the paper is the construction of a angle, introducing the “ angle chord” as a novel geometric property. This property challenges assumptions made by non-Euclidean methods and highlights the nuanced geometric properties crucial for rigorous constructions. The paper refutes the fallacy of relying solely on algebra for solutions to angles multiples of , emphasizing the necessity of embracing Euclidean geometry for geometric discoveries. The paper underscores the merits and resilience of Euclidean geometry, showcasing its independence and depth across historical and modern perspectives. The newly presented geometric construction not only resolves a longstanding question but also emphasizes the intrinsic strength and uniqueness of Euclidean principles in contrast to alternative methodologies.

Working on developing geometric coordinates with a special case - in the interest of human engineering - to fit with the humanities or with the famous psychological tests such as the Herman Brain Dominance Instrument Test, which is abbreviated as HBDI, or the Personal Compass test, which is abbreviated as (P.C).

The deflection of the vertical is an important parameter that combines both physical (astronomic) and geometric (geodetic) quantities. It is critical in such areas as datum transformation, reduction of astronomic observation to the geodetic reference surface, geoid modelling and geophysical prospecting. Although the deflection of the vertical is a physical property of the gravitational field of the earth; which almost all terrestrial survey measurements, with the exception of spatial distances, made on the earth surface are with respect to the Earth’s gravity vector, because a spirit bubble is usually used to align survey instruments. It has been ignored in most geodetic computation and adjustment. This research work is therefore aimed at computing the component of the deflection of the vertical component for part of Rivers State using a geometric method. This method involves the integration of Global Positioning System (GPS) to obtain the geodetic coordinate of points, precisely levelling to obtain the orthometric height of this point located within the study area. By least square using MATLAB program, the estimated deflections of vertical component parameters for the test station SVG/GPS-002 were; -0.0473” and 0.0393” arc seconds for the north-south and east-west components respectively. The associated standard errors of the North-south and East-west components were ±0.0093” and ±0.0060” arc seconds, respectively. The deflection of the vertical was also computed independently from gravimetric models of the earth as: ξ = 0.0204” ±0.0008814”, η = -0.0345” ±0.0014”; ξ =0.0157” ±0.000755”, η = -0.0246” ±0.0012”; ξ = -0.0546±0.0006014, η = -0.0208±0.0006014 for EGM 2008, EGM 1996 and EGM 1984 respectively. The two-tailed hypothesis test reveals that the estimated deflection component is statistically correct at 95% confidence interval. It was observed that the effect of the deflection of the vertical is directly proportional to the distance of the geodetic baseline. Therefore, including the derived component of deflection of the vertical to the ellipsoidal model will yield high observational accuracy since an ellipsoidal model is not tenable due to its far observational error in the determination of high-quality job. It is important to include the determined deflection of the vertical component for Rivers State, Nigeria.

The complexity of manufacturing photolithography has increased significantly. The increase in the level of integration has driven smaller feature-sized integrated circuits (ICs). The evolution in stepper technologies has been geometric. This has enabled the printing of printed ICs with a 45 nm feature size. Improvement in lithographic technology is moving towards 32 nm. This feature-size roadmap poses many challenges to semiconductor manufacturing technology. Advanced photomask synthesis, high-NA steppers, and computational lithography are some examples of the solution space. Optical proximity correction (OPC) and model-based optical proximity correction (MBOPC) are subsets of this solution space. OPC has matured significantly and is the de facto solution for manufacturing photomasks up to the 65 nm node. The OPC technique has been further refined as model-based OPC and has been applied to advanced printing technology of 45 nm. The OPC solution for 45 nm technology has limitations of mask rule check (MRC) and manufacturability restrictions. These restrictions are inevitable in OPC and MBOPC solutions because of the limits in lithographic technology. The technology evolution towards 32 nm has equally challenged the non-linear treatment of wafer-level problems in OPC solutions. PBOPC has limitations in reducing the wafer optical proximity error of the granny's issue, edge placement, mask rule check, etc. PBOPC also has limitations in reducing the mask error enhancement factor. With all these challenges, it is still a formidable solution methodology to address the wafer and mask level issues. Such a formidable solution architecture can result in a limited number of PBOPC solutions. This text looks at the performance of advanced PBOPC features on exposure tuning and the effects of higher-order wafer and aerial image effects. This text also discusses the performance of continuous process correction of masks, lenses, and scanners.