Compressive Sampling for Signal Classification

Haupt, Jarvis; Castro, Rui; Nowak, Robert; Fudge, G.L.; Yeh, Ago

doi:10.1109/acssc.2006.354994

Cited by 91 publications

(80 citation statements)

References 9 publications

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“…For instance, in many image processing and computer vision applications, data is acquired only for the purpose of making a detection, classification, or recognition decision. Fortunately, the CS framework is information scalable to a much wider range of statistical inference tasks [17]- [19]. Tasks such as detection do not require reconstruction, but only require estimates of the relevant sufficient statistics for the problem at hand.…”

Section: Information Scalability and The Smashed Filtermentioning

confidence: 99%

Single-pixel imaging via compressive sampling

Duarte

Davenport

Takhar

et al. 2008

IEEE Signal Process. Mag.

3,036

1,757

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Humans are visual animals, and imaging sensors that extend our reachcameras-have improved dramatically in recent times thanks to the introduction of CCD and CMOS digital technology. Consumer digital cameras in the megapixel range are now ubiquitous thanks to the happy coincidence that the semiconductor material of choice for large-scale electronics integration (silicon) also happens to readily convert photons at visual wavelengths into electrons. On the contrary, imaging at wavelengths where silicon is blind is considerably more complicated, bulky, and expensive. Thus, for comparable resolution, a US$500 digital camera for the visible becomes a US$50,000 camera for the infrared.In this article, we present a new approach to building simpler, smaller, and cheaper digital cameras that can operate efficiently across a much broader spectral range than conventional silicon-based cameras. Our approach fuses a new camera architecture

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Section: Information Scalability and The Smashed Filtermentioning

confidence: 99%

Single-pixel imaging via compressive sampling

Duarte

Davenport

Takhar

et al. 2008

IEEE Signal Process. Mag.

3,036

1,757

View full text Add to dashboard Cite

show abstract

“…In addition to enabling the design of new hyperspectral imaging hardware and acquisition methods, sparsity and other lowdimensional structures provide for new ways to efficiently process the data produced by these new sensors, in some cases without ever explicitly estimating the high-dimensional hyperspectral image [41,42].…”

Section: Hyperspectral Target Detection From Projection Measurementsmentioning

confidence: 99%

Sparsity and Structure in Hyperspectral Imaging : Sensing, Reconstruction, and Target Detection

Willett

Duarte

Davenport

et al. 2014

IEEE Signal Process. Mag.

181

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“…The CS theory can be further extended to address the detection, estimation and classification problems. In this context, the most relevant works are the discussions of compressive parameter estimation in [34], [35], compressive detection in [36], [42], [43] and compressive classification in [36], [43], [46], [47].…”

Section: ) Compressive Signal Processingmentioning

confidence: 99%

Application of Compressive Sensing in Cognitive Radio Communications: A Survey

Sharma

Lagunas

Chatzinotas

et al. 2016

IEEE Commun. Surv. Tutorials

210

112

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Abstract-Compressive Sensing (CS) has received much attention in several fields such as digital image processing, wireless channel estimation, radar imaging, and Cognitive Radio (CR) communications. Out of these areas, this survey paper focuses on the application of CS in CR communications. Due to the underutilization of the allocated radio spectrum, spectrum occupancy is usually sparse in different domains such as time, frequency and space. Such a sparse nature of the spectrum occupancy has inspired the application of CS in CR communications. In this regard, several researchers have already applied the CS theory in various settings considering the sparsity in different domains. In this direction, this survey paper provides a detailed review of the state of the art related to the application of CS in CR communications. Starting with the basic principles and the main features of CS, it provides a classification of the main usage areas based on the radio parameter to be acquired by a wideband CR. Subsequently, we review the existing CSrelated works applied to different categories such as wideband sensing, signal parameter estimation and Radio Environment Map (REM) construction, highlighting the main benefits and the related issues. Furthermore, we present a generalized framework for constructing the REM in compressive settings. Finally, we conclude this survey paper with some suggested open research challenges and future directions.

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Compressive Sampling for Signal Classification

Cited by 91 publications

References 9 publications

Single-pixel imaging via compressive sampling

Single-pixel imaging via compressive sampling

Sparsity and Structure in Hyperspectral Imaging : Sensing, Reconstruction, and Target Detection

Application of Compressive Sensing in Cognitive Radio Communications: A Survey

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