Interference Environment Research Articles

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.

Frequency response of the non-polarization-maintaining depolarized sagnac interferometer based on maximum fringe contrast

The Sagnac Interferometer offers a promising solution to the speckle problem in interferometric detection due to its insensitivity to environmental low-frequency interference, making it well-suited for applications such as laser ultrasonic detection. Control of the polarization state of light is imperative for achieving high sensitivity in optical fiber interferometer and ensuring optimal quiescent operating conditions. This study employed the coherence matrix method to analyze the theory of optimal polarization control in a non-polarization-maintaining depolarized Sagnac Interferometer, encompassing the deduction of the stable π/2 initial phase bias and the maximum fringe contrast of 50% in theory. In experimental observations during PZT simulated large amplitude ultrasound experiments, the fringe contrast reached 48.4%. Furthermore, the system's amplitude-frequency response was evaluated using an electro-optics phase modulator, demonstrating its capability to detect high-frequency signals within 100 MHz These findings provide essential insights into optimizing the Sagnac Interferometer's performance for laser ultrasonic detection and its potential applications in high-frequency signal detection and ultrasonic amplitude measurement.

Fenton-like membrane reactor assembled by electron polarization and defect engineering modifying Co3O4 spinel for flow-through removal of organic contaminants

The application of Fenton-like membrane reactors for water purification offers a promising solution to overcome technical challenges associated with catalyst recovery, reaction efficiency, and mass transfer typically encountered in heterogeneous batch reaction modes. This study presents a dual-modification strategy encompassing electron polarization and defect engineering to synthesize Al-doped and oxygen vacancies (OV)-enriched Co3O4 spinel catalysts (ACO-OV). This modification empowered ACO-OV with exceptional performance in activating peroxymonosulfate (PMS) for the removal of organic contaminants. Moreover, the ACO-OV@polyethersulfone (PES) membrane/PMS system achieved organic contaminant removal through filtration (with a reaction kinetic constant of 0.085 ms−1), demonstrating outstanding resistance to environmental interference and high operational stability. Mechanistic investigations revealed that the exceptional catalytic performance of this Fenton-like membrane reactor stemmed from the enrichment of reactants, exposure of reactive sites, and enhanced mass transfer within the confined space, leading to a higher availability of reactive species. Theoretical calculations were conducted to validate the beneficial intrinsic effects of electron polarization, defect engineering, and the confined space within the membrane reactor on PMS activation and organic contaminant removal. Notably, the ACO-OV@PES membrane/PMS system not only mineralized the targeted organic contaminants but also effectively mitigated their potential environmental risks. Overall, this work underscores the significant potential of the dual-modification strategy in designing spinel catalysts and Fenton-like membrane reactors for efficient organic contaminant removal.

Automatic Tracking Based on Weighted Fusion Back Propagation in UWB for IoT Devices.

The global population is progressively entering an aging phase, with population aging likely to emerge as one of the most-significant social trends of the 21st Century, impacting nearly all societal domains. Addressing the challenge of assisting vulnerable groups such as the elderly and disabled in carrying or transporting objects has become a critical issue in this field. We developed a mobile Internet of Things (IoT) device leveraging Ultra-Wideband (UWB) technology in this context. This research directly benefits vulnerable groups, including the elderly, disabled individuals, pregnant women, and children. Additionally, it provides valuable references for decision-makers, engineers, and researchers to address real-world challenges. The focus of this research is on implementing UWB technology for precise mobile IoT device localization and following, while integrating an autonomous following system, a robotic arm system, an ultrasonic obstacle-avoidance system, and an automatic leveling control system into a comprehensive experimental platform. To counteract the potential UWB signal fluctuations and high noise interference in complex environments, we propose a hybrid filtering-weighted fusion back propagation (HFWF-BP) neural network localization algorithm. This algorithm combines the characteristics of Gaussian, median, and mean filtering, utilizing a weighted fusion back propagation (WF-BP) neural network, and, ultimately, employs the Chan algorithm to achieve optimal estimation values. Through deployment and experimentation on the device, the proposed algorithm's data preprocessing effectively eliminates errors under multi-factor interference, significantly enhancing the precision and anti-interference capabilities of the localization and following processes.

DWSTr: a hybrid framework for ship-radiated noise recognition

The critical nature of passive ship-radiated noise recognition for military and economic security is well-established, yet its advancement faces significant obstacles due to the complex marine environment. The challenges include natural sound interference and signal distortion, complicating the extraction of key acoustic features and ship type identification. Addressing these issues, this study introduces DWSTr, a novel method combining a depthwise separable convolutional neural network with a Transformer architecture. This approach effectively isolates local acoustic features and captures global dependencies, enhancing robustness against environmental interferences and signal variability. Validated by experimental results on the ShipsEar dataset, DWSTr demonstrated a notable 96.5\% recognition accuracy, underscoring its efficacy in accurate ship classification amidst challenging conditions. The integration of these advanced neural architectures not only surmounts existing barriers in noise recognition but also offers computational efficiency for real-time analysis, marking a significant advancement in passive acoustic monitoring and its application in strategic and economic contexts.

Implementation of Underwater Electric Field Communication Based on Direct Sequence Spread Spectrum (DSSS) and Binary Phase Shift Keying (BPSK) Modulation.

Stable communication technologies in complex waters are a prerequisite for underwater operations. Underwater acoustic communication is susceptible to multipath interference, while underwater optical communication is susceptible to environmental impact. The underwater electric field communication established based on the weak electric fish perception mechanism is not susceptible to environmental interference, and the communication is stable. It is a new type of underwater communication technology. To address issues like short communication distances and high bit error rates in existing underwater electric field communication systems, this study focuses on underwater electric field communication systems based on direct sequence spread spectrum (DSSS) and binary phase shift keying (BPSK) modulation techniques. To verify the feasibility of the established spread spectrum electric field communication system, static communication experiments were carried out in a swimming pool using the DSSS-based system. The experimental results show that in fresh water with a conductivity of 739 μS/cm, the system can achieve underwater current electric field communication within a 11.2 m range with 10-6 bit errors. This paper validates the feasibility of DSSS BPSK in short-range underwater communication, and compact communication devices are expected to be deployed on underwater robots for underwater operations.