Dimitris G. Manolakis , a senior staff member in the Applied Space Systems Group, joined Lincoln Laboratory at the Massachusetts Institute of Technology in and has combined an extensive research career with a commitment to education. Manolakis' work has included the exploration and development of techniques in digital signal processing, adaptive filtering, array processing, pattern recognition, and remote sensing.
His recent research has focused on algorithms for hyperspectral target detection and modeling of spatio-temporal count data from down-looking sensors. Throughout his career, Dr. Manolakis has been involved in educating future engineers. He has taught undergraduate and graduate courses at the University of Athens, at which he earned a bachelor's degree in physics and a doctorate in electrical engineering; Northeastern University, at which he is an adjunct professor; Boston College; and Worcester Polytechnic Institute.
In addition, through an in-house technical education program, he conducts courses in digital and statistical signal processing and adaptive filtering to explain fundamental principles and concepts to Lincoln Laboratory staff members embarking on research in these areas. In , Dr. Manolakis was recognized with an IEEE Signal Processing Society Education Award for his dedication to advancing education through the development of curriculum materials, publication of scholarly texts, and teaching.
Manolakis is a prolific writer. He has authored or coauthored more than articles on topics ranging from digital signal processing to hyperspectral remote sensing of chemical plumes to hyperspectral image processing for automatic target detection; these articles have been cited in almost scientific publications. In addition, he has coauthored three textbooks that are widely used in academia: Digital Signal Processing: Principles, Algorithms, and Applications Prentice Hall, , 4th ed.
We're sorry! We don't recognize your username or password. Please try again. The work is protected by local and international copyright laws and is provided solely for the use of instructors in teaching their courses and assessing student learning. You have successfully signed out and will be required to sign back in should you need to download more resources. John G. If You're an Educator Download instructor resources Additional order info. Description Balanced coverage of digital signal processing theory and practical applications Digital Signal Processing presents the fundamental concepts and techniques of discrete-time signals, systems, and modern digital processing as well as related algorithms and applications for students in electrical engineering, computer engineering, and computer science.
Balanced coverage of digital signal processing theory and practical applications Includes many examples throughout the book and over homework problems, including computer problems. Answers to select problems are included in the back of the book for students to check their understanding. NEW - Covers multirate digital filter banks and wavelets in a new Chapter Explains the two-channel quadrature mirror filter QMF banks and multichannel filter banks that eliminate aliasing and provide perfect reconstruction of signals.
Treats the design of FIR filters for both two-channel and multichannel filter banks. Focuses on wavelets and the discrete wavelet transform in the second part of the chapter. Describes the construction of the discrete wavelet transform and the connections between wavelets and filter banks.
Describes the operations and techniques involved in the analog-to-digital conversion of analog signals Ch. Studies the characterization and analysis of linear time-invariant discrete-time systems and discrete-time signals in the time domain Ch. Considers both the bilateral and the unilateral z-transform, and describes methods for determining the inverse z-transform Ch.
Analyzes signals and systems in the frequency domain, and presents Fourier series and Fourier transform in both continuous-time and discrete-time signals Ch. Discusses how linear time-invariant LTI discrete systems are characterized in the frequency domain by their frequency response function and their response to periodic and aperiodic signals Ch.
NEW - Discusses reverberation filters in Chapter 5. Provides a thorough treatment of sampling of continuous-time signals and the reconstruction of the signals from their samples Ch. Treats the realization of IIR and FIR systems, including direct-form, cascade, parallel, lattice and lattice-ladder realizations.
Looks at sampling-rate conversion and its applications to multirate digital signal processing Ch. Treats linear prediction and optimum linear Wiener filters Ch. The newly redesigned interface looks and performs better than ever to make navigation a breeze. Enhanced search makes it easy to quickly find a key term or topic to study.
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In addition, DSP can solve problems that cannot be solved using ASP, like the spectral analysis of multicomponent signals, adaptive filtering, and operations at very Manolakis, D. Proakis, D. Hall, , 4th edition. About the cover photograph. Autumn leaves in the Gorges de l'Areuse, by Adrien Vetterli.
Besides being a beautiful picture, this photograph also illustrates a basic signal processing concept. The exposure time is on the order of a second, as can be seen from the fuzziness of the swirling leaves; in other words, the Ethem M.
Sozer, Milica Stojanovic, and John G. An underwater acoustic UWA local area network. LAN is designed and Availability of Software. These programs along with many Some practical examples of real-life DSP applications are then discussed briefly. The Chapter ends with a presentation of the course outline. Kay: Modern Front and rear panel diagrams, instruction and user manual, service and maintenance manual, software Exam Hours : Linear equations: Fields; system of linear equations, and its solution sets; elementary row operations and Exam Marks : Goal of the course — Advances in Digital Signal Processing involve variable sampling rates and thus the Sample number.
The meaning of negative frequencies. The problem is to find the frequency spectrum of the discrete signal shown in a. That is, we want to find the frequency and EC 5xx. Elective III. EC Semester III. Course Name. L-T-P Credits. Thesis -I Sequential Ckts. Mentor Graphics CAD software manuals. All Rights Reserved. Designed by Templatic.
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