Electrical Properties of Lead‐Free Na1/2Bi1/2TiO3 Relaxor Ferroelectric Ceramics Doped with Hafnium or Zirconium

Ferroelectric (Na_1/2Bi_1/2)TiO₃ and its various solid solutions have been drawing special attention as a new candidate for their lead‐based counterparts. In this study, therefore, hafnium‐ or zirconium‐doped grain‐oriented (Na_1/2Bi_1/2)TiO₃ ceramics with <001>_pc orientation were fabricated by templated grain growth method using anisotropically shaped SrTiO₃ template particles. These molten salt synthesized SrTiO₃ platelets were tape cast with calcined (Na_1/2Bi_1/2)TiO₃ powders, and then sintered at 1200°C for 6 h. Texture fractions up to 70% have been obtained. Unipolar strains up to >0.25% were measured. Doped ceramics showed a “relaxor‐like” ferroelectric behavior. A broad dielectric peak with a slim hysteresis loop was present for hafnium‐ or zirconium‐doped samples. Electrostrictive coefficient of hafnium‐doped (Na_1/2Bi_1/2)TiO₃ ceramics were found to be 0.032 m⁴/C², which is much larger than that of PMN‐based electrostrictive materials. Electromechanical properties of hafnium‐ and zirconium‐doped (Na_1/2Bi_1/2)TiO₃ ceramics under electric bias were studied as well.     

An intelligent diagnosis system for diabetes on Linear Discriminant Analysis and Adaptive Network Based Fuzzy Inference System: LDA-ANFIS

In this study, an intelligent diagnosis system for diabetes on Linear Discriminant Analysis (LDA) and Adaptive Network Based Fuzzy Inference System (ANFIS): LDA-ANFIS is presented. The structure of this LDA-ANFIS intelligent system for diagnosis of diabetes is composed by two phases: The Linear Discriminant Analysis (LDA) phase and classificiation by using ANFIS classifier phase. In first phase, Linear Discriminant Analysis (LDA) is used to separate features variables between healthy and patient (diabetes) data. In second phase, the healthy and patient (diabetes) features obtained in first phase are given to inputs of ANFIS classifier. The correct diagnosis performance of the LDA-ANFIS intelligent system is calculated by using sensitivity and specificity analysis, classification accuracy and confusion matrix respectively. The classification accuracy of this LDA-ANFIS intelligent system was obtained about 84.61%.     

Hydrogen generation from the hydrolysis of hydrazine-borane catalyzed by rhodium(0) nanoparticles supported on hydroxyapatite

Herein, we report the preparation and characterization of rhodium(0) nanoparticles supported on hydroxyapatite (Ca₁₀(OH)₂(PO₄)₆, HAP) and their catalytic use in the hydrolysis of hydrazine-borane, which attracts recent attention as promising hydrogen storage materials. Hydroxyapatite supported rhodium(0) nanoparticles were readily prepared by the hydrazine-borane reduction of rhodium(III)-exchanged hydroxyapatite in situ during the hydrolysis of hydrazine-borane at room temperature. Characterization of the resulting material by ICP–OES, TEM, SEM, EDX, XRD, XPS spectroscopies and N₂ adsorption–desorption technique, which shows the formation of rhodium(0) nanoparticles well dispersed on hydroxyapatite support. The catalytic performance of these new supported rhodium(0) nanoparticles in terms of activity, lifetime and reusability was tested in the hydrolysis of hydrazine-borane. They were found to be highly active, long-lived and reusable catalyst in this important catalytic reaction even at low temperatures and high initial [substrate]/[catalyst] conditions. This report also includes the detailed kinetic study of the hydrolysis of hydrazine-borane catalyzed by hydroxyapatite supported rhodium(0) nanoparticles depending on the catalyst concentration, substrate concentration, and temperature.     

A Highly Conductive Titanium Oxynitride Electron-Selective Contact for Efficient Photovoltaic Devices

High-quality carrier-selective contacts with suitable electronic properties are a prerequisite for photovoltaic devices with high power conversion efficiency (PCE). In this work, an efficient electron-selective contact, titanium oxynitride (TiO_xN_y), is developed for crystalline silicon (c-Si) and organic photovoltaic devices. Atomic-layer-deposited TiO_xN_y is demonstrated to be highly conductive with a proper work function (4.3 eV) and a wide bandgap (3.4 eV). Thin TiO_xN_y films simultaneously provide a moderate surface passivation and enable a low contact resistivity on c-Si surfaces. By implementation of an optimal TiO_xN_y-based contact, a state-of-the-art PCE of 22.3% is achieved for a c-Si solar cell featuring a full-area dopant-free electron-selective contact. Simultaneously, conductive TiO_xN_y is proven to be an efficient electron-transport layer for organic photovoltaic (OPV) devices. A remarkably high PCE of 17.02% is achieved for an OPV device with an electron-transport TiO_xN_y layer, which is superior to conventional ZnO-based devices with a PCE of 16.10%. Atomic-layer-deposited TiO_xN_y ETL on a large area with a high uniformity may help accelerate the commercialization of emerging solar technologies.     

Combined neural network model to compute wavelet coefficients

Abstract: In recent years a novel model based on artificial neural networks technology has been introduced in the signal processing community for modelling the signals under study. The wavelet coefficients characterize the behaviour of the signal and computation of the wavelet coefficients is particularly important for recognition and diagnostic purposes. Therefore, we dealt with wavelet decomposition of time-varying biomedical signals. In the present study, we propose a new approach that takes advantage of combined neural network (CNN) models to compute the wavelet coefficients. The computation was provided and expressed by applying the CNNs to ophthalmic arterial and internal carotid arterial Doppler signals. The results were consistent with theoretical analysis and showed good promise for discrete wavelet transform of the time-varying biomedical signals. Since the proposed CNNs have high performance and require no complicated mathematical functions of the discrete wavelet transform, they were found to be effective for the computation of wavelet coefficients.     

An expert system for speaker identification using adaptive wavelet sure entropy

In this study, an expert speaker identification system is presented for speaker identification using Turkish speech signals. Here, a discrete wavelet adaptive network based fuzzy inference system (DWANFIS) model is used for this aim. This model consists of two layers: discrete wavelet and adaptive network based fuzzy inference system. The discrete wavelet layer is used for adaptive feature extraction in the time–frequency domain and is composed of discrete wavelet decomposition and discrete wavelet entropy. The performance of the used system is evaluated by using repeated speech signals. These test results show the effectiveness of the developed intelligent system presented in this paper. The rate of correct classification is about 90.55% for the sample speakers.     

Automatic hepatitis diagnosis system based on Linear Discriminant Analysis and Adaptive Network based on Fuzzy Inference System

In this paper, an automatic diagnosis system based on Linear Discriminant Analysis (LDA) and Adaptive Network based on Fuzzy Inference System (ANFIS) for hepatitis diseases is introduced. This automatic diagnosis system deals with the combination of feature extraction and classification. This automatic hepatitis diagnosis system has two stages, which feature extraction – reduction and classification stages. In the feature extraction – reduction stage, the hepatitis features were obtained from UCI Repository of Machine Learning Databases. Then, the number of these features was reduced to 8 from 19 by using Linear Discriminant Analysis (LDA). In the classification stage, these reduced features are given to inputs ANFIS classifier. The correct diagnosis performance of the LDA-ANFIS automatic diagnosis system for hepatitis disease is estimated by using classification accuracy, sensitivity and specificity analysis, respectively. The classification accuracy of this LDA-ANFIS automatic diagnosis system for the diagnosis of hepatitis disease was obtained in about 94.16%.     

An expert system based on fuzzy entropy for automatic threshold selection in image processing

In pattern recognition and image processing, the selection of appropriate threshold is a very significant issue. Especially, the selecting gray-level thresholds is a critical issue for many pattern recognition applications. Here, the maximum fuzzy entropy and fuzzy c-partition methods are used for the aim of the gray-level automatic threshold selection method. The fuzzy theory has been successfully applied to many areas, such as image processing, pattern recognition, computer vision, medicine, control, etc. The images have some fuzziness in nature. In this study, expert maximum fuzzy-Sure entropy (EMFSE) method for the maximum fuzzy entropy and fuzzy c-partition processes in automatic threshold selection is proposed. The experimental studies were conducted on many images by testing maximum fuzzy-Sure entropy against maximum fuzzy-Shannon entropy (MFSHE), maximum fuzzy-Havrada and Charvat entropy (MFHCE) methods for selecting optimum 2-level threshold value, respectively. The obtained experimental results show that the used MFSE method is superior to other MFSHE and MFHCE methods on selecting the 2-level threshold value automatically and effectively.     

Modified mixture of experts employing eigenvector methods and Lyapunov exponents for analysis of electroencephalogram signals

Abstract: The use of diverse features in detecting variability of electroencephalogram (EEG) signals is presented. The classification accuracies of the modified mixture of experts (MME), which was trained on diverse features, were obtained. Eigenvector methods (Pisarenko, multiple signal classification – MUSIC, and minimum-norm) were selected to generate the power spectral density estimates. The features from the power spectral density estimates and Lyapunov exponents of the EEG signals were computed and statistical features were calculated to depict their distribution. The statistical features, which were used for obtaining the diverse features of the EEG signals, were then input into the implemented neural network models for training and testing purposes. The present study demonstrated that the MME trained on the diverse features achieved high accuracy rates (total classification accuracy of the MME is 98.33%).     

Probabilistic neural networks combined with wavelet coefficients for analysis of electroencephalogram signals

Abstract: In this paper, the probabilistic neural network is presented for classification of electroencephalogram (EEG) signals. Decision making is performed in two stages: feature extraction by wavelet transform and classification using the classifiers trained on the extracted features. The purpose is to determine an optimum classification scheme for this problem and also to infer clues about the extracted features. The present research demonstrates that the wavelet coefficients obtained by the wavelet transform are features which represent the EEG signals well. The conclusions indicate that the probabilistic neural network trained on the wavelet coefficients achieves high classification accuracies (the total classification accuracy is 97.63%).