Results 11 to 20 of about 3,716 (175)

T2* quantification using multi-echo gradient echo sequences: a comparative study of different readout gradients

open access: yesScientific Reports, 2023
To quantify T2*, multiple echoes are typically acquired with a multi-echo gradient echo sequence using either monopolar or bipolar readout gradients. The use of bipolar readout gradients achieves a shorter echo spacing time, enabling the acquisition of a
Seonyeong Shin   +2 more
doaj   +1 more source

Lowering the Cramer-Rao lower bounds of variance in randomized response sampling

open access: yesCommunications in Statistics - Simulation and Computation, 2020
In this paper, we lower the Cramer-Rao Lower bound of variance due to Singh and Sedory (2011, 2012) for the Odumade and Singh (2009) model in the sense that we propose a randomized response model that is more efficient. We investigate the properties of the proposed model under various situations for protection and efficiency.
Tonghui Xu   +2 more
openaire   +1 more source

СRAMER-RAO AND BHATTACHARYYA BOUNDS FOR ACCURACY ESTIMATION OF SUB-PIXEL IMAGE CO-REGISTRATION

open access: yesРадіоелектронні і комп'ютерні системи, 2018
The subject matter of the article is theoretical lower bounds of parameter estimates applied to the problem of image co-registration. The goal is to study and compare the Cramer-Rao and Bhattacharyya bounds.
Виталий Анатольевич Душепа
doaj   +1 more source

The hybrid Cramér-Rao lower bound for simultaneous self-localization and room geometry estimation

open access: yesEURASIP Journal on Advances in Signal Processing, 2021
This paper addresses the problem of tracking a moving source, e.g., a robot, equipped with both receivers and a source, that is tracking its own location and simultaneously estimating the locations of multiple plane reflectors.
Maya Veisman, Yair Noam, Sharon Gannot
doaj   +1 more source

Cramér–Rao lower bound for ATSC signal‐based passive radar systems

open access: yesElectronics Letters, 2019
In multistatic passive radar systems, the Cramér–Rao lower bound (CRLB) can be used to select the optimal illuminator of opportunity so that it provides the best estimation accuracy for target parameters.
M. Alslaimy, G.E. Smith
doaj   +1 more source

Optomechanical parameter estimation

open access: yesNew Journal of Physics, 2013
We propose a statistical framework for the problem of parameter estimation from a noisy optomechanical system. The Cramér–Rao lower bound on the estimation errors in the long-time limit is derived and compared with the errors of radiometer and ...
Shan Zheng Ang   +3 more
doaj   +1 more source

Cramér-Rao Lower Bound of Target Localization Method Based on TOA Measurements

open access: yesXibei Gongye Daxue Xuebao, 2019
The Cramér-Rao bound of target localization method based on time-of-arrival measurements is analyzed. For the localization error analysis, the CRLB is derived under the assumptions that the measurement errors are independent and characterized by zero ...

doaj   +1 more source

On stability of generalized (affine) phase retrieval in the complex case

open access: yesJournal of Inequalities and Applications, 2019
In this paper, we discuss the stability of generalized phase retrieval and generalized affine phase retrieval in the complex case. By the realification method, we obtain the bi-Lipschitz property in the absence of noise case and Cramer–Rao lower bound ...
Zhitao Zhuang
doaj   +1 more source

Recursive joint Cramér‐Rao lower bound for parametric systems with two‐adjacent‐states dependent measurements

open access: yesIET Signal Processing, 2021
Joint Cramér‐Rao lower bound (JCRLB) is very useful for the performance evaluation of joint state and parameter estimation (JSPE) of non‐linear systems, in which the current measurement only depends on the current state.
Xianqing Li   +2 more
doaj   +1 more source

Information-theoretic analysis of Hierarchical Temporal Memory-Spatial Pooler algorithm with a new upper bound for the standard information bottleneck method

open access: yesFrontiers in Computational Neuroscience, 2023
Hierarchical Temporal Memory (HTM) is an unsupervised algorithm in machine learning. It models several fundamental neocortical computational principles.
Shiva Sanati   +2 more
doaj   +1 more source

Home - About - Disclaimer - Privacy