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Advanced Science, EarlyView.This work demonstrates a heterojunction‐gated infrared phototransistor for broadband detection from 350 to 1700 nm. By suppressing defect states on nonpolar (100) facets of large PbS quantum dots via hybrid ligand passivation, the device achieves a room‐temperature detectivity of 5.7 × 1013 Jones at 1650 nm.
Hongkun Duan +14 more
wiley +1 more source
Multimodal Wearable Biosensing Meets Multidomain AI: A Pathway to Decentralized Healthcare
Advanced Science, EarlyView.Multimodal biosensing meets multidomain AI. Wearable biosensors capture complementary biochemical and physiological signals, while cross‐device, population‐aware learning aligns noisy, heterogeneous streams. This Review distills key sensing modalities, fusion and calibration strategies, and privacy‐preserving deployment pathways that transform ...
Chenshu Liu +10 more
wiley +1 more source
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Relative entropy fuzzy c-means clustering
Information Sciences, 2014zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zarandi, Mohammad Hossein Fazel +2 more
openaire +3 more sources
Journal of Intelligent & Fuzzy Systems, 2013
Many variants of fuzzy c-means (FCM) clustering method are applied to crisp numbers but only a few of them are extended to non-crisp numbers, mainly due to the fact that the latter needs complicated equations and exhausting calculations. Vector form of fuzzy c-means (VFCM), proposed in this paper, simplifies the FCM clustering method applying to non ...
Hadi, Mahdipour +2 more
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Many variants of fuzzy c-means (FCM) clustering method are applied to crisp numbers but only a few of them are extended to non-crisp numbers, mainly due to the fact that the latter needs complicated equations and exhausting calculations. Vector form of fuzzy c-means (VFCM), proposed in this paper, simplifies the FCM clustering method applying to non ...
Hadi, Mahdipour +2 more
openaire +1 more source
NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society, 2008
Clustering streaming data presents the problem of not having all the data available at one time. Further, the total size of the data may be larger than will fit in the available memory of a typical computer. If the data is very large, it is a challenge to apply fuzzy clustering algorithms to get a partition in a timely manner. In this paper, we present
P. Hore +3 more
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Clustering streaming data presents the problem of not having all the data available at one time. Further, the total size of the data may be larger than will fit in the available memory of a typical computer. If the data is very large, it is a challenge to apply fuzzy clustering algorithms to get a partition in a timely manner. In this paper, we present
P. Hore +3 more
openaire +1 more source
Variable Width Rough-Fuzzy c-Means
2017 13th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), 2017The richness of soft clustering algorithms in the scientific literature reflects from one side the complexity of the underlying problem and from the other the many attempts that have been made to preserve interpretability while modeling vagueness through different theories.
Ferone, Alessio +2 more
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Suppressed fuzzy c-means clustering algorithm
Pattern Recognition Letters, 2003Summary: Based on the defect of rival checked fuzzy \(c\)-means clustering algorithm, a new algorithm: suppressed fuzzy \(c\)-means clustering algorithm is proposed. The new algorithm overcomes the shortcomings of the original algorithm, establishes more natural and more reasonable relationships between hard \(c\)-means clustering algorithm and fuzzy \(
Fan, Jiu-Lun +2 more
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Bi-criteria fuzzy c-means analysis
Fuzzy Sets and Systems, 1994zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wang, Hsiao-Fan +2 more
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Improving Fuzzy C-Means Algorithm using Particle Swarm Optimization and Fuzzy C-Means++
2019 International Conference on Information and Digital Technologies (IDT), 2019The aim of the paper is to improve fuzzy particle swarm optimization. Our modification consists of modifying the hybrid fuzzy particle swarm optimization by replacing the random initialization of centers in the c-means fuzzy section with the FCM++ algorithm.
Olga Chovancova +2 more
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Categorical fuzzy entropy c-means
2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2020Hard and fuzzy clustering algorithms are part of the partition-based clustering family. They are widely used in real-world applications to cluster numerical and categorical data. While in hard clustering an object is assigned to a cluster with certainty, in fuzzy clustering an object can be assigned to different clusters given a membership degree.
Djiberou Mahamadou, Abdoul Jalil +3 more
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