research on digital signal processing

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Signal processing articles from across Nature Portfolio

Signal processing is the transmission of information with a modification, so that the new form of information may be exploited by downstream devices, as in sound converted to nerve impulses from ear to neurons. The scientific study of signal processing implicates information theory and is key to telecommunications or biology.

Latest Research and Reviews

SignalingProfiler 2.0 a network-based approach to bridge multi-omics data to phenotypic hallmarks

• Veronica Venafra
• Francesca Sacco
• Livia Perfetto

Transient frequency preference responses in cell signaling systems

• Candela L. Szischik
• Juliana Reves Szemere
• Alejandra C. Ventura

Signal amplification by cyclic extension enables high-sensitivity single-cell mass cytometry

Mass cytometry with signal amplification enables measurement of low-abundance proteins.

• Xiao-Kang Lun
• Kuanwei Sheng

Real-time analysis of large-scale neuronal imaging enables closed-loop investigation of neural dynamics

A real-time analysis system was developed for an up to 500-megabyte-per-second image stream. This system can extract activities from up to 100,000 neurons in larval zebrafish brains and enables closed-loop perturbations of brain-wide neural dynamics at cellular resolution.

• Chun-Feng Shang
• Yu-Fan Wang

A deep learning dataset for sample preparation artefacts detection in multispectral high-content microscopy

• Vaibhav Sharma
• Artur Yakimovich

Real-time prediction of bladder urine leakage using fuzzy inference system and dual Kalman filtering in cats

• Amirhossein Qasemi
• Alireza Aminian
• Abbas Erfanian

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DDSP: Differentiable Digital Signal Processing

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School of Electrical and Computer Engineering

College of engineering, digital signal processing.

Digital Signal Processing (DSP) involves the representation, processing, modeling, and analysis of signals, information, and physical phenomena. DSP interprets the captured data and enables visualization, analysis, manipulation, and control. DSP lies at the core of modern artificial intelligence (AI) and machine learning algorithms.

Digital Signal Processing is at the core of virtually all of today's information technology, and its impact is felt everywhere -- in telecommunications, medical technology, radar and sonar, and in seismic data analysis. The Digital Signal Processing Group at ECE operates the largest educational and research programs in the world in both size and impact. Nearly half of the DSP faculty hold the prestigious title of IEEE Fellow, and are world renowned for their leadership and expertise. DSP's faculty have authored or co-authored over 25 textbooks on DSP, which are studied at universities around the world.

The research at ECE's DSP group is supported by government and industry grants totaling over $4 million annually and encompasses virtually all areas in the theory and implementation of DSP systems. This research is supported by a network of state-of-the-art research laboratories. These resources offer students abundant opportunities to engage in cutting-edge research with DSP's world-renowned faculty

ECE's DSP group receives research support from a variety of funding sources including the Georgia Research Alliance, the National Science Foundation, the Defense Advanced Research Projects Agency, the U.S. Army Research Office, the Ballistic Missile Defense Organization, the National Aeronautical and Space Administration, John and Mary Franklin Foundation, Hewlett Packard, Texas Instruments, IBM, Analog Devices, Intel, NCR, Lanier Worldwide, COMPASS Design Automation, Kodak, and the Region of Lorraine, France.

Digital Signal Processing research falls within the following major areas:

• DSP and Machine Learning Theory
• High-dimensional Statistics
• Conversational Systems
• Robust and Explainable AI  
• Active and Reinforcement Learning 
• Image, Speech, Audio, and Video Processing and Learning 
• Radar and Array Processing

ECE offers the largest undergraduate and graduate DSP academic programs in the country. These programs provide a strong foundation in all aspects of digital signal processing from which students can continue graduate studies, work in industry, or enter schools of medicine, business or law. DSP faculty has played a major role in ECE's Computer Enhanced Education initiatives, and now an on-line master's program.  View the course listing .

Continuing Education Program

DSP faculty, in conjunction with industry experts, offer a variety of  continuing education courses  to practicing engineers on topics of current interest including fundamentals of digital signal processing, signal processing for telecommunications, multimedia signal processing, and video processing and compression.

Digital Signal Processing Faculty Members 

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Advanced Digital Signal Processing for Future Digital Communications

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section " Circuit and Signal Processing ".

Deadline for manuscript submissions: closed (1 May 2024) | Viewed by 15807

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Dear Colleagues,

Digital communication is a communication method that uses a digital signal as the carrier to transmit messages or uses a digital signal to digitally modulate the carrier and then transmits it. It has the characteristics of strong anti-interference ability, controllable transmission error, easy encryption, and easy storage. Digital communication systems are the dominant type of communication system and are widely used in mobile phones, computers, video telephones, network conferences, and so on. Digital communication systems are developing towards high speed, large capacity, and long distance. Digital signal processing (DSP) is a process of transforming analog signals into digital signals and using special technology to expand the processing. At present, digital signal processing technology has been applied in image processing, military, medical, communication, and other fields. In the future, digital signal processing will develop towards the research of fast and efficient algorithms, high-speed hardware implementation, and new application research.

This Special Issue focuses on the application of digital signal processing algorithms to future digital communication systems to help the reader clarify the motivations and methods of various signal processing algorithms to use them for hitherto undeveloped services as well as future scenarios of communication systems. Potential topics include, but are not limited to the following:

• Advanced coding and modulation/waveform techniques;
• Agile and efficient multiple access techniques;
• MIMO signal processing techniques;
• Radar signal processing techniques;
• Reconfigurable intelligent surfaces-assisted techniques;
• Underwater signal processing techniques;
• Channel modeling, sensing and measurement techniques;
• Sparse signal processing for grant-free massive connectivity;
• Signal processing optimization for federated learning.

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• Advanced Digital Signal Processing for Future Digital Communications: 2nd Edition in Electronics

Published Papers (11 papers)

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Course info.

• Prof. Alan V. Oppenheim

Departments

• Electrical Engineering and Computer Science

As Taught In

• Digital Systems
• Signal Processing

Learning Resource Types

Digital signal processing, introduction.

Signal processing using digital computers and special purpose digital hardware has taken on major significance in the past decade. The inherent flexibility of digital elements permits the utilization of a variety of sophisticated signal processing techniques which had previously been impractical to implement.

Advances in integrated circuit technology have had a major impact on the technical areas to which digital signal processing techniques and hardware are being applied. Applications of these techniques are now prevalent in such diverse areas as biomedical engineering, acoustics, sonar, radar, seismology, speech communication, telephony, nuclear science, image processing and many others. Thus, a thorough understanding of digital signal processing fundamentals and techniques is essential for anyone concerned with signal processing applications.

This set of lectures corresponds to a one-semester introduction to digital signal processing fundamentals. It is intended to provide an understanding and working familiarity with the fundamentals of digital signal processing and is suitable for a wide range of people involved with and/or interested in signal processing applications. Its goals are to enable you to apply digital signal processing concepts to your own field of interest, to make it possible for you to read the technical literature on digital signal processing, and to provide the background for the study of more advanced topics and applications.

Prerequisites

Advanced calculus and familiarity with introductory complex variable theory. Previous exposure to linear system theory for continuous-time signals, including Laplace and Fourier transforms, is required. No experience with discrete-time signals, z-transforms, or discrete Fourier transforms is assumed.

Course Topics

The course begins with a discussion of the analysis and representation of discrete-time signals and systems including a discussion of discrete-time convolution, difference equations, the z-transform and the discrete Fourier transform. Considerable emphasis is placed on the similarities with and distinctions between discrete-time and continuous-time signals and systems. The course then proceeds to a consideration of digital network structures for implementation of both recursive (infinite impulse response) and nonrecursive (finite impulse response) digital filters.

A major consideration in digital signal processing is the design of digital filters to meet prescribed specifications. Thus a set of four lectures is devoted to a detailed discussion of digital filter design for both recursive and nonrecursive filters. The course concludes with a thorough presentation of the fast Fourier transform algorithm for computation of the discrete Fourier transform.

Each lesson consists of a taped lecture, a reading assignment in the text, and problems. It is expected that each lesson will require approximately five hours—more or less depending on your ability and interests. The suggested sequence is to first view the lecture, then read the text and finally work the problems. In viewing the lecture you should feel free to run the lecture back and listen to some sections over again or in fact, to watch an entire lecture more than once if that would be helpful. In addition to the assigned reading in the text you may wish to read some of the sections not assigned. This is optional and probably most profitably done after the problems have been worked.

Perhaps the most important component of the course is the exercises. There is absolutely nothing like successfully completing an exercise to give you confidence that you have understood the lectures and the text and that you are ready to go on to new material. And, there is no surer indicator that you are not ready to go on than your not being able to solve an exercise. If you can’t solve an exercise after diligent effort (and don’t give up easily), look over the solution in the exercise solution book. If you have difficulty following the solution, get help. Don’t try to forge ahead thinking the next chapter or lecture will clear up the difficulty; it probably won’t, and you’ll be in still deeper trouble. In each lesson you should work all of the problems without an asterisk. The problems with asterisks are optional. If you have the time and feel that you would like more experience with the material you should try these also. If you wish to go still further, you may want to select some additional problems from the text.

Oppenheim, Alan V., and Ronald W. Schafer. Digital Signal Processing . Prentice Hall, 1975. ISBN: 9780132146357.

Recommended

Cooper, George, and Clare D. McGillem. Methods of Signal and System Analysis . Holt, Rinehart and Winston, 1967. ISBN: 9780030637452.

Lathi, B. P. Signals, Systems and Communication . John Wiley and Sons, 1965.

Oppenheim, Alan V., and A. S. Willsky. Signals and Systems . Prentice Hall, 1982. ISBN: 9780138097318.

Papoulis, A. The Fourier Integral and Its Applications . McGraw-Hill Book Company, 1962. ISBN: 9780070484474.

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Signal processing focuses on algorithms and hardware for analyzing, modifying and synthesizing signals and data, across a wide variety of application domains. As a technology it plays a key role in virtually every aspect of modern life including for example entertainment, communications, travel, health, defense and finance.

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Digital Signal Processing

An Introduction

• © 2024
• Latest edition
• D. Sundararajan 0

Department of Electrical and Computer Engineering, Formerly at Concordia University, Montreal, Canada

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• Written to be accessible to students of varying backgrounds, this textbook explains digital signal processing
• Presents concepts in a clear, concise and comprehensive manner, so that students can learn easily
• Provides detailed coverage of various types of filter design, including an introduction

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This textbook for a one semester  introductory course in digital signal processing for senior undergraduate and first year graduate students in electrical and computer engineering departments is concise,  highly readable, and  yet provides comprehensive coverage of the topic. Each new topic is presented with examples and figures. The highly mathematical content of the topic is presented lucidly to make the learning the subject easier. Practical aspects of the subject are clearly indicated so that the student can apply the principles in real applications.  Matlab programs for FIR and IIR filter design are provided as supplementary material online.

• Digital Signal Processing textbook
• Digital Filter design
• Fast Fourier Transform
• Wavelet Transform
• z-Transform

Table of contents (11 chapters)

Front matter, discrete-time signals.

• Dr. D. Sundararajan

Discrete-Time Systems

Discrete fourier transform, discrete-time fourier transform, power spectral density, the z -transform, finite impulse response filters, infinite impulse response filters, multirate digital signal processing, fast computation of the dft, effects of finite wordlength, back matter, authors and affiliations.

D. Sundararajan

About the author

Dr. D. Sundararajan was a former Professor at Concordia University, Montreal, Canada.  As the principal inventor of the latest family of DFT algorithms, he has written ten books, three Patents, and several papers in IEEE Transactions and in the Proceedings of IEEE Conferences.

Bibliographic Information

Book Title : Digital Signal Processing

Book Subtitle : An Introduction

Authors : D. Sundararajan

DOI : https://doi.org/10.1007/978-3-031-56740-7

Publisher : Springer Cham

eBook Packages : Engineering , Engineering (R0)

Copyright Information : The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024

Hardcover ISBN : 978-3-031-56739-1 Published: 20 June 2024

Softcover ISBN : 978-3-031-56742-1 Due: 04 July 2025

eBook ISBN : 978-3-031-56740-7 Published: 19 June 2024

Edition Number : 2

Number of Pages : XV, 439

Number of Illustrations : 175 b/w illustrations

Topics : Signal, Image and Speech Processing , Computer Imaging, Vision, Pattern Recognition and Graphics

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