New Jensen–Shannon divergence measures for intuitionistic fuzzy sets with the construction of a parametric intuitionistic fuzzy TOPSIS by Xinxing Wu, Qian Liu, Lantian Liu, Miin-Shen Yang, Xu Zhang

“…Inspired by this, a new Jensen–Shannon divergence measure for intuitionistic fuzzy sets (IFSs) is introduced and some basic properties for this new divergence measure are obtained. …”
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The continous Legendre transform, its inverse transform, and applications by P. L. Butzer, R. L. Stens, M. Wehrens

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Compressive narrowband interference detection and parameter estimation in direct sequence spread spectrum communication by Zengkai Shi, Yongshun Zhang, Zhaoyong Qian, Xiaoshuang Sun, Xiangtai Ma

“…Abstract The Shannon‐Nyquist sampling theorem that is based on narrowband interference (NBI) detection and parameter estimation methods in direct sequence spread spectrum (DSSS) communication is limited by the high sampling rate. …”
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Selective learning for sensing using shift-invariant spectrally stable undersampled networks by Ankur Verma, Ayush Goyal, Sanjay Sarma, Soundar Kumara

“…Abstract The amount of data collected for sensing tasks in scientific computing is based on the Shannon-Nyquist sampling theorem proposed in the 1940s. …”
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Application of compressive sensing techniques for advanced image processing and digital image transmission by Stefanović Nenad, Sazdić-Jotić Boban, Orlić Vladimir, Mladenović Vladimir, Ćirković Stefan

“…By leveraging the inherent sparsity in natural images, CS allows for significant reductions in the amount of data required for accurate reconstruction, thereby overcoming the limitations imposed by the traditional Shannon-Nyquist sampling theorem. We explore the theoretical foundations of CS, including the principles of sparsity and incoherence, and provide a detailed overview of the Orthogonal Matching Pursuit (OMP) algorithm, a prominent greedy algorithm used for sparse signal recovery. …”
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