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525.721 - Advanced Digital Signal Processing Course Homepage

Instructor Information

Benjamin Rodriguez

Email: benjamin.rodriguez@jhuapl.edu
Work Phone: (443) 778-9171

Dr. Rodriguez has a background in signal processing with a focus on data fusion and pattern recognition. His current work duties include research and development in recognition techniques and fusion of multiple sensors for ground targets as well as space targets and tracking. He has worked on projects related to target identification using SAR, Hyperspectral and Panchromatic imagery along with face recognition; fingerprint matching; voice recognition and breaking encoded messages within transmitted signals. Experienced research in radar, liar and optical sensors for target recognition using generated features, feature preprocessing techniques, classification models and fusion methods. Other areas of research experience include pattern recognition using image, signal and video processing techniques for face recognition, finger print matching, anomaly detection and voice recognition. Software Engineering experience using, Unix, Linux and Window operating systems and programming using assembly, C/C++, ENVI, Java, Matlab, PERL and Visual Studios.

Course Information

Course Description

The fundamentals of discrete-time statistical signal processing are presented in this course. Topics include optimal linear filter theory, classical and modern spectrum analysis, adaptive filtering, and the singular value decomposition and its application to least squares problems. Basic concepts of super-resolution methods are described, including an introduction to array processing. Computer experiments using Matlab illustrate some of the signal processing techniques.

Prerequisites

525.414 Probability and Stochastic Processes for Engineers, 525.427 Digital Signal Processing, and the basics of linear algebra.

Course Goal

The goals of this course is to give a graduate-level overview of diverse statistical digital signal processing theory and applications which include:

  • design optimum and adaptive filtering algorithms and apply them to various signals
  • modeling of spectral analysis using nonparametric as well as parametric approaches
  • introduce spectral estimation for nonparametric methods: power spectral density, autocorrelation and transfer functions
  • apply singular value decomposition to digital signals such as signal separation, detection, estimation and imaging
  • introduce array processing as it relates to antenna theory and applications

Course Objectives

  • The course objectives include an introduction to the theory of statistical signal processing methods and application developements as related to signal processing, such as Audio, Communications, Computer Vision, Imaging, Optics, Pattern Recognition, Radar, Speech, Video, Remote Sensing, Stochastic Control Systems.

When This Course is Typically Offered

  •  Summer at Montgomery County campus by Dr. Amir-Homayoon Najmi     
  •  Fall at the Johns Hopkins University Applied Physics Laboratory, Laurel MD by Dr. Benjamin Rodriguez

Syllabus

Topics Covered

  • Adaptive Signal Processing
  • Discrete-time Kalman Filtering
  • Eigenfilters
  • Finite Impulse Response (FIR)
  • Infinite Impulse Response (IIR)
  • Match Filters
  • Signal Modeling

Student Assessment Criteria

Homework 30%
Midterm Project (Implementation, Presentation, and Report) 30%
Final Project (Implementation, Presentation, and Report) 40%

  • The reports are to be submitted in IEEE format using either LaTeX or Microsoft Word.

Computer and Technical Requirements

  • Matlab
  • LaTeX
  • Microsoft Word 
  • Microsoft PowerPoint

Participation Expectations

  • Students are expected to do homework assignments on their own
  • Projects and reports can be done in groups of 2 or 3
  • Presentations are required for each project

Textbooks

Textbook information for this course is available online through the MBS Direct Virtual Bookstore.

Course Notes

There are no notes for this course.

Final Words from the Instructor


Required Text:
Statistical and Adaptive Signal Processing: Spectral Estimation, Signal Modeling, Adaptive Filtering and Array Processing
Dimitris G. Manolakis, Vinay K. Ingle, and Stephen M. Kogon
Artech House Publishers
ISBN 1580536107

Recommended Text:
Mathematical Methods and Algorithms for Signal processing
Todd K. Moon and Wynn C. Stirling
Prentice Hall
ISBN 0201361868

(Last Modified: 06-24-2009 at 7:46:28 PM)