Signal Modeling Research Articles

Linear prediction coefficients correction method for digital speech processing systems with data compression based on the autoregressive model of a voice signal

The problem of distortion of the autoregressive model of the voice signal under the influence of additive background noise in digital speech processing systems with data compression based on linear prediction is considered. In the frequency domain, these distortions are observed in the weakening of the main formants responsible for the intelligibility of the speaker’s speech. To compensate for formant attenuation, it is proposed to modify the parameters of the autoregressive model (linear prediction coefficients) using the impulse response of a recursive shaping filter. Along with the amplitude amplification of the formants, their frequencies remain unchanged to make the speaker’s voice recognizable. The effectiveness of the method was studied experimentally using specially developed software. Based on the experimental results, conclusions were drawn about a significant increase in the relative level of formants in the power spectrum of the corrected voice signal.

LC dynamic signal modeling and analysis of resonant frequency measurement error under rapid eye movements interference

Abstract LC resonant wireless passive intraocular pressure (IOP) sensors measure IOPs by mapping them to LC resonance frequencies. However, during the sweeping acquisition process of each LC signal frame, possible rapid eye movement (REM) can interfere with the wireless mutual inductance coupling, leading to the signal distortion and resulting in the error of resonance frequency measuring. Currently, there is a lack of modelling analysis of the errors generated by REM effect, resulting in an absence of guidance for reducing the impact. Therefore, here, we start from the perspective of the signal model and establish the LC dynamic signal model for sweeping acquisition. By means of the limit state analysis and Monte Carlo simulation, we analyze the influence of external REM parameters (including REM range and velocity) and internal parameters of the LC sensing system (including the quality coefficient of LC sensor, diameter, and number of turns of the reader coil, signal sweep acquisition speed and period) on the errors. We demonstrated theoretical relationship between the extreme errors with these parameters and verify it through a computer simulation. Based on the results, we propose to optimize the internal parameters of the LC sensing system to reduce the REMs effect on errors, safeguarding the quality of signal acquisition and improving the measurement accuracy.

The passive recording of the click trains of a beluga whale (Delphinapterus leucas) and the subsequent creation of a bio-inspired echolocation model.

A beluga-like model of click train signal is developed by observing beluga's sound recording. To reproduce the feature of the biosonar signal, this paper uses a signal extracting method with a correction factor of inter-click interval to acquire the parameter of click trains. The extracted clicks were analyzed in the time and frequency domain. Furthermore, a joint pulse-frequency representation was undertaken in order to provide a 2D energy distribution for an echolocation click train. The results from joint pulse-frequency representation indicate that click train can be adjusted its energy distribution by using a multi-component signal structure. To evaluate the capability of the click train to inform the whale of relevant target information perception for the click train, a finite element model is built to reproduce target discrimination by the bio-inspired click train. Numerical results indicate that the bio-inspired click train could enhance the echo-response by concentrating energy into the frequency bins for extracting target feature effectively. This proof-of-concept study suggests that the model of click train could be dynamically controlled to match the target properties, and show a promising way to use various types of echolocation click train to interrogate different features of the target by man-made sonar.

Anti-Rain Clutter Interference Method for Millimeter-Wave Radar Based on Convolutional Neural Network

Millimeter-wave radars are widely used in various environments due to their excellent detection capabilities. However, the detection performance in severe weather environments is still an important research challenge. In this paper, the propagation characteristics of millimeter-wave radar in a rainfall environment are thoroughly investigated, and the modeling of the millimeter-wave radar echo signal in a rainfall environment is completed. The effect of rainfall on radar detection performance is verified through experiments, and an anti-rain clutter interference method based on a convolutional neural network is proposed. The method combines image recognition and classification techniques to effectively distinguish target signals from rain clutter in radar echo signals based on feature differences. In addition, this paper compares the recognition results of the proposed method with VGGnet and Resnet. The experimental results show that the proposed convolutional neural network method significantly improves the target detection capability of the radar system in a rainfall environment, verifying the method’s effectiveness and accuracy. This study provides a new solution for the application of millimeter-wave radar in severe weather conditions.

Modelling of small signals in MISO DC-DC converters for hybrid energy sources

Modelling of small signals in MISO DC-DC converters for hybrid energy sources

Location of 5G base station antenna in substation taking into account path loss and RF radiation

Aiming at the engineering problem that 5G base station antenna is difficult to locate efficiently in complex electromagnetic environment, a two-stage positioning method of 5G base station antenna substation is proposed, which considers signal path loss and antenna RF radiation. Firstly, the path loss solution model of the 5G base station antenna signal in the substation is established, and the RF radiation solution model generated by the coupling excitation of 5G high-frequency electromagnetic wave and the metal shell of the electrical equipment is constructed based on the big element physical optics method. Secondly, combining the advantages of particle swarm optimization (PSO) and interior point algorithm, the antenna positioning method considering path loss and RF radiation is constructed. Among them, in the first stage, the minimum loss of 5G signal path in the substation is taken as the goal, and the antenna stations in the substation are searched globally to get the optimal pre-selected position. In the second stage, the antenna position is calibrated according to the pre-selected position, so that the interference of RF radiation is minimized, and the final position is output. Finally, a 500 kV 5G substation in Henan Province was taken as the simulation object. Compared with the original scheme, the simulation results ensured the minimum 5G path loss in the substation and took into account the electromagnetic compatibility of the equipment in the substation. The effectiveness of the location strategy for maintaining the safe operation of the substation is verified, and it can provide a reference for the 5G base station antenna positioning project of the substation.

Synthesis of a four-point model of an airplane

The modeling of echo signals from distributed radar objects taking into account the noise of their angular coordinates is considered. Relationships are presented that allow the transition from a multipoint model of an arbitrary object containing tens, hundreds and even thousands of emitting points to a model composed of four emitting points located at the vertices of a square. As an example, we synthesized an airplane model containing only four points. It is obtained on the basis of a multipoint model composed of 56 points. Using numerical modeling methods, it has been shown that angular noise generated by multi-point and four-point aircraft models have identical correlation functions and similar parameters of the probability density distribution of angular coordinate noise. The obtained result is also confirmed by semi-natural modeling methods using a matrix simulator.

Lexical Surprisal Shapes the Time Course of Syntactic Structure Building.

When we understand language, we recognize words and combine them into sentences. In this article, we explore the hypothesis that listeners use probabilistic information about words to build syntactic structure. Recent work has shown that lexical probability and syntactic structure both modulate the delta-band (<4 Hz) neural signal. Here, we investigated whether the neural encoding of syntactic structure changes as a function of the distributional properties of a word. To this end, we analyzed MEG data of 24 native speakers of Dutch who listened to three fairytales with a total duration of 49 min. Using temporal response functions and a cumulative model-comparison approach, we evaluated the contributions of syntactic and distributional features to the variance in the delta-band neural signal. This revealed that lexical surprisal values (a distributional feature), as well as bottom-up node counts (a syntactic feature) positively contributed to the model of the delta-band neural signal. Subsequently, we compared responses to the syntactic feature between words with high- and low-surprisal values. This revealed a delay in the response to the syntactic feature as a consequence of the surprisal value of the word: high-surprisal values were associated with a delayed response to the syntactic feature by 150-190 ms. The delay was not affected by word duration, and did not have a lexical origin. These findings suggest that the brain uses probabilistic information to infer syntactic structure, and highlight an importance for the role of time in this process.

Gravity and magnetic study of the Xitle volcanic field, Basin of Mexico – Inferences on the pre-eruptive topography

Results of a gravity and magnetic study over a lava field in the southern Basin of Mexico are used to investigate on lava emplacement and pre-eruptive topography. The characteristics of lava flows depend on multiple factors, including the lava composition, effusion rate, rheology, fluid content and underlying terrain. The ∼2000 years old volcanic field, formed by seven flow units emplaced by the Xitle eruption, covers an extensive area >80 km2 characterized by topographic relief on the Ajusco volcano flank and surrounding plain. The study is conducted on the northern end of the volcanic field. The gravity and magnetic study is based on regional-residual separation, vertical and horizontal derivatives, reduced to the pole, analytical signal, spectral analysis and forward modeling. The regional gravity field shows two anomaly lows in the central sector, with the residual field showing trends of gravity highs. The residual magnetic anomaly is characterized by trends of magnetic highs associated to the lava field. Depths estimated from spectral analysis of gravity and magnetic anomalies show shallow sources at ∼22 m and ∼46 m, and deep sources at ∼469 m and ∼268 m, respectively. The underground sequence is modeled as a volcanoclastic succession of varying thickness. The first vertical derivatives, gradients and analytical signal enhance the anomalies associated to lava thickness variations. Joint gravity and magnetic analysis constraints the deep/shallow anomaly sources in the volcanic field and relations to pre-eruptive structure.

A Novel Wiener‐Type Dynamic Neural Network Method for Large Signal Modeling of Power Amplifiers

ABSTRACTA novel Wiener‐type dynamic neural network (Wiener‐type DNN) method for large signal modeling of power amplifiers (PAs) is proposed in this paper. In this method, the proposed model structure for the PA contains two Wiener‐type DNNs to describe the input impedance and the amplification efficiency, respectively. The Wiener‐type DNN includes a simplified linear dynamic equation module and a nonlinear static equation module. For the simplification process of the linear dynamic equation, it is proposed to process fundamental frequency components of the large signal harmonic data of the PA by vector fitting. The neural network is used to implement the static equation in the Wiener‐type DNN. The formulas for training the proposed Wiener‐type DNN model of the PA using large signal data are derived. The training algorithm of the PA model is proposed to improve the training efficiency. A Motorola MOSFET PA example and a Freescale lateral double‐diffused MOSFET PA example are present to validate the proposed Wiener‐type DNN method. For the Motorola MOSFET PA, a dynamic neural network (DNN) model and a time‐delay deep neural network (TDDNN) are established for comparison. For the Freescale PA, the DNN model is used for comparison experiment. The comparison figures show that the Wiener‐type DNN model has better convergence properties than the DNN model in the nonlinear region of the PA. The established accurate PA model is conducive to the design of subsequent communication circuits.

The Method for Obtaining Shot Cloud Time Span and Range Dependence

When studying the behavior of a shot cloud in space, a variety of information and measurement systems can be applied, for example, based on light screens (photoelectronic blocking devices of a small ballistic measuring installation consisting of a linear emitter and an optical sensor based on a photodiode). To study the process of shot cloud movement in space and to assess its parameters (for example, length at a given range from the muzzle) correctly, it is advisable to have a simulation model of the optical sensor signal when the light screen is crossed by a shot cloud. Taking into account previous studies, it is necessary to know the magnitude of the scale parameter in the Rayleigh distribution describing the shot cloud in the direction of firing in time in order to obtain a similar model at some distance from the muzzle. A method that allows obtaining function of dependence of the mentioned scale parameter and the distance from the muzzle and based on real data from several optical sensors of light screens installed on the shooting track is proposed. The example of application the proposed method to obtain similar functions based on the results of processing optical sensor signals obtained in real experiments for cartridges loaded with three different shot types is given. The obtained dependences were approximated by polynomials of the first and second degree using the least squares method, and the error was estimated for various types of cartridges. Based on the obtained results, specific recommendations for the practical application of the developed method are proposed. The estimation of the confidence interval width for the expectation function of the shot cloud time span is performed. The ways of creating advanced signal simulation models from optical sensors of light screens when shooting with shot are outlined.

A new positive output DC–DC buck–boost converter based on modified boost and ZETA converters

A new DC–DC buck–boost converter with a wide conversion ratio is presented in this paper. The proposed buck–boost converter consists of a combination of modified boost converter and ZETA converter, which has the advantages of both converters such as continuous input/output current and positive polarity of the output voltage. The combination of these two converters achieves semi-quadratic voltage gain and makes the proposed converter suitable for industrial and renewable energy applications. With a voltage gain higher than that of the ZETA converter and modified boost converter, the proposed converter also reduces input current stress due to its continuity. Two operating states are available for this converter in continuous conduction mode. This converter has two switches that operate simultaneously and can be easily controlled. The converter's output voltage ripple and output capacitor current stress are reduced as a result of continuous output current. Computational analysis and the introduced structure efficiency considering the influence of parasitic elements are presented in this paper. The small signal modelling and closed-loop control, as well as simulation and experimental results are also presented. This converter has also been compared with other similar and recently presented topologies. Finally, a 40–60 W, 20–76 V for boost mode and 10 V for buck mode prototype was implemented to verify the accuracy of the computational analysis.

Wireless transmission method of electromagnetic wave of cross screw signal

Abstract Based on the near-bit azimuthing gamma measuring instrument developed by Changqing Drilling Company, this paper uses comsol multiphysics(COMSOL) numerical simulation software to build the electromagnetic wave wireless transmission model of cross-bolt signal while drilling. By using finite element method and a large amount of calculation and analysis, the influence law of source distance, drill wire conductivity, borehole and mud conductivity and other parameters on the response characteristics is obtained. The simulation modeling method presented in this paper and the conclusions obtained have important guiding significance for further optimizing and upgrading the near-bit wireless short-pass device and better understanding and mastering the signal transmission law of this kind of instrument.

Multi-Dimensional Medical Image Fusion With Complex Sparse Representation.

In the field of medical imaging, the fusion of data from diverse modalities plays a pivotal role in advancing our understanding of pathological conditions. Sparse representation (SR), a robust signal modeling technique, has demonstrated noteworthy success in multi-dimensional (MD) medical image fusion. However, a fundamental limitation appearing in existing SR models is their lack of directionality, restricting their efficacy in extracting anatomical details from different imaging modalities. To tackle this issue, we propose a novel directional SR model, termed complex sparse representation (ComSR), specifically designed for medical image fusion. ComSR independently represents MD signals over directional dictionaries along specific directions, allowing precise analysis of intricate details of MD signals. Besides, current studies in medical image fusion mostly concentrate on addressing either 2D or 3D fusion problems. This work bridges this gap by proposing a MD medical image fusion method based on ComSR, presenting a unified framework for both 2D and 3D fusion tasks. Experimental results across six multi-modal medical image fusion tasks, involving 93 pairs of 2D source images and 20 pairs of 3D source images, substantiate the superiority of our proposed method over 11 state-of-the-art 2D fusion methods and 4 representative 3D fusion methods, in terms of both visual quality and objective evaluation. The source code of our fusion method is available at https://github.com/Imagefusions/imagefusions/tree/main.

Electrostatic Modeling of Dust Impact Signals Based on the Expanding Plasma Cloud

Signals generated by dust impacting spacecraft can be detected by electric field instruments. These signals have been simulated by numerous models. However, few models can accurately characterize the expansion of the plasma cloud generated by dust impact. The COMSOL model presented in this paper provides a way to understand the expansion properties of ions and electrons. The model can also be used to analyze the various expected waveforms of dust impact signals as a function of different parameters, such as the spacecraft voltage and the ambient plasma temperature. The results show that close to 50% of ions and electrons in the impact plasma cloud are collected by spacecraft at weak spacecraft potentials and that a fraction of the ions is still collected rather than all of them streaming away from the impact location at V SC > 0 V. The model also confirms that in the expanding plasma cloud, ions are in the form of plumes, while electrons diffuse in an isotropic manner.

Cognitive Impairment in Cerebral Small Vessel Disease Is Associated with Corpus Callosum Microstructure Changes Based on Diffusion MRI.

to evaluate microstructural changes in the corpus callosum (CC) using diffusion MRI (D-MRI) approaches in cSVD patients with different severity of CI and reveal the most sensitive correlations of diffusion metrics with CI. the study included 166 cSVD patients (51.8% women; 60.4 ± 7.6 years) and 44 healthy volunteers (65.9% women; 59.6 ± 6.8 years). All subjects underwent D-MRI (3T) with signal (diffusion tensor and kurtosis) and biophysical (neurite orientation dispersion and density imaging, NODDI, white matter tract integrity, WMTI, multicompartment spherical mean technique, MC-SMT) modeling in three CC segments as well as a neuropsychological assessment. in cSVD patients, microstructural changes were found in all CC segments already at the subjective CI stage, which was found to worsen into mild CI and dementia. More pronounced changes were observed in the forceps minor. Among the signal models FA, MD, MK, RD, and RK, as well as among the biophysical models, MC-SMT (EMD, ETR) and WMTI (AWF) metrics exhibited the largest area under the curve (>0.85), characterizing the loss of microstructural integrity, the severity of potential demyelination, and the proportion of intra-axonal water, respectively. Conclusion: the study reveals the relevance of advanced D-MRI approaches for the assessment of brain tissue changes in cSVD. The identified diffusion biomarkers could be used for the clarification and observation of CI progression.

Disentangling signal and noise in neural responses through generative modeling.

Measurements of neural responses to identically repeated experimental events often exhibit large amounts of variability. This noise is distinct from signal , operationally defined as the average expected response across repeated trials for each given event. Accurately distinguishing signal from noise is important, as each is a target that is worthy of study (many believe noise reflects important aspects of brain function) and it is important not to confuse one for the other. Here, we describe a principled modeling approach in which response measurements are explicitly modeled as the sum of samples from multivariate signal and noise distributions. In our proposed method-termed Generative Modeling of Signal and Noise (GSN)-the signal distribution is estimated by subtracting the estimated noise distribution from the estimated data distribution. Importantly, GSN improves estimates of the signal distribution, but does not provide improved estimates of responses to individual events. We validate GSN using ground-truth simulations and show that it compares favorably with related methods. We also demonstrate the application of GSN to empirical fMRI data to illustrate a simple consequence of GSN: by disentangling signal and noise components in neural responses, GSN denoises principal components analysis and improves estimates of dimensionality. We end by discussing other situations that may benefit from GSN's characterization of signal and noise, such as estimation of noise ceilings for computational models of neural activity. A code toolbox for GSN is provided with both MATLAB and Python implementations.

FMCW Laser Fuze Structure with Multi-Channel Beam Based on 3D Particle Collision Scattering Model under Smoke Interference.

In the environment of smoke and suspended particles, the accurate detection of targets is one of the difficulties for frequency-modulated continuous-wave (FMCW) laser fuzes to work properly in harsh conditions. To weaken and eliminate the significant influence caused by the interaction of different systems in the photon transmission process and the smoke particle environment, it is necessary to increase the amplitude of the target echo signal to improve the signal-to-noise ratio (SNR), which contributes to enhancing the detection performance of the laser fuze for the ground target in the smoke. Under these conditions, the particle transmission of photons in the smoke environment is studied from the perspective of three-dimentional (3D) collisions between photons and smoke particles, and the modeling and Unity3D simulation of FMCW laser echo signal based on 3D particle collision is conducted. On this basis, a laser fuze structure based on multiple channel beam emission is designed for the combined effect of particle features from different systems and its impact on the target characteristics is researched. Simulation results show that the multiple channel laser emission enhances the laser target echo signal amplitude and also improves the anti-interference ability against the combined effects of multiple particle features compared with the single channel. Through the validation based on the laser prototype with four-channel beam emitting, the above conclusions are supported by the experimental results. Therefore, this study not only reveals the laser target properties under the 3D particle collision perspective, but also reflects the reasonableness and effectiveness of utilizing the target characteristics in the 3D particle collision mode to enhance the detection performance of FMCW laser fuze in the smoke.

Locating Insulation Defects in HV Substations Using HFCT Sensors and AI Diagnostic Tools.

In general, a high voltage (HV) substation can be made up of multiple insulation subsystems: an air insulation subsystem (AIS), gas insulation subsystem (GIS), liquid insulation subsystem (power transformers), and solid insulation subsystem (power cables), all of them with their grounding structures interconnected and linked to the substation earth. Partial discharge (PD) pulses, which are generated in a HV apparatus belonging to a subsystem, travel through the grounding structures of the others. PD analyzers using high-frequency current transformer (HFCT) sensors, which are installed at the connections between the grounding structures, are sensitive to these traveling pulses. In a substation made up of an AIS, several non-critical PD sources can be detected, such as possible corona, air surface, or floating discharges. To perform the correct diagnosis, non-critical PD sources must be separated from critical PD sources related to insulation defects, such as a cavity in a solid dielectric material, mobile particles in SF6, or surface discharges in oil. Powerful diagnostic tools using PD clustering and phase-resolved PD (PRPD) pattern recognition have been developed to check the insulation condition of HV substations. However, a common issue is how to determine the subsystem in which a critical PD source is located when there are several PD sources, and a critical one is near the boundary between two HV subsystems, e.g., a cavity defect located between a cable end and a GIS. The traveling direction of the detected PD is valuable information to determine the subsystem in which the insulation defect is located. However, incorrect diagnostics are usually due to the constraints of PD measuring systems and inadequate PD diagnostic procedures. This paper presents a diagnostic procedure using an appropriate PD analyzer with multiple HFCT sensors to carry out efficient insulation condition diagnoses. This PD procedure has been developed on the basis of laboratory tests, transient signal modeling, and validation tests. The validation tests were carried out in a special test bench developed for the characterization of PD analyzers. To demonstrate the effectiveness of the procedure, a real case is also presented, where satisfactory results are shown.

Damage-related imbalance identification for UAV composite propeller blades based on bidirectional temporal convolutional network and a flexible sensing system

Abstract Damage to the composite propeller blades could lead to rotational imbalance, which seriously affects the operational safety of unmanned aerial vehicles (UAVs), therefore, a novel method combining the Teager energy operator (TEO) and bidirectional temporal convolutional network (BiTCN) is proposed for detecting, localizing, and quantifying the damage-related imbalance in the blades. A flexible sensing system that contains Micro electro mechanical sensor accelerometers, signal conditioning, and wireless transmission is integrated with the composite propeller for in-situ signal acquisition of the propeller blades. TEO is applied to demodulate and enhance the pulse compositions in vibration signals and singular value decomposition (SVD) is employed to suppress random noise, resulting in denoised Teager energy spectrums for model input. Temporal convolutional network (TCN) has been widely used in sequence signal modeling because the causal dilated convolution could learn the context information of sequence signals while maintaining the advantages of parallel computing. To fully extract the signal features, BiTCN models are established to learn both the forward and backward signal features. Experimental verification results show that the proposed method detects the existence of imbalance with 100% accuracy, and the accuracies of localization and quantization are 99.65% and 98.61%, respectively, which are much higher than those of the models with the original signal as input. In addition, compared with the other four different algorithms, BiTCN is superior in terms of convergence speed and prediction accuracy.