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You searched for subject:(PRNU). Showing records 1 – 4 of 4 total matches.

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University of Southern California

1. McCleary, Brent. Digital imaging system testing and design using physical sensor characteristics.

Degree: PhD, Electrical Engineering, 2009, University of Southern California

 Image sensor testing and image quality enhancement methods that are geared towards commercial CMOS image sensors are developed in this thesis. The methods utilize sensor… (more)

Subjects/Keywords: CMOS image sensors; cross-talk; PRNU; sensor characterization

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

McCleary, B. (2009). Digital imaging system testing and design using physical sensor characteristics. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/600665/rec/1997

Chicago Manual of Style (16th Edition):

McCleary, Brent. “Digital imaging system testing and design using physical sensor characteristics.” 2009. Doctoral Dissertation, University of Southern California. Accessed October 18, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/600665/rec/1997.

MLA Handbook (7th Edition):

McCleary, Brent. “Digital imaging system testing and design using physical sensor characteristics.” 2009. Web. 18 Oct 2019.

Vancouver:

McCleary B. Digital imaging system testing and design using physical sensor characteristics. [Internet] [Doctoral dissertation]. University of Southern California; 2009. [cited 2019 Oct 18]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/600665/rec/1997.

Council of Science Editors:

McCleary B. Digital imaging system testing and design using physical sensor characteristics. [Doctoral Dissertation]. University of Southern California; 2009. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/600665/rec/1997


University of Adelaide

2. Matthews, Richard Henry Edwin. Towards a Unified Theory of Sensor Pattern Noise: An analysis of dark current, lens effects, and temperature bias in CMOS image sensors.

Degree: 2019, University of Adelaide

 Matching images to a discrete camera is of significance in forensic investigation. In the case of digital images, forensic matching is possible through the use… (more)

Subjects/Keywords: Digital Forensics; Dark Current; Temperature; PRNU; SPN; CMOS

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Matthews, R. H. E. (2019). Towards a Unified Theory of Sensor Pattern Noise: An analysis of dark current, lens effects, and temperature bias in CMOS image sensors. (Thesis). University of Adelaide. Retrieved from http://hdl.handle.net/2440/119974

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Matthews, Richard Henry Edwin. “Towards a Unified Theory of Sensor Pattern Noise: An analysis of dark current, lens effects, and temperature bias in CMOS image sensors.” 2019. Thesis, University of Adelaide. Accessed October 18, 2019. http://hdl.handle.net/2440/119974.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Matthews, Richard Henry Edwin. “Towards a Unified Theory of Sensor Pattern Noise: An analysis of dark current, lens effects, and temperature bias in CMOS image sensors.” 2019. Web. 18 Oct 2019.

Vancouver:

Matthews RHE. Towards a Unified Theory of Sensor Pattern Noise: An analysis of dark current, lens effects, and temperature bias in CMOS image sensors. [Internet] [Thesis]. University of Adelaide; 2019. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/2440/119974.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Matthews RHE. Towards a Unified Theory of Sensor Pattern Noise: An analysis of dark current, lens effects, and temperature bias in CMOS image sensors. [Thesis]. University of Adelaide; 2019. Available from: http://hdl.handle.net/2440/119974

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

3. Alhussainy, Amel Tuama. Forensic Source Camera Identification by Using Features in Machine Learning Approach : Identification d'appareils photos par apprentissage.

Degree: Docteur es, Informatique, 2016, Montpellier

L'identification d'appareils photos a récemment fait l'objet d'une grande attention en raison de son apport en terme sécurité et juridique. Établir l'origine d'un médias numériques,… (more)

Subjects/Keywords: Identification de l'appareil source; Prnu; Co-Occurrences; CFA interpolation; L'apprentissage en profondeur; Cnn; Camera Identification; Prnu; Co-Occurrences; CFA interpolation; Deep Learning; Cnn

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Alhussainy, A. T. (2016). Forensic Source Camera Identification by Using Features in Machine Learning Approach : Identification d'appareils photos par apprentissage. (Doctoral Dissertation). Montpellier. Retrieved from http://www.theses.fr/2016MONTS024

Chicago Manual of Style (16th Edition):

Alhussainy, Amel Tuama. “Forensic Source Camera Identification by Using Features in Machine Learning Approach : Identification d'appareils photos par apprentissage.” 2016. Doctoral Dissertation, Montpellier. Accessed October 18, 2019. http://www.theses.fr/2016MONTS024.

MLA Handbook (7th Edition):

Alhussainy, Amel Tuama. “Forensic Source Camera Identification by Using Features in Machine Learning Approach : Identification d'appareils photos par apprentissage.” 2016. Web. 18 Oct 2019.

Vancouver:

Alhussainy AT. Forensic Source Camera Identification by Using Features in Machine Learning Approach : Identification d'appareils photos par apprentissage. [Internet] [Doctoral dissertation]. Montpellier; 2016. [cited 2019 Oct 18]. Available from: http://www.theses.fr/2016MONTS024.

Council of Science Editors:

Alhussainy AT. Forensic Source Camera Identification by Using Features in Machine Learning Approach : Identification d'appareils photos par apprentissage. [Doctoral Dissertation]. Montpellier; 2016. Available from: http://www.theses.fr/2016MONTS024


EPFL

4. Boukhayma, Assim. Ultra Low Noise CMOS Image Sensors.

Degree: 2016, EPFL

 CMOS Image Sensors (CIS) overtook the charge coupled devices (CCDs) in low noise performance. Photoelectron counting capability is the next step for CIS for ultimate… (more)

Subjects/Keywords: Solid-State; CMOS Image Sensors (CIS); Pinned Photo Diode (PPD); Noise; 1/f; Flicker; Random Telegraph Signal (RTS); Thermal; Shot; Temporal Read Noise (TRN); Analog Circuit Design; PMOS; NMOS; Thick oxide; Thin oxide; Photon Transfer Curve (PTC); Photo Response Non-Uniformity (PRNU); Lag; Quantum Efficiency (QE); Correlated Double Sampling (CDS); CorrelatedMultiple Sampling (CMS); Terahertz (THz); CMOS; Responsivity; Passive Switched Capacitor; N-path Filter; Gm-C Filter

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Boukhayma, A. (2016). Ultra Low Noise CMOS Image Sensors. (Thesis). EPFL. Retrieved from http://infoscience.epfl.ch/record/222436

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Boukhayma, Assim. “Ultra Low Noise CMOS Image Sensors.” 2016. Thesis, EPFL. Accessed October 18, 2019. http://infoscience.epfl.ch/record/222436.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Boukhayma, Assim. “Ultra Low Noise CMOS Image Sensors.” 2016. Web. 18 Oct 2019.

Vancouver:

Boukhayma A. Ultra Low Noise CMOS Image Sensors. [Internet] [Thesis]. EPFL; 2016. [cited 2019 Oct 18]. Available from: http://infoscience.epfl.ch/record/222436.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Boukhayma A. Ultra Low Noise CMOS Image Sensors. [Thesis]. EPFL; 2016. Available from: http://infoscience.epfl.ch/record/222436

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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