Energy‐Aware Perturbation Optimization for Memristor‐Array Convolutional Neural Networks
Memristor‐array inference becomes more energy efficient when layer inputs are reshaped before computation. Sinusoidal perturbation encoding with dual‐threshold screening reduces active voltage pulses and contracts ADC input‐current ranges, jointly lowering crossbar and peripheral energy while preserving accuracy across hardware MNIST validation, deep ...
Ao Xu +6 more
wiley +1 more source
Coded Aperture Optimization in X-Ray Computed Tomography via Sparse Covariance Matrix Estimation. [PDF]
Jiang Y +6 more
europepmc +1 more source
Nonlinear Shrinkage of the Covariance Matrix for Portfolio Selection: Markowitz Meets Goldilocks
Olivier Ledoit, Michael Wolf
semanticscholar +1 more source
A scalable distributed observer framework for LiDAR sensor networks is presented for robust tracking and pose estimation of mobile robots. By combining adaptable 3D detection, clustering‐based communication reduction, and intermittent inertial fusion, accurate and efficient estimation is achieved under occlusions and sensing constraints, with formally ...
Isabella Luppi, Ehsan Hashemi
wiley +1 more source
Efficient epistasis inference via higher-order covariance matrix factorization. [PDF]
Shimagaki KS, Barton JP.
europepmc +1 more source
A smartphone‐embedded fabric‐strip sensing interface captures side‐pressure sequences and grip‐position patterns for behavioral authentication. Probability‐based soft stacking integrates class‐wise predictive probabilities from multiple learners to improve verification‐style discrimination, supporting a sensor‐integrated mobile‐security framework that ...
Wonki Hong
wiley +1 more source
Adaptive Covariance Matrix for UAV-Based Visual-Inertial Navigation Systems Using Gaussian Formulas. [PDF]
Cong Y +7 more
europepmc +1 more source
A Scalable and Resource‐Efficient Pipelined p‐Computer for Probabilistic Ising Machines
(a) Block diagram of the portfolio optimization problem: given M assets, the goal is to determine the optimal weights w that maximize the expected return (based on the mean historical assets return u), while minimizing the risk, quantified by the assets covariance matrix S.
Deborah Volpe +9 more
wiley +1 more source
Robust adaptive beamforming based on covariance matrix reconstruction with annular uncertainty set constraints. [PDF]
Xing G, Yao Z, Wei H, Hu Y.
europepmc +1 more source
A Multidimensional Perspective on Network Conditions in Telesurgery
A multidimensional framework systematically evaluates how combined network impairments influence telesurgical performance. By jointly analyzing latency, jitter, bandwidth, and packet loss, the proposed methodology identifies relevant network condition parameters and their interactions, supporting the design of reliable and safe telesurgical systems ...
Florian Heemeyer +4 more
wiley +1 more source

