Results 91 to 100 of about 53,606 (313)

An Extensive Study of Convolutional Neural Networks: Applications in Computer Vision for Improved Robotics Perceptions

Sensors
Convolutional neural networks (CNNs), a type of artificial neural network (ANN) in the deep learning (DL) domain, have gained popularity in several computer vision applications and are attracting research in other fields, including robotic perception ...
Ravi Raj, Andrzej Kos
doaj   +1 more source

Recent Advances in Laser‐Induced Graphene‐Based Gas Sensors: From Sensing Mechanisms to Biomedical Applications

Advanced Science, EarlyView.
Laser‐induced graphene (LIG) provides a scalable, laser‐direct‐written route to porous graphene architecture with tunable chemistry and defect density. Through heterojunction engineering, catalytic functionalization, and intrinsic self‐heating, LIG achieves highly sensitive and selective detection of NOX, NH3, H2, and humidity, supporting next ...
Md Abu Sayeed Biswas, Ankan Dutta, Shuvendu Das, Abu Musa Abdullah, Wanqing Zhang, Fatema Tuz Zohra, Huanyu Cheng   +6 more
wiley   +1 more source

Performance analysis of seven Convolutional Neural Networks (CNNs) with transfer learning for Invasive Ductal Carcinoma (IDC) grading in breast histopathological images. [PDF]

Sci Rep, 2022
Voon W, Hum YC, Tee YK, Yap WS, Salim MIM, Tan TS, Mokayed H, Lai KW.   +7 more
europepmc   +1 more source

Atomic Defects in Layered Transition Metal Dichalcogenides for Sustainable Energy Storage and the Intelligent Trends in Data Analytics

Advanced Science, EarlyView.
This review comprehensively summarizes the atomic defects in TMDs for their applications in sustainable energy storage devices, along with the latest progress in ML methodologies for high‐throughput TEM data analysis, offering insights on how ML‐empowered microscopy facilitates bridging structure–property correlation and inspires knowledge for precise ...
Zheng Luo, Ying Yang, Zizhen Fu, Fengqi Liu, Susu Fang, Kele Xu, Shanshan Wang   +6 more
wiley   +1 more source

The Application of Convolutional Neural Networks (CNNs) to Recognize Defects in 3D-Printed Parts. [PDF]

Materials (Basel), 2021
Wen H, Huang C, Guo S.
europepmc   +1 more source

A Fully Self‐Powered Digital Wearable System for the Auxiliary Treatment of Plantar Fasciitis

Advanced Science, EarlyView.
This study reports a system‐level fully self‐powered digital wearable system (FS‐DWS) for the auxiliary treatment of plantar fasciitis. By integrating arch support, energy harvesting, wearable sensing, and machine learning‐driven closed‐loop visualized feedback, the system enables effective plantar pressure reduction and self‐powered, real‐time plantar
Jiacheng Hou, Ying Hong, Shiyuan Liu, Qiqi Pan, Jingyu Zhang, Qingyang Xu, Qiyi Nie, Zhonghe Wang, Liming Xin, Yilong Wang, Biao Wang   +10 more
wiley   +1 more source

Convolutional neural network transformer (CNNT) for fluorescence microscopy image denoising with improved generalization and fast adaptation

Scientific Reports
Deep neural networks can improve the quality of fluorescence microscopy images. Previous methods, based on Convolutional Neural Networks (CNNs), require time-consuming training of individual models for each experiment, impairing their applicability and ...
Azaan Rehman, Alexander Zhovmer, Ryo Sato, Yoh-suke Mukouyama, Jiji Chen, Alberto Rissone, Rosa Puertollano, Jiamin Liu, Harshad D. Vishwasrao, Hari Shroff, Christian A. Combs, Hui Xue   +11 more
doaj   +1 more source

Effectiveness of Learning Systems from Common Image File Types to Detect Osteosarcoma Based on Convolutional Neural Networks (CNNs) Models. [PDF]

J Imaging, 2021
Loraksa C, Mongkolsomlit S, Nimsuk N, Uscharapong M, Kiatisevi P.   +4 more
europepmc   +1 more source

In Situ Quantization with Memory‐Transistor Transfer Unit Based on Electrochemical Random‐Access Memory for Edge Applications

Advanced Science, EarlyView.
By combining ionic nonvolatile memories and transistors, this work proposes a compact synaptic unit to enable low‐precision neural network training. The design supports in situ weight quantization without extra programming and achieves accuracy comparable to ideal methods. This work obtains energy consumption advantage of 25.51× (ECRAM) and 4.84× (RRAM)
Zhen Yang, Yuxiang Yang, Baiqian Wang, Yaoyu Tao, Zelun Pan, Lei Cai, Teng Zhang, Longhao Yan, Xianbin Li, Yuchao Yang   +9 more
wiley   +1 more source

A Hybrid Model Composed of Two Convolutional Neural Networks (CNNs) for Automatic Retinal Layer Segmentation of OCT Images in Retinitis Pigmentosa (RP). [PDF]

Transl Vis Sci Technol, 2021
Wang YZ, Wu W, Birch DG.
europepmc   +1 more source