The unscented transform as a method to assess circuit variability for emergent technologies

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2019.O presente trabalho propõe a transformada da incerteza (UT) como uma alternativa ao método de Monte Carlo (MC) para avaliar o funcionamento sob variabilidade de circuitos elétricos baseados em tecnologias emergentes. É mostrado que a aproximação discreta pela UT de funções de distribuição de probabilidade de variáveis aleatórias pode ser realizada a partir da quadratura Gaussiana. A tecnologia de nanotubos de carbono (CNT) possibilita a criação de circuitos de rádio frequência de comprimento moderado do canal e densidade de nanotubos. Como exemplo de aplicação, são comparadas as previsões de rendimento utilizando os métodos MC e UT de osciladores em anel construídos a partir de buffers de lógica em modo corrente. Apesar do escalonamento exponencial, este trabalho mostra evidências de sua aplicabilidade para a análise de circuitos menores de benchmark. Para circuitos maiores, a natureza determinística da UT permite a exploração de redundâncias inerentes ao circuito. Por fim, são apontados parâmetros da tecnologia com distribuição não-normais, que requerem novos algoritmos para computar a discretização utilizando a UT.The unscented transform (UT) is proposed as an alternative to the Monte Carlo (MC) method for assessing performance variability of electronic circuits based on emergent technologies. We show that the discrete UT approximation of a continuous probability distribution of random variables can be solved by Gaussian quadrature. Carbon nanotube (CNT) technology enables radio frequency circuits at moderate channel length and semiconducting tube density. As an application example, we compare UT and MC yield predictions for CNTFET current-mode-logic ring oscillators. Despite of the exponential scaling of the computational effort with system size, the UT proves to be a powerful tool for the analysis of small benchmark circuits. For larger circuits the deterministic nature of the UT allows to explore redundancies. Finally, we point out that technology parameters are not necessarily normal distributed, which calls for new algorithms to compute the UT discretization

Implementação e avaliação de desempenho do protocolo CSMA/CA IEEE 802.15.4 sobre a plataforma SimpliciTI e microcontroladores MSP430

Trabalho de conclusão de curso (graduação)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2015.Este trabalho apresenta a implementação do protocolo CSMA/CA IEEE 802.15.4, de tempo não compartimentado, sobre a plataforma SimpliciTI, da Texas Instruments. O desempenho do protocolo implementado é avaliado sobre uma rede de sensores baseada nos microcontroladores MSP430, e comparado com o desempenho do protocolo CSMA/CA original implementado na plataforma SimpliciTI. Os desempenhos de ambos os protocolos são avaliados sobre redes com diferentes números de nós e taxas de geração de pacotes, e comparados segundo o número de recuos ( backo s ) necessários para entrega de pacotes com sucessos, número de pacotes perdidos e vazão efetiva da rede.This work presents an implementation of the non-slotted CSMA/CA MAC protocol of the IEEE 802.15.4 standard over the SimpliciTI plataform, by Texas Instruments. The performance of the implemented protocol is evaluated over a sensor network based on MSP430 microcontrollers, and compared against the peformance of SimpliciTI's original CSMA/CA protocol. Both protocols are evaluated under di erent number of nodes and data rates, according to the number of backo stages needed to successfully deliver data packets, the number of data packets delivered unsuccessfully, and network throughput

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P < 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)