Results 31 to 40 of about 241,859 (276)
Identification of nonlinear lateral flow immunoassay state-space models via particle filter approach [PDF]
, 2012 This is the post-print of the Article. The official published version can be accessed from the link below - Copyright @ 2012 IEEEIn this paper, the particle filtering approach is used, together with the kernel smoothing method, to identify the state ...
Du, M, Li, Y, Liu, X, Wang, Z, Zeng, N
core +2 more sources
, 2021 Both the particle and Kalman filters attempt to approximate the minimum mean-square error (MMSE) estimate of the time-varying parameter. In this scenario, a prior model of the time evolution of the parameter of interest is assumed before the MMSE estimation takes place.
openaire +3 more sources
Optimal Intermittent Particle Filter
IEEE Transactions on Signal Processing, 2022 13 pages, 7 figures, 3 ...
Antoine Aspeel +3 more
openaire +3 more sources
Bernoulli, 2013 Subject of this master thesis is the presentation of a methodology, called Particle filters, a class of Sequential Monte Carlo (SMC) methods used to sample sequentially from a sequence of high-dimensional and complex probability distributions. A cloud of particles evolves over time as new observations become available to approximate the posterior ...
+7 more sources
Bridging the ensemble Kalman and particle filter
, 2012 In many applications of Monte Carlo nonlinear filtering, the propagation step is computationally expensive, and hence, the sample size is limited. With small sample sizes, the update step becomes crucial.
Frei, Marco, Künsch, Hans R.
core +1 more source
Particle filtering based semi-blind estimation for MIMO-OFDM time-varying channel
Tongxin xuebao, 2007 A semi-blind channel estimation method based on the particle filtering was proposed for the MIMO-OFDM wireless communication system.By optimizing the particles distribution locally,the traditional particle filtering algorithm was modified,and a local ...
JING Yuan, YIN Fu-liang, ZENG Shuo
doaj +2 more sources
Particle Learning and Smoothing
, 2010 Particle learning (PL) provides state filtering, sequential parameter learning and smoothing in a general class of state space models. Our approach extends existing particle methods by incorporating the estimation of static parameters via a fully-adapted
Carvalho, Carlos M. +3 more
core +2 more sources
Mathematical control of complex systems 2013 [PDF]
, 2014 Mathematical control of complex systems have already become an ideal research area for control engineers, mathematicians, computer scientists, and biologists to understand, manage, analyze, and interpret functional information/dynamical behaviours from ...
Dong, H +5 more
core +4 more sources
Open Physics, 2017 With the purpose of reinforcing correlation analysis of risk assessment threat factors, a dynamic assessment method of safety risks based on particle filtering is proposed, which takes threat analysis as the core.
Cui Jia +3 more
doaj +1 more source
Cell wall target fragment discovery using a low‐cost, minimal fragment library
FEBS Letters, EarlyView.LoCoFrag100 is a fragment library made up of 100 different compounds. Similarity between the fragments is minimized and 10 different fragments are mixed into a single cocktail, which is soaked to protein crystals. These crystals are analysed by X‐ray crystallography, revealing the binding modes of the bound fragment ligands.
Kaizhou Yan +5 more
wiley +1 more source