Results 81 to 90 of about 615,528 (247)

AI‐BioMech: Deep Learning Prediction of Mechanical Behavior in Aperiodic Biological Cellular Materials

Advanced Intelligent Discovery, EarlyView.
AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia, Marcelo A. Dias, Parvez Alam   +2 more
wiley   +1 more source

A Generative AI Framework to Predict Cardiomyocyte Contraction Function From Single Static Images

Advanced Intelligent Discovery, EarlyView.
A single static hiPSC‐cardiomyocyte image is fed into a U‐Net‐GAN framework, which directly predicts a pixel‐resolved contraction heatmap without time‐lapse imaging. StyleGAN2‐generated synthetic cell–heatmap pairs augment training, improving prediction fidelity (SSIM = 0.84).
Andrew Kowalczewski, Chenyan Wang, Xinrui Wang, Huaxiao Yang, Zhao Qin, Zhen Ma   +5 more
wiley   +1 more source

Motion blur removal from photographs [PDF]

, 2010
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 135-143).One of the long-standing challenges in photography is
Cho, Taeg Sang
core  

Ecoefficiency Analysis and Regression in Data Conversion for Spiking Neural Network Training

Advanced Intelligent Systems, EarlyView.
The environmental footprint of spiking neural networks is quantified during dataset encoding and training for autonomous driving regression across three benchmarks. Temporal depth emerges as the dominant driver of energy consumption and CO2 emissions, while the accuracy–energy trade‐off proves dataset‐dependent. On conventional hardware, spiking models
Fernando S. Martínez, Raúl Parada, Jordi Casas‐Roma, Laia Subirats   +3 more
wiley   +1 more source

Deblurring example suffering from a heavier blur.

, 2018
(a) denotes the input image and real blur kernel. (b) denotes the matched exemplar. (c) denotes the predicted ∇S. (d)—(j) denote the deblurring results of Krishnan[12], Xu eta.[17], Shan[15], Xu & Jia[7], Levin[16], Pan[9] and our method, respectively ...
Houjie Li (4918960), Shengyang Luan (4918963), Haiyu Song (4918954), Tianshuang Qiu (4918966), Linxiu Wu (4918957)   +4 more
core   +1 more source

Modeling Non-Stationary Asymmetric Lens Blur By Normal Sinh-Arcsinh Model [PDF]

, 2016
Department of Electrical EngineeringImages acquired by a camera show lens blur due to imperfection in the optical system. Lens blur is non-stationary in a sense the amount of blur depends on pixel locations in a sensor. Lens blur is also asymmetric in a
Jang, Jinhyeok, Joo Dong Yun, Jinhyeok, Jang, Yun, Joo Dong, Seungjoon Yang, Jinhyeok Jang, Yang, Seungjoon   +6 more
core   +1 more source

AI‐Powered Smart Displays and AR Glasses: Perspectives and Challenges

Advanced Intelligent Systems, EarlyView.
This perspective examines how AI is reshaping smart displays and AR glasses. The latest in image‐quality enhancement, sensor‐integrated display optimization, and physiological sensing in display systems, followed by AI‐assisted AR technologies including waveguide optimization, image compensation, foveated rendering, and multimodal sensing, is discussed.
Luke Benoit, Yizhou Qian, Yi‐Chien Chen, Ziyang Shen, Po‐Sheng Chiu, Youngeon Lee, Cheng‐Han Ke, Chih‐Lung Lin, Shin‐Tson Wu   +8 more
wiley   +1 more source

Blur image processing

, 2015
Image blur stems from camera sensor pixel recording light from multiple sources. There are three causes of blur: object motion, optical defocus and camera shake.
Zhang, Yi
core   +1 more source

SFMFNet: Spatial‐Frequency Multiscale Fusion Network for Left Ventricle Segmentation in Echocardiography

Advanced Intelligent Systems, EarlyView.
Automatic left ventricular segmentation in echocardiography is critical for cardiac function assessment but remains challenging due to limited crossdomain feature interaction. This paper proposes SFMFNet, a Spatial‐Frequency Multi‐Scale Fusion Network that integrates multiscale convolution, wavelet transform, and Fourier transform.
Dang Li, Patrick Pang, Chi‐Kin Lam, Tao Tan   +3 more
wiley   +1 more source

Deep Learning Classification of Pancreatic Cancer Cells Over a Progression of Epithelial to Mesenchymal States by Deformability Cytometry Under Microfluidic Viscoelastic Flows

Advanced Intelligent Systems, EarlyView.
Cancer cells progressively spread over intermediate epithelial‐to‐mesenchymal transition states that cause nuclear enlargement and circular redistribution of filamentous vimentin are classified by deformability cytometry coupled to a multi‐region deep learning model with convolutional feature extraction and attention‐based regional aggregation.
Javad Jarmoshti, Hanyuan Gao, Negin Zeinali, Abdullah‐Bin Siddique, Sara J. Adair, Todd W. Bauer, Matthew J. Lazzara, Xiaoxuan Yang, Nathan S. Swami   +8 more
wiley   +1 more source