Design of RF MEMS Based Oscillatory Neural Network for Ultra High Speed Associative Memories

Microelectromechanical systems are utilized alongside with transistor amplifiers and resistive connections for implementing of oscillatory associative memories. Phase locking is studied in such a network and all requirements of the circuit level implementation are satisfied. A very high gain trans-impedance amplifier operating in 1 GHz in addition to a novel automatic amplitude control circuit is employed to remove amplitude dynamics of the system. Requiring resonator characteristics are extracted and calculated as well. A new method for initialization of the network is proposed. Each neuron consumes 1.08 mW from a 1.8 V power supply. The convergence time of a typical network trained by Hebbian rule is less than 1.5 ns which results in an ultra high speed analog signal processing system.     

Development of multilayer perceptron artificial neural network (MLP-ANN) and least square support vector machine (LSSVM) models to predict Nusselt number and pressure drop of TiO2/water nanofluid flows through non-straight pathways

Abstract

In this study, Multilayer Perceptron Artificial Neural Network (MLP-ANN) model and Least Square Support Vector Machine (LSSVM), were developed to predict the thermal performance and pressure loss of nanofluid flow through coils as non-straight pathways. There different coils with various curvature ratios and coil pitches were constructed and used. Stable TiO₂ (50?nm)/water nanofluid in different concentrations from 0.0 to 2.0% were prepared using appropriate method. As it is expected, considerable enhancement of heat transfer was achieved by application of nanofluids instead of water in system. Volume concentration of nanofluid, Prandtl number (ranging from 4.82 to 9.11) and Helical number (106.80 to 1282.87) were introduced to the developed models to obtain Nusselt number (9.89 to 53.30) and pressure drop (291.35 to 18784?kPa) as the output data of the models. According to the output results of developed models, MLP-ANN model was able to predict both Nusselt number and pressure drop of nanofluid flow more precisely in comparison to LSSVM model. The developed MLP model of this study exceeded LSSVM model to high correlation coefficient value of 0.97.     

Study of photochemical and sonochemical processes efficiency for degradation of dyes in aqueous solution

The degradation of two commercially available dyestuffs (C.I. Reactive Black 5 and C.I. Disperse Orange 25) by ultraviolet radiation (UV), ultrasonic irradiation (US), UV/H₂O₂ and US/H₂O₂ processes was investigated in a laboratory-scale batch photoreactor equipped with a 55 W immersed-type low-pressure mercury vapor lamp and a sonoreactor with low frequency (42 kHz) plate type transducer at 170 W of acoustic power. The toxicity was also evaluated in acute toxicity studies using Daphnia magna. Results showed that color removal efficiencies by US and US/H₂O₂ processes were negligible for both dyes. Almost complete disappearance of Reactive Black 5 (97.9%) in UV/H₂O₂ process was possible after 5 min of irradiation. The maximum color removal efficiency of Disperse Orange 25 after 10 min of irradiation, however, was only 9.2% and reached a maximum value of 41% after 120 min of irradiation. Pseudo-first order kinetics with respect to dyestuffs concentrations was found to fit all the experimental data. The results clearly showed that both dyes examined were toxic to D. magna and resulted in quite low LC₅₀ values.     

可靠性：选择DC／DC稳压器时的另一个参数

DC／DC稳压器基本上都是密封在类似IC那样的一个封装中的完整DC／DC稳压器电路。假定用户已经研究了对几家厂商提供的产品进行选择的问题,而且所有这些产品都或多或少地提供了电源转换功能,并满足用户的大部分需求。接下来应如何决定哪一款是适合设计的最佳产品？对于开发和生产的最终产品,选择时要考虑客户重视的是什么？哪些DC／DC稳压器参数决定了性能？     

超低EMI DC/DC稳压器系统

本文介绍了目前Dc/Dc开关稳压器设计需满足的EMI标准,进而列出了凌力尔特公司满足超低EMI标准的几款产品,并对其EMI性能测试结果进行了详细说明.     

为EMI敏感和高速SERDES系统供电

本文为EMI敏感和高速的SERDES系统供电提供了一种新的参考方案,即采用一个新的DC-DC微型模块,这一方案像线性稳压器一样简单,同时具备更多优越性。     

Analysis of Ferroresonance Modes in Power Transformers Using Preisach-Type Hysteretic Magnetizing Inductance

The magnetic hysteresis phenomenon plays an important role in the ferroresonance behavior of a transformer. However, most existing transformer models for the analysis of the ferroresonance phenomenon either ignore the hysteresis effects or represent it based on the hysteresis major loop and scale it for lower current levels. Such a modeling approach does not reflect the actual physical behavior of the magnetic core with respect to the ferroresonance phenomenon. This paper, based on Preisach theory, introduces an accurate model of hysteretic inductor to represent a single-phase transformer for the investigation of the ferroresonance phenomenon. Based on the developed model, time-domain simulation studies show that even in the case of a single-phase transformer where no magnetic coupling exists between the three-phases, all known types of ferroresonance, including fundamental, sub-harmonic, quasi-periodic and chaotic modes, may occur. The effects of hysteresis and its initial conditions, grading capacitors and transformer winding capacitance on occurrence of ferroresonance and its change of mode are also discussed     

Is the time-dependent behaviour of the aortic valve intrinsically quasi-linear?

The widely popular quasi-linear viscoelasticity (QLV) theory has been employed extensively in the literature for characterising the time-dependent behaviour of many biological tissues, including the aortic valve (AV). However, in contrast to other tissues, application of QLV to AV data has been met with varying success, with studies reporting discrepancies in the values of the associated quantified parameters for data collected from different timescales in experiments. Furthermore, some studies investigating the stress-relaxation phenomenon in valvular tissues have suggested discrete relaxation spectra, as an alternative to the continuous spectrum proposed by the QLV. These indications put forward a more fundamental question: Is the time-dependent behaviour of the aortic valve intrinsically quasi-linear? In other words, can the inherent characteristics of the tissue that govern its biomechanical behaviour facilitate a quasi-linear time-dependent behaviour? This paper attempts to address these questions by presenting a mathematical analysis to derive the expressions for the stress-relaxation G(t) and creep J(t) functions for the AV tissue within the QLV theory. The principal inherent characteristic of the tissue is incorporated into the QLV formulation in the form of the well-established gradual fibre recruitment model, and the corresponding expressions for G(t) and J(t) are derived. The outcomes indicate that the resulting stress-relaxation and creep functions do not appear to voluntarily follow the observed experimental trends reported in previous studies. These results highlight that the time-dependent behaviour of the AV may not be quasi-linear, and more suitable theoretical criteria and models may be required to explain the phenomenon based on tissue’s microstructure, and for more accurate estimation of the associated material parameters. In general, these results may further be applicable to other planar soft tissues of the same class, i.e. with the same representation for fibre recruitment mechanism and discrete time-dependent spectra.     

Analysis of Resistive Open Defects in Drowsy SRAM Cells

New fault behaviors can emerge with the introduction of a drowsy mode to SRAMs. In this work, we show that, in addition to the data-retention faults that can occur during the drowsy mode, open defects in SRAM cells can also result in new fault behaviors when a memory is accessed immediately after wake-up. We first describe these new read-after-drowsy (RAD) fault behaviors and derive their corresponding fault primitives (FPs). Then, we propose a new March test, called March RAD, by inserting drowsy operations to a traditional test algorithm. Finally, the impact of the standby supply voltage on triggering the drowsy faults in SRAM cells is investigated. It is shown that, as the supply voltage is reduced in the drowsy mode to further cut down leakage, open defects with a parasitic resistance as small as 100 K Ω begin to cause faults.     

Prediction of geological hazardous zones in front of a tunnel face using TSP-203 and artificial neural networks

This research aims at improving the methods of prediction of hazardous geotechnical structures in the front of a tunnel face. We propose and showcase our methodology using a case study on a water supply system in Cheshmeh Roozieh, Iran. Geotechnical investigations had previously reported three measurements of the newly established method of TSP-203 (Tunnel Seismic Prediction) along 684 m of the 3200 m long tunnel up to a depth of 600 m. We use the results of TSP-203 in a trained artificial neural network (ANN) to estimate the unknown nonlinear relationships between TSP-203 results and those obtained by the methods of Rock Mass Rating classification (RMR – treated here as real values). Our results show that an appropriately trained neural network can reliably predict the weak geological zones in front of a tunnel face accurately.