Random Signal Processing Research Articles

Low frequency noise in β -Ga2O3 based nanoelectronic devices

In this work, low frequency noise in β-Ga2O3 nanowire-based (NW) electronic devices is analyzed, which exhibits different behaviors as the device size scales down. The noise spectrum for the narrower NW (∼80 nm) is closer to 1/f characteristics, whereas it starts to show evident 1/f2 components as the NW size gets thicker (∼200 nm), giving clear signs of distinctive features for the bunch of traps at the NW interface or in the bulk. Our results show that 1/f noise in these NW electronic devices seems predominantly originated from an aggregated effect of the intricate trap states close to the β-Ga2O3 NW surface or interface with a wide range distribution, while finite groups of active deep traps play a critical role in contributing 1/f2components via generation-recombination or random telegraph signal processes. Notably, as the bias voltage increases, the 1/f2 components in the noise spectra get more overwhelming and would shift toward lower frequencies, suggesting that electric ionization effects would screen the shallow traps close to the surface or interface based on the Poole–Frenkel model. The Hooge's constants extracted from the 1/f noise component for these β-Ga2O3 NW-based devices fall in the range of 0.008–0.019, which are comparable to those of the best reported devices based on other wide bandgap semiconductor with nanoscale structures, including GaN, ZnO, and SnO2. This work may give hints of revealing the sophisticated dynamic behaviors of traps in the surface/volume β-Ga2O3 materials and electronic devices in the nanoscale by low frequency noises.

Experimental determination of vibration conductivity by rocket sled structural elements in high-speed track tests of aircraft equipment

The development of new ballistic-type aircraft is characterized primarily by improved aerodynamic characteristics and higher speed limits. Ground track testing of aviation and rocket technology is a stage whose task is to confirm the efficiency and effectiveness of new developments. Track tests make it possible to simulate real loads, they are simpler and much cheaper than flight tests. Experimental installation "Rocket rail track 3500" of Scientific Test Range of Aviation Systems named after L. K. Safronov is constantly being upgraded in order to conduct track tests of products at a speed greater than 3M. The experimental setup includes a two-rail track, made on a special foundation, which excludes unacceptable rail deflection with a mass of up to 3000 kg. The rail track has an acceleration section with an angle of attack 2500 m long and a deceleration section. A tray filled with water is made on the braking section between the track rails. It is designed for hydrodynamic braking to a complete stop of the rocket sled with stored equipment. The movable rocket track sled is made of a massive steel plate to which three cross beams are welded. The front and rear beams end with axles on which sliding supports are pivotally mounted. On the rear and middle beams there are lodgements for fastening rocket engines of solid fuel. Depending on the required test speed, from one to five motors can be placed on the cradles. The test object is usually mounted on the front and middle beams along the axis of the sled and fixed in a cantilever, with the head part extended forward. The design of the supports - shoes is made to encircle the rail head in such a way that it provides sliding contact along the upper plane of the rail head, and in the event of a lifting force exceeding the weight of the sled at high speeds, it keeps the structure from free flight by contacting the lower surface of the rail head. Track high-speed tests of special equipment objects are always accompanied by intense vibration and shock effects of structural elements. Due to the desire to conduct track testing of products at a faster rate, it becomes necessary to reduce the level of dynamic loads and eliminate resonant interactions. The article presents an algorithm and methodology! for statistical processing of random signals of three-axis vibration acceleration sensors installed on the shoes of the rocket track sled and on the fairing of the test object. Due to the placement of registration data storage devices on the sled, experimental vibration data were stored when testing the product at a speed of more than IM. The autocorrelation functions of the signals of vibration accelerations of sensors placed on various elements of the rocket sled, the functions of mutual correlation of the corresponding signals, the density of the amplitude spectra, the density of the power spectra and the transfer functions that characterize the dynamic conductivity of vibrations from the shoes sliding along the rail guides to the test object were determined.

Fully Ferroelectric-FETs Reservoir Computing Network for Temporal and Random Signal Processing

Reservoir computing (RC), a derivation of recurrent neural networks (RNNs), is an energy-efficient computational framework suitable for temporal signal processing. Owing to the short-term and long-term memory capability, the ferroelectric field-effect transistor (FeFET) is regarded as a promising hardware component for implementing RC networks. This article aims to optimize the fully FeFETs RC network by evaluating the recognition accuracy in various classification tasks, which includes the operating voltage sequence, device numbers as well as connection methods. The physical random telegraph noise (RTN), working as an ideal temporal and random signal, is investigated and extended by using the optimized fully FeFET RC network, resulting in a rapid time constant extraction method. Our findings may provide the broad potential for hardware security and cyber security based on the fully FeFET RC network.

Using optical flow algorithm based on dynamic illumination mode to examine defects on highly reflective turbine blade surface

Notion of optical flow literally refers to the displacements of intensity patterns. In that sense, extracting interested information from 2D scene is analogy to modulation/demodulation in random signal processing. To address the limitations presented in computer vision based on static image, we propose a novel metal component defect detection method, specified as the instance of turbine blade surface detection, using optical flow estimation.To start the specified pattern recognition in 2D presentation, we modulate the brightness constancy assumption equation as illumination varying model, by sampling the second image with function whose frequency was chosen according to the Nyquist sampling theorem, and a sinusoidal factor was introduced as an additive factor. This tunable channel based on 2D image transfers intensity features into optical modes. Then, we implement optical flow estimation on two sequential images. Experimental results reveal grayscale space shows completness in representing the optical modes of turbine blade with various kinds of surface characteristics. By modifying the index of information content, we propose quantitative index to evaluate the performance of our method. Evaluation reveals optical flow algorithm is qualified to examine defects on highly reflective turbine blade, and our method extends the application of optical flow.

Impulse Processing Algorithm for Random Source Signals of Roadheaders That Is Based on Compound Interferometry

The boom-type roadheader is the main equipment for realizing the mechanization of coal drifting in coal mines, and it is an indispensable production equipment in major coal-producing countries. Substantial vibrations are generated during the operation of a roadheader; these vibrations carry substantial energy and, thus, can be regarded as a potential source and used for seismic advance detection purposes in mine drifts. Compared with a conventional exploration source, a roadheader source produces a complex continuous random signal. The shape of a seismic wavelet is uncertain and its duration is relatively long; thus, it must be processed into a conventional pulse signal before it can be used for subsequent processing and analysis. Therefore, based on the advantages of seismic interferometry in random signal processing, two seismic interference techniques, namely, deconvolution and cross-correlation, are introduced for constructing a compound interference algorithm. On the basis of a theoretically derived formula, a random signal impulse processing experiment is conducted using field-acquired source signals from a roadheader; this approach resolves the problem that cross-correlation alone cannot yield ideal results. Hence, a feasible algorithm for the impulse processing of a random signal, namely, the compound interference algorithm, is proposed. The algorithm deconvolves each seismic trace to obtain the reference trace and other receiver traces after compressing the wavelet. Then, the reference trace and each receiver trace are cross-correlated, and the wavelet time delay information of each correlated wavelet pulse, namely, the wavelet time delay information of the receiver trace relative to the reference trace, is obtained. Accordingly, the direct wave and reflected waves are recognized. To evaluate the performance of the algorithm, an algorithm application experiment is conducted for another group of random source signals that were collected by a roadheader under different coal drift conditions. Again, the algorithm processing results are consistent with the single-shot record characteristics of an explosive source. Consequently, the proposed algorithm can satisfy the requirements for engineering exploration and analysis. A comprehensive analysis further demonstrates that the compound interference algorithm is both feasible and effective and that the processed seismic signals can be used for subsequent processing and interpretation.

Random Error Reduction Algorithms for MEMS Inertial Sensor Accuracy Improvement-A Review.

Research and industrial studies have indicated that small size, low cost, high precision, and ease of integration are vital features that characterize microelectromechanical systems (MEMS) inertial sensors for mass production and diverse applications. In recent times, sensors like MEMS accelerometers and MEMS gyroscopes have been sought in an increased application range such as medical devices for health care to defense and military weapons. An important limitation of MEMS inertial sensors is repeatedly documented as the ease of being influenced by environmental noise from random sources, along with mechanical and electronic artifacts in the underlying systems, and other random noise. Thus, random error processing is essential for proper elimination of artifact signals and improvement of the accuracy and reliability from such sensors. In this paper, a systematic review is carried out by investigating different random error signal processing models that have been recently developed for MEMS inertial sensor precision improvement. For this purpose, an in-depth literature search was performed on several databases viz., Web of Science, IEEE Xplore, Science Direct, and Association for Computing Machinery Digital Library. Forty-nine representative papers that focused on the processing of signals from MEMS accelerometers, MEMS gyroscopes, and MEMS inertial measuring units, published in journal or conference formats, and indexed on the databases within the last 10 years, were downloaded and carefully reviewed. From this literature overview, 30 mainstream algorithms were extracted and categorized into seven groups, which were analyzed to present the contributions, strengths, and weaknesses of the literature. Additionally, a summary of the models developed in the studies was presented, along with their working principles viz., application domain, and the conclusions made in the studies. Finally, the development trend of MEMS inertial sensor technology and its application prospects were presented.

Evolution of Quantum Radar Concept to Noise Radar Concept

Preliminary results of the comparative analysis of quantum radar (QR) based upon quantum entanglement phenomenon and noise radar (NR) based upon classical coherence and correlation processing of random signals are presented in this article. Common and distinguishing features of NR and QR are briefly discussed. A novel concept of a stepped-frequency quantum radar (SF-QR) for design of QR with range resolution capability is suggested. Field programmable gate array based digital generation of entangled signals is proposed as well.

Aspects of Modern Systemic Approach (III): Implications of Random Processes in the Study of Dynamic Systems

The purpose of this study is to familiarize the reader with the diversity of concepts (notions) and stages of development specific to Probability Theory, with the definition and characterization of variables, vectors and random processes, respectively with the most important elements that give random processes. Among these we mention the distribution function, the probability density function, the statistical moments of a random process, the temporal averages, and respectively the correlation (and intercorrelation) of a random signal (process). Also, the implications of random processes in the study of dynamical systems are reviewed, as well as a series of applications specific to the analysis of dynamic behavior.

Processing of random roadway source signals based on a cross-correlation algorithm in the deconvolution domain

Advanced seismic detection technology utilising random roadway sources is advantageous method adapting to the development of dynamic intelligent technologies for the detection of hidden and disastrous geological structures. However, roadheaders generate complex signals, that are continuous and random with seismic wavelets that are wider and longer than conventional source wavelets, and these complex wavelets cannot be directly subjected to data processing. To address this problem, based on two basic techniques, namely, deconvolution and cross-correlation, a cross-correlation algorithm in the deconvolution domain is proposed to process continuous random roadheader signals, convert those signals into normal-impulse seismic data, and extract the arrivals of direct and reflected waves and other information. First, the feasibility of the algorithm is explained based on a derivation of theoretical formulas. Then, to verify the applicability of the algorithm to the processing of actual data, a random signal from a tamping source is processed; the continuous random signals are successfully converted into impulse signals, and the extracted in-phase direct waves are clear with good continuity. Moreover, the continuous random signal processing results are compared with an active source signal, and good consistency is found, verifying the effectiveness of the algorithm. Finally, an application analysis of the algorithm is carried out using an actual acquired roadheader source signal. After implementing the algorithm, the random roadheader signal is successfully converted into a normal-impulse seismic signal, and the direct wave arrivals are extracted. Based on the arrival times of the direct waves, the longitudinal wave velocity of the coal seam is calculated as 1918 m/s, which is consistent with the actual velocity. A comprehensive analysis of the test results confirms that the cross-correlation algorithm in the deconvolution domain is both feasible and effective and that the processed seismic signals can be used for subsequent processing and interpretation.

Adaptation of receiving channels to the spectrum of the received signal

The article deals with the implementation of optimal filtering in the correlation processing of random signals with a priori unknown characteristics of the spectrum. A scheme of the optimal processing device operating in a wide dynamic range of received signals is synthesized, adaptation to which requires the joint use of automatic gain control and automatic bandwidth adjustment.

Research on Tool Wear Detection Based on Genetic Neural Network

To improve the accuracy of tool wear detection, this paper proposes a tool wear detection method based on genetic neural network. Firstly, the vibration signals during tool processing are collected, and these signals are preprocessed to eliminate background noise. Then, in addition to the time-frequency analysis, the Ensemble Empirical Mode Decomposition which is more suitable for the processing of non-stationary random signals is also applied to extract tool wear sensitive features from signals. To reduce the computational complexity of the neural network, some minor components in the sensitive features can be omitted by kernel principal component analysis, leaving the principal components as the input of the neural network. Finally, aiming at the shortcomings of the BP neural network, the genetic algorithm is optimized in terms of chromosome coding, setting of control parameters and genetic operation, so that it can obtain better weights and thresholds to improve the BP neural network. The experimental result proves that the accuracy of BP neural network is 86.7% and that of genetic neural network is 96%. The tool wear detection method based on genetic neural network is more suitable for practical use.

Design of marine environmental noise signal generation system based on MATLAB, LabVIEW and FPGA

Marine environmental noise is a background interference field produced by various underwater sound sources. The characteristics of marine environment vary with time, place and frequency. In order to simulate the marine environmental noise signals with different time-frequency characteristics, a real-time generating system of marine environmental noise signals is designed based on MATLAB, LabVIEW and FPGA. According to the probability distribution of the measured frequency characteristics of marine environmental noise, the random signals with specific frequency response are calculated and generated in real time and continuously. The pool test results show that the signal generation system has the characteristics of stable real-time calculation and signal generation. Based on the marine environmental noise signal generation system designed in this paper, time-domain waveforms with specific spectrum characteristics are generated, which will provide technical support for the research of random signal processing algorithm under laboratory conditions and the performance analysis of underwater acoustic equipment under different marine background conditions.

ПРИМЕНЕНИЕ МЕДИАННЫХ ФИЛЬТРОВ ПРИ ОБРАБОТКЕ ЭКСПЕРИМЕНТАЛЬНЫХ ДАННЫХ ПО ДИНАМИКЕ КОЛЕБАНИЙ ПЛАВУЧИХ СООРУЖЕНИЙ РАЗЛИЧНЫХ ТИПОВ

The paper is devoted to the analysis of increasing the accuracy of the spectral characteristics obtained after processing experimental data of the vibrations of a physical model of the offshore drilling platform in the experimental tank. The increase in accuracy is provided by the recognition and correction of breakdowns and errors in the course of median filtering of vibration recordings of different positions of the platform relative to the propagation of waves in the tank and with different deepening of the platform support columns. The median filters are known to belong to nonlinear transformations; therefore, their characteristics are determined by the structure of their processing procedures. In this case, these are narrow-band, random oscillations with maximum spectra at wave frequencies, at fundamental frequency of the platform oscillation and harmonics, at frequencies that are multiple to the fundamental frequency. To use median filters in processing such signals, the amplitude-frequency characteristics of the median filter were determined as a function of the ratio of the characteristic period of oscillations to the equivalent filter base, i.e. to multiplying a number of points in the filter core by the discreteness of measurements. As a result, the condition for the effective application of median filtering in the processing of narrow-band random signals has been determined, which is used in analyzing the spectra of the processes under study. There are given characteristic examples of the spectra of angular oscillations (by roll and depth) of a semisubmersible platform with different drafts. It has been shown that at maximum draft, when the model is most stable, median filtering suppresses high-frequency noise components of the vibration spectra by 5-10 times and, at the same time, the main features of the spectra are not distorted, i.e. the main energy-carrying components are reproduced without visible distortion. In the other limiting case, with minimum platform draft, changes in the high-frequency part of the spectra are negligible due to increased intensity of angular oscillations. In this case, the oscillation spectra also reproduce the main features of dynamic processes. These data indicate the selective sensitivity of the median filter to the structure of the operating procedure and possibility of improving the accuracy of reproduction of spectral characteristics by suppressing the high-frequency noise components of a signal.

Unsupervised image thresholding: hardware architecture and its usage for FPGA-SoC platform

ABSTRACTOtsu’s global automatic image thresholding operation is used in various image processing applications. It needs computation of normalized cumulative histogram, mean and cumulative moments that are compute-intensive operations. In this paper, a custom architecture is presented for an efficient computation of Otsu’s algorithm along with its utilization as an intellectual property (IP) core in a field programmable gate array (FPGA) based system-on-chip (SoC) environment for the application of connected component analysis (CCA). A self-normalization technique is employed, where single-cycle, read–modify–write operations are performed with block random access memories (BRAMs) and digital signal processing (DSP)slices. The architecture is designed for 640 × 480 size of images that are captured by a high-resolution analouge camera and buffered in a DDR2 SDRAM of Xilinx ML-507 platform at 25.175 MHz clock frequency. The embedded PowerPC processor core is used to control the frame acquisition process. Experimental results on Virtex-5 xc5vfx70t FPGA device show that the architecture utilizes 1.4% slices, 2.7% BRAMs and 3.9% DSP48E slices. The total power consumption of the design is 1440.59 mW. The proposed architecture as an IP core is able to work in real-time with standard VGA resolution video and requires low computational resources.

Analysis of A-stationary random signals in the linear canonical transform domain

The notion of stationarity associated with the Fourier transform plays an important roll in random signals theory. As the linear canonical transform (LCT) has been shown to be a powerful tool in signal processing, the theories and properties of random signals in the LCT domain have been extensively studied. However, there are no results published associated with the generalized concept of stationarity for random signals in the LCT domain. Hence in this paper, the detailed analysis of A-stationary random signals in the LCT domain has been presented, which shows that for random signals are non-stationary in the standard formulation whereas can be A-stationary. First, the generalized concept of A-stationarity for random signals associated with the LCT has been introduced. Based on the concept, the LCT correlation function and the LCT power spectral density for A-stationary random signals have been derived. Then, we have redefined the notion of A-stationarity in terms of the A-Wigner–Ville distribution. In addition, the sampling theorem for A-stationary random signals have been obtained. Finally, the simulations and the potential applications are carried out to verify the validity and correctness of the proposed results.

Stationary Graph Processes and Spectral Estimation

Stationarity is a cornerstone property that facilitates the analysis and processing of random signals in the time domain. Although time-varying signals are abundant in nature, in many practical scenarios, the information of interest resides in more irregular graph domains. This lack of regularity hampers the generalization of the classical notion of stationarity to graph signals. This paper proposes a definition of weak stationarity for random graph signals that takes into account the structure of the graph where the random process takes place, while inheriting many of the meaningful properties of the classical time domain definition. Provided that the topology of the graph can be described by a normal matrix, stationary graph processes can be modeled as the output of a linear graph filter applied to a white input. This is shown equivalent to requiring the correlation matrix to be diagonalized by the graph Fourier transform; a fact that is leveraged to define a notion of power spectral density (PSD). Properties of the graph PSD are analyzed and a number of methods for its estimation are proposed. This includes generalizations of nonparametric approaches such as periodograms, window-based average periodograms, and filter banks, as well as parametric approaches, using moving-average, autoregressive, and ARMA processes. Graph stationarity and graph PSD estimation are investigated numerically for synthetic and real-world graph signals.

A Novel Baseline Correction Algorithm

This paper studies baseline correction algorithms for subtracting the background of real-word signal. A novel baseline correction algorithm is proposed that can be solved by random signal processing. With respect to generalized statistical features of the raw data, an appropriate threshold of standard deviation is set to extract the true baseline points unfailingly. Under the generalized meaning, the background at one signal point is substituted by the statistical features of its local window. By using this proposed algorithm, we established a time varying signal baseline independently and accurately. And performance evaluation shows that the proposed algorithm is more elaborate and tolerant of real-word data than the previous ones.

The Silence of the Amps: Integrated Circuits for Very-Low-Noise Processing of Random Signals from Radiation Detectors

In this article, I will offer a brief overview of the progress toward the current state of the art of integrated circuits (ICs) for the sciences that make use of ionizing radiation (X- and c rays, electrons, protons, ions, and alpha particles) for the discovery or study of fundamental phenomena in nature. Two examples of these are astrophysics, which use radiation spectrometry to study some of the fundamental processes occurring in and out of our galaxy [1], [2], and elementary particle physics, in which ionizing radiation is used to study the fundamental laws of matter [3]. Ionizing radiation is also commonly employed in several applications related to human life. Examples include medical imaging (CT, PET, X-ray radiography, and so on), radiotherapy, and X-ray scanners for security.

Continuous-Time Dynamic System Identification with Multisine Random Excitation Revisited

Abstract The paper presents a new, revisited and unified approach to a linear continuous-time dynamic single-input single-output system identification using input and output signal samples acquired with a deterministic constant or random sampling interval. The approach is based on a specially designed identification experiment with excitation of the form of a continuous-time multisine random signal and digital processing of the corresponding signal samples obtained without antialiasing analogue filtration in the case of disturbances satisfying or not satisfying the Shannon's sampling theorem. Properties of the proposed approach are discussed taking into account nonlinearity of the excitation generation and data acquisition systems with a focus on model identification in the case of input and output signal levels comparable with data acquisition system accuracy.

Signal Processing of Random Physiological Signals

This lecture book is intended to be an accessible and comprehensive introduction to random signal processing with an emphasis on the real-world applications of biosignals. Although the material has be