Interference Environment Research Articles

Disruption of Fuz in mouse embryos generates hypoplastic hindbrain development and reduced cranial nerve ganglia.

The brain and spinal cord formation is initiated in the earliest stages of mammalian pregnancy in a highly organized process known as neurulation. Environmental or genetic interferences can impair neurulation, resulting in clinically significant birth defects known collectively as neural tube defects. The Fuz gene encodes a subunit of the CPLANE complex, a macromolecular planar polarity effector required for ciliogenesis. Ablation of Fuz in mouse embryos results in exencephaly and spina bifida, including dysmorphic craniofacial structures due to defective cilia formation and impaired Sonic Hedgehog signaling. We demonstrate that knocking Fuz out during embryonic mouse development results in a hypoplastic hindbrain phenotype, displaying abnormal rhombomeres with reduced length and width. This phenotype is associated with persistent reduction of ventral neuroepithelial stiffness in a notochord adjacent area at the level of the rhombomere 5. The formation of cranial and paravertebral ganglia is also impaired in these embryos. This study reveals that hypoplastic hindbrain development, identified by abnormal rhombomere morphology and persistent loss of ventral neuroepithelial stiffness, precedes exencephaly in Fuz ablated murine mutants, indicating that the gene Fuz has a critical function sustaining normal neural tube development and neuronal differentiation.

Theoretical Analysis of a Magnetic Shielding System Combining Active and Passive Modes.

Considering the magnetic shielding requirements of both geomagnetic field and 50 Hz power-line frequency in the complex working conditions of the power grid, an electromagnetic shielding system combining active and passive modes is proposed in this article. A three-dimensional Helmholtz coil with a magnetic shielding barrel nested inside is established by the COMSOL simulation tool, and the magnetic shielding efficiency of the system is analyzed. Comparing different materials, the simulation results indicate that permalloy alloy exhibits better shielding performance than pure iron and nickel materials. Additionally, the overall shielding efficiency of the shielding barrel increases linearly with the number of multiple layers. Under the combined active and passive electromagnetic shielding conditions, the system achieves a shielding efficiency of SE = 113.98 dB, demonstrating excellent performance in shielding both AC and DC interference magnetic fields. This study provides theoretical guidance for the construction of magnetic shielding systems in electromagnetic interference environment.

Fe doping strategy induces peroxymonosulfate activation by ZIF-67 through a non-radical reaction pathway with dominant 1O2 generation

Zeolitic Imidazole Framework-67 (ZIF-67), which can be large-scale synthesized under mild conditions, has been widely reported as a promising and efficient catalyst in peroxymonosulfate (PMS)-based advanced oxidation process. However, ZIF-67 typically activates PMS through a monotonous free radical reaction pathway, which limits its reusability, resistance to environmental interference, and degradation selectivity. In this study, controlled doping of Fe atoms into the lattice structure of ZIF-67 (FexCo1-x-ZIF) was employed, resulting in the nearly complete conversion of PMS to 1O2 through a unique non-radical pathway. Series of experiments, characterizations, and theoretical calculations were employed to elucidate the micro-level mechanism of PMS activation. The results indicate that the tetrahedral coordinated Co-N4 sites in ZIF-67 are transformed into unsaturated Co-N2 sites due to the competitive coordination effect upon the introduction of Fe. PMS adsorption is enhanced as both oxygen atoms bind simultaneously to the Co-N2 site, inducing the removal of a hydrogen atom from PMS and electron transfer from PMS to the Co-N2 site, thus facilitating the generation of 1O2. The 1O2-dominated non-radical degradation reaction enhances the excellent degradation performance, reusability, and environmental tolerance of the FexCo1-x-ZIF/PMS system under diverse natural water conditions, presenting significant prospects for practical applications.

Selective and ultrafast oxidation of multiple pollutants by biomorphic diatomite-based catalyst and stable catalytic Fenton-like membrane: Degradation behavior and mechanism analysis

Carbon-driven advanced oxidations show great potential in water purification, but regulating structures and properties of carbon-based catalysts to achieve ultrafast Fenton-like reactions remains challenging. Herein, a biomorphic diatomite-based catalyst (BD-C) with Si–O doping was prepared using natural diatomite as silicon source and porous template. The results showed that the metal-free BD-C catalyst exhibited ultrafast oxidation performances (0.95–2.58 min−1) towards a variety of pollutants in PMS-based Fenton-like reaction, with the Fenton-like activity of metal-free catalyst comparable to metal-based catalysts or even single-atom catalysts. Pollutants (e.g., CP, BPA, TC, and PCM) with electron-donating groups exhibited extremely low PMS decomposition with overwhelmed electron transfer process (ETP), while high PMS consumption was induced by the addition of electron-withdrawing pollutants (e.g., MNZ and ATZ), which was dominated by radical oxidation. The BD-C/PMS system also showed a high ability to resist the environmental interference. In-depth theoretical investigations demonstrated that the coordination of Si–O can lower the potential barrier of PMS activation for accelerating the generation of radicals, and also promote the electron transfer from pollutants to the BD-C/PMS complexes. In addition, BD-C was deposited onto a polytetrafluoroethylene membrane (PTFEM) with 100% of pollutants removal over 10 h, thereby revealing the promising prospects of utilizing BD-C for practical applications.

Experimental study on quantitative characterization of cavitation internal structure based on the distribution of void fraction in pressure relief valve

Accurately quantify the complex structure of cavitation in pressure relief valve by experiment remains a challenging task. In this research, a quantitative method for assessing the vapor volume fraction in cavitation images by void fraction experimentally is proposed, this method is less affected by environmental interference and could achieve rapid and accurate characterization of cavitation. The results show that void fraction effectively represents changes in vapor volume fraction under different conditions. The influence of the grid division numbers on the vapor volume fraction is investigated, the optimal number of grid division is 93 in X direction, with a misjudgment number of 130 and a misjudgment rate of 1.62 %. The effects of grid numbers on vapor volume fraction under different cavitation image pixels is also discussed, the optimal number of grid division is 112 in flow direction, with a misjudgment number of 144 and a misjudgment rate of 1.21 %. Through processing the vapor volume fraction image using the Proper Orthogonal Decomposition (POD) method under different cavitation numbers, the results show that the cavitation number has an important influence on the proportion of re-entrant jet area and cavitation collapse area, when the cavitation number decreases to 0.047, the proportion of re-entrant jet area and cavitation collapse area achieve the maximum that 6.41 % and 8.68 %, respectively. Such quantitative analysis of the cavitation degrees under various working conditions can contribute to a better understanding of the impact of cavitation on valve dynamics mechanisms.

Dynamic stabilisation of magnetic fields measured inside a magnetically shielded room using an external coil system *

Magnetoencephalography (MEG) system based on optically pumped magnetometers (OPMs) requires a magnetically shielded room (MSR) to establish a stable near-zero field environment. Affected by external environmental electromagnetic interference, the magnetic noise in the MSR will become very severe. In order to overcome this problem, this paper proposes a method for dynamic stabilisation of magnetic fields measured inside a MSR using an external coil system. Firstly, the field form of the external compensation coil was analysed by taking the AC characteristics of the material into consideration. Then, the linear characteristic of the control system is studied and a high performance magnetic noise suppression controller is designed based on the environment noise characteristics. Finally, simulation and experimental are carried out through a self-developed 1250 mm × 1250 mm × 2100 mm MSR, which indicates that the proposed method can effectively suppress dynamic magnetic fluctuation and noise.

Ship Global Traveling Path Optimization via a Novel Non-Dominated Sorting Genetic Algorithm

Due to the intensification of economic globalization and the impact of global warming, the development of methods to reduce shipping costs and reduce carbon emissions has become crucial. In this study, a multi-objective optimization algorithm was designed to plan the optimal ship route for safe cross-ocean navigation under complex sea conditions. Based on the traditional non-dominated sorting genetic algorithm, considering ship stability and complex marine environment interference, a non-dominated sorting genetic algorithm model considering energy consumption was designed with the energy consumption and navigation time of the ship as the optimization objectives. The experimental results show that although the proposed method is 101.23 nautical miles more than the large ring route, and the voyage is increased by 10.1 h, the fuel consumption is reduced by 92.24 tons, saving 6.94%. Compared with the traditional genetic algorithm, the voyage distance and time are reduced by 216.93 nautical miles and 7.5 h, and the fuel consumption is reduced by 58.82 tons, which is almost 4.54%. Through experimental verification, the proposed model can obtain punctual routes, avoid areas with bad sea conditions, reduce fuel consumption, and is of great significance for improving the safety and economy of ship routes.

Relay selection in Underwater Acoustic Sensor Networks for QoS-based cooperative communication using game theory

Underwater Acoustic Sensor Networks (UASN) plays a crucial role in monitoring and transmitting environmental information for marine resource exploration. However, underwater communication faces significant challenges such as signal attenuation, noise interference, and unstable underwater environments. Moreover, with the deployment of a large number of nodes, inefficient resource allocation strategies in cooperative communication lead to strained communication resources as well as increased energy consumption. This paper introduces a novel relay selection approach based on game theory that takes into account both competition and cooperation among nodes. By building a potential game model and a utility function, we derive the optimal relay selection strategy that can enhance system Quality of Service (QoS). We also define conditions for the feasibility of pure strategy Nash Equilibria (NE) and develop a low-complexity QoS-based Relay Selection Iterative Algorithm (QoS-RSIA) to procure feasible pure strategy NE for the designed game. Through comparative analysis with other relay selection algorithms, simulation results show that the proposed method not only enhances system throughput and average spectral efficiency by at least 8.3%, but also significantly reduces time complexity compared to exhaustive search algorithms.

A Clutter Removal Method Based on the F-K Domain for Ground-Penetrating Radar in Complex Scenarios

Ground-penetrating radar (GPR) is a classic geophysical exploration method that utilizes the emission and reception of electromagnetic waves to non-destructively detect target objects in the target medium. It has been widely applied in various fields such as pipeline detection, cavity detection, and rebar detection. However, GPR systems are susceptible to environmental clutter interference, which poses challenges for data interpretation and subsequent processing. In this paper, the separability of clutter and target signal in the frequency-wavenumber (F-K) domain is validated through modeling, leading to the proposal of a comprehensive clutter removal method based on the F-K domain for complex scenarios. The direct coupling wave is initially eliminated by applying a peak matching mean subtraction filter, which avoids the artifacts. Subsequently, the F-K domain transformation is performed and surface clutter undulations are effectively removed using a method based on singular value decomposition and k-means clustering. Finally, an angle filter with Gaussian tapering at the edges is designed based on physical models to efficiently eliminate linear interference without undesired ringing interference. The commonly used clutter removal algorithms, including mean subtraction (MS), singular value decomposition (SVD), robust principal component analysis (RPCA), and traditional F-K filtering methods, are compared with the proposed algorithm on both the numerical simulated data and actual GPR data. The results from visual and quantitative analysis confirm that our proposed method is more effective than current commonly used clutter suppression algorithms. We have successfully enhanced the Signal-to-Clutter Ratio (SCR) of the GPR data, resulting in an Improvement Factor (IF) of 30.63 dB, 23.59 dB, and 30.60 dB for simulated data, experimental data, and TU1208 public data, respectively. The detection capability of buried targets is enhanced, thereby establishing a solid foundation for subsequent data interpretation and target identification.

Ratiometric electrochemical immunoassay based on 2D Co/Fe MOF decorated with toluidine blue and Fc-labeled Schiff base for accurate assay of alpha-fetoprotein in clinical serum

The high level of alpha-fetoprotein (AFP) expression is closely related to hepatocellular carcinoma (HCC). Herein, a dual signal ratiometric electrochemical immunosensor based on chitosan-ferrocenecarboxaldehyde-spindle gold (Chit-Fc-SAu) and Co/Fe metal-organic framework-toluidine blue/polydopamine (Co/Fe MOF-TB/PDA) was proposed for quantitative analysis of AFP. Specifically, Chit-Fc-SAu worked as a substrate to trap more primary antibodies (Ab1) generating the first electrochemical signal from Fc. Thanks to the large specific surface area, the synergistic and electronic effects of Co/Fe MOF nanosheets, and the rich functional groups of PDA, Co/Fe MOF-TB/PDA could load more secondary antibodies (Ab2) and signal molecules (TB) providing another amplified electrochemical signal. In the presence of AFP, Ab1-AFP-Ab2 formed a sandwich structure, and as the AFP concentration increased, the peak current ratio of TB to Fc (ITB/IFc) also increased. The dual signal ratiometric strategy can avoid environmental signal interference and achieve signal self-calibration, thereby improving the accuracy and reproducibility of detection. After a series of exploration, this self-calibrated ratiometric immunosensor exhibited a wide linear range (0.001−200 ng mL−1), a low detection limit (0.34 pg mL−1), and good repeatability. When applied to the assay of clinical serum samples, the detection results of ratiometric sensor were consistent with that of commercial electrochemiluminescence (ECL) immunoassay, significantly superior to that of non-ratiometric sensor. The self-calibrated strategy based on ratiometric sensor helps to improve the accuracy of AFP in clinical diagnosis.

Electrically‐Reconfigurable Extremely‐High Density Physical Unclonable Cryptographic Keys Based on Aurivillius Ferroelectrics

AbstractIn the era of information, emerging hardware security primitives, i.e., hardware random number generators and physically unclonable functions, hold profound promise for the establishment of exceptionally secure network information systems. Nonetheless, these potential solutions are constrained by inherent drawbacks, including the need for additional error correction circuits or algorithms, heightened susceptibility to environmental interference, and limited data density. This paper demonstrates the extremely high‐density, physical‐unclonable cryptographic keys by harnessing stochastically ferroelectric domain polarizations in Aurivillius ferroelectric material CaBi2Nb2O9 (CBNO). Ferroelectric polarization of CBNO nanodomains can serve as a robust source of physical unclonable entropy. Through the application of high‐speed voltage pulses, the inherent randomness of ferroelectric polarizations can be enhanced, thereby yielding cryptographic keys characterized by remarkable uniformity, uniqueness, and negligible environmental sensitivity. More importantly, the voltage pulse operations facilitate the configurability of the ferroelectric cryptographic keys with no correlation. An unprecedented data density of 435 Gbit µm−2 is therefore achievable with a miniaturized CBNO of less than 1 um2. Notably, the reconfigured keys successfully pass the National Institute of Standards and Technology random number tests without requiring additional post‐processing steps. Emanating from the inherent attributes of the material, these high‐density ferroelectric keys confer intrinsic advantages to information security, evincing substantial resistance to duplication or cloning.

The control method for ship tracking when navigating through narrow and curved sections

A track control method is proposed to solve the problem of manual steering difficulties in narrow and curved sections. The performance of a ship heading controller is affected by various factors, such as delay of the ship steering gear, external environmental disturbances, and variations in ship's speed. Firstly, a Smith optimal PID heading controller is designed. Then, a speed control method based on dichotomous method is proposed to accurately control ship speed and improve the rudder efficiency. Finally, a track control method based on model prediction and feedback compensation is proposed, considering the coupling relationship between course control and speed control. The Lantang Channel is selected as the experimental water area, and comparative simulation experiments are carried out under static water and environmental disturbance. The pre-planned route information is taken as input, and the target course at the current moment is calculated by the guidance method. The target course is used as the input of the course controller to calculate the rudder angle to control the ship's motion, and the new ship position is compared with the planned route information. The above steps are repeated to track the planned route. In order to make the ship pass the curve smoothly, the target speed of the control point is preset according to the sailing convention of decelerating first and accelerating later, and the variable speed time point and variable telegraph are solved by the dichotomy. The results are as follows: (1) The maximum track deviation and speed deviation of the Smith-OP PID are 2 m and 0.21 kn, respectively, without considering environmental interference. The maximum track deviation of the traditional PID is 8 m. (2) The maximum track deviation and speed deviation of the Smith-OP PID are 25 m and 0.27 kn, respectively, under the set environmental interference. The maximum track deviation of the traditional PID is 49 m. (3) The track control method is consistent with the practical practices of "deceleration before acceleration" and "hanging high and taking low". In summary, the track control method can be well applied to narrow and curved sections, and provide a theoretical basis for subsequent research on autonomous navigation and intelligent ships.

Magnetic resonance imaging at low signal-to-noise ratio, electromagnetic interferences and hardware imperfections

The problems of magnetic resonance (MR) scanning at low signal-to-noise ratio, electromagnetic interference (EMI) and equipment imperfections are considered. Attention is paid to pulsed interference, as well as interference represented by continuous radiation, the frequency and amplitude of which vary chaotically over time. Manifestations of hardware and software imperfections leading to distortions of MR images were noted. These include, in particular, failures in digital signal processing (DSP) and direct current (DC) offset correction algorithms. These imperfections can be caused by various reasons - software errors, poor shielding, aging equipment, etc. Recently, articles have appeared that recommend detecting EMI based on the analysis of electromagnetic coupling between sources and spatially distributed receiving coils connected to a multi-channel receiver, and based on this information, eliminating EMI from MRI data. If such resources are not available when conducting MRI studies, then simple procedures described in this paper can be used to minimize or suppress interference. They involve planning the research time taking into account the dynamics of the interference environment, setting an extremely narrow receiver bandwidth, as well as editing k-space points distorted by interference and hardware factors. The issues of optimizing scanning parameters are considered, in particular those related to setting the matrix size and sampling frequency, as well as practical aspects of data processing, in particular the use of apodization in k-space. The methods described in the article were used for 23Na MRI of the human head on a clinical 0.5 T scanner, the adaptation of which for detecting sodium signals was reduced only to modification of the proprietary receiving coil. It has been shown that with standard processing of MR data, images are uninformative. But if the k-space data is edited before the Fourier transform, then the information content of sodium images becomes comparable to proton ones if scans are performed with the same spatial resolution and similar tissue contrast.

Discovery and learning of feature geometry in underwater acoustic sensing: Case studies and challenges in active sonar applications

Interpretable artificial intelligence (AI) and related machine learning (ML) techniques are gaining popularity for underwater acoustic sensing applications. However, interpretability of machine learnt features poses a fundamental challenge to successful application of these powerful data science techniques to underwater acoustics. Autonomous sonar target recognition is especially interesting from the data science perspective as beyond target-specific features, a sonar ping response typically includes significant interference from the environment, e.g., clutter, multipath scattering, etc. Such environmental interference, resulting from complex, dynamic, unpredictable, and often unknown factors, manifest as identifiable structures in acoustic color or alternate multi-dimensional feature representations that can lead to machine learning and classification errors. In this talk, we will discuss these challenges commonly encountered in underwater acoustics using case studies from field experiments in active sonar target recognition. Some results will also include physics-driven simulations in this domain to provide robust ground truths. In particular, we will posit how braided feature geometry and its representation, as well geometry of overlap between features can render a sonar target feature informative, explainable, and discoverable. [Work funded by the ONR Grant Nos. N000142112420 and N000142312503 and DoD Navy (NEEC) Grant No. N001742010016.]

Correction of antenna far-field image error data based on spherical scanning

The antenna pattern is an important graph for measuring antenna performance, and various parameters of the antenna can be observed from the antenna pattern. The method of obtaining antenna far-field pattern through near-field measurement technology and near-to-far-field transformation algorithm has the advantages of being free from environmental interference, low cost, and fast speed, making it an effective research method. In the process of spherical near-field measurement, it is necessary to ensure the stable operation of the probe used for measurement. If one of the probe channels is abnormal, it will have a serious impact on the measurement of the antenna pattern. This article proposes an algorithm for repairing near-field measurement data through Newton interpolation based on spherical scanning. The feasibility of the proposed method is verified by using the HFSS simulation data and the measured data of the microwave anechoic chamber. It is proved that the algorithm proposed in this paper has obvious effect on improving the accuracy of antenna pattern.

DOA Estimation of Broadband Sources Using Dimension-Reduced Matrix Filter with Deep Nulling in a Strong Interference Environment

To enhance the performance of the adaptive matrix filter with nulling, a dimension-reduced matrix filter with deep nulling (DR-MFDN) is proposed in this paper and used for direction of arrival (DOA) estimation of broadband sources in a strong interference environment. Consider the change of noise after filtering, the gaussian matrix is used for prewhitening to prevent the noise from converting into colored noise without affecting the response characteristics of the beam-space. Finally, the multiple measurement vector orthogonal matching pursuit (MOMP) algorithm is used for DOA estimation of weak targets after filtering, and the simulation results show a better DOA estimation performance compared to the OMP and MUSIC algorithms with short snapshots.

Adaptive multipole models of optically pumped magnetometer data.

Multipole expansions have been used extensively in the Magnetoencephalography (MEG) literature for mitigating environmental interference and modelling brain signal. However, their application to Optically Pumped Magnetometer (OPM) data is challenging due to the wide variety of existing OPM sensor and array designs. We therefore explore how such multipole models can be adapted to provide stable models of brain signal and interference across OPM systems. Firstly, we demonstrate how prolate spheroidal (rather than spherical) harmonics can provide a compact representation of brain signal when sampling on the scalp surface with as few as 100 channels. We then introduce a type of orthogonal projection incorporating this basis set. The Adaptive Multipole Models (AMM), which provides robust interference rejection across systems, even in the presence of spatially structured nonlinearity errors (shielding factor is the reciprocal of the maximum fractional nonlinearity error). Furthermore, this projection is always stable, as it is an orthogonal projection, and will only ever decrease the white noise in the data. However, for array designs that are suboptimal for spatially separating brain signal and interference, this method can remove brain signal components. We contrast these properties with the more typically used multipole expansion, Signal Space Separation (SSS), which never reduces brain signal amplitude but is less robust to the effect of sensor nonlinearity errors on interference rejection and can increase noise in the data if the system is sub-optimally designed (as it is an oblique projection). We conclude with an empirical example utilizing AMM to maximize signal to noise ratio (SNR) for the stimulus locked neuronal response to a flickering visual checkerboard in a 128-channel OPM system and demonstrate up to 40 dB software shielding in real data.

Impact detection method based on the ratio of mechanoluminescence intensity

This paper proposes an impact detection method based on the ratio of mechanoluminescence (ML) intensity, as an improvement over traditional methods that rely on absolute ML intensity. The study utilized two types of ML materials, ZnS:Cu2+ and ZnS:Mn2+, which were physically mixed and embedded them in polydimethylsiloxane (PDMS) to fabricate a flexible film sensor. A drop ball experimental setup was constructed, and the ML spectrum of the film sensor under the impact of a small ball was collected through a spectrometer. This established the relationship between impact velocity and the ratio of ML intensities. Experiments proved that the distance of spectral collection has a minimal effect on the measurement results of this method. Compared to traditional methods based on absolute ML intensity, this method has stronger resistance to environmental interference and better applicability. This new impact detection method based on the ratio of ML intensities has broad application prospects in future engineering applications, safety assessments, and material testing.

A comprehensive analysis of gesture recognition systems: Advancements, challenges, and future direct

Gesture recognition emerges as a potent avenue for human-computer interaction, harnessing mathematical algorithms to interpret gestures. It promises to surpass text-based or graphical interfaces, enabling touchless device control through simple gestures. Our review of 7 papers encompassing various fields and methods underscores its diverse applications. Challenges persist, such as distinguishing genuine user intent from accidental actions amid environmental interference. Creating a universal EMG pattern recognition model demands intricate individual pre-training. Sensor-based gesture recognition grapples with real-world dynamics, necessitating adaptable models that discern user intent from non-intent actions. Addressing these gaps holds the key. Adaptable models and personalized approaches can enhance robustness and accuracy across applications, surmounting challenges in the gesture interaction technology realm.

Method for Fault Diagnosis of Track Circuits Based on a Time–Frequency Intelligent Network

In response to the limitations posed by noise interference in complex environments and the narrow focus of existing diagnosis methods for jointless track circuit faults, an innovative approach is put forward in this study. It involves the application of the continuous wavelet transform (CWT) for signal preprocessing, along with the integration of a deep belief network (DBN) and a genetic algorithm (GA) to improve the least-squares support vector machine (LSSVM) model for intelligent time–frequency fault diagnosis. Initially, the raw induced voltage signals are transformed using continuous wavelet transformation resulting in wavelet time–frequency representations that combine temporal and spectral information. Subsequently, these time–frequency representations are fed into the deep belief networks, which perform semi-supervised dimensionality reduction and feature extraction, thereby uncovering distinct fault characteristics in the track circuit. Finally, the genetic algorithms are employed to improve the kernel function and penalty factor parameters of the least-squares support vector machine, thus establishing an optimal DBN-GA-LSSVM diagnostic model. Experimental validation demonstrates the effectiveness of the proposed time–frequency intelligent network model by leveraging the advantages of deep belief networks in hierarchical feature extraction and the superior performance of the least-squares support vector machine in addressing high-dimensional pattern recognition problems with limited samples. The achieved accuracy rate on the testing dataset reaches an impressive 99.6%. Consequently, this comprehensive approach provides a viable solution for data-driven track circuit fault diagnosis.