A low power low ripple Schmitt trigger based PWM Boost Converter for Energy Harvesting Applications

Khan, Salma; Azeemuddin, Syed; Sohel, Mohammed Arifuddin

doi:10.1109/indicon49873.2020.9342419

Cited by 3 publications

(1 citation statement)

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“…It also can be utilized for the design of a clock generator efficiently. A low‐power Schmitt trigger‐based PWM Boost converter for EH applications is reported in Khan et al 52 In this paper, two distinct approaches are introduced to implement the switching functions: transistor‐based, and

μ

C‐based Schmitt trigger balancing circuits, both working on the principle of the Hysteresis mechanism. The first approach uses the Schmitt trigger circuit, illustrated in Figure 9, as a means of controlling the switches.…”

Section: Hybrid Energy Harvesting and Its Implementationmentioning

confidence: 99%

Modeling and implementation of a novel active voltage balancing circuit using deep recurrent neural network with dropout regularization

Noohi

Faraji

Sadrossadat

et al. 2022

Circuit Theory & Apps

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Recurrent neural networks (RNN) emerged as powerful tools to model and analyze the nonlinear behavior of electronic circuits accurately and quickly.Efforts to improve the accuracy of RNN will lead to the design of better-quality products, which is essential in various fields such as the design of energy harvesting (EH) systems. EH techniques can provide the electrical energy needed for low-power electronics without the need for a battery or with minimal dependency. Due to the importance of the active voltage balancing circuit in EH systems, we have proposed a new macromodeling method called dropout local-feedback deep recurrent neural network (Dropout-LFDRNN) to model and analyze this circuit along with two other nonlinear circuits asexamples. This technique is an advance over the LFDRNN macromodeling method, and based on the obtained results from the measurements, we were able to build a fast macromodel for the active balancing circuit, which outperforms conventional deep recurrent neural network modeling method in terms of accuracy without sacrificing speed. It is worth mentioning that this proposed technique in this paper can be considered a viable approach for modeling and analysis of nonlinear electronic components and circuits. In addition to the advantage of generating a more accurate model, the model based on the Dropout-LFDRNN approach is much faster than the existing transistor-level models.

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