scholarly journals Rapid evaluation of the spectral signal detection threshold and Stieltjes transform

2021 ◽  
Vol 47 (4) ◽  
Author(s):  
William Leeb
Keyword(s):  
Signal Detection ◽  
Detection Threshold ◽  
Rapid Evaluation ◽  
Stieltjes Transform ◽  
Spectral Signal

scholarly journals Field Theoretical Approach for Signal Detection in Nearly Continuous Positive Spectra II: Tensorial Data

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 795
Author(s):  
Vincent Lahoche ◽  
Mohamed Ouerfelli ◽  
Dine Ousmane Samary ◽  
Mohamed Tamaazousti
Keyword(s):  
Principal Component Analysis ◽  
Renormalization Group ◽  
Signal Detection ◽  
Detection Threshold ◽  
Principal Component ◽  
Vacuum Expectation ◽  
Vacuum Expectation Value ◽  
Component Analysis ◽  
Slight Generalization ◽  
Nearly Continuous

The tensorial principal component analysis is a generalization of ordinary principal component analysis focusing on data which are suitably described by tensors rather than matrices. This paper aims at giving the nonperturbative renormalization group formalism, based on a slight generalization of the covariance matrix, to investigate signal detection for the difficult issue of nearly continuous spectra. Renormalization group allows constructing an effective description keeping only relevant features in the low “energy” (i.e., large eigenvalues) limit and thus providing universal descriptions allowing to associate the presence of the signal with objectives and computable quantities. Among them, in this paper, we focus on the vacuum expectation value. We exhibit experimental evidence in favor of a connection between symmetry breaking and the existence of an intrinsic detection threshold, in agreement with our conclusions for matrices, providing a new step in the direction of a universal statement.

Electrical noise modulates perception of electrical pulses in humans: sensation enhancement via stochastic resonance

2014 ◽  
Vol 111 (6) ◽  
pp. 1238-1248 ◽  
Author(s):  
Fivos Iliopoulos ◽  
Till Nierhaus ◽  
Arno Villringer
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
Signal Detection ◽  
Stochastic Resonance ◽  
Detection Threshold ◽  
Left Index Finger ◽  
Electrical Noise ◽  
Low Pass ◽  
The Central Nervous System ◽  
Near Threshold

Although noise is usually considered to be harmful for signal detection and information transmission, stochastic resonance (SR) describes the counterintuitive phenomenon of noise enhancing the detection and transmission of weak input signals. In mammalian sensory systems, SR-related phenomena may arise both in the peripheral and the central nervous system. Here, we investigate behavioral SR effects of subliminal electrical noise stimulation on the perception of somatosensory stimuli in humans. We compare the likelihood to detect near-threshold pulses of different intensities applied on the left index finger during presence vs. absence of subliminal noise on the same or an adjacent finger. We show that (low-pass) noise can enhance signal detection when applied on the same finger. This enhancement is strong for near-threshold pulses below the 50% detection threshold and becomes stronger when near-threshold pulses are applied as brief trains. The effect reverses at pulse intensities above threshold, especially when noise is replaced by subliminal sinusoidal stimulation, arguing for a peripheral direct current addition. Unfiltered noise applied on longer pulses enhances detection of all pulse intensities. Noise applied to an adjacent finger has two opposing effects: an inhibiting effect (presumably due to lateral inhibition) and an enhancing effect (most likely due to SR in the central nervous system). In summary, we demonstrate that subliminal noise can significantly modulate detection performance of near-threshold stimuli. Our results indicate SR effects in the peripheral and central nervous system.

Effects of Hypoxia on Peripheral Visual Response to Dim Stimuli

1975 ◽  
Vol 41 (2) ◽  
pp. 467-474 ◽  
Author(s):  
John L. Kobrick
Keyword(s):  
Visual Field ◽  
Signal Detection ◽  
Response Times ◽  
Detection Threshold ◽  
Threshold Value ◽  
Hypoxic Exposure ◽  
Visual Response ◽  
Stimulus Luminance ◽  
Direct Relation ◽  
Stimulus Contrast

Response times (RTs) of 9 Ss were obtained for detection of 48 flash stimuli distributed throughout the visual field during 3 ¼-hr. exposures to each of 4 hypoxia conditions (0, 13,000, 15,000, 17,000 feet equivalent elevation). The luminances of all stimuli were set in common at the detection threshold value for the visual periphery. RTs were impaired in direct relation to hypoxic exposure severity, the peak impairments occurring within 90 min. followed by gradual recovery. Since the present results showed less impairment than previous data for brighter stimuli using the same task, it is concluded that stimulus contrast is more critical to peripheral signal detection than absolute stimulus luminance, particularly under hypoxic exposure.

Signal-Detection, Threshold, and Dual-Process Models of Recognition Memory: ROCs and Conscious Recollection

1996 ◽  
Vol 5 (4) ◽  
pp. 418-441 ◽  
Author(s):  
Andrew P. Yonelinas ◽  
Ian Dobbins ◽  
Michael D. Szymanski ◽  
Harpreet S. Dhaliwal ◽  
Ling King
Keyword(s):  
Recognition Memory ◽  
Signal Detection ◽  
Detection Threshold ◽  
Process Models ◽  
Dual Process ◽  
Dual Process Models ◽  
Conscious Recollection

scholarly journals Field Theoretical Approach for Signal Detection in Nearly Continuous Positive Spectra I: Matricial Data

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1132
Author(s):  
Vincent Lahoche ◽  
Dine Ousmane Samary ◽  
Mohamed Tamaazousti
Keyword(s):  
Signal Detection ◽  
Covariance Matrix ◽  
Degrees Of Freedom ◽  
Detection Threshold ◽  
Principal Component ◽  
Coarse Graining ◽  
Theoretical Investigations ◽  
Modern Physics ◽  
Highly Correlated ◽  
Nearly Continuous

Renormalization group techniques are widely used in modern physics to describe the relevant low energy aspects of systems involving a large number of degrees of freedom. Those techniques are thus expected to be a powerful tool to address open issues in data analysis when datasets are highly correlated. Signal detection and recognition for a covariance matrix having a nearly continuous spectra is currently one of these opened issues. First, investigations in this direction have been proposed in recent investigations from an analogy between coarse-graining and principal component analysis (PCA), regarding separation of sampling noise modes as a UV cut-off for small eigenvalues of the covariance matrix. The field theoretical framework proposed in this paper is a synthesis of these complementary point of views, aiming to be a general and operational framework, both for theoretical investigations and for experimental detection. Our investigations focus on signal detection. They exhibit numerical investigations in favor of a connection between symmetry breaking and the existence of an intrinsic detection threshold.

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