A Silicon Modulator Enabling RF Over Fiber for 802.11 OFDM Signals

We investigate the linearity properties of silicon modulators and show that, contrary to the traditional lithium niobate Mach–Zehnder modulators (MZMs), the third-order intermodulation distortion (IMD3) for silicon modulators is a function of the modulator bias point. The bias point for silicon modulators can be chosen to reduce the IMD3 well below that of standard lithium niobate MZMs. Given the cost and integration advantages of the silicon photonics technology, silicon modulators offer significant advantages for emerging radio over fiber applications. As an example, we examine, for the first time to our knowledge, a silicon modulator for converting analog 802.11 RF signals to the optical domain, achieving an error vector magnitude of −30 dB.      

Evaluation of Measurement Uncertainty in Determination of Lead in Glass Materials by a Standard Complexometric Method

Lead is an important constituent for the preparation of wide variety of glasses like high refractive index optical glasses, radiation shielding glasses, ceramic glazes, enamels, high electrical resistance glasses, glass soldiers and sealants, etc. Determination of exact quantity of lead is therefore very essential to obtain the desired property of different glasses. With a view to meet the necessity, the measurement uncertainty of the results of lead determination in different lead containing glasses have been evaluated. The lead content has been determined complexometrically at pH 4.3 using di-sodium salt of EDTA. The sources of uncertainty of the results of measurement have been identified as contributions from repeatability, standardization of EDTA, volume measurement by volumetric flask, burette, pipette and end point detection. Sources of uncertainty have been identified and combined following the EURACHEM guidelines. The results show that the major sources of uncertainty arise from standardization and end point detection.     

Er-Doped Y2O3 Nanoparticles: A Comparison of Different Synthesis Methods

Nanoparticles of erbium-doped yttria (Er:Y₂O₃) are important precursors to transparent ceramics for high-power solid-state lasers systems. As structure influences properties and, subsequently, performance the purpose of this work is to compare the morphological and chemical nature of the nanoparticles synthesized using two common methods: solution precipitation and combustion synthesis. A thorough characterization of as-prepared and calcined powders was performed using Fourier transform infrared spectroscopy, X-ray diffraction, conventional and high-resolution transmission electron microscopy, and Brunauer–Emmet–Teller methods. Solution precipitation was found to lead to two different precursor compositions (yttrium carbonate or yttrium hydroxide) depending on the precipitating reagent whereas combustion synthesis yielded only phase-pure, cubic Er:Y₂O₃. The hydroxide precipitation and combustion synthesis methods exhibited agglomerated particles with low surface area after calcining the precursors at 900°C. The addition of a small amount of ammonium sulfate during combustion synthesis was found to reduce the level of agglomeration, resultant particle size, and degree of crystallinity of the calcined Er:Y₂O₃ nanoparticles. The amount of carbon dioxide (CO₂) and water (H₂O) on the surface of the Er:Y₂O₃ powders is dependent on the powder surface area, however, increasing levels of gas absorption on the particle surfaces do not have a detrimental effect on the sinterability. The sintered density increases with increasing surface area and decreasing agglomeration.     

Nonconvex optimization of desirability functions

Desirability functions (DFs) are commonly used in optimization of design parameters with multiple quality characteristic to obtain a good compromise among predicted response models obtained from experimental designs. Besides discussing multi-objective approaches for optimization of DFs, we present a brief review of literature about most commonly used Derringer and Suich type of DFs and others as well as their capabilities and limitations. Optimization of DFs of Derringer and Suich is a challenging problem. Although they have an advantageous shape over other DFs, their nonsmooth nature is a drawback. Commercially available software products used by quality engineers usually do optimization of these functions by derivative free search methods on the design domain (such as Design-Expert), which involves the risk of not finding the global optimum in a reasonable time. Use of gradient-based methods (as in MINITAB) after smoothing nondifferentiable points is also proposed as well as different metaheuristics and interactive multi-objective approaches, which have their own drawbacks. In this study, by utilizing a reformulation on DFs, it is shown that the nonsmooth optimization problem becomes a nonconvex mixed-integer nonlinear problem. Then, a continuous relaxation of this problem can be solved with nonconvex and global optimization approaches supported by widely available software programs. We demonstrate our findings on two well-known examples from the quality engineering literature and their extensions.     

Performance analysis of an asymmetrical phase-converter-fedinduction motor

Of the various types of single-phase-to-three-phase static power converters used to interface a three-phase motor to single-phase supply, one simple type uses an open delta connection of a sinusoidal supply and a pulsewidth modulated (PWM) waveform to feed the motor. This paper presents an analysis of the performance of the three-phase motor when fed from such an asymmetrical supply. The analysis is based on the steady-state approximate-equivalent circuit of the motor, using the method of symmetrical components. The analysis is directed toward prediction of motor phase and line currents, along with the nature of generated torque. The computer-simulated waveforms are presented and compared with results from tests on a laboratory setup     

Computer-Aided Analysis of a Multiphase Chopper-Driven DC Series Motor and Test Results on a Microprocessor-Based Three-Phase Chopper Drive

A comprehensive digital computer-based analysis using a state-space model which predicts the performance of a dc series motor controlled by a multiphase chopper is presented. The analysis is based on the formulation of system equations in multivariable state-space form and takes into account the nonlinear magnetization curve and the variation of field inductance with load current. The predicted results based on this analysis are shown to agree with the test results on a practical drive system developed for this purpose in which the dc series motor is driven by a microprocessor-based three-phase thyristor chopper. The control circuit development for the practical drive is briefly described.     

A New Three-Phase Current Source Inverter with Flexible PWM Capability

A new three-phase current source inverter topology is presented, consisting of three single-phase bridge inverters connected in series and feeding the isolated windings of a standard three-phase induction motor. Because a current zero in one phase now does not affect the others, it enables the implementation of a wide range of current PWM patterns for the reduction and selective elimination of torque pulsations. Furthermore, this system allows for very fast control of the fundamental load current through the use of sinusoidal PWM, a method that was not possible to implement on existing inverter topologies.     

Online adaptive decision trees: pattern classification and function approximation

Recently we have shown that decision trees can be trained in the online adaptive (OADT) mode (Basak, 2004), leading to better generalization score. OADTs were bottlenecked by the fact that they are able to handle only two-class classification tasks with a given structure. In this article, we provide an architecture based on OADT, ExOADT, which can handle multiclass classification tasks and is able to perform function approximation. ExOADT is structurally similar to OADT extended with a regression layer. We also show that ExOADT is capable not only of adapting the local decision hyperplanes in the nonterminal nodes but also has the potential of smoothly changing the structure of the tree depending on the data samples. We provide the learning rules based on steepest gradient descent for the new model ExOADT. Experimentally we demonstrate the effectiveness of ExOADT in the pattern classification and function approximation tasks. Finally, we briefly discuss the relationship of ExOADT with other classification models.