Test for Aggregational Gaussianity (AG) in Petroleum Prices Returns

The work aims at investigating and establishing if Aggregational Gaussianity, (AG) is in the dynamics of petroleum prices. This AG aspect is the phenomenon in which the empirical distribution of log-returns tends to normality (or as the time scale over which the returns are calculated increases). In order to achieve this, the petroleum price series was tested for arch effects. In addition, tests for Aggregational Gaussianity, (AG) were carried out using qualitative (graphical) approach and inferential approach, (involving statistical inference). The study shows that the presence of arch effects does not guarantee existence of AG. It is also observed that qualitative (graphical) approach may suggest normality and hence, presence of AG, on the other hand, inferential approach (involving statistical tests) gives a better picture of the actual conclusion, of the presence (or otherwise) of AG in the data set, with a 99.97% rejection from normality by the three tests-Kolmogorov-simonorv,Shapiro-Wilks, and Anderson-darling. In the circumstance, there is no evidence to confirm a discernible presence of AG in the dynamics of petroleum prices. The non-existence of AG in the study shows the instability in the dynamics of petroleum prices, since one cannot invoke normality as an invariant property this, among other factors, make the economy unstable as it is oil- driven. However, since the highest percentage of the budget for the country is based on the petroleum sales, which as this study reveals is unstable, hence, diversification of the economy is proposed. The softwares used in the work are Eviews 10, Minitab 18, Spss 17, Easy-fit 5.6 professional, and R 3.2.2.

DOA Estimation for Sources with Large Power Differences

Sources with large power differences are very common, especially in complex electromagnetic environments. Classical DOA estimation methods suffer from performance degradation in terms of resolution when dealing with sources that have large power differences. In this paper, we propose an improved DOA algorithm to increase the resolution performance in resolving such sources. The proposed method takes advantage of diagonal loading and demonstrates that the invariant property of noise subspace still holds after diagonal loading is performed. We also find that the Cramer–Rao bound of the weak source can be affected by the power of the strong source, and this has not been noted before. The Cramer–Rao bound of the weak source deteriorates as the power of the strong source increases. Numerical results indicate that the improved algorithm increases the probability of resolution while maintaining the estimation accuracy and computational complexity.

Differential Attacks on CRAFT Exploiting the Involutory S-boxes and Tweak Additions

CRAFT is a lightweight tweakable block cipher proposed at FSE 2019, which allows countermeasures against Differential Fault Attacks to be integrated into the cipher at the algorithmic level with ease. CRAFT employs a lightweight and involutory S-box and linear layer, such that the encryption function can be turned into decryption at a low cost. Besides, the tweakey schedule algorithm of CRAFT is extremely simple, where four 64-bit round tweakeys are generated and repeatedly used. Due to a combination of these features which makes CRAFT exceedingly lightweight, we find that some input difference at a particular position can be preserved through any number of rounds if the input pair follows certain truncated differential trails. Interestingly, in contrast to traditional differential analysis, the validity of this invariant property is affected by the positions where the constant additions take place. We use this property to construct “weak-tweakey” truncated differential distinguishers of CRAFT in the single-key model. Subsequently, we show how the tweak additions allow us to convert these weak-tweakey distinguishers into ordinary secret-key distinguishers based on which key-recovery attacks can be performed. Moreover, we show how to construct MILP models to search for truncated differential distinguishers exploiting this invariant property. As a result, we find a 15-round truncated differential distinguisher of CRAFT and extend it to a 19-round key-recovery attack with 260.99 data, 268 memory, 294.59 time complexity, and success probability 80.66%. Also, we find a 14-round distinguisher with probability 2−43 (experimentally verified), a 16-round distinguisher with probability 2−55, and a 20-round weak-key distinguisher (2118 weak keys) with probability 2−63. Experiments on round-reduced versions of the distinguishers show that the experimental probabilities are sometimes higher than predicted. Finally, we note that our result is far from threatening the security of the full CRAFT.

Image Fusion Based on Nonsubsampled Shearlet Transform

In current days, an image fusion is a powerful method and developing field in the area of image processing. The image fusion is the process of combining two or more images into a single image then the resulting image will appear more informative than any of the input images. It is the process of assimilation of numerous input images into a new single fused image with highly informative than the input image. There are various image fusion transform techniques are proposed. Out of that techniques a Non-subsampled shearlet transform includes shift invariant property, highly directionality, feasible and more efficient information as compared to previous techniques such as wavelet transform(WT), DWT, LWT, MWT, CWT, Curvelet transform, Counterlet transform, and Nonsubsampled Counterlet transform(NSCT). This NSST technique is carried out by adjusting the levels with filter banks. Downsampling is used to reconstruct. NSST decomposition provides a simple hierarchical framework for image fusion with different geographical resolution.

Multibaseline Interferometric Phase Denoising Based on Kurtosis in the NSST Domain

Interferometric phase filtering is a crucial step in multibaseline interferometric synthetic aperture radar (InSAR). Current multibaseline interferometric phase filtering methods mostly follow methods of single-baseline InSAR and do not bring its data superiority into full play. The joint filtering of multibaseline InSAR based on statistics is proposed in this paper. We study and analyze the fourth-order statistical quantity of interferometric phase: kurtosis. An empirical assumption that the kurtosis of interferograms with different baselines keeps constant is proposed and is named as the baseline-invariant property of kurtosis in this paper. Some numerical experiments and rational analyses confirm its validity and universality. The noise level estimation of nature images is extended to multibaseline InSAR by dint of the baseline-invariant property of kurtosis. A filtering method based on the non-subsampled shearlet transform (NSST) and Wiener filter with estimated noise variance is proposed then. Firstly, multi-scaled and multi-directional coefficients of interferograms are obtained by NSST. Secondly, the noise variance is represented as the solution of a constrained non-convex optimization problem. A pre-thresholded Wiener filtering with estimated noise variance is employed for shrinking or zeroing NSST coefficients. Finally, the inverse NSST is utilized to obtain the filtered interferograms. Experiments on simulated and real data show that the proposed method has excellent comprehensive performance and is superior to conventional single-baseline filtering methods.

Exact dynamical behavior for a dual Kaup–Boussinesq system by symmetry reduction and coupled trial equations method

We propose a coupled trial equation method for a coupled differential equations system. Furthermore, according to the invariant property under the translation, we give the symmetry reduction of a dual Kaup–Boussinesq system, and then we use the proposed trial equation method to construct its exact solutions which describe its dynamical behavior. In particular, we get a cosine function solution with a constant propagation velocity, which shows an important periodic behavior of the system.