Random Signal Noise Research Articles

Narrowband feedback active noise control systems with secondary path modeling using gain-controlled additive random noise

This paper investigates estimation of the secondary path (SP) during the online operation of the filtered-x least mean square (FxLMS) algorithm-based feedback active noise control (FBANC) systems. The proposed method develops upon a previous work where two adaptive filters were used, one for active noise control (ANC) and the other for secondary path modeling (SPM). The proposed method essentially comprises a similar structure as that of the previous method. The objectives here are to suggest modifications to improve upon the slow convergence of SPM filter and the noise reduction (NR) performance in the previous method. The key idea is to employ a gain-controlled modeling signal (generated from the additive random noise signal) mixed with the cancellation signal. The gain-factor for the modeling signal is adjusted such that a large-level modeling signal is used during the transient state of the ANC system. This improves the converge of the SPM filter. As the ANC system converges, the level of the modeling signal is reduced to achieve good NR performance. Besides controlling the level of the modeling, the gain control parameter is employed in adjusting the various other parameters too, viz. fixed step-size, regularization parameter, convergence monitoring parameter, while computing the time-varying normalized step-size for the SPM filter. The simulation results demonstrate that the proposed method (equipped with the proposed modifications) outperforms the previous method and yet with a negligible increase in the computational complexity.

Random noise suppression of magnetic resonance sounding signal based on modified short-time Fourier transform

Magnetic resonance sounding (MRS) has the advantage of detecting groundwater content directly without drilling, but the signal-to-noise ratio (SNR) is extremely low which limits the application of the method. Most of the current researches focus on eliminating spikes and powerline harmonic noise in the MRS signal, whereas the influence of random noise cannot be ignored even though it is difficult to suppress due to the irregularity. The common method to eliminate MRS random noise is stacking which requires extensive measurement repetition at the cost of detection efficiency, and it is insufficient when employed in a high-level noise surrounding. To solve this problem, we propose a modified short-time Fourier transform(MSTFT) method, in which used is the short-time Fourier transform on the analytical signal instead of the real-valued signal to obtain the high-precision time-frequency distribution of MRS signal, followed by extracting the time-frequency domain peak amplitude and peak phase to reconstruct the signal and suppress the random noise. The performance of the proposed method is tested on synthetic envelope signals and field data. The using of the MSTFT method to handle a single recording can suppress the random noise and extract MRS signals when SNR is more than –17.21 dB. Compared with the stacking method, the MSTFT achieves an 27.88dB increase of SNR and more accurate parameter estimation. The findings of this study lay a good foundation for obtaining exact groundwater distribution by utilizing magnetic resonance sounding.

A weak fault diagnosis scheme for common rail injector based on MGOA-MOMEDA and improved hierarchical dispersion entropy

Aiming at the problem that the common rail injector’s early fault characteristics are very weak and susceptible to random noise and other signal interference, this paper proposes a new common rail injector weak fault diagnosis method based on multipoint optimal minimum entropy deconvolution adjusted based on modified grasshopper optimization algorithm optimization algorithm (MGOA-MOMEDA), improved hierarchical dispersion entropy, and least square support vector machine. First, the fault period T is determined using the multipoint kurtosis spectrum. Through the MGOA optimization algorithm, the optimal filter length L of MOMEDA is obtained adaptively, and the optimal performance filter is used for filter processing. Then, improved hierarchical discrete entropy is used to measure the complexity of the filtered fuel pressure signal to extract weak fault features. Finally, the fault feature vector is input into the LS-SVM multi-classifier to realize the weak fault diagnosis and recognition of the common rail injector. Through experimental verification, the proposed method can effectively achieve the weak fault diagnosis of the common rail injector.

Cluster analysis based fringe-activity range detector

A novel clustering-analysis approach is proposed in this work. This approach addresses the fringe-activity range-detection issue often encountered in vertical-scanning wideband interferometry. The proposed approach exploits the fact that the fringe signal formed by a wideband light source, which is composed of deterministic signals and a random noise signal. As a result, the number of clusters in the proposed clustering method can be easily specified as 2, in advance. The proposed analysis procedure and its development using a k-means method-based clustering scheme are described in this paper. To the best of the authors’ knowledge, this is the first investigation report regarding fringe-activity range detection. The details of the proposed approach are introduced within the framework of femtosecond optical frequency comb-based interferometry. Experimental results demonstrate the efficiency of the proposed algorithm with respect to the fringe-activity range detection.

Seismic Random Noise Suppression by Using Adaptive Fractal Conservation Law Method Based on Stationarity Testing

Attenuating the random noise and improving the signal-to-noise ratio (SNR) for the seismic data are of great significance in industrial exploration. In recent years, fractal conservation law (FCL) has been proposed and applied to seismic random noise suppression successfully. However, in conventional FCL, the filtering parameter selection strategy is relatively simple and a fixed parameter set is always used for the whole seismic record. In addition, it is very difficult to make an excellent tradeoff in random noise attenuation and signal preservation only by fixed parameters especially under the low-SNR conditions. Thus, accurately recognizing the effective signals and adaptively choosing appropriate filtering parameters is a feasible approach to improve the performance of the conventional FCL. In this article, an adaptive FCL methodology is proposed by combining the seismic noise analyzing theory and stationarity testing techniques. It is known that the random noise and reflection signals have different properties in stationarity and thus, the signal and noise segments can be divided by stationarity testing. As a consequence, different filtering parameters can be adopted for signal and noise segments to achieve the noise suppression and signal preservation simultaneously. Synthetic and field data experiments demonstrate that the proposed method can remove the random noise from seismic record and effectively preserve the reflection events.

Image Cryptosystem for Visually Meaningful Encryption Based on Fractal Graph Generating

Image information security is an important research direction in the field of information security. In existed image encryption schemes, original image is transformed to meaningless random noise signal which would draw attention and thus attract attacks. In this work, aiming to encrypt image to visually meaningful encrypted image, a novel image cryptosystem based on fractal graph generating is proposed which is called Visually Meaningful Image Encryption (VMIE) scheme. The VMIE scheme is to encrypt plain images into fractal scenery or fractal plant images by using fractal graph generation parameters as keys. According to these keys, receiver can get the plain image from fractal image by a reverse process. Based on the pixel value distribution characteristics of different plain images, different fractal transformations can be selected to produce different fractal images, which can better camouflage the plain images and make the encrypted plain images safer. In this paper, a new image encryption method is proposed, which is different from VMIE based on information hiding with limited hiding capacity, and from VMIE based on image sharing, which takes up a large amount of storage space and image size. Simulation experiments show that the proposed new scheme has low computation complexity and high security and the encrypted plain images have well understandability, strong resistance to compression and small data increase.

Reduction of random telegraph noise by high-pressure deuterium annealing for p-type omega-gate nanowire FET

In this work, we studied the effect of high-pressure deuterium annealing (HPDA) on a p-type omega-gate nanowire field effect transistor by random telegraph noise (RTN) signal analysis. After HPDA under conditions of 400 °C and 10 atm for 30 min, IOFF decreases by 41.2% and ION increases by up to 5.4%. Also, subthreshold swing (SS) is reduced from 72 mV dec−1 to 70 mV dec−1. In RTN analysis, multi-level RTN is reduced to single-level RTN due to the passivation of a fast trap site by HPDA. ΔID/ID is also decreased 1.3 and 1.1 times at |VOV| = 0.2 V and 0.4 V, respectively. From the low-frequency noise analysis, the reduction of trap density is observed by 86% at |VOV| = 0.4 V after HPDA. Through these results, we found that the HPDA reduces traps of gate dielectric and improves the quality of the interface between gate dielectric and NW channel in p-type OGNW FET.

Neural network based analysis of random telegraph noise in resistive random access memories

The characterization of random telegraph noise (RTN) signals in resistive random access memories (RRAM) is a challenge. The inherent stochastic operation of these devices, much different to what is seen in other electron devices such as MOSFETs, diodes, etc, makes this issue more complicated from the mathematical viewpoint. Nevertheless, the accurate modeling of these type of signals is essential for their use in digital and analog applications. RTN signals are revealed to be linked to the emission and capture of electrons by traps close to the conductive filament that can influence resistive switching operation in RRAMs. RTN features depend on the number of active traps, on the interaction between these traps at different times, on the occurrence of anomalous effects, etc Using a new representation technique, the locally weighted time lag plot (LWTLP), a highly efficient method in terms of computation, data from current–time (I–t) traces can be represented with a pattern that allows the analysis of important RTN signal features. In addition, self-organizing maps, a neural network devoted to clustering, can be employed to perform an automatic classification of the RTN traces that have similar LWTLP patterns. This pattern analysis allows a better understanding of RTN signals and the physics underlying them. The new technique presented can be performed in a reasonable computing time and it is particularly adequate for long (I–t) traces. We introduce here this technique and the most important results that can be drawn when applied to long RTN traces experimentally obtained in RRAMs.

Desert seismic noise suppression based on an improved low-rank matrix approximation method

The suppression of random noise is a crucial step before seismic data analysis. Random noise in desert areas has the characteristics of low frequency and non-stationary, and there is serious spectrum aliasing between random noise and effective signals, which makes it difficult to suppress such noise. In recent years, some methods based on signal rank minimization have achieved remarkable results in seismic random noise suppression. Since the implementation of low rank matrix approximation is an iterative process, noise estimation is an indispensable step before each iteration, but also an important step. The noise estimation method previously used is to calculate the residuals of the original noisy patch data and the corresponding iterative denoising version, which is intuitively considered as the filtered noise. This method may be very inaccurate in the case of high noise levels or complex seismic records. In this paper, a noise estimation method based on geometric texture is introduced to estimate the noise level by selecting weak textured patches in all seismic texture patches. At the same time, we reduce the loss of effective signals by truncating the singular values in each iteration. Experiments on both synthetic and field seismic data show that this method has better effect on suppressing random noise in desert areas.

Adaptive Noise Cancellation Algorithms Implemented onto FPGA-Based Electrical Impedance Tomography System

Electrical Impedance Tomography (EIT) as a non-invasive of electrical conductivity imaging method commonly employs the stationary-coefficient based filters (such as FFT) in order to remove the noise signal. In the practical applications, the stationary-coefficient based filters fail to remove the time-varying random noise which leads to the lack of impedance measurement sensitivity. In this paper, the implementation of adaptive noise cancellation (ANC) algorithms which are Least Mean Square (LMS) and Normalized Least Mean Square (NLMS) filters onto Field Programmable Gate Array (FPGA)-based EIT system is proposed in order to eliminate the time-varying random noise signal. The proposed method was evaluated through experimental studies with biomaterial phantom. The reconstructed EIT images with NLMS is better than the images with LMS by amplitude response AR = 12.5%, position error PE = 200%, resolution RES = 33%, and shape deformation SD = 66%. Moreover, the Analog-to-Digital Converter (ADC) performances of power spectral density (PSD) and the effective number of bit ENOB with NLMS is higher than the performances with LMS by SI = 5.7 % and ENOB = 15.4 %. The results showed that implementing ANC algorithms onto FPGA-based EIT system shows significantly more accurate image reconstruction as compared without ANC algorithms implementation.

Covert Electromagnetic Nanoscale Communication System in the Terahertz Channel

In this paper, an electromagnetic nano random communication system (EM-nRCS) has been proposed which ensures covert communication in the terahertz (THz) band. In the proposed system, the skewed alpha-stable noise shift keying method has been used to transmit random noise signals (RNSs) from the nano-transmitter (NT) by utilizing single-walled/carbon nanotubes-based true random number generator (SWCNTs-TRNG) and a graphene-based nano-antenna. A line-of-sight THz transparency window between 0.1[Formula: see text]THz and 0.5[Formula: see text]THz in the THz channel with spreading loss, molecular absorption loss and molecular absorption noise has been considered. Due to the broadband nature of the RNSs, the proposed EM-nRCS provides efficient transmission by overcoming the high path loss and intense channel noise arising from random fluctuations in the THz band. Non-coherent nano-receiver (NR) consisting of the modified extreme value method (MEVM) estimator has been proposed to extract the hidden binary information in the received RNSs. The bit error rate performance shows that the proposed EM-nRCS ensures high performance and covertness for future EM nanoscale communication devices.

Determination of Crack Signals Using the Deep Learning Technique Based on a 1D Convolutional Neural Network for Smart Detection of Structural Damage Cracking

Structures can be damaged by natural disasters such as earthquakes and typhoons. In particular, any damage to the foundation of a structure can present critical problems. Therefore, a smart monitoring technique such as the acoustic emission method is required to detect internal cracks and other types of structural damage. Many laboratory studies on this method have been conducted to estimate the locations and sizes of cracks as well as the resulting changes in structural durability using collected acoustic signals. However, the method has rarely been applied in the field because identifying damage signals from acquired signals, which can contain ambient noise, is difficult. We developed a deep learning algorithm based on a one-dimensional convolutional neural network method that can identify damage or crack signals generated from concrete failure from randomly synthesized signals. Using the developed algorithm, we were able to distinguish damage signals from random ambient noise signals. This algorithm enables real-time monitoring of concrete structures, thus providing a smart monitoring strategy. Keywords: Structure Health Monitoring, Machine Learning, 1D Convolution Network, Accelerometer

Detection of random noise and anatomy of continuous seismic waveforms in dense array data near Anza California

SUMMARY We develop a methodology to separate continuous seismic waveforms into random noise (RN), not random noise (NRN) produced by earthquakes, wind, traffic and other sources of ground motions, and an undetermined mixture of signals. The analysis is applied to continuous records from a dense seismic array on the San Jacinto fault zone. To detect RN signals, we cut hourly waveforms into non-overlapping 1 s time windows and apply cross-correlations to separate RN candidates from outliers. The cross-correlation coefficients between different RN candidates fall into a tight range (i.e. 0.09–0.35), while cross-correlation coefficients of RN candidates with NRN signals (e.g. seismic or air-traffic events) are lower. The amplitude spectra of RN candidates have a well-defined level, while the amplitude spectra of other signals deviate from that level. Using these properties, we examine the amplitude spectra of moving time windows and cross-correlation coefficients with RN templates in each hour. The hourly RN is quasi-stationary and the results cluster tightly in the parameter space of cross-correlation coefficients and L2 norm deviations from the mean spectra of RN candidates. Time windows with parameters in this tight cluster are identified as RN, windows that deviate significantly from the RN cluster are identified as NRN and windows with values in between are identified as mixed signals. Several iterations on each hourly data are used to update and stabilize the selection of RN templates and mean noise spectra. For the days examined, the relative fractions of RN, NRN and mixed signals in local day (night) times are about 26 (42 per cent), 40 (33 per cent) and 34 per cent (25 per cent), respectively.

3D Modal Analysis of a Loaded Tire with Binary Random Noise Excitation

ABSTRACT Modal analysis of tires has been a fundamental part of tire research aimed at capturing the dynamic behavior of a tire. An accurate expression of tire dynamics leads to an improved tire model and a more accurate prediction of tire behavior in real-life operations. Therefore, the main goal of this work is to improve the tire-testing techniques and data range to obtain the best experimental data possible using the current technology. With this goal in mind, we propose novel testing techniques such as piezoelectric excitation, high-frequency bandwidth data, and noncontact vibration measurement. High-frequency data enable us to capture the coupling between the wheel and tire as well as the coupling between airborne and structure-borne noise. Piezoelectric excitation eliminates the dynamic coupling of shakers and the inconsistency of force magnitude and direction of impact hammers as well as added mass effect. Noncontact vibration measurements using three-dimensional (3D) scanning laser Doppler vibrometer (SLDV) are superior to accelerometers because of no mass loading, a high number of measurement points in three dimensions, and high sensitivity. In this work, a modal analysis is carried out for a loaded tire in a static condition. Because of the highly damped nature of tires, multiple input excitation with binary random noise signal is used to increase the signal strength. Mode shapes of the tire are obtained and compared using both accelerometers and SLDV measurements.

High frequency realization of non-autonomous nonlinear transistor circuit

In this paper, an electronic implementation of a non-autonomous nonlinear transistor circuit is presented. This nonlinear circuit topology requires a minimal number of components, which consists of two resistors, two capacitors, and a single NPN bipolar junction transistor (BJT). This topology is of interest because it is relatively simple to construct and could be used for potential applications such as random number generators (RNGs) or noise signal generators (NSGs). The transistor portion of the circuit was analyzed using the Ebers-Moll model for a BJT. Using this model, time domain and phase space plots that qualitatively match the original systems dynamics were created. This model was also used to create bifurcation diagrams of the base voltage versus both frequency and amplitude, where periodic and chaotic solutions exist. The hardware realization was built using commercial-off-the-shelf (COTS) components with two different printed circuit board (PCB) designs. This PCB included the forcing function on the board with the transistor circuit. This circuit topology functioned over a wide range of frequencies, with an upper limit of approximately 5.1 MHz. Many potential applications could benefit from this high operation frequency.

A new technique to analyze RTN signals in resistive memories

A new technique to study random telegraph noise is proposed. It is based on an analysis of the variations of the sampled current instead of the current itself. These current variations are represented following a time-lag-plot scheme. The meaning of the graphical patterns obtained is discussed. Specifically, this new method is very effective for detecting current spikes due to the presence of fast traps. In addition, it can be used to easily detect an unsuitable sampling rate and achieve optimum RTN measurement conditions. This technique has been used for analyzing several current versus time traces for the state-of-the-art Resistive Random Access Memories (RRAMs). The results are analyzed and the patterns obtained with the proposed method are linked to the characteristic features of the corresponding random telegraph noise signals, showing the validity and effectiveness of the proposed method.

RTN in GexSe1-x OTS selector devices

Random telegraph noise (RTN) signals in GexSe1-x ovonic threshold switching (OTS) selector have been analysed in this work, both before and after the first-fire (FF) operation and at on- and off-states. It is observed that RTN appears after the FF, and its absolute amplitude at the off-state is small and negligible in comparison with the RTN signals in RRAM devices. At the on-state, large RTN signals are observed, which can either partially or fully block the conduction path, supporting that a conductive filament is formed or activated by FF and then modulated during switching. Statistical analysis reveals that the relative RTN amplitude at on-state in GexSe1-x OTS selector is smaller than or equivalent to those in RRAM devices.

Efficient Locality Classification for Indoor Fingerprint-Based Systems

Locality classification is an important component to enable location-based services. It entails two sequential queries: 1) whether a target is within the site or not, i.e., inside/outside region decision, and 2) if so, which area in the region the target is located, i.e., area classification. Locality classification is hence more coarse-grained and efficient as compared with pinpointing the exact target location in the region. The classification problem is challenging, because fingerprints may not exist outside the region for training. Furthermore, the target may sample an incomplete RSSI vector due to, say, random signal noise, momentary occlusion, or scanning duration. The algorithm also has to be computationally efficient. We propose INOA, a scalable and practical locality classification overcoming the above challenges. INOA may serve as a plug-in before any fingerprint-based localization, and can be incrementally extended to cover new areas or regions for large-scale deployment. Its preprocessor cherry-picks only those discriminating access points, which greatly enhances computational efficiency and accuracy. By formulating a “one-class” classifier using ensemble learning, INOA accurately decides whether the target is within the region or not. Extensive experimental trials in different sites validate the high efficiency and accuracy of INOA, without the need of full RSSI vectors collected at the target.

Влияние формы канала на амплитуду случайных телеграфных шумов в подпороговой области беспереходного FinFET-транзистора

In this work it is simulated the random telegraph noise signal amplitude dependence on gate voltage overdrive for silicon on insulator junctionless FinFET transistor with fin cross section in rectangle and trapeze form. It is shown in subthreshold region the noise signal amplitude is lower in case of trapeze cross section of the fin. Besides it approximately at the same condition the noise signal amplitude is essentially lower in junctionless fin field effect transistor than in bulk, fully depleted silicon on insulator and ordinary fin field effect transistors.

Simulation and investigations of a software implemented phase-locked loop with improved noise immunity

The improvement of noise immunity of a communication system is an effective way to increase the capacity of communication systems, which would provide more qualitative service for a larger number of users. This task can be solved by lowering the noise threshold of a phase-locked loop (PLL) in these systems if the dynamic properties of the device are preserved. The literature review indicates that such a device with improved noise immunity has already been implemented, but the effects of noise and modulation on its dynamic behavior were analyzed separately. This article is devoted to the analysis of the behavior of a digital firmware PLL under the simultaneous influence of noise and modulation of the input signal. The article depicts the structure of the classical digital PLL and its modifications and explains key differences between them. The simulation of the classical PLL with either absence or presence of noise at the device input was carried out. The simulation results show that the PLL is not able to detect all phase changes when the noise is present. Besides, the modified PLL has a wider working frequency range than the classical one under noisy conditions. The investigations of the PLL dynamic behavior with the simultaneous influence of random noise and Binary Phase Shift Keying (BPSK) modulated input signal was performed. The results of the research show that the duration of the transient processes during the processing of the BPSK modulated signal in the modified device is at least twice as low as that for the classical one. In addition, the number of errors during the signal detection increases faster for the classical PLL than for the modified one when the noise level rises. The use of the modified PLL in modern communication systems gives an opportunity to increase their capacity.