Compressive Sensing Technique Research Articles

Compression of electrocardiogram signals using compressive sensing technique based on curvelet transform toward medical applications

AbstractElectrocardiogram (ECG) signals can be monitored from many patients based on healthcare systems. To enhance these systems, the ECG signals should be collected and then stored in a cloud platform for later analysis. Hence, ECG signals can be utilized to diagnose heart diseases. However, the ECG signals require great internet capacity. So, compression techniques can be implemented to reduce a memory storage capacity for these signals. One of the potential compression techniques is the compressive sensing (CS). This paper proposes a CS technique to compress ECG signals. This technique is used to reduce sampling rates of the ECG signals to be less than the Nyquist rate. Moreover, a framework is suggested for the compression of maternal and fetal ECG signals. The compression of these signals is based on the curvelet transform (CT) to produce sparsity in ECG signals. The MIT-BIH database are utilized for testing the ECG signals. This database includes several ECG signals with various sampling rates, such as aberrant and normal signals. The proposed CS technique achieved a compression ratio (CR) of 15.7 with an accuracy of 98.2%. Also, a percentage root mean difference (PRD) is utilized to calculate the performance of the reconstructed ECG signals. The achieved value of the PRD is 2.0.

Multiple Complementary Priors for Multispectral Image Compressive Sensing Reconstruction.

Compressive sensing (CS) techniques using a few compressed measurements have drawn considerable interest in reconstructing multispectral imagery (MSI). Nonlocal-based tensor methods have been widely used for MSI-CS reconstruction, which employ the nonlocal self-similarity (NSS) property of MSI to obtain satisfactory results. However, such methods only consider the internal priors of MSI while ignoring important external image information, for example deep-driven priors learned from a corpus of natural image datasets. Meanwhile, they usually suffer from annoying ringing artifacts due to the aggregation of overlapping patches. In this article, we propose a novel approach for highly effective MSI-CS reconstruction using multiple complementary priors (MCPs). The proposed MCP jointly exploits nonlocal low-rank and deep image priors under a hybrid plug-and-play framework, which contains multiple pairs of complementary priors, namely, internal and external, shallow and deep, and NSS and local spatial priors. To make the optimization tractable, a well-known alternating direction method of multiplier (ADMM) algorithm based on the alternating minimization framework is developed to solve the proposed MCP-based MSI-CS reconstruction problem. Extensive experimental results demonstrate that the proposed MCP algorithm outperforms many state-of-the-art CS techniques in MSI reconstruction. The source code of the proposed MCP-based MSI-CS reconstruction algorithm is available at: https://github.com/zhazhiyuan/MCP_MSI_CS_Demo.git.

Achieving Multi-Time-Step Segment Routing via Traffic Prediction and Compressive Sensing Techniques

Achieving Multi-Time-Step Segment Routing via Traffic Prediction and Compressive Sensing Techniques

High-precision Solution of Monostatic Radar Cross Section based on Compressive Sensing and QR Decomposition Techniques

In solving the monostatic electromagnetic scattering problem, the traditional improved primary characteristic basis function method (IPCBFM) often encounters difficulties in constructing the reduced matrix due to the long computation time and low accuracy. Therefore, a new method combining the compressed sensing (CS) technique with IPCBFM is proposed and applied to solve the monostatic electromagnetic scattering problem. The proposed method utilizes the characteristic basis functions (CBFs) generated by the IPCBFM to achieve a sparse transformation of the surface-induced currents. Several rows in the impedance matrix and excitation vector are selected as the observation matrix and observation vector. The QR decomposition is adopted as the recovery algorithm to realize the recovery of surface-induced currents. Numerical simulations are performed for cylinder, cube, and almond models, and the results show that the new method has higher solution accuracy, shorter computation time, and stronger solution stability than the traditional IPCBFM. It is worth mentioning that the new method reduces the recovery matrix size and the number of CBFs quantitatively, and provides a novel solution for solving monostatic RCS of complex targets.

Biomedical signal compression using deep learning based multi-task compressed sensing

<span>Real-time transmission of biomedical signals is immensely challenging and requires cloud and internet of things (IoT) infrastructure. Security is also an important factor; however, to accomplish this, a reconstruction method is developed in which the entire signal is supplied as an input, the primary portion is considered here, and the signal is further encoded and transmitted to the destination. Electrocardiogram (ECG) compression for the lightweight wireless network is quite challenging for long-term healthcare monitoring. Compressed sensing (CS) involves efficient encoding mechanisms for error rate estimation for reconstruction and energy consumption for wireless transmission of data. We propose a multi-task compressed sensing (MT-CS) reconstruction mechanism in this study for ECG compression of data is most chosen for a wireless network system that has various sensors embedded in it. This model further extracts the essential adaptive features for correlation existing in the ECG signals. The performance of the proposed MT-CS reconstruction mechanism is evaluated on the multiparameter intelligent monitoring in intensive care (MIMIC-II) dataset, which ensures its robustness and generalization. The results obtained upon simulation ensure that the proposed MT-CS based reconstruction approach ensures excellent reconstruction signal with fewer measurements in comparison with the existing state-of-art CS techniques.</span>

Achieving Robust Compressive Sensing Seismic Acquisition with a Two-Step Sampling Approach

The compressive sensing (CS) framework offers a cost-effective alternative to dense alias-free sampling. Designing seismic layouts based on the CS technique imposes the use of specific sampling patterns in addition to the logistical and geophysical requirements. We propose a two-step design process for generating CS-based schemes suitable for seismic applications. During the first step, uniform random sampling is used to generate a random scheme, which is supported theoretically by the restricted isometry property. Following that, designated samples are added to the random scheme to control the maximum distance between adjacent sources (or receivers). The null space property theoretically justifies the additional samples of the second step. Our sampling method generates sampling patterns with a CS theoretical background, controlled distance between adjacent samples, and a flexible number of active and omitted samples. The robustness of two-step sampling schemes for reallocated samples is investigated and CS reconstruction tests are performed. In addition, using this approach, a CS-based 3D seismic survey is designed, and the distributions of traces in fold maps and rose diagrams are analyzed. It is shown that the two-step scheme is suitable for CS-based seismic surveys and field applications.

Novel dual compressive sensing method for solving the monostatic scattering problems of three-dimensional target

ABSTRACTAccording to the conventional dual compressive sensing method is limited to solving the electromagnetic scattering problems for two-dimensional targets, the Characteristic Basis Functions (CBFs) combined with the Compressive Sensing (CS) technique are proposed to solve the monostatic scattering problems for the three-dimensional (3D) target in this paper. The approach considered in this paper consists of successfully transforming the discrete surface-induced currents of Rao-Wilton-Glisson (RWG) basis functions using CBFs, which successfully enhance the sparsity of the induced currents. Then, the novel dual compressive sensing framework is obtained by applying the CBFs and discrete cosine transform matrices as sparse bases, respectively. The corresponding numerical calculations demonstrate that the proposed method can be used as an effective technical route to solve the monostatic scattering problem effectively.

Research on antenna design based on low-resolution digital-to-analog converters

In the ever-evolving field of communication, the optimization of antenna design and structure has garnered significant attention from professionals. Effective antenna design plays a crucial role in ensuring reliable and efficient communication systems. However, the challenge lies in overcoming the limitations imposed by current low-resolution digital-to-analog converters (DACs) on the attainment of highly efficient antenna structures. This study aims to address this issue by exploring the potential of low-resolution DACs for improving the structural design of antennas. To achieve this, we propose gradient-based and low-complexity heuristic solutions that leverage global optimization techniques. By optimizing the antenna structure using these innovative approaches, we aim to enhance the overall performance and efficiency of communication systems. The significance of this research lies in its potential to revolutionize antenna design by overcoming the constraints of low-resolution DACs. By incorporating Sigma-Delta modulation in the filter design, we anticipate significant improvements in performance when the number of transmitting antennas is comparable to the number of users. Furthermore, our investigation reveals that the application of Compressed Sensing techniques yields results that closely align with the optimal solution in scenarios where the number of transmitting antennas greatly exceeds the number of users.

Accurate ranging of adjacent well using compressive sensing-based current injection with a uniform sensor array

Drilling a relief well provides a last resort option in the event the blowout wellbore is inaccessible, where one of the most key points is to determine the distance of the target before uncertainty ellipsoids overlap, so as to guide the relief well to intersect with the target well. As the only commercially applicable technique presented by Haliburton, the current injection method has been proven to be effective for adjacent well ranging. However, the detection process is implemented by an openhole logging tool after pulling out of hole, where the logging tool is probably stuck especially in such a directional relief well. To realize an accurate measurement of the well spacing when the downhole tool is stuck, this paper proposes a compressive sensing (CS)-based ranging method for the adjacent well using current injection with a uniform sensor array. On the basis of the current injection signal model, the influences of the stuck tool on the detection signal with a magnetic sensor are analyzed. It is shown that the error term corresponding to the tool being stuck and the target term related to the well spacing are coupled in the received magnetic signal. In order to reduce the influence of the stuck error, we use the Gauss–Legendre quadrature to separate the error term and the target term in the detection signal with the uniform sensor array, so that the target vector can be extracted with the dimension reduction property of the CS technique. Moreover, an iterative CS-based ranging algorithm is proposed for the extracted target vector to accurately determine the distance between two wells by searching for the optimal solution in each iteration. Finally, simulation and experiment data are shown to verify the effectiveness of the proposed method, and the results show that this method can achieve accurate ranging, even in the case of a stuck logging tool.

Optimization-inspired Cumulative Transmission Network for image compressive sensing

Compressive Sensing (CS) techniques enable accurate signal reconstruction with few measurements. Deep Unfolding Networks (DUNs) have recently been shown to increase the efficiency of CS by emulating iterative CS optimization procedures by neural networks. However, most of these DUNs suffer from redundant update procedures or complex matrix operations, which can impair their reconstruction performances. Here we propose the optimization-inspired Cumulative Transmission Network (CT-Net), a DUN approach for natural image CS. We formulate an optimization procedure introducing an auxiliary variable similar to Half Quadratic Splitting (HQS). Unfolding this procedure defines the basic structure of our neural architecture, which is then further refined. A CT-Net is composed of Reconstruction Fidelity Modules (RFMs) for minimizing the reconstruction error and Constraint Gradient Approximation (CGA) modules for approximating (the gradient of) sparsity constraints instead of relying on an analytic solutions such as soft-thresholding. Furthermore, a lightweight Cumulative Transmission (CT) between CGAs in each reconstruction stage is proposed to facilitate a better feature representation. Experiments on several widely used natural image benchmarks illustrate the effectiveness of CT-Net with significant performance improvements and fewer network parameters compared to existing state-of-the-art methods. The experiments also demonstrate the scene and noise robustness of the proposed method.

Learned partial transform ensembles for exceptional optical compressive sensing

In most sensing processes, samples are acquired from a partial ensemble of measurements taken from a general transform defined by physically variable parameters. To follow the principle of parsimony, it is often desired to take the most compact ensemble of measurements possible. Compressive Sensing (CS) addresses this issue by significantly reducing the number of samples required in imaging systems. However, choosing the optimal set of physically realizable samples for CS can be challenging. In this work, we propose a novel Deep Learning (DL) method that jointly optimizes physically realizable CS matrices together with the reconstruction algorithm. Our method achieves unprecedented levels of optical compression, a few orders of magnitude higher compression ratio than what is typically achievable with classical CS techniques. We demonstrate the effectiveness of our approach through results on face imaging using as few as ten samples. Our method has the potential to significantly improve and enhance a wide range of imaging and sensing modalities.

Compressed Sensing Techniques Applied to Medical Images Obtained with Magnetic Resonance

The fast and reliable processing of medical images is of paramount importance to adequately generate data to feed machine learning algorithms that can prevent and diagnose health issues. Here, different compressed sensing techniques applied to magnetic resonance imaging are benchmarked as a means to reduce the acquisition time spent in the collection of data and signals that form the image. It is shown that by using these techniques, it is possible to reduce the number of signals needed and, therefore, substantially decrease the time to acquire the measurements. To this end, different algorithms are considered and compared: the iterative re-weighted least squares, the iterative soft thresholding algorithm, the iterative hard thresholding algorithm, the primal dual algorithm and the log barrier algorithm. Such algorithms have been implemented in different analysis programs that have been used to perform the reconstruction of the images, and it was found that the iterative soft thresholding algorithm gives the optimal results. It is found that the images obtained with this algorithm have lower quality than the original ones, but in any case, the quality should be good enough to distinguish each body structure and detect any health problems under an expert evaluation and/or statistical analysis.

Novel Duffing chaotic oscillator and its application to privacy data protection

Traditional Compressive Sensing (CS) achieves both compression and encryption of digital data. However, most existing compressive sensing methods present some shortcomings, including weak resistance to chosen-plaintext attacks and heavy key management burden. To overcome these shortcomings, this work presents a new combination of CS with optical transformation for digital image compression and encryption. The proposed compression-encryption scheme utilizes the interesting properties of CS and permutation-diffusion techniques to reduce the image size and encrypt the image data. A Novel Duffing Oscillator (NDO) is proposed, its dynamics is deeply analyzed, and its sequences are exploited to build a hardware-friendly measurement matrix for the CS process. This also contributes to reducing the total size of secret key sent to the receiving end. In addition, the final image compression-encryption output is obtained by applying one of the most significant optical encryption methods, namely Double Random Phase Encoding (DRPE). This contributes to further strengthen the security of the proposed scheme. Eventually, the experimental results imply that our scheme is effective in improving the resistance against various attacks, while guaranteeing good imperceptibility and reconstruction performance. It can then be employed in the information security communication field.

From Compressed-Sensing to Deep Learning MR: Comparative Biventricular Cardiac Function Analysis in a Patient Cohort.

Conventional segmented, retrospectively gated cine (Conv-cine) is challenged in patients with breath-hold difficulties. Compressed sensing (CS) has shown values in cine imaging but generally requires long reconstruction time. Recent artificial intelligence (AI) has demonstrated potential in fast cine imaging. To compare CS-cine and AI-cine with Conv-cine in quantitative biventricular functions, image quality, and reconstruction time. Prospective human studies. 70 patients (age, 39 ± 15 years, 54.3% male). 3T; balanced steady state free precession gradient echo sequences. Biventricular functional parameters of CS-, AI-, and Conv-cine were measured by two radiologists independently and compared. The scan and reconstruction time were recorded. Subjective scores of image quality were compared by three radiologists. Paired t-test and two related-samples Wilcoxon sign test were used to compare biventricular functional parameters between CS-, AI-, and Conv-cine. Intraclass correlation coefficient (ICC), Bland-Altman analysis, and Kendall's W method were applied to evaluate agreement of biventricular functional parameters and image quality of these three sequences. A P-value <0.05 was considered statistically significant, and standardized mean difference (SMD) < 0. 100 was considered no significant difference. Compared to Conv-cine, no statistically significant differences were identified in CS- and AI-cine function results (all P > 0.05), except for very small differences in left ventricle end-diastole volumes of 2.5 mL (SMD = 0.082) and 4.1 mL (SMD = 0.096), respectively. Bland-Altman scatter plots revealed that biventricular function results were mostly distributed within the 95% confidence interval. All parameters had acceptable to excellent interobserver agreements (ICC: 0.748-0.989). Compared with Conv-cine (84 ± 13 sec), both CS (14 ± 2 sec) and AI (15 ± 2 sec) techniques reduced scan time. Compared with CS-cine (304 ± 17 sec), AI-cine (24 ± 4 sec) reduced reconstruction time. CS-cine demonstrated significantly lower quality scores than Conv-cine, while AI-cine demonstrated similar scores (P = 0.634). CS- and AI-cine can achieve whole-heart cardiac cine imaging in a single breath-hold. Both CS- and AI-cine have the potential to supplement the gold standard Conv-cine in studying biventricular functions and benefit patients having difficulties with breath-holds. 1 TECHNICAL EFFICACY STAGE: 1.

Multilevel Privacy Preservation Scheme Based on Compressed Sensing

Although the extensive application of the Internet of Things brings a great convenience, it raises the concern of privacy leakage in the processes of data acquisition, analyzing and sharing as well. In this paper, a multilevel privacy protection via compressed sensing (CS) is proposed, which has the advantages of compressed sampling, protection of data privacy, and controllability of data access. At the data acquisition end, the CS technique suitable for a resource-constrained environment is employed to sample and encrypt signals with the assistance of discriminant component analysis (DCA). Then, the encrypted data will be transmitted to the cloud in time. On the cloud service, signals protected by CS rarely disclose private information to malicious attackers, and they will be accessed by two-class authorized entities. One is the semi-authorized user with low privilege who can only get the features from encrypted data for the subsequent inference; the other is the full-authorized user who is capable of reconstructing the original data. We demonstrate the scheme through two case studies of a face recognition system and a human activity recognition system and analyze its performance.

Hybridization of Bayesian Compressive Sensing and Array Dilation Technique for Synthesis of Linear Isophoric Sparse Antenna Arrays

This paper presents a deterministic hybrid algorithm combining the merits of Bayesian Compressive Sensing (BCS) and Array Dilation Technique (ADT) for the synthesis of aperiodic and isophoric (equally-fed) Linear Sparse Array Antennas (LSAA). The hybrid technique overcomes the shortcomings of BCS, i.e., non-uniform amplitude taper which results into a lower taper efficiency, utilizing hybridization with ADT resulting into an isophoric LSAA, thereby improving the taper efficiency. The proposed algorithm can be used to synthesize arbitrary antenna array patterns as per the reference pattern while complementing the constraints on the aperture length and the desired thinning levels. The hybrid algorithm provides a computationally efficient and deterministic technique for synthesizing LSAAs with low SLL and demonstrates a wide angular scanning with minimal loss in directivity and SLL over the scan volume. The results demonstrated are comparable or better than the prior literature on LSAAs.

Memristor-Based Signal Processing for Compressed Sensing.

With the rapid progress of artificial intelligence, various perception networks were constructed to enable Internet of Things (IoT) applications, thereby imposing formidable challenges to communication bandwidth and information security. Memristors, which exhibit powerful analog computing capabilities, emerged as a promising solution expected to address these challenges by enabling the development of the next-generation high-speed digital compressed sensing (CS) technologies for edge computing. However, the mechanisms and fundamental properties of memristors for achieving CS remain unclear, and the underlying principles for selecting different implementation methods based on various application scenarios have yet to be elucidated. A comprehensive overview of memristor-based CS techniques is currently lacking. In this article, we systematically presented CS requirements on device performance and hardware implementation. The relevant models were analyzed and discussed from the mechanism level to elaborate the memristor CS system scientifically. In addition, the method of deploying CS hardware using the powerful signal processing capabilities and unique performance of memristors was further reviewed. Subsequently, the potential of memristors in all-in-one compression and encryption was anticipated. Finally, existing challenges and future outlooks for memristor-based CS systems were discussed.

Compressed Sensing Σ-Δ Modulators and a Recovery Algorithm for Multi-Channel Wireless Bio-Signal Acquisition

Compressed sensing (CS) exploits signal sparsity in some domain to enable sub-Nyquist sampling which increases the energy efficiency of analog-to-digital conversion (ADC) and downstream data processing circuits–the sampling frequency is determined by the information rate, not the usual Nyquist rate. CS techniques for wireless multi-channel bio-signal recording applications based on sigma-delta modulation (SDM) are detailed and used to validate a multi-channel recovery algorithm. The SDM topology allows the required dot product calculations between the measurement and signal vectors to be performed in conjunction with its inherent integration using minimal additional circuitry. It eliminates opamp output saturation concerns and benefits directly from still ongoing Moore’s Law CMOS technology scaling. Finally, a sparse sensing matrix and recovery algorithm are described that exploit similar sparse signatures across multiple channels to improve both signal recovery accuracy and chip area efficiency. Simulation results validate the concepts.

Phonon hardening and the effect of phonon transport in cubic antiperovskites [formula omitted] (A = Li, Na; B = Se, Te) induced by quartic anharmonicity

In this article, we aim at the structural stability, lattice thermal conductivity, transport properties of four cubic antiperovskites A3FB using first-principle calculations combined with self-consistent phonon theory(SCP), compressive sensing techniques (CS), and Boltzmann transport equation (BTE), with a particular focus on the 4 phonon scattering rates and phonon frequency shifts caused by quartic anharmonicity. The results indicate that the strong quartic anharmonicity of alkali metal atoms Na atom harden low-frequency phonon branch to ensure dynamic stability of the material. We also find that the quartic anharmonicity play a crucial role in obtaining the reasonable temperature dependence of κL. In addition, we analyze the impact of phonon group velocities, the scattering rate and scattering phase space of 3ph and 4ph on phonon transport, revealing that the low κL of Na3FSe is due to the strong 4ph SRs which is dominated by redistribution and Umklapp processes. Our studies provide the microscopic understanding of the quartic anharmonicity together with their impact on lattice dynamics and phonon transport is not only of indispensable interest but also essential to design and explore materials with ultralow thermal conductivity.

Ionospheric Tomography: A Compressed Sensing Technique Based on Dictionary Learning

GNSS (Global Navigation Satellite System) observation insufficiency limits the development of the voxel-based computerized ionospheric tomography (CIT) technique. Electron densities of voxels without observation cannot be accurately estimated by the commonly used algebraic reconstruction techniques. In this study, we proposed a compressed sensing technique (CST) based on dictionary learning for ionospheric tomography. Specifically, the K-SVD (singular value decomposition) algorithm was used for dictionary learning based on training sets that are derived from the NeQuick model, hereafter referred to as the CST_NeQuick algorithm. K-SVD uses the orthogonal matching pursuit (OMP) for sparse coding and the SVD approach for dictionary updating. Both simulations and real experiments demonstrated the feasibility and superiority of the CST algorithm when compared to the widely used multiplicative algebraic reconstruction technique (MART). It was found that the CST_NeQuick algorithm’s tomographic performances were mostly superior to those of the MART algorithm in comparison with the independent slant total electron content (STEC) references. Another CST-based tomographic experiment was performed by using the MART-based solutions for dictionary learning, hereafter referred to as the CST_MART algorithm. It showed that the CST_MART algorithm can reduce the average root mean square (RMS) of the CIT-derived STEC by 37.3 % and 20.2 %, respectively, when compared to the MART and CST_NeQuick algorithms. Besides, the CST_MART algorithm’s electron density profiles also showed more agreement with the electron density profiles that were derived from radio occultation data.