Dynamic LR and QR factorization are fundamental problems that exist widely in the control field. However, the existing solutions under noises are lack of convergence speed and anti-noise ability. To this end, this paper incorporates the advantages of Dynamic-Coefficient Type (DCT) and Integration-Enhance Type (IET) Zeroing Neural Dynamic (ZND), and proposes an Adaptive and Robust-Enhanced Neural Dynamic (AREND). On this basis, a Strategy of Integration-Coupling (SIC) is proposed to address multiple error function problems, improving model stability and application scenarios. This strategy is experimentally proven to be effective and has potential expansion capability. After that, the convergence and robustness of our AREND is theoretically analyzed. Furthermore, the proposed AREND is verified by numerical experiments of low-to-high dimensional factorization in comparison with existing solutions. Finally, the real-time 3-D Angle of Arrival (AoA) localization in multiple high-noise conditions, is validated to the accuracy of the proposed model. Code is available at https://github.com/Alana2a3/AREND-Code-Implementation .
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11561276 | PMC |
| http://dx.doi.org/10.1038/s41598-024-76537-0 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Basic Science and Pathogenesis.
Alzheimers Dement
December 2024
Neurochemistry Laboratory, Department of Clinical Chemistry, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, North Holland, Netherlands.
Background: Transcriptomic and pathological studies indicate that microglia play a key role in the progression of Alzheimer's disease (AD). Throughout the stages of the AD continuum, there may be varying microglia phenotypes, such as the disease-associated microglia (DAM). Microglia proteins have been detected in cerebrospinal-fluid (CSF), providing a quantifiable avenue for potential stage-detection.
View Article and Find Full Text PDFConstructing Dynamical Symmetries for Quantum Computing: Applications to Coherent Dynamics in Coupled Quantum Dots.
Nanomaterials (Basel)
December 2024
Theoretical Physical Chemistry, UR MOLSYS, University of Liege, B4000 Liège, Belgium.
Dynamical symmetries, time-dependent operators that almost commute with the Hamiltonian, extend the role of ordinary symmetries. Motivated by progress in quantum technologies, we illustrate a practical algebraic approach to computing such time-dependent operators. Explicitly we expand them as a linear combination of time-independent operators with time-dependent coefficients.
View Article and Find Full Text PDFSimultaneous Prediction of Multiple Unmeasured Muscle Activations through Synergy Extrapolation.
J Biomech Eng
December 2024
Human-Centric Design Research Lab, Department of Mechanical Engineering, Texas Tech University, Lubbock, TX 79409, USA.
Estimating muscle forces is crucial for understanding joint dynamics and improving rehabilitation strategies, particularly for patients with neurological disorders who suffer from impaired muscle function. Muscle forces are directly proportional to muscle activations, which can be obtained using electromyography (EMG). EMG-driven modeling estimates muscle forces and joint moments from muscle activations.
View Article and Find Full Text PDFElectronic Coherences in Molecules: The Projected Nuclear Quantum Momentum as a Hidden Agent.
Phys Rev Lett
December 2024
Department of Physics, Rutgers University, Newark, New Jersey 07102, USA.
Electronic coherences are key to understanding and controlling photoinduced molecular transformations. We identify a crucial quantum-mechanical feature of electron-nuclear correlation, the projected nuclear quantum momenta, essential to capture the correct coherence behavior. For simulations, we show that, unlike traditional trajectory-based schemes, exact-factorization-based methods approximate these correlation terms and correctly capture electronic coherences in a range of situations, including their spatial dependence, an important aspect that influences subsequent electron dynamics and that is becoming accessible in more experiments.
View Article and Find Full Text PDFComputational modeling of superparamagnetic nanoparticle-based (affinity) diagnostics.
Front Bioeng Biotechnol
December 2024
Vascularized Composite Allotransplantation Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
Introduction: Magnetic nanoparticles (MNPs), particularly iron oxide nanoparticles (IONPs), are renowned for their superparamagnetic behavior, allowing precise control under external magnetic fields. This characteristic makes them ideal for biomedical applications, including diagnostics and drug delivery. Superparamagnetic IONPs, which exhibit magnetization only in the presence of an external field, can be functionalized with ligands for targeted affinity diagnostics.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!