Quantum Cellular Automata (QCA) is an emerging technology at the nanotechnology level. Lower power consumption, higher density and higher speed nature of QCA technology are very interesting. Nowadays, many applications of QCA technology are introduced and cryptography can be an attractive application of QCA technology. The implementation of the Serpent block cipher in Quantum Cellular Automata is the main purpose of this paper. The main modules of this cryptographic algorithm are implemented in this technology and the implementation results are discussed. The two methods of S-Box design, i.e. LUT-Based and Logic-Based methods are inspected. The Serpent's S-Boxes are designed and simulated by these two methods. The simulation results are obtained from QCADesigner software.
Schizotypy is a latent cluster of personality traits that denote a vulnerability for schizophrenia or a type of spectrum disorder. The aim of the study is to investigate parametric effective brain connectivity features for classifying high versus low schizotypy (LS) status. Electroencephalography (EEG) signals are recorded from 13 high schizotypy (HS) and 11 LS participants during an emotional auditory odd-ball task. The brain connectivity signals for machine learning are taken after the settlement of event-related potentials. A multivariate autoregressive (MVAR)-based connectivity measure is estimated from the EEG signals using the directed transfer functions (DTFs) method. The values of DTF power in five standard frequency bands are used as features. The support vector machines (SVMs) revealed significant differences between HS and LS. The accuracy, specificity, and sensitivity of the results using SVM are as high as 89.21%, 90.3%, and 88.2%, respectively. Our results demonstrate that the effective brain connectivity in prefrontal/parietal and prefrontal/frontal brain regions considerably changes according to schizotypal status. These findings prove that the brain connectivity indices offer valuable biomarkers for detecting schizotypal personality. Further monitoring of the changes in DTF following the diagnosis of schizotypy may lead to the early identification of schizophrenia and other spectrum disorders.
Steganography is the art of hiding information in a cover medium such that the existence of information is concealed. An image is a suitable cover medium for steganography because of its great amount of redundant spaces. One simple method of image steganography is the replacement of the least significant bit (LSB) of a cover image with a message bit. This represents a high embedding capacity but it is detectable by statistical analysis methods such as Regular-Singular (RS) and Chi-square analyses. Therefore, a new LSB algorithm is proposed here which can effectively resist statistical analysis. In this novel algorithm, every two sample's LSB bits are combined using addition modulo 2 which is compared to the secret message. If these two values are not equal, their difference is added to the second sample. Otherwise, no change is made. This paper proposes a hardware realization of this new algorithm. Furthermore, two scalable pixel interleaver and novel message bit randomizer with two different stego-keys are designed. Pixel interleaver can improve resistance against visual analysis by random selection of pixels.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.