Nonlinearity analysis and optimization of HBTs for optical mixer designs

Two types of photo heterojunction bipolar transistor (HBT) to directly down-convert optical signals to electronic signals have been reported in the literature: a conventional photo-HBT in which light penetrates through the area of the base-collector junction and an HBT where light shines through the base-collector edge for higher conversion efficiency. Although the performance in relation to bias conditions has been published, the detailed analyses for identifying the parameters and bias conditions that provide optimum direct down-conversion have not been examined. This paper provides a full explanation of the operation of the down-conversion for both HBT configurations based on low-frequency analyses. Such information is useful for both understanding the nonlinear mechanisms involved and designing for maximum efficiency. In addition, a new circuit has been developed from the basic HBT down-conversion circuit that provides improved performance.     

Radiometric sensor for temperature control of food processing

This paper describes a novel planar antenna sensor created for the purpose of noninvasive temperature measurements using microwave radiometry. In order to improve radiometric measurements in industrial applications, a new generation of sensors is introduced, composed of a metallic sheet. Simulations based upon the method of moments is used both to design and to determine their electromagnetic performances. This paper also describes a radiometric device using these sensors to measure and control the temperature of food products during deep freezing processes. The results and discussions are presented.     

Nanoscale Crystallization in Amorphous Fe–P–Mn Alloys

Mössbauer effect measurements and physicochemical analysis demonstrate that annealing of amorphous Fe–P–Mn alloys leads to the formation of a nanocrystalline structure.     

TEM Studies of High Temperature Corrosion Behaviour of TiAl Intermetallics with Surface Modifications

The oxidation/sulphidation behaviour of a Ti‐46.7Al‐1.9W‐0.5Si alloy with a TiAl₃ diffusion coating was studied in an environment of H₂/H₂S/H₂O at 850^oC. The kinetic results demonstrate that the TiAl₃ coating significantly increased the high temperature corrosion resistance of Ti‐46.7Al‐1.9W‐0.5Si. The SEM, EDX, XRD and TEM analysis reveals that the formation of an Al₂O₃ scale on the surface of the TiAl₃‐coated sample was responsible for the enhancement of the corroison resistance. The Ti‐46.7Al‐1.9W‐0.5Si alloy was also modified by Nb ion implantation. The Nb ion implanted and as received sampels were subjected to cyclic oxidation in an open air at 800^oC. The Nb ion implantation not only increased the oxidation resistance but also substantially improved the adhesion of scale to the substrate.     

Surface effects in nanocrystalline silicon

Exposure to ammonia (NH₃) increases the dark current (DC) in nanocrystalline silicon. Light soaking (LS) for short periods also enhances the dark current, which remains at a high value for a long time. Pumping alone is unable to restore the initial annealed state, but annealing brings it back. The final state obtained by LS and NH₃ exposure depends on the order in which they are performed. Evaporated selenium (Se) deposited on nanocrystalline silicon decreases the DC. These effects cannot be explained entirely by the presence of a-Si : H alone, in our sample. DC and photoluminescence (PL) measurements indicate the presence of two types of center in our sample, which behave differently when exposed to NH₃.     

Parameter Uncertainty and Policy Intensity: Some Extensions and Suggestions for Further Work

The effects of parameter uncertainty on optimal policy have been a matter of interest for academics, and even for some policymakers, for a long time. Two lines of literature have developed analytical results on this matter. The first line uses static models and the second dynamic models. In this dynamic line most of the results are confined to models with a single state and a single control variable. In this paper we want to encourage the analysis of more general dynamic cases. To do so, the results in the dynamic line are extended from one-state and one-control finite horizon models to models with a pair of control variables. We then discuss some of the hurdles which must be surmounted for the results to be made more general and suggests some lines for further research. JEL classification: C61; E61     

Synthesis of Rice Processing Plants. I. Development of Simplified Models

This article, the first of three articles on the synthesis of rice processing plants, focuses on the development of simplified mathematical models necessary for use in optimizing rice processing plants. The second concentrates on the optimal synthesis of a rice plant and the third on the sensitivity of the optimization to uncertainty in model parameters. Existing models for rice processing unit operations are not suitable for flowsheet optimization and new models need to be developed to overcome numerical difficulties that occur in optimization applications, specifically in mixed integer nonlinear programming (MINLP) applications. Simplified models of the drying, cooling, and tempering units are developed. In addition head rice yield models, used as a quality indicator, energy consumption, and economic models were also developed. Naturally, the new models exhibit some mismatch with respect to the existing models from which they were developed. However, a sensitivity analysis, presented in Part III, has shown that the optimal flowsheet structure was not sensitive to a lack of fit between the simplified and complex models. The simplified models were found adequate to be appropriate for use at the synthesis stage.     

Training and optimization of an artificial neural network controlling a hybrid power filter

A hybrid power compensator (HPC) consisting of a static VAr compensator and a dynamic compensator needs to be optimally controlled during the compensation of nonlinear loads. The HPC must be controlled to meet minimum requirements in terms of power factor and harmonic distortion, while at the same time minimizing its total cost. An artificial neural network (ANN) is used to control the HPC amidst a very dynamic power system environment. The performance of a reference ANN is evaluated while controlling an HPC connected to a typical nonlinear industrial load. The training and performance of the ANN is then optimized in terms of training set size, training set packing and ANN topology and the performance compared to the reference ANN. This paper highlights the importance of optimising the mentioned ANN parameters to achieve optimum ANN training and modeling accuracy. The results obtained reveals that the application of an ANN in controlling an HPC is feasible given that the ANN parameters are chosen appropriately.