Nonfluctuating Signals Research Articles

Metabolic resting-state brain networks in health and disease

The delineation of resting state networks (RSNs) in the human brain relies on the analysis of temporal fluctuations in functional MRI signal, representing a small fraction of total neuronal activity. Here, we used metabolic PET, which maps nonfluctuating signals related to total activity, to identify and validate reproducible RSN topographies in healthy and disease populations. In healthy subjects, the dominant (first component) metabolic RSN was topographically similar to the default mode network (DMN). In contrast, in Parkinson's disease (PD), this RSN was subordinated to an independent disease-related pattern. Network functionality was assessed by quantifying metabolic RSN expression in cerebral blood flow PET scans acquired at rest and during task performance. Consistent task-related deactivation of the "DMN-like" dominant metabolic RSN was observed in healthy subjects and early PD patients; in contrast, the subordinate RSNs were activated during task performance. Network deactivation was reduced in advanced PD; this abnormality was partially corrected by dopaminergic therapy. Time-course comparisons of DMN loss in longitudinal resting metabolic scans from PD and Alzheimer's disease subjects illustrated that significant reductions appeared later for PD, in parallel with the development of cognitive dysfunction. In contrast, in Alzheimer's disease significant reductions in network expression were already present at diagnosis, progressing over time. Metabolic imaging can directly provide useful information regarding the resting organization of the brain in health and disease.

Analysis of Steering Vector Mismatch on Adaptive Noncoherent Integration

Amplitude and phase errors can cause mismatches between the hypothesized signal vector and the steering vector of an antenna array. A generalized likelihood ratio test (GLRT) for signal detection with multiple coherent processing intervals (CPIs) in unknown interference is derived. The detection performance of the resulting adaptive noncoherent integration (NCI) algorithm is analyzed along with the effect of steering vector mismatch on detection performance. Detection performance and the effect of steering vector mismatch are illustrated with results for both a nonfluctuating signal model and a CPI-to-CPI fluctuating signal model. It is shown that a signal-to-interference-plus-noise ratio (SINR) loss factor inherent to the algorithm in effect limits the performance gain due to NCI of mismatched signals.

The Dependence of Fusion Gain on Signal-Amplitude Distributions and Position Errors

This paper considers statistical detection theory for a system of two geographically distributed sensors. The performance of the two main fusion rules (OR and AND) is evaluated in the absence and presence of position errors in the observations, by means of receiver operating characteristics (ROC) curves. The analysis is restricted to three distributions that are representative of a Rician: Rayleigh, one-dominant-plus-Rayleigh (1D+R), and nonfluctuating signal amplitude. Rice's distribution is considered to be relevant for sonar and radar applications. In the absence of position errors, the performance is derived theoretically. It is shown that the best fusion rule depends on the assumed signal amplitude distribution and on the signal-to-noise ratio (SNR). Under operational conditions, position inaccuracies of the multiple observations are of main importance, raising association problems. In this case, the ROC curves are evaluated by means of simulations. It is found that position errors degrade detection performance by 3-6 dB if the AND fusion is used, whereas OR fusion is hardly affected.

Characterization of a Linear Streak Artifact With Pulse Inversion Tissue Harmonics in Musculoskeletal Sonography

To understand a linear artifact that projects deep to reflective structures that move rapidly while using tissue harmonic imaging with pulse inversion (PI) sonography. We hypothesize that this artifact is due to a cancellation error between firings in PI imaging, and it is, therefore, similar in generation to the twinkling artifact in color Doppler sonography. This artifact could be studied with the use of surfaces of different roughness to represent different rates of motion, in which roughness corresponds to spatial fluctuations in surface height. Given very slight variations in beam focusing as occurs with sonographic imaging arrays, these spatial fluctuations translate into temporal fluctuations in the received signal as would occur with tissue motion. We scanned 4 different sandpaper grits and a smooth surface through a water path using fundamental and PI mode, 1- and 2-pulse techniques, respectively. The sandpaper and the smooth surface were scanned through a water path at mechanical indices of 0.1 to 0.7. Four independent images were subtracted pairwise to remove nonfluctuating signals. These noise pixels were counted and analyzed. Analysis of variance showed that the noise generated behind the different surfaces was highly significantly different. Two-tailed t tests generally showed significant differences in the quantity of noise between fundamental and harmonic imaging behind the roughest 3 grades of sandpaper. A multiple regression model showed significantly greater slopes for harmonic imaging for all grades of sandpaper and the smooth surface. The noise and, by extension, the linear streak artifact in musculoskeletal imaging are dependent on the mechanical index and are functions of sandpaper roughness. This would be equivalent to a subtraction error between 2 firings due to soft tissue motion, and the artifact may be a way to identify rapid soft tissue motion in PI images.

A fuzzy approach to signal integration

A new fuzzy concept for postdetection signal integration is presented. The fuzzy integrator is developed as a simple extension of the classic binary integrator by replacing the crisp binary threshold, which quantizes the observed data, with a fuzzy threshold. The performance of the fuzzy integrator is illustrated for detection of a simple nonfluctuating signal in Gaussian noise and is shown to exceed that of the binary integrator, approaching that of the optimal detector with Neyman-Pearson decision rule. Furthermore, the fuzzy integrator has the characteristic that the false alarm rate can be tuned using a single threshold, more easily than that of the dual-threshold binary integrator.

Optimum detection of fading signals in impulsive noise

This paper deals with the synthesis and the analysis of optimum receivers to detect one out of M equally likely, equi-energy, fading signals in impulsive noise, modelled as a compound Gaussian, possibly correlated process. We show that the conventional coherent and incoherent detectors are still optimum, independent of the noise as well as the fading probability density functions. The performance analysis has been carried on with reference to the general case of arbitrarily distributed disturbance: in order to simplify the analysis, asymptotical expressions have been developed for high signal-to-noise ratios as well as high signal space dimensionality. Interestingly enough, this allows separating the effect of the noise spikyness from that of the fading law. Results indicate that, for deep fading, the noise marginal distribution does not dramatically affect the error probability, nor is it influential on the limit operating characteristics corresponding to infinite signal space dimensions. For non-fluctuating signals, instead, the noise distribution plays a primary role: spiky noise usually produces performance impairment; moreover, the limit performance in impulsive disturbance may exhibit marked deviations from the well-known stepwise shape which is typical of Gaussian channels. >

Trimmed Generalized Sign and Modified Median Detectors for Multiple Target Situations

The trimmed generalized sign (TGS) nonparametric detector is introduced. The TGS and the modified median detector (MMD) are considered in situations when more than one target is present. Their performance is obtained through Monte Carlo simulations and compared with that of the generalized sign (GS) detector when detecting nonfluctuating signal in Gaussian noise.

The asymptotic performance of two-sample nonparametric detectors when detecting nonfluctuating signals in non-Gaussian noise (Corresp.)

The asymptotic relative efficiencies (ARE) of generalized sign (GS), Mann-Whitney (MW), modified Savage (MS), and modified rank squared (MRS) two-sample nonparametric detectors are compared with those of the mean and median parametric detectors. The signal is assumed to be nonfluctuating, and the background noise is assumed to be lognormal or to be characterized by a contaminated normal distribution. It is shown that using the asymptotic relative efficiency measure the performance of nonparametric detectors increases appreciably as the noise distribution deviates from normality.

Signal excess and detection probability of fluctuating sonar signals in noise

Signal excess is defined as the excess or deficiency of signal level relative to that required for a detection probability of 50%; curves of detection probability versus signal excess are sometimes called “transition curves.” In this paper, transition curves for a nonfluctuating signal in Gaussian noise and for Gaussian, Rayleigh, and log—normal signal fluctuations are obtained. A comparison with one set of detection data obtained in a sea test with a towed sound source indicates that log—normal signal excess fluctuations with a standard deviation of 6–8 dB give a reasonable fit to the data. The method suggested here—of using transition curves along with a known or assumed relation between transmission loss versus range—permits quicker computation of detection probability against range than does the conventional way of employing ROC curves, and can accommodate a signal excess that fluctuates with time about a mean value. [Work supported by Naval Surface Weapons Center and ASW Systems Project Office, Code ASW-13.]

Mean-Level Detection of Nonfluctuating Signals

Analysis of the performance of a mean-level threshold in the detection of nonfluctuating signals is performed. Formulas for the probability of detection are derived and a simple recursive method that can be used for computations is described. Binary integration is discussed, and it is shown that the loss in sensitivity due to the use of an adaptive threshold followed by binary integration is only a fraction of a decibel when compared with optimum binary integration. Binary integration results are given for both fluctuating and nonfluctuating signals.

Detection of Fluctuating Sonar Targets

The effect of amplitude fluctuation on sonar detection can be expressed quantitatively by means of receiver operating characteristic (ROC) curves having a fluctuation index k as a parameter. The fluctuation-modified ROCs are found to be similar to conventional ROCs for non-fluctuating signals (k = 1), but on double-probability paper have a slope that depends on the value of k. A simple example with Gaussian statistics is developed where the time mean of a signal fluctuates Gaussianly about the ensemble mean. From this idea, the analytical expressions for the “tilted” ROCs, as well as their plots, are derived. Two extremes of slow and fast fluctuations are discussed.

A unified approach to the detection of fluctuating pulsed signals in noise

The classical problem of detecting a fluctuating pulsed signal in noise is analyzed when the signal has a Rice amplitude distribution. The results provide a natural bridge between previous analyses of nonfluctuating signals and signals with Rayleigh envelopes.

Effects of signal fluctuation on the detection of pulse signals in noise

The Neyman-Pearson statistical theory on testing hypotheses has in previous work been applied to the problem of the detection of nonfluctuating constant-amplitude signals embedded in noise. This work is extended in this paper to the case of signal power fluctuating according to a prescribed probability distribution. The effect on system performance of possible correlation between successive signal pulses is taken into account. The introduction of signal fluctuation leads in general to some loss in system performance as compared to the case of nonfluctuating signals. This loss is most pronounced when there is complete correlation between successive signals, and is quite small when successive signals are independent of one another.