A Statistical Parsimony Method for Uncertainty Quantification of FDTD Computation Based on the PCA and Ridge Regression

Hu, Ruofei; Monebhurrun, Vikass; Himeno, Ryutaro; Yokota, Hideo; Costen, Fumie

doi:10.1109/tap.2019.2911645

Cited by 20 publications

(4 citation statements)

References 60 publications

(64 reference statements)

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“…The proposed attention-panel mechanism imitates such a situation, in which each panel member represents a participant of the subjection evaluation and judges the image quality from a different perspective. This way, the model can achieve a comprehensive evaluation of the image quality, thus reducing the prediction uncertainty (Hu et al 2019).…”

Section: Attention-panel Mechanismmentioning

confidence: 99%

Data-Efficient Image Quality Assessment with Attention-Panel Decoder

Qin

Liu

et al. 2023

AAAI

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Blind Image Quality Assessment (BIQA) is a fundamental task in computer vision, which however remains unresolved due to the complex distortion conditions and diversified image contents. To confront this challenge, we in this paper propose a novel BIQA pipeline based on the Transformer architecture, which achieves an efficient quality-aware feature representation with much fewer data. More specifically, we consider the traditional fine-tuning in BIQA as an interpretation of the pre-trained model. In this way, we further introduce a Transformer decoder to refine the perceptual information of the CLS token from different perspectives. This enables our model to establish the quality-aware feature manifold efficiently while attaining a strong generalization capability. Meanwhile, inspired by the subjective evaluation behaviors of human, we introduce a novel attention panel mechanism, which improves the model performance and reduces the prediction uncertainty simultaneously. The proposed BIQA method maintains a light-weight design with only one layer of the decoder, yet extensive experiments on eight standard BIQA datasets (both synthetic and authentic) demonstrate its superior performance to the state-of-the-art BIQA methods, i.e., achieving the SRCC values of 0.875 (vs. 0.859 in LIVEC) and 0.980 (vs. 0.969 in LIVE). Checkpoints, logs and code will be available at https://github.com/narthchin/DEIQT.

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Section: Attention-panel Mechanismmentioning

confidence: 99%

Data-Efficient Image Quality Assessment with Attention-Panel Decoder

Qin

Liu

et al. 2023

AAAI

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“…The excessive number of high-level features we learn by cls token in the transformer will affect the role of low level in quality assessment. We here use the least angle regression (LARS) method to select those influential high-level features and thus reduce the dimension of the high-level features [39].…”

Section: Least Angle Regression For Adaptive Feature Selectionmentioning

confidence: 99%

Enhancing Underwater Image Quality Assessment with Influential Perceptual Features

Liu,

Huang,

Xie

et al. 2023

Electronics

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In the multifaceted field of oceanic engineering, the quality of underwater images is paramount for a range of applications, from marine biology to robotic exploration. This paper presents a novel approach in underwater image quality assessment (UIQA) that addresses the current limitations by effectively combining low-level image properties with high-level semantic features. Traditional UIQA methods predominantly focus on either low-level attributes such as brightness and contrast or high-level semantic content, but rarely both, which leads to a gap in achieving a comprehensive assessment of image quality. Our proposed methodology bridges this gap by integrating these two critical aspects of underwater imaging. We employ the least-angle regression technique for balanced feature selection, particularly in high-level semantics, to ensure that the extensive feature dimensions of high-level content do not overshadow the fundamental low-level properties. The experimental results of our method demonstrate a remarkable improvement over existing UIQA techniques, establishing a new benchmark in both accuracy and reliability for underwater image assessment.

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“…The RR uses a regularization method (L2 regularization) to estimate the magnitude of the coefficient of multiple regression models when independent antenna variables (features) are highly correlated. This technique lowers the standard errors by introducing some bias into the usual linear regression for antenna parameter prediction [15].…”

Section: Ridge Regressionmentioning

confidence: 99%

Intelligent Design Prediction of a Circular Polarized Antenna for CubeSat Application Using Machine Learning Algorithms

Uddin,

Islam,

Ortiz

et al. 2023

Electronics

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This paper presents an intelligent design method for a corner-truncated microstrip patch antenna (CTMPA) operating at 32 GHz using various well-known machine learning (ML) techniques. Our objectives are to obtain a gain of >5 dBic across a 10% bandwidth, an axial ratio (AR) of <3 dB, and a return loss of <−10 dB. First, a dataset of 715 full-wave simulated samples is analyzed with four distinct antenna characteristics (viz. features), along with the related computed |S11|, gain, and AR. Using mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and R2 score, 12 ML regression models were examined to compare the training data with the new predicted values. Next, the model that best satisfies our objectives was chosen. Results showed that the artificial neural network (ANN) followed by k-nearest neighbor (KNN) regression produced the lowest error compared to all tested ML models. The design parameters that achieved our intended objectives were computed using the predicted results. The predicted design was validated using a full-wave simulation and a prototype measurement.

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A Statistical Parsimony Method for Uncertainty Quantification of FDTD Computation Based on the PCA and Ridge Regression

Cited by 20 publications

References 60 publications

Data-Efficient Image Quality Assessment with Attention-Panel Decoder

Data-Efficient Image Quality Assessment with Attention-Panel Decoder

Enhancing Underwater Image Quality Assessment with Influential Perceptual Features

Intelligent Design Prediction of a Circular Polarized Antenna for CubeSat Application Using Machine Learning Algorithms

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