Results 111 to 120 of about 20,128 (302)

A push–pull FVF LDO with full‐spectrum PSR and fast transient response

Electronics Letters
A push–pull low‐dropout regulator based on flipped voltage follower is proposed and designed in 65 nm CMOS, which has a push‐current mode and a pull‐current one.
Heng Zheng, Baixin Liu, Chen Wang, Xukun Wang, Bo Zhou   +4 more
doaj   +1 more source

The Effects of ELDRS at Ultra-Low Dose Rates [PDF]

, 2011
We present results on the effects on ELDRS at dose rates of 10, 5, 1, and 0.5 mrad(Si)/s for a variety of radiation hardened and commercial devices. We observed low dose rate enhancement below 10 mrad(Si)/s in several different parts.
Albarian, Rafi, Burns, Samuel, Carts, Martin, Chaumont, Geraldine, Chen, Dakai, Cox, Stephen, Duperray, Herve, Forney, James, Holcombe, Bruce, Kruckmeyer, Kirby, LaBel, Kenneth, Little, Bradley, Ouellet, Al, Pease, Ronald, Phan, Anthony, Salzman, James   +15 more
core   +1 more source

Capacitorless low-dropout regulator for power management applications

, 2021
This article aims to present the design of a 4.5-V, 450-mA low drop-out (LDO) voltage linear regulator based on a two-stage cascoded operational transconductance amplifier (OTA) as error amplifier. The aforementioned two-stage OTA is designed with cascoded current mirroring technique to boost up the output impedance.
Martínez García, Herminio, García Vílchez, Encarnación   +1 more
openaire   +1 more source

RPSLearner: A Novel Approach Based on Random Projection and Deep Stacking Learning for Categorizing Non‐Small Cell Lung Cancer

Advanced Intelligent Systems, EarlyView.
Identifying non‐small cell lung cancer (NSCLC) subtypes is essential for precision cancer treatment. Conventional methods are laborious, or time‐consuming. To address these concerns, RPSLearner is proposed, which combines random projection and stacking ensemble learning for accurate NSCLC subtyping. RPSLearner outperforms state‐of‐the‐art approaches in
Xinchao Wu, Jieqiong Wang, Shibiao Wan
wiley   +1 more source

Synthetic Electrocardiogram Spectrogram Generation Using Generative Adversarial Network‐Based Models: A Comparative Study

Advanced Intelligent Systems, EarlyView.
Cardiovascular diseases are leading death causes; electrocardiogram (ECG) analysis is slow, motivating machine learning and deep learning. This study compares deep convolutional generative adversarial network, conditional GAN, and Wasserstein GAN with gradient penalty (WGAN‐GP) for synthetic ECG spectrograms; Fréchet Inception Distance (FID) and ...
Giovanny Barbosa‐Casanova, Norelli Schettini, Winston Percybrooks, Begoña García‐Zapirain   +3 more
wiley   +1 more source

A Generalizable Transformer Framework for Gene Regulatory Network Inference from Single‐Cell Transcriptomes

Advanced Intelligent Systems, EarlyView.
FTGRN introduces an LLM‐enhanced framework for gene regulatory network inference through a two‐stage workflow. It combines a Transformer‐based model, pretrained on GPT‐4 derived gene embeddings and regulatory knowledge, with a fine‐tuning stage utilizing single‐cell RNA‐seq data.
Guangzheng Weng, Hyobin Kim, Patrick Martin, Junil Kim, Tae‐Hyung Kim, Gi‐Hoon Nam, Dongha Kim, Kyoung Jae Won   +7 more
wiley   +1 more source

Rural Facility Electric Power Quality Enhancement [PDF]

, 1991
Electric power disturbances are known to be more prevalent in small, isolated power systems than in larger interconnected grids which service most of the United States. This fact has given rise to a growing concern about the relative merits of different
Aspnes, J.D., Merritt, R.P., Spell, B.D., Wilson, M.   +3 more
core  

Multivariate Contrastive Predictive Coding with Sliding Windows for Disease Prediction from Electronic Health Records

Advanced Intelligent Systems, EarlyView.
Adaptive multi‐indicator contrastive predictive coding is introduced as a self‐supervised pretraining framework for multivariate EHR time series. An adaptive sliding‐window algorithm and 2D convolutional neural network encoder capture localized temporal patterns and global indicator dependencies, enabling label‐efficient disease prediction that ...
Hongxu Yuan, Xiaozhu Jing, Yuzheng Yan, Wuman Luo   +3 more
wiley   +1 more source

Adaptive Autonomy in Microrobot Motion Control via Deep Reinforcement Learning and Path Planning Synergy

Advanced Intelligent Systems, EarlyView.
This study introduces a data‐driven framework that combines deep reinforcement learning with classical path planning to achieve adaptive microrobot navigation. By training a surrogate neural network to emulate microrobot dynamics, the approach improves learning efficiency, reduces training time, and enables robust real‐time obstacle avoidance in ...
Amar Salehi, Bairong Zhu, Haoyu Liu, Tingting Yu   +3 more
wiley   +1 more source

Designing Low Dropout Regulator with Low Settling Time, High Power Supply Rejection and Low Line and Load Regulation

Majlesi Journal of Electrical Engineering
Low dropout regulators are one of the most important factures of many portable devices. Thus, consider to the complexity of the circuits and increasing request for portable devices, for increasing battery life and minimizing supply noise, regulators with
Najmeh Khanian, Abbas Golmakani
doaj