Convolutional Noise Research Articles

Enhancing image denoising: A novel non-local anisotropic diffusion framework based on Caputo derivatives and Gaussian convolution for the Perona–Malik model

In the field of image restoration, denoising is considered one of the most important techniques. It is a pre-processing approach aims to refine image clarity and enhance its overall quality by effectively reducing noise present in the image. The aim is to obtain good-quality images from a version degraded by additive noise or convolutional noise that introduces blur. As a result, more advanced treatments can be performed on the resulting image. In order to remove Gaussian noise from input images, we propose the following methodology using a fractional differential equation in time-space based on Gaussian convolution, where the integer and fractional order derivatives of Caputo can be discretized using finite difference and L1−approximations. Once the equation is solved numerically, the scheme is applied to grayscale digital images using the presented algorithm. The parameters must be optimized and adjusted. As a result of testing with natural images, we are able to successfully suppress the noise present in the images. Aside from that. Our model demonstrates strong visual quality, as verified by the calculation of indexes such as Peak Signal-To-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). Our denoising technique demonstrates its effectiveness in mitigating noise present in both MRI and X-ray images, resulting in improved image quality and diagnostic accuracy for these vital medical imaging modalities.

Study on Soil Total Nitrogen Content Prediction Method Based on Synthetic Neural Network Model

Rational utilization of soil total nitrogen is one of the keys to achieving sustainable agricultural development. By accurately measuring the content of total nitrogen in the soil, the utilization efficiency of nitrogen in the soil can be improved, and the scientific use of chemical fertilizers can reduce the pressure of agriculture on natural resources and realize the sustainable development of agriculture. In order to measure soil total nitrogen content simply and accurately, combined with the method of artificial olfactory systems, a new method of soil total nitrogen content detection based on convolutional noise reduction autoencoder (CDAE)–whale optimization algorithm (WOA)–deep residual shrinkage network (DSRN) is proposed. In order to obtain more salient features for fusion, the channel mechanism of the DSRN is improved by adding global Max pooling. The model uses a CDAE for the first filtering stage to automatically obtain data that filters simple noise and uses the WOA to automatically optimize hyperparameters. Finally, the optimized hyperparameters were used to train the DRSN for secondary filtering and predict the soil total nitrogen content. Experimental results show that the R2 of CAE-WOA-DSRN test set is 0.968, which is significantly better than the R2 of a traditional algorithm (0.873) and a simple BP network (0.877), and it can more accurately measure soil total nitrogen content.

Sensor attack detection based on active excitation response with uncertain delays

Cyber–physical systems face the threat of sensor attacks, which can result in incorrect measurements of the physical state for the controller and lead to erroneous decisions and control instructions. To address this issue, our paper introduces a sensor attack detection method involving the active injection of excitation into the system and a comparison of actual sensor data with the expected response. First, we address challenges posed by sensing noise and uncertain delays by compensating for them and presenting a probability interval derived from the convolution of noise and delay. Second, we create an anticipated response spectrum by adjusting the probability interval within a defined time window under specific active excitation. Third, the disparity between the expected response spectrum and the actual sensor data within the time window is utilized to calculate the probability of a sensor being attacked. Finally, we evaluate the effectiveness of our method through extensive simulations in a vehicle-platoon case study.

Research on Language Evolution and Language Diversity Based on Chinese Speech Pitch Deviation Features

Abstract This paper constructs a Chinese speech evaluation system based on the characteristics of the vocal mechanism of Chinese speech and uses the detection framework of statistical speech recognition to realize the automatic detection function of pronunciation bias. Assuming that the amplitude spectra of speech and noise obey the generalized Gamma distribution and Gaussian distribution, respectively, the MMSE estimator of the logarithmic spectrum of the speech signal is derived. The speech presence probability under the generalized Gamma speech model is derived as a correction to the MMSE estimation based on language evolution and diversity features. The correct recognition rate of the proposed algorithm reaches 64%, which indicates that the proposed algorithm can effectively suppress the interference of convolutional noise on the speech signal features and improve the subjective perceptual quality of diverse languages.

Adversarial Sample Generation Method Based on Global Convolution Noise Reduction Model

Adversarial Sample Generation Method Based on Global Convolution Noise Reduction Model

Analysis of Characteristics and Suppression Methods for Self-Defense Smart Noise Jamming

Self-defense smart noise jamming can automatically aim a signal frequency and obtain antenna gain and matching filtering processing gain, posing a huge threat to the normal performance of radar. In response to this situation, this article conducts an in-depth analysis of two typical smart noise jamming methods: noise convolution jamming methods and noise product jamming methods. The distribution characteristics of the two jamming methods in both time–frequency dimensions and their internal time–frequency relationships are analyzed. Based on this, a self-defense smart noise jamming suppression method based on pulse frequency stepping is proposed. This method obtains the true distance information of the target based on the phase difference caused by frequency stepping between adjacent pulses and uses this information to construct a filter to filter the radar echo, achieving jamming suppression. Simulation experiments have verified the effectiveness of this method.

Complete elimination of pump scattering in transient absorption spectroscopy using phase and amplitude modulation

Transient absorption (TA) spectroscopy is a powerful technique for studying ultrafast processes in physics, chemistry, biology, and materials. It is challenging to obtain high-quality TA spectra when there is severe light scattering in the sample. We propose an experimental approach to completely remove all noise caused by pump scattering in TA spectra using phase and amplitude modulation. We demonstrate through theoretical analysis and experiments that this method can remove the additive noise and convolution noise, all the noise in TA caused by pump scattering.

Fuzzy Based Convolutional Noise Clustering Classifier to Handle the Noise and Heterogeneity in Image Classification

Conventional Noise Clustering (NC) algorithms do not consider any spatial information in the image. In this study, three algorithms have been presented, Noise Local Information c-means (NLICM) and Adaptive Noise Local Information c-Means (ADNLICM), which use NC as the base classifier, and Noise Clustering with constraints (NC_S), which incorporates spatial information into the objective function of the NC classifier. These algorithms enhance the performance of classification by minimizing the effect of noise and outliers. The algorithms were tested on two study areas, Haridwar (Uttarakhand) and Banasthali (Rajasthan) in India. All three algorithms were examined using different parameters (distance measures, fuzziness factor, and δ). An analysis determined that the ADNLICM algorithm with Bray–Curtis distance measures, fuzziness factor m = 1.1, and δ = 106, outperformed the other algorithm and achieved 91.53% overall accuracy. The optimized algorithm returned the lowest variance and RMSE for both study areas, demonstrating that the optimized algorithm works for different satellite images. The optimized technique can be used to categorize images with noisy pixels and heterogeneity for various applications, such as mapping, change detection, area estimation, feature recognition, and classification.

The Residual ISI for Which the Convolutional Noise Probability Density Function Associated with the Blind Adaptive Deconvolution Problem Turns Approximately Gaussian.

In a blind adaptive deconvolution problem, the convolutional noise observed at the output of the deconvolution process, in addition to the required source signal, is—according to the literature—assumed to be a Gaussian process when the deconvolution process (the blind adaptive equalizer) is deep in its convergence state. Namely, when the convolutional noise sequence or, equivalently, the residual inter-symbol interference (ISI) is considered small. Up to now, no closed-form approximated expression is given for the residual ISI, where the Gaussian model can be used to describe the convolutional noise probability density function (pdf). In this paper, we use the Maximum Entropy density technique, Lagrange’s Integral method, and quasi-moment truncation technique to obtain an approximated closed-form equation for the residual ISI where the Gaussian model can be used to approximately describe the convolutional noise pdf. We will show, based on this approximated closed-form equation for the residual ISI, that the Gaussian model can be used to approximately describe the convolutional noise pdf just before the equalizer has converged, even at a residual ISI level where the “eye diagram” is still very closed, namely, where the residual ISI can not be considered as small.

Att-TasNet: Attending to Encodings in Time-Domain Audio Speech Separation of Noisy, Reverberant Speech Mixtures

Separation of speech mixtures in noisy and reverberant environments remains a challenging task for state-of-the-art speech separation systems. Time-domain audio speech separation networks (TasNets) are among the most commonly used network architectures for this task. TasNet models have demonstrated strong performance on typical speech separation baselines where speech is not contaminated with noise. When additive or convolutive noise is present, performance of speech separation degrades significantly. TasNets are typically constructed of an encoder network, a mask estimation network and a decoder network. The design of these networks puts the majority of the onus for enhancing the signal on the mask estimation network when used without any pre-processing of the input data or post processing of the separation network output data. Use of multihead attention (MHA) is proposed in this work as an additional layer in the encoder and decoder to help the separation network attend to encoded features that are relevant to the target speakers and conversely suppress noisy disturbances in the encoded features. As shown in this work, incorporating MHA mechanisms into the encoder network in particular leads to a consistent performance improvement across numerous quality and intelligibility metrics on a variety of acoustic conditions using the WHAMR corpus, a data-set of noisy reverberant speech mixtures. The use of MHA is also investigated in the decoder network where it is demonstrated that smaller performance improvements are consistently gained within specific model configurations. The best performing MHA models yield a mean 0.6 dB scale invariant signal-to-distortion (SISDR) improvement on noisy reverberant mixtures over a baseline 1D convolution encoder. A mean 1 dB SISDR improvement is observed on clean speech mixtures.

A Smart Noise Jamming Suppression Method Based on Atomic Dictionary Parameter Optimization Decomposition

Smart noise jamming is a new jamming style against radar, which plays an important role in modern radar electronic warfare. In order to solve the complex problem of convolutional smart noise jamming suppression with different time delay and jamming frequency modulation, a novel jamming suppression method based on the optimized atomic dictionary decomposition by estimating significant jamming parameters is proposed in this paper. First, the dual channel jamming signal information is obtained by designing twinning waveforms, then the independent estimation of time delay and jamming frequency parameters are established based on the spatial position sequences of the false targets. After that, the atomic dictionary is optimized by utilizing the obtained jamming parameters to achieve more effective decomposition. The performance of the new jamming suppression method is analyzed and verified by experimental simulations. The results show that this method can feasibly and practicably suppress the smart noise jamming with a deceptive effect, and the atomic decomposition efficiency is highly improved.

Surface defect detection of Si3N4 ceramic bearing ball based on improved homomorphic filter-Gaussian filter coupling algorithm

Considering the tiny surface defects of Si3N4 ceramic bearing balls and the low accuracy of defect detection using a single traditional algorithm, we find that the performance of the aerospace mechanical power system is poor. A coupling algorithm based on the improved homomorphic filter and Gaussian filter is proposed. The Si3N4 ceramic bearing ball nondestructive testing platform is established, by which the surface defect images of Si3N4 ceramic bearing balls are collected. The image acquisition card obtains the image information and then transmits it to the image storage module. The image processing module handles surface defect images. The gray transformation algorithm is used to complete image preprocessing. Gaussian noise in images is filtered out using the Gaussian filter algorithm. The homomorphic filtering algorithm is used to enhance the high frequency component, compress the low frequency component, and filter out the convolution noise and promiscuous signal. The contrast of the defect part has been reinforced using the coupling algorithm. It turns out that the accuracy of the coupling algorithm is 100%, 96.7%, 98.9%, and 94.4%.

Improved Approach for the Maximum Entropy Deconvolution Problem.

The probability density function (pdf) valid for the Gaussian case is often applied for describing the convolutional noise pdf in the blind adaptive deconvolution problem, although it is known that it can be applied only at the latter stages of the deconvolution process, where the convolutional noise pdf tends to be approximately Gaussian. Recently, the deconvolutional noise pdf was approximated with the Edgeworth Expansion and with the Maximum Entropy density function for the 16 Quadrature Amplitude Modulation (QAM) input but no equalization performance improvement was seen for the hard channel case with the equalization algorithm based on the Maximum Entropy density function approach for the convolutional noise pdf compared with the original Maximum Entropy algorithm, while for the Edgeworth Expansion approximation technique, additional predefined parameters were needed in the algorithm. In this paper, the Generalized Gaussian density (GGD) function and the Edgeworth Expansion are applied for approximating the convolutional noise pdf for the 16 QAM input case, with no need for additional predefined parameters in the obtained equalization method. Simulation results indicate that improved equalization performance is obtained from the convergence time point of view of approximately 15,000 symbols for the hard channel case with our new proposed equalization method based on the new model for the convolutional noise pdf compared to the original Maximum Entropy algorithm. By convergence time, we mean the number of symbols required to reach a residual inter-symbol-interference (ISI) for which reliable decisions can be made on the equalized output sequence.

Efficiency Improvement of Delta Sigma Modulator-Based Transmitter Using Complex Delta Sigma Modulator and Noise Reduction Loop

This paper proposes an architecture to enhance coding efficiency (CE) of the Delta Sigma Modulator (DSM) transmitters. In this architecture, a complex–low pass delta sigma modulator (LPDSM) is used instead of existing Cartesian–LPDSM and polar–low pass envelope delta sigma modulator (LPEDSM). Simulation results show that for Uplink long-term evolution (LTE) signal with 1.92[Formula: see text]MHz bandwidth and 7.8-dB peak to average power ratio (PAPR), the CE for the complex–LPDSM-based transmitter is equal to 41.7% in compare to 9.7% CE for Cartesian–LPDSM transmitter. Also, due to the resolving of noise convolution problem, the complex–LPDSM-based transmitter baseband part needs lower oversampling ratio (OSR) and clock speed than polar–LPEDSM transmitter baseband part to achieve the same signal-to-noise and distortion ratio (SNDR). In the next step, a quantization noise reduction loop is implemented in this architecture. By using this technique for an Uplink LTE signal with 1.92[Formula: see text]MHz bandwidth, with the same PAPR and OSR of 16, the CE is improved from 41.7% to 56.1% with 40[Formula: see text]dB SNDR.

Noise Reduction Power Stealing Detection Model Based on Self-Balanced Data Set

In recent years, various types of power theft incidents have occurred frequently, and the training of the power-stealing detection model is susceptible to the influence of the imbalanced data set and the data noise, which leads to errors in power-stealing detection. Therefore, a power-stealing detection model is proposed, which is based on Improved Conditional Generation Adversarial Network (CWGAN), Stacked Convolution Noise Reduction Autoencoder (SCDAE) and Lightweight Gradient Boosting Decision Machine (LightGBM). The model performs Generation- Adversarial operations on the original unbalanced power consumption data to achieve the balance of electricity data, and avoids the interference of the imbalanced data set on classifier training. In addition, the convolution method is used to stack the noise reduction auto-encoder to achieve dimension reduction of power consumption data, extract data features and reduce the impact of random noise. Finally, LightGBM is used for power theft detection. The experiments show that CWGAN can effectively balance the distribution of power consumption data. Comparing the detection indicators of the power-stealing model with various advanced power-stealing models on the same data set, it is finally proved that the proposed model is superior to other models in the detection of power stealing.

Modeling and Simulation of Three Distributed Cooperative Suppressive Jamming Modes

The distributed cooperative jamming based on joint modes is an effective means to counter the multi-function radar or netted radar, different joint modes will bring different jamming effects. This paper, analyzed two common suppressive jamming patterns, including noise convolution jamming and dense false target jamming, and presented the simulation suppressive effect aiming at LFM radar; illustrated the conception of cooperative jamming and cooperative mode, put forward three new cooperative modes and carried out theoretic analyses and simulation, the simulated results validated the cooperative jamming’ effectiveness.

Convolutional Recurrent Neural Network-Based Event Detection in Tunnels Using Multiple Microphones

This paper proposes a sound event detection (SED) method in tunnels to prevent further uncontrollable accidents. Tunnel accidents are accompanied by crashes and tire skids, which usually produce abnormal sounds. Since the tunnel environment always has a severe level of noise, the detection accuracy can be greatly reduced in the existing methods. To deal with the noise issue in the tunnel environment, the proposed method involves the preprocessing of tunnel acoustic signals and a classifier for detecting acoustic events in tunnels. For preprocessing, a non-negative tensor factorization (NTF) technique is used to separate the acoustic event signal from the noisy signal in the tunnel. In particular, the NTF technique developed in this paper consists of source separation and online noise learning. In other words, the noise basis is adapted by an online noise learning technique for enhancement in adverse noise conditions. Next, a convolutional recurrent neural network (CRNN) is extended to accommodate the contributions of the separated event signal and noise to the event detection; thus, the proposed CRNN is composed of event convolution layers and noise convolution layers in parallel followed by recurrent layers and the output layer. Here, a set of mel-filterbank feature parameters is used as the input features. Evaluations of the proposed method are conducted on two datasets: a publicly available road audio events dataset and a tunnel audio dataset recorded in a real traffic tunnel for six months. In the first evaluation where the background noise is low, the proposed CRNN-based SED method with online noise learning reduces the relative recognition error rate by 56.25% when compared to the conventional CRNN-based method with noise. In the second evaluation, where the tunnel background noise is more severe than in the first evaluation, the proposed CRNN-based SED method yields superior performance when compared to the conventional methods. In particular, it is shown that among all of the compared methods, the proposed method with the online noise learning provides the best recognition rate of 91.07% and reduces the recognition error rates by 47.40% and 28.56% when compared to the Gaussian mixture model (GMM)–hidden Markov model (HMM)-based and conventional CRNN-based SED methods, respectively. The computational complexity measurements also show that the proposed CRNN-based SED method requires a processing time of 599 ms for both the NTF-based source separation with online noise learning and CRNN classification when the tunnel noisy signal is one second long, which implies that the proposed method detects events in real-time.

Statistical sparsity

The main contribution of this paper is a mathematical definition of statistical sparsity, which is expressed as a limiting property of a sequence of probability distributions. The limit is characterized by an exceedance measure~$H$ and a rate parameter~$\rho > 0$, both of which are unrelated to sample size. The definition is sufficient to encompass all sparsity models that have been suggested in the signal-detection literature. Sparsity implies that $\rho$~is small, and a sparse approximation is asymptotic in the rate parameter, typically with error $o(\rho)$ in the sparse limit $\rho \to 0$. To first order in sparsity, the sparse signal plus Gaussian noise convolution depends on the signal distribution only through its rate parameter and exceedance measure. This is one of several asymptotic approximations implied by the definition, each of which is most conveniently expressed in terms of the zeta-transformation of the exceedance measure. One implication is that two sparse families having the same exceedance measure are inferentially equivalent, and cannot be distinguished to first order. A converse implication for methodological strategy is that it may be more fruitful to focus on the exceedance measure, ignoring aspects of the signal distribution that have negligible effect on observables and on inferences. From this point of view, scale models and inverse-power measures seem particularly attractive.

Convolutional Noise PDF at the Convergence State of a Blind Adaptive Equalizer

In the literature, the convolutional noise obtained at the output of a blind adaptive equalizer, is often modeled as a Gaussian process during the latter stages of the deconvolution process where the process is close to optimality. However, up to now, no strong mathematical basis was given supporting this phenomenon. Furthermore, no closed-form or closed-form approximated expression is given that shows what are the constraints on the system’s parameters (equalizer’s tap-length, input signal statistics, channel power, chosen equalization method and step-size parameter) for which the assumption of a Gaussian model for the convolutional noise holds. In this paper, we consider the two independent quadrature carrier input case and type of blind adaptive equalizers where the error that is fed into the adaptive mechanism which updates the equalizer’s taps can be expressed as a polynomial function of the equalized output up to order three. We show based on strong mathematical basis that the convolutional noise pdf at the latter stages of the deconvolution process where the process is close to optimality, is approximately Gaussian if complying on some constraints depending on the step-size parameter, input constellation statistics, channel power, chosen equalization method and equalizer’s tap-length. Simulation results confirm our findings.

General noise suppression scheme with reference detection in heterodyne nonlinear spectroscopy.

We devised a novel two-step reference scheme that can greatly suppress the additive and convolutional noises in heterodyne nonlinear spectroscopy. To optimally remove additive noise, we fully utilized the spectral correlation in multi-channel reference data through a linear combination and regression algorithm. Using our pump-probe 2D IR spectrometer, we demonstrated that our scheme can improve the signal-to-noise ratio by 10-30 times and reach the noise floor of the signal detector. The new algorithm is guaranteed to reduce noise, enables the use of unmatched reference detectors, and does not introduce baseline shift or signal distortion. This scheme is applicable to many heterodyne spectroscopic techniques.