In computer science, stream processing (also known as event stream processing, data stream processing, or distributed stream processing) is a programming paradigm which views data streams, or sequences of events in time, as the central input and output objects of computation. Stream processing encompasses dataflow programming, reactive programming, and distributed data processing. Stream processing systems aim to expose parallel processing for data streams and rely on streaming algorithms for efficient implementation. The software stack for these systems includes components such as programming models and query languages, for expressing computation; stream management systems, for distribution and scheduling; and hardware components for acceleration including floating-point units, graphics processing units, and field-programmable gate arrays. The stream processing paradigm simplifies parallel software and hardware by restricting the parallel computation that can be performed. Given a sequence of data (a stream), a series of operations (kernel functions) is applied to each element in the stream. Kernel functions are usually pipelined, and optimal local on-chip memory reuse is attempted, in order to minimize the loss in bandwidth, associated with external memory interaction. Uniform streaming, where one kernel function is applied to all elements in the stream, is typical. Since the kernel and stream abstractions expose data dependencies, compiler tools can fully automate and optimize on-chip management tasks. Stream processing hardware can use scoreboarding, for example, to initiate a direct memory access (DMA) when dependencies become known. The elimination of manual DMA management reduces software complexity, and an associated elimination for hardware cached I/O, reduces the data area expanse that has to be involved with service by specialized computational units such as arithmetic logic units. During the 1980s stream processing was explored within dataflow programming. An example is the language SISAL (Streams and Iteration in a Single Assignment Language). (Wikipedia).
Live Stream: Process control in C
Using fork(), exec(), and wait() in C programs. And, if there's time and interest, threading in Java.
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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
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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
Filters being used: - High and low frequency denoising - Spline-based image resizing to FullHD - Image stabilization http://kostackstudio.de
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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
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PNWS 2014 - Don’t cross the streams
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From playlist PNWS 2014
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AWS Kinesis Tutorial for Beginners | Introduction to Amazon Kinesis | AWS Training | Edureka
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LambdaConf 2015 - Scalaz Streams A Functional Approach to Compositional... Derek Chen Becker
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From playlist Course | Computer Systems Laboratory Colloquium (2006-2007)
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L21.9 Splitting a Bernoulli Process
MIT RES.6-012 Introduction to Probability, Spring 2018 View the complete course: https://ocw.mit.edu/RES-6-012S18 Instructor: John Tsitsiklis License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu
From playlist MIT RES.6-012 Introduction to Probability, Spring 2018
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