Statistical Signal Processing

 



559 Statistical Signal Processing jobs available on Indeed.com. Apply to Research Scientist, Customer Service Representative, Algorithm Engineer and more! Statistical Signal Processing of Complex-Valued Data: The Theory of Improper and Noncircular Signals Peter J. Schreier, Louis L. Scharf Cambridge University Press, Feb 4, 2010 - Technology & Engineering. EE262: Statistical Signal Processing.COURSES ARE SUBJECT TO CHANGE. Covers fundamental approaches to designing optimal estimators and detectors of deterministic and random parameters and processes in noise, and includes analysis of their performance.

Professor, Electrical & Computer Engineering, Cornell.
Vikram is also affiliated with Center of Applied Math and Mechanical & Aerospace Engineering at Cornell.
Vikram is a Fellow of IEEE, served as distinguished lecturer for the IEEE Signal Processing Society, Editor in Chief of IEEE Journal Selected Topics in Signal Processing. He was awarded an honorary doctorate from KTH, Sweden in 2013.
Vikram’s research interests are in statistical signal processing, stochastic control (POMDPs), stochastic optimization and inverse reinforcement learning with applications in social networks, human decision making and adaptive sensing.
Statistical
  • Click here for all my publications.
  • Click here for preprints on arXiv
  • Recent papers: JMLR,IEEE Info Theory, ICML, Nature Comm, IEEE Automatic Control, IEEE Signal Proc (Inverse RL for Identifying Cognitive Radar), IEEE Signal Proc (Inverse HMM Filters and Counter-adversarial Systems), IEEE Signal Proc (Anticipatory Decision Making and Quickest Change Detection).
Fundamental AreasApplication Areas
POMDPs & Controlled SensingSocial Networks (fusion & control)
Stochastic Optimization, Game TheoryCognitive Radar & Intent Inference
Stochastic Calculus, filtering (old stuff)Biosensors, Artificial Membranes
  • Partially Observed Markov Decision Processes book, Cambridge, 2016
  • Dynamics of Engineered Artificial Membranes & Biosensors,Cambridge, 2018.

Description

Statistical Signal Processing Examples

Processing

Statistical Signal Processing Scharf

Understand how random processing signals are characterized and how operations change signals require a combination of theory and application. Auto tune download vs device. This course introduces the concept of probability and sampling of signal processing with a wide variety of applications and mathematical approaches.

As the concepts of signal processing become clear, learn from increasingly complex examples of random processes. Practice using examples of commonly encountered processes, properties and calculations drawn from communications, signal processing, computer networks, circuits, and devices, among other areas.

Prerequisites

EE178 and linear systems; Fourier transforms at the level of EE102A, EE102B or EE261 .

Topics include

  • Random vectors and processes
  • Convergence and limit theorems
  • IID, independent increment, Markov, and Gaussian random processes
  • Autocorrelation and power spectral density
  • Mean square error estimation, detection, and linear estimation

Course Availability

The course schedule is displayed for planning purposes – courses can be modified, changed, or cancelled. Course availability will be considered finalized on the first day of open enrollment. For quarterly enrollment dates, please refer to our graduate education section.