Classover Announces Full Year 2025 Financial Results: Gross Margin Expands, Makes Strategic Advances in AI and Robotics - galvnews.com
Classover Announces Full Year 2025 Financial Results: Gross Margin Expands, Makes Strategic Advances in AI and Robotics galvnews.com
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Approximating Analytically-Intractable Likelihood Densities with Deterministic Arithmetic for Optimal Particle Filtering
arXiv:2512.01023v3 Announce Type: replace-cross Abstract: Particle filtering algorithms have enabled practical solutions to problems in autonomous robotics (self-driving cars, UAVs, warehouse robots), target tracking, and econometrics, with further applications in speech processing and medicine (patient monitoring). Yet, their inherent weakness at representing the likelihood of the observation (which often leads to particle degeneracy) remains unaddressed for real-time resource-constrained systems. Improvements such as the optimal proposal and auxiliary particle filter mitigate this issue under specific circumstances and with increased computational cost. This work presents a new particle filtering method and its implementation, which enables tunably-approximative representation of arbitra
ACCOR: Attention-Enhanced Complex-Valued Contrastive Learning for Occluded Object Classification Using mmWave Radar IQ Signals
arXiv:2512.11556v4 Announce Type: replace Abstract: Millimeter-wave (mmWave) radar provides robust sensing under adverse conditions and can penetrate thin materials for non-visual perception in industrial and robotic settings. Recent work with MIMO mmWave radar has demonstrated its ability to penetrate cardboard packaging for occluded object classification. However, existing models leave room for improvement and extensions across different sensing frequencies. Building on recent work with MIMO radar for occluded object classification, we propose ACCOR, an attention-enhanced complex-valued contrastive learning approach for radar, enabling robust occluded object classification. ACCOR processes complex-valued IQ radar signals via a complex-valued CNN backbone, a multi-head attention layer and
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On the Performance of Physical Layer Security for Continuous-Aperture Array (CAPA) Systems
arXiv:2412.13748v2 Announce Type: replace-cross Abstract: A continuous-aperture array (CAPA)-based secure transmission framework is proposed to enhance physical layer security. Continuous current distributions, or beamformers, are designed to maximize the secrecy transmission rate under a power constraint and to minimize the required transmission power for achieving a specific target secrecy rate. On this basis, the fundamental secrecy performance limits achieved by CAPAs are analyzed by deriving closed-form expressions for the maximum secrecy rate (MSR) and minimum required power (MRP), along with the corresponding optimal current distributions. To provide further insights, asymptotic analyses are performed for the MSR and MRP, which reveals that i) for the MSR, the optimal current distri
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