Signal Recovery Research Articles

Relaxed Inertial Projective Forward-backward Splitting Algorithms for Regularized Least Square Problems

This study presents flexible conditions of the inertial extrapolation parameter for easy implementation that is added to the algorithm in faster convergence. Modified inertial forward-backward splitting algorithms for solving variational inclusion problems are introduced to apply to solve the LASSO problem for image restoration and signal recovery, and the elastic net model for classification problem. Projection methods are used in the final step for narrowing down the search field, resulting in a better solution.

Filter-assisted direct detection-based field recovery scheme for complex-valued double-sideband signals

Double-sideband self-coherent detection (DSB-SCD) can achieve optical field recovery of complex-valued double-sideband (CV-DSB) signals without a local oscillator laser. To date, various proposed DSB-SCD schemes require a high carrier-to-signal power ratio (CSPR) to mitigate the inherent signal-signal beat interference (SSBI) induced by direct detection. Aiming at relaxing the high CSPR requirement for the DSB-SCD, filter-assisted direct detection (FADD) is proposed to retrieve the CV-DSB signals. In this paper, we clarify the concept of the FADD, which uses optical filters in the receiver to achieve optical field recovery of CV-DSB signals via direct detection. The CV-DSB signals can be retrieved at low CSPR with the aid of optical filters. We also propose a novel FADD receiver that uses an optical bandstop filter (OBSF) to obtain CV-DSB signals from self-coherent signals, called OBSF-DD with 90° hybrid. Considering the high complexity of the OBSF-DD with 90° hybrid, a simplified OBSF-DD receiver with 2 × 2 coupler is put forward as well, which uses a 2 × 2 coupler instead of optical hybrid, and three single-ended photodiodes are required to achieve signal reception. By using cost-effective FADD receiver, the proposed FADD scheme provides an effective approach for low CSPR field recovery.

On the Bregman-proximal iterative algorithm for the monotone inclusion problem in Banach spaces

In this paper, we focus on the solution of a class of monotone inclusion problems in reflexive Banach spaces. To reflect the geometry of the space and the operator, a more general proximal point iterative algorithm with Bregman divergence is proposed and some strong convergence results for the proposed scheme under standard assumptions are obtained. Meanwhile, the convex optimization problems and the critical point problems are studied in the applications, and the recovery of the sparse signal is simulated in the numerical experiments.

High-order block RIP for nonconvex block-sparse compressed sensing

Abstract This paper concentrates on the recovery of block-sparse signals, which are not only sparse but also nonzero elements are arrayed into some blocks (clusters) rather than being arbitrary distributed all over the vector, from linear measurements. We establish high-order sufficient conditions based on block RIP, which could ensure the exact recovery of every block s-sparse signal in the noiseless case via mixed l 2 / l p {l_{2}/l_{p}} minimization method, and the stable and robust recovery in the case that signals are not accurately block-sparse in the presence of noise. Additionally, a lower bound on necessary number of random Gaussian measurements is gained for the condition to be true with overwhelming probability. Furthermore, a series of numerical experiments are conducted to demonstrate the performance of the mixed l 2 / l p {l_{2}/l_{p}} minimization. To the best of the authors’ knowledge, the recovery guarantees established in this paper are superior to those currently available.

NIR-driven multifunctional PEC biosensor based on aptamer-modified PDA/MnO2 photoelectrode for bacterial detection and inactivation

Sensitive detection and effective inactivation of bacteria are essential in preventing foodborne bacterial infection that poses a significant threat to human health. Herein, a near-infrared (NIR)-driven multifunctional photoelectrochemical (PEC) biosensor was constructed for detection and inactivation of S. aureus. Based on the covalent bonding between amine and carboxyl groups, carboxyl-functionalized SA31 aptamer was immobilized on the PDA/MnO2 photoelectrode. In the presence of S. aureus, SA31 aptamer can specifically capture S. aureus, causing the decrease of photocurrent signal owing to steric hindrance effect. Leveraging photocurrent-off signal, there existed a satisfied linear relationship between the photocurrent variation and the logarithm of S. aureus concentration, achieving a wide linear range from 10 to 107 CFU/mL with a low detection limit of 2.0 CFU/mL. Notably, PDA/MnO2 with peroxidase-like activity facilitated the catalytic oxidation of S. aureus with assistance of hydrogen peroxide (H2O2) to cause the inactivation of S. aureus. Desorption of inactivated S. aureus from the photoelectrode led to a recovery of photocurrent signal, enabling a "signal on" switch. Simultaneously, the excellent photothermal performance of the PDA/MnO2 converted light energy into heat energy under the irradiation of NIR light (808 nm, 1.5 W/cm2), triggering the synergistic antibacterial effect against S. aureus (97.36%). This work provides a novel strategy for fabricating the detection and inactivation of bacteria in practical applications.

Assessment of SPECT Image Reconstruction in Liver Scanning Using 99mTc/ EDDA/ HYNIC-TOC

Purpose: Given that the Single Photon Emission Computed Tomography (SPECT) image quality is defined experimentally, developing a specialized scanning technique for each procedure is necessary to increase the diagnosis accuracy. This study aims to determine the optimal algorithm for liver scan reconstruction using 99mTc/SPECT. Materials and Methods: The Filtered Back-Projection (FBP) reconstruction method was used in liver scanning using 99mTc-EDDA/HYNIC-TOC (Tektrotyd) for SPECT images of 30 patients which were acquired with a dual-head EvoExel detector system. Using different types of filters in SPECT imaging, various optimal results can be achieved in the processed images, such as artifact reduction, noise reduction, or signal enhancement and recovery. To evaluate the effect of different filters on image quality, Signal-to-Noise-Ratio (SNR), Contrast-to-Noise-Ratio (CNR), and contrast parameters were calculated. Results: Applying filters enhanced contrast in the images in most cases as well as CNR and SNR. Metz (power = 2), Shepp-Logan (Cut-off frequency = 0.67) and Metz (power = 2) filters increase the CNR, contrast and SNR in images more than the other filters, respectively. The maximum improvement for CNR, contrast and SNR was from 0.62 to 2.35, 0.99 to 1.31, and 8.48 to 14.70, respectively. Conclusion: Based on the results, the Hamming filter, due to providing high-quality images for visual analysis of liver SPECT, and the Butterworth filter, due to balancing the image quality and noise for quantitative analysis, are recommended.

Joint Compressed Signal Recovery and RIS Diagnosis via Double-Sparsity Optimization

Joint Compressed Signal Recovery and RIS Diagnosis via Double-Sparsity Optimization

A novel Noor iterative method of operators with property (E) as concerns convex programming applicable in signal recovery and polynomiography

This work proposes a novel Noor iterative scheme, called CT‐iteration, to approximate the fixed points in the new context of generalized nonexpansive mappings with property . We establish the strong and weak convergence results in a uniformly convex Banach space. Additionally, numerical examples of the iterative technique are demonstrated using a signal recovery application in a compressed sensing situation. Furthermore, we show the use of the proposed method to generate polynomiographs. The proposed algorithm has been implemented and tested via numerical simulation in MATLAB. The simulation results show the effectiveness of the proposed algorithm.

Highly sensitive and selective demethylase FTO detection using a DNAzyme-mediated CRISPR/Cas12a signal cascade amplification electrochemiluminescence biosensor with C–CN/PCNV heterojunction as emitter

Fat mass and obesity-associated protein (FTO) has gained attention as the first RNA N6-methyladenosine (m6A) modification eraser due to its overexpression being associated with various cancers. In this study, an electrochemiluminescence (ECL) biosensor for the detection of demethylase FTO was developed based on DNAzyme-mediated CRISPR/Cas12a signal cascade amplification system and carboxylated carbon nitride nanosheets/phosphorus-doped nitrogen-vacancy modified carbon nitride nanosheets (C–CN/PCNV) heterojunction as the emitter. The biosensor was constructed by modifying the C–CN/PCNV heterojunction and a ferrocene-tagged probe (ssDNA-Fc) on a glassy carbon electrode. The presence of FTO removes the m6A modification on the catalytic core of DNAzyme, restoring its cleavage activity and generating activator DNA. This activator DNA further activates the trans-cleavage ability of Cas12a, leading to the cleavage of the ssDNA-Fc and the recovery of the ECL signal. The C–CN/PCNV heterojunction prevents electrode passivation and improves the electron-hole recombination, resulting in significantly enhanced ECL signal. The biosensor demonstrates high sensitivity with a low detection limit of 0.63 pM in the range from 1.0 pM to 100 nM. Furthermore, the biosensor was successfully applied to detect FTO in cancer cell lysate and screen FTO inhibitors, showing great potential in early clinical diagnosis and drug discovery.

Direction-of-Arrival Estimation via Sparse Bayesian Learning Exploiting Hierarchical Priors with Low Complexity.

For direction-of-arrival (DOA) estimation problems in a sparse domain, sparse Bayesian learning (SBL) is highly favored by researchers owing to its excellent estimation performance. However, traditional SBL-based methods always assign Gaussian priors to parameters to be solved, leading to moderate sparse signal recovery (SSR) effects. The reason is Gaussian priors play a similar role to l2 regularization in sparsity constraint. Therefore, numerous methods are developed by adopting hierarchical priors that are used to perform better than Gaussian priors. However, these methods are in straitened circumstances when multiple measurement vector (MMV) data are adopted. On this basis, a block-sparse SBL method (named BSBL) is developed to handle DOA estimation problems in MMV models. The novelty of BSBL is the combination of hierarchical priors and block-sparse model originating from MMV data. Therefore, on the one hand, BSBL transfers the MMV model to a block-sparse model by vectorization so that Bayesian learning is directly performed, regardless of the prior independent assumption of different measurement vectors and the inconvenience caused by the solution of matrix form. On the other hand, BSBL inherited the advantage of hierarchical priors for better SSR ability. Despite the benefit, BSBL still has the disadvantage of relatively large computation complexity caused by high dimensional matrix operations. In view of this, two operations are implemented for low complexity. One is reducing the matrix dimension of BSBL by approximation, generating a method named BSBL-APPR, and the other is embedding the generalized approximate message passing (GAMB) technique into BSBL so as to decompose matrix operations into vector or scale operations, named BSBL-GAMP. Moreover, BSBL is able to suppress temporal correlation and handle wideband sources easily. Extensive simulation results are presented to prove the superiority of BSBL over other state-of-the-art algorithms.

Dual-band filtering and enhanced directional via tunable acoustic metamaterial antennas

The detection of acoustic signals in strong background noise plays a crucial role in industrial non-destructive, mechanical equipment health monitoring and acoustic communication. The major bottleneck of this technology lies in the limited high-sensitivity and high-directivity of acoustic sensors. Here, this study proposes a tunable acoustic metamaterial antenna (TAMAA) with a double bandgap and near-zero refractive index. Different from the traditional geometric scatterer, a gear-shaped structure is introduced to enhance the controllability of the acoustic system. We theoretically demonstrate the physical properties of the structure with a double bandgap and near-zero refractive index. Remarkably, the gear-shaped honeycomb lattice structure exhibits an adjustable bandgap region, which enables the multiplexing of both acoustic shielding and acoustic enhancement functions by controlling the rotation angle of the scatterer. Furthermore, through numerical computational and experimental studies, we demonstrate that the proposed TAMAA exhibits dual-band filtering capabilities and provides excellent acoustic directional enhancement. Moreover, it allows for the recovery of weak acoustic signals even in the presence of extremely low signal-to-noise ratio and strong spatial noise interference. This work breaks through the detection limits of conventional acoustic sensing systems and provides new ideas for the development of acoustic sensing detection.

Damage Detection in Bridge Structures through Compressed Sensing of Crowdsourced Smartphone Data

Traditional bridge health monitoring methods that necessitate sensor installation are not only costly but also time-consuming. In contrast, utilizing smartphone data collected from vehicles as they traverse bridges offers an efficient and cost-effective alternative. This paper introduces a cutting-edge damage detection framework for indirect monitoring of bridge structures, leveraging a substantial volume of acceleration data collected from smartphones in vehicles passing over the bridge. Our innovative approach addresses the challenge of collecting and transmitting high-frequency data while preserving smartphone battery life and data plans through the integration of compressed sensing (CS) into the crowdsensing-based monitoring framework. CS employs random sampling and signal recovery from a significantly reduced number of samples compared to the requirements of the Nyquist–Shannon sampling theorem. In the proposed framework, acceleration signals from vehicles are initially acquired using smartphone sensors, undergo compression, and are then transmitted for signal reconstruction. Subsequently, feature extraction and dimensionality reduction are performed using Mel-frequency cepstral coefficients and principal component analysis. Damage indexes are computed based on the dissimilarity between probability distribution functions utilizing the Wasserstein distance metric. The efficacy of the proposed methodology in bridge monitoring has been substantiated through the utilization of numerical models and a lab-scale bridge. Furthermore, the feasibility of implementing the framework in a real-world application has been investigated, leveraging the smartphone data from 102 vehicle trips on the Golden Gate Bridge. The results demonstrate that damage detection using the reconstructed signals obtained through compressed sensing achieves comparable performance to that obtained with the original data sampled at the Nyquist measurement sampling rate. However, it is observed that to retain severity information within the signals for accurate damage severity identification, the compression level should be limited to 20%. These findings affirm that compressed sensing significantly reduces the data collection requirements for crowdsensing-based monitoring applications, without compromising the accuracy of damage detection while preserving essential damage-sensitive information within the dataset.

Unbalanced Distribution Network Cross-Country Fault Diagnosis Method with Emphasis on High-Impedance Fault Syndrome

Unusual fault scenarios can occur on the utility grid in a power system network. Cross-Country Faults (CCFs) connected to the High-Impedance Fault (HIF) syndrome are more prone to occur in forested areas due to thunderstorms, cyclones, and improper vegetation management and tree pruning. Finding and categorizing CCFs associated with HIF syndrome is a great challenge. This study employed the cross-correlation method to reconstruct the signals produced by CCFs with HIF, which were shown to be complicated, aperiodic, asymmetric, and nonlinear. A decreased sensitivity to random noise means that a given modification might not affect equally all component peaks. This allows for more precise signal recovery. The maximum voltage cross-correlation coefficients were carefully evaluated as distinguishing elements in the development of a suggested fault detection technique. The proposed concept was evaluated on a modified imbalanced IEEE 240 bus system under different case studies. These case studies cover a wide range of scenarios, such as the switching of a capacitor bank, feeder energization, and the effects of nonlinear loads under noisy conditions.

Timing of intraoperative neurophysiological monitoring (IONM) recovery and clinical recovery after termination of pediatric spinal deformity surgery due to loss of IONM signals

Background ContextIntraoperative neurophysiological monitoring (IONM) is used to reduce the risk of spinal cord injury during pediatric spinal deformity surgery. Significant reduction and/or loss of IONM signals without immediate recovery may lead the surgeon to acutely abort the case. The timing of when monitorable signals return remains largely unknown. PurposeThe goal of this study was to investigate the correlation between IONM signal loss, clinical examination, and subsequent normalization of IONM signals after aborted pediatric spinal deformity surgery to help determine when it is safe to return to the operating room. Study Design/SettingThis is a multicenter, multidisciplinary, retrospective study of pediatric patients (<18 years old) undergoing spinal deformity surgery whose surgery was aborted due to a significant reduction or loss of IONM potentials. Patient SampleSixty-six patients less than 18 years old who underwent spinal deformity surgery that was aborted due to IONM signal loss were enrolled into the study. Outcome MeasuresIONM data, operative reports, and clinical examinations were investigated to determine the relationship between IONM loss, clinical examination, recovery of IONM signals, and clinical outcome. MethodsInformation regarding patient demographics, deformity type, clinical history, neurologic and ambulation status, operative details, IONM information (eg, quality of loss [SSEPs, MEPs], laterality, any recovery of signals, etc.), intraoperative wake-up test, postoperative neurologic exam, postoperative imaging, and time to return to the operating were all collected. All factors were analyzed and compared with univariate and multivariate analysis using appropriate statistical analysis. ResultsSixty-six patients were enrolled with a median age of 13 years [IQR 11-14], and the most common sex was female (42/66, 63.6%). Most patients had idiopathic scoliosis (33/66, 50%). The most common causes of IONM loss were screw placement (27/66, 40.9%) followed by rod correction (19/66, 28.8%). All patients had either complete bilateral (39/66, 59.0%), partial bilateral (10/66, 15.2%) or unilateral (17/66, 25.8%) MEP loss leading to termination of the case. Overall, when patients were returned to the operating room 2 weeks postoperatively, nearly 75% (40/55) had monitorable IONM signals. Univariate analysis demonstrated that bilateral SSEP loss (p=.019), bilateral SSEP and MEP loss (p=.022) and delayed clinical neurologic recovery (p=.008) were significantly associated with having unmonitorable IONM signals at repeat surgery. Multivariate regression analysis demonstrated that delayed clinical neurologic recovery (> 72 hours) was significantly associated with unmonitorable IONM signals when returned to the operating room (p=.006). All patients ultimately made a full neurologic recovery. ConclusionsIn children whose spinal deformity surgery was aborted due to intraoperative IONM loss, there was a strong correlation between combined intraoperative SSEP/MEP loss, the magnitude of IONM loss, the timing of clinical recovery, and the time of electrophysiological IONM recovery. The highest likelihood of having a prolonged postoperative neurological deficit and undetectable IONM signals upon return to the OR occurs with bilateral complete loss of SSEPs and MEPs.

Time–Frequency-Multisqueezing Transform

In the real world, most signals encountered are nonstationary. It is essential to extract a time-frequency (TF) characteristics in such signals for an accurate description. Two parameters are usually applied to quantify the TF characteristics of a nonstationary signal, i.e., instantaneous frequency (IF) and group delay (GD). A post-processing strategy was adopted by two recently developed techniques, the synchrosqueezing transform (SST) and the time-reassigned SST (TSST) to accurately capture the change rules of IF and GD respectively. However, due to the diversity of modes in complex nonstationary signals, no existing technique has been used to effectively estimate both IF and GD simultaneously. To solve this problem, a post-processing analysis technique termed as time-frequency-multisqueezing transform (TFMST) is proposed in this paper where a so-called chirp rate (CR) discrimination criterion is established by considering the Gaussian window in the short-time Fourier transform. The proposed method can accurately categorize nonstationary signals containing harmonic- and impulsive-like components to achieve a concurrent description and ensure the recovery of original signals. The proposed method is validated by numerical simulation and real signal analyses.

Parallel reservoir computing based signal outlier detection and recovery method for structural health monitoring

The presence of outliers in signals collected by structural health monitoring systems, caused by sensor failure, equipment malfunction, or transmission interruption, can lead to misjudgments of a structure's working status and damage degree. This study proposes a novel, fast, accurate, and automatic method which are capable of reconstructing signals according to adjacent channels, detecting outliers by amplifying and sorting reconstructing errors, and recovering normal values to the corresponding locations. A parallel reservoir computing-based reconstructor with a decomposition module which purifies frequency components of input for each sub-network is utilized for improved precision. In addition, the adopted local outlier factor algorithm simplifies outlier detection work as simplex threshold comparison. The proposed method is analyzed for its effectiveness in detecting various types of outliers, such as spikes, abnormal segments, external trends, shifting, and baseline drift, using an acceleration dataset from the Shanghai Tower.

Self-Regulating Dynamic Measurement Method

A dynamic measuring system is considered. A new model of the measuring system, a method for processing of measurement results and a method of restoring the input signal of the system by the noisy output signal are proposed. The estimation of the accuracy of the method and a computational experiment demonstrating the efficiency of the signal recovery method are presented.

Fast T1 mapping MRI in preclinical and clinical settings using subspace-constrained joint-domain reconstructions

This work aims to develop fast T1 mapping methods for preclinical and clinical scanners based on subspace-constrained reconstructions. Two sequences are explored for rapid T1 characterizations: 1) Interleaved spatiotemporal encoding incorporating variable repetition times. 2) Inversion recovery gradient echo with random sampling of the phase-encoding (PE) dimension. For both sequences, the subspace reconstruction of the signal recovery was applied, to jointly reconstruct the down-sampled images while characterizing the T1 relaxation. In vivo scans on human brains and abdomens confirmed the efficiency of the proposed methods, including compatibility with breath-holding. In addition, Scans on animals with abdominal tumors and dynamic contrast-enhanced T1 mapping on kidneys support the applicability of the proposed methods also in preclinical settings.

A parallel inertial SP-iteration monotone hybrid algorithm for a finite family of G-nonexpansive mappings and its application in linear system, differential, and signal recovery problems

A parallel inertial SP-iteration monotone hybrid algorithm for a finite family of G-nonexpansive mappings and its application in linear system, differential, and signal recovery problems

Abstract 1217: Defining the anti-tumor activity and immune effects of the RASMULTI(ON) inhibitor RMC-7977 in preclinical models of NRAS-mutant melanoma

Abstract Introduction: Mutations in the small GTPase NRAS are the second most common oncogenic driver in cutaneous melanoma, being found in ~25% of cases. Targeted therapy strategies are currently lacking for NRAS-mutant melanoma. Here, we characterized the anti-tumor activity and immune effects of RMC-7977, a RASMULTI(ON) preclinical tool compound, representative of the investigational agent RMC-6236, in preclinical mouse and human models of NRAS-mutant melanoma. Methods: The in vitro potency of RMC-7977 was determined using mouse and human NRAS-mutant melanoma cell lines. Recovery of signaling was quantified by Western Blot and receptor tyrosine kinase (RTK) arrays. In vivo anti-tumor activity of RMC-7977 was determined in two syngeneic mouse melanoma models (SW1 NRASG13D and NRAS#5 NRASQ61R), and tumor immune infiltrate quantified using multiplexed IHC and flow cytometry. Mechanisms of resistance were defined using RNA-seq. Results: RMC-7977 had good activity in vitro against human cancer cell lines and in mouse models of NRAS-mutant melanoma. Time-course studies demonstrated RMC-7977 rapidly inhibited phospho-ERK signaling, which then began to recover after 8 hr of treatment. There was also evidence of adaptive receptor tyrosine kinase signaling by 48 hr. In syngeneic mouse models of NRAS-mutant melanoma, RMC-7977 led to a rapid inhibition of tumor growth, increased expression of tumor MHC class I and PD-L1 and an increase in CD4+ and CD8+ T cell infiltrate into tumors. Therapeutic escape was associated with decreased CD4+ and CD8+ T cell infiltration and increased numbers of immunosuppressive MDSCs. Cells collected from tumors resistant to RMC-7977 in vivo did not show any shift in sensitivity to the inhibitor when re-challenged in vitro, suggesting that the resistance observed in vivo resulted from mechanisms specific to the tumor microenvironment, for example adaptive RTK signaling or immune escape. RNA-seq and IHC analysis suggested that resistance was associated with decreased MHC expression and antigen presentation, as well as reduced co-stimulation of T cells. Combination studies of RMC-7977 with anti-PD-1 are ongoing to identify potential synergistic interactions in these preclinical models. Conclusion: RMC-7977 has anti-tumor activity in syngeneic mouse models of NRAS-mutant melanoma, associated with a robust immune response. Citation Format: Larissa A. Carvalho, Manali S. Phadke, Keiran S. M. Smalley. Defining the anti-tumor activity and immune effects of the RASMULTI(ON) inhibitor RMC-7977 in preclinical models of NRAS-mutant melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1217.