Decision based non-linear filtering using interquartile range estimator for Gaussian signals

Buch, Kaushal D.

doi:10.1109/indicon.2014.7030658

Cited by 7 publications

(6 citation statements)

References 17 publications

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“…Actually, the value of ν to be used significantly depends on the application type and datasets properties. Nevertheless, most applications in the literature opted for a scaling factor of 1.5, as it is agreed to be a typical acceptable value [38], [39]. In any case, if the training time showcases extreme values or high variability, a higher scaling factor would be preferable.…”

Section: B Proposed Non-stragglers Participant Selectionmentioning

confidence: 99%

Towards Reliable Participation in UAV-Enabled Federated Edge Learning on Non-IID Data

Cheriguene,

Jaafar,

Yanikomeroglu

et al. 2024

IEEE Open J. Veh. Technol.

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Federated Learning (FL) is a decentralized machine learning (ML) technique that allows a number of participants to train an ML model collaboratively without having to share their private local datasets with others. When participants are unmanned aerial vehicles (UAVs), UAV-enabled FL would experience heterogeneity due to the majorly skewed (non-independent and identically distributed -IID) collected data. In addition, UAVs may demonstrate unintentional misbehavior in which the latter may fail to send updates to the FL server due, for instance, to UAVs' disconnectivity from the FL system caused by high mobility, unavailability, or battery depletion. Such challenges may significantly affect the convergence of the FL model. A recent way to tackle these challenges is client selection, based on customized criteria that consider UAV computing power and energy consumption. However, most existing client selection schemes neglected the participants' reliability. Indeed, FL can be targeted by poisoning attacks, in which malicious UAVs upload poisonous local models to the FL server, by either providing targeted false predictions for specifically chosen inputs or by compromising the global model's accuracy through tampering with the local model. Hence, we propose in this article a novel client selection scheme that enhances convergence by prioritizing fast UAVs with high-reliability scores, while eliminating malicious UAVs from training. Through experiments, we assess the effectiveness of our scheme in resisting different attack scenarios, in terms of convergence and achieved model accuracy. Finally, we demonstrate the performance superiority of the proposed approach compared to baseline methods.

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Section: B Proposed Non-stragglers Participant Selectionmentioning

confidence: 99%

Towards Reliable Participation in UAV-Enabled Federated Edge Learning on Non-IID Data

Cheriguene,

Jaafar,

Yanikomeroglu

et al. 2024

IEEE Open J. Veh. Technol.

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“…Using the median and normalized interquartile range (NIQR) is another robust method for estimating the average and standard deviation of the window sample. This method is suitable for estimating symmetric and skewed distributions (Buch, 2014). If X is a sample with values x 1 , x 2 , .…”

Section: Window Sample Estimationmentioning

confidence: 99%

Deformation Feature Extraction for GNSS Landslide Monitoring Series Based on Robust Adaptive Sliding-Window Algorithm

Huang

Wang

et al. 2022

Front. Earth Sci.

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Global navigation satellite system technology has been widely used for high-precision, real-time monitoring of landslides. To improve forecasts and early warnings, the true deformation features must be extracted from the global navigation satellite system monitoring series. However, as the deformation rate changes at different creep stages, the relationship between noise and true deformation may also change, making it difficult to accurately describe the deformation. In this study, an adaptive sliding window algorithm is proposed to account for this relationship change. First, the window was defined with an equal window width and step length, which improved the efficiency of feature extraction. Second, the median and normalized interquartile ranges were used to estimate the window samples and obtain a continuous and reliable series. Finally, the window sample breakdown point was defined to adjust the window parameter. These steps were repeated for the adjusted window to achieve adaptive processing of the monitoring series. The results based on both simulated and real landslide monitoring series demonstrated that the proposed method can provide adaptive, robust, and reliable deformation information for landslide warnings. The adaptive sliding window method also successfully assisted in the early warning of a loess landslide in Heifangtai, Gansu province, northwest of the Chinese Loess Plateau, indicating its practical application potential.

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“…In the data adaptive transform methods, principal component analysis (PCA) and independent component analysis (ICA) are employed as transform tools for the conversion process [24].…”

Section: Literature Reviewmentioning

confidence: 99%

“…en it is subjected to denoising by using the hardthresholding algorithm [24]. By using inverse wavelet transforms and aligning the patches correctly, the denoised image is reconstructed [25].…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Effective Edge-Based Image Denoising Algorithm

Kumar¹,

Kumar²,

Naik³

et al. 2022

Scientific Programming

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This article proposes an edge-based denoising algorithm to restore the original image, which is highly degraded by the salt and pepper noise. Most of the existing image denoising algorithms consider edge as a noise. Here, the proposed algorithm can set out to resolve this ambiguity. The concept of directional filters is being used to delineate the edges from noise. The proposed algorithm performance is tested for different noise densities ranging from 5% to 90% on both the greyscale and colour images. It is compared with the current state of art techniques using several performance metrics such as peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM) values, and image enhancement factor (IEF). The results showed that the proposed algorithm has achieved an improvement of 60% over the state of art techniques.

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Decision based non-linear filtering using interquartile range estimator for Gaussian signals

Cited by 7 publications

References 17 publications

Towards Reliable Participation in UAV-Enabled Federated Edge Learning on Non-IID Data

Towards Reliable Participation in UAV-Enabled Federated Edge Learning on Non-IID Data

Deformation Feature Extraction for GNSS Landslide Monitoring Series Based on Robust Adaptive Sliding-Window Algorithm

A Novel Effective Edge-Based Image Denoising Algorithm

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