- published: 13 Dec 2011
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Signal processing is an enabling technology that encompasses the fundamental theory, applications, algorithms, and implementations of processing or transferring information contained in many different physical, symbolic, or abstract formats broadly designated as signals. It uses mathematical, statistical, computational, heuristic, and linguistic representations, formalisms, and techniques for representation, modelling, analysis, synthesis, discovery, recovery, sensing, acquisition, extraction, learning, security, or forensics.
According to Alan V. Oppenheim and Ronald W. Schafer, the principles of signal processing can be found in the classical numerical analysis techniques of the 17th century. Oppenheim and Schafer further state that the "digitalization" or digital refinement of these techniques can be found in the digital control systems of the 1940s and 1950s.
http://AllSignalProcessing.com for free e-book on frequency relationships and more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Introductory overview of the field of signal processing: signals, signal processing and applications, philosophy of signal processing, and language of signal processing
Source - http://serious-science.org/videos/278 MIT Prof. Gilbert Strang on the difference between cosine and wavelet functions, audio compression, and the pleasure of seeing the ideas actually used
A video by Jim Pytel for Renewable Energy Technology students at Columbia Gorge Community College
Learn about Signal Processing and Machine Learning
MATLAB Signal Processing Tutorial | Discrete Signal Processing
Enroll in Discrete-Time Signal Processing from MITx at https://www.edx.org/course/discrete-time-signal-processing-mitx-6-341x ↓ More info below. ↓ Follow on Facebook: www.facebook.com/edx Follow on Twitter: www.twitter.com/edxonline Follow on YouTube: www.youtube.com/user/edxonline Discrete-Time Signal Processing A focused view into the theory behind modern discrete-time signal processing systems and applications. About this Course 6.341x is designed to provide both an in-depth and an intuitive understanding of the theory behind modern discrete-time signal processing systems and applications. The course begins with a review and extension of the basics of signal processing including a discussion of group delay and minimum-phase systems, and the use of discrete-time (DT) systems for p...
Get an overview of signal processing topics related to machine learning. Get a Free MATLAB Trial: https://goo.gl/C2Y9A5 Ready to Buy: https://goo.gl/vsIeA5 Signals are ubiquitous across many research and development domains. Engineers and scientists need to process, analyze, and extract information from time-domain data as part of their day-to-day responsibilities. In a range of predictive analytics applications, signals are the raw data that machine learning systems must be able to leverage for the purpose of creating understanding and for informing decision-making. In this webinar we present an example of a classification system able to identify the physical activity that a human subject is engaged in, solely based on the accelerometer signals generated by his or her smartphone. We ...
Lecture Series on Digital Signal Processing by Prof.S. C Dutta Roy, Department of Electrical Engineering, IIT Delhi. For More details on NPTEL visit http://nptel.iitm.ac.in
We review some concepts from analog signal processing and introduce the terminology and notation of digital signal processing. Don't worry too much about understanding every equation just yet. This lecture is adapted from the ECE 410: Digital Signal Processing course notes developed by David Munson and Andrew Singer
MATLAB Signal Processing Tutorial | Discrete Signal Processing
Digital Signal Processing (DSP) - Speech Signal Processing
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