Characterization of abdominally acquired uterine electrical signals in humans, using a non-linear analytic method

2006 ◽  
Vol 44 (1-2) ◽  
pp. 117-123 ◽  
Author(s):  
William L. Maner ◽  
Lynette B. MacKay ◽  
George R. Saade ◽  
Robert E. Garfield
Keyword(s):  
Analytic Method ◽  
Electrical Signals ◽  
Non Linear

Non-linear imaging and characterization of atherosclerotic arterial tissue using combined SHG and FLIM microscopy

2015 ◽  
Vol 8 (4) ◽  
pp. 347-356 ◽  
Author(s):  
Riccardo Cicchi ◽  
Enrico Baria ◽  
Christian Matthäus ◽  
Marta Lange ◽  
Annika Lattermann ◽  
...  
Keyword(s):  
Arterial Tissue ◽  
Non Linear

Towards non-linear inversion for characterization of time-lapse phenomena through numerical modelling

2003 ◽  
Vol 51 (4) ◽  
pp. 285-293 ◽  
Author(s):  
A. Abubakar ◽  
P.M. van den Berg ◽  
J.T. Fokkema
Keyword(s):  
Numerical Modelling ◽  
Time Lapse ◽  
Linear Inversion ◽  
Non Linear

Adsorption of Iron (II) Ion by Using Magnetite-Bentonite-Based Monolith from Water

2021 ◽  
Vol 892 ◽  
pp. 10-16
Author(s):  
Ismi Nurul ◽  
Syamsuddin Yanna ◽  
Adisalamun ◽  
Aulia Sugianto Veneza ◽  
Darmadi
Keyword(s):  
Magnetic Properties ◽  
Magnetic Particles ◽  
Solution Concentration ◽  
Iron Solution ◽  
Monolayer Adsorption ◽  
Coprecipitation Process ◽  
Non Linear ◽  
Magnetic Loading ◽  
Langmuir Constants

In this study, iron removal was carried out by the adsorption process as a well-known method of removing heavy metal. Natural bentonite with magnetic properties in a monolithic form or Magnetite-Bentonite-based Monolith (MBM) adsorbent was used as an adsorbent to remove Iron (II) ion from the aqueous solution. The magnetic properties of adsorbents are obtained by adding magnetite (Fe3O4), which is synthesized by the coprecipitation process. The characterization of magnetic properties was performed using the Vibrating Sample Magnetometer (VSM). VSM results showed that the magnetic particles were ferromagnetic. Adsorption efficiency, isotherm model, and adsorption kinetics were investigated in a batch system with iron solution concentration varied from 2 to 10 mg/L and magnetite loading at 2% and 5% w/w. The highest removal efficiency obtained reached 89% with a 5% magnetite loading. The best fit to the data was obtained with the Langmuir isotherm (non-linear) with maximum monolayer adsorption capacity (Qo) at 5% magnetic loading MBM adsorbent is 0.203 mg/g with Langmuir constants KL and aL are 2.055 L/g and 10.122 L/mg respectively. The pseudo-first-order (non-linear) kinetic model provides the best correlation of the experimental data with the rate of adsorption (k1) with magnetite loading 2% and 5%, respectively are 0.024 min-1 and 0.022 min-1.

Synthesis, structure and characterization of a hybrid centrosymmetric material (4-dimethylaminopyridinium nitrate gallic acid monohydrate) well-designed for non-linear optics

2018 ◽  
Vol 1151 ◽  
pp. 126-134 ◽  
Author(s):  
Nasreddine Ennaceur ◽  
Boutheina Jalel ◽  
Rokaya Henchiri ◽  
Marie Cordier ◽  
Isabelle Ledoux-Rak
Keyword(s):  
Gallic Acid ◽  
Linear Optics ◽  
Non Linear Optics ◽  
Non Linear

In situ 3D characterization of membrane fouling by yeast suspensions using two-photon femtosecond near infrared non-linear optical imaging

2006 ◽  
Vol 280 (1-2) ◽  
pp. 124-133 ◽  
Author(s):  
David Hughes ◽  
Uday K. Tirlapur ◽  
Robert Field ◽  
Zhanfeng Cui
Keyword(s):  
Optical Imaging ◽  
Membrane Fouling ◽  
Near Infrared ◽  
Two Photon ◽  
Non Linear ◽  
Linear Optical

A NEW PARAMETER ESTIMATION METHOD FOR ONLINE SOFT TISSUE CHARACTERIZATION

2016 ◽  
Vol 16 (08) ◽  
pp. 1640019 ◽  
Author(s):  
JAEHYUN SHIN ◽  
YONGMIN ZHONG ◽  
JULIAN SMITH ◽  
CHENGFAN GU
Keyword(s):  
Soft Tissue ◽  
Linear Regression ◽  
Tissue Characterization ◽  
Soft Tissues ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Regression Method ◽  
Linear Regression Method ◽  
Non Linear

Dynamic soft tissue characterization is of importance to robotic-assisted minimally invasive surgery. The traditional linear regression method is unsuited to handle the non-linear Hunt–Crossley (HC) model and its linearization process involves a linearization error. This paper presents a new non-linear estimation method for dynamic characterization of mechanical properties of soft tissues. In order to deal with non-linear and dynamic conditions involved in soft tissue characterization, this method improves the non-linearity and dynamics of the HC model by treating parameter [Formula: see text] as independent variable. Based on this, an unscented Kalman filter is developed for online estimation of soft tissue parameters. Simulations and comparison analysis demonstrate that the proposed method is able to estimate mechanical parameters for both homogeneous tissues and heterogeneous and multi-layer tissues, and the achieved performance is much better than that of the linear regression method.

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