Double Thresholding with Sine Entropy for Thermal Image Segmentation

Traditional thresholding methods are often used for image segmentation of real images. However, due to distinct characteristics of infrared thermal images, it is difficult to ensure an optimal image segmentation using the traditional thresholding algorithms, and therefore, sometimes this can lead to over-segmentation, missing object information, and/or spurious responses in the output. To overcome these issues, we propose a new thresholding technique that makes use of the sine entropy-based criterion. Moreover, we build a double thresholding technique that makes use of two thresholds to get the final image thresholding result. Besides, we introduce the sine entropy concept as a supplement of the Shannon entropy in creating threshold-dependent criterion derived from the grayscale histogram. We found that the sine entropy is more robust in interpreting the strength of the long-range correlation in the gray levels compared to the Shannon entropy. We have experimented with our method on several infrared thermal images collected from standard image databases to describe the performance. On comparing with the state-of-art methods, the qualitative results from the experiments show that the proposed method achieves the best performance with an average sensitivity of 0.98 and an average misclassification error of 0.01, and second-best performance with an average sensitivity of 0.99 and an average specificity of 0.93.

Inverse centrifugal effect induced by collective motion of vortices in rotating thermal convection

When a fluid system is subject to strong rotation, centrifugal fluid motion is expected, i.e., denser (lighter) fluid moves outward (inward) from (toward) the axis of rotation. Here we demonstrate, both experimentally and numerically, the existence of an unexpected outward motion of warm and lighter vortices in rotating thermal convection. This anomalous vortex motion occurs under rapid rotations when the centrifugal buoyancy is sufficiently strong to induce a symmetry-breaking in the vorticity field, i.e., the vorticity of the cold anticyclones overrides that of the warm cyclones. We show that through hydrodynamic interactions the densely distributed vortices can self-aggregate into coherent clusters and exhibit collective motion in this flow regime. Interestingly, the correlation of the vortex velocity fluctuations within a cluster is scale-free, with the correlation length being proportional ( ≈ 30%) to the cluster length. Such long-range correlation leads to the counterintuitive collective outward motion of warm vortices. Our study brings insights into the vortex dynamics that are widely present in nature.

An Application of the Information Entropy to Nuclei

Shannon's information entropies in position- and momentum-space and their sum $S$ are calculated for various $s$-$p$ and $s$-$d$ shell nuclei using a correlated one-body density matrix depending on the harmonic oscillator size $b_0$ and the short range correlation parameter $y$ which originates from a Jastrow correlation function. It is found that the information entropy sum for a nucleus depends only on the correlation parameter $y$ through the simple relation $S= s_{0A} + s_{1A} y^{-\lambda_{sA}}$, where $s_{0A}$, $s_{1A}$ and $\lambda_{sA}$ depend on the mass number $A$. Finally, we propose a method to determine the correlation parameter from the above property of $S$ as well as the linear dependence of $S$ on the logarithm of the number of nucleons.

Wavelet-based multifractal analysis and fractal Signature of SARS-Cov-2 Coronavirus Variants genomes.

In this paper, the SARS-CoV-2 coronavirus variants of concern and of interest genomes are analyzed using the wavelet transform modulus maxima lines (WTMM) method. The goal is to track the monofractal behavior of the virus genomes and to investigate the Long-Range-Correlation (LRC) character through the estimation of the Hurst exponent. The obtained results demonstrate the multifractal and the anti-correlated characters in the variants of concern for the Knucleotidic and GC DNA coding. The fractal signatures of SARS-CoV-2 coronavirus variants are investigated through the indicator matrix maps of the genomes, they exhibit the same patterns for the variants (Alpha, Delta) and (Eta, Lota, Kappa) with moving positions, while the variants Beta, Gamma and Epsilon have different indicator matrixes. The fractal dimensions of SARS-CoV-2 variants are oscillating aroundI, except the Epsilon variant from USA, where the fractal dimension is 1.70.

The Impact of Reflectivity Gradients on the Performance of Range-Oversampling Processing

Range-oversampling processing is a technique that can be used to lower the variance of radar-variable estimates, reduce radar update times, or a mixture of both. There are two main assumptions for using range-oversampling processing: accurate knowledge of the range correlation and uniform reflectivity in the radar resolution volume. The first assumption has been addressed in previous research; this work focuses on the uniform reflectivity assumption. Earlier research shows that significant reflectivity gradients can occur in storms; we utilized those results to develop realistic simulations of radar returns that include effects of reflectivity gradients in range. An important consideration when using range-oversampling processing is the resulting change in the range weighting function. The range weighting function can change for different types of range-oversampling processing and some techniques, such as adaptive pseudowhitening, can lead to different range weighting functions at each range gate. To quantify the possible effects of differing range weighting functions in the presence of reflectivity gradients, we developed simulations to examine varying types of range-oversampling processing with two receiver filters: a matched receiver filter and a wider-bandwidth receiver filter (as recommended for use with range oversampling). Simulation results show that differences in range weighting functions are the only contributor to differences in radar reflectivity measurements. Results from real weather data demonstrate that the reflectivity gradients that occur in typical severe storms do not cause significant changes in reflectivity measurements, and the benefits from range-oversampling processing outweigh the possible isolated effects from large reflectivity gradients.