Compressed sensing mm-wave SAR for non-destructive testing applications using side information

Becquaert, Mathias; Cristofani, Edison; Pandey, Ganesh; Vandewal, Marijke; Stiens, Johan; Deligiannis, Nikos

doi:10.1109/radar.2016.7485244

Cited by 6 publications

(6 citation statements)

References 13 publications

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“…The values of this coefficient lie within the range [ − 1, 1], and the clusters are considered good if the value of this coefficient is high or close to 1. The silhouette coefficient has been calculated for k within the range [2,4], and according to the results shown in Figs 11-13, we can conclude that the optimum number of clusters is 2. This decision differs from the elbow result; however, it…”

Section: K-means Algorithmmentioning

confidence: 90%

“…The 3D-printed technology has a wide range of applications such as prototyping and producing complex objects [1], medical purposes [2], industrial applications such as rapid prototyping of RF components, and antenna fabrication of synthetic aperture radar (SAR) applications [3]. Due to the great dependency on this technology during the recent few years, there has been a great interest in investigating these objects to detect defects without destroying them [4]. The idea of imaging presented in this paper is based on SAR processing which is considered as one of the advanced technologies nowadays regarding electromagnetic imaging.…”

Section: Introductionmentioning

confidence: 99%

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Investigation on the scattering characteristics and unsupervised clustering of 3D printed samples

Elsaadouny

Barowski

Jebramcik

et al. 2020

Int. J. Microw. Wireless Technol.

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In this work, the scattering characteristics of 3D-printed samples are being investigated by using a single-polarized and a cross-polarized radar system. The 3D-printed technology participates in a wide range of applications nowadays. The idea of synthetic aperture radar (SAR) has been utilized to investigate the reflected electromagnetic energy from the 3D-printed samples by setting each of the radar systems in a fixed position and the mounting sample on an x-y positioning table which has been used to achieve rectangular-scan mode for SAR. The data have been ported and processed by the matched filter approach. For better image interpretation, the data have been further processed by the median filter in order to reduce noise level while preserving the main image details. Afterwards, the data have been further investigated for determining and classifying any possible defects. This process has been accomplished by deploying the unsupervised learning concept to cluster the SAR responses into two groups, compromising the defected positions responses and the non-defected responses. The obtained results of both radar sensors have been compared and evaluated using different quality assessment factors. Moreover, unsupervised learning techniques have been investigated and the obtained results show a high degree of efficiency in clustering the SAR responses.

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Section: K-means Algorithmmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Investigation on the scattering characteristics and unsupervised clustering of 3D printed samples

Elsaadouny

Barowski

Jebramcik

et al. 2020

Int. J. Microw. Wireless Technol.

View full text Add to dashboard Cite

show abstract

“…Furthermore, we assume that Assumption IV.2. Following the rearrangement in (30), at a given position i ∈ {p + 1, . .…”

Section: Measurement Bounds For Weighted N-1 Minimizationmentioning

confidence: 99%

“…The work also provided bounds on the number of Gaussian measurements for successful recovery when sufficient support information is available. Furthermore, recent studies investigated the performance of CS with single side information in practical applications, namely, compressive video foreground extraction [28], [29], magnetic resonance imaging [16], and mm-wave synthetic aperture radar imaging [30]. For instance, the work in [16] exploits the temporal similarity in MRI longitudinal scans to accelerate MRI acquisition.…”

Section: Introductionmentioning

confidence: 99%

Measurement Bounds for Sparse Signal Reconstruction with Multiple Side Information

Luong¹,

Seiler²,

Kaup³

et al. 2016

Preprint

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In the context of compressed sensing (CS), this paper considers the problem of reconstructing sparse signals with the aid of other given correlated sources as multiple side information. To address this problem, we theoretically study a generic weighted n-1 minimization framework and propose a reconstruction algorithm that leverages multiple side information signals (RAMSI). The proposed RAMSI algorithm computes adaptively optimal weights among the side information signals at every reconstruction iteration. In addition, we establish theoretical bounds on the number of measurements that are required to successfully reconstruct the sparse source by using weighted n-1 minimization. The analysis of the established bounds reveal that weighted n-1 minimization can achieve sharper bounds and significant performance improvements compared to classical CS. We evaluate experimentally the proposed RAMSI algorithm and the established bounds using synthetic sparse signals as well as correlated feature histograms, extracted from a multiview image database for object recognition. The obtained results show clearly that the proposed algorithm outperforms state-of-the-art algorithms-including classical CS, 1-1 minimization, Modified-CS, regularized Modified-CS, and weighted 1 minimizationin terms of both the theoretical bounds and the practical performance.

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“…The work also provided bounds on the number of Gaussian measurements for successful recovery when sufficient support information is available. Furthermore, recent studies proposed CS with side information in practical applications, i.e., compressive video foreground extraction [34][35][36], magnetic resonance imaging (MRI) [17], and synthetic aperture radar imaging [37].…”

mentioning

confidence: 99%

Sparse signal recovery with multiple prior information: Algorithm and measurement bounds

et al. 2018

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Compressed sensing mm-wave SAR for non-destructive testing applications using side information

Cited by 6 publications

References 13 publications

Investigation on the scattering characteristics and unsupervised clustering of 3D printed samples

Investigation on the scattering characteristics and unsupervised clustering of 3D printed samples

Measurement Bounds for Sparse Signal Reconstruction with Multiple Side Information

Sparse signal recovery with multiple prior information: Algorithm and measurement bounds

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