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Background: Temporal processing ability has been linked to speech understanding ability and older adults often complain of difficulty understanding speech in difficult listening situations. Temporal processing can be evaluated usi...
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Background: Temporal processing ability has been linked to speech understanding ability and older adults often complain of difficulty understanding speech in difficult listening situations. Temporal processing can be evaluated using gap detection procedures. There is some research showing that gap detection can be evaluated using an electrophysiological procedure. However, there is currently no research establishing gap detection threshold using the N1-P2 response.
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The use of LiDAR in the study of gap regimes has seen significant progress in recent years. Researchers have assumed that canopy gaps detected in this manner are ecologically equivalent to gaps sampled in situ by more traditional ...
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The use of LiDAR in the study of gap regimes has seen significant progress in recent years. Researchers have assumed that canopy gaps detected in this manner are ecologically equivalent to gaps sampled in situ by more traditional methods. However, those latter methods usually include canopy gaps only and ignore non-regenerating openings that are produced by causes limiting tree establishment. We developed a predictive model capable of discriminating between canopy gaps and non-regenerating openings using LiDAR-derived data. Selected predictive variables were related to conditions that limit tree establishment such as zones of moisture accumulation and steep slopes or to the resulting vegetation physiognomy. The model was applied to three old-growth maple forests to predict the fractions of canopy openings belonging to these two types. On average, non-regenerating openings represented 19.5% of the total area detected as canopy openings and occupied 1.37% of the sites. Canopy gaps formed 80.5% of the total area in canopy openings and covered 5.71% of sites that were studied. The non-regenerating opening seemed more frequent on thin surficial deposits. The canopy gap fraction was similar among study sites but had lower values than usually reported for temperate deciduous forest.
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A turnout, a device to guide tracks, is critical to the safety of high-speed railways. Detecting gaps in switch machines can monitor a turnout's working performance. Existing gap-detection systems, however, can barely perform at h...
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A turnout, a device to guide tracks, is critical to the safety of high-speed railways. Detecting gaps in switch machines can monitor a turnout's working performance. Existing gap-detection systems, however, can barely perform at high accuracy and with a low false alarm rate for a long time due to the complex operating conditions of switch machines. This study proposes an approach combining YOLO-based object detection architecture with image processing algorithms, of which YOLO is a deep learning network for object detection. First, YOLO detects target areas in gap images, and then image-processing algorithms identify gaps and calculate gap sizes. This approach targets various types of switch machines and particularly complicated situations. Experiments on gap images of S700K switch machines demonstrate that the accuracy of detecting gaps is 100%, and the accuracy of calculating gap sizes is higher than 99%. Additionally, the proposed approach can exhibit the same high performance on complex images, like overexposed and tilted ones.
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Objective: The minimum time interval between two stimuli that can be reliably detected is called the gap detection threshold. The present study examines whether an unconscious state, natural sleep affects the gap detection thresho...
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Objective: The minimum time interval between two stimuli that can be reliably detected is called the gap detection threshold. The present study examines whether an unconscious state, natural sleep affects the gap detection threshold. Methods: Event-related potentials were recorded in 10 young adults while awake and during all-night sleep to provide an objective estimate of this threshold. These subjects were presented with 2, 4, 8 or 16. ms gaps occurring in 1.5 duration white noise. Results: During wakefulness, a significant N1 was elicited for the 8 and 16. ms gaps. N1 was difficult to observe during stage N2 sleep, even for the longest gap. A large P2 was however elicited and was significant for the 8 and 16. ms gaps. Also, a later, very large N350 was elicited by the 16. ms gap. An N1 and P2 was significant only for the 16. ms gap during REM sleep. Significance: ERPs to gaps occurring in noise segments can therefore be successfully elicited during natural sleep. The gap detection threshold is similar in the waking and sleeping states.
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Differentiating objects separated by narrow gaps is a challenging and important task in analyzing microscopic images. These small separations provide useful information for applications that require detailed boundary information a...
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Differentiating objects separated by narrow gaps is a challenging and important task in analyzing microscopic images. These small separations provide useful information for applications that require detailed boundary information and/or an accurate particle count. We present a new approach to the modeling of these gaps based on a marked point process (MPP) framework. We propose to model narrow gaps as geometric structures called channels and define Gibbs energies for these models. The reversible jump Markov chain Monte Carlo (RJMCMC) algorithm embedded with simulated annealing is used as an optimization method, and the switching kernel in an RJMCMC is newly designed to speed up the algorithm. In this paper, we also propose a method to exploit a detected channel configuration to reduce bridging channel defects in conventional segmentation algorithms. The experimental results demonstrate that the proposed channel modeling methods are successful in detecting gaps between closely adjacent objects. The results also show that the proposed interaction parameter control method improves boundary precision in the segmentation of microscopic images. The implementation of this method is available at https://engineering.purdue.edu/MASSI.
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Objectives: Animals with induced tinnitus showed difficulties in detecting silent gaps in sounds, suggesting that the tinnitus percept may be filling the gap. The main purpose of this study was to evaluate the applicability of thi...
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Objectives: Animals with induced tinnitus showed difficulties in detecting silent gaps in sounds, suggesting that the tinnitus percept may be filling the gap. The main purpose of this study was to evaluate the applicability of this approach to detect tinnitus in human patients. The authors first hypothesized that gap detection would be impaired in patients with tinnitus, and second, that gap detection would be more impaired at frequencies close to the tinnitus frequency of the patient.
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The Auditory Brainstem Response (ABR) can be used to measure the early temporal activity of the auditory system. A gapin-noise ABR has been developed to measure the electrophysiological response to auditory stimulation without att...
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The Auditory Brainstem Response (ABR) can be used to measure the early temporal activity of the auditory system. A gapin-noise ABR has been developed to measure the electrophysiological response to auditory stimulation without attending to the task. In the present study, young adults passively listened to stimuli of various gap widths in separate sequences. In a single sequence, two identical 15 ms filtered noise bursts, with a center frequency of either 750 or 3750 Hz, were presented separated by a gap (2, 5, 10, 20, 30, 40 or 50 ms in duration), with the second noise burst followed by an interstimulus interval of no less than 50 ms. An ABR was recorded at the onset of the first noise burst, before the gap (pre-gap), and at onset of the second noise burst, after the gap (post-gap). The gap duration had a suppressive effect on the amplitude of wave V for the noise burst following the gap. In contrast, wave V amplitude before the gap (i.e. the control) remained relatively constant. A significant difference was found between the amplitude of wave V elicited before and after the gap for gap durations equal to and below 20 and 5 ms, for 750 and 3750 Hz, respectively. The gap-in-noise ABR can potentially provide frequency-specific information for the study of temporal resolution in populations with a variety of hearing disorders.
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In recent years, there has been increasing use of the gap detection reflex test to demonstrate induction of tinnitus in animals. Animals with tinnitus show weakened gap detection ability for background noise that matches the pitch...
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In recent years, there has been increasing use of the gap detection reflex test to demonstrate induction of tinnitus in animals. Animals with tinnitus show weakened gap detection ability for background noise that matches the pitch of the tinnitus. The usual explanation is that the tinnitus 'fills in the gap'. It has recently been shown, however, that tinnitus is commonly associated with hyperacusis-like enhancements of the acoustic startle response, a change which might potentially alter responses in the gap detection test. We hypothesized that such enhancements could lead to an apparent reduction of gap suppression, resembling that caused by tinnitus, by altering responses to the startle stimulus or the background noise. To test this hypothesis, we compared gap detection abilities in 3 subsets of noise exposed animals with those in unexposed controls. The results showed that exposed animals demonstrated altered gap detection abilities, but these alterations were sometimes explained as consequences of hyper-responsiveness to either the startle stimulus or to the background noise. Two of the three subsets of animals studied, however, displayed weakened gap detection abilities that could not be explained by enhanced responses to these stimuli or by reduced sound sensitivity or a reduction of temporal processing speed, consistent with the induction of tinnitus. These results demonstrate that not only hearing loss but also changes in sensitivity to background noise or to startle stimuli are potential confounds that, when present, can underlie changes in gap detection irrespective of tinnitus. We discuss how such confounds can be recognized and how they can be avoided. (C) 2015 Elsevier B.V. All rights reserved.
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The main objective of this paper is to initiate a systematic application of gap metric techniques to the performance analysis and design issues of detecting and isolating multiplicative faults in uncertain systems. To be specific,...
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The main objective of this paper is to initiate a systematic application of gap metric techniques to the performance analysis and design issues of detecting and isolating multiplicative faults in uncertain systems. To be specific, in the first part of this paper, the K-gap and L-2-gap metrics are introduced, which measure the distance between two kernel subspaces and serve as an efficient tool to deal with fault detection and isolation issues. Based on it, the K-gap and L(2-)gap aided analysis of residual dynamics with respect to model uncertainties is presented for the open-loop and feedback control systems, respectively. Furthermore, they are applied to deal with the performance analysis of fault detection systems. For this purpose, the concepts of fault detectability indicators and fault-to-uncertainty ratio are introduced. The second part of this paper is dedicated to the isolation of multiplicative faults. To this end, the definition for fault isolability is studied first with the aid of K-gap metric. The further efforts are devoted to the application of K-gap metric to two online fault isolation algorithms. The first one is an observer-based scheme, which adopts a bank of residual generators and an observer-based decision unit. The second scheme is based on the data-driven identification of the kernel representation of the faulty plant and the data-driven computation of the K-gap metric. Case and example studies are finally given to illustrate the proposed approaches. (C) 2020 Elsevier Ltd. All rights reserved.
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This paper studies the classical problem of detecting the locations of signal occurrences in a one-dimensional noisy measurement. Assuming the signal occurrences do not overlap, we formulate the detection task as a constrained lik...
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This paper studies the classical problem of detecting the locations of signal occurrences in a one-dimensional noisy measurement. Assuming the signal occurrences do not overlap, we formulate the detection task as a constrained likelihood optimization problem and design a computationally efficient dynamic program that attains its optimal solution. Our proposed framework is scalable, simple to implement, and robust to model uncertainties. We show by extensive numerical experiments that our algorithm accurately estimates the locations in dense and noisy environments, and outperforms alternative methods.
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