Results 241 to 250 of about 3,038,089 (290)

Smart Flexible Tactile Sensors: Recent Progress in Device Designs, Intelligent Algorithms, and Multidisciplinary Applications

Advanced Intelligent Discovery, EarlyView.
Flexible tactile sensors have considerable potential for broad application in healthcare monitoring, human–machine interfaces, and bioinspired robotics. This review explores recent progress in device design, performance optimization, and intelligent applications. It highlights how AI algorithms enhance environmental adaptability and perception accuracy
Siyuan Wang, Ming Dong, Rongrong Bao, Caofeng Pan   +3 more
wiley   +1 more source

Computer Vision Pipeline for Image Analysis for Freeze‐Fracture Electron Microscopy: Rosette Cellulose Synthase Complexes Case

Advanced Intelligent Discovery, EarlyView.
This paper presents a computer vision (deep learning) pipeline integrating YOLOv8 and YOLOv9 for automated detection, segmentation, and analysis of rosette cellulose synthase complexes in freeze‐fracture electron microscopy images. The study explores curated dataset expansion for model improvement and highlights pipeline accuracy, speed ...
Siri Mudunuri, Leala Carbonneau, Eric M. Roberts, Alison W. Roberts, Candace H. Haigler, Alexey V. Gulyuk, Yaroslava G. Yingling   +6 more
wiley   +1 more source

Empirical Transition Probability Indexing Sparse-Coding Belief Propagation (ETPI-SCoBeP) Genome Sequence Alignment

Cancer Informatics, 2015
Aminmohammad Roozgard, Nafise Barzigar, Shuang Wang, Xiaoqian Jiang, Samuel Cheng   +4 more
doaj  

Quantum sparse coding

Quantum Machine Intelligence, 2022
The ultimate goal of any sparse coding method is to accurately recover from a few noisy linear measurements, an unknown sparse vector. Unfortunately, this estimation problem is NP-hard in general, and it is therefore always approached with an ...
Yaniv Romano, H. Primack, Talya Vaknin, Idan Meirzada, Ilan Karpas, Dov Furman, C. Tradonsky, Ruti Ben-shlomi   +7 more
semanticscholar   +3 more sources

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Sparse coding with memristor networks.

Nature Nanotechnology, 2017
Sparse representation of information provides a powerful means to perform feature extraction on high-dimensional data and is of broad interest for applications in signal processing, computer vision, object recognition and neurobiology. Sparse coding is also believed to be a key mechanism by which biological neural systems can efficiently process a ...
P. Sheridan, F. Cai, Chao Du, Wen Ma, Zhengya Zhang, Wei D. Lu   +5 more
semanticscholar   +3 more sources

Discrete Sparse Coding

Neural Computation, 2017
Sparse coding algorithms with continuous latent variables have been the subject of a large number of studies. However, discrete latent spaces for sparse coding have been largely ignored. In this work, we study sparse coding with latents described by discrete instead of continuous prior distributions.
Exarchakis, Georgios, Lücke, Jörg
openaire   +3 more sources

Online Learning for Matrix Factorization and Sparse Coding

Journal of machine learning research, 2009
Sparse coding--that is, modelling data vectors as sparse linear combinations of basis elements--is widely used in machine learning, neuroscience, signal processing, and statistics.
J. Mairal, F. Bach, J. Ponce, G. Sapiro
semanticscholar   +1 more source

Laplacian Sparse Coding, Hypergraph Laplacian Sparse Coding, and Applications

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013
Sparse coding exhibits good performance in many computer vision applications. However, due to the overcomplete codebook and the independent coding process, the locality and the similarity among the instances to be encoded are lost. To preserve such locality and similarity information, we propose a Laplacian sparse coding (LSc) framework.
Shenghua, Gao, Ivor Wai-Hung, Tsang, Liang-Tien, Chia   +2 more
openaire   +2 more sources

Video Anomaly Detection with Sparse Coding Inspired Deep Neural Networks

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019
This paper presents an anomaly detection method that is based on a sparse coding inspired Deep Neural Networks (DNN). Specifically, in light of the success of sparse coding based anomaly detection, we propose a Temporally-coherent Sparse Coding (TSC ...
Weixin Luo, Wen Liu, Dongze Lian, Jinhui Tang, Lixin Duan, Xi Peng, Shenghua Gao   +6 more
semanticscholar   +1 more source

JPEG Artifacts Reduction via Deep Convolutional Sparse Coding

IEEE International Conference on Computer Vision, 2019
To effectively reduce JPEG compression artifacts, we propose a deep convolutional sparse coding (DCSC) network architecture. We design our DCSC in the framework of classic learned iterative shrinkage-threshold algorithm.
Xueyang Fu, Zhengjun Zha, Feng Wu, Xinghao Ding, J. Paisley   +4 more
semanticscholar   +1 more source