WIP: new dsp chapter

content/contentdev/_index.md

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+
+weight = 8
 archetype = "chapter"
-weight = 7
 title = "Content Development"
 date = "2017-02-15T06:49:44-05:00"
 

content/dsp/_index.md

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+
+weight = 7
+archetype = "chapter"
+title = "Digital Signal Processing"
+date = "2023-05-29T16:50:01-05:00"
+
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+
+Digital Signal Processing is the discrete realization of Analog Signal Processing
+operations used to condition, amplify, characterize, and transform. 
+Digital Signal Processing is essential when interfacing a digital computer
+to a physical process to enable reproducible and high-fidelity applications.

content/dsp/conditioning.md

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+weight = 2
+title = "Signal Conditioning"
+date = "2023-05-29T16:55:11-00:00"
+toc = true
+
+tags = [ "dsp", "algorithm", "signal-conditioning" ]
+categories = [ "dsp", "conditioning" ]
+series = [ "Digital Signal Processing" ]
+
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+
+Signal conditioning is the act of making signals more robust for further processing.
+

content/dsp/filters.md

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+weight = 3
+title = "Digital Filters"
+date = "2023-05-29T16:57:39-00:00"
+toc = true
+
+tags = [ "dsp", "algorithm", "filtering" ]
+categories = [ "dsp", "filtering" ]
+series = [ "Digital Signal Processing" ]
+
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+
+Digital Filtering is the act of focusing a signal to key features for observation.
+

content/dsp/indentification.md

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+weight = 4
+title = "Identification"
+date = "2023-05-29T16:58:21-00:00"
+toc = true
+
+tags = [ "dsp", "algorithm", "identification" ]
+categories = [ "dsp", "identification" ]
+series = [ "Digital Signal Processing" ]
+
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+
+Identification is the act of recognizing the signal in the presence of noise.
+
+When there are signals and noises, physicists try to identify signals by modeling them, 
+whereas statisticians oppositely try to model noise to identify signals. In this study, 
+we applied the statisticians' concept of signal detection of physics data with small-size 
+samples and high dimensions without modeling the signals. Most of the data in nature, 
+whether noises or signals, are assumed to be generated by dynamical systems; thus, there 
+is essentially no distinction between these generating processes. We propose that the 
+correlation length of a dynamical system and the number of samples are crucial for the 
+practical definition of noise variables among the signal variables generated by such 
+a system. Since variables with short-term correlations reach normal distributions 
+faster as the number of samples decreases, they are regarded to be ``noise-like'' 
+variables, whereas variables with opposite properties are ``signal-like'' variables. 
+Normality tests are not effective for data of small-size samples with high dimensions. 
+Therefore, we modeled noises on the basis of the property of a noise variable, that is, 
+the uniformity of the histogram of the probability that a variable is a noise. 
+We devised a method of detecting signal variables from the structural change of the 
+histogram according to the decrease in the number of samples. We applied our method 
+to the data generated by globally coupled map, which can produce time series data with 
+different correlation lengths, and also applied to gene expression data, which are 
+typical static data of small-size samples with high dimensions, and we successfully 
+detected signal variables from them. 

content/dsp/spectral.md

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+weight = 5
+title = "Spectral Analysis"
+date = "2023-05-29T17:04:34-00:00"
+toc = true
+
+tags = [ "dsp", "algorithm", "spectral-analysis" ]
+categories = [ "dsp", "analyzing" ]
+series = [ "Digital Signal Processing" ]
+
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+
+Spectral Analysis analyses the frequency components of a signal.
+

content/dsp/transforms.md

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+weight = 6
+title = "Transforms"
+date = "2023-05-29T17:05:17-00:00"
+toc = true
+
+tags = [ "dsp", "algorithm", "transforms" ]
+categories = [ "dsp", "transforming" ]
+series = [ "Digital Signal Processing" ]
+
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+
+Signal Transforms map the signal function to other functions for further analysis.
+