Results 91 to 100 of about 4,385,040 (261)
The Resistance Distance and Kirchhoff Index on Quadrilateral Graph and Pentagonal Graph
IEEE Access, 2019 The quadrilateral graph Q(G) is obtained from G by replacing each edge in G with two parallel paths of length 1 and 3, whereas the pentagonal graph W(G) is obtained from G by replacing each edge in G with two parallel paths of length 1 and 4.
Qun Liu
doaj +1 more source
Advanced Intelligent Discovery, EarlyView.Capacitive, charge‐domain compute‐in‐memory (CIM) stores weights as capacitance,eliminating DC sneak paths and IR‐drop, yielding near‐zero standbypower. In this perspective, we present a device to systems level performance analysis of most promising architectures and predict apathway for upscaling capacitive CIM for sustainable edge computing ...
Kapil Bhardwaj +2 more
wiley +1 more source
A Unifying Approach to Self‐Organizing Systems Interacting via Conservation Laws
Advanced Intelligent Discovery, EarlyView.The article develops a unified way to model and analyze self‐organizing systems whose interactions are constrained by conservation laws. It represents physical/biological/engineered networks as graphs and builds projection operators (from incidence/cycle structure) that enforce those constraints and decompose network variables into constrained versus ...
F. Barrows +7 more
wiley +1 more source
Low-Mach-number--slenderness limit for elastic Cosserat rods
, 2015 This paper deals with the relation of the dynamic elastic Cosserat rod model and the Kirchhoff beam equations. We show that the Kirchhoff beam without angular inertia is the asymptotic limit of the Cosserat rod, as the slenderness parameter (ratio ...
Baus, Franziska +3 more
core
Hardware‐Based On‐Chip Learning Using a Ferroelectric AND‐Type Array With Random Synaptic Weights
Advanced Intelligent Systems, EarlyView.This work demonstrates an energy‐efficient on‐chip learning system using an Metal‐Ferroelectric‐Insulator‐Semiconductor FeAND synaptic array. By employing a feedback alignment scheme with a separate backward array using fixed random weights, the system overcomes directional limitations of AND‐type arrays and achieves robust, low‐power learning suitable
Minsuk Song +8 more
wiley +1 more source
Further Results on Resistance Distance and Kirchhoff Index in Electric Networks
Discrete Dynamics in Nature and Society, 2016 In electric circuit theory, it is of great interest to compute the effective resistance between any pairs of vertices of a network, as well as the Kirchhoff index.
Qun Liu, Jia-Bao Liu, Jinde Cao
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Resistance Distance and Kirchhoff Index of
IEEE Access, 2019 Let Q(G) be the graph derived from G by inserting a new vertex into every edge of G and by connecting edges of these new vertices that are on adjacent edges of G, and Q-double join of G, G1 and G2 denoted by GQ v{G1, G2}, is the graph derived from Q(G ...
Weizhong Wang, Tingyan Ma, Jia-Bao Liu
doaj +1 more source
Device‐Level Implementation of Reservoir Computing With Memristors
Advanced Intelligent Systems, EarlyView.Reservoir computing (RC) is an emerging computing scheme that employs a reservoir and a single readout layer, which can be actualized in the nanoscale with memristors. As a comprehensive overview, the principles of RC and the switching mechanisms of memristors are discussed, followed by actual demonstrations of memristor‐based RC and the remaining ...
Sunbeom Park, Hyojung Kim, Ho Won Jang
wiley +1 more source
A Novel Milli‐Scale Magnetic Robot Exploiting Rotation for Controlled Magnetic Particles Release
Advanced Intelligent Systems, EarlyView.Delivering magnetic particles can become a game changer in minimally invasive medicine. To cope with this challenge, a magnetically actuated milli‐scale carrier leveraging rotation to perform on‐demand tunable release of magnetic particles across multiple release events is presented.
Giordano De Angelis +3 more
wiley +1 more source
A Flexible and Energy‐Efficient Compute‐in‐Memory Accelerator for Kolmogorov–Arnold Networks
Advanced Intelligent Systems, EarlyView.This article presents KA‐CIM, a compute‐in‐memory accelerator for Kolmogorov–Arnold Networks (KANs). It enables flexible and efficient computation of arbitrary nonlinear functions through cross‐layer co‐optimization from algorithm to device. KA‐CIM surpasses CPU, ASIC, VMM‐CIM, and prior KAN accelerators by 1–3 orders of magnitude in energy‐delay ...
Chirag Sudarshan +6 more
wiley +1 more source