17 papers · ranked by Valyu relevance
Yuhao Chen, Boxiang He, Junshan Luo, Shilian Wang + 2 more
—Specific emitter identification (SEI) distinguishes emitters by utilizing hardware-induced signal imperfections. However, conventional SEI techniques are primarily designed for single-emitter scenarios. This poses a fundamental limitation in distributed wireless networks, where simultaneous transmissions from multiple…
Mohd Saif Ali Khan, Karthik RM, Samar Agnihotri
Pilot contamination remains a major bottleneck in realizing the full potential of distributed massive MIMO systems. We propose two dynamic and scalable pilot assignment schemes designed for practical deployment in such networks. First, we present a low-complexity centralized scheme that sequentially assigns pilots to…
Jie Yuan, Yue Wang, Hao Ma, Wentao Liu + 1 more
Federated learning enables distributed model training across edge nodes without direct raw data sharing, but model parameter transmission still poses significant privacy risks. To address this vulnerability, a Distributed Logistic Regression Gaussian Perturbation (DLGP) algorithm is proposed, which integrates the…
Peter G. Hawkins, Eli M. Swanson, Megan Feichtel
The size of individual single cell samples continues to grow with advancing technologies, as do the number of samples included in individual experiments and across organizations. This presents challenges for processing this data at scale, both in terms of computational throughput and the required size of the machines…
Xin Chen, Jiale Li, Heinrich Wörtche
A robust multi-sensor recursive Expectation-Maximization (RMSREM) algorithm is proposed in this paper for autoregressive eXogenous (ARX) models, addressing the challenges of heavy-tailed noise, as well as the difficulty in simultaneously processing multi-sensor information. First, for the potential outliers in…
Authors not listed
Traditional electron-configuration notation (e.g. 1s^2 2s^2 2p^6) compresses multi-electron quantum information into integer occupancies that convey allowed maxima and most-probable arrangements but obscure the underlying probabilistic distribution and the spread of possible measurement outcomes. We present a…
David Silva-Sánchez, Erik H. Thiede, Roy R. Lederman, Pilar Cossio
Biomolecules are inherently dynamic, and understanding their conformational ensemble distributions is essential for understanding their dynamics and biological roles. Cryo-electron microscopy (cryo-EM), a technique that images individual biomolecules frozen in a thin layer of amorphous ice, has emerged as a leading…
Hirotaka Nishikori, Nobutaka Ito, Kouei Yamaoka, Norihiro Takamune + 1 more
Distributed microphone arrays composed of multiple subarrays enable blind source separation over a wide spatial area. Directly applying fast multichannel nonnegative matrix factorization (FastMNMF) to all subarrays can exploit observations from all subarrays, but it requires repeated inversions of large matrices…
Yuan Xu, Haoran Yin, Maosheng Yang, Lei Deng + 2 more
To increase information accuracy when using ultrawide-band (UWB) localization for robotic dogs, we introduce a switching method for a Student’s t-distributed extended Kalman filter (EKF) that achieves UWB localization under colored measurement noise (CMN). First, a distributed UWB localization framework under CMN is…
Angela Cortecchia, Davide Domini, Giovanni Ciatto, Roberto Casadei + 2 more
State estimation from uncertain, distributed observations is central in many cyber-physical applications. While Distributed Particle Filtering (DPF) algorithms address nonlinear and non-Gaussian estimations in distributed settings, most solutions remain tied to specific architectures and communication assumptions…
Said Mikki, Mark Girolami, Robertas Alzbutas, Hussein Rappel
As a rigorous and comprehensive foundation for electromagnetic information theory (EIT), we develop a general theory that elucidates the universal stochastic structure of radiated electromagnetic (EM) fields and induced currents in generic EM information transmission systems. The framework encompasses arbitrary random…
Kurt A. Vedros, Aleksandar Vakanski, Domenic J. Forte, Constantinos Kolias + 4 more
Side-Channel-based Anomaly Detection (SCAD) offers a powerful and non-intrusive means of detecting unauthorized behavior in IoT and cyber-physical systems. It leverages signals that emerge from physical activity-such as electromagnetic (EM) emissions or power consumption traces-as passive indicators of software…
SI Dimitriadis
Objective: Brain activity is measured using noninvasive electrophysiological techniques, such as electroencephalography (EEG) and magnetoencephalography (MEG). Data recorded from sensors outside the skull are regularly transformed into a virtual source space. Brain activity is typically parcellated into anatomical…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Mingyu Hu, Nan Liu, Wei Kang
—Over-the-air computation (AirComp) is a key enabler for distributed optimization, since it leverages analog waveform superposition to perform aggregation and thereby mitigates the communication bottleneck caused by iterative information exchange. However, AirComp is sensitive to wireless environment and conventional…
Sean O’Connell, Jonathan A. Michaels, Runming Wang, Sahit Mamidipaka + 6 more
Understanding how neural signals control muscle activity during behavior is a key challenge in motor neuroscience. To this end, recent advances in intramuscular multielectrode arrays have enabled high-quality multichannel recordings of many motor unit action potentials (MUAPs) in freely moving subjects. However…
Authors not listed
Olfaction arises from the interaction of odorants with olfactory receptors, a process shaped by molecular geometry, electron distribution, and conformational preference. We present ConfDENSE, a Set2Set enhanced PointNet model that learns directly from Hirshfeld promolecule electron-density point clouds, preserving full…