adaptive threshold
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Computing ◽  
2022 ◽  
Ruiping Wang ◽  
Liangcai Zeng ◽  
Shiqian Wu ◽  
Kelvin K. L. Wong

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8305
César Covantes-Osuna ◽  
Jhonatan B. López ◽  
Omar Paredes ◽  
Hugo Vélez-Pérez ◽  
Rebeca Romo-Vázquez

The brain has been understood as an interconnected neural network generally modeled as a graph to outline the functional topology and dynamics of brain processes. Classic graph modeling is based on single-layer models that constrain the traits conveyed to trace brain topologies. Multilayer modeling, in contrast, makes it possible to build whole-brain models by integrating features of various kinds. The aim of this work was to analyze EEG dynamics studies while gathering motor imagery data through single-layer and multilayer network modeling. The motor imagery database used consists of 18 EEG recordings of four motor imagery tasks: left hand, right hand, feet, and tongue. Brain connectivity was estimated by calculating the coherence adjacency matrices from each electrophysiological band (δ, θ, α and β) from brain areas and then embedding them by considering each band as a single-layer graph and a layer of the multilayer brain models. Constructing a reliable multilayer network topology requires a threshold that distinguishes effective connections from spurious ones. For this reason, two thresholds were implemented, the classic fixed (average) one and Otsu’s version. The latter is a new proposal for an adaptive threshold that offers reliable insight into brain topology and dynamics. Findings from the brain network models suggest that frontal and parietal brain regions are involved in motor imagery tasks.

2021 ◽  
Vol 2096 (1) ◽  
pp. 012019
A O Shcherbina ◽  
O O Lukovenkova ◽  
A A Solodchuk

Abstract The paper describes a new adaptive threshold scheme for detecting pulses in high-frequency signals against a background of non-stationary noise. The result of the scheme operation is to determine the pulse boundaries by comparing the signal amplitude-time parameters with the threshold. The threshold value is calculated in non-overlapping windows of fixed length and depends only on the background noise level. The detected pulses undergo additional shape checking, taking into account their characteristics. The parameters of the algorithms for detecting pulses and checking their shape can be adjusted for any type of high-frequency pulse signals. This threshold scheme is tuned to detect pulses in high frequency geoacoustic emission signals. The results of the scheme operation on an artificial signal and on fragments of a geoacoustic signal are given, a comparison is made between the proposed scheme and the previously used (outdated) one. The new threshold scheme proposed by the authors is less sensitive to the choice of the initial threshold value and it is more stable in operation. When processing 15-minute fragments of a geoacoustic signal, the new scheme correctly detects, on average, 5 times more pulses.

2021 ◽  
Vol 10 (11) ◽  
pp. 742
Xiaoyue Luo ◽  
Yanhui Wang ◽  
Benhe Cai ◽  
Zhanxing Li

Previous research on moving object detection in traffic surveillance video has mostly adopted a single threshold to eliminate the noise caused by external environmental interference, resulting in low accuracy and low efficiency of moving object detection. Therefore, we propose a moving object detection method that considers the difference of image spatial threshold, i.e., a moving object detection method using adaptive threshold (MOD-AT for short). In particular, based on the homograph method, we first establish the mapping relationship between the geometric-imaging characteristics of moving objects in the image space and the minimum circumscribed rectangle (BLOB) of moving objects in the geographic space to calculate the projected size of moving objects in the image space, by which we can set an adaptive threshold for each moving object to precisely remove the noise interference during moving object detection. Further, we propose a moving object detection algorithm called GMM_BLOB (GMM denotes Gaussian mixture model) to achieve high-precision detection and noise removal of moving objects. The case-study results show the following: (1) Compared with the existing object detection algorithm, the median error (MD) of the MOD-AT algorithm is reduced by 1.2–11.05%, and the mean error (MN) is reduced by 1.5–15.5%, indicating that the accuracy of the MOD-AT algorithm is higher in single-frame detection; (2) in terms of overall accuracy, the performance and time efficiency of the MOD-AT algorithm is improved by 7.9–24.3%, reflecting the higher efficiency of the MOD-AT algorithm; (3) the average accuracy (MP) of the MOD-AT algorithm is improved by 17.13–44.4%, the average recall (MR) by 7.98–24.38%, and the average F1-score (MF) by 10.13–33.97%; in general, the MOD-AT algorithm is more accurate, efficient, and robust.

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