Results 141 to 150 of about 1,958,095 (294)
Advanced Functional Materials, EarlyView.The physical realization of artificial neurons is a critical challenge for energy‐efficient neuromorphic computing. This review presents a comprehensive analysis of the evolution of artificial neuron implementations from conventional CMOS to emerging post‐CMOS technologies.
Kannan Udaya Mohanan +4 more
wiley +1 more source
Tensor nuclear norm minimization with linear constraints
We study the tensor nuclear norm minimization with general linear constraints, a fundamental model for low-rank tensor optimization that includes many practical applications in computer vision, recommendation systems, revenue management, transportation research.
Chen, Renjie, Zhang, Nanxi, Jiang, Bo
openaire +2 more sources
Advanced Functional Materials, EarlyView.This study advances SafeWax, a bio‐inspired, fatty‐acids‐based superhydrophobic coating, by identifying an agriculturally compliant solvent and revealing how solvent‐controlled Fatty acid crystallization governs the coating's microscopic structure and performance.
Niv Ben‐Arie +13 more
wiley +1 more source
Fast and Accurate Matrix Completion via Truncated Nuclear Norm Regularization
, 2013 Recovering a large matrix from a small subset of its entries is a challenging problem arising in many real applications, such as image inpainting and recommender systems.
Hu, Yao +4 more
core +1 more source
Advanced Functional Materials, EarlyView.A defect‐engineered Ag/Gd2O3:Nb2O5/Pt rare earth composite oxide memristor enables stable multilevel reservoir states through pulse driven conductance modulation. Experimentally measured device responses are incorporated into a device aware reservoir computing framework for CIFAR‐100 image classification, highlighting the potential of rare earth ...
Hammad Ghazanfar +9 more
wiley +1 more source
On Nuclear Norm Minimization in System Identification
, 2016 In system identification we model dynamical systems from measured data. This data-driven approach to modelling is useful since many real-world systems are difficult to model with physical principles. Hence, a need for system identification arises in many applications involving simulation, prediction, and model-based control.
openaire +1 more source
In this paper, we study the problem of recovering two unknown signals from their convolution, which is commonly referred to as blind deconvolution.
Krahmer, Felix +3 more
core +1 more source
System identification via Nuclear norm regularization [PDF]
, 2014 University of Minnesota M.S. thesis. July 2014. Major: Electrical Engineering. Advisor: Mihailo R. Jovanovic. 1 computer file (PDF); vi, 29 pages.We study the subspace method for system identification, and look at algorithms that rely on nuclear norm ...
Deshmane, Harshad
core
Robust polynomial classifier using L 1-norm minimization
, 2010 In this paper we present a robust polynomial classifier based on L1-norm minimization. We do so by reformulating the classifier training process as a linear programming problem. Due to the inherent insensitivity of the L1-norm to influential observations,
T. Shanableh +3 more
core +1 more source
Kinetics‐Controlled Architecture of Binder‐Free Organic Electrodes for Enhanced Ion Transport
Advanced Functional Materials, EarlyView.Pulse reverse electrodeposition controls nucleation and growth to create hierarchically porous Py‐derived oligomeric electrodes with enhanced ionic accessibility and reduced transport resistance. The resulting binder‐free architectures translate these transport advantages into practical sodium‐storage functionality.
Satendra Kumar +1 more
wiley +1 more source