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Comparative study of lithium niobate crystal cuts for use as high-voltage acoustic wave sensors

Type: Conference Paper

Author : Patel, N.; Branch, D.W.; Cular, S.; Schamiloglu, E.

Publisher: IEEE

Year: 2014

[Front matter]

Type: Conference Paper

Publisher: IEEE

Year: 2014

Simple linear inversion of soil electromagnetic properties from analytical model of electromagnetic induction sensor

Type: Conference Paper

Author : Vasic, D. , Ambrus, D. , Bilas, V.

Publisher: IEEE

Year: 2014

Feature Selective Validation applied to the comparison of calibration data

Type: Conference Paper

Author : Sanchez, Y. , Rosi, C. , Paez, E. , Azpurua, M.A.

Publisher: IEEE

Year: 2014

Biometrics on visual preferences: A “pump and distill” regression approach

Type: Conference Paper

Author : Segalin, C.; Perina, A.; Cristani, M.

Publisher: IEEE

Year: 2014

[Copyright notice]

Type: Conference Paper

Publisher: IEEE

Year: 2014

Ground effect experiments and model validation with Draganflyer X8 rotorcraft

Type: Conference Paper

Publisher: IEEE

Year: 2014

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Comparative study of lithium niobate crystal cuts for use as high-voltage acoustic wave sensors

[Front matter]

Simple linear inversion of soil electromagnetic properties from analytical model of electromagnetic induction sensor

Feature Selective Validation applied to the comparison of calibration data

Biometrics on visual preferences: A “pump and distill” regression approach

[Copyright notice]

Ground effect experiments and model validation with Draganflyer X8 rotorcraft

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Comparative study of lithium niobate crystal cuts for use as high-voltage acoustic wave sensors

[Front matter]

Simple linear inversion of soil electromagnetic properties from analytical model of electromagnetic induction sensor

Feature Selective Validation applied to the comparison of calibration data

Biometrics on visual preferences: A &#x201C;pump and distill&#x201D; regression approach

[Copyright notice]

Ground effect experiments and model validation with Draganflyer X8 rotorcraft

Biometrics on visual preferences: A “pump and distill” regression approach