An All-Digital Low-Noise Switching DC–DC Buck Converter Based on a Multi-sampling Frequency Delta-Sigma Modulation with Enhanced Light-Load Efficiency

2019 ◽  
Vol 45 (3) ◽  
pp. 1411-1419
Author(s):  
Hussain A. Alzaher ◽  
Mohammad K. Alghamdi
Keyword(s):  
Sampling Frequency ◽  
Low Noise ◽  
Buck Converter ◽  
Light Load ◽  
Delta Sigma ◽  
Delta Sigma Modulation

A New Improved $2^{nd}$-Order Delta-Sigma-Modulation Buck Converter with Low-Noise and Transient- Acceleration Loops

2021 ◽  
pp. 1-1
Author(s):  
Jiann-Jong Chen ◽  
Yuh-Shyan Hwang ◽  
Yitsen Ku ◽  
Jian-An Chen ◽  
Chien-Hung Lai
Keyword(s):  
Low Noise ◽  
Buck Converter ◽  
Delta Sigma ◽  
Delta Sigma Modulation

A Low-Electromagnetic-Interference Buck Converter With Continuous-Time Delta-Sigma-Modulation and Burst-Mode Techniques

2018 ◽  
Vol 65 (9) ◽  
pp. 6860-6869 ◽  
Author(s):  
Jiann-Jong Chen ◽  
Yuh-Shyan Hwang ◽  
Chih-Shiun Jheng ◽  
Yi-Tsen Ku ◽  
Cheng-Chieh Yu
Keyword(s):  
Electromagnetic Interference ◽  
Continuous Time ◽  
Buck Converter ◽  
Burst Mode ◽  
Delta Sigma ◽  
Delta Sigma Modulation

A low-voltage low-noise DC-DC flyback converter with delta-sigma modulation

2012 ◽  
Author(s):  
Bo-Han Hwang ◽  
Jay-Ann Yo ◽  
Jiann-Jong Chen ◽  
Yuh-Shyan Hwang ◽  
Cheng-Chieh Yu
Keyword(s):  
Low Voltage ◽  
Low Noise ◽  
Flyback Converter ◽  
Delta Sigma ◽  
Delta Sigma Modulation

1-bit Feedforward Distortion Compensation Technology for Bandpass Delta-Sigma Modulation

2016 ◽  
Vol E99.B (5) ◽  
pp. 1087-1092 ◽  
Author(s):  
Takashi MAEHATA ◽  
Suguru KAMEDA ◽  
Noriharu SUEMATSU
Keyword(s):  
Distortion Compensation ◽  
Delta Sigma ◽  
Delta Sigma Modulation

scholarly journals SCOUR: a stepwise machine learning framework for predicting metabolite-dependent regulatory interactions

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Justin Y. Lee ◽  
Britney Nguyen ◽  
Carlos Orosco ◽  
Mark P. Styczynski
Keyword(s):  
Machine Learning ◽  
Metabolic Networks ◽  
Sampling Frequency ◽  
Low Noise ◽  
Training Data ◽  
High Noise ◽  
Regulatory Interactions ◽  
Learning Framework ◽  
Metabolic Systems ◽  
Noise Data

Abstract Background The topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms—two characteristics that make it difficult to model metabolic networks accurately. While many computational methods have been built to unravel transcriptional regulation, there have been few approaches developed for systems-scale analysis and study of metabolic regulation. Here, we present a stepwise machine learning framework that applies established algorithms to identify regulatory interactions in metabolic systems based on metabolic data: stepwise classification of unknown regulation, or SCOUR. Results We evaluated our framework on both noiseless and noisy data, using several models of varying sizes and topologies to show that our approach is generalizable. We found that, when testing on data under the most realistic conditions (low sampling frequency and high noise), SCOUR could identify reaction fluxes controlled only by the concentration of a single metabolite (its primary substrate) with high accuracy. The positive predictive value (PPV) for identifying reactions controlled by the concentration of two metabolites ranged from 32 to 88% for noiseless data, 9.2 to 49% for either low sampling frequency/low noise or high sampling frequency/high noise data, and 6.6–27% for low sampling frequency/high noise data, with results typically sufficiently high for lab validation to be a practical endeavor. While the PPVs for reactions controlled by three metabolites were lower, they were still in most cases significantly better than random classification. Conclusions SCOUR uses a novel approach to synthetically generate the training data needed to identify regulators of reaction fluxes in a given metabolic system, enabling metabolomics and fluxomics data to be leveraged for regulatory structure inference. By identifying and triaging the most likely candidate regulatory interactions, SCOUR can drastically reduce the amount of time needed to identify and experimentally validate metabolic regulatory interactions. As high-throughput experimental methods for testing these interactions are further developed, SCOUR will provide critical impact in the development of predictive metabolic models in new organisms and pathways.

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