Mixed Interference Research Articles

Exploring Embodied Cognition and Brain Dynamics under Multi-tasks Target Detection in Immerse Projector-based Augmented Reality (IPAR) Scenarios.

Embodied cognition explores the intricate interaction between the brain, body, and the surrounding environment. The advancement of mobile devices, such as immersive interactive computing and wireless electroencephalogram (EEG) devices, has presented new challenges and opportunities for studying embodied cognition. To address how mobile technology within immersive hybrid settings affects embodied cognition, we propose a target detection multitask incorporating mixed body movement interference and an environmental distraction light signal. We aim to investigate human embodied cognition in immersive projector-based augmented reality (IPAR) scenarios using wireless EEG technology. We recruited and engaged fifteen participants in four multitasking conditions: standing without distraction (SND), walking without distraction (WND), standing with distraction (SD), and walking with distraction (WD). We pre-processed the EEG data using Independent Component Analysis (ICA) to isolate brain sources and K-means clustering to categorize Independent Components (ICs). Following that, we conducted time-frequency and correlation analyses to identify neural dynamics changes associated with multitasking. Our findings reveal a decline in behavioral performance during multitasking activities. We also observed decreases in alpha and beta power in the frontal and motor cortex during standing target search tasks, decreases in theta power, and increases in alpha power in the occipital lobe during multitasking. We also noted perturbations in theta band power during distraction tasks. Notably, physical movement induced more significant fluctuations in the frontal and motor cortex than distractions from social environment light signals. Particularly in scenarios involving walking and multitasking, there was a noticeable reduction in beta suppression. Our study underscores the importance of brain-body collaboration in multitasking scenarios, where the simultaneous engagement of the body and brain in complex tasks highlights the dynamic nature of cognitive processes within the framework of embodied cognition. Furthermore, integrating immersive augmented reality technology into embodied cognition research enhances our understanding of the interplay between the body, environment, and cognitive functions, with profound implications for advancing human-computer interaction and elucidating cognitive dynamics in multitasking.

Irregular Bloch-Zener oscillations in two-dimensional flat-band Dirac materials

When a static electrical field is applied to a two-dimensional (2D) Dirac material, Landau-Zener transition (LZT) and Bloch-Zener oscillations can occur. Employing $\ensuremath{\alpha}\text{\ensuremath{-}}{\mathcal{T}}_{3}$ lattices as a paradigm for a broad class of 2D Dirac materials, we uncover two phenomena. First, due to the arbitrarily small energy gaps near a Dirac point that make it more likely for LZTs to occur than in other regions of the Brillouin zone, the distribution of differential LZT probability in the momentum space can form a complicated morphological pattern. Second, a change in the LZT morphology as induced by a mutual switching of the two distinct Dirac points can lead to irregular Bloch-Zener oscillations characterized by a nonsmooth behavior in the time evolution of the electrical current density associated with the oscillation. These phenomena are due to mixed interference of quantum states in multiple bands modulated by the geometric and dynamic phases. We demonstrate that the adiabatic-impulse model describing Landau-Zener-St\"uckelberg interferometry can be exploited to calculate the phases, due to the equivalence between the $\ensuremath{\alpha}\text{\ensuremath{-}}{\mathcal{T}}_{3}$ lattice subject to a constant electrical field and strongly periodically driven two- or three-level systems. The degree of irregularity of Bloch-Zener oscillations can be harnessed by selecting the morphology pattern, which is potentially experimentally realizable.

Evaluation of AC corrosion under anodic polarization using microzone pH analysis

A method called the response surface methodology was designed to explore the corrosion characteristics under the combined action of an alternating current (AC) and a direct current (DC). High AC levels were found to inhibit anodic DC corrosion. The microzone pH increased significantly compared with that of the bulk solution when the anodic DC was coupled with a high AC. Ion migration at the interface under the interference was analyzed to evaluate the pH changes. By combining the effects of the pH variations with the Pourbaix diagram, a reasonable mechanism is proposed for the AC+DC mixed interference corrosion process.

Novel Ultrafast Molecular Imaging Based on the Combination of X-ray and Electron Diffraction

Recent development of X-ray free-electron lasers and megaelectronvolt radio-frequency electron guns have made ultrafast X-ray and electron diffraction measurements possible, thereby capturing chemical dynamics with atomic-spatial and femtosecond-temporal resolutions. We present a unified formulation of standard homodyne-detected and heterodyne-detected signals for both techniques. Noting that X-rays scatter from molecular electrons while electrons scatter from both molecular electrons and nuclei, we show how the two diffraction signals can be combined to reveal novel chemical information that is unavailable by solely using each technique alone. By subtracting the homodyne-detected X-ray and electron diffraction signals, a mixed electronic-nuclear interference in electron diffraction can be identified with a self-heterodyne nature for the direct imaging of attosecond electron dynamics where the scattering off molecular nuclei serves as a local oscillator for the scattering off molecular electrons. By subtracting heterodyne-detected X-ray and electron diffraction, the purely nuclear charge density can be singled out.

The Online Construction of Digital Spatial Representation in Alphanumeric Mixed Situations Is Not Automaticity

SNARC effect is called the joint effect of spatial digital response coding, which is the proof of the connection between number and space. There are mainly theoretical explanations, such as the mental number line explanation of long-term memory, the working memory explanation of short-term memory, and the dual path pattern explanation. Recent studies have demonstrated that SNARC effect is the online construction of digits in working memory during task execution, and the online construction of digits SNARC effect is interfered by letter processing in the mixed situation of digits and letters, which further supports the hypothesis of working memory. Although online encoding of SNARC effect has been proved, it is not clear whether online encoding of digital spatial representation in the mixed alphanumeric context has automaticity, and if so, whether it will be interfered by letter processing. In this study, three digit color judgment experiments were conducted, and the ratio of digits to letters was 1:1. 1:6. The paper explores whether the online construction of digital space representation is automaticity in the mixed interference situation of 6:1. To investigate the interference of letter processing on digital SNARC effect, explore whether the interference is regulated by the proportion of number letters, and further explore the automatic mechanism of the interference of letter processing on digital space coding. The results showed that SNARC effect was not observed in the mixed numbers and letters at different proportions. It shows that the online construction of digital space representation in the mixed alphanumeric situation does not have automaticity. When the task does not directly activate the size order information of the number letters, the letters will interfere with the online construction of the number SNARC effect, which is not proportional adjustment.

Anomaly Detection of Hyperspectral Image With Hierarchical Antinoise Mutual-Incoherence- Induced Low-Rank Representation

Hyperspectral image (HSI) anomaly detection (AD) generally considers background pixels as low-rank distribution and anomaly pixels as sparse distribution. However, it is usually difficult to construct an accurate background dictionary for the background pixels composed of different land-covers, and completely separate sparse anomaly targets from various complicated background pixels with complex mixed noise interference. To address these challenges, we propose an anti-noise hierarchical mutual-incoherence-induced discriminative learning (AHMID) method for AD of HSI. A structural incoherence constraint is designed to constrain the inherent dissimilarity and incoherence between background and anomalies for improving their separability. Then, a first-order statistic constraint is conducted on targets to enhance the anomaly representation, and a decentralization constraint is used on background to suppress the background representation. Meanwhile, a mixed noise model is constructed by ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1,1</sub> -norm and Frobenius norm to improve the anti-noise performance. Finally, a hierarchical alternating strategy is developed to gradually optimize the background and anomalies. Experiments on six HSI AD datasets show that the proposed method outperforms a few state-of-the-art AD algorithms. Code: https://github.com/HalongL/HAD-AHMID.

An Iterative Implementation-Based Approach for Joint Source Localization and Association Under Multipath Propagation Environments

We address the problem of joint source localization and association (JSLA) under a multipath propagation environment for sensor arrays. Taking into account inaccurate prior information in practical applications, we propose a JSLA algorithm based on the iterative implementation of the minimum mean-square error (MMSE) framework with semi-unitary and sparsity constraints and the subspace technique. The proposed algorithm exploits benefits of both spatial signals' sparse characteristic and coherence structure when localizing unknown sources in a mixed interference environment. In contrast with previous works, the proposed algorithm can associate the incident paths to each source from the complex propagation environment with improved association performance even under low signal-to-noise ratio conditions. Neither additional decorrelation preprocessing nor prior information pertaining to the multipath propagation is required. Both simulations and real data experiments demonstrate the effectiveness and robustness of the proposed algorithm.

Design of highly selective, and sensitive screen-printed electrochemical sensor for detection of uric acid with uricase immobilized polycaprolactone/polyethylene imine electrospun nanofiber

Uric acid (UA) plays a significant role in nerve center, human metabolism, and kidney system. Therefore, it is necessary to establish a simple, rapid, and sensitive detection method for UA. In recent years, researchers have been highly attracted by nanomaterials with satisfactory functions in electrochemical applications. In this study, polycaprolactone (PCL)/polyethylene imine (PEI) nanofiber membranes were prepared and used for the immobilization of uricase (UOx). The prepared membranes were characterized in terms of structure, composition, and morphology. Afterward, the quantum dot screen printed electrode (QD SPCE) was modified with PCL/PEI nanofiber membranes with and without methylene blue (MB) as an electron mediator. The electrochemical performance of the developed sensors was investigated by cyclic voltammetry (CV), differential pulse voltammetry (DPV), and electrochemical impedance spectroscopy (EIS). Under the optimal conditions, the modified sensors provided a broad linear range (5.0–52.0 µM) for the electrochemical detection of UA. Limit of detection (LoD) values were determined as 3.96 µM and 1.85 µM for the PCL/PEI/UOx/QD SPCE and PCL/PEI/UOx/MB/QD SPCE, respectively. The four-week stability results showed the change in current for the PCL/PEI/UOx/QD SPCE and PCL/PEI/UOx/MB/QD SPCE to be 92% and 87%, respectively. Additionally, in the mixed interference test remaining current ratios for UA were 95% and 82% for the PCL/PEI/UOx/QD SPCE and PCL/PEI/UOx/MB/QD SPCE, respectively. Most importantly, the effectiveness of the electrodes was also verified in real sample detection with satisfactory recovery (∼98-112%). Overall, the results showed that the PCL/PEI/UOx/QD SPCE and PCL/PEI/UOx/MB/QD SPCE have the advantages of simplicity, high sensitivity, and good selectivity for UA determination.

Optical coherence tomography – basic optical principles

AbstractOptical Coherence Tomography (OCT) is an optical analogue to ultrasound imaging. The much higher speed of light compared with sound allows for finer cross‐sectional views of the retina and anterior segment. Since it is extremely difficult to directly detect the shorter “echo” times it takes light to travel from different structures at axial distances within the eye, interferometry is used. Incident light is thus split into two beams, and the beam backscattered from the ocular tissue is then compared (“interfered”) with the beam that has travelled a known time from the reference mirror. Broadband (i.e., low “coherence”) light sources are used, because they produce a wider band of wavelengths, and thereby enable greater sensitivity in comparing the travel time differences of the two beams. In time‐domain OCT, the reference mirror position is altered, so that interference patterns are generated whenever the two beams have travelled almost the same amount of time. In spectral‐domain OCT, the reference mirror position is fixed and the mixed interference patterns are separated via spectral wavelength analysis. This lecture will explain underlying concepts of OCT with a discussion of cutting‐edge technological developments.

Attosecond Charge Migration in Molecules Imaged by Combined X-ray and Electron Diffraction.

We show how ultrafast gas-phase X-ray and electron diffraction signals can be combined to generate real-space movies of charge migration dynamics in molecules. Charge migration denotes short time electronic charge redistribution upon photoexcitation of molecules where the nuclei are frozen. In this regime, we identify a mixed electronic-nuclear interference term that can be cleanly singled out. Using the ground-state nuclear structure as a reference, the phase information in this signal allows its inversion to real space and the capture of electronic charge density movies on the attosecond time scale.

Adaptive filtering of microseismic data for monitoring a water-conducting fractured zone in a mine

Water-conducting fractured zones in a rock mass can cause problems in mining. Attempts have been made to monitor their development using microseismic signals. However, due to the lack of prior information, it is difficult to filter out mixed low-frequency interference with traditional denoising methods. In this work, the proposed adaptive filtering algorithm is applied after the wavelet packets are decomposed. It is based on a cross-correlation analysis. The algorithm takes a high-quality signal in the common source waveform as prior information and applies the corresponding correlation coefficients between subbands as a threshold. The algorithm was verified with simulations. The results show that low-frequency interference can be effectively suppressed by filtering. For single-frequency interference, the signal-to-noise ratio increased from − 10.18 to 13.97, and the root-mean-square error was 43.88. For multi-frequency interference, it increased from − 10.01 and − 2.63 to 13.50 and 7.99. The root-mean-square errors were 46.31 and 138.07. The narrower the main frequency band of the interference signal and the less the overlap of the main frequency band of the interference signal and the effective signal, the better the filtering effect. When the algorithm was applied to microseismic data collected in the field, the number of effective channels increased and the accuracy improved. The development of a water-conducting fractured zone in the field was consistent with the microseismic location obtained after interference was removed by the algorithm, which indicates that it is feasible to monitor a water-conducting fractured zone by analyzing microseismic waveforms with the adaptive filtering algorithm.

Correlation clustering for robust gas recognition under mixed interference

Gas recognition by electronic noses under mixed interference is a challenging problem. We propose correlation analysis for robust gas recognition by calculating the similarity of signals between target gases and mixtures. The gas sensing datasets were clustered according to the values of correlation coefficients with the target gases. The correlation analysis outperformed neural networks and other clustering algorithms on robust gas recognition under mixed interference. The correlation analysis maintained 100% accuracy even with a response change of about 40% up to an interference ratio of 13%. The excellent performance of correlation analysis can be ascribed to its powerful capacity for measuring the similarity between signals via relative variation. Correlation analysis is suggested to be a robust clustering algorithm for gas recognition.

Research on Detection Technology of Spoofing under the Mixed Narrowband and Spoofing Interference

The global navigation satellite system has achieved great success in the civil and military fields and is an important resource for space-time information services. However, spoof interference has always been one of the main threats to the application security of satellite navigation receivers. In order to further improve the application security of satellite navigation receivers, this paper focuses on the application scenarios where narrowband and spoofing interference exist at the same time, studies the problem of spoofing interference detection under mixed interference conditions, then proposes a spoofing interference detection method based on the tracking loop identification curve. This method can effectively deal with the detection of spoofing interference under the conditions of narrowband interference and, at the same time, it can effectively detect the spoofing interference of gradual deviation. Simulation experiments verify the effectiveness of the spoofing interference detection method, based on the tracking loop discrimination curve. In typical jamming and spoofing scenarios, when the spoofing signal is about 7.5 m away from the real signal, the method used in this paper can achieve effective detection. The proposed detection method is of great significance for improving the anti-spoofing capability of satellite navigation receivers.

Partial Discharge Pulse Segmentation Approach of Converter Transformers Based on Higher Order Cumulant

As one of the most effective methods to detect the partial discharge (PD) of transformers, high frequency PD detection has been widely used. However, this method also has a bottleneck problem; the biggest problem is the mixed pulse interference under the fixed length sampling. Therefore, this paper focuses on the study of a new pulse segmentation technology, which can separate the partial discharge pulse from the sampling signal containing impulse noise so as to suppress the interference of pulse noise. Based on the characteristics of the high-order-cumulant variation at the rising edge of the pulse signal, a method for judging the starting and ending time of the pulse based on the high-order-cumulant is designed, which can accurately extract the partial discharge pulse from the original data. Simulation results show that the location accuracy of the proposed method can reach 94.67% without stationary noise. The field test shows that the extraction rate of the PD analog signal can reach 79% after applying the segmentation method, which has a great improvement compared with a very low location accuracy rate of 1.65% before using the proposed method.

Iterative Implementation Method for Robust Target Localization in a Mixed Interference Environment

For the problem of target localization under the multipath propagation environment, the existing methods are mainly restricted to the limited prior information of complex reflections, especially when the target is embedded in a mixed interference environment. They may suffer from performance degradation due to the shortage of target classification ability. To address this problem, we propose a target localization method based on iterative implementation with semiunitary constraint and eigen-decomposition technique, where a practical propagation scenario based on the spherical Earth model is considered. Compared to the previous works, the proposed method can automatically distinguish a real target from the mixed interference environment with improved localization accuracy. Neither additional decorrelation preprocessing nor prior information of the dynamic scenario is required. Both simulations and real data experiments validate the effectiveness and robustness of the proposed method.

Robust gas recognition with mixed interference using a spiking neural network

Spiking neural networks (SNNs) have attracted significant interest owing to their high computing efficiency. However, few studies have focused on the robustness of SNNs and their application to electronic noses for gas recognition under strong interference. The goal of this study was to explore the robustness of a SNN for gas recognition under mixed interference. Data on mixed gases with different levels of interference were simulated by fitting experimental data. Two layers of a SNN based on leaky integrate-and-fire (LIF) neurons were constructed and the network was trained solely on datasets of pure targeted gases. Testing was then performed using data with mixed interference. The SNN achieved superior performance compared to other algorithms and remained 100% accurate for gas recognition up to a 10% interference ratio. The interval distance of spiking times between classes represents the robust capacity of the SNN according to the algorithm of the LIF neurons. SNNs have excellent capacity to maximize the differences between data of different classes and are promising candidates for electronic noses.

Mixed Numerology Interference Recognition Approach for 5G NR

The coexistence of multiple numerologies can cause interference and degrade system performance in 5G devices. In this letter, a convolutional neural networks (CNN)-based mixed numerology interference (MNI) recognition approach is devised to resolve the interference for user equipment (UE) to receive new radio (NR) downlink signal. The results are learned from the frequency domain magnitude and phase waveform, for the best precision and efficiency. We use as little sample data as possible to reduce memory/buffer costs and to alleviate the computing resources. Simulations are performed to verify the viability on various signal-noise ratio (SNR), channel condition, modulation, etc. It is shown that the accuracy can reach 97% or higher at varying SNR and fading channels. Furthermore, a dynamic filtering/windowing (DFW) scheme is proposed. The link level simulation results show that the scheme can improve the attainable Signal to Interference plus Noise Ratio (SINR) by 3~6dB for the indicated cases.

Coherent control mechanisms in bichromatic multiphoton ionization

Free electron vortices (FEVs) generated by multiphoton ionization (MPI) with ultrashort laser pulses have attracted significant attention due to their varied symmetries and unusual topological properties. We study two physical mechanisms of coherent control in atomic MPI with bichromatic polarization-shaped femtosecond laser pulses which give rise to the rich variety of FEVs. In the experiments, we combine pulse shaping of a carrier-envelope phase-stable supercontinuum with photoelectron tomography to generate and reconstruct three-dimensional photoelectron momentum distributions (PMDs). Simultaneous measurements of energetically separated photoelectrons from intraband and interband interference in a single PMD allow us to compare phase and polarization control of the angular distributions by both mechanisms. We investigate phase control in three scenarios: first, counterrotating circularly polarized pulses are employed to contrast the phase-insensitive angular momentum eigenstate created by intraband interference via frequency mixing with the phase-sensitive c 7 rotationally symmetric FEV from pure interband interference of two single-color ionization pathways. In the second scenario, we use orthogonal linearly polarized pulses to compare the phase control properties of a six-lobed angular momentum wave packet from intraband interference to those of a complex shaped interband PMD in the presence of phase fluctuations. Finally, we demonstrate phase control of a photoelectron hologram from mixed interband interference. In a (3 + 1) resonance enhanced MPI scheme, the red pump pulse induces a bound electron wave packet probed by the time-delayed blue pulse. The latter simultaneously creates a reference wave packet by three-photon ionization to form the photoelectron hologram. Rotation of the hologram with c 1 or c 5 rotational symmetry maps the time evolution of the bound wave packet. To analyze our results, we develop analytical expressions for the wave functions of intraband and interband interference in perturbative non-resonant MPI. The experiments are complemented with two-dimensional TDSE simulations to follow the FEV formation dynamics and to validate the physical pictures.

Inhibitory action of a new derivative of amino acids in aerated acidic media

The present work was effective in combining equimolar 4aminoacetophene with leucine amino acid in the synthesis of a new corrosion inhibitor (S2). 1H N.M.R., 13C N.M.R., C.H.N., and FTIR techniques proved its structure. At 298.15 K, 0.5 M hydrochloric acid solution was measured to affect the S2 inhibitor on the CS (X56) electrode. S2 has been verified as a suitable inhibitor and as a mixed interference to control both anodic and the cathodic response to delayed corrosion by blocking the active sites in the surface electrode by the results of electrochemical measurements as open circuit potential and Tafel scan. These observations were accompanied by FE-SEM images and mapping spectrums that showed a transparent surface protective layer.

A New Method for Separating EMI Signal Based on CEEMDAN and ICA

Aiming at the problem of electromagnetic interference signal separation, we propose a single-channel blind source separation method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and independent component analysis (ICA). Firstly, decompose the mixed interference signal by CEEMDAN to obtain a series of intrinsic mode functions (IMF). Secondly, combine them into a new multi-dimensional signal and solve its correlation matrix, and use singular value decomposition to obtain the eigenvalues of the matrix, and then use it to estimate the source number. Thirdly, select those IMFs whose correlation coefficients with the observed signal are bigger and regard them together with the original signal after denoising as new observed signals. Finally, recover the source signals by fast independent component analysis (Fast-ICA). We also extend our method to multi-channel BBS, and experiment results show that our method can eliminate unnecessary noise in the signal and perform well.