In signal processing, sampling is the reduction of a continuous-time signal to a discrete-time signal. A common example is the conversion of a sound wave to a sequence of "samples".A sample is a value of the signal at a point in time and/or space; this definition differs from the usage in statistics, which refers to a set of such values. A sampler is a subsystem or operation that extracts samples from a continuous signal. A theoretical ideal sampler produces samples equivalent to the instantaneous value of the continuous signal at the desired points. The original signal can be reconstructed from a sequence of samples, up to the Nyquist limit, by passing the sequence of samples through a type of low-pass filter called a reconstruction filter. (Wikipedia).
Frequency Domain Interpretation of Sampling
http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. Analysis of the effect of sampling a continuous-time signal in the frequency domain through use of the Fourier transform.
From playlist Sampling and Reconstruction of Signals
Quantization and Coding in A/D Conversion
http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. Real sampling systems use a limited number of bits to represent the samples of the signal, resulting in quantization of the signal amplitude t
From playlist Sampling and Reconstruction of Signals
Introduction to Sampling and Reconstruction
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. Introduction to the analysis of converting between continuous and discrete time forms of a signal using s
From playlist Sampling and Reconstruction of Signals
http://AllSignalProcessing.com for more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Frequency domain analysis of upsampling a discrete-time signal (increasing the effective sampling rate) by inserting zeros followed by lowpass filte
From playlist Sampling and Reconstruction of Signals
Reconstruction and the Sampling Theorem
http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. Analysis of the conditions under which a continuous-time signal can be reconstructed from its samples, including ideal bandlimited interpolati
From playlist Sampling and Reconstruction of Signals
http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. Practical requirements for an analog anti-aliasing filter to bandlimit continuous-time signals before sampling.
From playlist Sampling and Reconstruction of Signals
Practical DSP and Oversampling
http://AllSignalProcessing.com for more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Limitations of analog anti-aliasing and anti-imaging filters motivate a practical digital filtering approach in which high rates are used for sampli
From playlist Sampling and Reconstruction of Signals
Equivalent Analog Filtering (c)
http://AllSignalProcessing.com for more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Studies the equivalent analog filter corresponding to sampling a signal, applying a discrete-time filter, and reconstructing a continuous-time signa
From playlist Sampling and Reconstruction of Signals
http://AllSignalProcessing.com for more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Frequency domain analysis of downsampling a discrete-time signal (decreasing the effective sampling rate) by lowpass filtering followed by discardin
From playlist Sampling and Reconstruction of Signals
Lecture 17, Interpolation | MIT RES.6.007 Signals and Systems, Spring 2011
Lecture 17, Interpolation Instructor: Alan V. Oppenheim View the complete course: http://ocw.mit.edu/RES-6.007S11 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT RES.6.007 Signals and Systems, 1987
Lecture 19, Discrete-Time Sampling | MIT RES.6.007 Signals and Systems, Spring 2011
Lecture 19, Discrete-Time Sampling Instructor: Alan V. Oppenheim View the complete course: http://ocw.mit.edu/RES-6.007S11 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT RES.6.007 Signals and Systems, 1987
Random Processes and Stationarity
http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. Introduction to describing random processes using first and second moments (mean and autocorrelation/autocovariance). Definition of a stationa
From playlist Random Signal Characterization
Lecture 18, Discrete-Time Processing of Continuous-Time Signals | MIT RES.6.007 Signals and Systems
Lecture 18, Discrete-Time Processing of Continuous-Time Signals Instructor: Alan V. Oppenheim View the complete course: http://ocw.mit.edu/RES-6.007S11 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT RES.6.007 Signals and Systems, 1987
Introduction to Frame-Based Processing in Simulink
Watch a practical introduction to frame-based processing in Simulink®. Cover the what, why, and how of using frames and then compare against the default sample-based processing scheme. Watch the full series: https://youtube.com/playlist?list=PLn8PRpmsu08pS-Cq3Hl9T3jwLmOOQzEOp You’ll see
From playlist Using Frames in Simulink
MIT MIT 6.003 Signals and Systems, Fall 2011 View the complete course: http://ocw.mit.edu/6-003F11 Instructor: Dennis Freeman License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 6.003 Signals and Systems, Fall 2011
PROGRAM: Nonlinear filtering and data assimilation DATES: Wednesday 08 Jan, 2014 - Saturday 11 Jan, 2014 VENUE: ICTS-TIFR, IISc Campus, Bangalore LINK:http://www.icts.res.in/discussion_meeting/NFDA2014/ The applications of the framework of filtering theory to the problem of data assimi
From playlist Nonlinear filtering and data assimilation
Understanding Wavelets, Part 2: Types of Wavelet Transforms
Explore the workings of wavelet transforms in detail. •Try Wavelet Toolbox: https://goo.gl/m0ms9d •Ready to Buy: https://goo.gl/sMfoDr You will also learn important applications of using wavelet transforms with MATLAB®. Video Transcript: In the previous session, we discussed wavelet co
From playlist Understanding Wavelets
Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Remove an unwanted tone from a signal, and compensate for the delay introduced in the process using Signal Processing Toolbox™. For more on Signal Processing Toolbox, visi
From playlist Signal Processing and Communications
Digital Sampling, Signal Spectra and Bandwidth - A Level Physics
An A Level Physics revision video covering Digital Sampling, Signal Spectra and Bandwidth
From playlist A Level Physics Revision