Frequency Interference Research Articles

Effect of Mixing Methods and Black Conductive Fillers on Properties of Natural Rubber Composites

Carbon black, graphite, carbon nanofibers, carbon nanotubes, and metal fillers increase composite conductivity in natural rubber, which is electrically insulating. Depending on dispersion, conductive filler lowers insulating material resistivity. These materials are frequently used for electromagnetic/radio frequency interference (EMI/RFI) shielding, conductive flexible seals gaskets, and conductive mats used to prevent electrostatic damage to electronic devices. These elastomers could be used to make flexible solar cells or mechanical-to-electricity devices. Temperature, mixing time, shear rate, and cross-linking during vulcanization affect rubber electrical conductivity of composite. To study shear rate effects, vulcanizate of Natural Rubber-based composites filled with carbon black, millable carbon fiber powder, and synthetic graphite powder was prepared by open mixing (two roll mill) and close mixing (internal mixer). We compared how shear rate affects cure, stress-strain, and volume resistivity of conductive filler-based Natural Rubber composites. Increment in clearance of two roll mill during addition of rubber additives along with rubber of reduced the shearing force resulted in less dispersive and distributive mixing and stagnant points due to band formation on roll surface compared to intermix where compound movement had no stagnation point and long wings pushed material axially and two nogs pushed material in other chamber. Compared to two roll mill samples, the compound reached every point of the mixing chamber for best homogeneity, reducing cure time and improving stress-strain and volume resistivity.

Agile manipulation of the time-frequency distribution of high-speed electromagnetic waves

Controlling the temporal evolution of an electromagnetic (EM) wave’s frequency components, the so-called time-frequency (TF) distribution, in a versatile and real-time fashion remains very challenging, especially at the high speeds (> GHz regime) required in contemporary communication, imaging, and sensing applications. We propose a general framework for manipulating the TF properties of high-speed EM waves. Specifically, the TF distribution is continuously mapped along the time domain through phase-only processing, enabling its user-defined manipulation via widely-available temporal modulation techniques. The time-mapping operations can then be inverted to reconstruct the TF-processed signal. Using off-the-shelf telecommunication components, we demonstrate arbitrary control of the TF distribution of EM waves over a full bandwidth approaching 100 GHz with nanosecond-scale programmability and MHz-level frequency resolution. We further demonstrate applications for mitigating rapidly changing frequency interference terms and the direct synthesis of fast waveforms with customized TF distributions. The reported method represents a significant advancement in TF processing of EM waves and it fulfills the stringent requirements for many modern and emerging applications.

Emotional awareness and expression difficulties in relation to pain experiences in people with brain injury and chronic pain: preliminary investigation

ABSTRACT Objectives Preliminary examination of emotional awareness/expression relationships with pain in people with traumatic brain injury (TBI) and chronic pain (CP) and exploration of psychological factors as mediators or moderators of these relationships. Methods Cross-sectional study in adults (N = 59) with chronic TBI and CP using Toronto Alexithymia Scale-20 Difficulty Identifying and Describing Feelings subscales; Ambivalence over Emotional Expressiveness Questionnaire; Emotional Approach Coping Scale; PROMIS Pain Intensity and Pain Interference scales, Michigan Body Map (pain widespreadness); headache frequency; Pain Catastrophizing Scale; Brief Symptom Inventory-18 (psychological distress), and Post-traumatic Stress Checklist-Civilian. Results Difficulty Identifying Feelings was positively associated with pain intensity, pain interference, and headache frequency. Difficulty Describing Feelings was positively correlated with pain interference and headache frequency. Emotional Approach Coping was inversely correlated with headache frequency. Emotional awareness/expression relationships with pain outcomes were mediated by Pain Catastrophizing; Difficulty Describing Feelings relationships with Pain Interference and headache frequency were mediated by psychological distress; and Difficulty Describing Feelings associations with Pain Interference were mediated by post-traumatic stress. No moderators were identified. Conclusion These preliminary findings suggest that emotional awareness/expression is linked to pain in adults with TBI and CP, which may be connected via pain catastrophizing and psychological distress. If longitudinal studies with larger samples produce similar findings, researchers should explore training emotional awareness/expression for possible pain management after TBI.

Research on a multi-source rfi mitigation algorithm using a reference antenna array

Abstract We propose a multi-source RFI (Radio Frequency Interference) mitigation method based on a reference antenna array to address the challenge of RFI from multiple directions in radio observation equipment. It introduces a sampling point correction technique using a multi-channel parallel cross-correlation computation method, enhancing the effectiveness of frequency domain adaptive RFI fast mitigation algorithms. The design implements an RFI component detection method based on cross-correlation coefficient thresholds to effectively reduce new interference frequency components introduced by the reference antenna array. Simulated RFI signals and baseband signals of pulsar J0332+5434 observed by the NanShan 26-meter Radio Telescope (NSRT) were used to test the algorithm proposed in this paper. Simulation results demonstrate that the simulated radio telescope signals after RFI mitigation closely match the original pulsar data in profile and phase, confirming the effectiveness of the proposed method.

Segmenting RFI Using Meta’s Segment Anything Model

Radio Frequency Interference (RFI), radio signals from electronics, permeates through astronomical observations using radio telescopes. The interference results in lower sensitivity and higher noise when present. Existing methods, such as TFCrop and RFlag, flag RFI to prevent the contamination of observations. However, these approaches are partially automatic and require manual input to flag RFI. We explore artificial intelligence methods using Meta’s Segment Anything Model (SAM) and its parameter space in segmenting RFI. We have developed an open-source pipeline called SamRFI, which segments and flags RFI from measurement sets. We trained SAM’s masking weights from Hugging Face to fine-tune the model using Very Large Array data and synthetic waterfall plots injected with RFI. We define a metric, calcquality, to measure the performance of our models based on sensitivity and overflagging. Retrained SAM models identify RFI structure and allow for further exploration in RFI segmentation.

Bias and Deviation Map-Based Weighted Graph Search for NLOS Indoor RTLS Calibration

Recently, UWB-based technology providing centimeter-level accuracy has been developed and widely utilized in indoor real-time location tracking systems. However, location accuracy varies due to factors such as frequency interference, collisions, reflected signals, and whether line-of-sight (LOS) conditions are met, and it can be challenging to ensure high accuracy in specific environments. Fortunately, when anchor positions are fixed, the locations of large obstacles such as columns or furniture remain relatively stable, leading to similar patterns of positioning bias at specific points. This study proposes an algorithm that corrects inaccurate positioning to more closely reflect the actual location based on bias and deviation maps generated using natural neighbor interpolation. Initially, positioning bias and deviations at specific points are sampled, and bias and deviation maps are created using natural neighbor interpolation. During location tracking, the algorithm detects candidate clusters and determines the centroid to estimate the actual location by applying the bias and deviation maps to the measured positions derived through trilateration. To validate the proposed algorithm, experiments were conducted in a non-LOS (NLOS) indoor environment. The results demonstrate that the proposed algorithm can reduce the positioning bias of a UWB-based RTLS by approximately 71.34% compared to an uncalibrated system.

Galileo and BeiDou AltBOC Signals and Their Perspectives for Ionospheric TEC Studies.

For decades, GNSS code measurements were much noisier than phase ones, limiting their applicability to ionospheric total electron content (TEC) studies. Ultra-wideband AltBOC signals changed the situation. This study revisits the Galileo E5 and BeiDou B2 AltBOC signals and their potential applications in TEC estimation. We found that TEC noises are comparable for the single-frequency AltBOC phase-code combination and those of the dual-frequency legacy BPSK/QPSK phase combination, while single-frequency BPSK/QPSK TEC noises are much higher. A two-week high-rate measurement campaign at the ACRG receiver revealed a mean 100 sec TEC RMS (used as the noise proxy) of 0.26 TECU, 0.15 TECU, and 0.09 TECU for the BeiDou B2(a+b) AltBOC signal and satellite elevations 0-30°, 30-60°, and 60-90°, correspondingly, and 0.22 TECU, 0.14 TECU, and 0.09 TECU for the legacy B1/B3 dual-frequency phase combination. The Galileo E5(a+b) AltBOC signal corresponding values were 0.25 TECU, 0.14 TECU, and 0.09 TECU; for the legacy signals' phase combination, the values were 0.19 TECU, 0.13 TECU, and 0.08 TECU. The AltBOC (for both BeiDou and Galileo) SNR exceeds those of BPSK/QPSK by 7.5 dB-Hz in undisturbed conditions. Radio frequency interference (the 28 August 2022 and 9 May 2024 Solar Radio Burst events in our study) decreased the AltBOC SNR 5 dB-Hz more against QPSK SNR, but, due to the higher initial SNR, the threshold for the loss of the lock was never broken. Today, we have enough BeiDou and Galileo satellites that transmit AltBOC signals for a reliable single-frequency vTEC estimation. This study provides new insights and evidence for using Galileo and BeiDou AltBOC signals in high-precision ionospheric monitoring.

Measurement Campaign of Radio Frequency Interference in a Portion of the C-Band (4-5.8 GHz) for the Sardinia Radio Telescope.

Radio frequency interference (RFI) analysis is crucial for ensuring the proper functioning of a radio telescope and the quality of astronomical observations, as human-generated interference can compromise scientific data collection. The aim of this study is to present the results of an RFI measurement campaign in the frequency range of 4-5.8 GHz, a portion of the well-known C-band, for the Sardinia Radio Telescope (SRT), conducted in October-November 2023. In fact, this Italian telescope, managed by the Astronomical Observatory of Cagliari (OAC), a branch of the Italian National Institute for Astrophysics (INAF), was recently equipped with a new C-band receiver that operates from 4.2 GHz to 5.6 GHz. The measurements were carried out at three strategically chosen locations around the telescope using the INAF mobile laboratory, providing comprehensive coverage of all possible antenna pointing directions. The results revealed several sources of RFI, including emissions from radar, terrestrial and satellite communications, and wireless transmissions. Characterizing these sources and assessing their frequency band occupation are essential for understanding the impact of RFI on scientific observations. This work provides a significant contribution to astronomers who will use the SRT for scientific observations, offering a suggestion for the development of mitigation strategies and safeguarding the radio astronomical environment for future observational campaigns.

Research of the efficiency of Kravchenko weighting functions and combinations based on them in the problem of narrow band interference rejection

The influence of weight functions (WFs) of preliminary weighting on the quality of narrowband interference rejection based on direct and inverse discrete Fourier transforms has been studied. Classical and modern WFs are considered: Kravchenko, Kravchenko–Dolph–Chebyshev, Kravchenko–Gauss, Kravchenko–Bernstein–Rogozinsky. Quantitative estimates of efficiency were obtained – the coefficients of interference suppression and signal transmission, as well as their product – the total efficiency coefficient. Graphic examples of the behavior of the total efficiency coefficient depending on the interference frequency are shown. Estimates of the indicated quality indicators are presented when a fixed number of frequency samples are removed from the spectrum. The families of dependences of the probabilities of correctly performing a search for a spread spectrum signal on the “interference/signal” ratio when using modern WFs to weigh the implementations of an additive mixture of signal, interference and noise are presented. The significant advantage of modern WFs formed through combinations with Kravchenko functions in the problem of interference rejection has been demonstrated and confirmed.

NOMA Visible Light Communications with Distinct Optical Beam Configurations

Visible light communication (VLC) has been viewed as one promising candidate to mitigate the challenging spectrum crisis and radio frequency interference in future 6G mobile communications and networking. Due to the relatively limited baseband modulation bandwidth of VLC light sources—typically, light-emitting diodes—non-orthogonal multiple access (NOMA) techniques have been proposed and explored to enhance the spectral efficiency (SE) of VLC systems. However, almost all reported NOMA VLC schemes focus on well-discussed applications employing a Lambertian light beam configuration and ignore the potential applications with distinct non-Lambertian optical beam configurations. To address this issue, in this work, the performance of non-Lambertian optical beam configuration-based NOMA VLC is comparatively investigated for future 6G mobile networks. The numerical results demonstrate that, for a fundamental two-user application scenario with the far user located at the corner position, a maximum sum rate gain of about 15.6 Mbps could be provided by the investigated distinct non-Lambertian beam-based NOMA VLC, compared with the maximum sum rate of about 93.3 Mbps for the conventional Lambertian configuration with the same power splitting factor.

FAST Observations of Four Comets to Search for the Molecular Line Emissions between 1.0 and 1.5 GHz Frequencies

We used the Five-hundred-meter Aperture Spherical radio Telescope (FAST) to search for the molecular emissions in the L-band between 1.0 and 1.5 GHz toward four comets, C/2020 F3 (NEOWISE), C/2020 R4 (ATLAS), C/2021 A1 (Leonard), and 67P/Churyumov-Gerasimenko during or after their perihelion passages. Thousands of molecular transition lines fall in this low-frequency range, many attributed to complex organic or prebiotic molecules. We conducted a blind search for the possible molecular lines in this frequency range in those comets and could not identify clear signals of molecular emissions in the data. Although several molecules have been detected at high frequencies of greater than 100 GHz in comets, our results confirm that it is challenging to detect molecular transitions in the L-band frequency ranges. The non-detection of L-band molecular lines in the cometary environment could rule out the possibility of unusually strong lines, which could be caused by the masers or non-LTE effects. Although the line strengths are predicted to be weak, for FAST, using the ultra-wide bandwidth receiver and improving the radio frequency interference environments would enhance the detectability of those molecular transitions at low frequencies in the future.

Modeling and simulation study of acoustic response for dual-membrane capacitive MEMS microphone

Abstract As the micro-electro-mechanical systems (MEMS) technology matures, MEMS sensors have found widespread applications in mechatronics, robotics, and voice control. The high stability, high integration, and radio frequency interference resistance of MEMS microphones have rapidly led to their adoption in these domains, displacing electret condenser microphones.This article focuses on modeling and analyzing dual-membrane capacitive MEMS microphone, utilizing an lumped equivalent circuit model to calculate the microphone’s acoustic frequency response. Furthermore, the article employs the finite element method (FEM) to conduct a detailed analysis of the resonant frequency of the membrane, as well as to explore the effects of changes in the volume of the front and back chamber on the packaging performance of MEMS microphones. Finally, physical tests were conducted, and the simulation results of the two models showed considerable consistency with the curves obtained from the physical tests, verifying the accuracy of the models.

Radio frequency interference identification using dual cross-attention and multi-scale feature fusing

Radio astronomy plays a very important role in promoting scientific progress and unraveling the mysteries of the universe. However, radio telescopes are inevitably affected by radio frequency interference (RFI) when receiving radio signals, which leads to a reduction in data quality and has a serious impact on the formation of correct scientific conclusions. Therefore, it is essential to identify the RFI present in the observational data. In order to effectively identify RFI, improve the existing RFI identification methods that suffer from missed detections, and enhance the performance of RFI identification, this paper proposes a novel method that combines a dual cross-attention mechanism with multi-scale feature fusion. Experimental studies were conducted using the observational data from the 40-meter radio telescope at the Yunnan Astronomical Observatory of the Chinese Academy of Sciences. The proposed method achieved scores of 92.49%, 83.90%, and 87.99% in terms of precision, recall, and F1−score, respectively. It outperformed existing methods (U-Net, RFI-Net, R-Net6, RFI-GAN, EMSCA-UNet) in recall and F1−score, effectively reducing the occurrence of missed detections and improving the overall performance of radio frequency interference identification.

Study on the dynamic characteristics of the head cover-bolts-stay ring of a variable-speed pump-turbine

Abstract The design and operation of variable-speed pump-turbine units is more difficult than that of conventional units due to their adjustable speed, frequent start stop, and forward and reverse operation. Due to the development of variable-speed pump-turbines towards variable-speed, high head, and large capacity, the vibration instability caused by frequent switching between turbine and pump operating conditions is more prominent. The study on the dynamic characteristics of the head cover-bolts-stay ring system is an important guarantee for the long-term safe and stable operation of the units. This article uses the finite element method to conduct modal analysis on the head cover-bolts-stay ring system of a variable-speed pump-turbine. The natural frequency and mode shape are analyzed, and resonance analysis is carried out in combination with the dynamic and static interference characteristics of the variable-speed unit. The research results show that the dynamic and static interference frequency of the unit during the variable-speed process may be close to the natural frequency of the head cover-bolts-stay ring system, which needs to be paid attention to during operation. The results and findings in this article provide theory and practical basis for the research and design of high head, large capacity, and variable-speed pump-turbine units.

Radio Frequency Interference Detection and Automatic Modulation Recognition Based on Mask RCNN

The increasing complexity of wireless communication conditions presents noteworthy obstacles to the adaptability and flexibility of cognitive radio systems. The latter can alleviate the problem of spectrum shortages through proper spectrum management. However, cognitive radio has difficulty distinguishing between signals of interest and interference, which can be a general enemy since it is undesirable because it influences the signal's quality. Efficient detection and characterization of interference in wireless communication networks is critical for ensuring strong security. However, in this research, we use the Mask R-CNN methodology to present a new concept for automatic modulation recognition and radio frequency interference detection. Moreover, this algorithm can segment, detect, and recognize several types of interference that can affect wireless communication systems, such as chirp interference (CI), continuous wave interference (CWI), and multiple continuous wave interference (MCWI) within the signal of the interest (SOI), as well as the modulation kind present in the SoI. Overall, the combination of these distinct techniques can be very valuable in the field of signal processing, especially in anti-jamming strategies in wireless networks. Moreover, the proposed approach showcases exceptional performance in the validation dataset. For radio frequency interference detection, it achieves a mean average precision (mAP) of 0.946 and a mean average recall (mAR) of 0.954. For automatic modulation recognition, it attains a mAP of 0.898 and a mAR of 0.916.

Frequency of Pain, Its Severity and Interference in Patients Receiving Hemodialysis

Frequency of Pain, Its Severity and Interference in Patients Receiving Hemodialysis

Process deviation detection method for dry‐type air‐core reactor based on power factor frequency characteristics

AbstractTo address the problem that the process deviation of dry‐type air‐core reactors cannot be effectively detected, electrical parameters of reactor are numerically analysed at different frequencies. A new method of process deviation detection by the power factor frequency characteristic is proposed. Considering on‐site power frequency interference, a harmonic analysis method is proposed, which is based on the integer multiple of the power frequency cycle, to calculate the power factor. The results show that the resistance loss remains relatively constant at power frequency and gradually increases at higher frequencies with the number of deviation turns increases. The eddy current loss remains basically unchanged at different turn deviations and frequencies. The change laws of resistance loss and the resistance loss power factor are the same. In a double logarithmic coordinate system, resistance loss power factor changes from linear to non‐linear. The non‐linearity degree can be used to detect the process deviation. The test results are consistent with the theory, and the correctness of the detection method is verified.

Statistical analysis and effects of radio frequency interference in GPS signal quality in Thailand

The radio frequency interference (RFI) in global navigation satellite system (GNSS) signals has recently received much attention in the GNSS community because of frequent jamming issues. The carrier-to-noise density ratio (C/N0) is one of the common parameters to indicate the signal quality. In this work, we propose a real-time RFI analysis based on windowing and normalization of C/N0 observations. Specifically, the percentage of RFI values are analyzed based on the modified RFI detection. The steps to analyze the RFI levels (low, medium, high) are highlighted. In addition, we analyzed the occurrences of local RFI effects in areas surrounding the Suvarnabhumi International Airport as well as remote areas. We validate the modified RFI detection by using the GNSS reference stations at the urban, suburban, and outside the capital city in Thailand. The user positioning errors with the high (severe) RFI levels are investigated based on the single point positioning (SPP) and real-time kinematics (RTK). From the experimental simulations, the high RFI levels at the urban are higher than those at the suburban. As expected, the statistical analysis covering COVID-19 (2019 to 2023) shows that the high RFI levels in June 2023 (post COVID-19) are more than those in June 2020 and 2021 (lockdown COVID-19) by about twofold. Additionally, the SPP positioning errors with the medium/high RFI levels are clearly seen. There are more floating solutions in the RTK system in the year with more RFI presence.

Lightweight deep neural network for radio frequency interference detection and segmentation in synthetic aperture radar

Radio frequency interference (RFI) poses challenges in the analysis of synthetic aperture radar (SAR) images. Existing RFI suppression systems rely on prior knowledge of the presence of RFI. This paper proposes a lightweight neural network-based algorithm for detecting and segmenting RFI (LDNet) in the time-frequency domain. The network accurately delineates RFI pixel regions in time-frequency spectrograms. To mitigate the impact on the operational speed of the entire RFI suppression system, lightweight modules and pruning operations are introduced. Compared to threshold-based RFI detection algorithms, deep learning-based segmentation networks, and AC-UNet specifically designed for RFI detection, LDNet achieves improvements in mean intersection over union (MIoU) by 24.56%, 13.29%, and 7.54%, respectively.Furthermore, LDNet reduces model size by 99.03% and inference latency by 24.53% compared to AC-UNet.

Dynamic potential stochastic resonance for weak signal detection

To suppress the low-frequency interference noise, a dynamic potential stochastic resonance (DPSR) model is proposed in this paper for weak signal detection. The DPSR model introduces a single dynamic parameter k that simplifies parameter optimization. Its dynamic potential function can adaptively adjust to match noisy input signals. This model provides a new nonlinear model for triggering the SR phenomenon. Experimental results indicate that compared to the traditional methods that rely on clear interference frequency distributions to filter low-frequency components directly, the SR model offers greater flexibility and convenience. Unlike classical SR models, the proposed DPSR model demonstrates a 1.5 dB improvement in output performance for suppressing low-frequency interference. Therefore, the DPSR model not only robustly suppresses interference but also effectively enhances and detects characteristic signals in variant-noise environments. Application to sea trial signals highlights the superior performance of the DPSR model in significantly reducing low-frequency interference and improving target signal recognizability compared to other models.