Nonzero Staircase Modulation Scheme for Switching DC-DC Boost Converter

Salau, Ayodeji Olalekan; Eya, Candidus U.; Onyebuchi, Omeje Crescent

doi:10.1155/2020/8347462

Cited by 11 publications

(1 citation statement)

References 18 publications

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“…The research in [30] introduces a technique for nonzero staircase modulation to switch single-switched PWM DC-DC boost converters to reduce the effect of harmonics. The methodology is based on forming two pulse train signals if comparing a 50 Hz nonzero wave with a 1.5 Hz train wave.…”

Section: Related Workmentioning

confidence: 99%

Accurate Phase Detection for ZigBee Using Artificial Neural Network

Alqahtani¹,

Alsulami²,

Alahmari³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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The IEEE802.15.4 standard has been widely used in modern industry due to its several benefits for stability, scalability, and enhancement of wireless mesh networking. This standard uses a physical layer of binary phase-shift keying (BPSK) modulation and can be operated with two frequency bands, 868 and 915 MHz. The frequency noise could interfere with the BPSK signal, which causes distortion to the signal before its arrival at receiver. Therefore, filtering the BPSK signal from noise is essential to ensure carrying the signal from the sender to the receiver with less error. Therefore, removing signal noise in the BPSK signal is necessary to mitigate its negative sequences and increase its capability in industrial wireless sensor networks. Moreover, researchers have reported a positive impact of utilizing the Kalmen filter in detecting the modulated signal at the receiver side in different communication systems, including ZigBee. Meanwhile, artificial neural network (ANN) and machine learning (ML) models outperformed results for predicting signals for detection and classification purposes. This paper develops a neural network predictive detection method to enhance the performance of BPSK modulation. First, a simulation-based model is used to generate the modulated signal of BPSK in the IEEE802.15.4 wireless personal area network (WPAN) standard. Then, Gaussian noise was injected into the BPSK simulation model. To reduce the noise of BPSK phase signals, a recurrent neural networks (RNN) model is implemented and integrated at the receiver side to estimate the BPSK's phase signal. We evaluated our predictive-detection RNN model using mean square error (MSE), correlation coefficient, recall, and F1-score metrics. The result shows that our predictive-detection method is superior to the existing model due to the low MSE and correlation coefficient (R-value) metric for different signal-to-noise (SNR) values. In addition, our RNN-based model scored 98.71% and 96.34% based on recall and F1-score, respectively.

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Section: Related Workmentioning

confidence: 99%

Accurate Phase Detection for ZigBee Using Artificial Neural Network

Alqahtani¹,

Alsulami²,

Alahmari³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Design and analysis of batteryless cardiac pacemaker through combining thermoelectric generators along with DC‐DC converter

Shwetha¹,

Lakshmi

2021

Int Trans Electr Energ Syst

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Implantable cardiac pacemakers are equipped with batteries to provide power supply that have a limited lifetime. Battery-based implantable cardiac pacemakers need surgical intervention to replace batteries. This is an undesirable condition that can increase the risk of complications and costs for patients. To overcome the above shortcomings, a batteryless cardiac pacemaker is introduced, which harvests energy from the body temperature. In this article, a batteryless cardiac pacemaker is designed, which is powered by sustainable energy sources. Hence, thermoelectric generators (TEGs) are considered the sustainable renewable energy source that powers up cardiac pacemakers with effective reduction in dimension without limiting the capacity. Then, power electronic converters adapted with TEGs has enhanced the ability of controlling environmental conditions as well as maximizing energy harvesting. Therefore, a DC-DC converter is designed with TEG to solve environmental conditions issues of TEG. In this proposed methodology, a TEG with three different types of DC-DC converters is designed and validated to analyze the performance of converters such as boost converter, buck converter, and buckboost converter. The proposed design was analyzed based on a case example and simulated using Cadence Capture CIS. Finally, a prototype of our design was developed to evaluate the experimental outcome. Four modules are designed and validated: general pacemaker, TEG with pacemaker, TEG with boost converter, TEG with buck converter, and TEG with buck-boost List of Symbols and Abbreviations: ρ, density; Q, heat flux; Q, volumetric heat; C P , specific heat at consonant pressure; j !

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