Gaussian noise - PowerPoint PPT Presentation
Linear Filters
Linear Filters. T-11 Computer Vision University of Houston Christophoros Nikou. Images and slides from: James Hayes, Brown University, Computer Vision course Svetlana Lazebnik, University of North Carolina at Chapel Hill, Computer Vision course
By Albert_Lan
Why You Need to Measure Both BER and MER on QAM Digital Signals
Why You Need to Measure Both BER and MER on QAM Digital Signals . Presented by: Sunrise Telecom Broadband …a step ahead. Introduction. Most Digital Analyzers measure Modulation Error Ratio (MER) and Bit Error Rate (BER) MER and BER each have their limitations
By oshin
Fundamentals of Perceptual Audio Encoding
Fundamentals of Perceptual Audio Encoding. Craig Lewiston HST.723 Lab II 3/23/06. Goals of Lab. Introduction to fundamental principles of digital audio & perceptual audio encoding Learn the basics of psychoacoustic models used in perceptual audio encoding.
By fia
Noise
Noise. Lecture 6. Definition Sources of noise Noise Calculations. Definition:. Electrical noise may be said to be the introduction of any unwanted energy, which tend to interfere with the proper reception and reproduction of transmitted signals. . Sources of noise. External Atmospheric
By cicily
What to Do With Thousands of GPS Tracks
What to Do With Thousands of GPS Tracks. John Krumm, PhD Microsoft Research Redmond, WA. GPS Data. Microsoft Multiperson Location Survey (MSMLS). Garmin Geko 201 $115 10,000 point memory median recording interval 6 seconds 63 meters. 55 GPS receivers 227 subjects
By qabil
WIRELINE CHANNEL ESTIMATION AND EQUALIZATION
WIRELINE CHANNEL ESTIMATION AND EQUALIZATION. Ph.D. Defense Biao Lu Embedded Signal Processing Laboratory The University of Texas at Austin Committee Members Prof. Brian L. Evans Prof. Alan C. Bovik Prof. Joydeep Ghosh Prof. Risto Miikkulainen Dr. Lloyd D. Clark. OUTLINE.
By gordon
What to Do With Thousands of GPS Tracks
What to Do With Thousands of GPS Tracks. John Krumm, PhD Microsoft Research Redmond, WA. GPS Data. Microsoft Multiperson Location Survey (MSMLS). Garmin Geko 201 $115 10,000 point memory median recording interval 6 seconds 63 meters. 55 GPS receivers 227 subjects
By cicero
Time series analysis
Time series analysis. Example. Objectives of time series analysis. Classical decomposition: An example. Transformed data. Trend. Residuals. Trend and seasonal variation. Objectives of time series analysis. Unemployment data. Trend. Trend plus seasonal variation.
By veata
Fitting
Fitting. Fitting: Motivation. We’ve learned how to detect edges, corners, blobs. Now what? We would like to form a higher-level, more compact representation of the features in the image by grouping multiple features according to a simple model. 9300 Harris Corners Pkwy, Charlotte, NC. Fitting.
By taji
Decision Theory: Action Problems
Decision Theory: Action Problems. Decision theory goes Bad?. And thus the native hue of resolution Is sicklied o'er with the pale cast of thought, And enterprises of great pith and moment With this regard their currents turn awry, And lose the name of action.-- Shakespeare-Hamlet.
By makya
Matched Filtering and Digital Pulse Amplitude Modulation (PAM)
Matched Filtering and Digital Pulse Amplitude Modulation (PAM). Outline. Transmitting one bit at a time Matched filtering PAM system Intersymbol interference Communication performance Bit error probability for binary signals Symbol error probability for M -ary (multilevel) signals
By ion
Chapter 11
Chapter 11. Signal Processing with Wavelets. Objectives. Define and illustrate the difference between a stationary and non-stationary signal. Describe the relationship between wavelets and sub-band coding of a signal using quadrature mirror filters with the property of perfect reconstruction.
By ambrose
Digital Communication I: Modulation and Coding Course
Period 3 - 2007 Catharina Logothetis Lecture 3. Digital Communication I: Modulation and Coding Course. Last time we talked about:. Transforming the information source to a form compatible with a digital system Sampling Aliasing Quantization Uniform and non-uniform Baseband modulation
By hedda
the RASNIK Alignment System
the RASNIK Alignment System. Particle Physics CERN, Geneva, Swiss. pp collisions. 2) heavy collisions: A proton is a bag filled with quarks en gluonen. The ATLAS Experiment CERN, Geneva, Switzerland. ‘Tracking’ of charged particles Measurement of position of tracks
By brandon
Digital Image Processing
Digital Image Processing. Chapter 5: Image Restoration. A Model of the Image Degradation/Restoration Process. Degradation Degradation function H Additive noise Spatial domain Frequency domain. Restoration. Noise Models. Sources of noise Image acquisition, digitization, transmission
By terra
From Learning Models of Natural Image Patches to Whole Image Restoration
From Learning Models of Natural Image Patches to Whole Image Restoration. Daniel Zoran Interdisciplinary Center for Neural Computation Hebrew University of Jerusalem Yair Weiss School of Computer Science and Engineering Hebrew University of Jerusalem Presented by Eric Wang
By kreeli
Linear filtering
Linear filtering. Motivation: Image denoising. How can we reduce noise in a photograph?. 1. 1. 1. 1. 1. 1. 1. 1. 1. “box filter”. Moving average. Let’s replace each pixel with a weighted average of its neighborhood The weights are called the filter kernel
By tacey
EE 290P Project Proposal: Next-Generation Decoding Algorithms for BMI Systems
EE 290P Project Proposal: Next-Generation Decoding Algorithms for BMI Systems. Siddharth Dangi Suraj Gowda Paul Johnson. Preliminary work: Adaptive Kalman Filter. Dynamics of model. Variables. Kinematic state at time t. Firing rates at time t. Gaussian noise variables. State matrix.
By ozzy
Mining Historical Archives for Near-Duplicate Figures
Mining Historical Archives for Near-Duplicate Figures. Thanawin Rakthanmanon, Qiang Zhu, and Eamonn J. Keogh.
By kendra
Adaptive Filtering of Raster Map Images
Adaptive Filtering of Raster Map Images. Minjie Chen*, Mantao Xu and Pasi Fränti. Speech and Image Processing Unit (SIPU) School of Computing University of Eastern Finland , FINLAND. Raster Map Images. Topographic or road maps Few colors Detailed spatial structures.
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