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Linear Filters

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
(274 views)

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

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
(596 views)

Fundamentals of Perceptual Audio Encoding

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
(261 views)

Noise

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
(250 views)

What to Do With Thousands of GPS Tracks

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
(74 views)

WIRELINE CHANNEL ESTIMATION AND EQUALIZATION

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
(250 views)

What to Do With Thousands of GPS Tracks

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
(95 views)

Time series analysis

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
(207 views)

Fitting

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
(115 views)

Decision Theory: Action Problems

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
(128 views)

Matched Filtering and Digital Pulse Amplitude Modulation (PAM)

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
(382 views)

Chapter 11

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
(251 views)

Digital Communication I: Modulation and Coding Course

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
(259 views)

the RASNIK Alignment System

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
(188 views)

Digital Image Processing

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
(181 views)

From Learning Models of Natural Image Patches to Whole Image Restoration

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
(202 views)

Linear filtering

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
(95 views)

EE 290P Project Proposal: Next-Generation Decoding Algorithms for BMI Systems

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
(126 views)

Mining Historical Archives for Near-Duplicate Figures

Mining Historical Archives for Near-Duplicate Figures

Mining Historical Archives for Near-Duplicate Figures. Thanawin Rakthanmanon, Qiang Zhu, and Eamonn J. Keogh.

By kendra
(100 views)

Adaptive Filtering of Raster Map Images

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.

By koko
(88 views)

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