Domain Signal Research Articles

Potential multi-parametric OFDR system based on the wavelength-division cross-correlation method

In this study, an optical frequency domain reflectometer (OFDR) system for simultaneous measurement of temperature, strain, and relative humidity (RH) based on the wavelength-division cross-correlation method is proposed and experimentally demonstrated. The Rayleigh scattered spectrum shifts introduced by environmental changes can be demodulated by the cross-correlation operation, and the magnitude of the shifts exhibits variability in distinct wavelength subregions. Leveraging the distinct sensitivity characteristics of different parameters across different wavelength subregions, simultaneous multi-parameter decoupling can be achieved. In the signal processing flow, the frequency domain signals in different wavelength subregions are intercepted, and then the cross-correlation operation is performed separately to obtain the spectrum shift information in different wavelength subregions. The decoupling matrix is constructed to realize the decoupling of the three parameters based on the sensitivity differences of temperature, strain, and RH in different wavelength subregions. This novel and simple multi-parameter sensing system, to the best of our knowledge, has potential applications in engineering monitoring, chemical applications, and environmental measurements.

An adaptive continuous scanning laser Doppler vibrometry technique for measuring vibrational mode shapes of structures with holes

Abstract Continuous Scanning Laser Doppler Vibrometry (CSLDV) enables fast and full-field vibration measurement in an intact area of a structure. This paper further proposes a novel adaptive CSLDV method that can obtain the Operational Deflection Shapes (ODS) and mode shape of structures with holes. Firstly, an adaptive path planning method is applied to the surface of the tested structure, which can automatically adapt to the size and position of round and square holes, as well as the resolution requirements for the measurement. Secondly, based on the symmetry and other characteristics of the planned path, the time domain signal is processed by delay optimization and demodulation to obtain the ODS of each vibration mode. Finally, a modal test is conducted on a flat structure with a square and circle holes as an example. A validation experiment is also performed using SLDV. The results show that the mode shapes obtained from both methods are consistent and the Modal Assurance Criterion (MAC) between them are all above 0.96. The method can realize CSLDV of flat plate structures with arbitrary square and round holes, and has the advantages of high efficiency and dense measurement points, which enhances its engineering applicability.

Coherent HRRP Ranging Algorithm With Signal Domain Kalman Smoother for Wideband Radar

Coherent HRRP Ranging Algorithm With Signal Domain Kalman Smoother for Wideband Radar

Deep-learning-based design of synthetic orthologs of SH3 signaling domains

Evolution-based deep generative models represent an exciting direction in understanding and designing proteins. An open question is whether such models can learn specialized functional constraints that control fitness in specific biological contexts. Here, we examine the ability of generative models to produce synthetic versions of Src-homology 3 (SH3) domains that mediate signaling in the Sho1 osmotic stress response pathway of yeast. We show that a variational autoencoder (VAE) model produces artificial sequences that experimentally recapitulate the function of natural SH3 domains. More generally, the model organizes all fungal SH3 domains such that locality in the model latent space (but not simply locality in sequence space) enriches the design of synthetic orthologs and exposes non-obvious amino acid constraints distributed near and far from the SH3 ligand-binding site. The ability of generative models to design ortholog-like functions invivo opens new avenues for engineering protein function in specific cellular contexts and environments.

Delta-Sigma Domain Signal Processing Revisited with Related Topics in Stochastic Computing

Delta-Sigma Domain Signal Processing Revisited with Related Topics in Stochastic Computing

Wearable gait recognition system based on time domain features of Bluetooth inertial sensor network

Abstract As an important part of medical monitoring, gait recognition has been used in lower limb rehabilitation diagnosis and gait assessment. To meet practical requirements, we designed a wearable gait recognition system based on time domain features of Bluetooth inertial sensor network, which can be used for real-time acquisition and evaluation of gait signals. In this research, we used 6 sensor units to integrate 10 kinds of gait information, and designed a gait monitoring system covering the main joints of lower limbs based on Bluetooth network. The gait monitoring system can collect the inertial sensing data of lower limbs at a maximum rate of 90 Hz through the 6 sensor units, and has good electrical performance. In daily use, each unit can work for more than 3 hours and maintain a received signal strength indicator (RSSI) of more than -90 dBm at a spatial distance of 10 meters. On the gait dataset collected in the experiment, we used sliding window to segment the time domain signals and extract 10 kinds of time domain features for classification algorithm. We achieved 97.1% average identification accuracy of 7 different gait patterns by support vector machine (SVM).

An interpretable TFAFI-1DCNN-LSTM framework for UGW-based pre-stress identification of steel strands

Steel strands serve as the key load-bearing components of pre-stressed bridges, yet the identification of effective pre-stress for steel strands is a challenging task. In this study, a time and frequency adaptive fusion input-one dimensional convolutional neural network-long short-term memory (TFAFI-1DCNN-LSTM) framework was proposed for ultrasonic guided wave (UGW)-based effective pre-stress identification of steel strands. In this framework, the time and frequency domain signals of UGW were input into the network as two parallel branches, CNN and LSTM were utilized to extract spatial and serial-related features, and a trainable weight coefficient was introduced for adaptive fusion of time and frequency domain features. Firstly, ablation studies were conducted to investigate the influence of each key component in the proposed framework on the prediction results. Secondly, deep learning (DL) models of Res Net, Dense Net and Inception Net were introduced as comparisons, and the prediction results based on TFAFI-1DCNN-LSTM were compared with those based on the above DL models. Thirdly, the noise-robustness and performance under a few trainable samples of TFAFI-1DCNN-LSTM were also investigated. Finally, the outputs of various key layers of the proposed framework were subjected to dimensionality reduction and visualization to provide an intuitive demonstration of the feature extraction process, and the inherent mechanisms of the proposed framework were interpreted using local interpretable model-agnostic explanations (LIME). Results show that the architecture of TFAFI-1DCNN-LSTM is reasonably designed and all key components are necessary. The weights of features in time and frequency domain branches are rationally assigned due to the addition of an adaptive weight coefficient. Compared with other DL models, the proposed TFAFI-1DCNN-LSTM exhibits higher pre-stress identification accuracy, excellent noise-robustness, and superior performance under only a few trainable samples. The correlation between features extracted by the network and the pre-stress labels significantly increases with the deepening of the network, and the correlation between input UGW signals and predicted pre-stress values is effectively interpreted utilizing LIME, proving that the architecture of the proposed framework is reasonable and effective, the predicted results are reliable and credible.

Investigation on effects of water-shale interaction on acoustic characteristics of organic-rich shale in Ordos Basin, China

The water-shale interaction affect the shale structure, leading to wellbore instability and increasing drilling costs. The extent of structural changes within the shale can be determined non-destructively by analyzing its acoustic characteristics. Experiments were conducted to investigate the acoustic properties of shale from the Yanchang Formation in the Ordos Basin before and after exposure to brines of varying types, soaking times, and salinities. The study investigated the effects of brine type, soaking time, and salinity on shale’s acoustic properties, including changes in acoustic wave propagation speed, P/S wave velocity ratio, and both time-domain and frequency-domain amplitudes. The results indicate that although the type of brine has a limited impact on the water-shale interaction, KCl exhibits a significant inhibitory effect. However, the soaking time and the brine salinity have a significant impact on the acoustic properties of shale. As the soaking time increases, the decrease in wave velocity increases, the P/S wave velocity ratio increases, and the decrease in time-domain amplitude increases. The amplitude of the main frequency in the frequency domain signal also decreases with the increase of reaction time, which is consistent with the analysis results of the time domain signal. As the salinity of brine increases, the decrease in wave velocity decreases, the P/S wave velocity ratio decreases, and the decrease in time-domain amplitude decreases. The amplitude of the main frequency in the frequency domain signal also decreases with the increase of brine salinity, which is consistent with the analysis results of the time domain signal. This work establishes the relationship between water-shale interaction and acoustic characteristics, which can quantitatively evaluate the degree of interaction between water and shale without damaging shale. Furthermore, this research provides new insights and guidance for predicting drilling collapse cycles and optimizing drilling fluid compositions.

E3 ligase RNF2 inhibits porcine circovirus type 3 replication by targeting its capsid protein for ubiquitination-dependent degradation.

Porcine circovirus type 3 (PCV3) is closely associated with various diseases, such as the porcine dermatitis, nephropathy syndrome, and multisystemic clinicopathological diseases. PCV3-associated diseases are increasingly recognized as severe diseases in the global swine industry. Ring finger protein 2 (RNF2), an E3 ubiquitin ligase exclusively located in the nucleus, contributes to various biological processes. This ligase interacts with the PCV3 Cap. However, its role in PCV3 replication remains unclear. This study confirmed that the nuclear localization signal domain of the Cap and the RNF2 N-terminal RING domain facilitate the interaction between the Cap and RNF2. Furthermore, RNF2 promoted the binding of K48-linked polyubiquitination chains to lysine at positions 139 and 140 (K139 and K140) of the PCV3 Cap, thereby degrading the Cap. RNF2 knockdown and overexpression increased or decreased PCV3 replication, respectively. Moreover, the RING domain-deleted RNF2 mutant eliminated the RNF2-induced degradation of the PCV3 Cap and RNF2-mediated inhibition of viral replication. This indicates that both processes were associated with its E3 ligase activity. Our findings demonstrate that RNF2 can interact with and degrade the PCV3 Cap via its N-terminal RING domain in a ubiquitination-dependent manner, thereby inhibiting PCV3 replication.IMPORTANCEPorcine circovirus type 3 is a recently described pathogen that is prevalent worldwide, causing substantial economic losses to the swine industry. However, the mechanisms through which host proteins regulate its replication remain unclear. Here, we demonstrate that ring finger protein 2 inhibits porcine circovirus type 3 replication by interacting with and degrading the Cap of this pathogen in a ubiquitination-dependent manner, requiring its N-terminal RING domain. Ring finger protein 2-mediated degradation of the Cap relies on its E3 ligase activity and the simultaneous existence of K139 and K140 within the Cap. These findings reveal the mechanism by which this protein interacts with and degrades the Cap to inhibit porcine circovirus type 3 replication. This consequently provides novel insights into porcine circovirus type 3 pathogenesis and facilitates the development of preventative measures against this pathogen.

Cell autonomous functions of CD47 in regulating cellular plasticity and metabolic plasticity.

CD47 is a ubiquitously expressed cell surface receptor, which is widely known for preventing macrophage-mediated phagocytosis by interacting with signal regulatory protein α (SIRPα) on the surface of macrophages. In addition to its role in phagocytosis, emerging studies have reported numerous noncanonical functions of CD47 that include regulation of various cellular processes such as proliferation, migration, apoptosis, differentiation, stress responses, and metabolism. Despite lacking an extensive cytoplasmic signaling domain, CD47 binds to several cytoplasmic proteins, particularly upon engaging with its secreted matricellular ligand, thrombospondin 1. Indeed, the regulatory functions of CD47 are greatly influenced by its interacting partners. These interactions are often cell- and context-specific, adding a further level of complexity. This review addresses the downstream cell-intrinsic signaling pathways regulated by CD47 in various cell types and environments. Some of the key pathways modulated by this receptor include the PI3K/AKT, MAPK/ERK, and nitric oxide signaling pathways, as well as those implicated in glucose, lipid, and mitochondrial metabolism. These pathways play vital roles in maintaining tissue homeostasis, highlighting the importance of understanding the phagocytosis-independent functions of CD47. Given that CD47 expression is dysregulated in a variety of cancers, improving our understanding of the cell-intrinsic signals regulated by this molecule will help advance the development of CD47-targeted therapies.

Research on the algorithm for optimal selection of detection modes for rail crack detection

In the application of ultrasonic guided wave testing for rail crack detection, it is necessary to select a guided wave mode that is more sensitive to cracks as the detection mode. However, ultrasonic guided waves have multi-mode and dispersive characteristics. In order to extract mode information from complex signals, this paper proposes an optimal detection mode selection method based on the sensitivity of guided wave modes to cracks. This method is different from the traditional method of determining mode types by calculating the mode velocity through the arrival time of wave packets in the time domain signal. Based on the dispersion characteristics and mode features of guided wave modes, this paper establishes a crack sensitivity evaluation index. In a wide frequency band and among numerous modes, the guided wave modes suitable for detecting cracks in different regions of the full cross-section of rails are accurately selected. Experimental results show that the guided wave modes selected by the mode selection method proposed in this paper, based on the crack area energy and crack reflection intensity evaluation indexes, can accurately identify rail cracks, laying a foundation for the research on rail crack detection and localization methods.

V-DAFT: visual technique for texture image defect recognition with denoising autoencoder and fourier transform

Texture is the surface qualities and visual attributes of an object, determined by the arrangement, size, shape, density, and proportion of its fundamental components. In the manufacturing industry, products typically have uniform textures, allowing for automated visual inspections of the product surface to recognize defects. During this process, texture defect recognition techniques can be employed. In this paper, we propose a method that combines a convolutional autoencoder architecture with Fourier transform analysis. We employ a normal reconstructed template as defined in this study. Despite its simple structure and rapid training and inference capabilities, it offers recognition performance comparable to state-of-the-art methods. Fourier transform is a powerful tool for analyzing the frequency domain of images and signals, which is essential for effective defect recognition as texture defects often exhibit characteristic changes in specific frequency ranges. The experiment evaluates the recognition performance using the AUC metric, with the proposed method showing a score of 93.7%. To compare with existing approaches, we present experimental results from previous research, an ablation study of the proposed method, and results based on the high-pass filter used in the Fourier mask.

Hu8F4-CAR T cells with mutated Fc spacer segment improve target specificity and mediate anti-leukemia activity in vivo

Background aimsHu8F4 is a T-cell receptor–like antibody with high affinity for the leukemia-associated antigen PR1/HLA-A2 epitope. Adapted into a chimeric antigen receptor (CAR) format, Hu8F4-CAR is composed of the Hu8F4 single-chain variable fragment, the human IgG1 CH2CH3 extracellular spacer domain, a human CD28 costimulatory domain and the human CD3ζ signaling domain. We have demonstrated high efficacy of Hu8F4-CAR-T cells against PR1/HLA-A2-expressing cell lines and leukemic blasts from patients with acute myeloid leukemia in vitro. Previous studies have shown that modification of the Fc domains of IgG4 CH2CH3 spacer regions can eliminate activation-induced cell death and off-target killing mediated by mouse Fc gamma receptor–expressing cells. MethodsWe generated Hu8F4-CAR(PQ) with mutated Fc receptor binding sites on the CH2 domain of Hu8F4-CAR to prevent unwanted interactions with Fc gamma receptor–expressing cells in vivo. ResultsThe primary human T cells transduced with Hu8F4-CAR(PQ) can specifically lyse HLA-A2+ PR1-expressing leukemia cell lines in vitro. Furthermore, both adult donor-derived and cord blood-derived Hu8F4-CAR(PQ)-T cells are active and can eliminate U937 leukemia cells in NSG mice. ConclusionsHerein, we demonstrate that modification of the IgG1-based spacer can eliminate Fc receptor binding–induced adverse effects and Hu8F4-CAR(PQ)-T cells can kill leukemia in vivo.

Evaluation of grouting effectiveness for Semi-Rigid pavement base layer cracks based on Time-Frequency domain signal characteristics of 3D GPR

This study aims to address the deficiency of rapid, comprehensive, and non-destructive methods for evaluating the effectiveness of grouting treatment on semi-rigid pavement base layer cracks. By integrating radar simulation with empirical measurements, we establish a comprehensive evaluation method for the grouting treatment effectiveness of pavement base layer cracks. Initially, we propose a GprMax simulation of crack models with varying grout-filled volumes, analyzing the time–frequency domain signal characteristics before and after grouting. Based on data sensitivity, thirteen time–frequency domain signal characteristics are selected as grading evaluation indicators. Subsequently, relying on an engineering case study, we conduct a graded evaluation of the grouting effect on base layer cracks, analyzing the correlation and weight of evaluation indicators to derive a comprehensive evaluation method. Field coring results confirm the accuracy of the comprehensive graded evaluation method.

Masked cross-domain self-supervised deep learning framework for photoacoustic computed tomography reconstruction

Accurate image reconstruction is crucial for photoacoustic (PA) computed tomography (PACT). Recently, deep learning has been used to reconstruct PA images with a supervised scheme, which requires high-quality images as ground truth labels. However, practical implementations encounter inevitable trade-offs between cost and performance due to the expensive nature of employing additional channels for accessing more measurements. Here, we propose a masked cross-domain self-supervised (CDSS) reconstruction strategy to overcome the lack of ground truth labels from limited PA measurements. We implement the self-supervised reconstruction in a model-based form. Simultaneously, we take advantage of self-supervision to enforce the consistency of measurements and images across three partitions of the measured PA data, achieved by randomly masking different channels. Our findings indicate that dynamically masking a substantial proportion of channels, such as 80%, yields meaningful self-supervisors in both the image and signal domains. Consequently, this approach reduces the multiplicity of pseudo solutions and enables efficient image reconstruction using fewer PA measurements, ultimately minimizing reconstruction error. Experimental results on in-vivo PACT dataset of mice demonstrate the potential of our self-supervised framework. Moreover, our method exhibits impressive performance, achieving a structural similarity index (SSIM) of 0.87 in an extreme sparse case utilizing only 13 channels, which outperforms the performance of the supervised scheme with 16 channels (0.77 SSIM). Adding to its advantages, our method can be deployed on different trainable models in an end-to-end manner, further enhancing its versatility and applicability.

Few-shot bearing fault diagnosis by semi-supervised meta-learning with graph convolutional neural network under variable working conditions

Aiming at the problems of low accuracy and weak generalization ability in fault diagnosis caused by complex working conditions and limited fault samples of bearings, a few-shot bearing fault diagnosis method by semi-supervised meta-learning with simplifying graph convolutional neural network (semi-meta-sgc) is proposed. Firstly, the time domain signal is converted into the frequency domain by fast Fourier transform (FFT). Secondly, considering each spectrum sample as a node, a k-nearest neighbor (KNN) graph is constructed according to the adjacencies between nodes, thus transforming the bearing fault classification into a graphical node classification. Then, relying on meta-learning, graph convolutional neural network, and semi-supervised learning, a high-accuracy node classifier is obtained by using a small number of training samples to achieve the classification of bearing faults under variable working conditions. Finally, the proposed method is verified by four cases and compared with other methods. The results show that this method has higher recognition accuracy and generalization performance.

Damage identification of honeycomb sandwich structures based on Lamb waves and 1D-CNN

A specific method combining Lamb waves and 1D-CNN is proposed in this paper for damage assessment of honeycomb sandwich structures, which can identify the location and degree of damage simultaneously. The applicability of the Lamb waves method is verified by simulating the interaction between the Lamb waves and different degrees of damage on the honeycomb panel. In the experiment, four identical piezoelectric wafers were placed on a honeycomb plate in a square shape, and a mass block was placed inside to simulate damage. One of the sensors was used as an actuator to generate the Lamb waves signal, and the remaining three sensors received the response signals after interacting with the damage. The collected signals were denoised by Kalman filter, and the time domain signals were transformed into the frequency domain signals by Fourier transform, so as to extract the feature more effectively. The amplitude frequency characteristics of Lamb wave signals were used as inputs to the 1D-CNN model, and the damage categories were used as outputs. 365 samples were used for model testing, 364 were correctly identified with a correct recognition rate of 99.7 %. The result shows that the proposed method is feasible for damage identification of honeycomb sandwich structures.

Research into Prediction Method for Pressure Pulsations in a Centrifugal Pump Based on Variational Mode Decomposition-Particle Swarm Optimization and Hybrid Deep Learning Models.

Centrifugal pump pressure pulsation contains various signals in different frequency domains, which interact and superimpose on each other, resulting in characteristics such as intermittency, non-stationarity, and complexity. Computational Fluid Dynamics (CFD) and traditional time series models are unable to handle nonlinear and non-smooth problems, resulting in low accuracy in the prediction of pressure fluctuations. Therefore, this study proposes a new method for predicting pressure fluctuations. The pressure pulsation signals at the inlet of the centrifugal pump are processed using Variational Mode Decomposition-Particle Swarm Optimization (VMD-PSO), and the signal is predicted by Convolutional Neural Networks-Long Short-Term Memory (CNN-LSTM) model. The results indicate that the proposed prediction model combining VMD-PSO with four neural networks outperforms the single neural network prediction model in terms of prediction accuracy. Relatively high accuracy is achieved by the VMD-PSO-CNN-LSTM model for multiple forward prediction steps, particularly for a forward prediction step of 1 (Pre = 1), with a root mean square error of 0.03145 and an average absolute percentage error of 1.007%. This study provides a scientific basis for the intelligent operation of centrifugal pumps.

Development of GPC3-CAR-NK cells and optimization as a therapy for HCC.

Hepatocellular carcinoma (HCC) is a highly malignant tumor characterized by insidious onset and rapid progression, with limited treatment choices. One treatment modality, chimeric antigen receptor (CAR)-modified natural killer (NK) cell immunotherapy, has shown promise for various cancers. In this study, we developed two GPC3-specific CAR-NK-92 cell lines (GPC3-CAR-NK) and explored their antitumor efficacy for the treatment of HCC. Significant levels of cytokine production and in vitro cytotoxicity were produced following co-culture of GPC3+ HCC cells with the developed GPC3-CAR-NK cells. GC33-G2D-NK cells with NK cell-specific signaling domains showed better activation and killing abilities than GC33-CD28-NK cells containing T cell-specific signaling domains. Moreover, GC33-G2D-NK cells efficiently eliminated tumors in cell-derived xenograft and patient-derived xenograft mouse models. In an abdominal metastasis model, intraperitoneally delivered GC33-G2D-NK cells showed better antitumor ability than intravenously injected cells. Finally, the combination of microwave ablation with GC33-G2D-NK cell administration showed greater CAR-NK infiltration and tumor regression in ablated tumors than monotherapy alone. These findings indicate that administration of GPC3-CAR-NK cells may be a potential strategy for the treatment of HCC, and regional delivery or their combination with microwave ablation may optimize their efficacy against HCC and may have translational value.

EEG BASED SEQUENTIAL EPILEPTIC SEIZURE DETECTION USING CNN

The objective of the research work is to propose an electroencephalography based sequential approach for epileptic seizure detection method in a real time environment using chronological 2D convolutional neural network (CNN). Even though in link with CNN an electroencephalography (EEG) shows a substantial characters in observing the brain commotion of patients detecting epilepsy, it is pretended to investigate number of EEG illustration and its histories to perceive apprehensive epileptic activity. The proposed Model flows towards a BiLSTM (Bi-directional LSTM) to find multi-channel EEG signals and deliberates longitudinal temporal association, a feature in epileptic seizure discovery based on 2D convolutional layers. This research also been motivated to invoke and frame of CNN based raw electroencephalography indicators to advance the accuracy of finding epileptic seizure, as an alternative of regular feature abstraction to differentiate ictal, preictal, and interictal variations to find epileptic seizure detection. It was compared the routines of time and regularity domain signals in the detection of epileptic signals which is constructed and based on the health organization and its collaburation worldwide with scalp record which is transportable and potential in these parameters. Sorting the sequential approach and its consequences show that CNN has an approaching ability in the classification of EEG signals with a sequential verification and validation to recognition an accurate epileptic seizures by reaching 99.18% of global classification accuracy. Key words: Convolutional neural network (CNN), Bi-LSTM, Electroencephalography (EEG), Epileptic seizure.