scholarly journals The application of sparse arrays in high frequency transcranial focused ultrasound therapy: A simulation study

2013 ◽  
Vol 40 (12) ◽  
pp. 122901 ◽  
Author(s):  
Daniel Pajek ◽  
Kullervo Hynynen
Keyword(s):  
Simulation Study ◽  
High Frequency ◽  
Focused Ultrasound ◽  
Ultrasound Therapy ◽  
Sparse Arrays ◽  
Transcranial Focused Ultrasound

Numerical simulation of transcranial focused ultrasound therapy

2015 ◽  
Vol 138 (3) ◽  
pp. 1907-1907
Author(s):  
Aki T. Pulkkinen ◽  
Beat Werner ◽  
Ernst Martin ◽  
Kullervo Hynynen
Keyword(s):  
Numerical Simulation ◽  
Focused Ultrasound ◽  
Ultrasound Therapy ◽  
Transcranial Focused Ultrasound

scholarly journals Factors Associated with Heating Efficiency in Transcranial Focused Ultrasound Therapy

2020 ◽  
Vol 60 (12) ◽  
pp. 594-599
Author(s):  
Kazuaki YAMAMOTO ◽  
Hisashi ITO ◽  
Shigeru FUKUTAKE ◽  
Takashi ODO ◽  
Tetsumasa KAMEI ◽  
...  
Keyword(s):  
Focused Ultrasound ◽  
Ultrasound Therapy ◽  
Heating Efficiency ◽  
Factors Associated ◽  
Transcranial Focused Ultrasound

Ultrasound-based Safety Assessment during Moving Organ Tracking Towards In Vivo Focused Ultrasound Therapy

2019 ◽  
Author(s):  
A Mariani ◽  
◽  
L Morchi ◽  
A Diodato ◽  
A Cafarelli ◽  
...  
Keyword(s):  
Safety Assessment ◽  
Focused Ultrasound ◽  
Ultrasound Therapy

scholarly journals Bidirectional and state-dependent modulation of brain activity by transcranial focused ultrasound in non-human primates

2021 ◽  
Vol 14 (2) ◽  
pp. 261-272
Author(s):  
Pai-Feng Yang ◽  
M. Anthony Phipps ◽  
Sumeeth Jonathan ◽  
Allen T. Newton ◽  
Nellie Byun ◽  
...  
Keyword(s):  
Focused Ultrasound ◽  
Brain Activity ◽  
State Dependent ◽  
Transcranial Focused Ultrasound

scholarly journals Reducing the Bias of the Smoothed Log Periodogram Regression for Financial High-Frequency Data

Econometrics ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 40
Author(s):  
Erhard Reschenhofer ◽  
Manveer K. Mangat
Keyword(s):  
Simulation Study ◽  
High Frequency ◽  
Mean Squared Error ◽  
Estimation Methods ◽  
High Frequency Data ◽  
Frequency Data ◽  
Periodic Patterns ◽  
Typical Sample ◽  
Squared Error ◽  
Proper Interpretation

For typical sample sizes occurring in economic and financial applications, the squared bias of estimators for the memory parameter is small relative to the variance. Smoothing is therefore a suitable way to improve the performance in terms of the mean squared error. However, in an analysis of financial high-frequency data, where the estimates are obtained separately for each day and then combined by averaging, the variance decreases with the sample size but the bias remains fixed. This paper proposes a method of smoothing that does not entail an increase in the bias. This method is based on the simultaneous examination of different partitions of the data. An extensive simulation study is carried out to compare it with conventional estimation methods. In this study, the new method outperforms its unsmoothed competitors with respect to the variance and its smoothed competitors with respect to the bias. Using the results of the simulation study for the proper interpretation of the empirical results obtained from a financial high-frequency dataset, we conclude that significant long-range dependencies are present only in the intraday volatility but not in the intraday returns. Finally, the robustness of these findings against daily and weekly periodic patterns is established.

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