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Accurate Prediction of Phase Transitions in Compressed Sensing via a Connection to Minimax Denoising
Approximate Message Passing Lasso Group Lasso, Joint Sparsity, James- Stein, Minimax Risk over Nearly-Black Objects Minimax Risk of Soft Thresholding Minimax Risk of Firm Thresholding Minimax Shrinkage Nonconvex penalization State Evolution Total Variation Minimization Monotone Regression.
2015/8/21
Compressed sensing posits that, within limits, one can undersample a sparse signal and yet reconstruct it accurately. Knowing the precise limits to such undersampling is important both for theory and ...
Estimation Of Attenuation And Phase Delay In Driving Voltage Waveform Of An Ultra-High-Speed Image Sensor by Dimensional Analysis
Dimensional Analysis Elmore model RC network Signal Attenuation
2010/2/2
We present an explicit expression to estimate driving
voltage attenuation through RC networks representation of an ultrahigh-
speed image sensor. Elmore delay metric for a fundamental RC
chain is e...