DMD Implementation of a Single Pixel Camera Based on Compressed Sensing

The ideas presented here can be used to illustrate the links between data acquisition, linear algebra,basis expansions, inverse problems, compression, dimensionality reduction, and optimization in avariety of courses, from undergraduate or graduate digital signal processing to statistics and appliedmathematics.

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The ideas presented here can be used to illustrate the links between data acquisition, linear algebra,basis expansions, inverse problems, compression, dimensionality reduction, and optimization in avariety of courses, from undergraduate or graduate digital signal processing to statistics and appliedmathematics.

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The ideas presented here can be used to illustrate the links between data acquisition, linear algebra,basis expansions, inverse problems, compression, dimensionality reduction, and optimization in avariety of courses, from undergraduate or graduate digital signal processing to statistics and appliedmathematics.

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The ideas presented here can be used to illustrate the links between data acquisition, linear algebra,basis expansions, inverse problems, compression, dimensionality reduction, and optimization in avariety of courses, from undergraduate or graduate digital signal processing to statistics and appliedmathematics.

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The ideas presented here can be used to illustrate the links between data acquisition, linear algebra,basis expansions, inverse problems, compression, dimensionality reduction, and optimization in avariety of courses, from undergraduate or graduate digital signal processing to statistics and appliedmathematics.

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The ideas presented here can be used to illustrate the links between data acquisition, linear algebra,basis expansions, inverse problems, compression, dimensionality reduction, and optimization in avariety of courses, from undergraduate or graduate digital signal processing to statistics and appliedmathematics.

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The ideas presented here can be used to illustrate the links between data acquisition, linear algebra,basis expansions, inverse problems, compression, dimensionality reduction, and optimization in avariety of courses, from undergraduate or graduate digital signal processing to statistics and appliedmathematics.

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The ideas presented here can be used to illustrate the links between data acquisition, linear algebra,basis expansions, inverse problems, compression, dimensionality reduction, and optimization in avariety of courses, from undergraduate or graduate digital signal processing to statistics and appliedmathematics.

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The ideas presented here can be used to illustrate the links between data acquisition, linear algebra,basis expansions, inverse problems, compression, dimensionality reduction, and optimization in avariety of courses, from undergraduate or graduate digital signal processing to statistics and appliedmathematics.

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The ideas presented here can be used to illustrate the links between data acquisition, linear algebra,basis expansions, inverse problems, compression, dimensionality reduction, and optimization in avariety of courses, from undergraduate or graduate digital signal processing to statistics and appliedmathematics.

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The ideas presented here can be used to illustrate the links between data acquisition, linear algebra,basis expansions, inverse problems, compression, dimensionality reduction, and optimization in avariety of courses, from undergraduate or graduate digital signal processing to statistics and appliedmathematics.

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The ideas presented here can be used to illustrate the links between data acquisition, linear algebra,basis expansions, inverse problems, compression, dimensionality reduction, and optimization in avariety of courses, from undergraduate or graduate digital signal processing to statistics and appliedmathematics.

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The ideas presented here can be used to illustrate the links between data acquisition, linear algebra,basis expansions, inverse problems, compression, dimensionality reduction, and optimization in avariety of courses, from undergraduate or graduate digital signal processing to statistics and appliedmathematics.

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The ideas presented here can be used to illustrate the links between data acquisition, linear algebra,basis expansions, inverse problems, compression, dimensionality reduction, and optimization in avariety of courses, from undergraduate or graduate digital signal processing to statistics and appliedmathematics.

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The ideas presented here can be used to illustrate the links between data acquisition, linear algebra,basis expansions, inverse problems, compression, dimensionality reduction, and optimization in avariety of courses, from undergraduate or graduate digital signal processing to statistics and appliedmathematics.

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The ideas presented here can be used to illustrate the links between data acquisition, linear algebra,basis expansions, inverse problems, compression, dimensionality reduction, and optimization in avariety of courses, from undergraduate or graduate digital signal processing to statistics and appliedmathematics.

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The ideas presented here can be used to illustrate the links between data acquisition, linear algebra,basis expansions, inverse problems, compression, dimensionality reduction, and optimization in avariety of courses, from undergraduate or graduate digital signal processing to statistics and appliedmathematics.

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The ideas presented here can be used to illustrate the links between data acquisition, linear algebra,basis expansions, inverse problems, compression, dimensionality reduction, and optimization in avariety of courses, from undergraduate or graduate digital signal processing to statistics and appliedmathematics.

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