Statistical Properties Of Signal Research Articles

Features of application of fractal Gaussian noise type signals in telecommunication systems

Problem statement. At present, the properties of a separate type of pseudorandom signals such as fractal Gaussian noise remain insufficiently investigated. The existing discrete model of fractal Gaussian noise is characterized by the value of the Hurst exponent and the time scaling factor, the influence of which on the properties of fractal Gaussian noise is insufficiently investigated. The issue of synthesis of new types of fractal broadband signals of complex design, which can be used in noise-resistant telecommunication systems, is relevant. Objective. Based on the mathematical model of fractal Gaussian noise, investigate the effect of the time scaling factor on the statistical, correlation and energy properties of fractal Gaussian noise signals. Results. By the method of averaging over realizations the dependences of energy, correlation and statistical properties of signals of fractal Gaussian noise type, which can be used for coding information bits, on the time scaling factor were obtained. Practical significance. According to the results of research on the properties of fractal Gaussian noise, the regularities of the passage of self-similar flows of Internet traffic in the approximation that they are self-similar and are described by a mathematical model of fractal Gaussian noise are established. When the intensity is increased up to requests/hour, the results of modeling the average transit time of traffic through the system for flows with different types of distribution show significant differences in the value of the average transit time of each request.

A Family of Automatic Modulation Classification Models Based on Domain Knowledge for Various Platforms

Identifying the modulation type of radio signals is challenging in both military and civilian applications such as radio monitoring and spectrum allocation. This has become more difficult as the number of signal types increases and the channel environment becomes more complex. Deep learning-based automatic modulation classification (AMC) methods have recently achieved state-of-the-art performance with massive amounts of data. However, existing models struggle to achieve the required level of accuracy, guarantee real-time performance at edge devices, and achieve higher classification performance on high-performance computing platforms when deployed on various platforms. In this paper, we present a family of AMC models based on communication domain knowledge for various computing platforms. The higher-order statistical properties of signals, customized data augmentation methods, and narrowband convolution kernels are the domain knowledge that is specifically employed to the AMC task and neural network backbone. We used separable convolution and depth-wise convolution with very few residual connections to create our lightweight model, which has only 4.61k parameters while maintaining accuracy. On the four different platforms, the classification accuracy and inference time outperformed those of the existing lightweight models. Meanwhile, we use the squeeze-and-excitation attention mechanism, channel shuffle module, and expert feature parallel branch to improve the classification accuracy. On the three most frequently used benchmark datasets, the high-accuracy models achieved state-of-the-art average accuracies of 64.63%, 67.22%, and 65.03%, respectively. Furthermore, we propose a generic framework for evaluating the complexity of deep learning models and use it to comprehensively assess the complexity strengths of the proposed models.

The ideal observer: The forgotten second half of signal detection theory

In his seminal text “Signal Detection Theory and Psychophysics” (1966), coauthored with John Swets, Dave Green identifies two parts to the theory. The first entails the psychophysical methodology and procedures required to separate sensory and decision processes in detection, the second compares human performance to that of a theoretical ideal observer that optimizes decisions based on the statistical properties of signals and the demands of the detection task. The first part has received far wider application in psychophysics, but there are demonstrable advantages to applying both. In this talk I will give examples from work in our lab where the analysis of ideal observers framed in the methodology has been used to (1) give mathematical meaning to the constructs of stimulus uncertainty and similarity in informational masking; (2) determine sensory constraints on the use of invariant acoustic cues in sound source identification; and (3) isolate a fixed effect of listeners in multi-talker speech segregation across experiments involving vastly different stimuli and psychophysical tasks. [Work supported by NIDCD grant R01 DC001262.]

Sub-Nyquist optical pulse sampling for photonic blind source separation.

We propose and experimentally demonstrate an optical pulse sampling method for photonic blind source separation. The photonic system processes and separates wideband signals based on the statistical information of the mixed signals, and thus the sampling frequency can be orders of magnitude lower than the bandwidth of the signals. The ultra-fast optical pulses collect samples of the signals at very low sampling rates, and each sample is short enough to maintain the statistical properties of the signals. The low sampling frequency reduces the workloads of the analog to digital conversion and digital signal processing systems. In the meantime, the short pulse sampling maintains the accuracy of the sampled signals, so the statistical properties of the under-sampled signals are the same as the statistical properties of the original signals. The linear power range measurement shows that the sampling system with ultra-narrow optical pulse achieves a 30dB power dynamic range.

Unsupervised detection of rotary machine imbalance based on statistical signal properties

In the paper, a two-step method of unsupervised monitoring of rotary machine, based on statistical data processing, is proposed. The first step uses the chi-square non-parametric homogeneity test χ2 in the form introduced by Snedecor for checking whether two signals, the monitored one and the referential one, have the same distribution. This first step is used to detect the first significant fault occurrence in the monitored machine. In the second step, the value of the statistics is utilized as the measure whose dynamics reflects changes in the nature of the incompatibilities of the given signal and the referential ones. The appearance of a new change manifests the occurrence of a new fault in the tested machine. In a such way, the second step allows to show further fault development. The method is evaluated using real signals recorded from a machine experiencing shaft imbalance development and it is compared with results of detailed analysis using classical supervised methods.

Ultrasound Sample Entropy Imaging: A New Approach for Evaluating Hepatic Steatosis and Fibrosis.

Objective: Hepatic steatosis causes nonalcoholic fatty liver disease and may progress to fibrosis. Ultrasound is the first-line approach to examining hepatic steatosis. Fatty droplets in the liver parenchyma alter ultrasound radiofrequency (RF) signal statistical properties. This study proposes using sample entropy, a measure of irregularity in time-series data determined by the dimension n}{}mn and tolerance n}{}rn, for ultrasound parametric imaging of hepatic steatosis and fibrosis. Methods: Liver donors and patients were enrolled, and their hepatic fat fraction (HFF) (n}{}n =72n), steatosis grade (n}{}n =286n), and fibrosis score (n}{}n =65n) were measured to verify the results of sample entropy imaging using sliding-window processing of ultrasound RF data. Results: The sample entropy calculated using n}{}m =n 4 and n}{}r =0.1n was highly correlated with the HFF when a small window with a side length of one pulse was used. The areas under the receiver operating characteristic curve for detecting hepatic steatosis that was n}{}ge nmild, n}{}ge nmoderate, and n}{}ge nsevere were 0.86, 0.90, and 0.88, respectively, and the area was 0.87 for detecting liver fibrosis in individuals with significant steatosis. Discussion/Conclusions: Ultrasound sample entropy imaging enables the identification of time-series patterns in RF signals received from the liver. The algorithmic scheme proposed in this study is compatible with general ultrasound pulse-echo systems, allowing clinical fibrosis risk evaluations of individuals with developing hepatic steatosis.

A Comparison of Norm Based Antenna Selection and Random Antenna Selection with Regard to Energy Efficiency in Wireless System with Large Number of Users

Background: The paper considers the wireless system with large number of users (more than 50 users) and each user is assigned large number of antennas (around 200) at the Base Station (BS). Objective: The challenges associated with the defined system are increased power consumption and high complexity of associated circuitry. The antenna selection is introduced to combat these problems while the usage of linear precoding reduces computational complexity. The literature suggests number of antenna selection techniques based on statistical properties of signal. However, each antenna selection technique suits well to specific number of users. Methods: In this paper, the random antenna selection is compared with norm-based antenna selection. It is analysed that the random antenna selection leads to inefficient spectral efficiency if the number of users are more than 50 in Multi-User Multiple-Input Multiple Output (MU-MIMO) system. Results: The paper proposes the optimization of Energy-Efficiency (EE) with random transmit antenna selection for large number of users in MU-MIMO systems. Conclusion: Also the computation leads to optimization of number of transmit antennas at the BS for energy efficiency. The proposed algorithm results in improvement of the energy efficiency by 27% for more than 50 users.

Influence of a signal description model on the calculations of the efficiency indicators of optoelectronic systems

The work is aimed at establishing the boundaries of the use of models for describing signals in optoelectronic systems in calculating efficiency. A description of the signal formation process is proposed, taking into account the corpuscular and wave properties when registering signals in a wide range of intensities. A description of the statistical features of the output signals depending on the energy properties of the signal and noise components is proposed. It is shown that when describing the output signals of optoelectronic systems that register signals with different properties, Poisson and Gaussian distributions are used. The invariance of Poisson flows determines the description of an additive mixture of signal and background flows using Poisson flow. The efficiency of optoelectronic systems is calculated by the signal-to-noise ratio criterion based on the corpuscular and wave description of signals. Efficiency calculations have shown the expedience of using this criterion, provided that the statistical properties of signal and background flows are stabilized. It is shown that under the condition of changes in the energy characteristics of signals, from the point of view of the wave and corpuscular models, the statistical characteristics of the signals have different descriptions. The analysis of theoretical methods of signal analysis in optoelectronic systems is carried out, which is aimed at an adequate characteristic of the system operation, depending on the conditions of its operation. Taking into account the method of describing the process of receiving and processing signals will take into account additional statistical characteristics of signals, for example, an increase of the variance of the output signal. The use of adaptive methods for describing signals will make it possible to increase the efficiency of systems when receiving strong signals in a difficult interference environment, as well as when receiving weak signals

A Practical Statistical Approach to the Reconstruction Problem Using a Single Slice Rebinning Method

Abstract The paper presented here describes a new practical approach to the reconstruction problem applied to 3D spiral x-ray tomography. The concept we propose is based on a continuous-to-continuous data model, and the reconstruction problem is formulated as a shift invariant system. This original reconstruction method is formulated taking into consideration the statistical properties of signals obtained by the 3D geometry of a CT scanner. It belongs to the class of nutating reconstruction methods and is based on the advanced single slice rebinning (ASSR) methodology. The concept shown here significantly improves the quality of the images obtained after reconstruction and decreases the complexity of the reconstruction problem in comparison with other approaches. Computer simulations have been performed, which prove that the reconstruction algorithm described here does indeed significantly outperforms conventional analytical methods in the quality of the images obtained.

Modulation Order Reduction Method for Improving the Performance of AMC Algorithm Based on Sixth–Order Cumulants

For the last two decades a large number of different automatic modulation classification (AMC) algorithms were developed, and many improvements in classification performance are reported. This was commonly achieved by engaging complex structures of neural networks, or other adaptable mechanisms for achieving better precision, when it comes to decision-making. Still, from practical implementation point of view, low algorithm complexity, economical usage of resources and fast execution remain to represent very desirable properties of an AMC algorithm. These properties are recognized in AMC algorithms based on higher - order cumulants as classification features, so their further improvement is of interest. Previous performance analysis of an algorithm based on sixth - order cumulants, in scenarios with complex valued signals' classification, showed that improvements are possible in the context of resources engaged and speed of execution. In this paper a novel approach is presented, for improving the correctness of classification process with sixth-order cumulants and simple two-step feature extraction structure, by engaging a new method for reduction of observed signal's modulation order which directly improves the classification performance. While tested with sixth-order cumulants, proposed method preserves good statistical properties of signal's higher - order cumulants in general, so it can be adopted in other AMC algorithms as well. Proposed modulation order reduction method is described in details, tested through computer simulations within the sixth-order cumulant AMC algorithm, and achieved improvements in performance are presented and explained.

Does data cleaning improve brain state classification?

BackgroundNeuroscientists routinely seek to identify and remove noisy or artifactual observations from their data. They do so with the belief that removing such data improves power to detect relations between neural activity and behavior, which are often subtle and can be overwhelmed by noise. Whereas standard methods can exclude certain well-defined noise sources (e.g., 50/60 Hz electrical noise), in many situations there is not a clear difference between noise and signals so it is not obvious how to separate the two. Here we ask whether methods routinely used to “clean” human electrophysiological recordings lead to greater power to detect brain–behavior relations. New methodThis, to the authors’ knowledge, is the first large-scale simultaneous evaluation of multiple commonly used methods for removing noise from intracranial EEG recordings. ResultsWe find that several commonly used data cleaning methods (automated methods based on statistical signal properties and manual methods based on expert review) do not increase the power to detect univariate and multivariate electrophysiological biomarkers of successful episodic memory encoding, a well-characterized broadband pattern of neural activity observed across the brain. Comparison with existing methodsResearchers may be more likely to increase statistical power to detect physiological phenomena of interest by allocating resources away from cleaning noisy data and toward collecting more within-patient observations. ConclusionsThese findings highlight the challenge of partitioning signal and noise in the analysis of brain-behavior relations, and suggest increasing sample size and numbers of observations, rather than data cleaning, as the best approach to improving statistical power.

Widely Linear Complex-Valued Diffusion Subband Adaptive Filter Algorithm

The adaptive algorithms applied to distributed networks are usually real-valued diffusion subband adaptive filter algorithms. However, it cannot be used for processing the complex-valued signals. In this paper, a novel augmented complex-valued diffusion normalized subband adaptive filter (D-ACNSAF) algorithm is proposed for distributed estimation over networks. In order to deal with the noncircular complex-valued signals, the D-ACNSAF algorithm uses the widely linear model for a diffusion network. Due to the second-order statistical properties of signal, the D-ACNSAF algorithm can process the circular and non-circular complex-valued signals simultaneously. Moreover, the stability and mean-square steady-state analysis of the proposed algorithm are derived based on the spatial–temporal energy conservation principle. Computer simulation experiments on complex-valued system identification and prediction show that the proposed algorithm has better performance (lower mean-square deviation and faster convergence rate) than diffusion complex least-mean-square and diffusion augmented complex least-mean-square algorithms. And the simulation results are consistent with the analysis results.

Synchronization of the vector state estimation methods with unmeasurable coordinates for intelligent water quality monitoring systems in the river

One of the main problems of signal processing is the issue of estimation, which consists in reproducing a useful signal on the basis of another one, e.g. one burdened with unwanted interference. The use of classical filtering to eliminate disturbances carries the risk of removing an important part of the information about the useful signal. In order to avoid this phenomenon, optimal filters are used, utilizing the statistical properties of signals. Such a case of consideration concerns the issue of estimation for stochastic systems. An example is the Kalman filter in which the main aspect of research is the differential equation of the state estimate in which there is an amplification factor dependent on the covariance matrix of the estimation error. The value of this factor is determined from the non-linear Riccati differential equation, depending on the characteristics of signals affecting the object under investigation and the measurement. The paper proposes an adaptive algorithm to determine the value of the filter amplification factor which allows to avoid the inconveniences resulting from the need to estimate the characteristics of signals affecting the object under investigation. The presented approach to estimation issues does not require the need to characterize random signals due to the application of the adaptive method of state estimation. The analyzed process refers to an object described by ordinary differential equations with continuous measurements representing the state of pollution of the river using the so-called interpretation “along the characteristics”. The method used in the module of the intelligent analytical system allows for quick determination of hard-to-measure parameters of the quality of water in the river and to react to upcoming ecological threats without any delays dangerous for the system.

Gearbox damage identification using Ensemble Empirical Decomposition method

In this article, we have conducted a comparative analysis of vibration signals from helicopter aircraft propulsion transmissions, registered on an industrial research stand. We compared acceleration vibrations in the case of gears without physical damage and gears with one tooth missing. Based on recorded signals, we determined the values of indicators based on the statistical properties of signals and compared them with each other. For a more exact comparison, the distribution of the tested signals to the empirical modes using the EEMD (Ensemble Empirical Mode Decomposition) method was performed. This allows to treat individual modes as components of a signal at specific frequencies, and also prevents mixing of modes in individual components, which may take place in the classic EMD analysis. It should be noted that individual modes may correspond to characteristic frequencies for the operation of the transmission. When comparing the values of the most frequently used indicators, modes/frequencies in which the damage was most visible were indicated.

On the Statistical Properties of Constant Envelope Quantizers

Transmit signals that have constant magnitudes and discrete phases can decrease the power consumption of future wireless communication systems. To obtain signals fulfilling that property, constant envelope quantizers (CEQs) have to be deployed in the system. However, the increased power efficiency comes at the cost of degraded system performance. To mitigate this performance loss with conventional digital filters, e.g., Wiener Filter precoders, the statistical signal properties at the CEQ have to be known. In this context, we study the effect of the CEQ on the statistical signal properties and derive closed-form expressions of the correlation factors at the CEQ with complex-valued Gaussian input signals.

Study on Resolution Capability of Beam Space MUSIC Estimation Algorithm

Hydrophone arrays are generally used in modern sonar systems in which beam forming plays an important role. This paper analyzes the beam space MUSIC (multiple signal classification) algorithm for the weakly correlated sources according to changes in the statistical properties of signal and noise, then estimates the azimuth of multi-sources using array element space MUSIC algorithm and beam space MUSIC algorithm accurately, respectively. Finally, problems such as the computation cost of the algorithms, SNR resolution threshold and estimation deviation are discussed based on simulation tests. A conclusion could be drawn that beam space MUSIC algorithm is an effective way to resolve multiple targets in small angle domain.

Ground Clutter Detection Using the Statistical Properties of Signals Received With a Polarimetric Radar

Polarimetric weather radars provide additional measurements that allow better characterization of the targeted medium. Because ground clutter has different polarimetric characteristics from weather echoes, dual-polarization measurements can be used to distinguish one from the other. Ground clutter and weather signals also have different statistical properties which can be utilized to distinguish one from the other. A test statistic, obtained from the generalized likelihood ratio test (GLRT), and a simple Bayesian classifier (SBC), with inputs from the mean and covariance of the received signals, are developed to detect ground clutter in the presence of weather signals. It is found that the test statistic produces false detections caused by narrow-band zero-velocity weather signals while the SBC can effectively neutralize them. This work is aimed at detecting ground clutter based solely on data from each resolution volume. The performances of the test statistic and SBC are shown by applying them to radar data collected with the University of Oklahoma-Polarimetric Radar for Innovation in Meteorology and Engineering.

On the Stationarity of Signal Scattering by Two-Dimensional Slightly Rough Random Surfaces

We present statistical properties of signal scattering by two-dimensional slightly rough random surface. The work concerns the intermediate-field zone. Calculations are carried out within the framework of the first-order small perturbation method. The surface is assumed to be ergodic and stationary and the height distribution to be Gaussian. Under an oblique incidence, we demonstrate that the first-order scattered field is not wide-sense stationary. For a given altitude, under the normal incidence, the scattered field is a strictly stationary random process. By using the stationary phase method, we show that the scattered field becomes asymptotically a strictly stationary random process when increasing the altitude of the observation point. We also define the condition that must be satisfied by an antenna transfer function so that the measured scattered field becomes stationary and ergodic.

Unformatted Digital Fiber-Optic Data Transmission for Radio Astronomy Front-Ends

ABSTRACTWe report on the development of a prototype integrated receiver front-end that combines all conversions from RF to baseband, from analog to digital, and from copper to fiber into one compact assembly, with the necessary gain and stability suitable for radio astronomy applications. The emphasis in this article is on a novel digital data link over optical fiber which requires no formatting in the front-end, greatly reducing the complexity, bulk, and power consumption of digital electronics inside the antenna, facilitating its integration with the analog components, and minimizing the self-generated radio-frequency interference (RFI) which could leak into the signal path. Management of the serial data link is performed entirely in the back-end based on the statistical properties of signals with a strong random noise component. In this way, the full benefits of precision and stability afforded by conventional digital data transmission are realized with far less overhead at the focal plane of a radio telescope.

Spatial uncertainty and proximity effects in informational masking identically related to the Simpson–Fitter metric of target-masker separation

Further evidence is provided suggesting a primary dependence of informational masking (IM) on the stochastic separation of target and masker given by Simpson-Fitter’s da [Lutfi et al. J. Acoust. Soc. Am. 132, EL109-113 (2012)]. The stimuli were brief bursts of Gaussian noise or words played in sequence as masker-target-masker triads. The apparent position of bursts (words), from left to right, was varied independently and at random on each presentation using KEMAR HTRFs. In the 2IFC procedure, the listener’s task was to choose the target positioned further to the right. The effect on performance of spatial uncertainty regarding the masker was examined by manipulating the position variance of the masker. The effect on performance of spatial proximity of target to masker was examined by manipulating the position mean difference between target and masker. In both cases, the data were well described by a single linear function relating d’ performance to da; intercepts differed across listeners, but slopes were similar. Comparable results presented at this meeting for the effects of spectral uncertainty and similarity of target and masker suggest that the statistical properties of signals may be a more significant determinant of IM than their specific acoustic properties.