Classification of Hyperspectral Image using SVM Post-Processing for Shape Preserving Filter and PCA

This paper is based on an experimentation to preserve shapes of the natural classes in a hyperspectral image post classification of the image using SVM. The classifier classifies the vegetation types present in the hyperspectral image and then estimates the crop types present in the image. In doing so it preserves the spatial shapes of the vegetation types spread in the image using an Edge-preserving filter. The shape-preserving filter was applied prior to dimension reduction where by the low information content spectral components are discarded using Principal Component Analysis. The classification of the features is performed using SVM. The result has been found very effective in characterizing significant spectral and spatial structures of objects in a scene.

Better sensing with variable-range interactions

The typical bound on parameter estimation, known as the standard quantum limit (SQL), can be surpassed by exploiting quantum resources such as entanglement. To estimate the magnetic probe field, we propose a quantum sensor based on a variable-range many-body quantum spin chain with a moderate transverse magnetic field. We report the threefold benefits of employing a long-range system as a quantum sensor. Firstly, sensors with quasi long-range interactions can always beat SQL for all values of the coordination number while a sensor with long-range interactions does not have this ubiquitous quantum advantage. Secondly, a long-range Hamiltonian outperforms a nearest-neighbor (NN) Hamiltonian in terms of estimating precision. Finally, we observe that the system with long-range interactions can go below SQL in the presence of a high temperature of the initial state while sensors having NN interactions cannot. Furthermore, a sensor based on the long-range Ising Hamiltonian proves to be robust against impurities in the magnetic field and when the time-inhomogeneous dephasing noise acts during interaction of the probe with the system.Comment: 12 pages, 9 figure

Predicting Topological Quantum Phase Transition via Multipartite Entanglement from Dynamics

An exactly solvable Kitaev model in a two-dimensional square lattice exhibits a topological quantum phase transition which is different from the symmetry-breaking transition at zero temperature. When the ground state of a non-linearly perturbed Kitaev model with different strengths of perturbation taken as the initial state is quenched to a pure Kitaev model, we demonstrate that various features of the dynamical state, such as Loschmidt echo, time-averaged multipartite entanglement, can determine whether the initial state belongs to the topological phase or not. Moreover, the derivatives of the quantifiers can faithfully identify the topological quantum phase transition, present in equilibrium. When the individual qubits of the lattice interact with the local thermal bath repeatedly, we observe that block entanglement can nevertheless distinguish the phases from which the system starts evolution.Comment: 10 pages, 5 figure

Framework of dynamical transitions from long-range to short-range quantum systems

A quantum many-body system undergoes phase transitions of distinct species with variations of local and global parameters. We propose a framework in which a dynamical quantity can change its behavior for quenches across global (coarse-grained criterion) or local system parameters (fine-grained criterion), revealing the global transition points. We illustrate our technique by employing the long-range extended Ising model in the presence of a transverse magnetic field. We report that by distinguishing between algebraic and exponential scaling of the total correlation in the steady state, one can identify the first transition point that conventional indicators such as the rate function fail to detect. To determine the second one, we exploit the traditional local quenches. During quenches with and without crossing the critical points along the local parameter, total correlation follows either the same or different scaling laws depending on its global phase.Comment: v1: 13 pages, 5 figures; v2: new results added and title change