'Noise parameters' diaporamas de présentation

Sensor and Graph Mining

Sensor and Graph Mining. Christos Faloutsos Carnegie Mellon University & IBM www.cs.cmu.edu/~christos. Joint work with. Anthony Brockwell (CMU/Stat) Deepayan Chakrabarti (CMU) Spiros Papadimitriou (CMU) Chenxi Wang (CMU) Yang Wang (CMU). Outline. Introduction - motivation

761 views • 63 slides

Data Assimilation in Meteorology and Oceanography

824 views • 61 slides

Mysteries of Noise Figure Sander Weinreb, California Institute of Technology

Mysteries of Noise Figure Sander Weinreb, California Institute of Technology sweinreb@caltech.edu , June 6, 2014.

497 views • 40 slides

Chapter 8: Image Restoration

Chapter 8: Image Restoration. Image enhancement: Overlook degradation processes, deal with images intuitively Image restoration: Known degradation processes; model the processes and reconstruct images based on the inverse model ? Degradations

650 views • 37 slides

Low Noise Amplifiers for Phased Array Feeds Sander Weinreb, California Institute of Technology

Low Noise Amplifiers for Phased Array Feeds Sander Weinreb, California Institute of Technology Presented to Workshop, Phased Array Antenna Systems for Radio Astronomy, Brigham Young University, Provo, Utah, May 4, 2010.

355 views • 34 slides

Bivariate (multivariate) analysis

Bivariate (multivariate) analysis. Aim: to estimate the model: Y t =b*X t + N t ; b= the regression coefficient expressing the effect of X on Y N t =noise term (error term), including other causes of Y besides X.

467 views • 8 slides