An integrated high voltage digital-to-analog converter for a reconfigurable antenna array

This paper presents an integrated high voltage digital-to-analog converter, which is fabricated in a 0.35 \({\upmu }\) m high voltage CMOS technology (AMS H35), and can be applied in high voltage applications up to 115 V. The high voltage output is controlled by 8-bit digital input code. In order to improve the accuracy and decrease the required area, it is implemented by a low voltage DAC and a high voltage amplifier for boosting the controllable output voltage. Since the current consumption from the high voltage power supply is only 80 \({\upmu }\) A, it can be powered by a charge pump which generates 115 V from a battery with 3.7 V DC. Combining with a 8*8 external high voltage switch array, the proposed high voltage digital-to-analog converter can drive up to 64 individual channels of antenna array with a voltage from 0 to 115 V. It will greatly reduce the complexity and cost of mobile applications which use tunable microwave devices and require high voltages. The feasibility is proved by post-simulation result and experimental test result. The proposed concept of the high voltage ASIC is also proved by a demonstrator.     

Dynamic portfolio choice: a simulation-and-regression approach

Simulation-and-regression algorithms have become a standard tool for solving dynamic programs in many areas, in particular financial engineering and computational economics. In virtually all cases, the regression is performed on the state variables, for example on current market prices. However, it is possible to regress on decision variables as well, and this opens up new possibilities. We present numerical evidence of the performance of such an algorithm, in the context of dynamic portfolio choices in discrete-time (and thus incomplete) markets. The problem is fundamentally the one considered in some recent papers that also use simulations and/or regressions: discrete time, multi-period reallocation, and maximization of terminal utility. In contrast to that literature, we regress on decision variables and we do not rely on Taylor expansions nor derivatives of the utility function. Only basic tools are used, bundled in a dynamic programming framework: simulations—which can be black-boxed—as a representation of exogenous state variable dynamics; regression surfaces, as non-anticipative representations of expected future utility; and nonlinear or quadratic optimization, to identify the best portfolio choice at each time step. The resulting approach is simple, highly flexible and offers good performance in time and precision.     

An Approach of the Sintering YBa2Cu3O7−δ System

The abnormal glow discharge applied to the sintering process is a recent technique used for processing both metallic and ceramic materials. In this paper, we use the abnormal glow discharge as an alternative method for the sintering step of the YBa₂Cu₃O_7?δ superconductor material. The physical properties of the sintered samples by glow discharge were compared with the properties of sintered samples in resistive furnace, which is commonly used for production of high temperature superconductor ceramics. The structural analysis of the YBa₂Cu₃O_7?δ samples was carried out by the X-ray diffraction technique, microstructural analysis by Scanning Electron Microscopy and electrical resistivity by the four probes method (resistance measurements as a function of temperature). The experimental results permitted to establish the similar structure and morphology for all samples: produced by plasma and resistive furnace sintering. The superconducting behavior was corroborated for both sintering processes.     

Defining amplifier’s gain to maximize the transmission rate in optical systems using evolutionary algorithms and swarm intelligence

Optical networks are currently the only technology capable of providing extremely high data transmission rates. Because of this, systems must be increasingly efficient and immune to failures. One way to improve network efficiency is to use dynamic approaches like Adaptive Control of Operating Point, which consists of autonomously choosing the best operating point for optical amplifiers on the link, thus providing the best configuration concerning Quality of transmission. Unlike the previous works that focused on optimizing Optical Signal-To-Noise Ratio, our proposal and analysis are focused on maximizing the transmission rate. In this paper, we compare the results obtained by five different and widely used evolutionary and swarm-based algorithms in the search for maximizing the transmission rate in optical links. We have observed that the differential evolution provided the best results in the analyzed scenarios.

     

Local and global approaches for the adaptive control of a cascade of amplifiers

In this work we present and analyze three approaches for the adaptive control of the operating point of a cascade of erbium-doped fiber amplifiers (EDFAs), aimed at optical networks performance enhancement. The first approach is called Annealing Search Heuristic with Backpropagation and flexible output (AsHB flex) and uses machine learning concepts to update the amplifier gains through an iterative process. The second one (Exhaustive Method) uses an exhaustive search to evaluate all possible solutions for the problem and obtain the optimum solution. The last one (MaxGain) is a heuristic method that uses previous knowledge about the problem to obtain the solutions. The amplifier characteristics and specifications were obtained experimentally through measurements of gain, noise figure, gain ripple and power consumption on commercially available EDFAs. We performed comparisons among these approaches and others found in the literature, and the results show that the three proposals outperformed the previous ones in terms of noise figure, gain ripple and BER. For example, in a link with four amplifiers the Exhaustive Method achieved a reduction in the cascade noise figure from 10.05 to 5.18 dB, a reduction in the gain ripple from 24.08 to 18.56 dB and a reduction in the BER in almost two orders of magnitude, when compared with the traditional approach, which defines the gain to compensate the loss of the previous link. However, the computation time of Exhaustive Method becomes prohibitive as the number of amplifiers in the link increases. Both MaxGain and AsHBflex obtained similar solutions, close to the optimum operation point in a reasonable time.     

Fermentation patterns of gramicidin S formation by Bacillus brevis ATCC 9999 in high-yielding synthetic medium

Addition of the gramicidin S (GS)-constituent amino acids, other than the limiting precursor L -phenylalanine, to the high-yielding chemically defined F3/6 and G3/6 media, enhanced growth and volumetric GS-production by Bacillus brevis ATCC 9999 considerably, but did not yield a higher specific GS-production level. L -Leucine alone could duplicate this stimulatory effect in G3/6 medium. Replacing the fructose component of F3/6 medium by these four amino acids yielded a high specific GS-production level, but resulted in poor growth and low volumetric antibiotic production. Nutrient-utilisation patterns in F3/6 medium revealed that B. brevis initially grew at the expense of L -glutamine and L -arginine. After a diauxic lag period, D -fructose was consumed together with L -histidine. L -Proline was mainly used during the stationary phase. L -Methionine was broken down gradually throughout the whole fermentation cycle. L -Phenylalanine was metabolised only after GS formation started, and its disappearance was proportional to the amount of GS produced. Lowering the aeration rate caused an acidification of the medium, resulting in a slower and incomplete, although similar, nutrient-utilisation pattern. At a controlled pH of 7.3, under lowered aeration, utilisation patterns were again comparable with those of a fully aerated fermentation, but GS levels were enhanced considerably (0.220 mg of GS mg^?1 dry cell wt). Depending on environmental culture conditions, B. brevis also excreted different amino acids (L -lysine, L -alanine, L -valine, L -serine), which were in turn metabolised during late growth and differentiation stages. The onset of GS synthesis occurred on depletion of L -glutamine and L -arginine. Soluble GS synthetase 1 and 2 peaks coincided with ‘diauxic’ lag phases; this supported the idea that a high growth rate is incompatible with GS synthetase formation.     

Recovery of semivolatile organic compounds during sample preparation: implications for characterization of airborne particulate matter

Semivolatile compounds present special analytical challenges not met by conventional methods for analysis of ambient particulate matter (PM). Accurate quantification of PM-associated organic compounds requires validation of the laboratory procedures for recovery over a wide volatility and polarity range. To meet these challenges, solutions of n-alkanes (nC12-nC40) and polycyclic aromatic hydrocarbons PAHs (naphthalene to benzo[ghi]perylene) were reduced in volume from a solvent mixture (equal volumes of hexane, dichloromethane and methanol), to examine recovery after reduction in volume. When the extract solution volume reached 0.5 mL the solvent was entirely methanol, and the recovery averaged 60% for n-alkanes nC12-nC25 and PAHs from naphthalene to chrysene. Recovery of higher MW compounds decreased with MW, because of their insolubility in methanol. When the walls of the flasks were washed with 1 mL of equal parts hexane and dichloromethane (to reconstruct the original solvent composition), the recovery of nC18 and higher MW compounds increased dramatically, up to 100% for nC22-nC32 and then slowly decreasing with MW due to insolubility. To examine recovery during extraction of the components of the High Capacity Integrated Gas and Particle Sampler, the same standards were used to spike its denuders and filters. For XAD-4 coated denuders and filters, normalized recovery was >95% after two extractions. Recovery from spiked quartz filters matched the recovery from the coated surfaces for alkanes nC18 and larger and for fluoranthene and larger PAHs. Lower MW compounds evaporated from the quartz filter with the spiking solvent. This careful approach allowed quantification of organics by correcting for volatility- and solubility-related sample preparation losses. This method is illustrated for an ambient sample collected with this sampler during the Texas Air Quality Study 2000.     

Greenhouse gas emission reductions from domestic anaerobic digesters linked with sustainable sanitation in rural China

Anaerobic digesters provide clean, renewable energy (biogas) by converting organic waste to methane, and are a key part of China's comprehensive rural energy plan. Here, experimental and modeling results are used to quantify the net greenhouse gas (GHG) reduction from substituting a household anaerobic digester for traditional energy sources in Sichuan, China. Tunable diode laser absorption spectroscopy and radial plume mapping were used to estimate the mass flux of fugitive methane emissions from active digesters. Using household energy budgets, the net improvement in GHG emissions associated with biogas installation was estimated using global warming commitment (GWC) as a consolidated measure of the warming effects of GHG emissions from cooking. In all scenarios biogas households had lower GWC than nonbiogas households, by as much as 54%. Even biogas households with methane leakage exhibited lower GWC than nonbiogas households, by as much as 48%. Based only on the averted GHG emissions over 10 years, the monetary value of a biogas installation was conservatively estimated at US$28.30 ($16.07 ton(-1) CO(2)-eq), which is available to partly offset construction costs. The interaction of biogas installation programs with policies supporting improved stoves, renewable harvesting of biomass, and energy interventions with substantial health cobenefits are discussed.