Signal Correlation Research Articles

Single-pixel sensing of rotating objects in Laguerre-Gaussian spectral domain

Single-pixel sensing (SPS) has proven to be a powerful tool for image-free object analysis. However, challenges arise when applying this technique to identify rotating objects, such as biological molecules and astronomical stars, because rotation disrupts the inherent correlation among numerous projections. Here, we propose and experimentally demonstrate a Laguerre-Gaussian (LG) spectral domain SPS approach. Benefiting from the circular symmetry of LG mode, the LG amplitude spectrum of an object exhibits rotation invariance, which is utilized to reconstruct the signal correlations of various projections. We prepare amplitude-type projection modes through the superposition of conjugate LG modes, which provides robustness against the phase fluctuations of light. The convolutional neural network (CNN) is trained using the LG spectra of static objects. In experiment, our method demonstrates high accuracies of 90.80%, 88.64%, and 82.5% when recognizing static objects, static objects after a random rotation, and rotating objects at 4831 rpm, respectively. Our approach opens new possibilities for extending the function of SPS to identify fast-rotating objects, which has potential applications in fundamental research areas such as biology and astronomy, as well as in industrial fields.

Discovery of the First Efficacious Adenosine 2A Receptor Negative Allosteric Modulators for High Adenosine Cancer Immunotherapies.

Inhibition of the adenosine 2A receptor (A2AR) is recognized as a promising immunotherapeutic strategy but is challenged by the ubiquity of A2AR function in the immune system. To develop a safe yet efficacious immunotherapy, the discovery of a novel negative allosteric modulator (NAM) was preferred. Leveraging an in-house, sensitive, high-throughput screening cellular assay, novel A2AR NAM scaffolds were identified, followed by an extensive structure-activity relationship (SAR) study, leading to the discovery of potent 2-amino-3,5-dicyanopyridine derivatives. The allosteric mode of action of active compounds was confirmed by progressive fold-shift assay, nonlinearity of the Schild plot analysis, biophysical measurements, and retained satisfactory potencies in high-adenosine concentrations. Further correlation of A2AR engagement and downstream signaling was done in a human blood translational assay, clearly showcasing the potential of A2AR allosteric modulation as a novel approach for efficient and safer cancer immunotherapies.

Exploring the Correlation of Physiological Stress Signals with Student Exam Performance: A Preliminary Study.

Stress responses in real-world settings are less studied compared to controlled laboratory environments, limiting our understanding of their impact on cognitive performance. This study investigates the relationship between physiological stress signals and academic performance using an open-access dataset of 10 students assessed across three exam sessions (Midterm 1, Midterm 2, and Final Exam). Physiological measures, including electrodermal activity (EDA), heart rate (HR), and skin surface temperature (TEMP), along with exam grades, were analyzed using traditional hypothesis testing, bootstrap method, correlation analysis, and regression tree modeling. To address the small sample size, we validated traditional hypothesis test results with the bootstrap method, and both approaches were compatible. Hypothesis testing revealed no significant differences in physiological measures across exam sessions, supporting the null hypothesis. Grades differed significantly between the final exam and both midterms (p < 0.05). Stress fluctuations were also analyzed across three periods (beginning, middle, and end) for each exam, revealing temporal response variations. Correlation analysis showed a moderate negative relationship between EDA and HR (r = -0.504, p < 0.01) and a weak positive relationship between EDA and TEMP (r = 0.417, p < 0.05), both intensifying during the final exam. Regression analysis explained 78% of the variance in grades (R2 = 0.78), with regression tree modeling identifying lower skin temperature (< 28°C) and higher EDA (≥ 0.19) as predictors of poorer performance. These findings underscore the interplay between physiological stress responses and academic outcomes, emphasizing the need for further research and interventions to support student success.

Precise Sizing and Collision Detection of Functional Nanoparticles by Deep Learning Empowered Plasmonic Microscopy.

Single nanoparticle analysis is crucial for various applications in biology, materials, and energy. However, precisely profiling and monitoring weakly scattering nanoparticles remains challenging. Here, it is demonstrated that deep learning-empowered plasmonic microscopy (Deep-SM) enables precise sizing and collision detection of functional chemical and biological nanoparticles. Image sequences are recorded by the state-of-the-art plasmonic microscopy during single nanoparticle collision onto the sensor surface. Deep-SM can enhance signal detection and suppresses noise by leveraging spatio-temporal correlations of the unique signal and noise characteristics in plasmonic microscopy image sequences. Deep-SM can provide significant scattering signal enhancement and noise reduction in dynamic imaging of biological nanoparticles as small as 10nm, as well as the collision detection of metallic nanoparticle electrochemistry and quantum coupling with plasmonic microscopy. The high sensitivity and simplicity make this approach promising for routine use in nanoparticle analysis across diverse scientific fields.

Impaired macroscopic CSF flow by sevoflurane in humans - both during and after anesthesia.

According to the model of the glymphatic system, the directed flow of cerebrospinal fluid (CSF) is a driver of waste clearance from the brain. In sleep, glymphatic transport is enhanced, but it is unclear how it is affected by anesthesia. Animal research indicates partially opposing effects of distinct anesthetics but corresponding results in humans are lacking. Thus, this study aims to investigate the effect of sevoflurane anesthesia on CSF flow in humans, both during and after anesthesia. Using data from a functional magnetic resonance imaging (fMRI) experiment in 16 healthy human subjects before, during, and 45 minutes after sevoflurane mono-anesthesia of 2vol%, we related grey matter blood-oxygenation-level dependent (BOLD) signals to CSF flow, indexed by fMRI signal fluctuations, across the basal cisternae. Specifically, CSF flow was measured by CSF fMRI signal amplitudes, global grey matter (gGM) functional connectivity by the median of inter-regional GM fMRI Spearman rank correlations, and gGM-CSF basal cisternae coupling by Spearman rank correlations of fMRI signals. Anesthesia decreased cisternal CSF peak-to-trough amplitude (median difference Mdn-diff = 1.00, 95% CI [0.17 1.83], p = .013), disrupted the global, cortical BOLD-fMRI-based connectivity (Mdn-diff = 1.5, 95% CI [0.67, 2.33], p < 0.001) and, global grey matter (gGM)-CSF coupling (Mdn-diff = 1.19, 95% CI [0.36, 2.02], p = 0.002). Remarkably, the impairments of global connectivity (Mdn-diff = 0.94, 95% CI [0.11, 1.77], p = 0.022) and gGM-CSF coupling (Mdn-diff = 1.06, 95% CI [0.23, 1.89], p = 0.008) persisted after re-emergence from anesthesia. Collectively, our data show that sevoflurane impairs macroscopic CSF flow via a disruption of coherent gGM activity. This effect persists, at least for a short time, after regaining consciousness. Future studies need to elucidate whether this contributes to the emergence of postoperative neurocognitive symptoms, especially in older patients or those with dementia.

Enhancing the Transformer Model with a Convolutional Feature Extractor Block and Vector-Based Relative Position Embedding for Human Activity Recognition.

The Transformer model has received significant attention in Human Activity Recognition (HAR) due to its self-attention mechanism that captures long dependencies in time series. However, for Inertial Measurement Unit (IMU) sensor time-series signals, the Transformer model does not effectively utilize the a priori information of strong complex temporal correlations. Therefore, we proposed using multi-layer convolutional layers as a Convolutional Feature Extractor Block (CFEB). CFEB enables the Transformer model to leverage both local and global time series features for activity classification. Meanwhile, the absolute position embedding (APE) in existing Transformer models cannot accurately represent the distance relationship between individuals at different time points. To further explore positional correlations in temporal signals, this paper introduces the Vector-based Relative Position Embedding (vRPE), aiming to provide more relative temporal position information within sensor signals for the Transformer model. Combining these innovations, we conduct extensive experiments on three HAR benchmark datasets: KU-HAR, UniMiB SHAR, and USC-HAD. Experimental results demonstrate that our proposed enhancement scheme substantially elevates the performance of the Transformer model in HAR.

Macromolecule Modelling for Improved Metabolite Quantification Using Short Echo Time Brain 1H-MRS at 3 T and 7 T: The PRaMM Model.

To improve reliability of metabolite quantification at both, 3 T and 7 T, we propose a novel parametrized macromolecules quantification model (PRaMM) for brain 1H MRS, in which the ratios of macromolecule peak intensities are used as soft constraints. Full- and metabolite-nulled spectra were acquired in three different brain regions with different ratios of grey and white matter from six healthy volunteers, at both 3 T and 7 T. Metabolite-nulled spectra were used to identify highly correlated macromolecular signal contributions and estimate the ratios of their intensities. These ratios were then used as soft constraints in the proposed PRaMM model for quantification of full spectra. The PRaMM model was validated by comparison with a single-component macromolecule model and a macromolecule subtraction technique. Moreover, the influence of the PRaMM model on the repeatability and reproducibility compared with those other methods was investigated. The developed PRaMM model performed better than the two other approaches in all three investigated brain regions. Several estimates of metabolite concentration and their Cramér-Rao lower bounds were affected by the PRaMM model reproducibility, and repeatability of the achieved concentrations were tested by evaluating the method on a second repeated acquisitions dataset. Although the observed effects on both metrics were not significant, the fit quality metrics were improved for the PRaMM method (p ≤ 0.0001). Minimally detectable changes are in the range 0.5-1.9 mM, and the percentage coefficients of variations are lower than 10% for almost all the clinically relevant metabolites. Furthermore, potential overparameterization was ruled out. Here, the PRaMM model, a method for an improved quantification of metabolites, was developed, and a method to investigate the role of the MM background and its individual components from a clinical perspective is proposed.

Practical, low-cost integrity testing of diesel particle filters using remote sensing measurement at the campus entrance gate.

This work investigates the detection of defunct or absent diesel particle filters by drive-through remote sensing measurement at the Czech University of Life Sciences main vehicular entrance gate. An exhaust sample was collected by a line attached to the road surface in the center of the travel lane. A non-volatile particle number (nvPN) counter and electric mobility particle size classifier were used to measure particle number concentrations, and an FTIR analyzer was used to measure CO2, CO, and NO concentrations. Of 59.7% of 526 entering vehicles, peak CO2 concentrations above 100ppm were observed, suggesting that the entrance gate is ideal for sampling-style remote sensing measurements due to high signal strength and controlled spacing between vehicles. On 48 vehicles, the measurements were compared to a 10-s periodic technical inspection (PTI) style measurement of tailpipe nvPN concentrations at idle. The results indicate nearly absolute agreement in distinguishing diesel vehicles with and without a functional DPF, with one outlier with a relatively weak CO2 signal and weak correlation between roadside nvPN and CO2 concentrations. Using a linear regression approach to calculate the remote sensing emissions factors (EF) and restricting the data to those with peak CO2 at least 100ppm yielded an absolute agreement not only between tailpipe nvPN concentrations at idle and remote sensing nvPN EF but also between tailpipe nvPN and roadside concentration of both nvPN and total particle number in 25-560nm size bins. The reported setup presents, in the authors' opinion, a relatively simple, robust, non-obtrusive, and practical means of checking DPF functionality in locations interested in excluding high emitters, such as university campuses, airports, large parking garages, national park entrances, and similar locations where entering vehicles are fully warmed up and not cooled down by extended waiting in line. Relatively strong CO2 signal yielding high accuracy of detection of non-DPF diesels with laboratory instrument presents, in the opinion of the authors, a very high chance of successful exploitation of diffusion chargers, nvPN counters intended for PTI use in several EU countries, and other low-cost particle number sensors, with detection limit in thousands of #/cm3, and possibly low-cost black carbon monitors, along with a low-cost NDIR CO2 sensor and a camera or other means of identifying individual tested vehicles, for detecting defunct diesel particle filters. The CO2 signal alone can identify vehicles using internal combustion engines. Extending this measurement to CO and NO concentrations may also detect non-functional three-way and NOx-reducing catalysts, as demonstrated on three L-category vehicles, one quad, and two motorcycles. The setup also could be used as a drive-through, loaded-mode PTI test.

Characterization of contaminants in the Lyman-alpha forest auto-correlation with DESI

Baryon Acoustic Oscillations can be measured with sub-percent precision above redshift two with the Lyman-α (Lyα) forest auto-correlation and its cross-correlation with quasar positions. This is one of the key goals of the Dark Energy Spectroscopic Instrument (DESI) which started its main survey in May 2021. We present in this paper a study of the contaminants to the Lyα forest which are mainly caused by correlated signals introduced by the spectroscopic data processing pipeline as well as astrophysical contaminants due to foreground absorption in the intergalactic medium. Notably, an excess signal caused by the sky background subtraction noise is present in the Lyα auto-correlation in the first line-of-sight separation bin. We use synthetic data to isolate this contribution, we also characterize the effect of spectro-photometric calibration noise, and propose a simple model to account for both effects in the analysis of the Lyα forest. We then measure the auto-correlation of the quasar flux transmission fraction of low redshift quasars, where there is no Lyα forest absorption but only its contaminants. We demonstrate that we can interpret the data with a two-component model: data processing noise and triply ionized Silicon and Carbon auto-correlations. This result can be used to improve the modeling of the Lyα auto-correlation function measured with DESI.

Polar Networks Mediate Ion Conduction of the SARS-CoV-2 Envelope Protein.

The SARS-CoV-2 E protein conducts cations across the cell membrane to cause pathogenicity to infected cells. The high-resolution structures of the E transmembrane domain (ETM) in the closed state at neutral pH and in the open state at acidic pH have been determined. However, the ion conduction mechanism remains elusive. Here, we use solid-state NMR spectroscopy to investigate the side chain structure, dynamics, and interactions of five polar residues at the N-terminal entrance of the channel and three polar residues at the C-terminal end. The chemical shifts of the N-terminal Glu8 reveal that the Glu side chain interacts with protons, Ca2+ and two neighboring Thr residues, and adopts distinct motionally averaged conformational ensembles. These polar interactions are sensitive to the presence of negatively charged lipids in the membrane. A T9I mutation, prevalent in the Omicron variants of SARS-CoV-2 E, perturbs these interactions and partially immobilizes the N-terminal segment. Deeper into the channel, two polar residues, Asn15 and Ser16, form interhelical hydrogen bonds in the closed state but become separated by water molecules in the open state. This is manifested by Asn15-Ser16 correlation signals at neutral pH and the loss of these correlations and the appearance of water cross peaks with Ser16 at acidic pH in the presence of Ca2+. Finally, the guanidinium side chain of the C-terminal Arg38 undergoes fast reorientations in the closed state but becomes more restricted in the open state. These results provide evidence for a dynamic and hydrogen-bonded N-terminal polar network that recruits and relays protons and Ca2+ in a lipid-dependent manner. Once inside, the ions permeate past the hydrophobic middle of the transmembrane domain with the help of enhanced hydrophilicity of the C-terminal channel lumen due to the insertion of the Arg38 side chain into the pore.

MS-CLSTM: Myoelectric Manipulator Gesture Recognition Based on Multi-Scale Feature Fusion CNN-LSTM Network.

Surface electromyography (sEMG) signals reflect the local electrical activity of muscle fibers and the synergistic action of the overall muscle group, making them useful for gesture control of myoelectric manipulators. In recent years, deep learning methods have increasingly been applied to sEMG gesture recognition due to their powerful automatic feature extraction capabilities. sEMG signals contain rich local details and global patterns, but single-scale convolutional networks are limited in their ability to capture both comprehensively, which restricts model performance. This paper proposes a deep learning model based on multi-scale feature fusion-MS-CLSTM (MS Block-ResCBAM-Bi-LSTM). The MS Block extracts local details, global patterns, and inter-channel correlations in sEMG signals using convolutional kernels of different scales. The ResCBAM, which integrates CBAM and Simple-ResNet, enhances attention to key gesture information while alleviating overfitting issues common in small-sample datasets. Experimental results demonstrate that the MS-CLSTM model achieves recognition accuracies of 86.66% and 83.27% on the Ninapro DB2 and DB4 datasets, respectively, and the accuracy can reach 89% in real-time myoelectric manipulator gesture prediction experiments. The proposed model exhibits superior performance in sEMG gesture recognition tasks, offering an effective solution for applications in prosthetic hand control, robotic control, and other human-computer interaction fields.

Effect of Core Material on Random Vibration Behaviour of Foam-based Laminated Sandwich Panels Using a Modified Layerwise Theory

Abstract The vibration response of foam-based laminated sandwich panels (FLSP) under random loading has been investigated. A modified layerwise displacement theory has been employed to account for the variable material properties of layers through the thickness. The dynamic behaviour of the FLSP was determined using random input excitation. Various foam materials have been used to investigate the effect of core material on the random vibration behaviours of FLSPs. The influencing random parameters, including mean, standard deviation, variance, and autocorrelation, have been determined for various core materials. A proof-of-concept experimental setup was devised to illustrate the performance and accuracy of the proposed modified layerwise theory in random vibration analysis of FLSPs. It was observed that as the softness of the core material increases, the natural frequency of FLSP decreases. Furthermore, the higher the core stiffness, the higher the response parameters of random vibration response, including mean value, standard deviation, and variance. In addition, the FLSP filled with stiffer core material exhibits a higher signal correlation than softcore material.&amp;#xD;

Band Correlation‐Based Multichannel Multiscale Convolution Network for Intelligent Interference Recognition

AbstractIn recent years, with the development and extensive application of wireless communication technology, the communication system should have stronger anti‐Jamming ability. Therefore, interference recognition is particularly important as a prerequisite for anti‐interference. However, the existing traditional and intelligent interference recognition algorithms have problems such as complicated feature extraction and low recognition accuracy under low interference‐to‐noise ratio. In order to solve the above problems, this paper introduces parallel multi‐channel multi‐scale convolution to improve the speed and accuracy of network recognition. In addition, combined with frequency band correlation and long‐short‐term memory network (LSTM), an innovative wireless communication interference identification model based on frequency band correlation is proposed, which uses LSTM to detect the frequency band correlation of interference signals and improve the accuracy of interference identification under low Jamming noise ratio (JNR). Experiments prove that the model proposed in this article has faster recognition speed and better generalization. The introduction of frequency band correlation increases the recognition accuracy to more than 99% with low JNR. Therefore, the model proposed in this paper is an effective and available model in complex electromagnetic environments. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

The impact of functional correlations on task information coding.

State-dependent neural correlations can be understood from a neural coding framework. Noise correlations-trial-to-trial or moment-to-moment covariability-can be interpreted only if the underlying signal correlation-similarity of task selectivity between pairs of neural units-is known. Despite many investigations in local spiking circuits, it remains unclear how this coding framework applies to large-scale brain networks. Here, we investigated relationships between large-scale noise correlations and signal correlations in a multitask human fMRI dataset. We found that task-state noise correlation changes (e.g., functional connectivity) did not typically change in the same direction as their underlying signal correlation (e.g., tuning similarity of two regions). Crucially, noise correlations that changed in the opposite direction as their signal correlation (i.e., anti-aligned correlations) improved information coding of these brain regions. In contrast, noise correlations that changed in the same direction (aligned noise correlations) as their signal correlation did not. Interestingly, these aligned noise correlations were primarily correlation increases, suggesting that most functional correlation increases across fMRI networks actually degrade information coding. These findings illustrate that state-dependent noise correlations shape information coding of functional brain networks, with interpretation of correlation changes requiring knowledge of underlying signal correlations.

Diffusion‐Based Causality‐Preserving Neural Network for Dementia Recognition

ABSTRACTAnalyzing large‐scale functional brain networks for brain disorders often relies on undirected correlations in activation signals between brain regions. While focusing on co‐occurring activations, this approach overlooks the potential for directionality inherent in brain connectivity. Established research indicates the causal nature of brain networks, suggesting that activation patterns co‐occur and potentially influence one another. To this end, we propose a novel dffusion vector auto‐regressive (Diff‐VAR) method, enabling the assessment of whole‐brain effective connectivity (EC) as a directed and weighted network by integrating the search objectives into the deep neural network model as learnable parameters. The EC learned by our method identifies widespread differences in flow of influence within the brain network for individuals with impaired brain function compared to those with normal brain function. Moreover, we introduce an adaptive smoothing mechanism to enhance the stability and reliability of inferred EC. We evaluated the results of our proposed method on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The model's performance is compared with existing correlation‐based and causality‐based methods. The results revealed that the brain networks constructed by our method achieve high classification accuracy and exhibit features consistent with physiological mechanisms. The code is available at https://github.com/SaqibMamoon/Diff‐VAR.

Unresponsiveness induced by sevoflurane and propofol is associated with reduced basal forebrain cholinergic nuclei functional connectivity in humans, a pilot exploratory study

Studies suggest the cholinergic system is involved in anesthesia-induced unconsciousness, hence unresponsiveness. A significant source of cholinergic innervation comes from basal forebrain cholinergic nuclei (BFCN), with bi-directional connections between anterior BFCN and the default mode network (DMN). Since DMN functional connectivity (FC) is consistently reduced during anesthesia-induced unresponsiveness in humans, we hypothesized that BFCN-FC during anesthesia-induced unresponsiveness is reduced and particularly, anterior BFCN-FC reductions might be related to DMN-FC reductions. Resting-state fMRI (rs-fMRI) signal correlations (i.e., a proxy for FC) were calculated. FC seeds were anterior and posterior BFCN and the DMN. Rs-fMRI data come from healthy male controls during wakefulness and anesthesia with sevoflurane (n = 15) (at fixed concentrations: 2 and 3 vol%) and propofol titrated to the endpoint of clinical unresponsiveness (n = 12), respectively. FC state differences were tested via paired t-tests; FC changes for anterior BFCN and DMN were associated via correlation analysis. We found reduced anterior and posterior BFCN-FC with sevoflurane and propofol compared to wakefulness. The correlation between reduced DMN-FC-and anterior BFCN-FC reductions was r = 0.57 (p = 0.01) for sevoflurane 3 vol%, r = 0.34 (p = 0.11) for sevoflurane 2 vol% and r = 0.47 (p = 0.06) for propofol. In summary, in this exploratory pilot study, we demonstrated reduced BFCN-FC and a potential correlation between reduced anterior BFCN-FC and DMN-FC during sevoflurane and propofol anesthesia. This suggests DMN changes as a potential factor of anterior BFCN-FC reductions during anesthesia-induced unresponsiveness and BFCN-FC reduction as a potential sign of such state.

Online milling chatter detection based on signal correlation and optimized variational mode decomposition

Online milling chatter detection based on signal correlation and optimized variational mode decomposition

Longitudinal changes in electrophysiology and widefield calcium imaging following electrode implantation

Objective. Intracortical microelectrode arrays often fail to deliver reliable signal quality over chronic recordings, and the effect of an implanted recording array on local neural circuits is not completely understood.Approach. In this work we examined the degree of correlation and the spatial dependence of that relationship between widefield calcium imaging and electrophysiology in awake mice from 4 to 44 d post-implantation. Both correlation maps and spike-triggered averaging (STA) are used to characterize the relationship.Main results. We find that calcium imaging and electrophysiological signal are highly correlated in all animals, however, spatial variability in the correlation is affected by inherent correlation in the calcium imaging signal. Some animals exhibit a high degree of apparent neuronal synchrony in the vicinity of the probe at 4 d, while more diversity of response is detected at later time points.Significance. Degree of synchrony appears to be related to the acute injury response to the implanted electrode, with later time points displaying less synchrony. STA may be used to uncover the diverse cortical connections of spiking units.

A 12-Lead ECG signal correlation analysis in multiple domains

A 12-Lead ECG signal correlation analysis in multiple domains

Research on frequency-domain characteristics of acoustic emission signals generated during wood damage and fracture process

ABSTRACT This research conducts three-point bending tests on air-dried beech, elm, pine, and cypress specimens to research their damage and fracture behaviour. Wavelet transform is utilized to decompose original acoustic emission signals generated during the damage and fracture process into five layers. Then, based on the principle of signal correlation, the wavelet-decomposed detail signals are adaptively reconstructed. The K-means algorithm is used to classify reconstructed AE signals into different groups based on their frequency-domain characteristic. Research results indicate that the peak frequency of low-frequency AE signals generated during the wood damage and fracture process is approximately 21 kHz. These AE signals may be generated by damage, delamination, bucking, and collapse of the cell-wall layer. When wood specimens are in the later stage of nonlinear deformation and in the macroscopic fracture stage, in addition to the mentioned microscopic damage above, there will also be formation and expansion of microcrack damage zones, as well as cell-wall rupture and macroscopic fracture of wood fibres. In addition to the low-frequency AE signals mentioned above, these AE signals are medium-frequency signals with peak frequency in the range of 25–60 kHz, and high-frequency signals with a peak frequency of approximately 165 kHz.