Design and implementation of a practical semi‐lumped high power low‐pass filter

In this article, a compact, semi‐lumped and high power low‐pass filter in VHF band frequency is designed, fabricated, and measured. A semi‐lumped structure is used to decrease the size of the filter and improve its power handling. In high power analysis, all effects of critical points in distributed and lumped structures are considered. The experimental measurements show close agreement with the simulation results. This filter has a cut off frequency at 180 MHz, 0.02 dB ripples in pass band, return loss better than 21 dB in the pass band, 0.2 dB insertion losses, 1.6 dB/MHz shape factor, a 75% miniaturization against conventional structures with distributed elements, and wide out of band rejection. Moreover, 10 and 1 KW are the peak power and the average power handling of the filter. © 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:605–614, 2014.     

Limitations of LDD types of structures in deep-submicrometer MOStechnology

Lightly doped drain (LDD) types of MOSFET structures have been analyzed in detail in order to understand the issues and trade-offs in the application of these structures to deep-submicrometer technology (L _gate⩽0.35 μm) in which the minimum feature size of the technology is exploited as the total gate length of the device. Because of considerable channel doping compensation resulting from the desired graded-drain profile of the N^- region, larger and unacceptable charge-sharing effects are encountered. The problem can be avoided if the LDD N^- region is shallow and steeply profiled. However, this leads to unacceptably high hot-carrier generation rates. This major conflict in design requirements (suppression of charge sharing as well as reduction of hot-carrier effects) results in serious limitations on the applicability of LDD MOSFETs to deep-submicrometer technology     

Electron and hole quantization and their impact on deep submicronsilicon p- and n-MOSFET characteristics

A first-principles approach to inversion layer quantization, valid for arbitrarily complex band structures, has been developed. This has allowed, for the first time, hole quantization and its effects on p-MOSFET device characteristics to be studied. In addition, electron quantization effects are revisited, improving on previous, simpler approaches. In particular, the impact of quantization on the threshold voltages and “effective” gate oxide thicknesses of p- and n-MOSFETs is investigated. A simple compact model is provided to quantitatively describe the threshold voltage shifts at 300 K as a function of the doping concentration and the oxide thickness. The significance of hole quantization for buried channel p-MOS structures is also studied. The results can be used to both identify and model these effects using popular device simulators     

Improved parallelism and scheduling in multi-core software routers

Recent technological advances in commodity server architectures, with multiple multi-core CPUs, integrated memory controllers, high-speed interconnects, and enhanced network interface cards, provide substantial computational capacity, and thus an attractive platform for packet forwarding. However, to exploit this available capacity, we need a suitable software platform that allows effective parallel packet processing and resource management. In this paper, we at first introduce an improved forwarding architecture for software routers that enhances parallelism by exploiting hardware classification and multi-queue support, already available in recent commodity network interface cards. After evaluating the original scheduling algorithm of the widely-used Click modular router, we propose solutions for extending this scheduler for improved fairness, throughput, and more precise resource management. To illustrate the potential benefits of our proposal, we implement and evaluate a few key elements of our overall design. Finally, we discuss how our improved forwarding architecture and resource management might be applied in virtualized software routers.     

Global minimization of multi-funnel functions using particle swarm optimization

In this paper, we consider the problem of finding the global minimum of multi-funnel-shaped functions with many local minima, which is a well-known and interesting problem in computational biology. First, the particle swarm optimization algorithms are briefly reviewed. Then, we have applied a variant of it with linear decreasing inertia weight to solve the underlying global optimization problem. Our computational experiments on several known test problems show the efficiency of the particle swarm optimization algorithm in comparison with global convex quadratic underestimator algorithms that are widely used in the literature.     

Form-birefringence structure fabrication in GaAs by use of SU-8 as a dry-etching mask

A thin layer of a SU-8 submicrometer pattern produced by holographic lithography was directly used as the dry-etching mask in a chemically assisted ion-beam-etching system. With optimized etching parameters, etching selectivity of 7:1 was achieved together with a smooth vertical profile. As an application, a half-wavelength retardation plate for a 1.55-microm wavelength was produced and evaluated.     

Generalization performance of support vector machines and neural networks in runoff modeling

Effective one-day lead runoff prediction is one of the significant aspects of successful water resources management in arid region. For instance, reservoir and hydropower systems call for real-time or on-line site-specific forecasting of the runoff. In this research, we present a new data-driven model called support vector machines (SVMs) based on structural risk minimization principle, which minimizes a bound on a generalized risk (error), as opposed to the empirical risk minimization principle exploited by conventional regression techniques (e.g. ANNs). Thus, this stat-of-the-art methodology for prediction combines excellent generalization property and sparse representation that lead SVMs to be a very promising forecasting method. Further, SVM makes use of a convex quadratic optimization problem; hence, the solution is always unique and globally optimal. To demonstrate the aforementioned forecasting capability of SVM, one-day lead stream flow of Bakhtiyari River in Iran was predicted using the local climate and rainfall data. Moreover, the results were compared with those of ANN and ANN integrated with genetic algorithms (ANN-GA) models. The improvements in root mean squared error (RMSE) and squared correlation coefficient (R²) by SVM over both ANN models indicate that the prediction accuracy of SVM is at least as good as that of those models, yet in some cases actually better, as well as forecasting of high-value discharges.     

Solid-Sample 11B Nuclear Magnetic Resonance and X-ray Photoelectron Spectroscopy Study of Boron-Doped β-Silicon Carbide

Solid-sample magic angle spinning (MAS) nuclear magnetic resonance (NMR) and X-ray photoelectron spectroscopy (XPS), in conjunction with scanning electron microscopy (SEM), were used to investigate the fate of boron used as a sintering aid for silicon carbide. The results of the NMR studies indicated that the boron penetrated the silicon carbide grain boundaries during sintering, and was incorporated in a tetrahedral form in the bulk, regardless of the gas used during the process. The NMR spectrum of a sample sintered under nitrogen indicated the formation of a trigonal form of boron as well. XPS identified this trigonal boron as boron nitride; however, no boron was detected by XPS in any form on the fracture surface of the silicon carbide sintered under argon, even though the NMR results confirmed the presence of tetrahedral boron in the bulk sample. The SEM results indicated that the fracture process for these materials was predominantly intergranular. This suggested that the boron in the silicon carbide sintered under argon penetrated the grains and left the grain boundaries depleted of boron.     

Influence of oxygen plasma treatment parameters on the properties of carbon fiber

This study is focused on the impact of oxygen plasma treatment on properties of carbon fibers and interfacial adhesion behavior between the carbon fibers and epoxy resin. The influences of the main parameters of plasma treatment process, including duration, power, and flow rate of oxygen gas were studied in detail using interlaminar shear strength (ILSS) of carbon fiber composites. The ILSS of composites made of carbon fibers treated by oxygen plasma for 1 min, at power of 125 W, and oxygen flow rate of 100 sccm presented a maximum increase of 28% compared to composites made of untreated carbon fibers. Furthermore, carbon fibers were characterized by scanning electron microscopy (SEM), tensile strength test, attenuated total reflectance Fourier transform infrared (ATR-FTIR), and Raman spectroscopy analyses. It was found that the concentration of reactive functional groups on the fiber surface was increased after the plasma modification, as well the surface roughness, which finally improved the interfacial adhesion between carbon fibers and epoxy resin. However, high power and long exposure times could partly damage the surface of carbon fibers and decrease the tensile strength of filaments and ILSS of treated fiber composites.