Showing papers on "Noise (signal processing) published in 1993"

Noise enhancement of information transfer in crayfish mechanoreceptors by stochastic resonance

Abstract: IN linear information theory, electrical engineering and neurobiology, random noise has traditionally been viewed as a detriment to information transmission. Stochastic resonance (SR) is a nonlinear, statistical dynamics whereby information flow in a multistate system is enhanced by the presence of optimized, random noise1–4. A major consequence of SR for signal reception is that it makes possible substantial improvements in the detection of weak periodic signals. Although SR has recently been demonstrated in several artificial physical systems5,6, it may also occur naturally, and an intriguing possibility is that biological systems have evolved the capability to exploit SR by optimizing endogenous sources of noise. Sensory systems are an obvious place to look for SR, as they excel at detecting weak signals in a noisy environment. Here we demonstrate SR using external noise applied to crayfish mechanoreceptor cells. Our results show that individual neurons can provide a physiological substrate for SR in sensory systems.

Higher-order spectral analysis

Abstract: Absltacl The purpose of this keynote lecture of the Signal Analysis Track is U) present the motivation behind he use of higher-order spectra (HOS) in signal processing as well as the definitions, properties, and biomedica1 signal processing applications of higher-order spectra. This lecture will also emphasize the state of science of the higher-order spectra field, especially as it applies to non-stadonary signal analysis.

Multi-channel signal separation by decorrelation

Abstract: Identification of an unknown system and recovery of the input signals from observations of the outputs of an unknown multiple-input, multiple-output linear system are considered. Attention is focused on the two-channel case, in which the outputs of a 2*2 linear time invariant system are observed. The approach consists of reconstructing the input signals by assuming that they are statistically uncorrelated and imposing this constraint on the signal estimates. In order to restrict the set of solutions, additional information on the true signal generation and/or on the form of the coupling systems is incorporated. Specific algorithms are developed and tested. As a special case, these algorithms suggest a potentially interesting modification of Widrow's (1975) least-squares method for noise cancellation, where the reference signal contains a component of the desired signal. >

Subspace-based signal analysis using singular value decomposition

Abstract: A unified approach is presented to the related problems of recovering signal parameters from noisy observations and identifying linear system model parameters from observed input/output signals, both using singular value decomposition (SVD) techniques. Both known and new SVD-based identification methods are classified in a subspace-oriented scheme. The SVD of a matrix constructed from the observed signal data provides the key step in a robust discrimination between desired signals and disturbing signals in terms of signal and noise subspaces. The methods that are presented are distinguished by the way in which the subspaces are determined and how the signal or system model parameters are extracted from these subspaces. Typical examples, such as the direction-of-arrival problem and system identification from input/output measurements, are elaborated upon, and some extensions to time-varying systems are given. >

Optimal Fingerprints for the Detection of Time-dependent Climate Change

Abstract: An optimal linear filter (fingerprint) is derived for the detection of a given time-dependent, multivariate climate change signal in the presence of natural climate variability noise. Application of the fingerprint to the observed (or model simulated) climate data yields a climate change detection variable (detector) with maximal signal-to-noise ratio. The optimal fingerprint is given by the product of the assumed signal pattern and the inverse of the climate variability covariance matrix. The data can consist of any, not necessarily dynamically complete, climate dataset for which estimates of the natural variability covariance matrix exist. The single-pattern analysis readily generalizes to the multipattern case of a climate change signal lying in a prescribed (in practice relatively low dimensional) signal pattern space: the single-pattern result is simply applied separately to each individual base pattern spanning the signal pattern space. Multipattern detection methods can be applied either t...

AM-FM energy detection and separation in noise using multiband energy operators

Abstract: This paper develops a multiband or wavelet approach for capturing the AM-FM components of modulated signals immersed in noise. The technique utilizes the recently-popularized nonlinear energy operator Psi (s)=(s)/sup 2/-ss to isolate the AM-FM energy, and an energy separation algorithm (ESA) to extract the instantaneous amplitudes and frequencies. It is demonstrated that the performance of the energy operator/ESA approach is vastly improved if the signal is first filtered through a bank of bandpass filters, and at each instant analyzed (via Psi and the ESA) using the dominant local channel response. Moreover, it is found that uniform (worst-case) performance across the frequency spectrum is attained by using a constant-Q, or multiscale wavelet-like filter bank. The elementary stochastic properties of Psi and of the ESA are developed first. The performance of Psi and the ESA when applied to bandpass filtered versions of an AM-FM signal-plus-noise combination is then analyzed. The predicted performance is greatly improved by filtering, if the local signal frequencies occur in-band. These observations motivate the multiband energy operator and ESA approach, ensuring the in-band analysis of local AM-PM energy. In particular, the multi-bands must have the constant-Q or wavelet scaling property to ensure uniform performance across bands. The theoretical predictions and the simulation results indicate that improved practical strategies are feasible for tracking and identifying AM-FM components in signals possessing pattern coherencies manifested as local concentrations of frequencies. >

LUM filters: a class of rank-order-based filters for smoothing and sharpening

Abstract: A new class of rank-order-based filters, called lower-upper-middle (LUM) filters, is introduced. The output of these filters is determined by comparing a lower- and an upper-order statistic to the middle sample in the filter window. These filters can be designed for smoothing and sharpening, or outlier rejection. The level of smoothing done by the filter can range from no smoothing to that of the median filter. This flexibility allows the LUM filter to be designed to best balance the tradeoffs between noise smoothing and signal detail preservation. LUM filters for enhancing edge gradients can be designed to be insensitive to low levels of additive noise and to remove impulsive noise. Furthermore, LUM filters do not cause overshoot or undershoot. Some statistical and deterministic properties of the LUM filters are developed, and a number of experimental results are presented to illustrate the performance. These experiments include applications to 1D signals and to images. >

An unbiased signal-to-noise ratio measure for magnetic resonance images.

Abstract: Estimating the true signal‐to‐noise ratio (SNR) of magnetic resonance(MR)images with low signal is confounded by the magnitude presentation of the data. This paper suggests a simple solution to this problem. A common method of measuring SNR compares the mean signal to the standard deviation of the noise. This SNR measure was found to be satisfactory for high but not low signal‐to‐noise image regions because of noise bias. These inconsistencies are removed by introducing unbiased definitions of the signal and noise levels in terms of their root‐mean‐square values. The approaches are compared by evaluating the SNR values for MRmedical images.

Attenuation correction using count-limited transmission data in positron emission tomography.

Abstract: Poisson noise in transmission data can have a significant influence on the statistical uncertainty of PET measurements, particularly at low transmission count rates. In this paper, we investigate the effect of transmission data processing on noise and quantitative accuracy of reconstructed PET images. Differences in spatial resolution between emission and transmission measurements due to transmission data smoothing are shown to have a significant influence on quantitative accuracy and can lead to artifacts in the reconstructed image. In addition, the noise suppression of this technique is insufficient to greatly reduce transmission scan times. Based on these findings, improved strategies for processing count-limited transmission data have been developed, including a method using segmentation of attenuation images. Using this method, accurate attenuation correction can be performed using transmission scan times as low as 2 min without increasing noise in reconstructed PET images.

Adaptive noise reduction circuit for a sound reproduction system

Abstract: A noise reduction circuit for a hearing aid having an adaptive filter for producing a signal which estimates the noise components present in an input signal. The circuit includes a second filter for receiving the noise-estimating signal and modifying it as a function of a user's preference or as a function of an expected noise environment. The circuit also includes a gain control for adjusting the magnitude of the modified noise-estimating signal, thereby allowing for the adjustment of the magnitude of the circuit response. The circuit also includes a signal combiner for combining the input signal with the adjusted noise-estimating signal to produce a noise reduced output signal.

HNS: Speech modification based on a harmonic+noise model

Abstract: HNS (harmonic plus noise synthesis), an analysis/modification/synthesis model based on a harmonic plus noise representation of the speech signal, is presented. Significant improvements over previous work on the subject are proposed at both the analysis and the synthesis stages: a model of harmonically related sinusoids with linearly varying complex amplitudes for the representation of the deterministic part of the signal; a joint time-domain and frequency-domain representations of the stochastic part of the signal; and a pitch-synchronous PSOLA (pitch-synchronized overlap-add)-like synthesis scheme. Informal listening-tests demonstrate the effectiveness of this approach for time-scale modifications. >

Method and apparatus for canceling spread-spectrum noise

Abstract: A spread-spectrum noise canceller (182) is provided. A received phase and a received amplitude for a first (216) and a second (236) component of a received spread-spectrum signal (200) is determined. The second component (236) is structurally similar to [a replica of] the first component (216), but differs by being received at a different time, being transmitted along a different path, or having a different phase [which was received at a different time]. In addition, the spread-spectrum signal (200) includes a first and a second known signal. A portion of a spread-spectrum noise signal in the received signal (200) is canceled by generating an estimated signal (270) by spreading (260) the second known signal at the second component received phase (204) with the first known signal at the first component received phase (224) and adjusting a gain (268) of an integrated form of the spread second known signal as a function of the received amplitudes of the first (216) and the second (236) components. Subsequently, the second known signal is processed out of the received spread-spectrum signal (200) by subtracting (166) the estimated signal (270) from a demodulated form (216, 236) of the received spread-spectrum signal (200).

Computer analysis of electrophysiological signals

Abstract: Introduction. Digital Recording of Analogue Signals. Analogue Signal Conditioning. Signal Analysis: Measurement of Signal Waveform Characteristics. Statistical Analysis and Presentation of Measurements. Mathematical Modelling and Curve Fitting. Analysis of Voltage-Activated Currents. Analysis of Single Channel Currents. Analysis of Ionic Current Fluctuations: Noise Analysis. Appendices: An Introduction to Computers. Chapter Summaries. Reference. Suppliers.

High Resolution Order Tracking at Extreme Slew Rates Using Kalman Tracking Filters

Abstract: The analysis of the periodic components in noise and vibration signals measured on rotating equipment such as car power trains, must be done more and more under rapid changes of an axle, or reference RPM. Normal tracking filters (analog or digital implementations) have limited resolution in such situations; wavelet methods, even when applied after resampling the data to be proportional to an axle RPM, must compromise between time and frequency resolution. The authors propose the application of nonstationary Kalman filters for the tracking of periodic components in such noise and vibration signals. These filters are designed to accurately track signals with a known structure among noise and signal components of different, “unknown,” structure. The tracking characteristics of these filters, i.e., the predicted signal amplitude versus time values versus exact signal amplitude versus time values, can be tailored to accurate tracking of harmonics buried in other signal components and noise, even at high rates of change of the reference RPM. A key to the successful construction is the precise knowledge of the structure of the signal to be tracked. For signals that vary with an axle RPM, an accurate estimate of the instantaneous RPM is essential, and procedures to this end will also be presented.

Signal processing for microcalorimeters

Abstract: Most of the power in the signals from microcalorimeters occurs at relatively low frequencies. At these frequencies, typical amplifiers will have significant amounts of 1/f noise. Our laboratory systems can also suffer from pickup at several harmonics of the AC power line, and from microphonic pickup at frequencies that vary with the configuration of the apparatus. We have developed some optimal signal processing techniques in order to construct the best possible estimates of our pulse heights in the presence of these non-ideal effects. In addition to a discussion of our laboratory systems, we present our plans for providing this kind of signal processing in flight experiments.

Differential delay/Doppler ML estimation with unknown signals

Abstract: Previously published analyses on the maximum-likelihood estimation of joint frequency and time offsets between two noisy versions of a common signal have assumed a Gaussian random signal with a known power spectrum. In many applications, the common signal is not Gaussian and there may be no prior knowledge of its detailed structure. Instead, estimation of a hypothesized common signal can be construed as another element in the estimation process. The analysis is straightforward and shows that the complex ambiguity function (CAF) still represents the ML approach when the noise is Gaussian and spectrally flat. Additional interpretation shows that use of interference-rejection filtering followed by a CAF is an attractive suboptimum approach in an environment of narrowband interferers. >

Simultaneous Switching Noise of CMOS Devices and Systems

Abstract: List of Figures. List of Tables. 1. Introduction. 2. Packages/Scaled CMOS Devices. 3. Methods of Calculating Simultaneous Switching Noise (SSN). 4. Power Distribution Inductance Modeling. 5. Signal Conductors over a Perforated Reference Plane. 6. Dynamic Noise Immunity, and Skewing/Damping SSN Waveform. 7. Application Specific Output Drivers to Reduce SSN. 8. SSN Simulator Architecture. 9. Signal Conductors over a Noisy Reference Plane. 10. Conclusions. 11. Discussion and Future Work. Appendices. References. Index.

All-Optical Signal Regenerator

Abstract: It is widely recognised that all-optical processing is the key to overcoming electronic bottle-necks in high-speed communication networks. Such systems, which are likely to include an abundance of fibre amplifiers, are still limited in performance by timing jitter (the Gordon-Haus effect1) and signal to noise degradation due to the build up of amplified spontaneous emission. The use of all-optical signal regeneration to restore pulse timing and to remove noise and intensity fluctuations could therefore greatly extend the range and bit-rate of such a system. In this paper, we demonstrate experimentally a complete all-optical signal regenerator exploiting a novel method of all-optical clock recovery2 and a nonlinear loop mirror3. We show that the regenerated data has less intensity variation and less temporal jitter than the incoming data.

On importance sampling in digital communications. I. Fundamentals

Abstract: A simulation algorithm design strategy based on the combination of event simulation, conditional importance sampling, and asymptotically optimal biasing of Gaussian noise inputs is discussed. The utility of this approach is illustrated by presenting numerical results for a satellite channel model that includes uplink and downlink noise sources, a travelling-wave tube amplifier (TWTA) nonlinearity, and intersymbol interference (ISI) from both uplink and downlink filtering. An overview and comparison of various simulation design strategies and some results on the optimization of general mean translation and variance scaling biasing schemes for nonlinear systems are presented. >

A method and apparatus for noise reduction

Abstract: A noise reducing method and associated apparatus for use in a medical radiographic imaging system wherein an image represented by an array of pixels is processed and the processed image is recorded on a recording medium or visualized on a display monitor. The processing comprises the steps of a) decomposing an original image into a sequence of detail images or into an array of coefficients representing detail strength at multiple resolution levels and a residual image, b) pixelwise attenuating the detail images or the coefficient arrays according to the locally estimated amount of relevant signal present and in accordance with an estimated noise level, c) reconstructing a processed image by accumulating detail obtained from the attenuated detail images or from the attenuated detail coefficients, and further adding the residual image.

On the Cramer-Rao Bound for Direction Finding of Correlated Signals

Abstract: In this paper we present compact closed form formulas for the Cramer Rao Bound corresponding to the joint estimation of the directions-of-arrival, the signal covariance matrix, and the noise variance. Using these formulas we investigate the effect of signal correlation on the achievable accuracy of direction finding system in a correlated signal environment. As expected, estimation accuracy decreases with increasing correlation magnitude. We observe that under certain conditions (small aperture, high correlation magnitude), correlation phase has a strong effect on DOA estimation accuracy.

Method of determining range data in a time-of-flight ranging system

Abstract: A time-of-flight range sensing system for a vehicle such as a mobile robot comprised of a number of sensors that are situated at preset locations and aligned with particular orientations on the vehicle. Each sensor may consist of single or multiple transmitting transducers and single or multiple receiving transducers. Each sensor provides a means for changing the effective sensing volume of the sensors or it has two or more separate processing channels for processing the time-of-flight information with several different effective sensing volumes using the sensor signal processor. The sensor signal processor also provides a means for collecting information regarding the echo signal peak value and the noise present in the received signal before reception of the first echo. A sensor array processor excites the transducers, measures the time-of-flight of arrival of the first echo above a predetermined threshold and combines the data from the multiple signal processing channels so as to increase the range data gathering speed and to improve the fidelity of the range data received. The sensor array processor determines from the range data received and the signal and noise information the position and orientation of gross geometric features in the environment (flat surfaces, posts inside corners and outside corners) relative to the sensor array. This information may be used by a navigation control processor to avoid collisions, recognize features in the environment, or map the environment.

Special purpose correlation functions for improved signal detection and parameter estimation

Abstract: A compilation of several correlation functions which were developed by the author and have been used for several years in signal analysis applications is given. The affine invariant pseudometric is a correlation function normalized to be independent of power, DC bias, and phase rotation. It was developed to track radar video sync pulses. It has recently been successfully used to track glottal pulses in voiced speech. The cross-power spectrum represents a significant improvement over standard power-spectral methods for recovering weak stationary tones in noise. The harmonic rejecting correlation function is a variant of the Wigner transform which resolves fundamentals from harmonic and subharmonic features produced by periodic waveforms. Each of these algorithms has been tested and used on a variety of data. Most importantly, for each of the methods described here, closed form solutions are derived, which enable easy implementations. >

Soliton transmission control in time and frequency domains

Abstract: Soliton transmission control techniques in both the time and frequency domains designed to enable ultra-long-distance soliton transmission are described in detail. Soliton transmission control in the time domain, which can be realized by synchronous modulation, is a retiming technique which removes jitter and nonlinear interaction between adjacent solitons. In addition, a transfer function reduces noise and the noise power eventually converges to a low level for any transmission distance. Soliton transmission control in the frequency domain, which can be realized with a bandpass optical filter, stabilizes the soliton pulse. A million-kilometer transmission experiment confirms the usefulness of these techniques. >

Application of Filtering Techniques for Incident Detection

Abstract: Traffic data, essential for the detection of freeway incidents, are often corrupted by impulsive noise and short‐term traffic inhomogeneities that may impair detection performance. Rigorous data filtering can reduce the undesirable noise and enhance the incident signal. A new incident detection algorithm is developed that employs short‐term time averaging (low‐pass filter) to reduce the adverse effects of short‐term traffic fluctuations and impulsive noise in the detection process. The detection algorithm traces the filtered spatial occupancy difference between adjacent detector stations through time, and detects an incident when this difference changes significantly in a short time period. The new algorithm was evaluated with data from a congested freeway in the Minneapolis‐St. Paul metropolitan area and compared against major existing algorithms. Test results indicate the ability of the Minnesota algorithm to significantly alleviate the false‐alarm problem, while preserving high detection performance, a...

Superdirective arrays for hearing aids.

Abstract: Microphone arrays are the most effective of the techniques that have been proposed for improving speech intelligibility in noise for the hearing impaired. However, classical delay‐and‐sum beamforming provides very small amounts of array gain at low frequencies, while adaptive array processing has been shown to cancel the desired signal in the presence of strong room reflections. Superdirective arrays offer a heretofore overlooked solution in which optimal performance can be obtained for a stationary random noise field, but where the desired signal will not be canceled. A short constrained superdirective array suitable for hearing‐aid applications is proposed in this paper, and its theoretical performance is evaluated.