Analytical Solutions to the Hulthen and the Morse Potentials by using the Asymptotic Iteration Method

We present the exact analytical solution of the radial Schr\"{o}dinger equation for the deformed Hulth\'{e}n and the Morse potentials within the framework of the Asymptotic Iteration Method. The bound state energy eigenvalues and corresponding wave functions are obtained explicitly. Our results are in excellent agreement with the findings of the other methods.Comment: 13 pages and 2 table

The Energy Eigenvalues of the Two Dimensional Hydrogen Atom in a Magnetic Field

In this paper, the energy eigenvalues of the two dimensional hydrogen atom are presented for the arbitrary Larmor frequencies by using the asymptotic iteration method. We first show the energy eigenvalues for the no magnetic field case analytically, and then we obtain the energy eigenvalues for the strong and weak magnetic field cases within an iterative approach for $n=2-10$ and $m=0-1$ states for several different arbitrary Larmor frequencies. The effect of the magnetic field on the energy eigenvalues is determined precisely. The results are in excellent agreement with the findings of the other methods and our method works for the cases where the others fail.Comment: 13 pages and 5 table

Any ll-state solutions of the Hulth\'en potential by the asymptotic iteration method

In this article, we present the analytical solution of the radial Schr\"{o}dinger equation for the Hulth\'{e}n potential within the framework of the asymptotic iteration method by using an approximation to the centrifugal potential for any $l$ states. We obtain the energy eigenvalues and the corresponding eigenfunctions for different screening parameters. The wave functions are physical and energy eigenvalues are in good agreement with the results obtained by other methods for different $\delta$ values. In order to demonstrate this, the results of the asymptotic iteration method are compared with the results of the supersymmetry, the numerical integration, the variational and the shifted 1/N expansion methods.Comment: 14 pages and 1 figur

Arbitrary l-state solutions of the rotating Morse potential by the asymptotic iteration method

For non-zero $\ell$ values, we present an analytical solution of the radial Schr\"{o}dinger equation for the rotating Morse potential using the Pekeris approximation within the framework of the Asymptotic Iteration Method. The bound state energy eigenvalues and corresponding wave functions are obtained for a number of diatomic molecules and the results are compared with the findings of the super-symmetry, the hypervirial perturbation, the Nikiforov-Uvarov, the variational, the shifted 1/N and the modified shifted 1/N expansion methods.Comment: 15 pages with 1 eps figure. accepted for publication in Journal of Physics A: Mathematical and Genera

ESTATE: A Large Dataset of Under-Represented Urban Objects for 3D Point Cloud Classification

Cityscapes contain a variety of objects, each with a particular role in urban administration and development. With the rapid growth and implementation of 3D imaging technology, urban areas are increasingly surveyed with high-resolution point clouds. This technical advancement extensively improves our ability to capture and analyse urban environments and their small objects. Deep learning algorithms for point cloud data have shown considerable capacity in 3D object classification but still face problems with generally under-represented objects (such as light poles or chimneys). This paper introduces the ESTATE dataset (https://github.com/3DOM-FBK/ESTATE), which combines available datasets of various sensors, densities, regions, and object types. It includes 13 classes featuring intensity and/or colour attributes. Tests using ESTATE demonstrate that the dataset improves the classification performance of deep learning techniques and could be a game-changer to advance in the 3D classification of urban objects

DEEP LEARNING BASED AERIAL IMAGERY CLASSIFICATION FOR TREE SPECIES IDENTIFICATION

Forest monitoring and tree species categorization has a vital importance in terms of biodiversity conservation, ecosystem health assessment, climate change mitigation, and sustainable resource management. Due to large-scale coverage of forest areas, remote sensing technology plays a crucial role in the monitoring of forest areas by timely and regular data acquisition, multi-spectral and multi-temporal analysis, non-invasive data collection, accessibility and cost-effectiveness. High-resolution satellite and airborne remote sensing technologies have supplied image data with rich spatial, color, and texture information. Nowadays, deep learning models are commonly utilized in image classification, object recognition, and semantic segmentation applications in remote sensing and forest monitoring as well. We, in this study, selected a popular CNN and object detection algorithm YOLOv8 variants for tree species classification from aerial images of TreeSatAI benchmark. Our results showed that YOLOv8-l outperformed benchmark’s initial release results, and other YOLOv8 variants with 71,55% and 72,70% for weighted and micro averaging scores, respectively