Random Signal Noise Research Articles

Rank-based detection of weak random signals in a multiplicative noise model

Multiplicative noise is known to be useful in modeling some environment, which is difficult to describe by additive noise model. In this paper, nonparametric detection of weak random signals in multiplicative noise is considered. The locally optimum detector based on signs and ranks of observations is derived for good weak-signal detection performance under any noise probability density function. The detector has similarities to the locally optimum detector for random signals in multiplicative noise. It is shown that the nonparametric detector asymptotically has almost the same performance as the locally optimum detector.

Measurements of normal incidence surface impedance using the single transfer function method with multitone periodic random noise signal

Commonly used signals for determining the transfer function of a linear filter are pure tone excitation, white noise, or a pseudorandom noise (MLS) signal. However, it is known that the accuracy of this measurement depends strongly on the kind of signal used. In order to try to improve the quality of the measurement, multitone periodic random noise signal properties are investigated in the case of the normal incidence surface impedance measurement. Kundt’s tubes and free-field impedance results using a single microphone transfer function method are given in a large frequency range (20–4000 Hz) for a multitone periodic random noise signal. These results are compared with those obtained with pure tone, white noise, and MLS signal excitation. Also, the influence of noise immunity is investigated for each type of signal.

Automated reverberation time experiments employing artificially generated noise

Using an IBM compatible PC, an inexpensive and simple method was developed to conduct acoustic absorption measurements in a reverberant chamber. The technique provided random noise distributed around 1/3 octave band center frequencies from 200 to 1000 Hz, through a D/A converter, to speakers within the chamber. Turbo-Pascal software was written to create a random noise file for each of the 1/3 octave bands. As a consequence, while a random noise signal was sent by the computer, that signal was identical for each experiment, for a specific 1/3 octave band. The noise signal, of 3-s duration, was programmed to increase to maximum intensity, and decrease to zero over a period of 25 μs, eliminating a steplike transient to the dynamic system at termination. The decaying noise was sampled 1000 times per second using an A/D converter, and the decay rate calculated. This technique was initiated with a keystroke and produced a sound-pressure level versus time plot file, allowing determination of the reverberation time and absorption coefficient of the chamber.

Low bit-rate speech coding by perceptually optimized noise excitation modulation

A novel low bit-rate high-quality speech coding technique is presented based on a perceptually optimized signal reconstruction method. According to this parametric speech model, the signal's spectral envelope is reconstructed from non-linear spectral filtering of an excitation signal, which is a combination of a random broadband noise signal with a number of discrete spectral pulses extracted from the original speech using a perceptual model. This general coding platform allows variable bit-rate implementations, starting from 1.9kbit/s, at which sufficient intelligibility (more than 92%) was measured, while at higher bit-rates (2.8 kbit/s) intelligibility scores were better than 94% with sufficient naturalness in the coded speech. In all cases, the complexity of the proposed system is very low.

Electro-acoustic system analyzer

A method and apparatus for analyzing performance parameters of an electro-acoustic system. The bandwidth of the electro-acoustic system is determined by applying a broad band stimulus signal to its input, and picking up the resulting acoustic signal with a microphone. The microphone output is then applied to a state variable filter having a low-pass filter output, a high-pass filter output, and a band-pass filter output. The operating frequency of the state variable filter is changed incrementally through each of a plurality of frequencies. The high and low frequency responses of the electro-acoustic system is determined on the basis of the operating frequencies of the state variable filter at which accumulated values of the outputs of the state variable filter bear certain relationships to each other. The thermal power limit of the electro-acoustic system is analyzed by applying a gradually increasing random noise signal to the input, and monitoring the amplitude of the resulting acoustic signal to determine when the acoustic signal no longer tracks the input signal. The equalizability of the electro-acoustic system is determined by comparing the phase of a swept input signal with the phase of the resulting acoustic signal and displaying the change in phase per spectra. Finally, the spurious vibration of the electro-acoustic system is analyzed by generating a noise signal having its frequency components excluded at swept frequency, and detecting any resulting acoustic signal at the excluded frequency.

Computer generation of colored noise for time-domain analysis of integrated circuits

In many designs it is necessary to establish how noise affects the performance of the circuit under consideration. The type of noise analysis supported by most simulators is a frequency domain small-signal analysis around a DC operating point. This approach cannot be applied to circuits designed to operate in heavily nonlinear conditions. This paper presents an algorithm to generate random time-domain noise signals with a specified power density spectrum. The algorithm can be implemented easily and efficiently in a circuit simulator, and the noise signal thus generated can be used in ordinary transient analysis. Use of this algorithm is demonstrated in the analysis of the noise performance of a comparator and of an optical amplifier.

Experimental study of a rapidly convergent adaptive controller applied to suppression of random noise transmission

In this letter, an efficient, rapid-convergent, control algorithm will be developed and compared with finite impulse response (FIR), filtered-x, LMS [Widrow et al., Proc. IEEE 63, 1692–1711 (1975)], and a modified version of Godard’s algorithm [D. Godard, IBM J. Res. Develop., pp. 267–273 (1974)]. The experiments will be performed on an active noise control (ANC) system. Comparisons of the random noise attenuation capability, transient and convergence performance, and computational requirements of each algorithm will be made as the order of the controller and relevant convergence parameters are varied. The ANC system used for these experiments is a test bed of noise suppression technology for expendable launch vehicles. It consists of a flexible plate backed by a rigid cavity. Piezoelectric actuators are mounted on the plate and polyvinylidene fluoride (PVDF) microphones are used to sense the reference and error signals. The plate is bombarded with an amplified random noise signal, and the control system is used to suppress the cavity noise generated by the sound source.

Random telegraph voltages in high-Tc thin films at zero magnetic field

New class of random telegraph voltage noise signals has been observed in zero field cooled BiSrCaCuO films at currents several times exceeding the critical current of the sample. The amplitude of the telegraph signal scales lienarly with current bias. Average lifetimes demonstrate distinct behavior in up and down voltage states indicating that the energy barrier associated with the down state decreases linearly with increasing current while that of the up state is nonlinear and reaches a maximum at a symmetric telegraph signal.

A CFAR processor for the detection of unknown random signals in nonstationary correlated noise

Signal detection in a nonstationary environment has been widely studied in the literature, either in the context of known deterministic signals in unknown correlated noise or in the context of unknown random signals in white noise. However, the detection of unknown random signals in unknown correlated noise has not been adequately described although it is of practical importance. Application of the conventional normalization-plus-integration technique to such situations results in performance degradation and failure to achieve a constant false-alarm rate (CFAR). An efficient and computationally simple CFAR detection procedure is proposed in this paper, based on the comparison of the covariance structure of the data vector under test with that of the interference. The theoretical detection performance of the new test, in a closed form, is presented. It is shown that for interference with moderate and high autocorrelation, the new test provides about 6 to 12 dB improvement over the conventional normalization technique.

Identifying a signal in a markovian symmetric cauchy random noise

An unknown signal θ being received in the presence of a Markovian symmetric Cauchy random noise whose probability law is known, we examine the problem of identifying θ with any given signal θo. We use two quite different methods to test the hypothesis “θ = θ o”against an alternative hypothesis as large as possible. Several situations are presented in which the problem under consideration can, or cannot, be solved. In the first case, consistent tests are given.

On the score functions of the two-sample locally optimum rank test statistic for random signals in additive noise

The test statistic of the two-sample locally optimum rank detector is derived for weak random signals in additive noise. It is shown that the two-sample locally optimum rank detector for random signals has interesting similarities to the one-sample locally optimum rank detector for random signals. The detector may also be viewed as a generalization of the two-sample locally optimum rank detector for known signals. Some examples and properties of the score functions constituting the locally optimum rank test statistic are investigated.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Reconstruction of multilayered dielectric models using oblique transverse electric or transverse magnetic incidence

Subsurface probing of inhomogeneous dielectric media has received considerable attention from the research community. Extensive work in this area has been presented either in the time or frequency domain. However, most of the algorithms presented are mainly devoted to continuous dielectric profile inversion. The fast Fourier transform (FFT) inversion method is newly introduced for discrete multilayer inversion. This method has the advantages of simplicity, a short computation time, and robustness with respect to noise. The FFT inversion method is applied to a discrete homogeneous multilayer dielectric model using oblique incidence and a limited bandwidth, provided that all dielectric media are lossless and nondispersive. For a given model, a uniform plane wave is obliquely incident on the top of its surface, with its polarization being either a transverse electric or a transverse magnetic wave. In each case, the corresponding reflection coefficient is sampled over a finite frequency range. The time-domain spectrum of the reflection coefficient is obtained by applying the FFT algorithm to its samples. An algorithm is developed for the deduction of the height and permittivity of each layer. The algorithm is tested by using the simulated data of a three-layer half-space dielectric model. The resulting relative errors in the inversion process are almost less than 10 -4 for all of the model parameters. When a random noise signal is superimposed on the sampled input data, the relative errors are comparable to the noise-to-level ratio.

Distributed cell-averaging CFAR detection in dependent sensors

Constant false alarm rate detection is considered in a decentralized, two-sensor context. Cases with observations which are dependent from sensor to sensor are investigated, for which results have been lacking. The in-phase and quadrature components of the received narrowband observation at each sensor consist of a common weak random signal in additive Gaussian combined clutter and noise. The sensors use cell-averaging CFAR tests to each generate binary decisions which are sent to a fusion center. Optimal sensor thresholds are found and the performance of the best schemes using AND and OR fusion rules are compared. The ability of these schemes to maintain constant false alarm probability in the presence of clutter edges is also studied.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Detection of random signals in Gaussian mixture noise

A locally optimal detection algorithm for random signals in dependent noise is derived and applied to independent identically distributed complex-valued Gaussian mixture noise. The resulting detector is essentially a weighted sum of power detectors-the power detector is the locally optimal detector for random signals in Gaussian noise. The weighting functions are modified to enhance the detection performance for small sample sizes. An implementation of the mixture detector, using the expectation-maximization algorithm, is described. Moments of these detectors are calculated from piecewise-polynomial approximations of the weighting functions. The sum of sufficiently many independent identically distributed detector outputs is then approximated by a normal distribution. Probability distributions are also derived for the power detector in Gaussian mixture noise. For a particular set of noise parameters, the theoretical distributions are compared with those obtained from Monte Carlo simulation and seen to be quite close. The theoretical distributions are then used to compare the performance of the mixture and power detectors in Gaussian mixture noise over a range of parameters and to assess the impact of parameter error on detection performance. In this study, the signal gain of the mixture detectors varies from 15 to 38 dB, and the degradation of the probability of detection due to parameter estimation error is relatively minor.

High-frequency random telegraph voltage noise in high-Tc thin films.

The vortex mechanism, leading to very high-frequency random telegraph voltage noise signals switching with MHz frequencies, is discussed. The mechanism assumes that vortices do not flow freely across dc-current-biased superconducting films but undergo subsequent processes of trapping and releasing from pinning centers. Random transitions of vortices between pinned and flow states result in the appearance of a Lorentzian component in a fluctuating-voltage power spectrum. It is shown that fluctuations of randomly distributed Abrikosov vortex density, rigidly moving across the strip, contribute an oscillating component to the noise spectrum. The distance between oscillating peaks corresponds to the time of flight of vortices across the strip.

Random-signal interaction with flowing lattice of Abrikosov vortices

The interaction of a random-noise signal with a flowing lattice of Abrikosov vortices leads to strong modifications of the original noise spectrum. The detailed shape of the spectrum depends on the lattice-flow velocity. At low velocities of the lattice flow the spectrum is attenuated at low frequencies similarly to the case of a motionless lattice. For the velocities above the crossover velocity the spectrum magnitude oscillates with a characteristic frequency depending on the time of flight of the vortices across the strip dimension perpendicular to the direction of current flow. For sufficiently high magnetic fields the bias current determining the crossover to the oscillatory regime depends only on the temperature and film thickness.

The impact of background noise on the determination of the fractal and statistical properties of cosmic ray time series

This paper investigates the influence of background white noise on the determination of the colored random noise properties of cosmic ray experimental signals. We consider fractal methods (Grassberger and Procaccia method, scaling exponent method and fractal length method) and statistical methods (comparison with the Gaussian and Rice curves, and the multivariate scaling analysis technique). The investigation is carried out using two experimental time series with different color (spectral indices α = 1.6 and α = 1.2) and, as a reference, several computer simulations of pure noise‐free colored random noise signals with 0 ≤ α ≤ 3 with the same number of points. The results of the investigation is that the presence of noise in a experimental signal “deceives” the algorithms employed in the various analyses causing them to sample noiselike properties (e.g., an infinite or very large fractal dimension and Gaussian or quasi‐Gaussian behavior), irrespective of the physical characteristics of the particular data set.

An adaptive maximum likelihood receiver for correlated Rayleigh-fading channels

The paper develops a receiver structure for random Gaussian signals in additive noise based on the classic maximum likelihood (M-L), estimator-correlator derivation of Kailath [1960], and applies it to differential phase shift keying (DPSK) on the correlated Rayleigh-fading channel. It is shown to lower the error floor found in the performance of conventional DPSK receivers by orders of magnitude. In addition, the maximum-likelihood procedure is shown to make uncorrelated symbol decisions. The performance of both conventional and optimal receivers, which require knowledge of the channel statistics, is examined analytically. A recursive, channel-adaptive version of the optimal receiver, utilizing decision feedback to estimate the channel statistics, is developed. Its simulated performance shows no penalty compared to theoretical calculations which require explicit knowledge of the channel statistics.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Long term study of random noise and signal uniformity in spin-echo brain imaging.

Random noise and MR signal uniformity were analyzed for a period of 9 months using two head coils on a 1.5 T commercial imager. Signal response from a cylindrical phantom filled with a 0.1 mmol/L CuSO4 solution was incorporated in a correction scheme and the effect of correction on uniformity and accuracy of tissue volume determination was measured. There was little change in random noise (CV < 5%) and image uniformity (CV < 15%) over the time of the study. The uniformity in a 130 cm2 region was 4% for a double saddle coil and 5.3% for a mirror coil. After correction, uniformity was improved to 1.1% for the double saddle coil and 1.2% for the mirror coil. In a 40 cm2 central region the uniformity was approximately three times better than in the 130 cm2 region. A second phantom, mimicking the relaxation times of CSF and the brain, consisted of saline encapsulated in vials of volume ranging from 35.6 ml to 249.6 ml and placed in a 0.1 mmol/L solution of MnCl2. Uniformity correction reduced the average error in volume measurement from 8.6% to 6.1%.

Effective scale: a natural unit for measuring scale-space lifetime

A manner in which a notion of effective scale can be introduced in a formal way is developed. For continuous signals, a scaling argument directly gives a natural unit for measuring scale-space lifetime in terms of the logarithm of the ordinary scale parameter. That approach is, however, not appropriate for discrete signals since an infinite lifetime would be assigned to structures existing in the original signal. It is shown how such an effective scale parameter can be defined to give consistent results for both discrete and continuous signals. The treatment is based on the assumption that the probability that a local extremum disappears during a short-scale interval should not vary with scale. As a tool for the analysis, estimates are given of how the density of local extrema can be expected to vary with scale in the scale-space representation of different random noise signals both in the continuous and discrete cases.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>