Modelação não-linear de transistores de potência para RF e microondas

Doutoramento em Engenharia Electrotécnic

Distorsión no lineal en un transmisor polar debida a la característica Ron(VDD) del dispositivo GaN HEMT

In this paper, the possible impact of RF switching device ON resistance variation with drain supply voltage, Ron(VDD) characteristic, on polar transmitter distortion is considered. Using Pulsed I/V measurement results over a 15 W GaN HEMT, the deviation in the Vdd-to-AM modulation profile is estimated. System-level calculations, in the presence of gateto- drain capacitance contribution to carrier feedthrough, allow the evaluation of the secondary role of this dispersion effect

Predistorsión digital selectiva en transmisores polares

This paper presents a selective polar transmitter predistortion technique validated with EDGE communication signals. The methodology used, complements the drain bias modulation, vDD(t) with a proper input drive level, vg(t). It is believed to improve the linearity, while maintaining the efficiency of such transmitters, and it was validated in a real laboratory test setup

Diseño de la etapa final de un transmisor polar considerando los estadísticos de la señal

This paper presents the design of a 60W GaN HEMT based high-level modulating stage to be used in a IEEE 802.16e Mobile WiMAX polar transmitter. Following a transmission-line class E topology the load condition for maximizing the efficiency is selected close to the maximum value of the power generating function, as defined in, for an OFDMA modulating signal. The possible effect of such average-based design on the polar transmitter linearity is also studied

Evidence for the formation of metallic In after laser irradiation of InP

Structural and electronic changes induced by laser irradiation are currently of interest owing to the possibility to tune the mechanical, optical, and transport properties of the irradiated materials. In this work, we investigate the effects of laser irradiation on indium phosphide, InP, by modifying the electronic temperature, Te, of the system within the density functional theory framework and performing molecular dynamics simulations to prove that the laser irradiation also provokes a local thermalization effect. We found that the process can be described by a two-stage mechanism. First, at low Te values (0–1.0 eV), the laser energy induces electronic transitions, while the InP lattice remains undisturbed and cool. In the second stage (with Te in the range of 1.0–4.0 eV), both electron-electron scattering and electron-phonon coupling processes are triggered, increasing the energy of the lattice so as to provoke a Coulomb explosion, which changes some physical chemical properties of InP. The close agreement between the simulations helps explain the formation of metallic In as it is observed in the transmission electron microscopy images

Impacto del autocalentamiento en la linealización de un transmisor polar basado en un amplificador a GaN HEMT

In this paper, the impact of self heating on the linearity of a polar transmitter, with a saturated GaN HEMT switched mode power amplifier (PA) as final RF stage, is studied. The thermal circuit parameters of a commercial GaN HEMT device were extracted in order to evaluate the temperature rise dependence on power dissipation. A temperature dependent characterization of the PA modulation profiles was realized and its behaviour under two tone excitations with different frequency spacing was obtained through measurements and simulations, being the self heating effects clearly masked by other nonidealities, as the case of feedthrough. In this sense, linearization techniques as memoryless digital predistortion and I/Q vector hole punching were applied to the two tone excitation with the aim of extracting the real self heating contribution. Finally, temperature dynamics influence on the system residual distortion under a real communication signal excitation is shown

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study

Background: The ISARIC prospective multinational observational study is the largest cohort of hospitalized patients with COVID-19. We present relationships of age, sex, and nationality to presenting symptoms. Methods: International, prospective observational study of 60 109 hospitalized symptomatic patients with laboratory-confirmed COVID-19 recruited from 43 countries between 30 January and 3 August 2020. Logistic regression was performed to evaluate relationships of age and sex to published COVID-19 case definitions and the most commonly reported symptoms. Results: ‘Typical’ symptoms of fever (69%), cough (68%) and shortness of breath (66%) were the most commonly reported. 92% of patients experienced at least one of these. Prevalence of typical symptoms was greatest in 30- to 60-year-olds (respectively 80, 79, 69%; at least one 95%). They were reported less frequently in children (≤ 18 years: 69, 48, 23; 85%), older adults (≥ 70 years: 61, 62, 65; 90%), and women (66, 66, 64; 90%; vs. men 71, 70, 67; 93%, each P < 0.001). The most common atypical presentations under 60 years of age were nausea and vomiting and abdominal pain, and over 60 years was confusion. Regression models showed significant differences in symptoms with sex, age and country. Interpretation: This international collaboration has allowed us to report reliable symptom data from the largest cohort of patients admitted to hospital with COVID-19. Adults over 60 and children admitted to hospital with COVID-19 are less likely to present with typical symptoms. Nausea and vomiting are common atypical presentations under 30 years. Confusion is a frequent atypical presentation of COVID-19 in adults over 60 years. Women are less likely to experience typical symptoms than men