Blind signal separation with Noise Reduction for efficient speaker identification

Hammam, Hossam; El‐Shafai, Walid; Hassan, Emad S.; El-Azm, A. E. Abu; Dessouky, Moawad I.; Elhalawany, Mohamed E.; El‐Samie, Fathi E. Abd

doi:10.1007/s10772-019-09641-6

Cited by 6 publications

(2 citation statements)

References 20 publications

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“…This technique used the fusion and segmentation methods. Several research studies have worked on medical image fusion from several perspectives, as in [20][21][22][23][24][25].…”

Section: Related Workmentioning

confidence: 99%

An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

El‐Shafai¹,

El-Hag²,

Sedik³

et al. 2023

Computers, Materials &Amp; Continua

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Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and therapy. Deep learning provides a high performance for several medical image analysis applications. This paper proposes a deep learning model for the medical image fusion process. This model depends on Convolutional Neural Network (CNN). The basic idea of the proposed model is to extract features from both CT and MR images. Then, an additional process is executed on the extracted features. After that, the fused feature map is reconstructed to obtain the resulting fused image. Finally, the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching (HM), Histogram Equalization (HE), fuzzy technique, fuzzy type , and Contrast Limited Histogram Equalization (CLAHE). The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement quality. Different realistic datasets of different modalities and diseases are tested and implemented. Also, real datasets are tested in the simulation analysis.

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“…This technique used the fusion and segmentation methods. Several research studies have worked on medical image fusion from several perspectives, as in [20][21][22][23][24][25].…”

Section: Related Workmentioning

confidence: 99%

An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

El‐Shafai¹,

El-Hag²,

Sedik³

et al. 2023

Computers, Materials &Amp; Continua

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“…Traditional encryption algorithms can be used for encrypting biometric templates, but they have some difficulties in implementation, and they require much time. On the other hand, chaoticbased algorithms are simple in implementation and may be implemented with permutation operations only [10]. That is why chaotic encryption is more recommended for biometric encryption.…”

Section: Introductionmentioning

confidence: 99%

Efficient Hardware Design of a Secure Cancellable Biometric Cryptosystem

elazm¹,

El‐Shafai²,

Ibrahim³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Biometric security is a growing trend, as it supports the authentication of persons using confidential biometric data. Most of the transmitted data in multimedia systems are susceptible to attacks, which affect the security of these systems. Biometric systems provide sufficient protection and privacy for users. The recently-introduced cancellable biometric recognition systems have not been investigated in the presence of different types of attacks. In addition, they have not been studied on different and large biometric datasets. Another point that deserves consideration is the hardware implementation of cancellable biometric recognition systems. This paper presents a suggested hybrid cancellable biometric recognition system based on a 3D chaotic cryptosystem. The rationale behind the utilization of the 3D chaotic cryptosystem is to guarantee strong encryption of biometric templates, and hence enhance the security and privacy of users. The suggested cryptosystem adds significant permutation and diffusion to the encrypted biometric templates. We introduce some sort of attack analysis in this paper to prove the robustness of the proposed cryptosystem against attacks. In addition, a Field Programmable Gate Array (FPGA) implementation of the proposed system is introduced. The obtained results with the proposed cryptosystem are compared with those of the traditional encryption schemes, such as Double Random Phase Encoding (DRPE) to reveal superiority, and hence high recognition performance of the proposed cancellable biometric recognition system. The obtained results prove that the proposed cryptosystem enhances the security and leads to better efficiency of the cancellable biometric recognition system in the presence of different types of attacks.

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Automatically Abnormal Detection for Radiator Fans Through Sound Signals Using a Deep Learning Technique

Nguyen,

Tran

2024

Lecture Notes in Mechanical Engineering

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Blind signal separation with Noise Reduction for efficient speaker identification

Cited by 6 publications

References 20 publications

An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

Efficient Hardware Design of a Secure Cancellable Biometric Cryptosystem

Automatically Abnormal Detection for Radiator Fans Through Sound Signals Using a Deep Learning Technique

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