Signal Separation Algorithm Research Articles

DPSM-based edge blurring suppression of ultrasonic measurement of contact state

Ultrasonic technology evaluates the state of contact between parts by calculating the reflection coefficient. However, edge blurring occurs when detecting the contact boundary, which reduces the reliability of contact stiffness and contact stress assessment. In this paper, a blurred signal separation algorithm and an interference signal prediction model based on the distributed point source method (DPSM) are proposed. First, the separation algorithm is introduced and a simulation model is built to prove the feasibility of the algorithm. Then, an interference signal prediction model is established based on the DPSM technique, which is more accurate in predicting the interference signal. Finally, the experimental results prove the practicality of the separation algorithm, and the algorithm-corrected reflection coefficients fit the reference reflection coefficients better. The experimental results show that the method can effectively suppress the edge blurring, which is of great significance to improve the reliability of boundary assessment.

Small sample composite fault diagnosis of hydraulic bearings based on improved VME algorithm and mRVM

The working environment of rolling bearings is complex. Once a fault occurs, various parts will affect each other and produce a compound fault. Traditional methods often use signal separation algorithms to separate different types of signals for fault diagnosis, but it is difficult to analyze specific faults efficiently and accurately. To solve this problem, this paper combines variational mode decomposition (VMD), Laplace energy index (LE) and variational mode extraction (VME) for signal extraction. Multi-class relevance vector machine (mRVM) and DS evidence theory are used for intelligent fault diagnosis, focusing on the context of small sample data. First, the VMD-LE-VME method is used to extract effective fault information from the fault signal and obtain multi-domain features. Then, the multi-domain features are input into mRVM for fault identification. Finally, the classification results are fused through DS evidence theory to obtain the final classification results. The effectiveness and superiority of this method in processing small sample data are verified by experiments.

Edge blurring suppression of ultrasonic reflection coefficients in contact state measurement

Ultrasonic reflection coefficient is the key to contact state evaluation of mechanical devices. Edge blurring can lead to contact state (such as stress concentrations) measurement errors. This reduces the reliability of performance evaluations and introduces potential security risks. In this study, an edge blurring suppression method based on matching pursuit algorithm was proposed. Firstly, the interference signal prediction model is built based on the Nakagami model. Then, a blurred signal separation algorithm based on matching pursuit is proposed to obtain the effective signal. Finally, the reflection coefficient with and without edge blurring effect were obtained. The simulation results show that the maximum relative error of the reflection coefficient is reduced from 219 % to 15 %. The effectiveness of the proposed method is also verified by experiments. The experimental results show that the relative error of the reflection coefficient after edge blurring suppression is reduced from 64 % to 16 %. This indicates that the proposed method can effectively suppress edge blurring, which provides an effective method for edge blurring suppression in various application fields of ultrasonic measurement and improve the reliability of product quality evaluation.

Music Curriculum Research Using a Large Language Model, Cloud Computing and Data Mining Technologies

This paper presents a method to enhance the scientific nature of the music curriculum model by integrating a large language model, cloud computing and data mining technology for the analysis of the music teaching curriculum model. To maintain the integrity of the mixing matrix while employing the frequency hopping frequency, the paper suggests dividing the mixing matrix into a series of sub-matrices along the vertical time axis. This approach transforms wideband music signal processing into a narrowband processing problem. Additionally, two hybrid matrix estimation algorithms are proposed in this paper using underdetermined conditions. Furthermore, utilizing the estimated mixing matrix and the detected time-frequency support domain, the paper employs the subspace projection algorithm for underdetermined blind separation of music signals in the time-frequency domain. This procedure, along with the integration of the estimated direction of arrival (DoA), enables the completion of frequency-hopping network station music signal sorting. Extensive simulation teaching demonstrates that the music curriculum model proposed in this paper, based on a large language model, cloud computing and data mining technologies, significantly enhances the quality of modern music teaching.

FastICA based single-channel radar signal separation algorithm

FastICA based single-channel radar signal separation algorithm

Comprehensive Separation Algorithm for Single-Channel Signals Based on Symplectic Geometry Mode Decomposition.

This paper aims to explore the difficulty of obtaining source signals from complex mixed signals and the issue that the FastICA algorithm cannot directly decompose the received single-channel mixed signals and distort the signal separation in low signal-to-noise environments. Thus, in this work, a comprehensive single-channel mixed signal separation algorithm was proposed based on the combination of Symplectic Geometry Mode Decomposition (SGMD) and the FastICA algorithm. First, SGMD-FastICA uses SGMD to decompose single-channel mixed signals, and then it uses the Pearson correlation coefficient to select the Symplectic Geometry Components that exhibit higher correlation coefficients with the mixed signals. Then, these components are expanded with the single-channel mixed signals into virtual multi-channel signals and input into the FastICA algorithm. The simulation results show that the SGMD algorithm could eliminate noise interference while keeping the raw time series unchanged, which is achievable through symplectic geometry similarity transformation during the decomposition of mixed signals. Comparative experiment results also show that compared with the EMD-FastICA and VMD-FastICA, the SGMD-FastICA algorithm has the best separation effect for single-channel mixed signals. The SGMD-FastICA algorithm represents an improved solution that addresses the limitations of the FastICA algorithm, enabling the direct separation of single-channel mixed signals, while also addressing the challenge of proper signal separation in noisy environments.

ADS-B overlapping signals separation based on minimum dispersion criterion

As a new generation of air traffic management (ATM) monitoring system, the airborne automatic dependent surveillance-broadcast (ADS-B) transmitters will broadcast their position, speed, and other information to enhance the efficiency and safety of air traffic. However, due to the asynchronous nature of ADS-B transmitters broadcasting their information on the same frequency, it is inevitable for multiple ADS-B signals from different aircraft to overlap. This paper proposes an ADS-B signal separation algorithm based on the minimum dispersion criterion (MD), which takes advantage of the non-Gaussian distribution properties exhibited by ADS-B signals. Firstly, the non-Gaussian distribution properties of overlapping ADS-B signals are illustrated by analyzing their probability density distribution and kurtosis. Secondly, the separation problem is formulated as a non-convex optimization problem based on the minimum dispersion criterion. Finally, the alternating direction method of multipliers (ADMM) is employed to tackle the formulated optimization problem. Simulations will demonstrate the effectiveness of the proposed algorithm.

A high-precision and efficient algorithm for space-based ADS-B signal separation

AbstractSpace-based automatic dependent surveillance-broadcast (ADS-B) receivers can cover thousands of aircraft, each transmitting 6 ⋅ 2 signals per second. As a result, ADS-B signals are very prone to overlap. When the number of aircraft covered by a receiver reaches 3,000, about 90 % of the signals will be overlapping. Overlapped signals can reduce the decoding accuracy of receivers, so that aircraft information cannot be accurately transmitted to the air traffic control (ATC) surveillance system, hence threatening aviation flight safety. It is necessary to propose signal separation algorithms for space-based ADS-B systems. An orthogonal projection linear constrained minimum variance (OPLCMV) algorithm is proposed, which can separate two signals simultaneously based on the linearly constrained minimum variance algorithm by exploiting the characteristics of overlapped signals. Compared with the state-of-the-art extended projection algorithm and the fast independent component analysis algorithm, the OPLCMV method has a higher decoding accuracy for multiple overlapping signals with a small direction difference of arrival or frequency shift. Moreover, the OPLCMV algorithm has a low computational complexity when the number of overlapped signal sources is less than seven.

Optimization of vocal singing training method based on intelligent big data technology

Abstract With the advancement of science and technology, the relationship between vocal singing training methods and big data technology is getting closer and closer. The application of training methods is a means of expressing vocal compositions. Emotional expression is the ultimate purpose and meaning of method optimization. Training methods and emotional expression are equally important in vocal singing, and both are indispensable. In this paper, a VSSA-separation model is established based on the Vocal Signal Separation Algorithm (VSSA). Focus on optimizing the training method of vocal singing. Through in-depth discussion of existing works, I try to find its inner influence and outer connection. In order to better understand the grasp and application of vocal singing method and emotional expression balance. It can be seen from the calculation that the image shaping in the song language accounts for 83% of the optimization of the training method. Inverse word processing in song language has a different proportion of the optimization of the training method. It accounts for 71% of English operas. It accounts for only 13% of Chinese operas. Artistic effects have the highest average optimization difficulty among the training methods. Its optimization difficulty is as high as 89%.

An efficient method for parameter estimation and separation of multi-component LFM signals

In this paper, an efficient and robust algorithm for fast parameter estimation and separation of multi-component LFM signals is proposed, which is suitable for processing overlapping and intersect LFM signals in the time-frequency plane. First, the key innovative idea of the algorithm is to use the fourth-order origin moment of fractional Fourier transform spectrum to formulate a reasonable search strategy for the optimal rotation order to achieve parameter estimation, and propose a step-by-step filtering technique for signals in the fractional Fourier domain achieve the signal separation we are interested in. Then, the proposed algorithm overcomes the problems of low estimation accuracy and large computational of traditional two-dimensional search and time-frequency analysis methods by virtue of good impulse characteristics and anti-noise performance. Different from existing solutions, the method avoid interference problems between multiple signals with close time-frequency distance, overlapping and cross components. Moreover, the error analysis of the linear frequency modulation signal is carried out, and the Cramer-Rao lower bound for the unbiased estimation of the parameters is derived. Finally, the simulation results demonstrate the accuracy and effectiveness of the proposed method in noisy environments by comparing with traditional methods and existing methods.

Анализ эффективности использования методов слепого разделения сигналов для борьбы с помеховым излучением электрических ракетных двигателей в системах космической связи

The article considers the use of blind signal separation algorithms to improve interference immunity of the satellite communication channel with octal phase shift keying as applied to the interference, that is caused by the operation of stationary plasma thrusters (SPT), and represents one of the varieties of electric propulsion [1]. During the study, a simulation model of communication channel with an additional channel for receiving interference from SPT was developed. While modeling, some pulsed noise-like signals, formed as a result of SPT operation, were considered as interference in the communication channel. The blind signal separation algorithm ncFastICA used in the study is based on the method of independent component analysis. The main task of the modeling was to verify the applicability of blind signal separation methods to counter the interference from SPT in the communication channel. The simulation results proved the possibility of efficient use of blind signal separation algorithms to counter SPT interference. Application of the considered algorithm allows achieving a gain in bit error rate by two and more orders of magnitude. Thus, one of the worst cases of signal reception was considered: the gain from the use of the ncFastICA algorithm is achieved when the bit signal-to- noise ratio is more than 10 dB and when the interference-to-signal ratio in the signal-receiving channel is 10 dB and in the interference receiving channel from the SPT is 30 dB.

A MLE-based blind signal separation method for time–frequency overlapped signal using neural network

The blind signal separation (BSS) algorithm obtains each original/source signal from the observed signal collected by the receiving antenna or sensor. Objective/loss/cost function and optimization method are two key parts of BSS algorithm. Modifying the objective function and optimization from the perspective of neural network (NN) is a novel concept in BSS domain. L_2 regularization is adopted as a term of maximum likelihood estimation (MLE)-based objective function like in Liu et al. (Sensors 21(3):973, 2021); however, we modified the probability density function (PDF) term of the objective function and used the kernel density estimation method for time–frequency overlapped digital communication signal. Multiple optimizers are studied in this paper, and we figure out the right optimizer for our application scenario. A varies of comparison experiments—whose separation results will be provided in forms of correlation coefficient and performance index—are carried out, which indicate our method can converge quickly and achieve satisfactory separation results with performance index (PI) lower than 0.02 when signal-to-noise ratio (SNR) no less than 10dB. Additionally, it demonstrates performance of our method is better than that of typical separation—FastICA, especially for the lower SNR environment, and it shows that our method is not sensitive to the frequency overlap level (FOL) of the source signal, even FOL as high as 100%; it still can get high-precision separation results with textrm{PI}<0.02.

Somatosensory cortical signature of facial nociception and vibrotactile touch-induced analgesia.

Pain relief by vibrotactile touch is a common human experience. Previous neurophysiological investigations of its underlying mechanism in animals focused on spinal circuits, while human studies suggested the involvement of supraspinal pathways. Here, we examine the role of primary somatosensory cortex (S1) in touch-induced mechanical and heat analgesia. We found that, in mice, vibrotactile reafferent signals from self-generated whisking significantly reduce facial nociception, which is abolished by specifically blocking touch transmission from thalamus to the barrel cortex (S1B). Using a signal separation algorithm that can decompose calcium signals into sensory-evoked, whisking, or face-wiping responses, we found that the presence of whisking altered nociceptive signal processing in S1B neurons. Analysis of S1B population dynamics revealed that whisking pushes the transition of the neural state induced by noxious stimuli toward the outcome of non-nocifensive actions. Thus, S1B integrates facial tactile and noxious signals to enable touch-mediated analgesia.

Accelerated Conjugate Gradient for Second-Order Blind Signal Separation

This paper proposes a new adaptive algorithm for the second-order blind signal separation (BSS) problem with convolutive mixtures by utilising a combination of an accelerated gradient and a conjugate gradient method. For each iteration of the adaptive algorithm, the search point and the search direction are obtained based on the current and the previous iterations. The algorithm efficiently calculates the step size for the accelerated conjugate gradient algorithm in each iteration. Simulation results show that the proposed accelerated conjugate gradient algorithm with optimal step size converges faster than the accelerated descent algorithm and the steepest descent algorithm with optimal step size while having lower computational complexity. In particular, the number of iterations required for convergence of the accelerated conjugate gradient algorithm is significantly lower than the accelerated descent algorithm and the steepest descent algorithm. In addition, the proposed system achieves improvement in terms of the signal to interference ratio and signal to noise ratio for the dominant speech outputs.

Investigation of Stepped-Frequency Hybrid Modulation Radar for Respiration Monitoring, 2-D Localization, and Moving Target Tracking

This article presents a stepped-frequency hybrid modulation (SFHM) methodology, which is proposed to work in conjunction with a frequency scanning array to effectively perform respiration monitoring, 2-D localization for stationary human subjects, and target trajectory tracking for moving ones. Conventional frequency-shift keying (FSK) radar can accurately detect the absolute distance and small displacement but suffers from the problem of a maximum unambiguous range. In comparison, continuous-wave radar with a chirp signal has no range limitation but suffers from a limited displacement measurement accuracy. To solve the two problems, SFHM is proposed to make full use of the advantages of both FSK mode and chirp mode, collaboratively resolving each other&#x2019;s issues. In addition, the high flexibility of SFHM facilitates the implementation of a frequency scanning array, which provides a simple and cost-efficient means for fast beam steering. A prototype of the radar system is implemented, and a new signal separation algorithm was derived based on the SFHM theory. Various types of experiments confirm the ability of the radar to accurately provide the respiration and range information of stationary subjects and trajectory information of moving subjects.

Series-Constellation Feature Based Blind Modulation Recognition for Beyond 5G MIMO-OFDM Systems With Channel Fading

Due to the shortage of radio spectrum in the current 5G and upcoming 6G systems, the cognitive radio (CR) technique is indispensable for spectrum management and can put the unutilized spectrum to good use. As the core technology of CR, blind modulation recognition (BMR) plays a pivotal role in improving spectral efficiency. However, the BMR research on MIMO-OFDM systems still lacks enough attention. Given the prosperity of deep learning, we propose a series-constellation multi-modal feature network (SC-MFNet) to recognize the modulation types of MIMO-OFDM subcarriers. Without any prior information, a blind signal separation algorithm is employed to reconstruct the impaired transmitted signal. Considering the insufficient features of signal series, we propose a segment accumulated constellation diagram (SACD) strategy to produce the striking constellation features. Moreover, the proposed multi-modal feature fusion network is employed to collect the advantages of series and SACD features, which are extracted by one-dimensional convolution (Conv1DNet) branch and improved EfficientNet branch, respectively. Experimental results demonstrate that in a realistic non-cooperative cognitive communication scenario where prior information is exempted, the proposed SC-MFNet outperforms the traditional feature-based methods and the state-of-the-art neural networks which are based on either constellation features or series features.

SoDar: Multitarget Gesture Recognition Based on SIMO Doppler Radar

In recent years, various intelligent activity recognition systems have been developed based on radio frequency signals such as radar, Wi-Fi, and radio frequency identification (RFID). When only one target is present, these systems can often provide high accuracy in recognizing different activities. However, such activity identification systems often fail to work due to signal interference when multiple targets coexist. To address this problem, we propose a multitarget gesture recognition system, named SoDar, based on a commercial single-input multi-output (SIMO) dual-channel Doppler radar. First, we employ endpoint detection, low-pass filtering, and discrete wavelet transform for data preprocessing. Then, we design a multitarget signal separation algorithm by maximizing the signal-to-noise ratio, and further refine the obtained signal based on principle component analysis. Afterward, we put forward a two-stage feature extraction method to extract both static and dynamic features from each separated signal. Finally, a classification model is trained to recognize the gestures of multiple targets. To verify the performance of SoDar, we selected nine different combinations of six gestures for two targets and collected more than 8000 data samples. Experimental results showed that the accuracy of two-target gesture recognition is above 90&#x0025;.

Multi-Stream Convolutional Neural Networks for Rotating Machinery Fault Diagnosis under Noise and Trend Items.

In recent years, rotating machinery fault diagnosis methods based on convolutional neural network have achieved much success. However, in real industrial environments, interfering signals are unavoidable, which may reduce the accuracy of fault diagnosis seriously. Most of the current fault diagnosis methods are of single input type, which may lead to the information contained in the vibration signal not being fully utilized. In this study, theoretical analysis and comprehensive comparative experiments are completed to investigate the time domain input, frequency domain input, and two types of time–frequency domain input. Based on this, a new fault diagnosis model, named multi-stream convolutional neural network, is developed. The model takes the time domain, frequency domain, and time–frequency domain images as input, and it automatically fuses the information contained in different inputs. The proposed model is tested based on three public datasets. The experimental results suggested that the model achieved pretty high accuracy under noise and trend items without the help of signal separation algorithms. In addition, the positive implications of multiple inputs and information fusion are analyzed through the visualization of learned features.

Bimodal Tactile Sensor without Signal Fusion for User-Interactive Applications

Tactile sensors with multimode sensing ability are cornerstones of artificial skin for applications in humanoid robotics and smart prosthetics. However, the intuitive and interference-free reading of multiple tactile signals without involving complex algorithms and calculations remains a challenge. Herein a pressure-temperature bimodal tactile sensor without any interference is demonstrated by combining the fundamentally different sensing mechanisms of optics and electronics, enabling the simultaneous and independent sensing of pressure and temperature with the elimination of signal separation algorithms and calculations. The bimodal sensor comprises a mechanoluminescent hybrid of ZnS-CaZnOS and a poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) thermoresistant material, endowing the unambiguous transduction of pressure and temperature into optical and electrical signals, respectively. This device exhibits the highest temperature sensitivity of -0.6% °C-1 in the range of 21-60 °C and visual sensing of the applied forces at a low limitation of 2 N. The interference-free and light-emitting characteristics of this device permit user-interactive applications in robotics for encrypted communication as well as temperature and pressure monitoring, along with wireless signal transmission. This work provides an unexplored solution to signal interference of multimodal tactile sensors, which can be extended to other multifunctional sensing devices.

Performance of Digital Drone Signage System Based on DUET

In this letter, we study a scenario based on degenerate unmixing estimation technique (DUET) that separates original signals from mixture of FHSS signals with two antennas. We have shown that the assumptions for separating mixed signals in DUET can be applied to drone based digital signage recognition signals and proposed the DUET-based separation scheme (DBSS) to classify the mixed recognition drone signals by extracting the delay and attenuation components of the mixture signal through the likelihood function and the short-term Fourier transform (STFT). In addition, we propose an iterative algorithm for signal separation with the conventional DUET scheme. Numerical results showed that the proposed algorithm is more separation-efficient compared to baseline schemes. DBSS can separate all signals within about 0.56 seconds when there are fewer than nine signage signals.