Coding of time-varying electric field amplitude modulations in a wave-type electric fish

1996 ◽  
Vol 75 (6) ◽  
pp. 2280-2293 ◽  
Author(s):  
R. Wessel ◽  
C. Koch ◽  
F. Gabbiani
Keyword(s):  
Mutual Information ◽  
Electric Fish ◽  
Signal To Noise Ratio ◽  
Cutoff Frequency ◽  
Spike Trains ◽  
Time Varying ◽  
Signal To Noise ◽  
Single Spike ◽  
The Mean ◽  
P Type

1. The coding of time-varying electric fields in the weakly electric fish, Eigenmannia, was investigated in a quantitative manner. The activity of single P-type electroreceptor afferents was recorded while the amplitude of an externally applied sinusoidal electric field was stochastically modulated. The amplitude modulation waveform (i.e., the stimulus) was reconstructed from the spike trains by mean square estimation. 2. From the stimulus and the reconstructions we calculated the following: 1) the signal-to-noise ratio and thus an effective temporal bandwidth of the units; 2) the coding fraction, i.e., a measure of the fraction of the time-varying stimulus encoded in single spike trains; and 3) the mutual information provided by the reconstructions about the stimulus. 3. Signal-to-noise ratios as high as 7:1 were observed and the bandwidth ranged from 0 up to 200 Hz, consistent with the limit imposed by the sampling theorem. Reducing the cutoff frequency of the stimulus increased the signal-to-noise ratio at low frequencies, indicating a nonlinearity in the receptors' response. 4. The coding fraction and the rate of mutual information transmission increased in parallel with the standard deviation (i.e., the contrast) of the stimulus as well as the mean firing rate of the units. Significant encoding occurred 20-40 Hz above the spontaneous discharge of a unit. 5. When the temporal cutoff frequency of the stimulus was increased between 80 and 400 Hz, 1) the coding fraction decreased, 2) the rate of mutual information transmission remained constant over the same frequency range, and 3) the reconstructed filter changed. This is in agreement with predictions obtained in a simplified neuronal model. 6. Our results suggest that 1) the information transmitted by single spike trains of primary electrosensory afferents to higherorder neurons in the fish brain depends on the contrast and the cutoff frequency of the stimulus as well as on the mean firing rate of the units; and 2) under optimal conditions, more than half of the information about a Gaussian stimulus that can in principle be encoded is carried in single spike trains of P-type afferents at rates up to 200 bits per second.

Auditory Frequency Selectivity in Normal and Presbycusic Subjects

1980 ◽  
Vol 23 (3) ◽  
pp. 603-613 ◽  
Author(s):  
Robert H. Margolis ◽  
Seth M. Goldberg
Keyword(s):  
Signal To Noise Ratio ◽  
Cutoff Frequency ◽  
Frequency Selectivity ◽  
Hearing Impaired ◽  
Critical Ratio ◽  
Signal To Noise ◽  
Noise Masker ◽  
Low Pass ◽  
Noise Ratio ◽  
Stable Estimate

Auditory frequency selectivity was inferred from measurements of the detectability of tonal signals as a function of the cutoff frequency of a low-pass computer-generated noise masker. In Experiment I the effect of small changes in signal-to-noise ratio on inferred auditory frequency selectivity was studied. In Experiment II, frequency selectivity was determined for five normal-hearing subjects and four subjects with sensorineural hearing loss due to presbycusis. Critical ratios (signal-to-noise ratio at masked threshold) also were determined in Experiment II. The results of Experiment I indicate that the low-pass masking experiment provides a stable estimate of the width, but not the position, of the critical masking band. Experiment II revealed elevated critical ratios for three of the four presbycusic subjects. Some hearing-impaired subjects appeared to have normal frequency selectivity despite elevated critical ratios. Other presbycusic subjects demonstrated impaired auditory frequency selectivity. The results suggest that critical ratio and critical masking band data are free to vary independently in hearing-impaired subjects.

Power spectral analysis of the diaphragm electromyogram

1983 ◽  
Vol 54 (6) ◽  
pp. 1579-1584 ◽  
Author(s):  
T. K. Aldrich ◽  
J. M. Adams ◽  
N. S. Arora ◽  
D. F. Rochester
Keyword(s):  
Power Spectrum ◽  
Signal To Noise Ratio ◽  
Normal Subjects ◽  
Chronic Obstructive ◽  
Signal To Noise ◽  
Low Frequencies ◽  
Mean Values ◽  
The Mean ◽  
Noise Ratio ◽  
Diaphragm Fatigue

We studied the power spectrum of the diaphragm electromyogram (EMG) at frequencies between 31 and 246 Hz in four young normal subjects and five patients with chronic obstructive lung disease (COPD). Diaphragm EMGs were analyzed during spontaneous breathing and maximum inspiratory efforts to determine the effect of signal-to-noise ratio on the power spectrum and if treadmill exercise to dyspnea was associated with diaphragm fatigue. We found that the centroid frequencies of the power spectra (fc) were strongly correlated (r = 0.93) with ratios of power at high frequencies to power at low frequencies (H/L) for all subjects. Of the two indices, H/L had the largest standard deviation expressed as a percentage of the mean. The mean values of both of these decreased significantly after exercise, fc from 100.2 to 97.3 and H/L from 1.07 to 0.97. Signal-to-noise ratios were higher in maximal inspiratory efforts and after exercise in normal subjects and higher in COPD patients. The signal-to-noise ratio was correlated negatively with fc and H/L, indicating that these indices of the shape of the power spectrum are influenced by signal strength and noise levels as well as muscle function. We conclude that the fc and H/L index similar qualities of the power spectrum, that they are partially determined by the signal-to-noise ratio, and that, in some cases, exercise to dyspnea is associated with apparently mild diaphragm fatigue.

Detecting and Predicting Early Faults of Complex Rotating Machinery Based on Cyclostationary Time Series Model

2006 ◽  
Vol 128 (5) ◽  
pp. 666-671 ◽  
Author(s):  
Z. S. Chen ◽  
Y. M. Yang ◽  
Z. Hu ◽  
G. J. Shen
Keyword(s):  
Signal To Noise Ratio ◽  
Rotating Machinery ◽  
Ar Model ◽  
Model Parameters ◽  
Time Varying ◽  
Signal To Noise ◽  
Vibration Signals ◽  
Novel Method ◽  
Cyclostationary Signals ◽  
Gear Crack

Vibration signals of complex rotating machinery are often cyclostationary, so in this paper one novel method is proposed to detect and predict early faults based on the linear (almost) periodically time-varying autoregressive (LPTV-AR) model. At first the algorithms of identifying model parameters and order are presented using the higher-order cyclic-cumulant, which can suppress additive stationary noises and improve the signal to noise ratio (SNR). Then numerical simulations are done and the results indicate that this model is more effective for cyclostationary signals than the classical AR model. In the end the proposed method is used for detecting incipient gear crack fault in a helicopter gearbox. The results demonstrate that the approach can be used to detect and predict early faults of complex rotating machinery by the kurtosis of the residual signal.

Optical Range and Range Rate Estimation for Teleoperator Systems

1974 ◽  
Vol 18 (5) ◽  
pp. 487-492
Author(s):  
Nicholas L. Shields ◽  
Mark Kirkpatrick ◽  
Thomas B. Malone ◽  
Carl T. Huggins
Keyword(s):  
Signal To Noise Ratio ◽  
Size Estimation ◽  
Image Size ◽  
Signal To Noise ◽  
Estimation Errors ◽  
Television System ◽  
Rate Estimation ◽  
Rate Controlled ◽  
The Mean ◽  
Range Rate

Range and range rate are crucial parameters which must be available to the operator during remote controlled orbital docking operations. A method was developed for the estimation of both these parameters using an aided television system. An experiment was performed to determine the human operator's capability to measure displayed image size using a fixed reticle or movable cursor as the television aid. The movable cursor was found to yield mean image size estimation errors on the order of 2.3 percent of the correct value. This error rate was significantly lower than that for the fixed reticle. Performance using the movable cursor was found to be less sensitive to signal-to-noise ratio variation than was that for the fixed reticle. The mean image size estimation errors for the movable cursor correspond to an error of approximately 2.25 percent in range suggesting that the system has some merit. Determining the accuracy of range rate estimation using a rate controlled cursor will require further experimentation.

SIGNAL‐TO‐NOISE RATIO AND RECORD QUALITY

Geophysics ◽  
1964 ◽  
Vol 29 (6) ◽  
pp. 922-925 ◽  
Author(s):  
Arne Junger
Keyword(s):  
Phase Shift ◽  
Signal To Noise Ratio ◽  
Random Phase ◽  
Large Change ◽  
Seismic Record ◽  
Signal To Noise ◽  
Change In The Mean ◽  
The Mean ◽  
Noise Ratio

The appearance of a seismic record is a function of the signal‐to‐noise ratio. This ratio is expressed quantitatively, but it can not be measured on the record. The quality of the record is expressed by the lineup of events and constancy of character across the record, but is generally not expressed numerically. The appearance of the record is here expressed numerically by the mean phase shift from perfect lineup of various events. A statistical relationship is established between this mean phase shift and the signal‐to‐noise ratio. A seismic record may be approximated by considering the signal to have a sinusoidal waveform and the noise to be a continuous sine wave with the same frequency as the signal and with random phase shift with respect to the signal on various traces. The resulting record will show a random phase shift, the mean value of which is a function of the signal‐to‐noise ratio. A plot of these two values shows that with increasing signal‐to‐noise ratio there is very little change in the mean phase shift, and thus of the quality of the record, until a value of one‐half for the signal‐to‐noise ratio is reached, showing that the noise dominates the record up to this point. For values of the signal‐to‐noise ratio between one‐half and two, there is a large change in the mean phase shift, indicating a strong visual improvement for this range. For a signal‐to‐noise ratio larger than two, the signal predominates visually, and only a slight improvement in quality can be obtained with additional improvements in the signal‐to‐noise ratio. These conclusions are in agreement with experimental data published elsewhere.

An MMSE Overlay Cognitive Wireless System

2013 ◽  
Vol 5 (1) ◽  
pp. 1-13
Author(s):  
Monirosharieh Vameghestahbanati ◽  
Hasan S. Mir ◽  
Mohamed El-Tarhuni
Keyword(s):  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Wireless System ◽  
Transmitter Power ◽  
Channel Estimate ◽  
Squared Error ◽  
The Mean ◽  
Noise Ratio ◽  
New System

In this paper, the authors propose a framework that allows an overlay (new) system to operate simultaneously with a legacy (existing) system. By jointly optimizing the transmitter and the receiver filters of the overlay system, the sum of the mean-squared error (MSE) of the new system plus the excess MSE in the existing system due to the introduction of the overlay system is minimized. The effects of varying key parameters such as the overlay transmitter power and the amount of overlap between the legacy and the overlay systems are investigated. Furthermore, the sensitivity of the system to accuracy of signal-to-noise ratio (SNR) estimate and the channel estimate is also examined.

Frequency resolution of Fourier and maximum entropy spectral estimates

Geophysics ◽  
1982 ◽  
Vol 47 (9) ◽  
pp. 1303-1307 ◽  
Author(s):  
S. L. Marple
Keyword(s):  
Maximum Entropy ◽  
Signal To Noise Ratio ◽  
Frequency Resolution ◽  
Sampled Data ◽  
Signal To Noise ◽  
Analytic Determination ◽  
Spectral Estimates ◽  
The Mean ◽  
Better Than

An analytic determination of the frequency resolution for maximum entropy and conventional Blackman‐Tukey spectral estimates is made for the case of known autocorrelation. As the signal‐to‐noise ratio decreases, the maximum entropy resolution is no better than that achievable by the Blackman‐Tukey spectral estimate. The mean resolution of an ensemble of spectra constructed from sampled data sequences agrees with the analytic result.

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