Results 51 to 60 of about 4,036 (224)

Low‐Power Control Of Resistance Switching Transitions in First‐Order Memristors

open access: yesAdvanced Electronic Materials, EarlyView.
Joule losses are a serious concern in modern integrated circuit design. In this regard, minimizing the energy necessary for programming memristors should be handled with care. This manuscript presents an optimal control framework, allowing to derive energy‐efficient programming voltage protocols for resistance switching devices. Following this approach,
Valeriy A. Slipko   +3 more
wiley   +1 more source

Enhanced High Dimensionality and the Information Processing Capacity in Interfered Spin Wave‐Based Reservoir Computing, Achieved With Eight Detectors

open access: yesAdvanced Electronic Materials, EarlyView.
Physical reservoir computing (PRC) based on spin wave interference has demonstrated high computational performance, yet room for improvement remains. In this study, we fabricated this concept PRC with eight detectors and evaluated the impact of the number of detectors using a chaotic time series prediction task.
Sota Hikasa   +6 more
wiley   +1 more source

On generalized Frame-Stewart numbers [PDF]

open access: yes, 2012
13 pages ; 3 figuresInternational audienceFor the multi-peg Tower of Hanoi problem with $k \geqslant 4$ pegs, so far the best solution is obtained by the Stewart's algorithm based on the the following recurrence relation: $\mathrm{S}_k(n)=\min_{1 ...
Matsuura, Akihiro   +3 more
core   +1 more source

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

open access: yesAdvanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu   +4 more
wiley   +1 more source

DeepMapper: Attention‐Based AutoEncoder for System Identification in Wound Healing and Stage Prediction

open access: yesAdvanced Intelligent Discovery, EarlyView.
The authors develop a deep learning model for real‐time tracking of wound progression. The deep learning framework maps the nonlinear evolution of a time series of images to a latent space, where they learn a linear representation of the dynamics. The linear model is interpretable and suitable for applications in feedback control.
Fan Lu   +11 more
wiley   +1 more source

Indicator method for a recurrence relation for order statistics [PDF]

open access: yes
An indicator method is used to derive a recurrence relation satisfied by the distribution of order statistics from n random variables having an arbitrary joint distribution.Order statistics recurrence relation indicator of ...
Balakrishnan, N., Balasubramanian, K.
core  

Physical uniqueness of higher-order Korteweg-de Vries theory for continuously stratified fluids without background shear [PDF]

open access: yes, 2017
The 2nd-order Korteweg-de Vries (KdV) equation and the Gardner (or extended KdV) equation are often used to investigate internal solitary waves, commonly observed in oceans and lakes.
Shimizu,Kenji, Kenji Shimizu
core   +1 more source

An Autonomous Large Language Model‐Agent Framework for Transparent and Local Time Series Forecasting

open access: yesAdvanced Intelligent Discovery, EarlyView.
Architecture of the proposed large language model (LLM)‐based agent framework for autonomous time series forecasting in thermal power generation systems. The framework operates through a vertical pipeline initiated by natural language queries from users, which are processed by the LLM Agent Core powered by Llama.cpp and a ReAct loop with persistent ...
William Gouvêa Buratto   +5 more
wiley   +1 more source

On recurrence relations for order statistics [PDF]

open access: yes
The main purpose of this paper is to provide a unified approach to the treatment of linear recurrence relations for single or pairs of order statistics.
David, H. A.
core  

On the differences of the generalized factorials at an arbitrary point and their combinatorial applications [PDF]

open access: yes, 1983
The nth order difference [Δhn(x)m,g]x=a, where Δh is the difference operator with increment h defined by Δhf(x) = f(x+h)−f(x) and (x)m,g = x(x−g)(x−2g)…(x−mg+g) is the generalized factorial of degree m and increment g, is the subject of this paper.
Koutras, M., Charalambides, Ch.A.
core   +1 more source

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