Amirkabir University of Technology

About: Amirkabir University of Technology is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Nonlinear system & Fuzzy logic. The organization has 15254 authors who have published 31165 publications receiving 487551 citations. The organization is also known as: Tehran Polytechnic & Tehran Polytechnic University.

Fractality and a wavelet-chaos-methodology for EEG-based diagnosis of Alzheimer disease.

Abstract: Recently the senior author and his associates developed a spatiotemporal wavelet-chaos methodology for the analysis of electroencephalograms (EEGs) and their subbands for discovering potential markers of abnormality in Alzheimer disease (AD). In this study, fractal dimension (FD) is used for the evaluation of the dynamical changes in the AD brain. The approach presented in this study is based on the research ideology that nonlinear features, such as FD, may not show significant differences between the AD and the control groups in the band-limited EEG, but may manifest in certain subbands. First, 2 different FD algorithms for computing the fractality of EEGs are investigated and their efficacy for yielding potential mathematical markers of AD is compared. They are Katz FD (KFD) and Higuchi FD. Significant features in different loci and different EEG subbands or band-limited EEG for discrimination of the AD and the control groups are determined by analysis of variation. The most discriminative FD and the corresponding loci and EEG subbands for discriminating between AD and healthy EEGs are discovered. As KFD of all loci in the β subband showed very high ability (P value <0.001) in discriminating between the groups, all KFDs are abstracted in 1 global KFD by averaging across loci in each of the 2 eyes-closed and eyes-open conditions. This leads to a more robust classification in terms of common variation of electrode positions than a classification based on separate KFDs of certain loci. Finally, based on the 2 global features separately and together, linear discriminant analysis is used to classify EEGs of AD and elderly normal patients. A high accuracy of 99.3% was obtained for the diagnosis of the AD based on the global KFD in the β-band of the eyes-closed condition with a sensitivity of 100% and a specificity of 97.8%.

Hidden attractors in a chaotic system with an exponential nonlinear term

Abstract: Studying systems with hidden attractors is new attractive research direction because of its practical and threoretical importance. A novel system with an exponential nonlinear term, which can exhibit hidden attractors, is proposed in this work. Although new system possesses no equilibrium points, it displays rich dynamical behaviors, like chaos. By calculating Lyapunov exponents and bifurcation diagram, the dynamical behaviors of such system are discovered. Moreover, two important features of a chaotic system, the possibility of synchronization and the feasibility of the theoretical model, are also presented by introducing an adaptive synchronization scheme and designing a digital hardware platform-based emulator.

Self cleaning TiO2 coating on polycarbonate: Surface treatment, photocatalytic and nanomechanical properties

Abstract: A developed route to form TiO 2 self cleaning coatings on polycarbonate substrates is reported. TiO 2 coatings on plastics may find widespread application in auto and construction industries if possess desired photocatalytic and mechanical properties. A chemical surface treatment method was used to create hydrophilic groups on the surface. X-ray photoelectron spectroscopy showed the treatment led to the oxidation of surface groups. TiO 2 deposition was based on wet coating using an anatase sol of TiO 2 nanoparticles of 30 nm size. The sol was synthesized using a sol–gel route. A pre-coat of peroxotitanium complex was employed to improve adhesion and inhibit the substrate degradation. The coating reduced the transparency for 10–15%. The photocatalytic activity was found linearly dependent on the thickness. The reaction rate constant for methylene blue degradation was estimated 0.024 s − 1 for films of 150 nm thickness. The mechanical properties were also improved after coating, as demonstrated by nano-indentation and nano-scratch tests. The hardness and scratch resistance were improved by 2.5 and ∼6.4 folds.

Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions

Abstract: The evaluation and precise prediction of safety factor (SF) of slopes can be useful in designing/analyzing these important structures. In this study, an attempt has been made to evaluate/predict SF of many homogenous slopes in static and dynamic conditions through applying various hybrid intelligent systems namely imperialist competitive algorithm (ICA)-artificial neural network (ANN), genetic algorithm (GA)-ANN, particle swarm optimization (PSO)-ANN and artificial bee colony (ABC)-ANN. In fact, ICA, PSO, GA and ABC were used to adjust weights and biases of ANN model. In order to achieve the aim of this study, a database composed of 699 datasets with 5 model inputs including slope gradient, slope height, friction angle of soil, soil cohesion and peak ground acceleration and one output (SF) was established. Several parametric investigations were conducted in order to determine the most effective factors of GA, ICA, ABC and PSO algorithms. The obtained results of hybrid models were check considering two performance indices, i.e., root-mean-square error and coefficient of determination $$(R^{2})$$ . To evaluate capability of all hybrid models, a new system of ranking, i.e., the color intensity rating, was developed. As a result, although all predictive models are able to approximate slope SF values, PSO-ANN predictive model can perform better compared to others. Based on $$R^{2}$$ , values of (0.969, 0.957, 0.980 and 0.920) were found for testing of ICA-ANN, ABC-ANN, PSO-ANN and GA-ANN predictive models, respectively, which show higher efficiency of the PSO-ANN model in predicting slope SF values.