Results 81 to 90 of about 222,299 (265)

Intelligent Processing Methods

open access: yesRUDN Journal of Engineering Research
Nowadays, in the era of information technology, intelligent data processing methods play an important role in various spheres of life. These methods, together with modern algorithms and computer models, allow extracting valuable information from huge ...
Veronika V. Tolmanova, Denis A. Andrikov
doaj   +1 more source

Multilayer Self‐Limiting Electrospray Deposition via Stepped Voltage Bias

open access: yesAdvanced Engineering Materials, EarlyView.
Self‐limiting electrospray deposition (SLED) uses a high voltage to generate and deposit a charged payload on a target surface. The coating retains its charge, repelling newly arriving material. SLED thickness can be decreased by applying a secondary bias to the target.
Madhuri Deb   +3 more
wiley   +1 more source

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

open access: yesAdvanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone   +11 more
wiley   +1 more source

Intermediate Resistive State in Wafer‐Scale Vertical MoS2 Memristors Through Lateral Silver Filament Growth for Artificial Synapse Applications

open access: yesAdvanced Functional Materials, EarlyView.
In MOCVD MoS2 memristors, a current compliance‐regulated Ag filament mechanism is revealed. The filament ruptures spontaneously during volatile switching, while subsequent growth proceeds vertically through the MoS2 layers and then laterally along the van der Waals gaps during nonvolatile switching.
Yuan Fa   +19 more
wiley   +1 more source

Interpolation Methods for Stochastic Processes Spaces

open access: yesAbstract and Applied Analysis, 2013
The scales of classes of stochastic processes are introduced. New interpolation theorems and boundedness of some transforms of stochastic processes are proved. Interpolation method for generously monotonous processes is entered. Conditions and statements
E. Nursultanov, T. Aubakirov
doaj   +1 more source

Functional Methods in Stochastic Systems [PDF]

open access: yes, 2012
Plenary talk presented at Mathematical Modeling and Computational Science.
openaire   +2 more sources

Integration of Low‐Voltage Nanoscale MoS2 Memristors on CMOS Microchips

open access: yesAdvanced Functional Materials, EarlyView.
This article presents the first monolithic integration of nanoscale MoS2‐based memristors into the back‐end‐of‐line of foundry‐fabricated CMOS microchips in a one‐transistor‐one‐resistor (1T1R) architecture. The MoS2‐based 1T1R cells exhibit forming‐free, nonvolatile resistive switching with ultra‐low operating voltages, low cycle‐to‐cycle variability ...
Jimin Lee   +16 more
wiley   +1 more source

Projection methods for stochastic structural dynamics

open access: yesMATEC Web of Conferences, 2018
A set of novel hybrid projection approaches are proposed for approximating the response of stochastic partial differential equations which describe structural dynamic systems.
Pryse Sion Eilir   +2 more
doaj   +1 more source

Three‐Dimensional Anomalous Hall Sensing Enabled By Spin–Orbit Torque Driven Domain Reconfiguration In Chiral Multilayers

open access: yesAdvanced Functional Materials, EarlyView.
A three‐dimensional magnetic field sensor is realized in chiral W/CoFeB/MgO multilayers, where spin–orbit torques reversibly reconfigure homochiral stripe domains. The symmetry of the torque enables offset‐free in‐plane sensing, while the reproducible domain reconfiguration extends the linear out‐of‐plane range. An anomalous Hall readout delivers large‐
Sabri Koraltan   +16 more
wiley   +1 more source

Adam: A Method for Stochastic Optimization

open access: yes, 2014
We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems ...
Kingma, D.P., Ba, L.J.
openaire   +2 more sources

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