Memristive Learning Cellular Automata: Theory and Applications

Memristors are novel non volatile devices that manage to combine storing and processing capabilities in the same physical place.Their nanoscale dimensions and low power consumption enable the further design of various nanoelectronic processing circuits and corresponding computing architectures, like neuromorhpic, in memory, unconventional, etc.One of the possible ways to exploit the memristor's advantages is by combining them with Cellular Automata (CA).CA constitute a well known non von Neumann computing architecture that is based on the local interconnection of simple identical cells forming N-dimensional grids.These local interconnections allow the emergence of global and complex phenomena.In this paper, we propose a hybridization of the CA original definition coupled with memristor based implementation, and, more specifically, we focus on Memristive Learning Cellular Automata (MLCA), which have the ability of learning using also simple identical interconnected cells and taking advantage of the memristor devices inherent variability.The proposed MLCA circuit level implementation is applied on optimal detection of edges in image processing through a series of SPICE simulations, proving its robustness and efficacy

Exsolution-enhanced reverse water-gas shift chemical looping activity of Sr2FeMo0.6Ni0.4O6-δ double perovskite

This study investigates the structural evolution and redox characteristics of the double perovskite Sr2FeMo0.6Ni0.4O6-delta (SFMN) during hydrogen (H2) and carbon dioxide (CO2) redox cycles and explores the material performance in the Reverse Water-Gas Shift Chemical Looping (RWGS-CL) reaction. In-situ and ex-situ X-Ray Diffraction (XRD) and High-Resolution Transmission Electron Microscopy (HRTEM) studies reveal that H2 reduction at temperatures above 800 degrees C leads to the exsolution of bimetallic Ni-Fe alloy particles and the formation of a Ruddlesden-Popper (RP) phase. A core-shell structure with Ni-Fe core and a perovskite oxide shell is formed with subsequent redox cycles, and the resulting material exhibits better performance and high stability in the RWGS-CL process. Thermogravimetric (TGA) and Temperature Programmed Reduction (TPR) and Oxidation (TPO) analyses show that the optimal reduction and oxidation temperatures for maximizing the CO yield are around 850 degrees C and 750 degrees C respectively, and that the cycled material is able to work steadily under isothermal conditions at 850 degrees C

What Are Spectral and Spatial Distributions of EEG-EMG Correlations in Overground Walking? An Exploratory Study

You probably believe that a latent relationship between the brain and lower limbs exists and it varies across different walking conditions (e.g., walking with or without an exoskeleton). Have you ever thought what the distributions of measured signals are? To address this question, we simultaneously collected electroencephalogram (EEG) and electromyogram (EMG) signals while healthy participants were conducting four overground walking conditions without any constraints (e.g., specific speed). The EEG results demonstrated that a wide range of frequencies from delta band to gamma band were involved in walking. The EEG power spectral density (PSD) was significantly different in sensorimotor and posterior parietal areas between exoskeleton-assisted walking and non-exoskeleton walking. The EMG PSD difference was predominantly observed in the theta band and the gastrocnemius lateralis muscle. EEG-EMG PSD correlations differed among walking conditions. The alpha and beta bands were primarily involved in consistently increasing EEG-EMG PSD correlations across the walking conditions, while the theta band was primarily involved in consistently decreasing correlations as observed in the EEG involvement. However, there is no dominant frequency band as observed in the EMG involvement. Channels located over the sensorimotor area were primarily involved in consistently decreasing EEG-EMG PSD correlations and the outer-ring channels were involved in the increasing EEG-EMG PSD correlations. Our study revealed the spectral and spatial distributions relevant to overground walking and deepened the understanding of EEG and EMG representations during locomotion, which may inform the development of a more human-compatible exoskeleton and its usage in motor rehabilitation

Le visage du Soleil dans la pensée grecque ancienne

This doctorate is composed of three parts (in 398 pages) : I - The Sun as a natural phenomen : The observation of the sky had provoked to the Ancient Greeks, from the beginning, religious and philosophical feelings, under the principe of the recognition between the macrocosme and microcosme. Gradually the astronomy coming out of the astrology developed through the archaic poetry, later the philosophy, and at the end through the scientific study of the celestial bodies mouvements. II - The Sun in the human life : According to the principles of the first part, we studied the religious aspects (the Sun as a god, as a distinct figure - the Sun's face, the Sun's cults) and the manifestations in the every day life (calendars, meteorological effects, apotelesmatic astronomy, astrology). III - The Sun into the human representations (literature - arts) : It's a panoramic view of the various aspects of the Sun into the ancient Greek civilisation and culture which came out of a deel clear Greek origin.La thèse se compose trois parties (occupant 398 p.) : I - Le soleil comme phénomène naturel : L'observation du ciel a d'abord provoqué chez les Grecs anciens l'éveil de sentiments religieux et philosophiques, avec le principe de reconnaissance entre macrocosme et microcosme. Se dégageant progressivement de l'astrologie, l'astronomie s'est développée à travers la philosophie et enfin par l'étude scientifique du mouvement de corps célestes. II - Le soleil dans la vie des hommes : En harmonie avec les principes de la première partie, on étudie les aspects religieux (le Soleil comme divinité, en tant que figure propre - le visage du Soleil, les cultures solaires), les manifestations du Soleil dans la vie quotidienne (calendrier, les effets météorologiques, l'astronomie apotélesmatique, l'astrologie). III - Le soleil dans les représentations des hommes (littérature - arts) : C'est un panorama des différents aspects du Soleil dans la civilisation et la culture grecques antiques qui est ainsi présenté, avec ses fondements proprement helléniques

Experimental study and modeling analysis of ion transport membranes for methane partial oxidation and oxyfuel combustion

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2017.Cataloged from PDF version of thesis.Includes bibliographical references (pages 211-223).The atmospheric concentration of CO 2 has recently exceeded 400 (ppm) (up from 285 (ppm) in 1850), largely because of the burning of fossil fuels. Despite the growth of alternatives, these fuels will continue to play a major role in the energy sector for many decades. In accordance with international agreements, action to curtail C02 emissions is necessary, including carbon capture, reuse and storage. For this purpose, some of the leading technologies are oxy-combustion for power generation and partial oxidation for syngas production. Both require significant quantities of oxygen, whose production can impose considerable energy and economic penalties. Alternative technologies, such as intermediate-temperature ceramic membranes, operating under reactive conditions, promise to ameliorate both. Challenges include the long term stability of the material, reactor design and integration into the overall system. The goal of this thesis is to develop a framework for the thermochemical and electrochemical modeling of oxygen-conducting membranes that can be used in reactor design, based on experimental measurements and detailed surface exchange kinetics and charged species transport. La0. 9Ca0.1Fe03-[delta] (LCF) perovskite membranes have been used because of their long term stability in a reducing environment. Using experimental measurements, we examine the impact of hydrogen, carbon monoxide and methane on oxygen permeation and defect chemistry. While LCF exhibits low flux under non-reactive conditions, in the presence of fuel oxygen permeation increases by more than one order of magnitude. Our experiments confirm that hydrogen surface oxidation is faster compared to carbon monoxide. With methane, syngas production is slow and oxygen permeation is limited by surface exchange on the permeate side. Adding C02 to the fuel stream doubles the oxygen flux and increases syngas production by an order of magnitude. Our modeling analysis shows that different oxidation states of Fe participate in the electron transfer process. To account for this dependency, oxygen transport is modeled using a multi-step (fuel dependent) surface reaction mechanism that preserves thermodynamic consistency and conserves site balance and electroneutrality. Charged species diffusion is modeled using the dilute-limit Poisson-Nernst-Planck formulation that accounts for transport due to concentration gradient as well as electromigration. We use the experimental data to extract kinetic parameters of the model. We couple the aforementioned model with CFD of the gas-phase transport and thermochemistry in an effort to develop a numerical tool that allows the design of membrane reactors that exhibit high oxygen permeation and fuel conversion.by Georgios T. Dimitrakopoulos.Ph. D

XGRN: Reconstruction of Biological Networks Based on Boosted Trees Regression

In Systems Biology, the complex relationships between different entities in the cells are modeled and analyzed using networks. Towards this aim, a rich variety of gene regulatory network (GRN) inference algorithms has been developed in recent years. However, most algorithms rely solely on gene expression data to reconstruct the network. Due to possible expression profile similarity, predictions can contain connections between biologically unrelated genes. Therefore, previously known biological information should also be considered by computational methods to obtain more consistent results, such as experimentally validated interactions between transcription factors and target genes. In this work, we propose XGBoost for gene regulatory networks (XGRN), a supervised algorithm, which combines gene expression data with previously known interactions for GRN inference. The key idea of our method is to train a regression model for each known interaction of the network and then utilize this model to predict new interactions. The regression is performed by XGBoost, a state-of-the-art algorithm using an ensemble of decision trees. In detail, XGRN learns a regression model based on gene expression of the two interactors and then provides predictions using as input the gene expression of other candidate interactors. Application on benchmark datasets and a real large single-cell RNA-Seq experiment resulted in high performance compared to other unsupervised and supervised methods, demonstrating the ability of XGRN to provide reliable predictions