Signal Frequency Research Articles

Multi-Frequency Microwave Sensing System with Frequency Selection Method for Pulverized Coal Concentration

The accurate measurement of pulverized coal concentration (PCC) is crucial for optimizing the production efficiency and safety of coal-fired power plants. Traditional microwave attenuation methods typically rely on a single frequency for analysis while neglecting valuable information in the frequency domain, making them susceptible to the varying sensitivity of the signal at different frequencies. To address this issue, we proposed an innovative frequency selection method based on principal component analysis (PCA) and orthogonal matching pursuit (OMP) algorithms and implemented a multi-frequency microwave sensing system for PCC measurement. This method transcended the constraints of single-frequency analysis by employing a developed hardware system to control multiple working frequencies and signal paths. It measured insertion loss data across the sensor cross-section at various frequencies and utilized PCA to reduce the dimensionality of high-dimensional full-path insertion loss data. Subsequently, the OMP algorithm was applied to select the optimal frequency signal combination based on the contribution rates of the eigenvectors, enhancing the measurement accuracy through multi-dimensional fusion. The experimental results demonstrated that the multi-frequency microwave sensing system effectively extracted features from the high-dimensional PCC samples and selected the optimal frequency combination. Filed experiments conducted on five coal mills showed that, within a common PCC range of 0–0.5 kg/kg, the system achieved a minimum mean absolute error (MAE) of 1.41% and a correlation coefficient of 0.85. These results indicate that the system could quantitatively predict PCC and promptly detect PCC fluctuations, highlighting its immediacy and reliability.

Meta‐analysis of the acoustic adaptation hypothesis reveals no support for the effect of vegetation structure on acoustic signalling across terrestrial vertebrates

ABSTRACTAcoustic communication plays a prominent role in various ecological and evolutionary processes involving social interactions. The properties of acoustic signals are thought to be influenced not only by the interaction between signaller and receiver but also by the acoustic characteristics of the environment through which the signal is transmitted. This conjecture forms the core of the so‐called “acoustic adaptation hypothesis” (AAH), which posits that vegetation structure affects frequency and temporal parameters of acoustic signals emitted by a signaller as a function of their acoustic degradation properties. Specifically, animals in densely vegetated “closed habitats” are expected to produce longer acoustic signals with lower repetition rates and lower frequencies (minimum, mean, maximum, and peak) compared to those inhabiting less‐vegetated “open habitats”. To date, this hypothesis has received mixed results, with the level of support depending on the taxonomic group and the methodology used. We conducted a systematic literature search of empirical studies testing for an effect of vegetation structure on acoustic signalling and assessed the generality of the AAH using a meta‐analytic approach based on 371 effect sizes from 75 studies and 57 taxa encompassing birds, mammals and amphibians. Overall, our results do not provide consistent support for the AAH, neither in within‐species comparisons (suggesting no overall phenotypically plastic response of acoustic signalling to vegetation structure) nor in among‐species comparisons (suggesting no overall evolutionary response). However, when considering birds only, we found weak support for the AAH in within‐species comparisons, which was mainly driven by studies that measured frequency bandwidth, suggesting that this variable may exhibit a phenotypically plastic response to vegetation structure. For among‐species comparisons in birds, we also found support for the AAH, but this effect was not significant after excluding comparative studies that did not account for phylogenetic non‐independence. Collectively, our synthesis does not support a universal role of vegetation structure in the evolution of acoustic communication. We highlight the need for more empirical work on currently under‐studied taxa such as amphibians, mammals, and insects. Furthermore, we propose a framework for future research on the AAH. We specifically advocate for a more detailed and quantitative characterisation of habitats to identify frequencies with the highest detection probability and to determine if frequencies with greater detection distances are preferentially used. Finally, we stress that empirical tests of the AAH should focus on signals that are selected for increased transmission distance.

A Radio Frequency Interference Screening Framework—From Quick-Look Detection Using Statistics-Assisted Network to Raw Echo Tracing

Synthetic aperture radar (SAR) is often affected by other high-power electromagnetic devices during ground observation, which causes unintentional radio frequency interference (RFI) with the acquired echo, bringing adverse effects into data processing and image interpretation. When faced with the task of screening massive SAR data, there is an urgent need for the global perception and detection of interference. The existing RFI detection method usually only uses a single type of data for detection, ignoring the information association between the data at all levels of the real SAR product, resulting in some computational redundancy. Meanwhile, current deep learning-based algorithms are often unable to locate the range of RFI coverage in the azimuth direction. Therefore, a novel RFI processing framework from quick-looks to single-look complex (SLC) data and then to raw echo is proposed. We take the data of Sentinel-1 terrain observation with progressive scan (TOPS) mode as an example. By combining the statistics-assisted network with the sliding-window algorithm and the error-tolerant training strategy, it is possible to accurately detect and locate RFI in the quick looks of an SLC product. Then, through the analysis of the TOPSAR imaging principle, the position of the RFI in the SLC image is preliminarily confirmed. The possible distribution of the RFI in the corresponding raw echo is further inferred, which is one of the first attempts to use spaceborne SAR data to ‌elucidate the RFI location mapping relationship between image data and raw echo. Compared with directly detecting all of the SLC data, the time for the proposed framework to determine the RFI distribution in the SLC data can be shortened by 53.526%. All the research in this paper is conducted on Sentinel-1 real data, which verify the feasibility and effectiveness of the proposed framework for radio frequency signals monitoring in advanced spaceborne SAR systems.

Measuring Microplastic Concentrations in Water by Electrical Impedance Spectroscopy

Plastics are vital for society, but their usage has grown exponentially and contributes to the growth of pollution worldwide. The World Health Organization, WHO, already reported that microplastics (MPs) are found everywhere, in waste and fresh water, and in the air and soil. Regarding water effluents, waste-water treatment plants only minimize the problem, trapping only larger size particles. In contrast, smaller ones remain in oxidation ponds or sewage sludges, or are even released to aquifers environment. Classic procedures for MPs detection are still quite laborious, and are usually conducted off-line, involving several steps and expensive equipment. Electrical Impedance Spectroscopy, EIS, is a technique that allows the analysis of a system’s electrical response, yielding helpful information about its domain-dependent on physical-chemical properties. Due to the superficial electronegativity of MPs’ particles, EIS may allow to attain the purpose of the present work: to provide a fast and reliable method to detect/estimate MPs’ concentration in water effluents. Among the most common microplastics are Polyethylene, PE, and Polyvinyl Chloride, PVC. Using the developed setup and experimental data collection methodology, the authors could differentiate between MPs’ suspensions containing the same concentration of the different evaluated MPs, PVC and PE, and assess PVC concentration variation, in the interval between 0.03 to 0.5 g (w/w), with an error, estimated based on the obtained impedance modulus, around or below 3% for the entire stimulus signal frequency range (from 100 Hz to 40 MHz) for the PVC particles.

Two-Dimensional Differential Positioning with Global Navigation Satellite System Signal Frequency Division Relay Forwarding to Parallel Leaky Coaxial Cables in Tunnel

To address the issue of GNSS receivers being unable to function properly in tunnels due to the loss of Global Navigation Satellite System (GNSS) signals, this paper proposes a two-dimensional differential positioning system for tunnel environments based on dual leaky coaxial (LCX) cables with GNSS signal frequency relay forwarding. The system receives mixed GNSS signals from open environments and utilizes the frequency selection capabilities of the MAX2769E chip to separate and generate radio frequency signals at different frequencies corresponding to GPS, BDS, and GLONASS. These signals are then used to drive three ports of the LCX cables, which are laid in parallel within the tunnel. By leveraging the uniform radiation characteristics of the LCX cables, stable GNSS signal coverage is achieved throughout the tunnel. On the receiving end, the GNSS receiver achieves two-dimensional positioning by utilizing inter-satellite pseudorange differences and reference point error correction. The simulation results indicate that the dual T-shaped radiating LCX cables configuration offers excellent positioning accuracy and noise resistance, achieving meter-level positioning accuracy in tunnel environments.

Bearing Electrical Erosion Signal Detection Technique Based on Current Signal Analysis for Electric Motors

Abstract In modern industries, enhancing the efficiency and performance of electric motors is a critical requirement. Pulse width modulation (PWM) inverters utilized to enhance the energy efficiency of electric motors generate complex shaft voltages and bearing currents, leading to bearing electrical erosion. This study proposes a new method for detecting high-frequency (HF) circulating current and electric discharge machining (EDM) current signals in bearings for electric motors. The proposed method utilizes common mode voltage (CMV) and bearing current data, analyzing the relationship between these signals. Subsequently, it applies filtering techniques and differentiation for signal preprocessing. The Interquartile Range (IQR) method is used to detect outliers, classify HF circulating current and EDM current signals, and perform time-series data clustering to determine the occurrence frequency and timing of EDM signals. Finally, the implementation outcomes of the proposed method are validated, and its classification efficacy is evaluated and benchmarked against established methodologies through a comprehensive performance analysis. The proposed technique is anticipated to be applicable to the maintenance and prediction of modern electric motors in future developments, contributing to enhanced durability and reliability.

Characterizing EEG signal dynamics in healthy, seizure-free, and seizure states using the chaos decision tree algorithm

Abstract Epilepsy is a multifaceted neurological condition marked by repetitive seizures that arise from irregular electrical activity in the brain. To understand this condition, a thorough examination of brain signals captured in different states is needed. In order to examine the dynamic behavior of brain signals in three different conditions: healthy, seizure-free, and seizure periods, this study uses the chaos decision tree algorithm. The findings show notable variations in these situations’ dynamics. Chaos is evident during seizure moments, showing extremely chaotic activity. The signals mostly exhibit stochastic behavior in the healthy condition, which is consistent with typical brain dynamics. It is noteworthy that an intermediate state exhibiting a blend of stochastic and chaotic signal dynamics is exhibited throughout the seizure-free time. Furthermore, the research shows that the frequency of chaotic signals rises with increasing proximity to the epileptogenic zone. These discoveries clarify the complex nature of epilepsy and offer insightful information about the dynamic properties of brain signals in various stages, aiding in improved understanding and potential diagnostic approaches.

Research on impedance measurement based on Gallium Nitride Transistor

Abstract Impedance measurement has broad applications in fields such as electrochemistry, biology, medicine, and food safety. Typically, frequency response analyzers are used for small signal disturbance measurements, but they come with drawbacks such as high cost, large size, and limited excitation capabilities. To address the need for wide-band, large-signal, and portable impedance measurement in electrochemical applications, a novel scheme based on a Gallium Nitride device excitation circuit and amplitude-phase discrimination module is proposed. A multiphase cascade frequency-doubling buck circuit is used to inject disturbance signals into the impedance under test, and impedance parameters are obtained using discrete fourier transformafter signal sampling. By varying the frequency of the disturbance signal, impedance information across different frequencies can be captured. In this approach, the amplitude-phase discrimination module replaces the frequency response analyzer, reducing the overall size of the measurement system, while the power device-based excitation circuit enables large signal disturbances.

Turning four-wave mixing on and off in elliptically birefringent fibers via narrow frequency detuning

Controlling four-wave mixing (FWM) is vital for several applications, including fiber optical communication, optical signal processing, optical amplification, and frequency generation. This paper presents a novel, to our knowledge, approach to control unidirectional FWM in elliptically birefringent fibers. By leveraging the frequency-dependent polarization eigenmodes of these fibers and detuning the optical frequency of one of the pump fields by a few megahertz, we can turn the FWM interaction on and off, thus controlling the generation of signal and idler fields. Moreover, this approach allows us to turn off the FWM interaction at any desired frequency, enabling all-optical switching and narrowband filtering applications.

Optimization algorithm of UAV‐assisted federated learning resources based on wireless energy harvesting

AbstractFederated learning (FL) enhances data privacy and security by enabling multiple terminals to collaboratively train a global model without transferring data to a central location. To tackle the challenges of limited terrestrial communication resources and device energy in FL, a UAV‐assisted federated learning and energy collection resource optimization algorithm is proposed. This algorithm harvests the working energy of FL terminals from radio frequency signals transmitted by the UAV via wireless energy collection. Considering energy causality and limited resources, we formulate a problem aimed at minimizing the completion time of FL training tasks. As this problem is NP‐hard, we initially employ a greedy algorithm to optimize the UAV's position, then decompose it into three sub‐problems: computing resources, power control, and bandwidth allocation. We derive and establish the optimization objective function for computing resources as convex, obtaining the expression for optimal computing resources. The Brent algorithm, based on golden section interpolation, iteratively solves the power distribution sub‐problem, while the Lagrange‐quasi‐Newton algorithm optimizes bandwidth allocation for each user. Simulation results demonstrate that the proposed algorithm effectively reduces training completion time while maintaining training quality.

High-precision IFM receiver based on random forest algorithm

Abstract The six-port network-based Instantaneous Frequency Measurement (IFM) receiver is widely utilized in industrial applications for signal frequency demodulation. However, current IFM receivers face the issue of low algorithmic demodulation accuracy (ACC). According to the four-channel output characteristics of the IFM receiver, we use the Random Forest algorithm to extract features, train and test the output voltage data set, and recognize frequency labels. The experimental results show that the ACC of the proposed algorithm reaches 0.9583, which is 13.99% higher than that of the traditional polynomial fitting algorithm. To evaluate the noise reduction performance of the algorithm, we add Gaussian white noise to the original data and demodulate the signal with noise. Comparative tests prove the advantages of the algorithm in high stability and flexibility. Finally, multiple model evaluation indicators demonstrate that the algorithm features strong applicability and robustness to the experimental data with IFM receiver.

Permutation Entropy for Signal Analysis

Shannon Entropy is the preeminent tool for measuring the level of uncertainty (and conversely, information content) in a random variable. In the field of communications, entropy can be used to express the information content of given signals (represented as time series) by considering random variables which sample from specified subsequences. In this paper, we will discuss how an entropy variant, the \textit{permutation entropy} can be used to study and classify radio frequency signals in a noisy environment. The permutation entropy is the entropy of the random variable which samples occurrences of permutation patterns from time series given a fixed window length, making it a function of the distribution of permutation patterns. Since the permutation entropy is a function of the relative order of data, it is (global) amplitude agnostic and thus allows for comparison between signals at different scales. This article is intended to describe a permutation patterns approach to a data driven problem in radio frequency communications research, and includes a primer on all non-permutation pattern specific background. An empirical analysis of the methods herein on radio frequency data is included. No prior knowledge of signals analysis is assumed, and permutation pattern specific notation will be included. This article serves as a self-contained introduction to the relationship between permutation patterns, entropy, and signals analysis for studying radio frequency signals and includes results on a classification task.

Non-contact monitoring of human cardiorespiratory activity during sleep using FMCW millimeter wave radar

Millimeter wave radar can be utilized for non-contact, long-term, and accurate health monitoring. This paper presents an application for vital signs detection including respiratory rate (RR) and heart rate (HR) during sleep, as well as facilitating rapid detection and extended recording of apnea syndrome. This paper employs frequency spectrum analysis for RR estimation and develops an algorithm of template matching estimation (TME) for real-time, accurate HR estimation by comparing radar signal fragments to a template with a frequency signature. The time required for HR estimation is reduced to 1 s. In addition, a neural network-based model was developed for detecting the apnea events with the accuracy of 93.60% and calculates the duration in seconds. The system underwent testing using irregular analog signals of vital signs. In the actual scene testing, the accuracy of the RR estimation is 90.58%, and 90.25% for HR estimation.

Electrocardiogram sampling frequency for optimal performance of complexity analysis and machine learning models: Discrimination between subjects with and without paroxysmal atrial fibrillation using sinus rhythm electrocardiograms

BackgroundCurrent clinical practice to diagnose atrial fibrillation (AF) requires repeated, episodic monitoring and significantly underperform in their ability to detect AF episodes. ObjectiveThere is therefore potential for artificial intelligence based methods to assist in the detection of AF. Better understanding the optimal parameters for this detection can potentially improve the sensitivity in detecting AF. MethodTen second, 12-lead electrocardiogram signals were analysed using complexity algorithms combined with machine learning techniques to predict patients who had a previously detected AF episode but had since returned to normal sinus rhythm. An investigation was performed into the impact of sampling frequency of the electrocardiogram signal on the accuracy of the machine learning models used. ResultsUsing a single complexity algorithm showed a peak accuracy of 0.69 when using signals sampled at 125Hz. In particular it was noted that improved accuracy occurred when using lead V6 compared to other available leads. ConclusionBased on these results there is potential for 12-lead electrocardiogram signals to be recorded at 125Hz as standard and used in conjunction with complexity analysis to aid in the detection of subjects with AF.

Observation of biological and emulsion samples by newly developed three-dimensional impedance scanning electron microscopy

Imaging at nanometre-scale resolution is indispensable for many scientific fields such as biology, chemistry, material science and nanotechnology. Scanning electron microscopes (SEM) are widely used as important tools for the nanometre-scale analysis of various samples. However, because of the vacuum inside the SEM, a typical analysis requires fixation of samples, a drying process, and staining with heavy metals. Therefore, there is a need for convenient and minimally invasive methods of observing samples in solution. Recently, we have developed a new type of impedance microscopy, multi-frequency impedance SEM (IP-SEM), which allows nanoscale imaging of various specimens in water with minimal radiation damage. Here, we report a new IP-SEM system equipped with a linear-array terminal, which allows eight tilted images to be observed in a single capture by applying eight frequencies of input signals to each electrode. Furthermore, we developed a three-dimensional (3D) reconstruction method based on the Simulated Annealing (SA) algorithm, which enables us to construct a high-precision 3D model from the 8 tilted images. The method reported here can be easily used for 3D structural analysis of various biological samples, organic materials, and nanoparticles.

A novel method for rapidly simulating X-ray pulsar signals at a spacecraft

This paper addresses the challenges posed by the low photon flux and limited atmospheric penetration capabilities of X-ray pulsar signals, which are further exacerbated by technical, economic, and temporal constraints during in-orbit observations. These challenges result in a lack of continuous and high-fidelity data support for ground-based X-ray pulsar navigation (XPNAV) research and validation. To overcome these obstacles, we propose an innovative method for the efficient and rapid simulation of X-ray pulsar signals as received by spacecraft. Building on the physical process that, although the arrival times of the same pulsar signal differ between the spacecraft and the Solar System Barycenter (SSB), these times can be calibrated to the same phase at the SSB using a timing model, we leverage the relationship between the pulsar signal’s arrival rate at the SSB and its arrival phase. Using the Order Statistical Method (OSM), we directly and effectively generate the arrival phase series of pulsar signals received by the spacecraft, which follow a non-homogeneous Poisson process (NHPP). Subsequently, from these phase series, we derive the arrival time series of spacecraft-received pulsar signals, incorporating multiple physical effects based on a relationship model between arrival time series and phases. This method significantly reduces the required simulation time while maintaining high accuracy, thereby greatly enhancing simulation efficiency. Validation against data from the Rossi X-ray Timing Explorer (RXTE) and the Neutron Star Interior Composition Explorer (NICER) shows that the correlation coefficients between the accumulated profiles of simulated and measured data exceed 0.99. Furthermore, our method reduces the simulation time of high-flux pulsar signals by 25.7%, high spin frequency signals by 40.8%, and long observation duration signals by 25.38% compared to the latest fast simulation techniques, highlighting its advantages in accuracy, speed, and versatility for simulating pulsar signals across varying flux rates, spin frequencies, and observation durations. This approach provides continuous and effective data support for XPNAV research, significantly advancing theoretical and practical developments in the field.

Signal growth in a pure time-modulated transmission line and the loss effect

We present the first comprehensive study for signal growth in transmission lines (TL) with purely time-modulated characteristic impedance Z o (infinite superluminality). This study pioneers the investigation into the effects of varying the cell’s electrical length and the impact of loss on momentum bandgaps and amplification levels. It also thoroughly examines how time-modulated transmission line truncation by a static load influences the sensitivity of amplification gain to the relative phase between the incoming signal and modulation, comparing these findings with the case of parametric amplification. Varying Z o is accomplished by loading TLs with a sinusoidally time-modulated capacitor (TMC). The study starts with a simple lumped model cell to facilitate understanding of the phenomena. Following this, transmission lines are introduced, and the effects of incorporating loss are examined. To accomplish this, three models are investigated: a lossless L-C TL lumped model loaded with a shunt lossless TMC and a TL loaded with a shunt lossless and lossy TMC. Dispersion diagrams are plotted and momentum bandgaps are identified at a modulation frequency double the signal frequency. Within the momentum bandgap, only imaginary frequencies are found and correlated to momentum bandgap width and signal growth level. Signal growth is confirmed using harmonic balance and transient simulations, and the results are consistent with the dispersion diagram outcomes.

A frequency channel-attention based vision Transformer method for bearing fault identification across different working conditions

Fault identification of rolling bearings plays a crucial role in maintaining the efficient and stable operation of equipment. Although traditional fault identification methods have made certain progress, they still lack in model feature extraction capabilities and generalization ability. In this paper, a frequency channel-attention based vision Transformer method is proposed for rolling bearings intelligent fault identification. Using frequency domain channel-attention mechanism, the proposed method is able to preserve fundamental fault information and integrate the frequency characteristics of the vibration signals. The proposed method also leverages the inherent self-attention mechanism of vision Transformer to recognize long-range dependencies within the signal data. This integration of attention not only enhances the model’s sensitivity to signal frequency characteristics but also enables the visualization of the attention mechanism, thereby increasing the model’s interpretability. Additionally, a shift linear layer is proposed to reduce the model’s computational demands while maintaining its robust feature extraction capabilities. This proposed method directly uses the collected vibration raw signals to achieve precise fault identification of rolling bearings, and experimental validation on two datasets demonstrates the model’s diagnostic accuracy under across working conditions

Quasi-distributed acoustic sensing based on optical path difference demodulation for high-frequency and large-amplitude using an optical fiber interferometer array.

The phase-sensitive optical time domain reflectometry scheme has attracted great research interest in distributed acoustic sensing (DAS) but suffers from a trade-off between the dynamic range and signal frequency. In this paper, an optical path difference (OPD) demodulation method is applied to an interferometer array composed of an ultra-weak fiber Bragg grating (UWFBG) array and an imbalanced Michelson interferometer to solve this problem. As far as we know, this is the first time that OPD demodulation has been applied to DAS. The UWFBG array is interrogated by a frequency-modulated pulse, and the acoustic signal sensed by the fiber between any two adjacent UWFBGs can be retrieved by demodulating the variation of residual OPD of the interferometer formed by them. The proposed method is analyzed theoretically and validated experimentally; compared with the phase demodulation method, a 100-times boost in bandwidth is achieved for a signal with an amplitude of 0.4 µε. Results also show that the proposed method offers increasing signal-to-noise ratios as the frequency increases.

Freely tunable phase-locked dual-frequency microwave signal generation from injection-locked broadband optoelectronic oscillator

We propose and experimentally demonstrate an injection-locked broadband optoelectronic oscillator (OEO) to generate freely tunable phase-locked dual-frequency microwave signals. When two single-tone signals inside and outside the passband of the electrical broadband bandpass filter (BPF) are, respectively, injected into the OEO, a phase-locked dual-frequency microwave signal with ultra-low near-end side-mode spurs can be generated from the OEO cavity. Therefore, one frequency of the output signal is equal to the frequency of the injected signal within the BPF, and the other frequency is equal to the sum frequency or the differential frequency of two injected signals. By changing the frequencies of two injected signals, two frequencies of the generated dual-frequency microwave signal can be arbitrarily tuned over the passband of the BPF. In our experiment, freelytu nable phase-locked dual-frequency microwave signals within the frequency range of 8∼12 GHz can be generated, where the side-mode suppression ratios (SMSRs) are more than 90 dB during the frequency tuning.