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In recent times, there are considerable research efforts in utilising millimetre wave cellular system for meeting rising mobile traffic volume and escalating data rate demand. In this regard, several analytical models have been developed for obtaining Channel State Information (CSI) for the design of combiner and precoder architectures for millimetre wave communication. One prominent method that has caught the attention of most researchers is the Orthogonal Matching Pursuit (OMP) which performs poorly in noisy condition. As a result, this work introduces Compressed Sampling Matching Pursuit (CoSAMP) as alternative to OMP for obtaining CSI for the design of hybrid combiner. A comparative analysis of all the cases examined reveals that CoSAMP surpasses OMP and Least Square (LS) across SNR range of −30 dB to −10 dB, recording lowest Normalized Mean Square Error (NMSE), highest Spectral Efficiency (SE) and bit rate. For instance at −20 dB, NMSE of CoSAMP exhibits 95% and 86% gain over LS and OMP, respectively while SE and bit rate of CoSAMP are better than OMP and LS by 21% and 91%, respectively. It is therefore seen that CoSAMP is a better technique for estimating CSI and for combiner design in such condition. [ABSTRACT FROM AUTHOR]

Extremely large-scale massive MIMO (XL-MIMO) emerges as a crucial technology for future 6G communication. Existing XL-MIMO hybrid-field channel models primarily model channels between uniform linear arrays and single-antenna users, employing the last-hop model of scatterers. However, if both transmitter and receiver are equipped with linear arrays, existing hybrid-field channel estimation schemes become inadequate. To address this, this paper proposed a channel modeling approach for XL-MIMO scenarios with both transmitter and receiver equipped with large-scale linear arrays, utilizing the SalehValenzuela model. Additionally, it introduced a hybrid-field channel estimation algorithm based on orthogonal matching pursuit (OMP). This algorithm initially estimated far-field components using the angle-domain transformation matrix, followed by estimating near-field components using the polar-domain transformation matrix. Furthermore, it introduced the Cramer-Rao lower bound (CRLB) to evaluate the proposed algorithm. Simulation results demonstrate approximately 0. 6 dB improvement in channel estimation performance compared to algorithms considering only far-field and near-field components. [ABSTRACT FROM AUTHOR]

The sparse representation, which is based on the orthogonal matching pursuit (OMP) algorithm, is a useful technique for identifying defect characteristics in rolling element bearings. However, OMP is easily influenced by noise interference and is prone to choosing irrelevant atoms during the sparse decomposition process, resulting in a reduction in reconstruction accuracy. A clustering-based regularized orthogonal matching pursuit (CROMP) algorithm is proposed for bearing fault diagnosis. The clustering technique can successfully eliminate redundant atoms from the dictionary, improving the system's stability and performance, while regularization can enhance the program's capacity to recover sparse signals. The suggested technique may successfully recover transient signals from loud noise, according to simulation simulations. The approach performs well in extracting notable fault impacts, according to actual testing. The suggested approach takes less time to run and extracts early defect information more effectively than the OMP algorithm. [ABSTRACT FROM AUTHOR]

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Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The shallow sea underwater acoustic channel exhibits a significant sparse multipath structure. The temporally multiple sparse Bayesian learning (TMSBL) algorithm can effectively estimate this sparse multipath channel. However, the complexity of the algorithm is high, the signal-to-noise ratio (SNR) of shallowsea underwater acoustic communication is low, and the estimation performance of the TMSBL algorithm is greatly affected by noise. To address this problem, an improved TMSBL underwater acoustic channel estimation method based on a dictionary learning noise reduction algorithm is proposed. Firstly, the K-Singular Value Decomposition (K-SVD) dictionary learning method is used to reduce the noise of the received pilot matrix, reducing the influence of noise on the signal. Then, the Generalized Orthogonal Matching Pursuit (GOMP) channel estimation method is combined to obtain a priori information such as the perceptual matrix and hyperparameter matrix for TMSBL channel estimation; and the noise variance is obtained by using the null subcarrier calculation instead of iteratively updating the noise variance in the TMSBL, to improve the estimation accuracy and reduce the algorithmic complexity. Finally, the TMSBL channel estimation method is used to estimate the underwater acoustic channels of different symbols jointly. The simulation results show that the normalized mean square error of the channel estimation of the improved TMSBL method is reduced by about 92.2% compared with the TMSBL algorithm, obtaining higher estimation accuracy; running time is reduced by about 45.6%, and there is also better performance in terms of the running speed, which provides a reference for underwater acoustic channel estimation. [ABSTRACT FROM AUTHOR]

The wide‐angle electromagnetic scattering problems can be rapidly solved by using the method of moments in conjunction with compressive sensing theory. The method mainly has two computational parts: measurement and recovery. To further enhance the performance, an improved orthogonal matching pursuit algorithm with a randomised atom selection process is proposed, significantly reducing the computational complexity of the recovery part. Additionally, a restart mechanism is designed to eliminate possible errors that might occur during the random process, ensuring the accuracy of recovery results. Numerical experiments with objects of different shapes validate the effectiveness and efficiency of the proposed scheme. [ABSTRACT FROM AUTHOR]

This study focused on improving the clustering performance of hyperspectral imaging (HSI) by employing the Generalized Orthogonal Matching Pursuit (GOMP) algorithm for feature extraction. Hyperspectral remote sensing imaging technology, which is crucial in various fields like environmental monitoring and agriculture, faces challenges due to its high dimensionality and complexity. Supervised learning methods require extensive data and computational resources, while clustering, an unsupervised method, offers a more efficient alternative. This research presents a novel approach using GOMP to enhance clustering performance in HSI. The GOMP algorithm iteratively selects multiple dictionary elements for sparse representation, which makes it well-suited for handling complex HSI data. The proposed method was tested on two publicly available HSI datasets and evaluated in comparison with other methods to demonstrate its effectiveness in enhancing clustering performance. [ABSTRACT FROM AUTHOR]

In this paper, we generalize the conditions for the exact or stable recovery of weighted k-sparse signals in weighted sparse phase retrieval in our previous work [11] from the weighted ℓ 1 minimization to the weighted ℓ q (0 < q ≤ 1) minimization in a broad sense. Specifically, we first present that the weighted null space property (WNSP) is a sufficient and necessary condition to guarantee the exact recovery of a weighted k-sparse signal from its noiseless phaseless measurements via the weighted ℓ q (0 < q ≤ 1) minimization in both the real and complex cases. In addition, we establish a general strong weighted restricted isometry property (SWRIP) condition for the stable recovery of a weighted k-sparse signal from its noisy phaseless measurements via the weighted ℓ q (0 < q ≤ 1) minimization in the real case. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to improve the mechanical property and processing performance and reduce the cost of the polylacticacid/polybutyleneadipate-co-terephthalate(PLA/PBAT) composites, the calcium carbonate (CaCO3) and compatibilizer styrene-maleicanhydride copolymer (SMA-2025) were added to the PLA/PBAT system, and the effect of CaCO3 and SMA-2025 on the morphology, structure, mechanical property, thermal property, thermalstability and shape memory property of the CaCO3/PLA/PBAT composites were studied and discussed. Design/methodology/approach: The CaCO3/PLA/PBAT shape memory composites were prepared via melt-blending and hot-pressing methods, and the effect of CaCO3 and SMA-2025 on the property of the composites was investigated via scanning electron microscope, universal testing instrument, Fourier transform infrared spectroscopy, differential scanning calorimetry, thermogravimetric analysis and DMA, respectively. Findings: The interface property, mechanical property, thermal stability, shape memory recovery ratios and recovery stresses, and processing performance of the CaCO3/PLA/PBAT shape memory composites were significantly improved by adding of CaCO3 and SMA-2025. Moreover, the CaCO3/PLA/PBAT composites have good blowing film processing performance. Originality/value: This study will provide a reference for the research, processing and application of the high-performance CaCO3/PLA/PBAT shape memory composites. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to conduct a thorough investigation of a stochastic eddy-current testing problem, when the geometric parameters of the system under study are characterized by uncertainty. Focusing on the case of subsurface defect detection, we devise reliable surrogates for the quantities of interest (QoI) based on the principles of the generalized polynomial chaos (PC) and using the orthogonal matching pursuit (OMP) solver to promote sparsity in the approximate models. In addition, a variance-based approach is implemented for the sequential construction of the necessary sample set, enabling more accurate estimation of the statistical metrics without imposing additional computational overhead. Apart from quantifying the inherent uncertainty, a sensitivity analysis is performed that assesses the impact of each geometric variable on the QoI, via the computation of Sobol indices. The efficiency of the OMP-PC algorithm is demonstrated in two variants of the subsurface-discontinuity problem, yielding at the same time useful conclusions regarding the properties of the stochastic outputs. [ABSTRACT FROM AUTHOR]

This article presents an effective computational method based on the orthogonal collocation on finite element for nonlinear pursuit‐evasion differential game problems. The original problems are transformed into two dynamic optimization problems at first, so that the difficulty of obtaining the solution is reduced. To improve the convergence rate and the efficiency, the sensitivities describing the influence of control and interval parameters on state are derived through the discretized dynamic equations for the resulting nonlinear programming problem. The convergence speed is introduced to measure the performance in the upper level iteration. The main structure and the algorithm of the method are also given. Two demonstrative differential game problems with different scenarios from practice are studied. Compared with the approach without sensitivity information, the proposed method needs less function evaluations and saves at least 68.4% of the computational time. The research results show the effectiveness of proposed approach. [ABSTRACT FROM AUTHOR]

Artificial neural networks (ANNs) have gained a lot of attention from researchers in the past few years and have been employed on a large scale. They have also been gaining momentum in wireless communication systems. For efficient vehicle-to-vehicle (V2V) channel communication, a sparse multipath channel issue must be studied. To minimize the multipath effect, a time reversal (TR) operation and time division synchronization orthogonal frequency division multiplexing (TDS-OFDM) have been appealing because of their fast synchronization and active spectral efficiency. To improve the transceiver's execution in a frequency-selective fading channel environment, an OFDM system is used to reduce inter- symbol interference (ISI). Simultaneous Orthogonal Matching Pursuit (SOMP) channel state estimator algorithm suffer from high computational cost and high computational complexity. The ANN algorithm has better performance than SOMP algorithm. The proposed neural network technologies have lower complexity than the SOMP algorithm. The application of ANN is capable of solving complex problems, such as those encountered in image, signal processing and have been implemented for channel estimation in OFDM. The proposed ANN outperformed the SOMP algorithm with regard to signal compensation. Overall, the ANN algorithm achieved the best performance. This study proposes an ANN-based sparse channel state estimator. Regarding the bit error rate (BER) metric, the proposed estimator outperforms the channel estimation approach based on the SOMP. The simulation results confirm the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

An editorial focuses on advancing seismic data reconstruction methods through the integration of anti-aliasing techniques with vector Projection onto Convex Sets (POCS) methods. It introduces an enhanced approach to reconstruct multicomponent seismic data, addressing the limitations of existing methods that often reconstruct each component independently and lack anti-aliasing mechanisms.

This paper addresses the problem of complex and challenging disturbance localization in the current power system operation environment by proposing a disturbance localization method for power systems based on group sparse representation and entropy weight method. Three different electrical quantities are selected as observations in the compressed sensing algorithm. The entropy weighting method is employed to calculate the weights of different observations based on their relative disturbance levels. Subsequently, by leveraging the topological information of the power system and pre-designing an overcomplete dictionary of disturbances based on the corresponding system parameter variations caused by disturbances, an improved Joint Generalized Orthogonal Matching Pursuit (J-GOMP) algorithm is utilized for reconstruction. The reconstructed sparse vectors are divided into three parts. If at least two parts have consistent node identifiers, the node is identified as the disturbance node. If the node identifiers in all three parts are inconsistent, further analysis is conducted considering the weights to determine the disturbance node. Simulation results based on the IEEE 39-bus system model demonstrate that the proposed method, utilizing electrical quantity information from only 8 measurement points, effectively locates disturbance positions and is applicable to various disturbance types with strong noise resistance. [ABSTRACT FROM AUTHOR]

To better meet communication needs, 6G proposes Integrated Satellite-Terrestrial Networks. Integrated Sensing and Communication (ISAC) is one of the key technologies of Integrated Satellite-Terrestrial Networks, which can reduce the energy consumption of the system, improve communication efficiency, and increase the utilization rate of spectrum resources. In the existing technology, the Modulated Wideband Converter (MWC) system can provide support for the miniaturization and intelligence of wireless device sensing and communication systems. Therefore, the MWC system can be used as a preliminary application of ISAC technology. However, the reconstruction effect of the conventional MWC system under the influence of noise is not stable. Therefore, we propose a signal processing optimization scheme for the MWC system based on spatial information entropy. First, the subsequent reconstruction algorithm is considered to require the dynamic and flexible processing of the sampled signals to reduce the influence of noise. Second, for the shortcomings of the original Orthogonal Matching Pursuit (OMP) algorithm, the concept of the genetic algorithm is used to optimize the algorithm by constructing the feature factor through spatial information gain and spatial information features. According to the simulation results, compared with the traditional MWC system, the scheme proposed in this paper is improved in all indicators. [ABSTRACT FROM AUTHOR]

This paper investigates single-user uplink and two-user downlink channel estimation in reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) wireless communication systems. Because of the difficulty associated with the estimation of channels in RIS-aided wireless communication systems, channel state information (CSI) is assumed to be known at the receiver in some previous works in the literature. By assuming that prior knowledge of the line-of-sight (LoS) channel between the RIS and the base station (BS) is known, two compressive sensing-based channel estimation schemes that are based on simultaneous orthogonal matching pursuit and structured matching pursuit (StrMP) algorithms are proposed for estimation of uplink channel between RIS and user equipment (UE), and joint estimations of downlink channels between BS and a UE, and between RIS and another UE, respectively. The proposed channel estimation schemes exploit the inherent common sparsity shared by the angular domain mmWave channels at different subcarriers. The superiority of one of the proposed channel estimation techniques, the StrMP-based channel estimation technique, with negligibly higher computational complexity cost compared with other channel estimators, is documented through extensive computer simulation. Specifically, with a reduced pilot overhead, the proposed StrMP-based channel estimation scheme exhibits better performance than other channel estimation schemes considered in this paper for signal-to-noise ratio (SNR) between 0 dB and 5 dB upward at different instances for both uplink and downlink scenarios, respectively. However, below these values of SNR the proposed StrMP-based channel estimation scheme will require higher pilot overhead to perform optimally. [ABSTRACT FROM AUTHOR]

In recent years, there has been extensive research and application of unsupervised monocular depth estimation methods for intelligent vehicles. However, a major limitation of most existing approaches is their inability to predict absolute depth values in physical units, as they generally suffer from the scale problem. Furthermore, most research efforts have focused on ground vehicles, neglecting the potential application of these methods to unmanned aerial vehicles (UAVs). To address these gaps, this paper proposes a novel absolute depth estimation method specifically designed for flight scenes using a monocular vision sensor, in which a geometry-based scale recovery algorithm serves as a post-processing stage of relative depth estimation results with scale consistency. By exploiting the feature correspondence between successive images and using the pose data provided by equipped navigation sensors, the scale factor between relative and absolute scales is calculated according to a multi-view geometry model, and then absolute depth maps are generated by pixel-wise multiplication of relative depth maps with the scale factor. As a result, the unsupervised monocular depth estimation technology is extended from relative depth estimation in semi-structured scenes to absolute depth estimation in unstructured scenes. Experiments on the publicly available Mid-Air dataset and customized data demonstrate the effectiveness of our method in different cases and settings, as well as its robustness to navigation sensor noise. The proposed method only requires UAVs to be equipped with monocular camera and common navigation sensors, and the obtained absolute depth information can be directly used for downstream tasks, which is significant for this kind of vehicle that has rarely been explored in previous depth estimation studies. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) represent a significant health concern in the present era, with Electrocardiogram (ECG) serving as a crucial bio-signal for their detection. Efficient health monitoring necessitates rapid and precise diagnosis, thereby mandating the utilization of Compressive Sensing (CS) alongside Machine Learning (ML) algorithms. CS functions as a sensing methodology that reduces sample numbers by capturing sparse or compressible representations, simplifying and expediting the acquisition process. In this proposed study, ECG signals are compressively sensed and preprocessed using CS reconstruction algorithms, followed by the application of various ML algorithms for diagnostic purposes. The assessment of the reconstructed ECG signal entails the assessment of Peak Signal-to-Noise Ratio (PSNR) values and Percentage Root-mean-square Difference (PRD). Concurrently, ML algorithms are evaluated based on metrics including accuracy, specificity, and sensitivity. This work demonstrates exceptional performance in terms of acquisition time and computational complexity through the application of CS technology. Comparative analysis with the existing methodologies for CVD diagnosis reveals the proposed approach's remarkable efficacy. Notably, the reduction in data volume and hardware complexity serves as a significant advantage over conventional methods. The integration of CS and ML algorithms in the proposed methodology proves highly effective in diagnosing CVDs, achieving a classification accuracy of 94.7%. These results underscore the methodology's ability to deliver both speed and accuracy in diagnosis, positioning it as a promising approach for health monitoring. [ABSTRACT FROM AUTHOR]

With the widespread application of digital images, image processing technology plays an important role in fields such as computer vision and image analysis. Based on the orthogonal matching pursuit algorithm, an image processing method is proposed. In the process, sparse representation and reconstruction algorithm are used for image compressed sensing to complete image sampling operation. Afterwards, the theory of overcomplete sparse representation is introduced to optimize sparse representation, and an overcomplete dictionary is used to remove Gaussian noise, achieving the goal of image processing. The experimental results indicate that the research method do not show significant deficiencies in signal reconstruction when testing reconstructed signals under sparsity of 8; When testing the calculation time, the calculation time of the research method is about 0.212 s when the sparsity is 5 in the Lenna; In the error test, the mean square difference of the research method in the Lenna is stable at about 14.6; When conducting application analysis, the variance eigenvalues of the research method remained below 9.4. This indicates that the research method has good performance and can effectively process images, providing new technical support for image processing. [ABSTRACT FROM AUTHOR]

Blind compressive sensing generates a reduced set of measurements of a signal without the knowledge of the sparsifying basis. In this context, the unique recovery and reconstruction of the underlying signal is a challenging problem. One of the properties of the Householder operator is to transform a non-sparse vector in a finite dimensional vector space to a sparse representation of the vector, preserving its ℓ 2 -norm. In this work, we present an algorithm that invokes the sparsifying property of the Householder transforms for the recovery and reconstruction of a signal from its reduced set of blind measurements. The algorithm is an iterative procedure using two objective functions. The first objective function is to estimate the sparse set of representing coefficients. The second objective function jointly estimates the unknown sparsifying transform and the unknown signal. Both the objective functions are entangled to ensure that the estimates meet the fidelity requirement with the available reduced set of measurements. An ℓ 1 -trend filtering is applied to minimize overfitting the estimate of the signal. The signal is uniquely recovered and reconstructed upto an acceptable lower error bound. [ABSTRACT FROM AUTHOR]

This study is aimed at investigating the resilience degradation caused by traffic accidents and developing relevant resilience optimization strategies. A two-stage accident resilience triangle framework was proposed by comparing the differences between natural disasters and traffic accidents. To maximize system resilience, a network-wide traffic signal optimization model was presented. Spillback constraints and equilibrium constraints were established to enhance the capacity of urban-road networks to minimize congestion escalation, in addition to rapid recovery. A two-level algorithm based on greedy strategy and gradient descent was designed to solve the proposed non-linear programming model. In the experiment, a virtual road network was constructed based on the Simulation of Urban Mobility (SUMO) platform for validation and sensitivity analysis. The experimental results revealed that: (1) Compared to the traditional resilience framework, the proposed two-stage accident resilience framework can more reasonably describe the change mechanism of road network resilience under disturbance. (2) The proposed resilience-based traffic signal optimization model improved the system resilience under different conditions of traffic demand, accident severity, and rescue time in terms of the maximum performance degradation and recovery time. Precisely, the resilience loss is reduced by a maximum of 1.4%. Finally, the proposed model was further implemented with field data. The resilience improvement was significant during the evening rush hour. The results of this study contribute toward transportation resilience research and accident rescue strategies with respect to traffic management and control. [ABSTRACT FROM AUTHOR]

The choice of the sensing matrix is crucial in compressed sensing. Random Gaussian sensing matrices satisfy the restricted isometry property, which is crucial for solving the sparse recovery problem using convex optimization techniques. However, tight-frame sensing matrices result in minimum mean- squared-error recovery given oracle knowledge of the support of the sparse vector. If the sensing matrix is not tight, could one achieve the recovery performance assured by a tight frame by suitably designing the recovery strategy? This is the key question addressed in this paper. We consider the analysis-sparse l1-minimization problem with a generalized l2-norm-based data-fidelity and show that it effectively corresponds to using a tight-frame sensing matrix. The new formulation offers improved performance bounds when the number of nonzeros is large. One could develop a tight- frame variant of a known sparse recovery algorithm using the proposed formalism. We solve the analysis-sparse recovery problem in an unconstrained setting using proximal methods. Within the tight-frame sensing framework, we rescale the gradients of the data-fidelity loss in the iterative updates to further improve the accuracy of analysis-sparse recovery. Experimental results show that the proposed algorithms offer superior analysis-sparse recovery performance. Proceeding further, we also develop deep-unfolded variants, with a convolutional neural network as the sparsifying operator. On the application front, we consider compressed sensing image recovery. Experimental results on Set11, BSD68, Urban100, and DIV2K datasets show that the proposed techniques outperform the state-of-the-art techniques, with performance measured in terms of peak signal-to-noise ratio and structural similarity index metric. [ABSTRACT FROM AUTHOR]

6G wireless communication employ Extremely Large Multiple Input Multiple Output (ELMIMO) antenna arrays at the Base Station (BS) to expand the Radiative Near Field Region (RNFR) boundary by several meters. The existing Channel Estimation (CE) schemes in the Far Field Region (FFR) and RNFR had shown poor estimation accuracy at RNFR distance up to 1 km. In order to address this problem, RNFR boundary range within which a spherical wave can exists is deduced. A sparse three-dimensional (3D) Polar Depth Domain (PDD) codebook matrix has been developed using a new Depth Domain (DD) with finite range for each depth applicable within the obtained RNFR boundary range. A novel Compressive Sensing (CS) based CE algorithm named Depth Domain Orthogonal Matching Pursuit algorithm (DDOMP) is developed using generated 3D PDD codebook matrix. The Normalized Mean Square Error (NMSE) performance of the proposed algorithm has been analyzed in terms of depth, Pilot Length (PL), and Signal to Noise Ratio (SNR). It has been found that the DDOMP algorithm improves the CE accuracy when compared to the existing FFR and NFR CE schemes. It significantly reduces pilot overhead too. The performance of the DDOMP CE scheme is validated by simulation results. [ABSTRACT FROM AUTHOR]

A sparse adaptive channel estimation algorithm based on compressed sensing is presented to estimate sparse unknown channels in intelligent reflecting surface (IRS) assisted communication systems. First, the relationship between the l2 norm of the channel residual under the orthogonal matching pursuit (OMP) algorithm and the sparsity of the channel is studied, and the iteration termination conditions for the OMP to restore the sparsity of unknown channels are obtained. Then, a two-stage sparse adaptive channel estimation algorithm is presented. In the first stage, the channel sparseness is estimated, and in the second stage, the supporting set atoms are added or deleted to minimize the channel vector errors recovered. The simulation results show that the performance and robustness of the proposed algorithm are better than those of classical least squares, OMP with known sparsity, and sparse adaptive matching pursuit (SAMP). [ABSTRACT FROM AUTHOR]

Simple Summary: The implementation of antibiotic-free (ABF) and no-antibiotics-ever (NAE) poultry production has prompted interest in identifying viable options to reduce the need for medically important antibiotics (MIA) in animal feed. The Magni-Phi® Ultra (MPU) nutritional specialty product is mainly composed of natural plant extracts (Quillaja saponaria and Yucca schidigera), which are known for their numerous benefits. By evaluating the effects of feeding MPU on the immune response and intestinal permeability of broilers during coccidia challenge at the peak and recovery phases of infection, this study found that intestinal integrity was enhanced and growth performance and immune response were improved when feeding MPU. This study assessed the impact of Magni-Phi Ultra (MPU) inclusion on intestinal integrity and immunity in broiler chickens challenged with coccidia during peak and recovery phases. A total of 128 male Ross 708 broiler chicks were randomly allotted to one of four treatment groups (four chicks/cage). Treatments included an uninfected control (UUC); a coccidial challenge (CC) infected control (IUC); a CC fed salinomycin at 66 ppm (SAL); and a CC fed Magni-Phi Ultra at 0.11 g/kg of diet (MPU). At 16 days post-hatch, all birds in the CC groups were orally gavaged with a 3× dose of a live coccidia vaccine. At 5 dpi, the birds fed MPU and SAL showed decreased plasma FITC-d, oocyte shedding, and lesion scores and higher BWG compared to the IUC birds (p < 0.05). Jejunum IL-17, IL-10, and IFN-ϒ mRNA expression was higher in the IUC compared to the UUC (p < 0.05) group at 5 dpi. At 12 dpi, the birds fed MPU or SAL had lower plasma FITC-d and jejunum IFN-ϒ and IL-10 mRNA expression compared to the IUC birds (p < 0.05). This study indicates that MPU supports intestinal integrity and mucosal immune responses during the peak and recovery phases of infection, which may lead to improved health and performance. [ABSTRACT FROM AUTHOR]

In this article, a fast algorithm is studied to recover the sparse signals. It can be regarded as an extension of the parameterized fast iterative shrinkage‐thresholding algorithm from convex optimization to nonconvex optimization. Numerical results show that the proposed fast algorithm is efficient and fast in recovering the sparse signals. [ABSTRACT FROM AUTHOR]

In this article, two Hager–Zhang (HZ) type projection algorithms are presented for large-dimension nonlinear monotone problems and sparse signal recovery in compressed sensing. This goal is attained by conducting singular value analysis of a nonsingular HZ-type search direction matrix as well as applying the idea by Piazza and Politi [J Comput Appl Math. 2002;143(1):141–144] and minimizing the Frobenius norm of an orthornormal matrix. The paper attempts to fill the gap in the work of Hager and Zhang [Pac J Optim. 2006;2(1):35–58], Waziri et al. [Appl Math Comput. 2019;361:645–660], Sabi'u et al. [Appl Numer Math. 2020;153:217–233] and Babaie-Kafaki [4OR-Q J Oper Res. 2014;12:285-292], where the sufficient descent or global convergence condition is not satisfied when the HZ parameter is in the interval $ (0,\frac {1}{4}) $ (0 , 1 4). The proposed schemes are also suitable for solving non-smooth nonlinear problems. Also, by employing some mild conditions, global convergence of the schemes are established, while numerical comparison with four effective HZ-type methods show that the new methods are efficient. Furthermore, to illustrate their practical application, both methods are applied to solve the $ \ell _1 $ ℓ 1 -norm regularization problems to recover a sparse signal in compressive sensing. The experiments conducted in that regard show that the methods are promising and perform better than two other methods in the literature. [ABSTRACT FROM AUTHOR]

In magnetic resonance (MR) image reconstruction, the orthogonal matching pursuit (OMP) is widely recognized for its simplicity and competitive performance. However, OMP designs a termination condition based on some prior information such as sparsity and noise intensity. In practice, the unknown prior information of MR images cannot guarantee accurate reconstruction. To make OMP suitable for magnetic resonance imaging (MRI), we propose an automatic threshold OMP algorithm based on QR decomposition (ATOMP-QR). The termination condition of ATOMP-QR, which utilizes the mutual incoherence property of the sensing matrix, is related to whether the residual vector includes the orthogonal projection component of measurements. Then, to avoid the computation of pseudo-inverse and accelerate reconstruction speed, we perform QR decomposition on the measurement matrix. We conduct the MRI experiments to evaluate the superiority and effectiveness of ATOMP-QR in peak signal-to-noise ratio (PSNR), structure similarity index measure (SSIM), and running time. Specifically, for the T1-w image with a sparsity of 10, the PSNR was improved from 24 to 32 dB; the SSIM was increased from 0.87 to 0.99. The maximum time consumed decreased from 0.2276 to 0.0107 s. [ABSTRACT FROM AUTHOR]

• Analytical solution of viscoelastic laminated beams in hygro-thermo-mechanical condition is proposed. • The proposed solution is more accurate than simplified solutions and is efficient over finite element counterpart. • A superposition principle for viscoelastic laminated beams in hygro-thermo-mechanical condition is derived. In order to predict the creep and recovery behaviors of for viscoelastic laminated beam in hygro-thermo-mechanical (HTM) coupled condition, an exact analytical solution is proposed. This solution considers two effect mechanisms: temperature and humidity, including the expansion difference and the variation of viscoelastic properties. In the analytical model, the stresses and displacements of each lamina are described based on the hygro-thermo-elasticity theory combined with the Boltzmann superposition. By employing series expansion, the transfer matrix method, and Laplace transform, the solution is determined analytically. Notably, the entire process of Laplace inverse transformation is carried out analytically without relying on any numerical approximations. The comparison analysis indicates that the proposed solution is more computationally efficient than the finite element counterpart and is more accurate than the solutions based on transverse shear deformation assumption. By analyzing the components of the proposed solution, a modified superposition principle applicable to HTM coupled conditions and a superposition principle for creep recovery are derived. In the end, the HTM coupled behavior, the bending creep and recovery as well as the optimization of expansion coefficient are investigated in detail. [ABSTRACT FROM AUTHOR]

The use of Block Compressed Sensing (BCS) as an alternative to conventional Compressed Sensing (CS) in image sampling and acquisition has gained attention due to its potential benefits. However, BCS can suffer from blocking artifacts and blurs in the reconstructed images, which can degrade the overall image quality. To address these issues and improve reconstruction performance, Adaptive Block Compressed Sensing (ABCS) techniques can be used. ABCS minimizes the blurs and artifacts that occur during the reconstruction process by adaptively selecting samples from different image blocks. To further enhance the sampling efficiency and overall performance in underwater image compression, a new approach called adaptive reweighted sampling-based ABCS (ARS-ABCS) in Fast Haar Wavelet Transform (FHWT) domain is proposed in the paper. This reweighting process allows the system to allocate more samples to the areas where reconstruction quality is low or artifacts are prevalent, improving the overall image reconstruction in a targeted manner. Performance is measured in terms of Peak Signal to Noise Ratio (PSNR), Structural SIMilarity index (SSIM), Normalized Cross-Correlation (NCC) and Normalized Absolute Error (NAE). The results demonstrate that the proposed ARS-ABCS has achieved 1.5 to 5dB increase in PSNR with respect to other non-weighted adaptive schemes. It has produced space saving of 60 to 70% with utilization of only around 30% of total samples in the image. SSIM and NCC values obtained are closer to 1 with low NAE values. [ABSTRACT FROM AUTHOR]

Owing to its success in 4G, Orthogonal Frequency Division Multiplexing (OFDM) made its contenting presence in 5G communications. To accommodate massive 5G data, it would be quite opportune to maintain significant reduction in overhead, while also ensuring reliable symbol detection at the receiver. For instance, reducing pilot symbol transmissions required for OFDM channel estimation, still retaining the efficacy of symbol detection is propitious. In this regard, contemporary compressive sensing framework with sparse recovery approaches such as Orthogonal Matching Pursuit (OMP) and Sparse Bayesian Learning (SBL) have been widely investigated in the literature. However study on approaches such as Least Absolute Shrinkage and Selection Operator (LASSO), Split Augmented Lagrangian Shrinkage Algorithm (SALSA) and corresponding investigations with experimentally acquired data frames are very less explored. In this work, by modifying the group LASSO optimization, a sparse recovery approach is proposed for OFDM channel estimation, particularly for a severely less pilot scenario. A non-coherent OFDM wireless link is developed using two Universal Software Radio Peripheral (USRP) devices, and several complex baseband OFDM frames are extracted in an indoor wireless lab environment for different receiver gains. Sparse recovery approaches are incorporated in two scenarios, namely number of pilots less than and severely less than channel length. The results of Mean Square Error (MSE) and Bit Error Rate (BER) versus receiver gain, show that the proposed approach outperforms all the other considered approaches. Particularly, while estimating CIR of length 20 with the proposed approach, employing 17 pilots resulted in BER of 0.0050, which degraded to only 0.0099 in a 5 pilot scenario, thus accommodating severe reduction in pilot overhead. [ABSTRACT FROM AUTHOR]

The appearance of the same object may vary in different scene images due to occlusions between objects. Humans can quickly identify the same object, even if occlusions exist, by completing the occluded parts based on its complete canonical image in the memory. Achieving this ability is still challenging for existing models, especially in the unsupervised learning setting. Inspired by such an ability of humans, we propose a novel object-centric representation learning method to identify the same object in different scenes that may be occluded by learning global object-centric representations of complete canonical objects without supervision. The representation of each object is divided into an extrinsic part, which characterizes scene-dependent information (i.e., position and size), and an intrinsic part, which characterizes globally invariant information (i.e., appearance and shape). The former can be inferred with an improved IC-SBP module. The latter is extracted by combining rectangular and arbitrary-shaped attention and is used to infer the identity representation via a proposed patch-matching strategy with a set of learnable global object-centric representations of complete canonical objects. In the experiment, three 2D scene datasets are used to verify the proposed method's ability to recognize the identity of the same object in different scenes. A complex 3D scene dataset and a real-world dataset are used to evaluate the performance of scene decomposition. Our experimental results demonstrate that the proposed method outperforms the comparison methods in terms of same object recognition and scene decomposition. [ABSTRACT FROM AUTHOR]

Practitioners employ operator splitting methods--such as alternating direction method of multipliers (ADMM) and its "dual" Douglas-Rachford method (DR)--to solve many kinds of optimization problems. We provide a gentle introduction to these algorithms, and illustrations of their duality-like relationship in the context of solving basis pursuit problems for audio signal recovery. Recently, researchers have used the dynamical systems associated with the iterates of splitting methods to motivate the development of schemes to improve performance. These developments include a class of methods that act by iteratively minimizing surrogates for a Lyapunov function for the dynamical system. An exemplar of this class is currently state-of-the-art for the feasibility problem of finding wavelets with special structure. Early experimental evidence has also suggested that, when implemented in a primal-dual (ADMM and DR) framework, this exemplar may provide improved performance for basis pursuit problems. We provide a reasonable way to compute the updates for this exemplar, and we study the application of this method to the aforementioned basis pursuit audio problems. We provide experimental results and visualizations of the dynamical system for the dual DR sequence. We observe that for highly structured problems with real data, the algorithmic behavior is noticeably different than for randomly generated problems. [ABSTRACT FROM AUTHOR]

Sparse representation has garnered significant attention across multiple fields, including signal processing, statistics, and machine learning. The fundamental concept of this technique is that we can express the signal as a linear combination of only a few elements from a known basis. Compressed sensing (CS) is an interesting application of this technique. It is valued for its potential to improve data collection and ensure efficient acquisition and recovery from just a few measurements. In this paper, we propose a novel approach for the high-order CS problem based on the Einstein product, utilizing a tensor dictionary instead of the commonly used matrix-based dictionaries in the Tucker model. Our approach provides a more general framework for compressed sensing. We present two novel models to address the CS problem in the multidimensional case. The first model represents a natural generalization of CS to higher-dimensional signals; we extend the traditional CS framework to effectively capture the sparsity of multidimensional signals and enable efficient recovery. In the second model, we introduce a complexity reduction technique by utilizing a low-rank representation of the signal. We extend the OMP and the homotopy algorithms to solve the high-order CS problem. Through various simulations, we validate the effectiveness of our proposed method, including its application to solving the completion tensor problem in 2D and 3D colored and hyperspectral images. [ABSTRACT FROM AUTHOR]

We propose the Rescaled Matching Pursuit (RMP) algorithm to recover sparse signals in high-dimensional Euclidean spaces. The RMP algorithm has less computational complexity than other greedy-type algorithms, such as Orthogonal Matching Pursuit (OMP). We show that if the restricted isometry property is satisfied, then the upper bound of the error between the original signal and its approximation can be derived. Furthermore, we prove that the RMP algorithm can find the correct support of sparse signals from random measurements with a high probability. Our numerical experiments also verify this conclusion and show that RMP is stable with the noise. So, the RMP algorithm is a suitable method for recovering sparse signals. [ABSTRACT FROM AUTHOR]

A novel source enumeration method based on criterion of searching for the best-matched preset sparse dictionary is presented in this paper, which is applicable under the condition of both white and colored noise. In this method, different source numbers are mapped with different sparse dictionaries innovatively in advance, which are constructed by utilizing the K-SVD algorithm and the training data under the same SNR. Then, in the source enumeration stage, all the preset dictionaries are employed to sparsely encode the observed signal through the orthogonal matching pursuit (OMP) algorithm, and the best-matched one which corresponds to the correct source number leads to the least information loss after sparse encoding. Due to the difficulty of directly evaluating the information loss through each error signal which is the difference between the observed signal and the sparse reconstructed signal, the energy norm for the singular values, which is calculated after performing singular value decomposition (SVD) on error signal, is proposed to reflect the information loss indirectly. Simultaneously, the source number corresponding to the best-matched preset sparse dictionary is the final estimated result. The experimental results show the superiority of our proposed method compared to other advanced methods. [ABSTRACT FROM AUTHOR]

The realization of a BRST cohomology of the 4D topological gauge-affine gravity is established in terms of a superconnection formalism. The identification of fields in the quantized theory occurs directly as is usual in terms of superconnection and its supercurvature components with the double covering of the general affine group G A ¯ (4 , R) . Then, by means of an appropriate decomposition of the metalinear double-covering group S L ¯ (5 , R) with respect to the general linear double-covering group G L ¯ (4 , R) , one can easily obtain the enlargements of the fields while remaining consistent with the BRST algebra. This leads to the descent equations, allowing us to build the observables of the theory by means of the BRST algebra constructed using a s a ¯ (5 , R) algebra-valued superconnection. In particular, we discuss the construction of topological invariants with torsion. [ABSTRACT FROM AUTHOR]

Numerous physiological processes are cyclical, but sampling these processes densely enough to perform frequency decomposition and subsequent analyses can be challenging. Mathematical approaches for decomposition and reconstruction of sparsely and irregularly sampled signals are well established but have been under-utilized in physiological applications. We developed a basis pursuit denoising with polynomial detrending (BPWP) model that recovers oscillations and trends from sparse and irregularly sampled timeseries. We validated this model on a unique dataset of long-term inter-ictal epileptiform discharge (IED) rates from human hippocampus recorded with a novel investigational device with continuous local field potential sensing. IED rates have well established circadian and multiday cycles related to sleep, wakefulness, and seizure clusters. Given sparse and irregular samples of IED rates from multi-month intracranial EEG recordings from ambulatory humans, we used BPWP to compute narrowband spectral power and polynomial trend coefficients and identify IED rate cycles in three subjects. In select cases, we propose that random and irregular sampling may be leveraged for frequency decomposition of physiological signals. Trial Registration:NCT03946618. Author summary: Circadian and multiday cycles are an important part of many long-term neuro-behavioral phenomena such as pathological inter-ictal epileptiform discharges (IEDs) and seizures in epilepsy. Long-term, ambulatory, neuro-behavioral monitoring in human patients involves complex recording systems that can be subject to intermittent, irregular data loss and storage limitations, resulting in sparse, irregularly sampled data. Cycle identification in sparse data or irregular data using traditional frequency decomposition techniques typically requires interpolation to create a regular timeseries. Using unique, long-term recordings of pathological brain activity in people with epilepsy implanted with an investigational device, we developed a method to identify cycles in sparse, irregular neuro-behavioral data without interpolation. We anticipate this approach will enable retrospective cycle identification in sparse neuro-behavioral timeseries and support prospective sparse sampling in monitoring systems to enable long-term monitoring of patients and to extend storage capacity in a variety of ambulatory monitoring applications. [ABSTRACT FROM AUTHOR]

Multi-rotor aircraft have the advantages of a simple structure, low cost, and flexible operation in the unmanned aerial vehicle (UAV) family, and have developed rapidly in recent years. Radar surveillance and classification of the growing number of multi-rotor aircraft has become a challenging problem due to their low-slow-small (LSS) characteristics. Estimation of the blade number is an important step in distinguishing LSS targets. However, most of the current research on micro-motion parameters estimation has focused on the analysis of rotational frequency, length, and the initial phase of blades with a prior of blade number, affecting its ability to identify LSS targets. In this article, a micro-motion parameters estimation method for multi-rotor targets without a prior is proposed. On the basis of estimating the flashing frequency of the blades, a validation function is constructed through spectral analysis to judge the number of blades, and then the rotational frequency is estimated. The blade length is calculated by estimating the maximum Doppler shift. Moreover, the variational mode decomposition (VMD)-based atomic scaling orthogonal matching pursuit (AS-OMP) method is jointly applied to estimate the blade length when suffering from the low PRF and insufficient SNR conditions. Extensive experiments on the simulated and measured data demonstrate that the proposed method outperforms robust micro-motion parameter estimation capability in low PRF and insufficient SNR conditions compared to the traditional time-frequency analysis methods. [ABSTRACT FROM AUTHOR]

Space–time adaptive processing (STAP) is an effective technology in clutter suppression and moving target detection for airborne radar. When working in the heterogeneous environment, the number of training samples that satisfy independent and identically distributed (IID) conditions is insufficient, making it difficult to ensure the estimation accuracy of the clutter plus noise covariance matrix for traditional STAP methods. Sparse recovery–based STAP (SR–STAP) methods have received widespread attention in the past few years. The accurate estimation of the clutter plus noise covariance matrix can be achieved using only a few training samples. The iterative adaptive approach (IAA) can quickly and accurately estimate the power spectrum, but applying this method directly to the STAP method cannot produce good performance. In this paper, a fast IAA–based SR–STAP method is proposed. Based on the weighted l 1 problem, the IAA spectrum is used as a weighted term to obtain a good approximation. In order to obtain an analytical solution, we use the weighted l 2 norm to approximate the weighted l 1 norm without loss of performance. Compared with the IAA–STAP method, the proposed method is more robust to errors. Moreover, the proposed method has a fast computational speed. The effectiveness of the proposed method is demonstrated by simulations. [ABSTRACT FROM AUTHOR]

The problem of separating multicomponent micro-Doppler (m-D) signals is common in the field of radar signal processing. In some implementations, it is necessary to separate the multicomponent m-D signal that contains missing samples. To address this issue, an optimization model has been developed to recover and decompose multicomponent m-D signals with missing samples. To solve the underlying optimization problem, a two-algorithm-based alternate iteration framework is proposed. This method uses three techniques—the null space property, ridge regression method, and matching pursuit principle—to estimate the individual component, complex-valued differential operator, and regularization parameter. Finally, as shown by both simulation and measured data processing results, the proposed method can accurately separate the multicomponent m-D signal from incomplete data. [ABSTRACT FROM AUTHOR]

Document-level relation extraction aims to uncover relations between entities by harnessing the intricate information spread throughout a document. Previous research involved constructing discrete syntactic matrices to capture syntactic relationships within documents. However, these methods are significantly influenced by dependency parsing errors, leaving much of the latent syntactic information untapped. Moreover, prior research has mainly focused on modeling two-hop reasoning between entity pairs, which has limited applicability in scenarios requiring multi-hop reasoning. To tackle these challenges, a syntax-enhanced multi-hop reasoning network (SEMHRN) is proposed. Specifically, the approach begins by using a dependency probability matrix that incorporates richer grammatical information instead of a sparse syntactic parsing matrix to build the syntactic graph. This effectively reduces syntactic parsing errors and enhances the model's robustness. To fully leverage dependency information, dependency-type-aware attention is introduced to refine edge weights based on connecting edge types. Additionally, a part-of-speech prediction task is included to regularize word embeddings. Unrelated entity pairs can disrupt the model's focus, reducing its efficiency. To concentrate the model's attention on related entity pairs, these related pairs are extracted, and a multi-hop reasoning graph attention network is employed to capture the multi-hop dependencies among them. Experimental results on three public document-level relation extraction datasets validate that SEMHRN achieves a competitive F1 score compared to the current state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The demand for precise positioning in noisy environments has propelled the development of research on array antenna radar systems. Although the orthogonal matching pursuit (OMP) algorithm demonstrates superior performance in signal reconstruction, its application efficacy in noisy settings faces challenges. Consequently, this paper introduces an innovative OMP algorithm, DTM_OMP_ICA (a dual-threshold mask OMP algorithm based on independent component analysis), which optimizes the OMP signal reconstruction framework by utilizing two different observation bases in conjunction with independent component analysis (ICA). By implementing a mean mask strategy, it effectively denoises signals received by array antennas in noisy environments. Simulation results reveal that compared to traditional OMP algorithms, the DTM_OMP_ICA algorithm shows significant advantages in noise suppression capability and algorithm stability. Under optimal conditions, this algorithm achieves a noise suppression rate of up to 96.8%, with its stability also reaching as high as 99%. Furthermore, DTM_OMP_ICA surpasses traditional denoising algorithms in practical denoising applications, proving its effectiveness in reconstructing array antenna signals in noisy settings. This presents an efficient method for accurately reconstructing array antenna signals against a noisy backdrop. [ABSTRACT FROM AUTHOR]

The relaxed CQ algorithm is one of the most important algorithms for solving the split feasibility problem. We study the issue of strong convergence of the relaxed CQ algorithm in Hilbert spaces together with estimates on the convergence rate. Under a kind of Hölderian type bounded error bound property, strong convergence of the relaxed CQ algorithm is established. Furthermore, qualitative estimates on the convergence rate is presented. In particular, for the case when the involved exponent is equal to 1, the linear convergence of the relaxed CQ algorithm is established. Finally, numerical experiments are performed to show the convergence property of the relaxed CQ algorithm for the compressed sensing problem. [ABSTRACT FROM AUTHOR]

Analytic signals constitute a class of signals that are widely applied in time–frequency analysis such as extracting instantaneous frequency (IF) or phase derivative in the characterization of ultrashort laser pulse. The purpose of this paper is to investigate the phase retrieval (PR) problem for analytic signals in C N by short-time Fourier transform (STFT) measurements since such measurements enjoy some nice structures. Since generic analytic signals are not sparse in the time domain, the existing PR results for sparse (in time domain) signals do not apply to analytic signals. In this paper, the windows are required to be bandlimited such that they usually have the full support length N and allow us to get much better resolutions on low frequencies. By exploiting the structure of the STFT associated with bandlimited windows for analytic signals, we prove that the STFT-based phase retrieval (STFT-PR for short) of generic analytic signals can be achieved by their (3 ⌊ N 2 ⌋ + 1) measurements, where ⌊ x ⌋ denotes the largest integer that is not larger than x. Since the generic analytic signals are (⌊ N 2 ⌋ + 1) -sparse in the Fourier domain, such a number of measurements is lower than 4 N + O (1) and O (k 3) which are required in the literature for STFT-PR of all signals and of k 2 -sparse (in the Fourier domain) signals in C N 2 , respectively. Moreover, we also prove that if the length N is even and the windows are also analytic, then the number of measurements can be reduced to (3 N 2 - 1) . As an application of this, we get that the instantaneous frequency (IF) of a generic analytic signal can be exactly recovered from the STFT measurements. [ABSTRACT FROM AUTHOR]

We develop a convex‐optimization clustering algorithm for heterogeneous financial networks, in the presence of arbitrary or even adversarial outliers. In the stochastic block model with heterogeneity parameters, we penalize nodes whose degree exhibit unusual behavior beyond inlier heterogeneity. We prove that under mild conditions, this method achieves exact recovery of the underlying clusters. In absence of any assumption on outliers, they are shown not to hinder the clustering of the inliers. We test the performance of the algorithm on semi‐synthetic heterogenous networks reconstructed to match aggregate data on the Korean financial sector. Our method allows for recovery of sub‐sectors with significantly lower error rates compared to existing algorithms. For overlapping portfolio networks, we uncover a clustering structure supporting diversification effects in investment management. [ABSTRACT FROM AUTHOR]