Signal Frequency Research Articles

Research on Frequency Discrimination Method Using Multiplicative-Integral and Linear Transformation Network

In this paper, a frequency discrimination method using a multiplicative-integral and linear transformation network is proposed. In this method, two preset differential frequency signals and frequency modulation signals are transformed by multiplication and integration, and then the instantaneous frequency parameters of the frequency modulation signal are accurately analyzed by the linear transformation network to restore the original modulation signal. Compared with the phase discriminator, the simulation results show that this method has a higher frequency discrimination bandwidth. In addition, this method has better anti-noise performance, and the frequency discrimination distortion caused by noise with a different Signal-to-Noise Ratio is reduced by 33.80% on average compared with the phase discriminator. What is more, the carrier center frequency error has little influence on the frequency discrimination quality of this method, which solves the problem that most common frequency discriminators are seriously affected by the carrier center frequency error. This method requires a low accuracy of carrier center frequency, which makes it extremely suitable for digital frequency discrimination technology and can meet the needs of various frequency discrimination occasions.

Photonic architecture for remote multi-parameter measurement and transmission of microwave signals

A photonic architecture for remote multi-parameter measurement and transmission of microwave signals is proposed and demonstrated, which utilizes a dual-parallel dual-drive Mach-Zehnder modulator (DP-DDMZM) in the antenna unit and a dual-drive Mach-Zehnder modulator (DDMZM) in the processing unit. Doppler frequency shift (DFS) and angle of arrival (AOA) can be determined by analyzing the down-converted intermediate frequency signals. Introducing a reference signal in the processing unit ensures DFS measurement without directional ambiguity. The proposed architecture can also be applied for instantaneous frequency measurement based on down-conversion. Due to the use of optical single sideband modulation, long-distance transmission of radio frequency (RF) signals without dispersion-induced power fading can be achieved. Experiments for accurate and stable DFS and AOA measurement as well as long-distance RF signal transmission with dispersion-induced power fading are presented. The approach avoids the use of optical filters and polarization-related devices, facilitating wideband and stable operation, which is highly desirable. The proposed architecture is a potential solution for microwave photonic antenna remoting, offering support for both remote transmission and multi-parameter measurement.

Indirect temperature protection of an asynchronous generator by stator winding resistance measurement with superimposition of high-frequency pulse signals

Indirect temperature protection of an asynchronous generator by stator winding resistance measurement with superimposition of high-frequency pulse signals

Speech emotion recognition using a combination of variational mode decomposition and Hilbert transform

The objective of automated speech emotion recognition (SER) is to recognize each emotion of the speech signal uniquely and efficiently using machines like computers, mobile devices, etc. The popularity of SER has been widely acknowledged among researchers due to its extensive applicability in practical contexts. The use of SER has shown to be advantageous in several domains, including medical treatment, enhancement of security systems, surveillance operations, online marketing strategies, online educational platforms, online search engines, personal communication, customer relationship management, reinforcement of machine and human connection, and numerous other areas. Numerous authors have used various techniques, including the combination of multiple features (acoustic, non-acoustic, or both) and classifiers (machine learning, deep learning, or both), in order to enhance the efficacy of emotion categorization. In the present work, our objective is to enhance the performance of emotion classification (PECL) by using a combined approach of the variational mode decomposition (VMD) and Hilbert transform (HT) techniques. Using the VMD method, we decomposed the speech signal frame into many sub-signals, or intrinsic mode functions (IMFs). Then HT is applied to each VMD-based IMF signal to find the mode instantaneous amplitude (MIA) and mode instantaneous frequency (MIF) signal vectors. We extracted proposed features such as HT-based approximate entropy (HTAE), HT-based permutation entropy (HTPE), HT-based increment entropy (HTIE), and HT-based sample entropy (HTSE) using each MIA and MIF signal vector. The combination of the proposed HT-based features is called HT-based entropy (HTE) features. Then, we accessed the PECL using the HTE and MFCC features alone and in conjunction with a deep neural network (DNN) classifier. The experiment's results showed that the combinations of the proposed feature (MFCC + HTE) using a DNN classifier outperformed the individual features and obtained a SER accuracy of 86.92% for the EMOVO dataset and 91.63% for the EMO-DB dataset.

Experimental Evaluation of an SDR-Based UAV Localization System.

UAV communications have seen a rapid rise in the last few years. The drone class of UAV has particularly become more widespread around the world, and illicit behavior using drones has become a problem. Therefore, localization, tracking, and even taking control of drones have also gained interest. Knowing the frequency of a target signal, its position can be determined (as the angle of arrival with respect to a fixed receiver point) using radio frequency-based localization techniques. One such technique is represented by the subspace-based algorithms that offer highly accurate results. This paper presents the implementation of the MUSIC algorithm on an SDR-based system using a uniform circular antenna array and its experimental evaluation in relevant outdoor environments for drone localization. The results show the capability of the system to indicate the AoA of the target signal. The results are compared with the actual direction computed from the log files of the drone application and validated with a professional direction-finding solution (i.e., Narda SignalShark equipped with the automatic direction-finding antenna).

Three modes of electrochemical impedance spectroscopy measurements performed on vanadium redox flow battery

This article presents an innovative approach to monitor working redox flow batteries using dynamic electrochemical impedance spectroscopy, diverging from the commonly sequential impedance methods carried out under potentiostatic or galvanostatic conditions close to the open circle voltage. The authors introduce a fresh variation of dynamic impedance measurement that leverages an amplitude-modulated multi-frequency alternating current perturbation signal. This technique leads to a reduction in measurement time, making it possible to monitor impedance in real-time under typical operational conditions. Consequently, it effectively addresses the limitations stemming from the absence of stationary conditions during impedance measurements. There is no doubt that measurement techniques enabling the study of processes during the operational life of a battery provide the most valuable insights into the properties of these systems. The potential of this proposed approach is exemplified through the examination of a full vanadium redox flow battery as a case study. Classical impedance measurements were also conducted under potentiostatic and galvanostatic conditions with sequential frequency signal change, which requires stopping the battery operation, resulting in electrode potential changes of over 200 mV compared to the working system. Spectra for all modes of measurements were compared. In the case of dynamic measurements, such significant differences in spectra during charging and discharging are not observed, which is characteristic of classical measurements. The authors deliberately and consciously refrain from analyzing the results by fitting equivalent circuits.

Broadband photoelectric fusion transceiver-multiplexed conversion system with high spurious rejection

In this paper, a broadband photoelectric fusion transceiver-multiplexed system is proposed to realize a frequency converter. The system achieves a high spurious suppression ratio through two frequency conversions that utilize the advantages of microwave and photonics technology simultaneously to reduce the complexity of the system and improve the effective spectrum utilization. In addition, the core components, such as the Mach–Zehnder modulator (MZM), are multiplexed in the up and down frequency conversion link. High-frequency local oscillator (LO) signals are used to keep image frequency signals and various kinds of spurious signals obtained by beating frequency outside the system bandwidth. Experimental results demonstrate that the operating frequency ranges from 2 to 18 GHz with high performance for both transmitter and receiver. The image rejection is 57.35 dB for up-conversion and 46.56 dB for down-conversion, and the in-band spurious suppression achieves at least 55.02 dB. At the same time, the spurious-free dynamic range (SFDR) can reach at least 89.11dB⋅Hz2/3.

A modified virtual time reversal method for enhancing monitoring sensitivity of bolt preloads based on ultrasonic guided waves

Bolted joints are prone to loosening due to inaccurate initial preloads and time-varying service conditions. Real-time monitoring of bolt preloads is therefore an important means to warrant the reliability and safety of a bolted structure, though it remains a daunting task to implement precise monitoring of early bolt loosening. A modified virtual time reversal (MVTR) method is developed in this study, aimed at enhanced sensitivity of bolt preload monitoring. To begin with, the frequency response function (FRF) of an intact bolted structure is ascertained using the excitation wave and the received wave signal through the bolt. Then a received wave signal captured from the bolted structure under monitoring is time-reversed, and a refocused signal is calculated in virtue of the time-reversed signal and the FRF in the intact state. Bolt tightness indices are established by the peak amplitude or energy of the refocused wave packet. To facilitate interpretation of the principle of the proposed MVTR, a semi-analytic model is developed, which combines the waveform superposition method and finite element method. With the model, the effects of interface contact length, wave mode and signal frequency on the tightness indices are investigated. The effectiveness of the proposed MVTR is experimentally verified using three representative bolted lap structures featuring single bolt or multiple bolts. The results confirm the findings from the semi-analytic model, demonstrating that the MVTR enhances monitoring sensitivity compared to prevailing approaches using linear or nonlinear guided wave features.

РАДІОВИМІРЮВАЛЬНИЙ ПЕРЕТВОРЮВАЧ ВОЛОГОСТІ ҐРУНТУ НА БАЗІ ПЕРВИННОГО СЕНСОРА YL-69

Soil moisture monitoring in agriculture is becoming an integral part of ensuring optimal conditions for plant growth and achieving high yields, as well as an important environmental aspect for preserving natural resources and ecosystems. The increasing popularity of wireless sensor networks for measuring soil moisture underscores the need for the development of modern technologies in this field. This enables a continuous flow of real-time data on soil conditions, which is crucial for effective agricultural management and ecological balance. The paper presents the development of a radio-based soil moisture transducer with a moisture-sensitive resistive element based on a transistor structure with negative resistance. The YL-69 resistive sensor was utilized as the primary soil moisture sensor. An analytical expression for the conversion function of the radio-based soil moisture transducer is determined. Computer simulation of the transducer's operation was conducted in the OrCAD environment, resulting in a family of current-voltage characteristics for the investigated radio-based transducer. A region with negative differential resistance is observed on the current-voltage characteristics. An experimental conversion function of the radio-based soil moisture transducer based on the YL-69 primary sensor was obtained. From the conversion function, it is evident that as soil moisture increases in the range from 0% to 50%, the frequency of the output signal of the radio-based soil moisture transducer decreases from 680 kHz to 452 kHz. The highest sensitivity value of the radio-based soil moisture transducer is achieved in the soil moisture measurement range from 20% to 45% and is 8.7 kHz/%. The operation of radio-based soil moisture transducers is based on the functional dependence of semiconductor device impedance on soil moisture, which opens up wide prospects for improving agricultural technologies and preserving the environment. This transducer can be used for measuring soil moisture in agriculture, which can be utilized to create automatic systems for monitoring soil moisture and watering plants.

Research on an Alpha Navigation Signal Detection Method Based on Multichannel Orthogonal Correlation

The Alpha navigation system is the only operating radio system based on very-low-frequency (VLF) signals that can be used to research VLF navigation, timing, and ionospheric characteristics. The detection of the Alpha navigation signal is the key step in the Alpha receiver; however, the received Alpha navigation signal is susceptible to noise and mutual interference, which deteriorates signal detection performance. This paper presents a multichannel orthogonal correlation method for Alpha navigation signal detection. Once the three frequency signals of the Alpha navigation system are obtained using a notch filter, station identification is realized using a multichannel orthogonal correlation method and signal format. The selection of key parameters and the detection performance under noise and mutual interference are analyzed. This method’s detection probability exceeds 90% when the signal-to-noise ratio (SNR) is greater than −10 dB. The influence of mutual interference on the signal correlation peak is less than 1% when the signal-to-interference ratio (SIR) of the mutual interference is greater than −28 dB. The proposed method is verified using an actual signal collected using an Alpha receiver. The results show that an Alpha signal can be detected at an extremely low SNR. This method has strong practicability and satisfies the application requirements of an Alpha receiver.

Reconstruction of elasticity modulus distribution base on semi-supervised neural network

Accurate reconstruction of tissue elasticity modulus distribution has always been an important challenge in ultrasound elastography. Considering that existing deep learning-based supervised reconstruction methods only use simulated displacement data with random noise in training, which cannot fully provide the complexity and diversity brought by in-vivo ultrasound data, this study introduces the use of displacement data obtained by tracking in-vivo ultrasound radio frequency signals (i.e., real displacement data) during training, employing a semi-supervised approach to enhance the prediction accuracy of the model. Experimental results indicate that in phantom experiments, the semi-supervised model augmented with real displacement data provides more accurate predictions, with mean absolute errors and mean relative errors both around 3%, while the corresponding data for the fully supervised model are around 5%. When processing real displacement data, the area of prediction error of semi-supervised model was less than that of fully supervised model. The findings of this study confirm the effectiveness and practicality of the proposed approach, providing new insights for the application of deep learning methods in the reconstruction of elastic distribution from in-vivo ultrasound data.

Memristive arrangements of nanofluidic pores.

We demonstrate that nanofluidic diodes in multipore membranes show a memristive behavior that can be controlled not only by the amplitude and frequency of the external signal but also by series and parallel arrangements of the membranes. Each memristor consists of a polymeric membrane with conical nanopores that allow current rectification due to the electrical interaction between the ionic solution and the pore surface charges. This surface charge-regulated ionic transport shows a rich nonlinear physics, including memory and inductive effects, which are characterized here by the current-voltage curves and electrical impedance spectroscopy. Also, neuromorphiclike potentiation of the membrane conductance following voltage pulses (spikes) is observed. The multipore membrane with nanofluidic diodes shows physical concepts that should have application for information processing and signal conversion in iontronics hybrid devices.

Multi-scale ground penetrating radar full waveform inversion with hybrid Tikhonov and total-variation regularization for different geometric structure

Abstract Full waveform inversion (FWI) is a high-resolution technique to estimate the parameters of dielectric permittivity (ϵ) and electrical conductivity (σ) and identify the structure of the subsurface for Ground Penetrating Radar (GPR) application. However, permittivity and conductivity parameters can be coupled in bi-parameter GPR inversion. This coupling effect leads to the crosstalk in the FWI result. To solve this problem, we propose a novel approach to use the multi-scale FWI with the hybrid regularization method, which combines Tikhonov and total-variation (TV) regularizers that simultaneously invert the ϵ and the σ parameters, which improve the inversion accuracy and reduce the crosstalk effect. The multi-scale strategy uses the Wiener filtering to process the GPR data in different frequency ranges. Then, the low frequencies signal updates the bottom part and subsequently increases the frequencies to invert for the shallow areas. The Tikhonov regularization stabilizes the reconstruction of the smoothly varying background part. In contrast, Total Variation (TV) regularization can recover the large contrasts associated with the LNAPL model. The new Tikhonov-TV (TT) regularization can mitigate the crosstalk caused by the parameter coupling effect. Numerical tests with typical GPR models demonstrate that the proposed multi-scale TT-FWI strategy can effectively eliminate the crosstalk and improve the reconstruction accuracy when the model parameters have a different structure.

Defect detection of grouting sleeve connection with energy ratio change in frequency domain

Controlling the grouting density is the key to ensure the construction quality of precast structures. Since the grouting sleeve is embedded in concrete, it is difficult to detect the grouting compactness of the sleeve. In this paper, the frequency domain analysis technique is used for non-destructive testing of concrete components with grouting defects. The collected ultrasonic testing signals are calibrated-denoised-decomposed-reconstructed. The damage degree of the grouting signal is evaluated and analyzed by quantifying the energy value of different frequency signals with two norms. Considering the effect of the random distribution of aggregates on the propagation of ultrasonic stress waves, a three-dimensional multi-point detection model and a two-dimensional plane single-point model based on piezoelectric effect are set up respectively. Results show that frequency energy distribution of the grout-deficient signal is concentrated to low-frequency range than that of grout-compact signal. When grouting defect accumulates to a certain extent, the main frequency may move from high-frequency band to lower-frequency band. Although the aggregates in different concrete components are randomly distributed, the test results of the two-dimensional plane model show that the validity of the defect quantitative index is 72%. The experimental tests results show that the method of using frequency energy transfer to lower-frequency band to determine grouting defects is effective, with a detection validity of 66.7%. Therefore, the non-destructive testing method for grouting sleeve compactness proposed in this paper can provide reference for engineering testing application.

Phase demodulation method of high line density grating interferometric signal based on wavelet transform

The increasing line density of the reference grating and the accelerating miniaturization of ultra-precision displacement measurement technology necessitate more stable interferometric signal processing methods for high line density gratings, particularly in low signal-to-noise ratio scenarios. This paper presents a phase demodulation method for dynamic interferometric signals for high line density gratings. The Morlet wavelet transform is utilized to obtain the instantaneous frequency of the interferometric signal, integration of which yields the relative displacement, while adding adjacent relative displacements without gaps provides the absolute displacement during dynamic motion of the grating. In simulations with a signal-to-noise ratio ranging from 40 to 70 dB, the proposed method demonstrates greater robustness compared to the traditional method. By establishing a platform for repeated experiments and comparing it with traditional methods, it was found that the maximum deviation between calculation results obtained using this method and traditional methods is 0.8 nm, further confirming its potential application.

Enhanced Tracer Particle Detection in Dynamic Bulk Systems Based on Polarimetric Radar Signature Correlation.

This contribution focuses on the detection of tracer particles within non-homogeneous bulk media, aiming to enhance insights into particulate systems. Polarimetric radar measurements are employed, utilizing cross-polarizing channels in order to mitigate interference from bulk media reflections. To distinguish the tracer particle in the measurements, a resonant cross-polarizing structure is constructed, facilitating the isolation of frequency signatures from the surrounding bulk clutter. In addition to characterizing the bulk and tracer components, this study provides a detailed presentation and discussion of the measurement setup, along with the employed signal processing methods. The effectiveness of the proposed methods is demonstrated through comprehensive measurements, where a tracer particle is systematically positioned at various locations. The results affirm the feasibility and efficacy of the approach, highlighting its applicability for enhanced dynamic monitoring in particulate systems within industrial processes.

Flexibility Potential in the Austrian Building Sector

What would happen if 15% of the Austrian building stock in 2030 – both refurbished and newly-built – would use flexible heating/cooling systems to offer their flexibility on a market? How much annual residual loads from volatile renewable energy sources (RES) could be absorbed? Our simple agent-market model says: ~5% of RES surpluses and around 2% of RES residual loads. The cost of this operation is additional energy demand of about 30%, indicating an efficiency of approx. 70% in using these volatile supply peaks in flexible building operation. In this model, buildings are agents offering additional electricity consumption from pre-emptive HVAC operation, effectively using the building mass as storage, their energy demand is modelled in a simple thermal RC model. The grid flexibility demand is derived from future residual load scenarios and the offered flexibility depends both on signal parameters, mainly signal frequency and duration, and on key building parameters indoor temperature comfort bounds, building mass and thermal envelope quality.

High-order iterative rearrangement transform for time–frequency characterization of bearing fault impact

Time–frequency analysis (TFA) can effectively characterize features of non-stationary signals. Traditional TFA algorithms construct signal models in the time domain and make the assumption that the instantaneous characteristics of each component are continuous. However, the instantaneous frequency (IF) of the transient signal is discontinuous in the time domain and exhibits a multifaceted relationship with time, such as shock, vibration wave, damped sound wave, etc. Additionally, in most existing TFA methods, low-order group delay (GD) is used to describe transient signals, which leads to unsatisfactory energy concentration and calculation accuracy. To address about issues, a novel TFA technique, termed high-order iterative rearrangement transform (HOIRT), is developed in this research. First, the signal model is defined within the frequency domain, and the frequency ridge of the transient signal is described by a high-order GD (HOGD), which is similar to the IF. Second, a HOGD-based iterative synchrosqueezing operator is defined to reassign time–frequency coefficients into the GD trajectories along the time direction. Finally, the HOGD-based frequency extraction operator is constructed to only retain the target time–frequency information of the transient signal from the rearranged results, such that the noise interference is eliminated and the energy-concentrated TFR is obtained. A simulation signal with nonlinear GDs is employed to illustrate the effectiveness of the HOIRT. Compared with the other seven typical TFA algorithms, the developed technique has the smallest calculation error and Rényi entropy, showing that the HOIRT has the highest accuracy and energy concentration. Analysis result of the bearing fault impact signal shows that the proposed HOIRT can display the time when pulses occur while ensuring high time–frequency resolution, making it suitable for detecting bearing faults.

Distributed quantized secure bipartite consensus of linear multi‐agent systems with switching topologies and sequential scaling attacks

AbstractThis paper considers the secure distributed control consensus problem of linear multi‐agent systems (MASs) under switching topologies, subject to intermittent sequential scaling attacks, which was compelled to scaling factor, attack frequency, duration and cooperative‐competitive networks. First, the scenario of a fixed topology is considered, and a novel control protocol combined with a logarithmic quantizer and relative state measurements of neighbouring agents is discussed. The sighed graph is utilized to characterize the communication topology determined by the information flow directions and captured by the graph Laplacian matrix. After that, sufficient conditions for effectiveness of the developed control methods in guiding the MASs to secure bipartite leader‐following consensus are constructed. Second, the scenario of switching topologies is considered, and it is derived that the secure bipartite consensus will be achieved if the designed state feedback control protocol with the scaling factor, the attack duration, attack frequency and switching signal are selected properly. At last, to prove the effectiveness of the designed controllers, a simulation example depended on the real‐word actual military is introduced.

Robust cascaded frequency conversion through shortcuts to adiabaticity

We propose a cascaded frequency conversion technique based on Stark-chirped rapid shortcuts to adiabatic passage (SCRSAP). The implementation of this technique corrects the coupling function in Stark-chirped rapid adiabatic passage (SCRAP) technique by means of shortcuts to adiabaticity (STA) counter-diabatic (CD) driving. The results of numerical simulations show that SCRSAP technique allows efficient frequency conversion at lower pump laser intensity and a wider range of delay parameters than SCRAP technique, and the STA technique improves the robustness of the process to temperature and signal frequency variations in adiabatic frequency conversion.