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Synthesis Lectures on Signal Processing
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#1Henry Braun (ASU: Arizona State University)H-Index: 6
#2Pavan Turaga (ASU: Arizona State University)H-Index: 24
Last.Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 26
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Abstract Compressed sensing (CS) allows signals and images to be reliably inferred from undersampled measurements. Exploiting CS allows the creation of new types of high-performance sensors includi...
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#1Nasser Kehtarnavaz (UTD: University of Texas at Dallas)H-Index: 32
#2Fatemeh Saki (UTD: University of Texas at Dallas)H-Index: 9
Last.Adrian Duran (UTD: University of Texas at Dallas)
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A typical undergraduate electrical engineering curriculum incorporates a signals and systems course. The widely used approach for the laboratory component of such courses involves the utilization of MATLAB to implement signals and systems concepts. This lecture series book presents a newly developed laboratory paradigm where MATLAB codes are made to run on smartphones, which most students already possess. This smartphone-based approach enables an anywhere-anytime platform for students to conduct...
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#1Danilo Orlando (University of Salento)H-Index: 23
#2Francesco Bandiera (University of Salento)H-Index: 15
Last.Giuseppe Ricci (University of Salento)H-Index: 27
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Adaptive detection of signals embedded in correlated Gaussian noise has been an active field of research in the last decades. This topic is important in many areas of signal processing such as, just to give some examples, radar, sonar, communications, and hyperspectral imaging. Most of the existing adaptive algorithms have been designed following the lead of the derivation of Kelly's detector which assumes perfect knowledge of the target steering vector. However, in realistic scenarios, mismatch...
82 CitationsSource
#1Yanwei WangH-Index: 8
#2Jian LiH-Index: 75
Last.Petre StoicaH-Index: 90
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Spectral estimation is important in many fields including astronomy, meteorology, seismology, communications, economics, speech analysis, medical imaging, radar, sonar, and underwater acoustics. Most existing spectral estimation algorithms are devised for uniformly sampled complete-data sequences. However, the spectral estimation for data sequences with missing samples is also important in many applications ranging from astronomical time series analysis to synthetic aperture radar imaging with a...
56 CitationsSource
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