Praveen Kumar Singh | Searcharxiv

arXiv2026

Chaos in Liquid Crystal Directrons

Biological systems often operate at the boundary between order and chaos, transitioning from directed to irregular dynamics to achieve adaptability and robustness. Reproducing such transitions in artificial soft matter remains a central challenge. Here, we report a biomimetic regime of directron dynamics in achiral nematic liquid crystals, in which coherent, directed motion collectively evolves into chaos. Driven by multi-directron interactions, the system develops coexisting directron families with competing trajectories, displaying randomized motion, dynamic assembly formation and spontaneous fission of high energy to low energy daughter directrons - all of which mimics the phenotypic diversity observed in biological groups. Above a critical electric field, these interactions drive the system into a chaotic state that is distinct from the directed behaviours reported previously. We further introduce a minimal dipole-based model that qualitatively captures the underlying physics of this transition. Together, our results establish an artificial active system in which chaos emerges intrinsically from interactions, offering a versatile platform to study biological dynamics and opening new avenues for liquid-crystal-based soft-matter applications involving adaptive transport, cargo delivery, and energy transduction

cond-mat.soft↗

arXiv2014

Block Outlier Methods for Malicious User Detection in Cooperative Spectrum Sensing

Block outlier detection methods, based on Tietjen-Moore (TM) and Shapiro-Wilk (SW) tests, are proposed to detect and suppress spectrum sensing data falsification (SSDF) attacks by malicious users in cooperative spectrum sensing. First, we consider basic and statistical SSDF attacks, where the malicious users attack independently. Then we propose a new SSDF attack, which involves cooperation among malicious users by masking. In practice, the number of malicious users is unknown. Thus, it is necessary to estimate the number of malicious users, which is found using clustering and largest gap method. However, we show using Monte Carlo simulations that, these methods fail to estimate the exact number of malicious users when they cooperate. To overcome this, we propose a modified largest gap method.

stat.AP↗