Ideal Tightly Couple (t,m,n) Secret Sharing

As a fundamental cryptographic tool, (t,n)-threshold secret sharing ((t,n)-SS) divides a secret among n shareholders and requires at least t, (t<=n), of them to reconstruct the secret. Ideal (t,n)-SSs are most desirable in security and efficiency among basic (t,n)-SSs. However, an adversary, even without any valid share, may mount Illegal Participant (IP) attack or t/2-Private Channel Cracking (t/2-PCC) attack to obtain the secret in most (t,n)-SSs.To secure ideal (t,n)-SSs against the 2 attacks, 1) the paper introduces the notion of Ideal Tightly cOupled (t,m,n) Secret Sharing (or (t,m,n)-ITOSS ) to thwart IP attack without Verifiable SS; (t,m,n)-ITOSS binds all m, (m>=t), participants into a tightly coupled group and requires all participants to be legal shareholders before recovering the secret. 2) As an example, the paper presents a polynomial-based (t,m,n)-ITOSS scheme, in which the proposed k-round Random Number Selection (RNS) guarantees that adversaries have to crack at least symmetrical private channels among participants before obtaining the secret. Therefore, k-round RNS enhances the robustness of (t,m,n)-ITOSS against t/2-PCC attack to the utmost. 3) The paper finally presents a generalized method of converting an ideal (t,n)-SS into a (t,m,n)-ITOSS, which helps an ideal (t,n)-SS substantially improve the robustness against the above 2 attacks

Brain MR Image Segmentation Based on an Adaptive Combination of Global and Local Fuzzy Energy

This paper presents a novel fuzzy algorithm for segmentation of brain MR images and simultaneous estimation of intensity inhomogeneity. The proposed algorithm defines an objective function including a local fuzzy energy and a global fuzzy energy. Based on the assumption that the local image intensities belonging to each different tissue satisfy Gaussian distributions with different means, we derive the local fuzzy energy by utilizing maximum a posterior probability (MAP) and Bayes rule. The global fuzzy energy is defined by measuring the distance between the original image and the corresponding inhomogeneity-free image. We combine the global fuzzy energy with the local fuzzy energy using an adaptive weight function whose value varies with the local contrast of the image. This combination enables the proposed algorithm to address intensity inhomogeneity and to improve the accuracy of segmentation and its robustness to initialization. Besides, the proposed algorithm incorporates neighborhood spatial information into the membership function to reduce the impact of noise. Experimental results for synthetic and real images validate the desirable performances of the proposed algorithm

Co-pyrolysis of Corn-cob and Waste Cooking-oil in a Fixed Bed Reactor with HY Upgrading Process

AbstractCorn cob and waste oil were co-pyrolysed in a fixed bed at the temperatures of 500°C, 550°C, 600°C, respectively, under nitrogen atmosphere. Co-pyrolysis products were investigated with focus on the physical and chemical properties of oily products characterized by means of GC–MS and elemental analyser. The results show 550°C seems to be the optimum temperature considering maximum bio oil yields and bio oil properties. Co-pyrolysis of corn cob and waste oil produced more amount of liquid and less amount of solid residue than that of pyrolysis of corn cob solely. While weight ratio of waste oil: corn cob increases from 0 to 0.87, bio-oil yield increases dramatically from 44.7wt% to 70.62wt% with increasing acids content and decreasing phenols, acohols, ketones content. The upgraded bio-oil has the potential to be an alternative fuel for engine after upgraded by HY zeolite

Attosecond betatron radiation pulse train

High-intensity X-ray sources are essential diagnostic tools for science, technology and medicine. Such X-ray sources can be produced in laser-plasma accelerators, where electrons emit short-wavelength radiation due to their betatron oscillations in the plasma wake of a laser pulse. Contemporary available betatron radiation X-ray sources can deliver a collimated X-ray pulse of duration on the order of several femtoseconds from a source size of the order of several micrometres. In this paper we demonstrate, through particle-in-cell simulations, that the temporal resolution of such a source can be enhanced by an order of magnitude by a spatial modulation of the emitting relativistic electron bunch. The modulation is achieved by the interaction of the that electron bunch with a co-propagating laser beam which results in the generation of a train of equidistant sub-femtosecond X-ray pulses. The distance between the single pulses of a train is tuned by the wavelength of the modulation laser pulse. The modelled experimental setup is achievable with current technologies. Potential applications include stroboscopic sampling of ultrafast fundamental processes