2D AMESING multi-echo 31P-MRSI of the liver at 7T allows transverse relaxation assessment and T2-weighted averaging for improved SNR

2016 ◽  
Vol 34 (2) ◽  
pp. 219-226 ◽  
Author(s):  
Jurgen Henk Runge ◽  
Wybe J.M. van der Kemp ◽  
Dennis W.J. Klomp ◽  
Peter R. Luijten ◽  
Aart J. Nederveen ◽  
...  
Keyword(s):  
Weighted Averaging ◽  
Transverse Relaxation ◽  
31P Mrsi

The Effect of Kalman-Weighted Averaging and Artifact Rejection on Residual Noise During Auditory Brainstem Response Testing

2019 ◽  
Vol 28 (1) ◽  
pp. 114-124
Author(s):  
Linda W. Norrix ◽  
Julie Thein ◽  
David Velenovsky
Keyword(s):  
Repeated Measures ◽  
Auditory Brainstem ◽  
Weighted Averaging ◽  
Auditory Brainstem Responses ◽  
Residual Noise ◽  
Electrophysiological Recordings ◽  
Artifact Rejection ◽  
Brainstem Response ◽  
Active Motor ◽  
Averaging Time

Purpose Low residual noise (RN) levels are critically important when obtaining electrophysiological recordings of threshold auditory brainstem responses. In this study, we examine the effectiveness and efficiency of Kalman-weighted averaging (KWA) implemented on the Vivosonic Integrity System and artifact rejection (AR) implemented on the Intelligent Hearing Systems SmartEP system for obtaining low RN levels. Method Sixteen adults participated. Electrophysiological measures were obtained using simultaneous recordings by the Vivosonic and Intelligent Hearing Systems for subjects in 2 relaxed conditions and 4 active motor conditions. Three averaging times were used for the relaxed states (1, 1.5, and 3 min) and for the active states (1.5, 3, and 6 min). Repeated-measures analyses of variance were used to examine RN levels as a function of noise reduction strategy (i.e., KWA, AR) and averaging time. Results Lower RN levels were obtained using KWA than AR in both the relaxed and active motor states. Thus, KWA was more effective than was AR under the conditions examined in this study. Using KWA, approximately 3 min of averaging was needed in the relaxed condition to obtain an average RN level of 0.025 μV. In contrast, in the active motor conditions, approximately 6 min of averaging was required using KWA. Mean RN levels of 0.025 μV were not attained using AR. Conclusions When patients are not physiologically quiet, low RN levels are more likely to be obtained and more efficiently obtained using KWA than AR. However, even when using KWA, in active motor states, 6 min of averaging or more may be required to obtain threshold responses. Averaging time needed and whether a low RN level can be attained will depend on the level of motor activity exhibited by the patient.

Frequency-dependence of the magnetic transverse relaxation in a random anisotropy system: a-DyNi

1989 ◽  
Vol 50 (1) ◽  
pp. 91-98 ◽  
Author(s):  
B. Dieny ◽  
X. Labouze ◽  
B. Barbara ◽  
G. Pillion ◽  
J. Filippi
Keyword(s):  
Frequency Dependence ◽  
Transverse Relaxation ◽  
System A ◽  
Random Anisotropy

TRANSVERSE RELAXATION IN A RANDOM ANISOTROPY SYSTEM : AMORPHOUS DyNi

1988 ◽  
Vol 49 (C8) ◽  
pp. C8-1249-C8-1250 ◽  
Author(s):  
B. Dieny ◽  
X. Labouze ◽  
B. Barbara ◽  
G. Fillion ◽  
J. Filippi
Keyword(s):  
Transverse Relaxation ◽  
Random Anisotropy

scholarly journals Estimating Software Development Efforts Using a Random Forest-Based Stacked Ensemble Approach

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1195
Author(s):  
Priya Varshini A G ◽  
Anitha Kumari K ◽  
Vijayakumar Varadarajan
Keyword(s):  
Deep Learning ◽  
Random Forest ◽  
Software Development ◽  
Weighted Averaging ◽  
Software Project ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Single Model ◽  
Ensemble Techniques ◽  
Project Estimation

Software Project Estimation is a challenging and important activity in developing software projects. Software Project Estimation includes Software Time Estimation, Software Resource Estimation, Software Cost Estimation, and Software Effort Estimation. Software Effort Estimation focuses on predicting the number of hours of work (effort in terms of person-hours or person-months) required to develop or maintain a software application. It is difficult to forecast effort during the initial stages of software development. Various machine learning and deep learning models have been developed to predict the effort estimation. In this paper, single model approaches and ensemble approaches were considered for estimation. Ensemble techniques are the combination of several single models. Ensemble techniques considered for estimation were averaging, weighted averaging, bagging, boosting, and stacking. Various stacking models considered and evaluated were stacking using a generalized linear model, stacking using decision tree, stacking using a support vector machine, and stacking using random forest. Datasets considered for estimation were Albrecht, China, Desharnais, Kemerer, Kitchenham, Maxwell, and Cocomo81. Evaluation measures used were mean absolute error, root mean squared error, and R-squared. The results proved that the proposed stacking using random forest provides the best results compared with single model approaches using the machine or deep learning algorithms and other ensemble techniques.

Proton magnetic relaxation of water sorbed by methaemoglobin crystals

1973 ◽  
Vol 331 (1587) ◽  
pp. 457-468 ◽  
Keyword(s):  
Relaxation Rate ◽  
Relaxation Times ◽  
Transverse Relaxation Rate ◽  
Ferric Ions ◽  
Transverse Relaxation ◽  
Water Binding ◽  
Adsorption Sites ◽  
Protein Molecules ◽  
Proton Magnetic Relaxation ◽  
Limiting Value

P. m. r. relaxation times ( T 1 and T 2 ) have been measured as a function of regain and temperature for water sorbed by lyophilized methaemoglobin. The purpose of the work was to gain information regarding the nature and extent of water binding by the protein molecules. The T 1 results are interpreted in terms of an exchange between the sixth ligand position of the Fe (III) and other adsorption sites on the protein. At high temperatures the relaxation rate at a given regain reaches a limiting value which allows the fraction of ferric ions hydrated to be calculated. Above 16% regain all the Fe (III) is hydrated. At 21 and 35% regains a maximum appears in the relaxation rate at about -46 °C indicating a contribution from a more mobile phase which produces a T 1 minimum at that temperature. The T 2 data are consistent with a model in which the main contribution to the transverse relaxation rate comes from a tightly bound fraction of the water with ω 0 Ƭ c ≫1. The temperature dependence of T 2 exhibits three different regions: ( a ) a low temperature region where lg T 2 ∝ T -1 ; ( b ) an intermediate region with a steeper increase of T 2 with temperature; and ( c ) a high temperature where T 2 levels off.

Radiator threat evaluation with missing data based on improved technique for order preference by similarity to ideal solution

2021 ◽  
pp. 1-10
Author(s):  
Yu-Heng Xu ◽  
Si-Yi Cheng ◽  
Hu-Biao Zhang
Keyword(s):  
Missing Data ◽  
Prior Knowledge ◽  
Attribute Reduction ◽  
Estimation Algorithm ◽  
Weighted Averaging ◽  
Ideal Solution ◽  
Attribute Weights ◽  
Order Preference ◽  
Improved Technique ◽  
Threat Evaluation

To solve the problem of the missing data of radiator during the aerial war, and to address the problem that traditional algorithms rely on prior knowledge and specialized systems too much, an algorithm for radiator threat evaluation with missing data based on improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) has been proposed. The null estimation algorithm based on Induced Ordered Weighted Averaging (IOWA) is adopted to calculate the aggregate value for predicting missing data. The attribute reduction is realized by using the Rough Sets (RS) theory, and the attribute weights are reasonably allocated with the theory of Shapley. Threat degrees can be achieved through quantization and ranking of radiators by constructing a TOPSIS decision space. Experiment results show that this algorithm can solve the incompleteness of radiator threat evaluation, and the ranking result is in line with the actual situation. Moreover, the proposed algorithm is highly automated and does not rely on prior knowledge and expert systems.

scholarly journals Approach to Multi-Attribute Decision-Making Methods for Performance Evaluation Process Using Interval-Valued T-Spherical Fuzzy Hamacher Aggregation Information

Axioms ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 145
Author(s):  
Yun Jin ◽  
Zareena Kousar ◽  
Kifayat Ullah ◽  
Tahir Mahmood ◽  
Nimet Yapici Pehlivan ◽  
...  
Keyword(s):  
Decision Making ◽  
Fuzzy Set ◽  
Evaluation Process ◽  
Aggregation Operators ◽  
Weighted Averaging ◽  
Membership Degree ◽  
Operational Laws ◽  
Multi Attribute Decision Making ◽  
Interval Valued ◽  
Do So

Interval-valued T-spherical fuzzy set (IVTSFS) handles uncertain and vague information by discussing their membership degree (MD), abstinence degree (AD), non-membership degree (NMD), and refusal degree (RD). MD, AD, NMD, and RD are defined in terms of closed subintervals of that reduce information loss compared to the T-spherical fuzzy set (TSFS), which takes crisp values from intervals; hence, some information may be lost. The purpose of this manuscript is to develop some Hamacher aggregation operators (HAOs) in the environment of IVTSFSs. To do so, some Hamacher operational laws based on Hamacher t-norms (HTNs) and Hamacher t-conorms (HTCNs) are introduced. Using Hamacher operational laws, we develop some aggregation operators (AOs), including an interval-valued T-spherical fuzzy Hamacher (IVTSFH) weighted averaging (IVTSFHWA) operator, an IVTSFH-ordered weighted averaging (IVTSFHOWA) operator, an IVTSFH hybrid averaging (IVTSFHHA) operator, an IVTSFH-weighted geometric (IVTSFHWG) operator, an IVTSFH-ordered weighted geometric (IVTSFHOWG) operator, and an IVTSFH hybrid geometric (IVTSFHHG) operator. The validation of the newly developed HAOs is investigated, and their basic properties are examined. In view of some restrictions, the generalization and proposed HAOs are shown, and a multi-attribute decision-making (MADM) procedure is explored based on the HAOs, which are further exemplified. Finally, a comparative analysis of the proposed work is also discussed with previous literature to show the superiority of our work.

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