Soft computing based compressive sensing techniques in signal processing: A comprehensive review

Mishra, Ishani; Jain, Sanjay

doi:10.1515/jisys-2019-0215

Cited by 16 publications

(5 citation statements)

References 62 publications

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“…As mentioned above, this study focuses on sparse modeling [15]. Sparse modeling was separately developed for sparse coding [16] and compressed sensing [17]. Sparse coding was developed mainly for signal processing, such as for images and audio.…”

Section: Related Workmentioning

confidence: 99%

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Comparative Analysis of Digital Elevation Model Generation Methods Based on Sparse Modeling

Fuse,

Imose

2023

Remote Sensing

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With the spread of aerial laser bathymetry (ALB), seafloor topographies are being measured more frequently. Nevertheless, data deficiencies occur owing to seawater conditions and other factors. Conventional interpolation methods generally need to produce digital elevation models (DEMs) with sufficient accuracy. If the topographic features are considered as a basis, the DEM should be reproducible based on a combination of such features. The purpose of this study is to develop new DEM generation methods based on sparse modeling. Based on a review of the definitions of sparsity, we developed DEM generation methods based on a discrete cosine transform (DCT), DCT with elastic net, K-singular value decomposition (K-SVD), Fourier regularization, wavelet regularization, and total variation (TV) minimization, and conducted a comparative analysis. The developed methods were applied to artificially deficient DEM and ALB data, and their accuracy was evaluated. Thus, as a conclusion, we can confirm that the K-SVD method is appropriate when the percentage of deficiencies is low, and that the TV minimization method is appropriate when the percentage of deficiencies is high. Based on these results, we also developed a method integrating both methods and achieved an RMSE of 0.128 m.

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Section: Related Workmentioning

confidence: 99%

“…prox λ g (r) = F T so f t(Fr, λ) (17) where the function so f t(y, λ) is called the "soft threshold function" and is defined according to Equation (18).…”

Section: Compressed Sensing Using Fourier Transformmentioning

confidence: 99%

Comparative Analysis of Digital Elevation Model Generation Methods Based on Sparse Modeling

Fuse,

Imose

2023

Remote Sensing

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“…Topics covered include the system architecture, various signal processing approaches, and performance testing. The results of the experiments show that the combined compressive sensing (CS) and DTCWT approach is effective for dependable signal recovery while using a low amount of electricity [10], [11]. CS and DTCWT processing modules nested within an FPGA-based mm-wave transceiver are two solutions that we recommend [12] for resolving the challenges at hand.…”

Section: Introductionmentioning

confidence: 99%

FPGA implementation of DTCWT architecture's high-speed DA structure for OFDM-based transceiver with CS

Sindgi,

Mahadevaswamy

2024

Bulletin EEI

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Communication systems at millimeter-wave (mm-wave) frequencies with high propagation losses use radio frequency (RF) budget analysis. RF system gains and losses ensure the receiver can recover the broadcast signal. Modern communication systems use compressive sensing (CS) and discrete wavelet transform (DWT). Hardware implementation is hard. Fieldprogrammable gate arrays (FPGA) adaptability, configurability, and processing speed make them popular. More mm-wave transceivers use FPGAs and advanced signal processing. FPGA-based mm-wave transceivers use compressed sensing and dual-tree complex wavelet transform (DTCWT). RF budget analysis recovers receiver signals. Energy and data efficiency transceivers have baseband processors, transmitters, and receivers. RF-to-mm-wave transmitter. Receiver demodulation and baseband conversion. CS and DTCWT processing modules boost baseband signal processing 5 Gbps Xilinx virtex-6 FPGAs. The system retrieves the signal while conserving power, according to simulations and testing. This study found that FPGA-based mm-wave transceivers can use advanced signal processing in future high-speed communication systems.

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“…There is a compulsory need to compress the ECG signal before sending it to the cloud to meet the bandwidth capacity and to serve multiple parallel remote patients. It is also important that the quality of the decompressed signal should be good enough to yield accurate data analytics results in machine learning algorithms to predict arrhythmia [12,13].…”

Section: Introductionmentioning

confidence: 99%

Review on Compressive Sensing Algorithms for ECG Signal for IoT Based Deep Learning Framework

Kumar

Ramachandran

2022

Applied Sciences

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Nowadays, healthcare is becoming very modern, and the support of Internet of Things (IoT) is inevitable in a personal healthcare system. A typical personal healthcare system acquires vital parameters from human users and stores them in a cloud platform for further analysis. Acquiring fundamental biomedical signal, such as with the Electrocardiograph (ECG), is also considered for specific disease analysis in personal healthcare systems. When such systems are scaled up, there is a heavy demand for internet channel capacity to accommodate real time seamless flow of discrete samples of biomedical signals. So, there is a keen need for real time data compression of biomedical signals. Compressive Sensing (CS) has recently attracted more interest due to its compactness and its feature of the faithful reconstruction of signals from fewer linear measurements, which facilitates less than Shannon’s sampling rate by exploiting the signal sparsity. The most common biomedical signal that is to be analyzed is the ECG signal, as the prediction of heart failure at an early stage can save a human life. This review is for a vast use-case of IoT framework in which CS measurements of ECG are acquired, communicated through Internet to a server, and the arrhythmia are analyzed using Machine learning (ML). Assuming this use-case specific for ECG, in this review many technical aspects are considered regarding various research components. The key aspect is on the investigation of the best sensing method, and to address this, various sensing matrices are reviewed, analyzed and recommended. The next aspect is the selection of the optimal sparsifying method, and the review recommends unexplored ECG compression algorithms as sparsifying methods. The other aspects are optimum reconstruction algorithms, best hardware implementations, suitable ML methods and effective modality of IoT. In this review all these components are considered, and a detailed review is presented which enables us to orchestrate the use-case specified above. This review focuses on the current trends in CS algorithms for ECG signal compression and its hardware implementation. The key to successful reconstruction of the CS method is the right selection of sensing and sparsifying matrix, and there are many unexplored sparsifying methods for the ECG signal. In this review, we shed some light on new possible sparsifying techniques. A detailed comparison table of various CS algorithms, sensing matrix, sparsifying techniques with different ECG dataset is tabulated to quantify the capability of CS in terms of appropriate performance metrics. As per the use-case specified above, the CS reconstructed ECG signals are to be subjected to ML analysis, and in this review the compressive domain inference approach is discussed. The various datasets, methodologies and ML models for ECG applications are studied and their model accuracies are tabulated. Mostly, the previous research on CS had studied the performance of CS using numerical simulation, whereas there are some good attempts for hardware implementations for ECG applications, and we studied the uniqueness of each method and supported the study with a comparison table. As a consolidation, we recommend new possibilities of the research components in terms of new transforms, new sparsifying methods, suggestions for ML approaches and hardware implementation.

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Soft computing based compressive sensing techniques in signal processing: A comprehensive review

Cited by 16 publications

References 62 publications

Comparative Analysis of Digital Elevation Model Generation Methods Based on Sparse Modeling

Comparative Analysis of Digital Elevation Model Generation Methods Based on Sparse Modeling

FPGA implementation of DTCWT architecture's high-speed DA structure for OFDM-based transceiver with CS

Review on Compressive Sensing Algorithms for ECG Signal for IoT Based Deep Learning Framework

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