Precise simulation of immittance functions using the CFOA

A simple new circuit realising negative immittances using a single commercially available CFOA is presented. The simulated immittance values can orthogonally be adjusted and all passive sensitivities are no more than unity. For the circuit the phase angle of the realised immittances are independent of the three CFOA non-idealities and the circuit allows easy compensation for these active errors.     

Performance and exhaust emissions of a biodiesel engine

In this study, the applicabilities of Artificial Neural Networks (ANNs) have been investigated for the performance and exhaust-emission values of a diesel engine fueled with biodiesels from different feedstocks and petroleum diesel fuels. The engine performance and emissions characteristics of two different petroleum diesel-fuels (No. 1 and No. 2), biodiesels (from soybean oil and yellow grease), and their 20% blends with No. 2 diesel fuel were used as experimental results. The fuels were tested at full load (100%) at 1400-rpm engine speed, where the engine torque was 257.6 Nm. To train the network, the average molecular weight, net heat of combustion, specific gravity, kinematic viscosity, C/H ratio and cetane number of each fuel are used as the input layer, while outputs are the brake specific fuel-consumption, exhaust temperature, and exhaust emissions. The back-propagation learning algorithm with three different variants, single layer, and logistic sigmoid transfer function were used in the network. By using weights in the network, formulations have been given for each output. The network has yielded R² values of 0.99 and the mean % errors are smaller than 4.2 for the training data, while the R² values are about 0.99 and the mean % errors are smaller than 5.5 for the test data. The performance and exhaust emissions from a diesel engine, using biodiesel blends with No. 2 diesel fuel up to 20%, have been predicted using the ANN model.     

Artificial Neural Network Channel Estimation Based on Levenberg-Marquardt for OFDM Systems

The many advantages responsible for the widespread application of orthogonal frequency division multiplexing (OFDM) systems are limited by the multipath fading. In OFDM systems, channel estimation is carried out by transmitting pilot symbols generally. In this paper, we propose an artificial neural network (ANN) channel estimation technique based on levenberg-marquardt training algorithm as an alternative to pilot based channel estimation technique for OFDM systems over Rayleigh fading channels. In proposed technique, there are no pilot symbols which added to OFDM. Therefore, this technique is more bandwidth efficient compared to pilot-based channel estimation techniques. Also, this technique is making full use of the learning property of neural network. By using this feature, there is no need of any matrix computation and the proposed technique is less complex than the pilot based techniques. Simulation results show that ANN based channel estimator gives better results compared to the pilot based channel estimator for OFDM systems over Rayleigh fading channel.     

脉冲ArF准分子激光淀积SiC/Si(100)薄膜的最佳晶化温度及光致发光

用脉冲 Ar F准分子激光熔蚀 Si C陶瓷靶 ,在 80 0℃ Si(10 0 )衬底上淀积 Si C薄膜 ,经不同温度真空 (10 - 3Pa)退火后 ,用 FTIR、XRD、TEM、XPS、PL 谱等分析方法 ,研究了薄膜最佳晶化温度及表面形态、结构、组成 ,并对在最佳退火温度处理后的样品进行了化学态、微结构及光致发光的研究 .结果表明 ,在 Si(10 0 )上 80 0℃淀积的样品为非晶Si C薄膜 .经 85 0— 10 5 0℃不同温度真空退火后 ,Si C薄膜经非晶核化 -长大过程 ,在 980℃完成最佳晶化 .随退火温度的变化 ,薄膜中可能存在 3C- Si C与 6 H- Si C的竞争生长或 /和 3C- Si C相的长、消 (最佳温度     

耦合纵缝馈电的短路波导宽边纵缝阵的分析与设计

基于三层结构（馈电缝、耦合缝、辐射缝）平面缝隙阵天线设计的需要,用场分析法对由耦合纵缝馈电的两端短路波导宽边直线纵缝阵进行了分析和综合。该法考虑了所有的互耦、高次模及波导壁厚的影响,得到了耦合纵缝馈电的两端短路波民宽边纵缝直线阵在辐射缝偏心距相同和缝长相同时直线缝阵口径分布等幅同相的结论。利用所得结论并结合泰勒线阵综合法提出了适合于对文中系统进行综合的有效方法。     

Copolymer of ketonic resin–polyacrylonitrile

The polymerization of acrylonitrile in the presence of Ce(IV) salts and ketonic resin such as methyl ethyl ketone/formaldehyde and cyclohexanone/formaldehyde resin was investigated. Block copolymer of ketonic resin–polyacrylonitrile was produced. The effect of Ce(IV) concentration, temperature, time, and monomer concentration on the yield and molecular weight was studied. Maximum yield was obtained at 50°C and ceric ammonium nitrate concentration of 0.033 mol/L.     

Enhancing impedance imaging through multimodal tomography

Several noninvasive modalities including electrical impedance tomography (EIT), magnetic induction tomography (MIT), and induced-current EIT (ICEIT) have been developed for imaging the electrical conductivity distribution within a human body. Although these modalities differ in how the excitation and detection circuitry (electrodes or coils) are implemented, they share a number of common principles not only within the image reconstruction approaches but also with respect to the basic principle of generating a current density distribution inside a body and recording the resultant electric fields. In this paper, we are interested in comparing differences between these modalities and in theoretically understanding the compromises involved, despite the increased hardware cost and complexity that such a multimodal system brings along. To systematically assess the merits of combining data, we performed 3-D simulations for each modality and for the multimodal system by combining all available data. The normalized sensitivity matrices were computed for each modality based on the finite element method, and singular value decomposition was performed on the resultant matrices. We used both global and regional quality measures to evaluate and compare different modalities. This study has shown that the condition number of the sensitivity matrix obtained from the multimodal tomography with 16-electrode and 16-coil is much lower than the condition number produced in the conventional 16-channel EIT and MIT systems, and thus, produced promising results in terms of image stability. An improvement of about 20% in image resolution can be achieved considering feasible signal-to-noise ratio levels.