scholarly journals Integration of motion energy from overlapping random background noise increases perceived speed of coherently moving stimuli

2016 ◽  
Vol 116 (6) ◽  
pp. 2765-2776 ◽  
Author(s):  
Jason Chuang ◽  
Emily C. Ausloos ◽  
Courtney A. Schwebach ◽  
Xin Huang
Keyword(s):  
Noise Level ◽  
Background Noise ◽  
Visual Motion ◽  
Random Motion ◽  
Spatial Integration ◽  
Neural Basis ◽  
Motion Energy ◽  
Background Stimulus ◽  
Speed Perception ◽  
Perceived Speed

The perception of visual motion can be profoundly influenced by visual context. To gain insight into how the visual system represents motion speed, we investigated how a background stimulus that did not move in a net direction influenced the perceived speed of a center stimulus. Visual stimuli were two overlapping random-dot patterns. The center stimulus moved coherently in a fixed direction, whereas the background stimulus moved randomly. We found that human subjects perceived the speed of the center stimulus to be significantly faster than its veridical speed when the background contained motion noise. Interestingly, the perceived speed was tuned to the noise level of the background. When the speed of the center stimulus was low, the highest perceived speed was reached when the background had a low level of motion noise. As the center speed increased, the peak perceived speed was reached at a progressively higher background noise level. The effect of speed overestimation required the center stimulus to overlap with the background. Increasing the background size within a certain range enhanced the effect, suggesting spatial integration. The speed overestimation was significantly reduced or abolished when the center stimulus and the background stimulus had different colors, or when they were placed at different depths. When the center- and background-stimuli were perceptually separable, speed overestimation was correlated with perceptual similarity between the center- and background-stimuli. These results suggest that integration of motion energy from random motion noise has a significant impact on speed perception. Our findings put new constraints on models regarding the neural basis of speed perception.

scholarly journals Automatic Modulation Classification Based on Deep Feature Fusion for High Noise Level and Large Dynamic Input

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2117
Author(s):  
Hui Han ◽  
Zhiyuan Ren ◽  
Lin Li ◽  
Zhigang Zhu
Keyword(s):  
Neural Network ◽  
Frequency Domain ◽  
Noise Level ◽  
Background Noise ◽  
Dynamic Range ◽  
Feature Fusion ◽  
Superior Performance ◽  
Modulation Classification ◽  
High Background ◽  
Automatic Modulation Classification

Automatic modulation classification (AMC) is playing an increasingly important role in spectrum monitoring and cognitive radio. As communication and electronic technologies develop, the electromagnetic environment becomes increasingly complex. The high background noise level and large dynamic input have become the key problems for AMC. This paper proposes a feature fusion scheme based on deep learning, which attempts to fuse features from different domains of the input signal to obtain a more stable and efficient representation of the signal modulation types. We consider the complementarity among features that can be used to suppress the influence of the background noise interference and large dynamic range of the received (intercepted) signals. Specifically, the time-series signals are transformed into the frequency domain by Fast Fourier transform (FFT) and Welch power spectrum analysis, followed by the convolutional neural network (CNN) and stacked auto-encoder (SAE), respectively, for detailed and stable frequency-domain feature representations. Considering the complementary information in the time domain, the instantaneous amplitude (phase) statistics and higher-order cumulants (HOC) are extracted as the statistical features for fusion. Based on the fused features, a probabilistic neural network (PNN) is designed for automatic modulation classification. The simulation results demonstrate the superior performance of the proposed method. It is worth noting that the classification accuracy can reach 99.8% in the case when signal-to-noise ratio (SNR) is 0 dB.

scholarly journals Spatial and Temporal Integration of Visual Motion Signals for Smooth Pursuit Eye Movements in Monkeys

2009 ◽  
Vol 102 (4) ◽  
pp. 2013-2025 ◽  
Author(s):  
Leslie C. Osborne ◽  
Stephen G. Lisberger
Keyword(s):  
Random Walk ◽  
Eye Movements ◽  
Smooth Pursuit ◽  
Visual Motion ◽  
Target Motion ◽  
Linear Filter ◽  
Smooth Pursuit Eye Movements ◽  
Spatial Averaging ◽  
Spatial Integration ◽  
Pursuit Eye Movements

To probe how the brain integrates visual motion signals to guide behavior, we analyzed the smooth pursuit eye movements evoked by target motion with a stochastic component. When each dot of a texture executed an independent random walk such that speed or direction varied across the spatial extent of the target, pursuit variance increased as a function of the variance of visual pattern motion. Noise in either target direction or speed increased the variance of both eye speed and direction, implying a common neural noise source for estimating target speed and direction. Spatial averaging was inefficient for targets with >20 dots. Together these data suggest that pursuit performance is limited by the properties of spatial averaging across a noisy population of sensory neurons rather than across the physical stimulus. When targets executed a spatially uniform random walk in time around a central direction of motion, an optimized linear filter that describes the transformation of target motion into eye motion accounted for ∼50% of the variance in pursuit. Filters had widths of ∼25 ms, much longer than the impulse response of the eye, and filter shape depended on both the range and correlation time of motion signals, suggesting that filters were products of sensory processing. By quantifying the effects of different levels of stimulus noise on pursuit, we have provided rigorous constraints for understanding sensory population decoding. We have shown how temporal and spatial integration of sensory signals converts noisy population responses into precise motor responses.

Prediction of Threshold Sand Rates from Acoustic Monitors Using Artificial Intelligence

2021 ◽  
Author(s):  
Ronald E. Vieira ◽  
Bohan Xu ◽  
Asad Nadeem ◽  
Ahmed Nadeem ◽  
Siamack A. Shirazi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Noise Level ◽  
Background Noise ◽  
Pipe Wall ◽  
Flow Patterns ◽  
Operating Conditions ◽  
Flow Conditions ◽  
Background Noise Level ◽  
Input Parameters

Abstract Solids production from oil and gas wells can cause excessive damage resulting in safety hazards and expensive repairs. To prevent the problems associated with sand influx, ultrasonic devices can be used to provide a warning when sand is being produced in pipelines. One of the most used methods for sand detection is utilizing commercially available acoustic sand monitors that clamp to the outside of pipe wall and measures the acoustic energy generated by sand grain impacts on the inner side of a pipe wall. Although the transducer used by acoustic monitors is especially sensitive to acoustic emissions due to particle impact, it also reacts to flow induced noise as well (background noise). The acoustic monitor output does not exceed the background noise level until a sufficient sand rate is entrained in the flow that causes a signal output that is higher than the background noise level. This sand rate is referred to as the threshold sand rate or TSR. A significant amount of data has been compiled over the years for TSR at the Tulsa University Sand Management Projects (TUSMP) for various flow conditions with stainless steel pipe material. However, to use this data to develop a model for different flow patterns, fluid properties, pipe, and sand sizes is challenging. The purpose of this work is to develop an artificial intelligence (AI) methodology using machine learning (ML) models to determine TSR for a broad range of operating conditions. More than 250 cases from previous literature as well as ongoing research have been used to train and test the ML models. The data utilized in this work has been generated mostly in a large-scale multiphase flow loop for sand sizes ranging from 25 to 300 μm varying sand concentrations and pipe diameters from 25.4 mm to 101.6 mm ID in vertical and horizontal directions downstream of elbows. The ML algorithms including elastic net, random forest, support vector machine and gradient boosting, are optimized using nested cross-validation and the model performance is evaluated by R-squared score. The machine learning models were used to predict TSR for various velocity combinations under different flow patterns with sand. The sensitivity to changes of input parameters on predicted TSR was also investigated. The method for TSR prediction based on ML algorithms trained on lab data is also validated on actual field conditions available in the literature. The AI method results reveal a good training performance and prediction for a variety of flow conditions and pipe sizes not tested before. This work provides a framework describing a novel methodology with an expanded database to utilize Artificial Intelligence to correlate the TSR with the most common production input parameters.

Improvement of Recognition on a Multi-Modal Pattern Discrimination Test

1968 ◽  
Vol 26 (2) ◽  
pp. 431-441 ◽  
Author(s):  
Daniel Cappon ◽  
Robin Banks ◽  
Craig Ramsey
Keyword(s):  
Noise Level ◽  
Background Noise ◽  
Corrective Feedback ◽  
Pattern Discrimination ◽  
Repeated Exposure ◽  
Practice Effects ◽  
Discrimination Test ◽  
Passive Touch ◽  
Modal Test ◽  
Treatment Variation

A multi-modal test of pattern discrimination, including vision, hearing, active and passive touch, is described. It measures changes in veridicality of recognition as a result of two kinds of treatment: variation in pattern definition or context and practice effects. The test consists essentially of stable familiar geometrical figures in the foreground against a background of graduated “noise” in the same modality as the embedded figure. 240 Ss, divided into four groups (one of each modality) were employed. Ss were exposed to corrective feedback, repeated exposure or a control condition and to a random presentation of varying background for each of the foreground figures in a particular modality. Results indicated that both practice and background noise level affected veridicality of recognition.

ОПЫТ ИСПОЛЬЗОВАНИЯ В УСЛОВИЯХ МКС ТЕХНИЧЕСКИХ СРЕДСТВ НАУЧНОЙ АППАРАТУРЫ «БАР». ЧАСТЬ 1. АКУСТИЧЕСКИЕ ПРИБОРЫ

2021 ◽  
Vol 55 (2) ◽  
pp. 90-97
Author(s):  
E.A. Deshevaya ◽  
◽  
V.B. Bychkov ◽  
M.P. Malakh ◽  
V.O. Orlov ◽  
...  
Keyword(s):  
Noise Level ◽  
Background Noise ◽  
Narrow Band ◽  
Oral Communication ◽  
Leak Detector ◽  
Narrow Band Noise ◽  
Band Noise ◽  
Operational Testing ◽  
The Us ◽  
Research Equipment

The paper presents the results of operational testing of two acoustic components of research equipment «Bar» (a leak detector and ultrasound analyzer) onboard the ISS Russian segment. It was found that the ultrasonic (US) background noise interferes in leak search; besides, it exceeds dramatically the adopted noise level constraint thus impacting crew health and efficiency, and oral communication. The narrow-band noise spectra were measured and the expressed tone property of the US noise was demonstrated. The authors make the point that the present noise level constraints for space habitats do not take into account all realities of the life of cosmonauts and lack a solid medical and acoustic background.

Stimulus Specificity and Temporal Dynamics of Working Memory for Visual Motion

2003 ◽  
Vol 90 (4) ◽  
pp. 2757-2762 ◽  
Author(s):  
Tatiana Pasternak ◽  
Daniel Zaksas
Keyword(s):  
Visual Motion ◽  
Temporal Dynamics ◽  
Random Motion ◽  
Neural Mechanisms ◽  
Precise Location ◽  
Storage Mechanism ◽  
Motion Stimulus ◽  
Selective Interference ◽  
Stimulus Specificity ◽  
Stimulus Direction

When asked to compare two moving stimuli separated by a delay, observers must not only identify stimulus direction but also store it in memory. We examined the properties of this storage mechanism in two macaque monkeys by sequentially presenting two random-dot stimuli, sample and test, in opposite hemifields and introducing a random-motion mask during the delay. The mask interfered with performance only at the precise location of the test, 100–200 ms after the start of the delay, and when its size and speed matched those of the remembered sample. This selective interference suggests that the representation of the motion stimulus in memory preserves its direction, speed, and size and is most fragile shortly after the completion of the encoding phase of the task. This precise preservation of sensory attributes of the motion stimulus suggests that the neural mechanisms involved in the processing of visual motion may also be involved in its storage.

scholarly journals Operation Testing of an Advanced Personalized Ventilation System

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1596 ◽  
Author(s):  
Csáky ◽  
Kalmár ◽  
Kalmár
Keyword(s):  
Air Quality ◽  
Thermal Comfort ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Distribution System ◽  
Noise Level ◽  
Background Noise ◽  
Ventilation System ◽  
Air Distribution ◽  
Personalized Ventilation

Using personalized ventilation systems in office buildings, important energy saving might be obtained, which may improve the indoor air quality and thermal comfort sensation of occupants at the same time. In this paper, the operation testing results of an advanced personalized ventilation system are presented. Eleven different air terminal devices were analyzed. Based on the obtained air velocities and turbulence intensities, one was chosen to perform thermal comfort experiments with subjects. It was shown that, in the case of elevated indoor temperatures, the thermal comfort sensation can be improved considerably. A series of measurements were carried out in order to determine the background noise level and the noise generated by the personalized ventilation system. It was shown that further developments of the air distribution system are needed.

Modeling Speech Level as a Function of Background Noise Level and Talker-to-Listener Distance for Talkers Wearing Hearing Protection Devices

2017 ◽  
Vol 60 (12) ◽  
pp. 3393-3403 ◽  
Author(s):  
Rachel E. Bouserhal ◽  
Annelies Bockstael ◽  
Ewen MacDonald ◽  
Tiago H. Falk ◽  
Jérémie Voix
Keyword(s):  
Noise Level ◽  
Background Noise ◽  
Speech Sound ◽  
Sound Level ◽  
Hearing Protection ◽  
Background Noise Level ◽  
Vocal Effort ◽  
Protection Devices ◽  
The Difference ◽  
The Relationship

Purpose Studying the variations in speech levels with changing background noise level and talker-to-listener distance for talkers wearing hearing protection devices (HPDs) can aid in understanding communication in background noise. Method Speech was recorded using an intra-aural HPD from 12 different talkers at 5 different distances in 3 different noise conditions and 2 quiet conditions. Results This article proposes models that can predict the difference in speech level as a function of background noise level and talker-to-listener distance for occluded talkers. The proposed model complements the existing model presented by Pelegrín-García, Smits, Brunskog, and Jeong (2011) and expands on it by taking into account the effects of occlusion and background noise level on changes in speech sound level. Conclusions Three models of the relationship between vocal effort, background noise level, and talker-to-listener distance for talkers wearing HPDs are presented. The model with the best prediction intervals is a talker-dependent model that requires the users' unoccluded speech level at 10 m as a reference. A model describing the relationship between speech level, talker-to-listener distance, and background noise level for occluded talkers could eventually be incorporated with radio protocols to transmit verbal communication only to an intended set of listeners within a given spatial range—this range being dependent on the changes in speech level and background noise level.

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