The fractional Fourier transform as a biomedical signal and image processing tool: A review

Gómez-Echavarría, Alejandro; Ugarte, Juan; Tobón, Catalina

doi:10.1016/j.bbe.2020.05.004

Cited by 35 publications

(8 citation statements)

References 91 publications

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“…Additionally, they proposed a classifier fusion strategy that combined predictions from different classifiers using majority voting. The experimental results demonstrated that by utilizing learned image features and the classification fusion strategy, the recall and F1-score were further improved to 99.20% and 99.40%, respectively [32].…”

Section: Literature Reviewmentioning

confidence: 97%

COVID-19 Diagnosis by Extracting New Features from Lung CT Images Using Fractional Fourier Transform

Nokhostin,

Rashidi

2024

Fractal Fract

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COVID-19 is a lung disease caused by a coronavirus family virus. Due to its extraordinary prevalence and associated death rates, it has spread quickly to every country in the world. Thus, achieving peaks and outlines and curing different types of relapses is extremely important. Given the worldwide prevalence of coronavirus and the participation of physicians in all countries, information has been gathered regarding the properties of the virus, its diverse types, and the means of analyzing it. Numerous approaches have been used to identify this evolving virus. It is generally considered the most accurate and acceptable method of examining the patient’s lungs and chest through a CT scan. As part of the feature extraction process, a method known as fractional Fourier transform (FrFT) has been applied as one of the time-frequency domain transformations. The proposed method was applied to a database consisting of 2481 CT images. Following the transformation of all images into equal sizes and the removal of non-lung areas, multiple combination windows are used to reduce the number of features extracted from the images. In this paper, the results obtained for KNN and SVM classification have been obtained with accuracy values of 99.84% and 99.90%, respectively.

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Section: Literature Reviewmentioning

confidence: 97%

COVID-19 Diagnosis by Extracting New Features from Lung CT Images Using Fractional Fourier Transform

Nokhostin,

Rashidi

2024

Fractal Fract

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“…In recent years, many new wavelet transform methods have emerged in various signal processing applications, such as image processing, audio processing, and biomedical engineering [21][22][23]. These methods include non-orthogonal wavelet transforms and continuous wavelet transforms.…”

Section: Traditional Wavelet Transformmentioning

confidence: 99%

Robust Zero-Watermarking by Circular Features and 1-D NRDPWT Transformation

Hsiu-Chi Tseng,

Hsiu-Chi Tseng

2024

Journal of Computers

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<p>This paper introduces a secure and robust zero-watermarking framework that leverages the advantages of zero-watermarking, ensuring non-destructive modification of original images and unlimited capacity. The proposed method enables robust watermark embedding while preserving the original image. It employs a novel feature extraction approach using circular areas based on image radius, enhancing feature resilience. Additionally, applying one-dimensional non-recursive discrete periodized wavelet transform (1-D NRDPWT) converts feature values into phi, contributing to enhanced stability and robustness. Enhanced security is achieved through the use of Shuffle and Pseudo-Random Number Generator (PRNG). Experimental results, evaluated using metrics such as Bit Error Rate (BER) and Normalized Correlation (NC), validate the exceptional performance of this watermarking technique. These findings underscore the framework’s robustness, security, reliability, and integrity against both general and geometric noise attacks, making it a secure and robust solution for modern digital image copyright protection. In summary, our method offers an effective defense against various noise attacks while ensuring the highest watermark quality without compromising the original image. It is a significant advancement in copyright protection applications.</p> <p> </p>

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“…The frequency of chirp signals either increases (up-chirp) or decreases (down-chirp) with time [23]. They are frequently encountered in various signal processing applications, such as sonar [24], radar, and biomedical devices [25]. Some of these applications rely on chirp signal transmission as in the case of sonar [24], while others model the received signal after Doppler spread as chirp signals, e.g., in synthetic aperture radars (SARs) [26], and heart sound signals [27].…”

Section: Introductionmentioning

confidence: 99%

Characterization and Selection of WiFi Channel State Information Features for Human Activity Detection in a Smart Public Transportation System

Alizadeh,

Savaria,

Nerguizian

2024

IEEE Open J. Intell. Transp. Syst.

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Robust methods are needed to detect how people are moving in smart public transportation systems. This paper proposes and characterizes effective means to accurately detect passengers. We analyze a public WiFi-based activity recognition (WiAR) dataset to extract human activity features from Channel State Information (CSI) data. To do so, CSI power changes caused by nearby human activity are analyzed. Our method first extracts multi-dimensional features using a Short-Time Fourier Transform (STFT) of CSI data to capture the relevant signal features. Since the environment of a transportation system changes dynamically and non-deterministically, we propose analyzing these changes with a heuristic algorithm that leverages a decision tree to automate a decision-making solution for feature selection. Principal Component Analysis (PCA) is performed before the decision tree algorithm. Reported results are compared with those obtained from the existing methods. Based on these results, we explore the effectiveness of various features such as the chirp rate, delta band power, spectral flux, and frequency of movement. This allows identifying and recommending the most effective features for the explored detection task according to observed variability, information gain, and correlation between features. The reported classification results show that using only the chirp rate generated from CSI information as a feature, we achieve precision = 83%, True Positive (T P ) = 94%, True Negative (T N ) = 91% and F1-score = 87%. Considering delta band power as an additional feature adds more information and allows getting higher performance with precision = 100%, T P = 97%, T N = 95% and F1-score = 95%.

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The fractional Fourier transform as a biomedical signal and image processing tool: A review

Cited by 35 publications

References 91 publications

COVID-19 Diagnosis by Extracting New Features from Lung CT Images Using Fractional Fourier Transform

COVID-19 Diagnosis by Extracting New Features from Lung CT Images Using Fractional Fourier Transform

Robust Zero-Watermarking by Circular Features and 1-D NRDPWT Transformation

Characterization and Selection of WiFi Channel State Information Features for Human Activity Detection in a Smart Public Transportation System

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