Results 111 to 120 of about 1,587,623 (364)

DESIGN AND SIMULATION OF BUILDING BLOCKS OF A LOW-DROPOUT VOLTAGE REGULATOR [PDF]

, 2022
Arief Wisnu Wardhana, Azis Wisnu Widhi Nugraha, Eko Murdyantoro Atmojo, Ari Fadli   +3 more
openalex   +1 more source

Data‐Driven Feedback Identifies Focused Ultrasound Exposure Regimens for Improved Nanotheranostic Targeting of the Brain

Advanced Science, EarlyView.
Machine learning models predict in real time the onset of harmful microbubble collapse during microbubble‐enhanced focused ultrasound (MB‐FUS) and enable dynamic adjustment of sonication to prevent cavitation‐induced damage. This predictive control expands the safe operating window for bloodbrain barrier opening, enhancing nanoparticle delivery and ...
Hohyun Lee, Victor Menezes, Shiqin Zeng, Chulyong Kim, Cynthia M. Baseman, Jae Hyun Kim, Samhita Padmanabhan, Pranav Premdas, Naima Djeddar, Anton Bryksin, Nikhil Pandey, Pavlos Anastasiadis, Anthony J. Kim, Tobey J. MacDonald, Chetan Bettegowda, Graeme F. Woodworth, Felix J. Herrmann, Costas Arvanitis   +17 more
wiley   +1 more source

A Fast-Transient Output-Capacitor-Less Low-Dropout Regulator With Direct-Coupled Slew Rate Enhancement

IEEE Access
An output capacitorless low-dropout (OCL-LDO) regulator with a direct-coupled slew rate enhancement (DCSRE) technique. This paper proposes a low-dropout regulator with a simple structure, fast transient response, and the ability to reduce overshoot and ...
Shao-Ku Kao, Jian-Jiun Chen, Chien-Hung Liao, Yu-Jen Lu, Jer-Chyi Wang   +4 more
doaj   +1 more source

A Low Dropout Voltage Regulated Bulk-driven CMOS Current Mirror

Procedia Computer Science, 2016
AbstractNovel bulk-driven current mirror (CM) and bulk-driven cascode CM are presented in this paper. Bulk-driven technique is employed to overcome a threshold voltage limitation. Proposed circuits operate at 1V power supply. By using a negative feedback, high accuracy input and output transfer characteristic over wide current range is achieved.
Minwong, Nutthawut, Sooksood, Kriangkrai, Thanachayanont, Apinunt   +2 more
openaire   +1 more source

A tri-loop low-dropout regulator with fast transient response based on flipped voltage follower [PDF]

, 2023
Yi Wang, Yao Xu, Zeyuan Lin, Tao Bai, Na Bai, Junwu Ma   +5 more
openalex   +1 more source

CACLENS: A Multitask Deep Learning System for Enzyme Discovery

Advanced Science, EarlyView.
CACLENS, a multimodal and multi‐task deep learning framework integrating cross‐attention, contrastive learning, and customized gate control, enables reaction type classification, EC number prediction, and reaction feasibility assessment. CACLENS accelerates functional enzyme discovery and identifies efficient Zearalenone (ZEN)‐degrading enzymes.
Xilong Yi, Yingzhu Tan, Huikang Lin, Guoqing Zhang, Ye Tian, Aibo Wu   +5 more
wiley   +1 more source

A CMOS-Based Power Management Circuit with a Reconfigurable Rectifier and an LDO Regulator for Piezoelectric Energy Harvesting in IoT Applications

Nanoenergy Advances
The technological advances in internet of things (IoT) devices have raised the demand for cost-efficient and sustainable energy sources. Piezoelectric energy harvesters (PEHs) are promising low-cost and eco-friendly energy sources but require robust ...
Suany E. Vázquez-Valdés, Primavera Argüelles-Lucho, Rosa M. Woo-García, Edith Osorio-de-la-Rosa, Francisco López-Huerta, Agustín L. Herrera-May   +5 more
doaj   +1 more source

Special studies of AROD system concepts and designs [PDF]

Signal to noise ratios in airborne range and orbit determination system, and carrier and range loop performance ...
Baghdady, E. J., Kruse, K. W., Meer, S. A.   +2 more
core   +1 more source

Inferring Gene Regulatory Networks From Single‐Cell RNA Sequencing Data by Dual‐Role Graph Contrastive Learning

Advanced Science, EarlyView.
RegGAIN is a novel and powerful deep learning framework for inferring gene regulatory networks (GRNs) from single‐cell RNA sequencing data. By integrating self‐supervised contrastive learning with dual‐role gene representations, it consistently outperforms existing methods in both accuracy and robustness.
Qiyuan Guan, Jiating Yu, Jieyi Pan, Fan Yuan, Jiadong Ji, Rusong Zhao, Zhi‐Ping Liu, Bingqiang Liu, Ling‐Yun Wu, Duanchen Sun   +9 more
wiley   +1 more source

Generating Dynamic Structures Through Physics‐Based Sampling of Predicted Inter‐Residue Geometries

Advanced Science, EarlyView.
While static structure prediction has been revolutionized, modeling protein dynamics remains elusive. trRosettaX2‐Dynamics is presented to address this challenge. This framework leverages a Transformer‐based network to predict inter‐residue geometric constraints, guiding conformation generation via physics‐based iterative sampling. The resulting method
Chenxiao Xiang, Wenkai Wang, Zhenling Peng, Jianyi Yang   +3 more
wiley   +1 more source