Results 251 to 260 of about 127,164 (309)

A CLIP-based framework for multiclass lung histopathology classification with prompt engineering and class-imbalance-aware focal optimization. [PDF]

Sci Rep
Munawar S, Hanif F, Raza A, Farman M, Hincal E.   +4 more
europepmc   +1 more source

Deep Learning-Based Generation of Retinal Nerve Fibre Layer Thickness Maps from Fundus Photographs: A Comparative Analysis of U-Net Architectures for Accessible Glaucoma Assessment. [PDF]

Life (Basel)
Ohn K, Jun H, Kim YS, Whang WJ.
europepmc   +1 more source

A Computerized Adaptive Test for the Knowledge of Effective Parenting Test-Internalizing Module: Instrument Validation Study. [PDF]

JMIR Form Res
Lindhiem O, Gallagher HD, Tomlinson CS, Vaughn-Coaxum R, Kolko DJ, Pilkonis PA, Yu L.   +6 more
europepmc   +1 more source

Stopping set elimination for LDPC codes

2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2017
This work studies the Stopping-Set Elimination Problem, namely, given a stopping set, how to remove the fewest erasures so that the remaining erasures can be decoded by belief propagation in k iterations (including k =∞). The NP-hardness of the problem is proven. An approximation algorithm is presented for k = 1.
Anxiao Andrew Jiang, Pulakesh Upadhyaya, Ying Wang, Krishna R. Narayanan, Hongchao Zhou, Jin Sima, Jehoshua Bruck   +6 more
openaire   +3 more sources

Generalized Stopping Sets and Stopping Redundancy

2007 Information Theory and Applications Workshop, 2007
Iterative decoding for linear block codes over erasure channels may be much simpler than optimal decoding but its performance is usually not as good. Here, we present a general iterative decoding technique that gives a more refined trade-off between complexity and performance. In each iteration, a system of equations is solved.
Khaled A.S. Abdel-Ghaffar, Jos H. Weber
openaire   +2 more sources

Pseudocodeword weights and stopping sets

International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings., 2004
We examine the structure of pseudocodewords in Tanner graphs and derive lower bounds of pseudocodeword weights. The weight of a pseudocodeword is related to the size of its support set, which forms a stopping set in the Tanner graph.
Kelley, C, Sridhara, D, Xu, J, Rosenthal, J   +3 more
openaire   +2 more sources

Some of the next articles are maybe not open access.

Related searches:

Stopping Sets of Hermitian Codes

IEEE Transactions on Information Theory, 2016
Combinatorial structures called stopping sets are useful in analyzing the performance of a linear code when coupled with an iterative decoding algorithm over an erasure channel. In this paper, we consider stopping sets of Hermitian codes.
Sarah E. Anderson, Gretchen L. Matthews
openaire   +1 more source