Compressive Sensing Algorithm Research Articles

AMS-Net: Adaptive Multi-Scale Network for Image Compressive Sensing

Recently, deep convolutional neural networks have been applied to image compressive sensing (CS) to improve reconstruction quality while reducing computation cost. Existing deep learning-based CS methods can be divided into two classes: sampling image at single scale and sampling image across multiple scales. However, these existing methods treat the image low-frequency and high-frequency components equally, which is an obstruction to get a high reconstruction quality. This paper proposes an adaptive multi-scale image CS network in wavelet domain called AMS-Net, which fully exploits the different importance of image low-frequency and high-frequency components. First, the discrete wavelet transform is used to decompose an image into four sub-bands, namely the low-low (LL), low-high (LH), high-low (HL), and high-high (HH) sub-bands. Considering that the LL sub-band is more important to the final reconstruction quality, the AMS-Net allocates it a larger sampling ratio, while allocating the other three sub-bands a smaller one. Since different blocks in each sub-band have different sparsity, the sampling ratio is further allocated block-by-block within the four sub-bands. Then a dual-channel scalable sampling model is developed to adaptively sample the LL and the other three sub-bands at arbitrary sampling ratios. Finally, by unfolding the iterative reconstruction process of the traditional multi-scale block CS algorithm, we construct a multi-stage reconstruction model to utilize multi-scale features for further improving the reconstruction quality. Experimental results demonstrate that the proposed model outperforms both the traditional and state-of-the-art deep learning-based methods.

Compressive sensing-based channel estimation for high mobile systems with delay Doppler effects

In this paper, channel overhead is reduced by exploiting channel sparsity for multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) system. Where, compressive sensing (CS) based dictionary design algorithms has been adopted as a channel estimation technique in high mobile systems with minimal number of pilots, such as high-speed train (HST) systems. A novel framework design of the dictionary-based CS was proposed considering both delay and Doppler effects in order to correctly recover the channel response. The channel under consideration is a 2 by 2 space-time block code (STBC) MIMO channel. Simulation tests according to the international telecommunication union (ITU) channel model demonstrated the suitability of the proposed dictionary for estimating the channel impulse response (CIR) of a liner time varying (LTV) channel with a mobility approaches 675 Km/h related to a Doppler frequency of 1500 Hz and 2.4 GHz carrier frequency. Two CS recovery algorithms were applied; orthogonal matching pursuit (OMP) and basis pursuit (BP), where by about 7 dB gain in signal to noise ratio (SNR) was achieved with mobility of 675 Km/h using OMP as compared to BP at a bit error rate (BER) of with 128 OFDM subcarriers.

Supervised Dictionary Learning for Block Threshold Feature in Compressive Spectrum Sensing

With the development of communication systems towards the high-frequency band, the demand for spectrum resources is ever-increasing, where the research interest has changed from narrowband spectrum sensing to wideband spectrum sensing. The Nyquist-rate-based wideband spectrum sensing with high-rate sampling is being questioned whether it is suitable for real-time applications. On the contrary, the well-known compressive spectrum sensing (CSS) is more appealing due to the compressive sensing (CS) technology, resulting in lower signal acquisition costs. However, the CS algorithms for recovering sparse spectrum generally require multiple iterations, which presents a challenge to the low complexity implementation of spectrum sensing. To address this issue, this paper proposes a novel method for solving the block CSS (BCSS) problem, where the spectrum of primary users is modeled as a block structure signal. Specifically, the block threshold feature (BTF) is utilized to reconstruct the spectrum while bypassing any iterative operations. Furthermore, to improve the performance of the BTF-based BCSS, we develop a novel supervised dictionary learning (SDL) model, based on the theoretical analysis of mutual incoherence and restricted isometry properties. Simulation results not only verify the compatibility of BTF and the SDL model, but also demonstrate the effectiveness and robustness of SDL-BTF-based BCSS for practical implementation.

Uncertainty-aware physics-driven deep learning network for free-breathing liver fat and R2 * quantification using self-gated stack-of-radial MRI.

To develop a deep learning-based method for rapid liver proton-density fat fraction (PDFF) and R2 * quantification with built-in uncertainty estimation using self-gated free-breathing stack-of-radial MRI. This work developed an uncertainty-aware physics-driven deep learning network (UP-Net) to (1) suppress radial streaking artifacts because of undersampling after self-gating, (2) calculate accurate quantitative maps, and (3) provide pixel-wise uncertainty maps. UP-Net incorporated a phase augmentation strategy, generative adversarial network architecture, and an MRI physics loss term based on a fat-water and R2 * signal model. UP-Net was trained and tested using free-breathing multi-echo stack-of-radial MRI data from 105 subjects. UP-Net uncertainty scores were calibrated in a validation dataset and used to predict quantification errors for liver PDFF and R2 * in a testing dataset. Compared with images reconstructed using compressed sensing (CS), UP-Net achieved structural similarity index >0.87 and normalized root mean squared error <0.18. Compared with reference quantitative maps generated using CS and graph-cut (GC) algorithms, UP-Net achieved low mean differences (MD) for liver PDFF (-0.36%) and R2 * (-0.37 s-1 ). Compared with breath-holding Cartesian MRI results, UP-Net achieved low MD for liver PDFF (0.53%) and R2 * (6.75 s-1 ). UP-Net uncertainty scores predicted absolute liver PDFF and R2 * errors with low MD of 0.27% and 0.12 s-1 compared to CS + GC results. The computational time for UP-Net was 79 ms/slice, whereas CS + GC required 3.2 min/slice. UP-Net rapidly calculates accurate liver PDFF and R2 * maps from self-gated free-breathing stack-of-radial MRI. The pixel-wise uncertainty maps from UP-Net predict quantification errors in the liver.

Efficient experimental characterization of quantum processes via compressed sensing on an NMR quantum processor

We employ the compressed sensing (CS) algorithm and a heavily reduced data set to experimentally perform true quantum process tomography (QPT) on an NMR quantum processor. We obtain the estimate of the process matrix \(\chi \) corresponding to various two- and three-qubit quantum gates with a high fidelity. The CS algorithm is implemented using two different operator bases, namely the standard Pauli basis and the Pauli-error basis. We experimentally demonstrate that the performance of the CS algorithm is significantly better in the Pauli-error basis, where the constructed \(\chi \) matrix is maximally sparse. We compare the standard least square (LS) optimization QPT method with the CS-QPT method and observe that, provided an appropriate basis is chosen, the CS-QPT method performs significantly better as compared to the LS-QPT method. In all the cases considered, we obtained experimental fidelities greater than 0.9 from a reduced data set, which was approximately 5–6 times smaller in size than a full data set. We also experimentally characterized the reduced dynamics of a two-qubit subsystem embedded in a three-qubit system and used the CS-QPT method to characterize processes corresponding to the evolution of two-qubit states under various J-coupling interactions.

A Study on Millimeter Wave SAR Imaging for Non-Destructive Testing of Rebar in Reinforced Concrete.

In this study, we investigate a millimeter wave (mmWave) synthetic aperture radar (SAR) imaging scheme utilizing a low-cost frequency modulated continuous wave (FMCW) radar to take part in non-destructive testing which could be a useful tool for both civilian and military demands. The FMCW radar working in the frequency range from 76 GHz to 81 GHz is equipped with a 2-D moving platform aiming to reconstruct the 2-D image of the shape of the target object. Due to the lab environment containing several devices and furniture, various noise and interference signals from the floor are not avoidable. Therefore, the digital signal processing algorithms are joined to remove the undesired signals as well as improve the target recognition. This study adopts the range migration algorithms (RMAs) on the processed reflected signal data to form the image of the target because of its verified ability in this type of mission. On the other hand, the integration of compressed sensing (CS) algorithms into the SAR imaging system is also researched which helps to improve the performance of the system by reducing the measurement duration while still maintaining the image quality. Three minimization algorithms are used involving the imaging system as the CS solvers reconstruct the radar data before being processed by RMA to form the image. The proposed imaging scheme demonstrates its good ability with high azimuth resolution in the mission of detecting tiny cracks in the rebar of reinforced concrete. In addition, the participation of CS algorithms improves the performance of the scheme as the cracks on the rebar can be located on the images, which are reconstructed from only 30% of the dataset. The comparison of CS solvers shows that ADMM outperforms the other candidates in the reconstruction task.

Fetal 3D cardiovascular cine image acquisition using radialsampling and compressed sensing.

To explore a fetal 3D cardiovascular cine acquisition using a radial image acquisition and compressed-sensing reconstruction and compare image quality and scan time with conventional multislice 2D imaging. Volumetric fetal cardiac data were acquired in 26 volunteers using a radial 3D balanced SSFP pulse sequence. Cardiac gating was performed using a Doppler ultrasound device. Images were reconstructed using a parallel-imaging and compressed-sensing algorithm. Multiplanar reformatting to standard cardiac views was performed before image analysis. Clinical 2D images were used for comparison. Qualitative and quantitative image evaluation were performed by two experienced observers (scale: 1-4). Volumes, mass, and function were assessed. Average scan time for the 3D imaging was 6min, including one localizer. A 2D imaging stack covering the entire heart including localizer sequences took at least 6.5min, depending on planning complexity. The 3D acquisition was successful in 7 of 26 subjects (27%). Overall image contrast and perceived resolution were lower in the 3D images. Nonetheless, the 3D images had, on average, a moderate cardiac diagnostic quality (median [range]: 3 [1-4]). Standard clinical 2D acquisitions had a high cardiac diagnostic quality (median [range]: 4 [3, 4]). Cardiac measurements were not different between 2D and 3D images (all p> 0.16). The presented free-breathing whole-heart fetal 3D radial cine MRI acquisition and reconstruction method enables retrospective visualization of all cardiac views while keeping examination times short. This proof-of-concept work produced images with diagnostic quality, while at the same time reducing the planning complexity to a single localizer.

Distributed ranking-based resource allocation for sporadic M2M communication

This work proposes a novel scheme for distributed ranking-based and contention-free resource allocation in large-scale machine-to-machine (M2M) communication networks. We partition a network of N devices into disjoint clusters based on service type, and assign to each cluster a cluster-specific signature for active cluster members to indicate their active status. The devices in each cluster are totally ordered in some a priori-known manner, which gives rise to an active ranking of active cluster members. In order to tackle complexity issues in large-scale M2M networks with a massive number of devices, we propose a distributed resource allocation scheme using the framework of compressed sensing (CS), which mainly consists of three phases: (i) In a full-duplex acquisition phase, the devices transmit their cluster-specific signatures simultaneously and the network activation pattern is collected in a distributed manner. (ii) The base station detects the active clusters and the number of active devices per cluster using block sketching, and allocates resources to each active cluster accordingly. (iii) Each active device determines its active ranking in the cluster and accesses a specific resource according to the ranking position. By exploiting the sparsity in the activation pattern of the M2M devices, the proposed scheme is formulated as a CS support recovery problem for a particular binary block-sparse signal xin {mathbb{B}}^N – with block sparsity K_{B} and in-block sparsity K_{I} over block size d. Our analysis shows that the proposed scheme efficiently reduces the signature length to mathcal {O}(max {K_{B}log N, K_{B}K_{I}log d}) and achieves less computational complexity of mathcal {O}(dK_{I}^{2}+frac{N}{d}log N) compared with standard CS algorithms. Moreover, numerical results suggest strong robustness of the proposed scheme under noisy conditions.

Compressed Sensing Technique for the Localization of Harmonic Distortions in Electrical Power Systems.

The present work proposes to locate harmonic frequencies that distort the fundamental voltage and current waves in electrical systems using the compressed sensing (CS) technique. With the compressed sensing algorithm, data compression is revolutionized, a few samples are taken randomly, a measurement matrix is formed, and according to a linear transformation, the signal is taken from the time domain to the frequency domain in a compressed form. Then, the inverse linear transformation is used to reconstruct the signal with a few sensed samples of an electrical signal. Therefore, to demonstrate the benefits of CS in the detection of harmonics in the electrical network of this work, power quality analyzer equipment (commercial) is used. It measures the current of a nonlinear load and issues its results of harmonic current distortion (THD-I) on its screen and the number of harmonics detected in the network; this equipment acquires the data based on the Shannon–Nyquist theorem taken as a standard of measurement. At the same time, an electronic prototype senses the current signal of the nonlinear load. The prototype takes data from the current signal of the nonlinear load randomly and incoherently, so it takes fewer samples than the power quality analyzer equipment used as a measurement standard. The data taken by the prototype are entered into the Matlab software via USB, and the CS algorithm run and delivers, as a result, the harmonic distortions of the current signal THD-I and the number of harmonics. The results obtained with the compressed sensing algorithm versus the standard measurement equipment are analyzed, the error is calculated, and the number of samples taken by the standard equipment and the prototype, the machine time, and the maximum sampling frequency are analyzed.

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

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.

Complex object wave retrieval using single-pixel compressive imaging of an off-axis Fresnel hologram

A complex wavefield imaging using single-pixel compressive sensing of an off-axis digital Fresnel hologram is proposed. A digital micromirror device (DMD) sampling prior to the single-pixel detection does the sparse representation of the hologram. The digital hologram is computationally recovered using a compressive sensing (CS) algorithm, and then the complex object wavefield is reconstructed using a complex wave retrieval method. The impact of the use of two measurement operators namely random and Walsh–Hadamard measurement matrices on the reconstruction quality of the hologram and complex object wavefield is compared. The cases of CS algorithm using different sparsification criteria i.e. Haar wavelet, Fresnelet sparsification and without sparsification of the digital hologram are analysed. The computer simulation results of the proposed CS method reveals that the Walsh–Hadamard sampling with Fresnelet sparsification achieves good quality reconstructions of both intensity and phase of the object wavefield.

Partial Hadamard encoded synthetic transmit aperture for high frame rate imaging with minimal l 2-norm least squares method

Objective. Synthetic transmit aperture (STA) ultrasound imaging is well known for ideal focusing in the full field of view. However, it suffers from low signal-to-noise ratio (SNR) and low frame rate, because each transducer element must be activated individually. In our previous study, we encoded all the transducer elements with partial Hadamard matrix and reconstructed the complete STA dataset with compressed sensing (CS) algorithm (CS-STA). As all the elements are activated in each transmission and the number of transmissions is smaller than that of STA, this method can achieve higher SNR and higher frame rate. Its main drawback is the time-consuming CS reconstruction (∼hours). In this study, we propose to accelerate the complete STA dataset reconstruction with minimal l 2-norm least squares method. Approach. Partial Hadamard apodized plane wave (PW) transmissions were performed to acquire the PW dataset. Thereafter, the complete STA dataset can be reconstructed from the PW dataset with minimal l 2-norm least squares method. Due to the orthogonality of partial Hadamard matrix, the minimal l 2-norm least squares solution can be easily calculated. Main results. The proposed method is tested with simulation data and experimental phantom and in-vivo data. The results demonstrate that the proposed method achieves ∼5 × 103 times faster reconstruction speed than CS algorithm. The simulation results demonstrate that the proposed method is capable of achieving the same accuracy as the conventional CS-STA method for the STA dataset reconstruction. The simulations, phantom and in-vivo experiments show that the proposed method is capable of improving the generalized contrast-to-noise ratio (gCNR) and SNR with maintained spatial resolution and fewer transmissions, compared with STA. Significance. In conclusion, the improved image quality and reduced computational time of LS-STA pave the way for its real-time applications in the clinics.

DL-CSNet: Dictionary Learning based Compressed Sensing Neural Network

In this paper, we propose a novel neural network for Compressed Sensing (CS) application: the Dictionary Learning based Compressed Sensing neural Network (DL-CSNet). It is fairly simple but highly effective, which consists of only three layers: 1) a DL layer for latent sparse features extraction; 2) a smoothing layer via Total Variation (TV) like constraint; and 3) a CS acquisition layer for neural network training. In particular, the TV-like smoothing layer is a perfect complement to the sparsity-oriented DL layer to achieve smooth images. The trained DL-CSNet can learn the optimal dictionary matrix so that images can be reconstructed in high quality. At last, extensive experiments have been carried out on binary images and compared to most classical CS algorithms, which shows the superior performance of the proposed DL-CSNet.

Generalized residual ratio thresholding

Recovering sparse high dimensional vectors from low dimensional linear measurements is an important compressive sensing (CS) problem. Many CS algorithms assume a priori knowledge of parameters like signal sparsity or noise variance which are unavailable in most real-life problems. It is also difficult to efficiently estimate these parameters with finite sample guarantees. This article proposes a support recovery technique called generalized residual ratio thresholding (GRRT) that can operate many popular sparsity or noise variance dependent sparse recovery algorithms in a sparsity and noise variance oblivious fashion with finite sample guarantees when the noise is composed of independent and identically distributed (i.i.d) Gaussian random variables and design matrix satisfies certain regularity conditions. Numerical simulations and theoretical results in sparse estimation scenarios like single measurement vector, multiple measurement vectors, block sparsity etc. indicate that the performance of algorithms operated using GRRT is comparable to the performance of same algorithms when operated with a priori knowledge of sparsity and noise variance.

Temporal Correlation Enhanced Sparse Activity Detection in MIMO Enabled Grant-Free NOMA

Exploiting the sparse user activity induced by sporadic transmission, compressed sensing (CS) has been widely applied in multiple-input multiple-output (MIMO) enabled non-orthogonal multiple access (NOMA) for efficient multiuser detection. However, most of the existing detection schemes in MIMO enabled NOMA systems focus mainly on the spatial structure in user activity induced by multiantenna reception, while the temporal correlation in user activity has rarely been incorporated for further performance improvement. To address this issue, we propose a novel Multiuser Detection framework in MIMO enabled NOMA (MDMN), which explicitly integrates the temporal correlation in user activity into the detection process to improve the user detection accuracy. Specifically, we first formulate multiuser detection with multiantenna reception in MDMN as a block CS problem, by exploiting the spatial correlation in user activity. Furthermore, through incorporating the temporal correlation in active user sets into block CS, an adaptive CS algorithm based on subspace pursuit is developed for MDMN, named spatial-temporal correlation enhanced adaptive subspace pursuit (STASP). In particular, STASP does not require any prior knowledge of the user sparsity, as the cross validation designed in STASP can properly terminate the algorithm. The superior performance of the proposed MDMN framework and the corresponding algorithms is corroborated by extensive simulation results.

Compressive Sensing-Based Secure Uplink Grant-Free Systems

Compressive sensing (CS) has been extensively employed in uplink grant-free communications, where data generated from different active users are transmitted to a base station (BS) without following the strict access grant process. Nevertheless, the state-of-the-art CS algorithms rely on a highly limited category of measurement matrix, that is, pilot matrix, which may be analyzed by an eavesdropper (Eve) to infer the user’s channel information. Thus, the physical layer security becomes a critical issue in uplink grant-free communications. In this article, the channel reciprocity in time-division duplex systems is utilized to design environment-aware (EA) pilots derived from transmission channels to prevent eavesdroppers from acquiring users’ channel information. The simulation results show that the proposed EA-based pilot approach possesses a high level of security by scrambling the Eve’s normalized mean square error performance of channel estimation.

Efficient Instantaneous Channel Propagation Modeling for Aeronautical Communications Systems With Compressed Sensing

Establishing an instantaneous channel propagation model in aircraft engineering simulators is important for aircraft systems development. This article investigates an instantaneous channel propagation modeling scheme with compressed sensing (CS) recovery and electromagnetic (EM) interpolation for aeronautical communications systems to reduce the cost of flight experiments. Specifically, the system architecture introduces the proposed modeling process of the instantaneous aeronautical channel. The mathematical models are analyzed to show the sparsity of the aeronautical channel with a limited scattering environment. The channel modeling algorithm is detailed by explaining the CS algorithm and the EM interpolation scheme. The experimental results show the effectiveness of the proposed method. Most recovery relative errors between the recovering channel model and the real sampled channel responses are lower than 0.1, i.e., −10 dB. More than 75% of the sampling points can be reduced by the proposed method compared with the real flight experiments. Moreover, the proposed single-input-single-output (SISO) approach can also be applied in the potential aeronautical communications systems with multiple-input-multiple-output (MIMO) transmission.

Efficient Tomographic Inversion Based on Refined Scatterer Pre-Estimation

During the last decade, synthetic aperture radar (SAR) tomography (TomoSAR) technique in 3D reconstruction is significantly improved. In particular, compressed sensing (CS) has become the best suit for urban tomographic inversion. Among CS algorithms, the orthogonal matching pursuit (OMP) combined with Bayesian information criterion (BIC) has relatively low computational complexity, however, its scatterer estimation accuracy needs to be improved. To address the problem, a prior scatterer estimation with an innovative triplethreshold detection algorithm is proposed which not only greatly reduces number of iterations but also helps suppress artifacts. Experiments on real data show that the priori overlay times maps provided by the proposed algorithm are closer to the reference maps than other two pre-estimation algorithms. Artifacts in 3D reconstruction results are well suppressed at less than half the time cost of original OMP-BIC.

FB Dictionary Based SSBL-EM and Its Application for Multi-Class SSVEP Classification Using Eight-Channel EEG Signals

Many applications of signal processing require efficient reconstruction and economical processing of a signal. A classical approach for efficient reconstruction and economical processing of signal is compression. In recent studies, compressed sensing (CS) is an emerging field for compression and reconstruction. Moreover, CS is well-known for energy-efficient signal compression. However, most CS algorithms do not provide efficient reconstruction of physiological signals, such as electroencephalogram (EEG) and electrocardiogram (ECG). Efficient reconstruction is not possible due to non-stationary and non-sparsity characteristics of these signals. On the other hand, processing of these multichannel physiological signals is also time-consuming since the processing time is directly proportional to the number of channels. Hence, we have proposed a Fourier–Bessel (FB) dictionary-based spatiotemporal sparse Bayesian learning (SSBL) algorithm with expectation–maximization (EM) method for efficient reconstruction of multichannel physiological signals. This proposed method is based on the existing SSBL-EM method in which the authors used discrete cosine transform (DCT) dictionary for efficient reconstruction. In our work, we have used FB dictionary instead of DCT dictionary as the Bessel functions have non-stationary characteristic and provide good compression for signals such as EEG. The proposed method has been tested on two different databases for steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) classification using multichannel EEG signals and provides 100% classification accuracy with 50% compression and 2-s time duration using the first database. Classification has been performed using L1-regularized multiway canonical correlation analysis (L1-RMCCA) method. The performance of the proposed method has been compared with the DCT-dictionary-based SSBL-EM method. The classification accuracy result makes this proposed method more beneficial for uninterrupted wireless telemonitoring of multichannel EEG signals.

SAR Change Imaging in the Sparse Transforming Domain Based on Compressed Sensing

Since compressed sensing (CS) theory broke through the limitation of the traditional Nyquist sampling theory, it has attracted extensive attention in the field of microwave imaging. However, conventional CS-based imaging models always suffer from the limitation of sparse properties of the scene itself. In this article, a novel change imaging in the transforming domain based on CS is proposed, which converts the recovery of the scene itself to that of scene change from the historical observation to the current observation. Firstly, a new complex-data sparse microwave imaging model in the transforming domain is built by the real-imaginary separated operation. Then a scene transform method named inverse-whitening processing is introduced to confirm the relationship between the real part, imaginary part, and amplitude part of a complex scene, and the sparse transforming domain is constructed based on this processing and historical observation. At last, a CS algorithm is used to recover this change with undersampling echo, and the scene of the current observation can be achieved by integrating the recovered change with the historical observation. The effectiveness of change imaging in the transforming domain is verified on both simulated and real SAR images.