Random Signal Noise Research Articles

Adaptive Variational Mode Decomposition and Principal Component Analysis-Based Denoising Scheme for Borehole Radar Data

To address the significant impact of noise on the target detection performance of borehole radar (BHR), a key type of ground-penetrating radar (GPR), a denoising scheme based on the whale optimization algorithm (WOA) for adaptive variational mode decomposition (VMD) and multiscale principal component analysis (MSPCA) is proposed. This study initially conducts the modal decomposition of BHR data using an improved adaptive VMD method based on the WOA; it then automatically selects modes meeting specific frequency band standards. The correlation coefficients between these modes and the original signal are computed, discarding weakly correlated modes before signal reconstruction. Finally, MSPCA further suppresses noise, yielding denoised BHR data. Simulations show that the proposed scheme increases the signal-to-noise ratio by 17.964 dB or higher, surpassing the more established denoising techniques of robust principal component analysis (RPCA), MSPCA, and empirical mode decomposition (EMD), and obtains the most favorable results in terms of the RMSE and MSE metrics. The experimental results demonstrate that the proposed scheme more effectively suppresses vertical and random noise signals in BHR data. Both the numerical simulations and experimental results confirm the effectiveness of this scheme in noise reduction for BHR data.

Bias‐Independent True Random Number Generator Circuit using Memristor Noise Signals as Entropy Source

Inherent noise characteristics of memristor devices can be utilized in stochastic computing applications such as true random number generators (TRNGs). However, the ratio between capture and emission time can significantly affect the randomness of generated bit streams by TRNGs. Herein, a bias‐independent TRNG circuit is presented, utilizing the random telegraph noise (RTN) signal of the memristor as a random entropy source. This design considers the condition‐dependent RTN characteristics, including capture time and emission time constants, which vary with read voltage (Vread) and temperature conditions in the high‐resistance state of the fabricated memristor. The TRNG circuit, comprising an edge detection circuit and an N‐bit counter, is experimentally demonstrated to validate hardware feasibility with the optimized external clock frequency, which can mitigate the biases induced by Vread and temperature. Finally, the performance of the designed TRNG circuit is evaluated using autocorrelation functions and National Institute of Standards and Technology tests, confirming its capability to produce random number bitstreams.

Ultraviolet spectral broadening by stimulated rotational Raman scattering on nitrogen pumped with signal laser injection

We present experimental results on kilojoule ultraviolet laser output with 1% spectral broadening. Through stimulated rotational Raman scattering (SRRS) with signal laser injection, we achieve effective spectral broadening in short-range propagation, with good retention of the original near-field distribution and time waveform. Theoretical calculations show that 2% bandwidth spectral broadening can be achieved by injecting 20 kW/cm2 signal light at 2.2 GW/cm2 flux of the pump laser. In addition, high-frequency modulation in the near field can be effectively avoided through replacement of the original random noise signal light by the controllable signal light. The SRRS in the atmospheric environment excited with signal laser injection can provide wide-band light output with controllable beam quality without long-distance propagation, representing an important potential route to realization of broadband laser drivers.

Sign of slant receiver for skewed alpha-stable noise shift keying-based random communication system

Purpose In contrast to traditional communication systems, slower data rate has always remained a weak link for non-traditional random communication systems (RCSs), which use alpha-stable (a-stable) noise as a carrier. This paper aims to introduce a fast receiver for skewed a-stable noise shift keying (SkaSNSK)-based RCSs. Design/methodology/approach The introduced receiver is based on the sign of slant estimator (SoSE), which provides rapid estimation of the skewed a-stable random noise signals (RNSs) received from the additive white Gaussian noise channel. The SoSE-based receiver minimizes the number of samples required to extract the encoded information from the received RNSs. This is achieved by manipulating the antipodal properties of the slant/skewness parameter of the a-stable carrier. Hence, a high data rate with relatively low complexity is guaranteed. Findings In comparison with the previously introduced sinc, logarithmic and modified extreme value method-based receivers, the proposed SoSE-based receiver also achieves improved bit error rate (BER) along with the better covertness values so that the essence of security provided by SkaSNSK-based RCSs remains intact. Research limitations/implications Because of the selected range of the associated parameters of the a-stable noise as a carrier, the BER vs MSNR results are may lack applicability for the complete range of values. Therefore, further research is required to produce results in different ranges. Practical implications The study includes implications for the hardware development based on the proposed communication scheme. Originality/value It can be seen that the paper fulfils the desired need of a fast receiver design for RCS.

A new parameter-free entropy based on fragment oscillation and its application in fault diagnosis

For finite time series, it is difficult to provide a convincing determination scheme for specifying relevant parameters of entropy. This article proposes a new entropy, σkEn, based on the amplitude fluctuations of time series segments. It uses the optimal variance estimation under the principle of minimizing AMSE(σˆ2) to adaptively select the window width, and then realizes the no-argument calculation process of volatility entropy through kernel density estimation. By testing with artificial data and applying to real-world data, it has been verified anti-noise and stability properties of σkEn. In a background noise environment with an intensity of 20%, σkEn distinguish various chaotic systems and random signals efficiently. In addition, this manuscript also proposes the plane of (σkEn,NkEn) and (σ̄,σkEn) , which also successfully identifies 6 types of chaotic systems, and 3 random noise signals. Furthermore, (<σ̄>,<σkEn>) curve exhibits different dynamical behavior patterns for these chaotic systems. Finally, we employ (σ̄,σkEn) as a feature and take 8 usual machine learning models, such as Linear Discriminant, Gaussian Naive Bayes and Subspace Discriminant, etc, to perform anomaly detection and fault classification tests on 6 open datasets. Given the appropriate classifiers, our method can achieve a classification accuracy of 100% for four tasks, and an average accuracy of over 96% for the remaining two. This is significantly superior than the accuracy obtained by using Permutation Entropy as a feature.

Study on weak sound signal separation and pattern recognition under strong background noise in marine engineering

The extraction of weak acoustic signals under strong background noise is of great significance in the applications of target identification and localization. In this paper, the pulse signal with high randomness is set as the weak signal sound source, random noise and sine sound are used as the background noise. Under the condition of a signal-to-noise ratio of −20 dB, combined with blind source separation and neural network methods, the collected observation signals are subjected to weak sound signal separation and recognition research. The optimization method of centralization and scaling processing is used to eliminate the unfavorable influence of the uncertainty of the separated signal amplitude caused by the blind source separation method on the pattern recognition. The recognition result is verified by the combination of “weak impulse acoustic signal” and “random noise signal,” and the output vector (0.99 0.01 0.01) approaches (1 0 0), which is recognized as impulse acoustic signal. By combining blind source separation and neural network methods, the separation and identification of weak pulse signals under the condition of a signal-to-noise ratio of −20 dB can be achieved.

Multipath Detection and Mitigation of Random Noise Signals Propagated through Naturally Lossy Dispersive Media for Radar Applications

This paper describes a methodological analysis of the Brillouin precursor formation to understand the impairments undergone by like-noise and random noise waveforms propagating through naturally dispersive media commonly found in radar applications. By means of a frequency-domain methodology based on considering the frequency response of the medium under study, the effect of these dispersive media on the evolution of an input signal can be seen as frequency filtering. The simulations were performed at a center frequency of 1.5 GHz and for a signal bandwidth of 3 GHz. Four random noise signals were considered: Barker codes, PRBS codes, Frank codes, Costas codes and additive white Gaussian noise. The experienced impairments were assessed in terms of cross-correlation function (CCF) degradation. The differences in the behavior of each type of phase and frequency coded signal to face the dispersive propagation have been demonstrated in terms of parameters used for information retrieval: peak amplitude decay, CCF secondary sidelobe level and multipath detectability. Finally, a frequency filtering approach is proposed to mitigate the impairments due to dispersive propagation under multipath conditions.

Deep Neural Network-Based Fusion Localization Using Smartphones

Indoor location-based services (LBS) have tremendous practical and social value in intelligent life due to the pervasiveness of smartphones. The magnetic field-based localization method has been an interesting research hotspot because of its temporal stability, ubiquitousness, infrastructure-free nature, and good compatibility with smartphones. However, utilizing discrete magnetic signals may result in ambiguous localization features caused by random noise and similar magnetic signals in complex symmetric and large-scale indoor environments. To address this issue, we propose a deep neural network-based fusion indoor localization system that integrates magnetic and pedestrian dead reckoning (PDR). In this system, we first propose a ResNet-GRU-LSTM neural network model to achieve magnetic localization more accurately. Afterward, we put forward a multifeatured-driven step length estimation. A hierarchy GRU (H-GRU) neural network model is proposed, and a multidimensional dataset using acceleration and a gyroscope is constructed to extract more valid characteristics. Finally, more reliable and accurate pedestrian localization can be achieved under the particle filter framework. Experiments were conducted at two trial sites with two pedestrians and four smartphones. Results demonstrate that the proposed system achieves better accuracy and robustness than other traditional localization algorithms. Moreover, the proposed system exhibits good generality and practicality in real-time localization with low cost and low computational complexity.

Deep learning based structural damage identification for the strain field of a subway bolster

Strain-based structural health monitoring technology has been widely used in the field of transportation. The existing strain damage identification methods have defects such as complex process, lag in state evaluation, and low intelligence. This paper adopts the deep learning method to establish a network model that uses the strain field information to map directly to the damage information, and takes a subway bolster as the engineering background to realise the end-to-end automatic damage identification. Firstly, the problem of damage identification in strain field is described, combined with the idea of fully convolutional network. The basic structure of damage identification network is modularised, and the overall design framework is proposed. Then, the damage simulation method is determined, and the feasibility of using this method to construct a strain field damage dataset is verified. The batch random damage model generation and the random noise signal addition program are coded, and the datasets of the bolster under static/dynamic force are obtained. Finally, the deep learning model is applied to the bolster damage dataset, and a residual module BolRes_Att that integrates spatial attention and channel attention mechanism is proposed. It has better damage identification performance without an increase in model parameters. The average number of faulty elements on the two bolster test sets of the improved damage identification network is 3.02 and 2.92, respectively, accounting for about 0.016% of all elements. The average processing time for a set of data is only 0.014 s. The results show that the deep learning model constructed in this paper can accurately and quickly identify the damage information of elements according to the strain field information.

Extraction of Flaw Signals from the Mixed 1-D Signals by Denoising Autoencoder

Ultrasonic testing (UT) is one of the most popular non-destructive evaluation (NDE) techniques used in many industries to evaluate structural integrity. The commonly used NDE techniques are basic inspection techniques, such as visual testing (VT), penetration testing (PT), and magnetic testing (MT), and advanced inspection techniques, such as UT, radiography testing (RT), eddy current testing (ECT), and phased array ultrasonic testing (PAUT). Among the numerous advanced techniques, ultrasonic testing (UT) is usually used for the inspection of welds in various industries. However, the application of UT still has some shortcomings to overcome. One major shortcoming that reduces the precision of UT is the extra signals from the geometrical interface of a specimen. UT uses the reflection indications of the ultrasonic beam. However, the reflection signals from the welding interface and geometry along with the target flaw signal produce mixed signals. The inspectors use a 1-D reflection outcome called the ultrasonic A-scan to evaluate the welding integrity. The mixed ultrasonic A-scan signals are often very difficult to analyze because inspectors must distinguish the target flaw signal of welding from the mixed ultrasonic A-scan signal, which includes the flaw indication as well as the background signal. Therefore, a method to distinguish between the flaw signal and the background signal must be developed for the efficiency of UT. Autoencoder is an artificial neural network that is made for feature extraction from the input. Denoising autoencoder (DAE) is one of the derivative models of the autoencoder which adds or eliminates random noise signals to extract the prominent features. DAE is already widely used in the denoising of images and sound data. The characteristics of DAE are used in this research to distinguish the ultrasonic flaw signal from the mixed ultrasonic A-scan signal. For the training, 2463 mixed A-scan signals were obtained from 45 different standard blocks in which 5 different types of flaws were embedded. For testing, we used 1000 mixed A-scan signals. The performance of the network was evaluated using a point-by-point comparison method. The autoencoder was trained to denoise the background signal from the mixed ultrasonic A-scan, and the target flaw signal was extracted from the original A-scan signal.

Objective Bayesian Approach for Binary Hypothesis Testing of Multivariate Gaussian Observations

This paper proposes an objective Bayesian detector for the binary hypothesis testing problem in which the observation vectors are IID and follow multivariate Gaussian distribution under both the null and alternative hypotheses. The mean vector and covariance matrix under each hypothesis are not known, they also do not have special structure other than the covariance matrices are partially ordered in the positive definite cone with their difference positive semi-definite. An example of such a problem is the detection of Gaussian random signal in Gaussian noise. Non-informative priors are imposed on the unknown parameters for computing the marginals and evaluating the Bayes factor test statistic, where the prior for the covariance matrix is applied to the Cholesky factorization of the precision matrix. For the null hypothesis, we propose conjugate priors for the unknowns. For the alternative hypothesis, we propose uniform prior on the mean vector, and a class of objective priors that encompasses the Jeffreys, Independence Jeffreys, Geisser and Cornfield, Haar Measure and Reference priors for the precision matrix. The proposed priors enable closed-form expressions for the marginals and lead to an objective Bayes factor having the posteriors mainly governed by the data. The proposed detector is analyzed for justifying the priors used, deriving the theoretical moments, and approximating the false alarm and detection probability densities by the shifted Gamma distributions. The developed detector exhibits improvement in detection performance over the energy detector (ED) and the generalized likelihood ratio test (GLRT).

Cross-Comparison of Radiation Response Characteristics between the FY-4B/AGRI and GK-2A/AMI in China

In this study, we compare the data of the advanced geostationary radiation imager (AGRI) on board the FY-4B and the advanced meteorological imager (AMI) on board the GK-2A, in terms of overall data, different reflectivity/brightness temperature intervals, different regions, and different underlying surfaces. The results show that the AGRI and AMI data are generally consistent; the mean biases for reflectivity channels show a range of 0.50% to 1.69%, with channel VIR004 being exceptionally good, while brightness temperature (TB) differences in the IR channels ranging from 0.11 to 0.57 K, with channel IR120 being the most accurate. The reflectivity of the AGRI is higher than that of the AMI in terms of mean bias. The dispersion of the reflectivity difference between the AGRI and AMI is smaller at the short-wavelength channels than that at the longer-wavelength channels. The TB data observed by the AGRI are higher than those of AMI at conditions above 310 K. In the case of observing the same target, the difference in infrared brightness temperature due to the random noise signal is small. The differences between the two sensors can be considerably reduced by revising mean biases. In the following studies of quantitative product algorithms, the characteristics of sensor data need to be further analyzed in detail.

Seismic Random Noise Suppression Model Based on Downsampling and Superresolution

Seismic random noise suppression presents two main challenges: achieving thorough noise suppression while simultaneously ensuring complete restoration of effective signal content. However, due to the complexity of random noise, existing denoising methods often only achieve an awkward balance between removing random noise and restoring effective signals. In this paper, we propose a novel random noise suppression model based on downsampling and super-resolution. By decoupling the denoising and signal restoration processes, our method reduces the difficulty of addressing these two challenges and mitigates the likelihood of suboptimal results. On the one hand, the high fitting-capacity Downsampling network uses non-linear transformations to separate random noise and effective signals while purifying the high-order features of effective signals. On the other hand, the Super-resolution network expands the low-dimensional seismic signal content containing the high-order features of the signal to restore the signal structure. Moreover, we propose a new adversarial loss by introducing the gradient between the generated data and the real data, which enhances the perceptual quality of the super-resolution results and recovers the content of effective signals better. Because both subnetworks are not affected by signal/noise features during processing, the model exhibits strong fitting and generalization abilities. The experimental evaluation on four different types of seismic data demonstrates the superiority of our method in suppressing random noise and restoring the content of effective signals.

A Twice Denoising Autoencoder Framework for Random Seismic Noise Attenuation

Many seismic denoising algorithms are easily trapped in the dilemma of signal leaking and noise remaining. But between the two situations, more complex and common situations happens that both signal leaking and noise remaining exist. We have presented a denoising framework for random seismic noise attenuation to mitigate the dilemma and achieve a better denoising performance. The framework consists of a denoising autoencoder (DAE) and data generator. The DAE attenuates random noise without ground truth and works with a vectoring patching technique to reduce time complexity. In the data generator, local correlation is first developed to nonlinear local correlation to detect and extract the signal leakage with noise components suppressed. Then, the extracted signal leakage is compensated back to the DAE output in a supersaturated way to generate a new record. After that, DAE is used again to suppress the remaining noise in the new record. The supersaturated compensation of the first signal leakage counteracts the signal leakage of the second DAE noise attenuation. Formally, the proposed framework can be implemented like a nonlinear geological inversion by thinking of the data generator as the forward operator. Four 2D/3D synthetic and field examples test the performance of the proposed framework. Experiments demonstrate that the proposed method is effective in random noise attenuation and signal leakage compensation, and thus significantly mitigates the mentioned dilemma.

Effective Audio Encryption Using Pseudo Noise Generation Architecture

Encryption of audio signal is performed in this work. Pseudo random noise signal is used to encrypt the audio signal. Generation of pseudo random numbers are done by two interconnected Linear Feedback Shift Registers, the first of which controls the second. Different formulae are presented depending on the seed word, and therefore appropriate random numbers are generated. The usage of two LFSR increases the randomness of the random number generated and also increases the periodicity of generated random numbers making it more secure. The random numbers are generated on Xilinx. The suggested method has high frequency, less delay and more randomization as well as user set modes.&#x0D; For real-time applications, an audio signal is considered, which is encrypted by performing an xor operation with the key to obtain an encrypted audio signal. Decryption is performed by performing an xor operation on the encrypted audio signal with the same key to obtain the original audio signal, demonstrated in Matlab. The encrypted histogram shows better signal distribution like white noise which ensures more security.

Effect of the recurring random signal waveform on SBS and self-pulsing in a phase-modulated narrow-linewidth linearly polarized fiber amplifier

A high power, narrow linewidth, linearly polarized all-fiber amplifier with recurring random signal (RRS) or white noise signal (WNS) phase modulation is structured. The influence of the RRS waveforms on SBS and self-pulsing characteristics is experimentally studied. We find that choosing an appropriate RRS waveform can effectively improve the SBS and self-pulsing threshold. For that, the RRS phase modulation can provide larger than 1.6 times limited power compared to WNS phase modulation at the same linewidth. Meanwhile, we first demonstrate that the output power of a fiber amplifier based on RRS phase modulation is limited by the continuous wave (CW) SBS, while it is limited by the self-pulsing effect in the case of WNS phase modulation. The influence of random signal waveforms on SBS is analyzed theoretically. The results prove that the Stokes peak intensity is closely related to the fine structure of the modulated spectrum determined by the signal waveforms, and which cannot be characterized only from the spectral linewidth. The theoretical analysis is consistent with the experimental results. Finally, we obtain a 1.23 kW narrow linewidth linearly polarized laser output with a linewidth of ∼4 GHz based on RRS phase modulation.

Chaos-Based Fast Image Encryption Scheme with Double Zigzag Permutation and Secure SHA256

Encryption is generally used to protect the content of images. This paper proposes a simple chaos-based color image encryption algorithm with permutation and diffusion stages dependent on the plain image to increase plain text sensitivity by collaboratively leveraging a sampled random noise signal, SHA-256, Henon chaotic map, double zigzag scanning and Lorenz system. Specifically, a hash value of the plain image and a sampled true random noise signal are generated using SHA-256 hash function and considered as an initial one-time key, which is then used to locate values from the sampled true random noise signal array to define the initial values and parameters for chaotic Henon map and Lorenz system. The Henon chaotic map creates an output that defines the starting point for double zigzag pixel scanning in the permutation stage, whereas the Lorenz system generates three sequences to diffuse the image’s three components; red, green, and blue using the XOR operation in order to get an encrypted image. Finally, statistical analysis results are offered to evaluate the complexity and ascertain security functionality for the proposed algorithm. Results show that the proposed image encryption algorithm is superior in comparison to other existing algorithms.

Gaussian Distributed Noise Generator Design Using MCU-STM32

The random noise signal is widely used as a test signal to identify a physical or biological system. In particular, the Gaussian distributed white noise signal (Gaussian White Noise) is popularly used to simulate environmental noise in telecommunications system testing, input noise in testing ADC (Analog to Digital Converter) devices as well as testing other digital systems. Random noise signal generation can be done using resistors or diodes. The weakness of the noise generator system using physical components is the statistical distribution. An alternative solution is to use a Pseudo-Random System that can be adjusted for distribution and other statistical parameters. In this study, the implementation of the Gaussian distributed pseudo noise generation algorithm based on the Enhanced Box-Muller method is described. Prototype of noise generation system using a minimum system board based on Cortex Microcontroller or MCU-STM32F4. From the test results, it was found that the Enhanced Box-Muller (E Box-Muller) method can be applied to the MCU-STM32F4 efficiently, producing signal noise with Gaussian distribution. The resulting noise signal has an amplitude of ±1Volt, is Gaussian distributed and has a relatively wide frequency spectrum. The noise signal can be used as a jamming device in a certain frequency band using an Analog modulator.

An improved multiscale distribution entropy for analyzing complexity of real-world signals

Assessment of the dynamical complexity of signals or systems is very crucial in medical diagnostics, fault analysis of mechanical systems, astrophysics and many more. Although there have been tremendous improvements in entropy measures as complexity estimator, most of these measures are affected by short data length and are highly sensitive to predetermined parameters. These issues are addressed quite successfully by distribution entropy (DistEn), a robust estimator of complexity for many signals. However, it fails to discriminate random noise, pink noise and Henon map-based chaotic signals. Furthermore, it underestimates the complexity of chaotic signals at higher scales. To circumvent these problems, we propose an improved distribution entropy (ImDistEn), which utilizes embedded vectors' orientation, ordinality and ℓ1-norm distance information for its computation. Simulation results show that ImDistEn can provide clear distinction of different classes of real-world signals, besides accurately assessing the complexity of various synthetic signals.

Intelligent Roller Bearing Fault Diagnosis in Industrial Internet of Things

Advanced research studies on industrial Internet of things require effective feature extraction and accurate machinery health state evaluation. For roller bearing, a well-known mechanical component most extensively used in the industry, its running status directly affects the operation of the entire machinery and equipment. For intelligent fault diagnosis of roller bearing, the selection of the intrinsic mode function (IMF) modes in approaches of ensemble empirical mode decomposition (EEMD)/variational mode decomposition (VMD) becomes a tricky problem. To solve this problem, this study proposed an efficient scheme on roller bearing fault diagnosis that combines the refined composite multivariate multiscale sample entropy (RCMMSE) with different classifiers. Firstly, the synthetic noise signals are introduced to compare the effectiveness of the multiscale sample entropy (MSE) and the RCMMSE models. Secondly, the random noise signals are used to compare the performance of EEMD and VMD methods, where the envelope spectrum characteristics of fault signals are well described. Moreover, EEMD/VMD methods are utilized to decompose the roller bearing vibration signals into various modes to get the entropy values. Finally, the obtained RCMMSE is adopted as a feature vector and subsequently employed as an input of support vector machine, random forest, and probabilistic neural network models to conduct roller bearing fault identification. The extensive experimental results prove that this proposed scheme performs well and the classification accuracy of VMD-RCMMSE is higher than EEMD-RCMMSE.