Measurement Matrix Research Articles

How does environmental regulation affect the city’s domestic value-added rate of export? New spatial evidence from Chinese cities

Protecting the environment and increasing the domestic value-added rate of exports (DVARE) are hot issues for high-quality economic development. This paper explores how environmental regulation affects the DVARE. This paper constructs a dynamic spatial econometric model using the data of 285 Chinese cities from 2000 to 2020. It empirically tests the impact of environmental regulation on the DVARE. The research results show that environmental regulation has a nonlinear impact on the DVARE. Environmental regulation has a U-shaped effect on DVARE that first decreases and then increases. Furthermore, environmental regulation also has a positive spatial spillover effect. The heterogeneity analysis also shows that cities have different administrative levels, geographical locations, size, and environmental regulations’ impact and spillover effects on the DVARE are also different. This paper also finds that the spatial spillover effect of environmental regulation is limited to 1100 KM by constructing a spatial attenuation matrix. This result is still robust after selecting appropriate instrumental variables, replacing DVARE, environmental regulation intensity measure and spatial weight matrix. The final mechanism test found that environmental regulation has promoted the increase of the DVARE through cost effects and resource allocation effects. However, the innovation effect mechanism is insignificant.

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.

Hyperspectral fruit image restoration using non-convex optimization

Hyperspectral (HS) imaging captured at low proximity ranges has become an effective method for assessing the quality and safety of food products in recent years. The majority of Hyperspectral cameras are push-broom type, which often contributes random noise and stripes to the spectral images. As a result, the quality of the collected HS images and the precision of the subsequent processing will both suffer. Despite the fact that there are several denoising techniques that have been studied and recorded in the literature, they frequently fail to fully restore the original HS images when there are strong stripe noises and other intricate mixed noises. In this study, we introduce a new method based on tensor logarithmic Schatten-p norm minimization with anisotropic spatial spectral total variation (ASSTV) regularization for simultaneous denoising and destriping of line scan-based hyperspectral images. Tensor logarithmic Schatten-p norm improves denoising performance by deleting the smaller singular values while protecting the larger singular values. Through the application of ASSTV, stripes and Gaussian noise are eliminated while maintaining the spatial–spectral smoothness of HS images and the directional characteristics of the stripes. The augmented Lagrange multiplier (ALM) approach is used to address the ensuing optimization problem. The quantitative and visual assessments reveal that the suggested technique has improved performance than its counterparts.

Identification and classification for multiple cyber attacks in power grids based on the deep capsule CNN

Cyber-attacks have become one of the main threats to the security, reliability, and economic operation of power systems. Detection and classification of multiple cyber-attacks pose challenges for ensuring the stability and security of power systems. To address this issue, this study proposes an automatic identification and classification method for multiple cyber-attacks based on the deep capsule convolution neural network. Spatial correlations among different nodes and temporal features from history operation status in the transmitted data packets are extracted by the convolution neural network. Capsules in the proposed structure have important implications for maintaining the topological consistency contained in the measurement matrix. Furthermore, the proposed method is model-free and avoids the impact of system parameters uncertainties on detection performance. Multiple kinds of typical cyber-attacks, including false data injection attacks, replay attacks, denial of service attacks, time-delay attacks, and deception attacks, are considered and modeled in this paper. Numerical results on the IEEE 39-bus test system show that the proposed method can achieve 99.97% detection accuracy on single cyber-attacks and 96.25% detection accuracy on multiple cyber-attacks. Comparative results illustrate the proposed method outperforms than traditional neural networks. This approach provides a solution for the problem of multiple cyber-attack detection and classification.

Diffusion Nonlinear Estimation and Distributed UAV Path Optimization for Target Tracking with Intermittent Measurements and Unknown Cross-Correlations

This paper focuses on distributed state estimation (DSE) and unmanned aerial vehicle (UAV) path optimization for target tracking. First, a diffusion cubature Kalman filter with intermittent measurements based on covariance intersection (DCKFI-CI) is proposed, to address state estimation with the existence of detection failure and unknown cross-correlations in the network. Furthermore, an alternative transformation of DCKFI-CI based on the information form is developed utilizing a pseudo measurement matrix. The performance of the proposed DSE algorithm is analyzed using the consistency and the bounded error covariance of the estimate. Additionally, the condition of the bounded error covariance is derived. In order to further improve the tracking performance, a UAV path optimization algorithm is developed by minimizing the sum of the trace of fused error covariance, based on the distributed optimization method. Finally, simulations were conducted to verify the effectiveness of the proposed algorithm.

An image compression encryption based on the semi-tensor product and the DFT measurement matrix

As the volume of image data increases, optical image encryption algorithms often require more time and space to operate. To solve the problem, compression is generally used, and compressed sensing algorithms are among the most frequently used compression algorithms recently. However, most existing compressed sensing algorithms suffer from low reconstruction quality and large measurement matrix size. To address this issue, we proposed a novel compression encryption scheme using the semi-tensor product, DFT (discrete Fourier transform) measurement matrix, and cascaded diffraction system. When the amount of image data grows, the key space grows according to the factorial growth rate without an upper limit, making brute-force attacks virtually impossible. Compared with algorithms without using a semi-tensor product, the storage space shrinks to one-fourth of its original size. Experiment results proved better reconstruction quality compared with existing compressed sensing algorithms. Furthermore, our approach is robust against various attacks, which is validated through rigorous testing.

Composite Fabry-Perot interferometric gas pressure and temperature sensor utilizing four hole fiber with sensitivity boosted by high-order harmonic Vernier effect.

It is an enormous challenge for optical fiber sensors to intuitively achieve the simultaneous measurement of both gas pressure and temperature with high sensitivity. To address this challenge, the Fabry-Perot interferometer (FPI) based on high-order harmonic Vernier effect is combined with the fiber Bragg grating (FBG). A novel fiber sensor built with a cascaded FPI and an FBG for the simultaneous measurement of gas pressure and temperature is designed and experimentally investigated by virtue of the temperature sensing property of FBG and its independence on gas pressure-induced refractive index change, where a high-order harmonic Vernier effect was utilized to boost the gas pressure sensitivity of the sensor. As gas pressure increases from 0 to 1 MPa, the internal envelope of composite FBG and FPI based 10-order harmonic Vernier effect exhibits redshift with maximal sensitivities of 146.64 nm/MPa and a high magnification factor of 43. FBG is insensitive to gas pressure change, whereas, the spectral response of the internal envelope 10-order harmonic Vernier effect and FBG monotonously move and undergo blueshift and redshift as the temperature increases from 30 °C to 120 °C with maximal sensitivities of -0.48 and 0.011 nm/°C, respectively. Therefore, the distinct sensitivities of FBG and FPI to gas pressure and temperature result in extraction of both gas pressure and temperature information simultaneously by constructing measurement matrixes.

Visually meaningful image encryption scheme using multi-parameter fractal theory and block synchronous sorting diffusion

Traditional visually meaningful image encryption (VMIE) works by embedding a secret image (SI) into a visually meaningful carrier image (CAI), thus achieving the dual protection of both data information and appearance features. However, the current VMIE algorithm still suffers from problems of reconstruction quality and transmission efficiency. To address these issues, this paper proposes an innovative VMIE algorithm that utilizes a newly designed two-dimensional hyperchaotic map, multi-parameter fractal matrix (MPFM) theory and compressive sensing. The proposed algorithm achieves dual protection of both the semantic information and appearance image data. First, a newly designed 2D infinite triangle folding map (2D-ITFM) is presented to generate a binary key-controlled measurement matrix to measure and compress a plain image (PI) to generate measured image (MI) and decrease the amount of transmission information. Next, based on the hyperchaotic map 2D-ITFM and MPFM, we present the inter-block scrambling (IBS) algorithm and intra-block synchronous sorting diffusion (IBSSD) algorithm, which are used to process the MI and generate SI. Then, the matrix encoding embedding (MEE) technique is utilized to hide the SI into the CAI to produce a visually secure cipher image (CII). Finally, the experimental results demonstrate that our scheme is effective in improving the anti-attack ability while guaranteeing good imperceptibility and reconstruction performance. This scheme can be employed in the field of information security communication.

A novel method to repair missing vibration data in rolling bearing vibration signals based on improved optimized measurement matrix

Missing data, caused by many factors such as equipment short circuits or data cleaning, affect the accuracy of condition monitoring for rotating machinery. To improve the precision of missing data recovery, a compressed sensing-based vibration data repair method is developed. First, based on the Gaussian random matrix, an improved optimized measurement matrix (OMM) is proposed to accurately sample data. Then, a sparse representation of the vibration signal, through a discrete cosine transform, is utilized to make the noisy vibration signal sparse. Finally, the orthogonal matching pursuit algorithm is employed to reconstruct the missing signal. The effectiveness of the proposed method is verified by analyzing constant and variable speed time series of rolling bearings. Compared with other data repair methods, it is shown that the OMM has a higher repair precision at different loss rates.

FECG compressed sensing mode based on joint block sparsity

The development of medical Internet of Things technology has enabled the rise of telemedicine. For medical signals, real-time transmission, accuracy of signals and portability of equipment are required to be strict. The telemedicine system based on Compressed Sensing (CS) technology can avoid sampling a large number of redundant information, and reduce the complexity of data processing and computing time. However, the performance of existing Compressed Sensing algorithms in processing and reconstructing medical signals is poor. It is difficult to meet the strict requirements. In this paper, the sparse representation method which is most suitable for Fetal Electrocardiogram (FECG) signals is obtained by comparison experiment. And a Logistics-Tent Sine Bernoulli Measurement Matrix (LTSBM) construction algorithm is proposed for more convenient hardware applications. Based on the characteristics of FECG signals, a Joint Block Multiple Orthogonal Least Squares (JBMOLS) algorithm is proposed to improve the reconstruction performance in this paper. Combined with the above algorithms, the FECG compressed sensing mode is constituted, which is used to improve the compression, sampling and reconstruction performance of FECG signals. The experimental results show that, compared with traditional compressed sensing technology, the FECG compressed sensing mode improves the overall signal reconstruction performance, the running time is greatly reduced, and the practicability is stronger.

Meta-Heuristic Device-Free Localization Algorithm under Multiple Path Effect.

In the scenario of device-free localization under multiple effects, the accuracy of localization based on compressed sensing theory is severely affected. Most existing localization techniques directly ignore multiple path effects. However, it is not practical to ignore the multiple path effect due to its high signal strength, which can provide localization information. In this paper, we formulate the sensing matrix optimization problem in compressed sensing for device-free localization scenarios based on multiple reflections. To solve this problem, we model it as a constrained combinatorial optimization problem and propose a hybrid meta-heuristic algorithm. First, smart reflection surfaces and virtual node models are used to construct the desired communication links. Second, we iteratively improve the properties of the measurement matrix by using K-means clustering to obtain reasonable thresholds, and use a meta-heuristic algorithm to optimize the sensing matrix. Finally, the simulation results show that the proposed method efficiently optimizes the sensing matrix and achieves fast and high-precision localization while conserving communication resources.

A hybrid interferometer sensor for simultaneous measurement of temperature and gas pressure based on tapered seven-core fiber and PDMS

A novel hybrid interferometer sensor composed of a tapered seven-core fiber (TSCF) and a polydimethylsiloxane (PDMS) cap at the end face of a TSCF is proposed for simultaneous measurement of temperature and gas pressure. TSCF forms a Michelson interferometer (MI), and the PDMS cap on the end surface of TSCF forms a Fabry–Pérot interferometer (FPI). The sensing head consisted of a cascade of MI and FPI. When the external temperature or gas pressure changes, owing to the thermal effect or elastic deformation of PDMS, the interference spectrum of the FPI shifts significantly, so the FPI is very sensitive to temperature and gas pressure. MI, which is made of quartz optical fiber, is sensitive only to temperature and is not to gas pressure. The experimental results show that FPI has a temperature sensitivity of −0.22 nm °C−1 in the temperature range of 40 °C–80 °C, and a gas pressure sensitivity of −2.27 nm MPa−1 in the gas pressure range of 0–0.3 MPa. MI has a temperature sensitivity of 0.05 nm °C−1 in the temperature range of 40 °C–80 °C, and a gas pressure sensitivity of zero in the gas pressure range of 0–0.3 MPa. Using the temperature and gas pressure sensitivities of FPI and MI to construct a measurement matrix, it is possible to simultaneously measure temperature and gas pressure, eliminating their cross-sensitivity. This sensor has the comprehensive advantages of compact structure, small size, easy manufacturing, low cost, high reliability, and high sensitivity, and is expected to be applied in industrial practice.

CS-based multi-task learning network for arrhythmia reconstruction and classification using ECG signals

Objective. Although deep learning-based current methods have achieved impressive results in electrocardiograph (ECG) arrhythmia classification issues, they rely on using the original data to identify arrhythmia categories. However, a large amount of data generated by long-term ECG monitoring pose a significant challenge to the limited-bandwidth and real-time systems, which limits the application of deep learning in ECG monitoring. Approach. This paper, therefore, proposed a novel multi-task network that combined compressed sensing and convolutional neural networks, namely CSML-Net. According to the proposed model, the ECG signals were compressed by utilizing a learning measurement matrix and then recovered and classified simultaneously via shared layers and two task branches. Among them, the multi-scale feature module was designed to improve model performance. Main results. Experimental results on the MIT-BIH arrhythmia dataset demonstrate that our proposed method is superior to all the approaches that have been compared in terms of reconstruction quality and classification performance. Significance. Consequently, the proposed model achieving the reconstruction and classification in the compressed domain can be an improvement and become a promising approach for ECG arrhythmia reconstruction and classification.

Bivariate Pilot Optimization for Compressed Channel Estimation in RIS-Assisted Multiuser MISO-OFDM Systems

In reconfigurable intelligent surface (RIS)-assisted wireless communication systems, channel estimation is of paramount importance to unleash the full advantage of the high passive beamforming gains from RIS. However, it is particularly challenging due to the massive number of RIS reflecting elements which lack signal perception and processing capabilities, thus incurring prohibitively high pilot overhead between transceivers. To address this challenge, we focus on an RIS-assisted multiuser multiple-input single-output orthogonal frequency division multiplexing (MU-MISO-OFDM) system, and propose a practical transmission protocol with non-uniformly spaced comb -type pilots for compressed channel estimation and data transmission, by exploiting the sparsity of RIS-associated channels. To solve a bivariate pilot optimization problem with the objective of minimizing the mutual coherence of the measurement matrix influenced by pilot positions and pilot powers simultaneously, we propose two enhanced whale optimization algorithm (EWOA)-based approaches by leveraging the second order cone programming (SOCP) and compressed coding scheme (CCS). Simulation results validate the effectiveness of the proposed methods over benchmark schemes in various aspects. It corroborates that considering the coupling relationship between pilot positions and powers can help improve the channel estimation performance of the RIS-assisted MU-MISO-OFDM system with limited pilot overhead.

Stochastic modeling with robust Kalman filter for real-time kinematic GPS single-frequency positioning

The centimeter-level positioning accuracy of real-time kinematic (RTK) depends on correctly resolving integer carrier-phase ambiguities. To improve the success rate of ambiguity resolution and obtain reliable positioning results, an enhanced Kalman filtering procedure has been developed. Based on a posteriori residuals of measurements and state predictions, the measurement noise variance–covariance matrix for double-differenced measurements is adaptively estimated, rather than approximated by an empirical function which uses satellite elevation angle as input. Since, in real-world situations, unexpected outliers and carrier-phase outages can degrade the filter performance, a stochastic model based on robust Kalman filtering is proposed, for which the double-differenced measurement noise variance–covariance matrix is computed empirically with a modified version of the IGG (Institute of Geodesy and Geophysics) III method in order to detect and identify outliers. The performance of the proposed method is assessed by two tests, one with simulated data and one with real data. In addition, the performance of F-ratio and W-ratio tests as proxies for the success of ambiguity fixing is investigated. Experimental results reveal that the proposed method can improve the reliability and robustness of relative kinematic positioning for simulation scenarios as well as in a real urban test.

Fast Frequency-Diverse Radar Imaging Based on Adaptive Sampling Iterative Soft-Thresholding Deep Unfolding Network

Frequency-diverse radar imaging is an emerging field that combines computational imaging with frequency-diverse techniques to interrogate the high-quality images of objects. Despite the success of deep reconstruction networks in improving scene image reconstruction from noisy or under-sampled frequency-diverse measurements, their reliance on large amounts of high-quality training data and the inherent uninterpretable features pose significant challenges in the design and optimization of imaging networks, particularly in the face of dynamic variations in radar operating frequency bands. Here, aiming at reducing the latency and processing burden involved in scene image reconstruction, we propose an adaptive sampling iterative soft-thresholding deep unfolding network (ASISTA-Net). Specifically, we embed an adaptively sampling module into the iterative soft-thresholding (ISTA) unfolding network, which contains multiple measurement matrices with different compressed sampling ratios. The outputs of the convolutional layers are then passed through a series of ISTA layers that perform a sparse coding step followed by a thresholding step. The proposed method requires no need for heavy matrix operations and massive amount of training scene targets and measurements datasets. Unlike recent work using matrix-inversion-based and data-driven deep reconstruction networks, our generic approach is directly adapted to multi-compressed sampling ratios and multi-scene target image reconstruction, and no restrictions on the types of imageable scenes are imposed. Multiple measurement matrices with different scene compressed sampling ratios are trained in parallel, which enables the frequency-diverse radar to select operation frequency bands flexibly. In general, the application of the proposed approach paves the way for the widespread deployment of computational microwave and millimeter wave frequency-diverse radar imagers to achieve real-time imaging. Extensive imaging simulations demonstrate the effectiveness of our proposed method.

Multicoset-based deterministic measurement matrices for compressed sensing of sparse multiband signals

Signal acquisition is a basic function in signal processing systems. In the context of digital communications, the analog-to-digital (ADC) converters have to be carefully designed as part of the radio frequency front end, to reduce power consumption while avoiding aliasing. When operating with sparse multiband signals, compressive sampling becomes an attractive alternative to enable low sampling rates at the front end. Starting from previous results on multicoset sampling and compressed sensing, we analyze sampling strategies for sparse multiband signals based on the operation of a set of ADC converters working in parallel. Different architectures are considered, with equal and different sampling rates in the ADCs, and their associated deterministic measurement matrices and sampling patterns are studied. The matrices involved in the corresponding reconstruction equations for the different architectures are also analyzed in terms of their mutual coherence. As a result, we are able to design different universal multicoset sampling patterns. We obtain the parameters for the different sampling architectures that guarantee reconstruction of the multiband sparse signal, even at high compression rates. Simulation results show the effectiveness of the theoretical designs for sparse reconstruction of this type of signals.

Simultaneous measurement three parameters of temperature, strain, and curvature by thin-core fiber based-Mach-Zehnder interferometer

A Mach-Zehnder interferometer (MZI) sensor for simultaneous measurement of three parameters is developed. The sensor structure is spliced by single-mode fiber, no-core fiber, thin-core fiber (TCF) and single-mode fiber in turn, where the sensing element is TCF. Due to the different core diameters of three types of optical fibers, the light in the transmission process excites a number of higher-order modes and interacts to form a mode interferometer. The interference spectrum produces many resonance peaks that are sensitive to different parameters. We select three resonant peaks sensitive to temperature, strain and bending for measuring temperature, strain and curvature. The experimental results display that the resonant wavelength of the sensor transmission spectrum drifts linearly as a function of the environmental parameters, with a maximum sensitivity of 248.7 pm/℃ for temperature, −2 pm/με for strain and −24.784 nm/m−1 for curvature. Selecting three resonance peaks to construct a measurement matrix realizes three-parameters simultaneous measurement and eliminates the cross-sensitivity among the three physical quantities. In addition, the proposed sensor possesses appealing characteristics of facile manufacturing process, compact size, high sensitivity, good linear fitting, robustness and low cost, and the sensor has a broad application prospect in the field of multi-parameter simultaneous measurement. In short, only one MZI in the optical fiber can realize the simultaneous monitoring of temperature, strain and bending, which provides a new solution for creating a compact multi-parameter sensor for structural health monitoring.

Compressed ghost imaging based on deep image prior using singular value decomposition

To improve the low-quality reconstruction of compressed ghost imaging (CGI) caused by non-negative measurement matrix, and avoid the requirements of large amount of training dataset and time-consuming training process, compressed ghost imaging based on deep image prior using singular value decomposition (SVDCGI-DIP) is proposed. The measurement matrix and corresponded measurements are first generated through numerical simulation and then simultaneously optimized with singular value decomposition without the change of optical setup of CGI. Secondly, deep image prior (DIP) method is applied to untrained generative network models to avoiding the long training process, and learned regularization is incorporated to further optimize network parameters to iteratively reconstruct the original image from only a few measurements of an image without training. Numerical experiments verify the superiority of our proposed SVDCGI-DIP method.

The IPTA Nashville Consensus Conference on Post-Transplant lymphoproliferative disorders after solid organ transplantation in children: III - Consensus guidelines for Epstein-Barr virus load and other biomarker monitoring.

The International Pediatric Transplant Association convened an expert consensus conference to assess current evidence and develop recommendations for various aspects of care relating to post-transplant lymphoproliferative disorders after solid organ transplantation in children. In this report from the Viral Load and Biomarker Monitoring Working Group, we reviewed the existing literature regarding the role of Epstein-Barr viral load and other biomarkers in peripheral blood for predicting the development of PTLD, for PTLD diagnosis, and for monitoring of response to treatment. Key recommendations from the group highlighted the strong recommendation for use of the term EBV DNAemia instead of "viremia" to describe EBV DNA levels in peripheral blood as well as concerns with comparison of EBV DNAemia measurement results performed at different institutions even when tests are calibrated using the WHO international standard. The working group concluded that either whole blood or plasma could be used as matrices for EBV DNA measurement; optimal specimen type may be clinical context dependent. Whole blood testing has some advantages for surveillance to inform pre-emptive interventions while plasma testing may be preferred in the setting of clinical symptoms and treatment monitoring. However, EBV DNAemia testing alone was not recommended for PTLD diagnosis. Quantitative EBV DNAemia surveillance to identify patients at risk for PTLD and to inform pre-emptive interventions in patients who are EBV seronegative pre-transplant was recommended. In contrast, with the exception of intestinal transplant recipients or those with recent primary EBV infection prior to SOT, surveillance was not recommended in pediatric SOT recipients EBV seropositive pre-transplant. Implications of viral load kinetic parameters including peak load and viral set point on pre-emptive PTLD prevention monitoring algorithms were discussed. Use of additional markers, including measurements of EBV specific cell mediated immunity was discussed but not recommended though the importance of obtaining additional data from prospective multicenter studies was highlighted as a key research priority.