Performance of error correction based on Compressed Sensing

Zorlein, Henning; Lazich, Dejan E.; Bossert, Martin

doi:10.1109/iswcs.2011.6125372

Cited by 6 publications

(2 citation statements)

References 8 publications

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“…BASC sensing matrix [19] construction yields minimal coherence between code words, but they had to be adapted the feasible hardware implementation. They were the best performing matrices with observed improvement as shown in Table I and Fig.…”

Section: Resultsmentioning

confidence: 99%

Performance Evaluation of Spatial Modulation Patterns in Compressive Sensing Terahertz Imaging

2022

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Terahertz (THz) imaging traditionally uses pixelto-pixel mechanical raster scanning, which is slow and limits the resolution and potential applications. Compressive Sensing Terahertz (CS-THz) imaging has the potential to solve these challenges by reducing the number of required measurements. However, there is an existing research gap between the current CS-THz implementations, which often use random binary masks without further consideration, and specialized Compressive Sensing works, which focus on RIP and coherence reduction but ignore the physics underlying THz wave propagation such as the effect of diffraction on masks patterns. This paper discusses and evaluates the use of low-coherence sensing matrices as mask patterns for CS-THz imaging. Although not previously used in THz imaging, Best Antipodal Spherical Code masks show the best image reconstruction performance among the considered alternatives. We also demonstrate the feasibility of phase-only modulation.

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Section: Resultsmentioning

confidence: 99%

Performance Evaluation of Spatial Modulation Patterns in Compressive Sensing Terahertz Imaging

2022

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“…Since the ENCRUST scheme is designed using CS concepts, we use means square error (MSE) as the performance metric for K-sparse signals. If the MSE is less than 10 −6 , then the reconstruction is regarded as successful [24], [52]. To measure the error recovery capability, we use different channel parameters as discussed in the following subsection.…”

Section: Simulation Resultsmentioning

confidence: 99%

A Novel Efficient Secure and Error-Robust Scheme for Internet of Things Using Compressive Sensing

Kuldeep

Zhang

2021

IEEE Access

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In most of existing Internet of Things (IoT) applications, data compression, data encryption and error/erasure correction are implemented separately. To achieve reliable communication, in particular, in harsh wireless environment with strong interference, error/erasure correction codes with higher correction capability or Automatic repeat request (ARQ) scheme are desirable but at the cost of increasing complexity and energy consumption. Due to resource-constrained IoT device, it is often challenging to implement all of them. In this paper, we propose a novel lightweight efficient secure error-robust scheme, ENCRUST, which is able to achieve these three functions using simple matrix multiplication. ENCRUST is built on the new theoretical foundation of projection-based encoding presented in this paper, by leveraging the sparsity inherent in the signal. We perform theoretical analysis and experimental study of the proposed scheme in comparison with the conventional schemes. It shows that the proposed scheme can work in low SINR range and the reconstructed signal quality shows graceful degradation. Furthermore, we apply the proposed scheme on real-life electrocardiogram (ECG) dataset and images. The results demonstrate that ENCRUST achieves decent compression, information secrecy as well as strong error recovery in one go.

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Unbiasing in Iterative Reconstruction Algorithms for Discrete Compressed Sensing

Fischer

Sippel

2022

Compressed Sensing in Information Processing

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Performance of error correction based on Compressed Sensing

Cited by 6 publications

References 8 publications

Performance Evaluation of Spatial Modulation Patterns in Compressive Sensing Terahertz Imaging

Performance Evaluation of Spatial Modulation Patterns in Compressive Sensing Terahertz Imaging

A Novel Efficient Secure and Error-Robust Scheme for Internet of Things Using Compressive Sensing

Unbiasing in Iterative Reconstruction Algorithms for Discrete Compressed Sensing

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