Effect of Different Thresholding Techniques for Denoising of EMG Signals by using Different Wavelets

Thukral, Ruchika; Kumar, Ashwani; Arora, Ajat Shatru; Gulshan,

doi:10.1109/icct46177.2019.8969036

Cited by 22 publications

(4 citation statements)

References 19 publications

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“…Our approach enhances the classifier head by introducing a degree of uncertainty to the extracted features. However, it is worth noting that introducing uncertainty into the image domain has been explored in previous works [52][53][54]. For instance, in the Seg-based method [52], researchers trained their model using segmentation images.…”

Section: Effect Of Backbonementioning

confidence: 99%

“…We have incorporated a similar approach into our ship dataset, generating segmentation images to create a new dataset for training our ship detector. Additionally, the NoiBased approach [53,54] method enriches datasets by introducing noise to input images and employing denoising techniques to bolster system robustness. Drawing inspiration from this observation, we conducted training sessions for our ship dataset both with and without the introduction of noise.…”

Section: Effect Of Backbonementioning

confidence: 99%

“…The NoiBased method [53,54] offers a potential remedy by augmenting the dataset through the introduction of noise into the input images. This augmentation leads to a modest performance improvement.…”

Section: Effect Of Backbonementioning

confidence: 99%

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Image-Based Ship Detection Using Deep Variational Information Bottleneck

Ngo,

Vo,

Nguyen

et al. 2023

Sensors

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Image-based ship detection is a critical function in maritime security. However, lacking high-quality training datasets makes it challenging to train a robust supervision deep learning model. Conventional methods use data augmentation to increase training samples. This approach is not robust because the data augmentation may not present a complex background or occlusion well. This paper proposes to use an information bottleneck and a reparameterization trick to address the challenge. The information bottleneck learns features that focus only on the object and eliminate all backgrounds. It helps to avoid background variance. In addition, the reparameterization introduces uncertainty during the training phase. It helps to learn more robust detectors. Comprehensive experiments show that the proposed method outperforms conventional methods on Seaship datasets, especially when the number of training samples is small. In addition, this paper discusses how to integrate the information bottleneck and the reparameterization into well-known object detection frameworks efficiently.

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Section: Effect Of Backbonementioning

confidence: 99%

Section: Effect Of Backbonementioning

confidence: 99%

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Image-Based Ship Detection Using Deep Variational Information Bottleneck

Ngo,

Vo,

Nguyen

et al. 2023

Sensors

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“…The 3D model reconstructed with the help of GPS receiver and laser range sensor for preservation of cultural heritages is discussed in (Kumar Aggarwal, 2017). An ensemble of wavelets has been used for removal of noise in EMG signals in (Thukral et al, 2019). The extrinsic parameters of a camera used for estimation of position of GPS receiver has been estimated using a sequence of images in (Kume et al, 2010).…”

Section: Related Workmentioning

confidence: 99%

Enhancement of GPS Position Accuracy Using Machine Vision and Deep Learning Techniques

Aggarwal¹

2020

Journal of Computer Science

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The accuracy of GPS position estimation in urban cities is an issue which need to be resolved using machine vision and deep learning techniques. The accuracy of GPS in horizontal direction is better than in the vertical direction. Although for most of the navigation applications in intelligent transportation systems, horizontal positioning accuracy is vital, but vertical position accuracy gives idea about road slanting conditions. Several statistical methods like median filtering, homomorphic filtering and k-means clustering, etc., can be used to improve upon the position accuracy of GPS signals. Such methods are useful for offline applications where a lot many GPS measurements are taken at a single point and afterwards filtering is applied to batch of measurement. In this study, the GPS positioning errors which are caused by sensor noise, ionospheric effects, occlusions by building facades, etc., have been considered for online improvement in position estimation using computer vision and deep learning methods by empirically choosing hyper-parameters.

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