Statistical signal processing

Innovation (signal processing)

In time series analysis (or forecasting) — as conducted in statistics, signal processing, and many other fields — the innovation is the difference between the observed value of a variable at time t and the optimal forecast of that value based on information available prior to time t. If the forecasting method is working correctly, successive innovations are uncorrelated with each other, i.e., constitute a white noise time series. Thus it can be said that the innovation time series is obtained from the measurement time series by a process of 'whitening', or removing the predictable component. The use of the term innovation in the sense described here is due to Hendrik Bode and Claude Shannon (1950) in their discussion of the Wiener filter problem, although the notion was already implicit in the work of Kolmogorov. In contrast, the residual is the difference between the observed value of a variable at time t and the optimal updated state of that value based on information available till (including) time t. (Wikipedia).

Video thumbnail

Introduction to Signal Processing

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, phi

From playlist Introduction and Background

Video thumbnail

Manufacturing robots are going to revolutionize the workplace

These manufacturing robots can make sushi and produce motorbikes. They are going to speed up the production process. Find out more information at https://bit.ly/3L1EeHP To get the latest science and technology news, subscribe to our newsletter “The Blueprint” at https://bit.ly/3BDdN5e

From playlist Radical Innovations

Video thumbnail

Brief History of Signal Processing

http://AllSignalProcessing.com for more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Describes several key events in development of the field of signal processing.

From playlist Introduction and Background

Video thumbnail

Is 3D printing a revolution or just a trend?

Additive manufacturing and 3D printing can reshape our future. Companies are now using these technologies to print everything from fully functional cars to Michelin-stared dinners. Watch this video to learn more about how 3d printing and additive manufacturing might change the future! T

From playlist Radical Innovations

Video thumbnail

Determining Signal Similarities

Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Find a signal of interest within another signal, and align signals by determining the delay between them using Signal Processing Toolbox™. For more on Signal Processing To

From playlist Signal Processing and Communications

Video thumbnail

Signal Processing Framework

http://AllSignalProcessing.com for more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Introduces three pervasive problems in signal processing: filtering, equalization, and system identification.

From playlist Introduction and Background

Video thumbnail

3D Printing - Science of Innovation

A three-dimensional, digital representation of an object created with a computer and then sent to an inkjet printer that builds the prototype in three-dimensions. This innovative tool is giving scientists, engineers and backyard inventors a faster, easier and less expensive way to turn the

From playlist 3D Printing

Video thumbnail

Signal Smoothing

Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Learn how to smooth your signal using a moving average filter and Savitzky-Golay filter using Signal Processing Toolbox™. For more on Signal Processing Toolbox, visit: htt

From playlist Signal Processing and Communications

Video thumbnail

Hybrid sparse stochastic processes and the resolution of (...) - Unser - Workshop 2 - CEB T1 2019

Michael Unser (EPFL) / 12.03.2019 Hybrid sparse stochastic processes and the resolution of linear inverse problems. Sparse stochastic processes are continuous-domain processes that are specified as solutions of linear stochastic differential equations driven by white Lévy noise. These p

From playlist 2019 - T1 - The Mathematics of Imaging

Video thumbnail

GRCon21 - Keynote: Future Interference Management, Future Spectrum Monitoring

Presented by John Chapin at GNU Radio Conference 2021

From playlist GRCon 2021

Video thumbnail

Measurements vs. Bits: Compressed Sensors and Info Theory

October 18, 2006 lecture by Dror Baron for the Stanford University Computer Systems Colloquium (EE 380). Dror Baron discusses the numerous rich insights information theory has to offer Compressed Sensing (CS), an emerging field based on the revelation that optimization routines can reco

From playlist Course | Computer Systems Laboratory Colloquium (2006-2007)

Video thumbnail

Probabilistic inverse problems (Lecture 1) by Daniela Calvetti

DISCUSSION MEETING WORKSHOP ON INVERSE PROBLEMS AND RELATED TOPICS (ONLINE) ORGANIZERS: Rakesh (University of Delaware, USA) and Venkateswaran P Krishnan (TIFR-CAM, India) DATE: 25 October 2021 to 29 October 2021 VENUE: Online This week-long program will consist of several lectures by

From playlist Workshop on Inverse Problems and Related Topics (Online)

Video thumbnail

Michael Unser: Wavelets and stochastic processes: how the Gaussian world became sparse

Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Mathematics Library: http://library.cirm-math.fr. And discover all its functionalities: - Chapter markers and keywords to watch the parts of your choice in the video - Videos enriched with abstracts, b

From playlist 30 years of wavelets

Video thumbnail

Innovations in Electrical Engineering - Stanford EE Opportunities

Innovations in electrical engineering have led to major applications and products that fundamentally alter our lifestyles -- from personal computing to environmental sensing. How will we tackle the opportunities and challenges ahead in this dynamic and changing field? Simon Wong, Stanford

From playlist Engineering

Video thumbnail

Cancer Classification from Healthy DNA - Shuki Bruck - 6/7/2019

Changing Directions & Changing the World: Celebrating the Carver Mead New Adventures Fund. June 7, 2019 in Beckman Institute Auditorium at Caltech. The symposium features technical talks from Carver Mead New Adventures Fund recipients, alumni, and Carver Mead himself! Since 2014, this Fu

From playlist Carver Mead New Adventures Fund Symposium

Video thumbnail

Duality between estimation and control - Sanjoy Mitter

PROGRAM: Data Assimilation Research Program Venue: Centre for Applicable Mathematics-TIFR and Indian Institute of Science Dates: 04 - 23 July, 2011 DESCRIPTION: Data assimilation (DA) is a powerful and versatile method for combining observational data of a system with its dynamical mod

From playlist Data Assimilation Research Program

Video thumbnail

GRCon19 - AI and SDR: Software Meets Hardware Again... by Manuel Uhm

AI and SDR: Software Meets Hardware Again... by Manuel Uhm, Jason Vidmar Over the course of the last 30 years, SDR has become the de facto industry standard for the implementation of waveforms for communications, both military and commercial. During that time, the desire for waveforms to

From playlist GRCon 2019

Video thumbnail

Peter Singer - Saving Brains: Innovations to Help Children Thrive

Keynote speaker Peter Singer discusses developing and scaling up products, services and policies that protect and nurture early brain development at the 2016 Childx Symposium. Childx is a dynamic, TED-style conference designed to inspire innovation that improves pediatric and maternal hea

From playlist Stanford Childx Conference 2016

Video thumbnail

Convolution and Unit Impulse Response

The Dirac delta function, the Unit Impulse Response, and Convolution explained intuitively. Also discusses the relationship to the transfer function and the Laplace Transform. Signal Analysis for Linear Systems. My Patreon page is at https://www.patreon.com/EugeneK

From playlist Physics

Related pages

White noise | Signal processing | Kalman filter | Claude Shannon | Filtering problem (stochastic processes) | Statistics | Wiener filter