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W. El-Shafai
Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt

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Short Biography

Walid El-Shafai was born in Alexandria, Egypt. He received the B.Sc. degree (Hons.) in Electronics and Electrical Communication Engineering from Faculty of Electronic Engineering (FEE), Menoufia University, Menouf, Egypt in 2008, M.Sc. degree from Egypt-Japan University of Science and Technology (E-JUST) in 2012, and PhD degree from the Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt in 2019. He is currently working as a Lecturer and an Assistant professor in ECE Dept. FEE, Menoufia University. His research interests are in the areas of Wireless Mobile and Multimedia Communications Systems, Image and Video Signal Processing, Efficient 2D Video/3D Multi-View Video Coding, Multi-view Video plus Depth coding, 3D Multi-View Video Coding and Transmission, Quality of Service and Experience, Digital Communication Techniques, Cognitive Radio Networks, Adaptive Filters Design, 3D Video Watermarking, Steganography, and Encryption, Error Resilience and Concealment Algorithms for H.264/AVC, H.264/MVC and H.265/HEVC Video Codecs Standards, Cognitive Cryptography, Medical Image Processing, Speech Processing, Security Algorithms, Software Defined Networks, Internet of Things, Medical Diagnoses Applications, FPGA Implementations for Signal Processing Algorithms and Communication Systems, Cancellable Biometrics and Pattern Recognition, Image and Video Magnification, Artificial Intelligence for Signal Processing Algorithms and Communication Systems, Modulation Identificat

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Journal article
Published: 13 July 2021 in Applied Sciences
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There is a massive growth in malicious software (Malware) development, which causes substantial security threats to individuals and organizations. Cybersecurity researchers makes continuous efforts to defend against these malware risks. This research aims to exploit the significant advantages of Transfer Learning (TL) and Fine-Tuning (FT) methods to introduce efficient malware detection in the context of imbalanced families without the need to apply complex features extraction or data augmentation processes. Therefore, this paper proposes a visualized malware multi-classification framework to avoid false positives and imbalanced datasets’ challenges through using the fine-tuned convolutional neural network (CNN)-based TL models. The proposed framework comprises eight different FT CNN models including VGG16, AlexNet, DarkNet-53, DenseNet-201, Inception-V3, Places365-GoogleNet, ResNet-50, and MobileNet-V2. First, the binary files of different malware families were transformed into 2D images and then forwarded to the FT CNN models to detect and classify the malware families. The detection and classification performance was examined on a benchmark Malimg imbalanced dataset using different, comprehensive evaluation metrics. The evaluation results prove the FT CNN models’ significance in detecting malware types with high accuracy that reached 99.97% which also outperforms the performance of related machine learning (ML) and deep learning (DL)-based malware multi-classification approaches tested on the same malware dataset.

ACS Style

Walid El-Shafai; Iman Almomani; Aala AlKhayer. Visualized Malware Multi-Classification Framework Using Fine-Tuned CNN-Based Transfer Learning Models. Applied Sciences 2021, 11, 6446 .

AMA Style

Walid El-Shafai, Iman Almomani, Aala AlKhayer. Visualized Malware Multi-Classification Framework Using Fine-Tuned CNN-Based Transfer Learning Models. Applied Sciences. 2021; 11 (14):6446.

Chicago/Turabian Style

Walid El-Shafai; Iman Almomani; Aala AlKhayer. 2021. "Visualized Malware Multi-Classification Framework Using Fine-Tuned CNN-Based Transfer Learning Models." Applied Sciences 11, no. 14: 6446.

Research article
Published: 28 June 2021 in International Journal of Communication Systems
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Cloud computing is the current computing standard, which provides information technology (IT) services over the Internet on demand. In the cloud environment, a task is mapped with an available resource to attain a good result. Task scheduling is the technique that is used to allocate tasks on virtual machines (VMs) of a server based on its capacity of workload. Tasks are scheduled to the server in such a way to minimize traffic and time delay. Particle swarm optimization (PSO) is the best existing algorithm used to schedule a task to an existing resource on the environment of the cloud. By PSO, the task is scheduled for an existing resource to reduce computational cost. In this paper, a hybrid swarm optimization (HSO) algorithm, which is the combination of PSO and salp swarm optimization (SSO), is proposed to resolve task scheduling issues in the cloud environment. The main goal of HSO is to schedule the task to the available resource in such a way to reduce the execution time and computation cost. Multilayer logistic regression (MLR) is an approach used to detect the overloaded VMs, so that a task can be scheduled to a VM according to its capacity of workload. The proposed HSO algorithm with MLR is simulated on the cloudsim toolkit, and the results reveal the efficiency of the proposed algorithm in terms of cost, execution time, and makespan. Compared to the existing algorithms such as the genetic algorithms (GAs), the improved efficiency evolution (IDEA), and the PSO, the proposed algorithm reveals superiority in terms of efficiency, resource utilization, and speed.

ACS Style

Heba M. Eldesokey; Saied M. Abd El‐Atty; Walid El‐Shafai; Mohammed Amoon; Fathi E. Abd El‐Samie. Hybrid swarm optimization algorithm based on task scheduling in a cloud environment. International Journal of Communication Systems 2021, e4694 .

AMA Style

Heba M. Eldesokey, Saied M. Abd El‐Atty, Walid El‐Shafai, Mohammed Amoon, Fathi E. Abd El‐Samie. Hybrid swarm optimization algorithm based on task scheduling in a cloud environment. International Journal of Communication Systems. 2021; ():e4694.

Chicago/Turabian Style

Heba M. Eldesokey; Saied M. Abd El‐Atty; Walid El‐Shafai; Mohammed Amoon; Fathi E. Abd El‐Samie. 2021. "Hybrid swarm optimization algorithm based on task scheduling in a cloud environment." International Journal of Communication Systems , no. : e4694.

Article
Published: 07 June 2021 in Annals of Data Science
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Seizure detection and prediction are a very hot topics in medical signal processing due to their importance in automatic medical diagnosis. This paper presents three efficient frameworks for applications related to electroencephalogram (EEG) signal processing. The first one is an automatic seizure detection framework based on the utilization of scale-invariant feature transform (SIFT) as an extraction tool. The second one depends on the utilization of the fast Fourier transform (FFT) and an artificial neural network for epileptic seizure prediction. Finally, an automated patient-specific framework for channel selection and seizure prediction is presented based on FFT. The simulation results show the success of the proposed frameworks for automated medical diagnosis.

ACS Style

Heba M. Emara; Mohamed Elwekeil; Taha E. Taha; Adel S. El-Fishawy; El-Sayed M. El-Rabaie; Walid El-Shafai; Ghada M. El Banby; Turky Alotaiby; Saleh A. Alshebeili; Fathi E. Abd El-Samie. Efficient Frameworks for EEG Epileptic Seizure Detection and Prediction. Annals of Data Science 2021, 1 -36.

AMA Style

Heba M. Emara, Mohamed Elwekeil, Taha E. Taha, Adel S. El-Fishawy, El-Sayed M. El-Rabaie, Walid El-Shafai, Ghada M. El Banby, Turky Alotaiby, Saleh A. Alshebeili, Fathi E. Abd El-Samie. Efficient Frameworks for EEG Epileptic Seizure Detection and Prediction. Annals of Data Science. 2021; ():1-36.

Chicago/Turabian Style

Heba M. Emara; Mohamed Elwekeil; Taha E. Taha; Adel S. El-Fishawy; El-Sayed M. El-Rabaie; Walid El-Shafai; Ghada M. El Banby; Turky Alotaiby; Saleh A. Alshebeili; Fathi E. Abd El-Samie. 2021. "Efficient Frameworks for EEG Epileptic Seizure Detection and Prediction." Annals of Data Science , no. : 1-36.

Journal article
Published: 06 June 2021 in International Journal of Communication Systems
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ACS Style

Mohamad I. Elhadad; Walid El‐Shafai; El‐Sayed M. El‐Rabaie; Mohammed Abd‐Elnaby; Fathi E. Abd El‐Samie. Optimized two‐level scheduler for video traffic in LTE downlink framework. International Journal of Communication Systems 2021, 34, e4704 .

AMA Style

Mohamad I. Elhadad, Walid El‐Shafai, El‐Sayed M. El‐Rabaie, Mohammed Abd‐Elnaby, Fathi E. Abd El‐Samie. Optimized two‐level scheduler for video traffic in LTE downlink framework. International Journal of Communication Systems. 2021; 34 (12):e4704.

Chicago/Turabian Style

Mohamad I. Elhadad; Walid El‐Shafai; El‐Sayed M. El‐Rabaie; Mohammed Abd‐Elnaby; Fathi E. Abd El‐Samie. 2021. "Optimized two‐level scheduler for video traffic in LTE downlink framework." International Journal of Communication Systems 34, no. 12: e4704.

Journal article
Published: 28 May 2021 in Digital Signal Processing
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Cancellable biometrics is the art of generating distorted or encrypted templates of original biometric templates. The evolution of cancellable biometrics is attributed to the advanced hacking technologies that can capture the original stored biometrics from databases. One of the solutions for this problem is to store cancellable biometric templates in the database rather than the original ones. This paper presents a cancellable face recognition scheme that is based on face image encryption with Fractional-Order (FO) Lorenz chaotic system. The basic idea is to generate user-specific random keys to be XORed with the red, green, and blue components of color face images. These keys are generated from the fractional-order Lorenz chaotic system. In addition, some post-processing is implemented on the encrypted color components of the face images with rotation and transposition of matrices. Finally, a wavelet fusion process is applied on these encrypted and processed face image components. The reason behind the utilization of wavelet fusion is to generate a single cancellable template for each color face image. Furthermore, wavelet fusion after post-processing of encrypted color components leads to a better degree of diffusion in the encrypted face templates. Actually, the encryption with the proposed algorithm is not full encryption, but it is appropriate for cancellable biometric applications. Moreover, the proposed scheme is secure due to the power of fractional-order Lorenz chaotic system that is very sensitive to initial conditions selected by the user. In addition, the post-processing incorporated with wavelet fusion is a non-invertible process. The validation of the proposed scheme is performed with experiments on FERET, LFW, and ORL databases. Evaluation metrics including Equal Error Rate (EER), Area under the Receiver Operating Characteristic (AROC) curves are utilized in the proposed scheme. Numerical values reveal EER levels close to zero and AROC values of 100% at low and mild noise levels.

ACS Style

Iman S. Badr; Ahmed G. Radwan; El-Sayed M. El-Rabaie; Lobna A. Said; Ghada M. El Banby; Walid El-Shafai; Fathi E. Abd El-Samie. Cancellable face recognition based on fractional-order Lorenz chaotic system and Haar wavelet fusion. Digital Signal Processing 2021, 116, 103103 .

AMA Style

Iman S. Badr, Ahmed G. Radwan, El-Sayed M. El-Rabaie, Lobna A. Said, Ghada M. El Banby, Walid El-Shafai, Fathi E. Abd El-Samie. Cancellable face recognition based on fractional-order Lorenz chaotic system and Haar wavelet fusion. Digital Signal Processing. 2021; 116 ():103103.

Chicago/Turabian Style

Iman S. Badr; Ahmed G. Radwan; El-Sayed M. El-Rabaie; Lobna A. Said; Ghada M. El Banby; Walid El-Shafai; Fathi E. Abd El-Samie. 2021. "Cancellable face recognition based on fractional-order Lorenz chaotic system and Haar wavelet fusion." Digital Signal Processing 116, no. : 103103.

Journal article
Published: 25 May 2021 in IEEE Access
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Various cancelable biometric techniques have been proposed to maintain user data security. In this work, a cancelable biometric framework is introduced to satisfy user data security and keeping the original biometric template safe away from intruders. Thus, our main contribution is presenting a novel authentication framework based on the evolutionary Genetic Algorithm (GA)-based encryption technique. The suggested framework produces an entirely unrecognized biometric template by hiding the whole discriminative features of biometric templates; this is with exploiting the outstanding characteristics of the employed Genetic operations of the utilized encryption technique. Firstly, the GA initiates its search from a population of templates, not a single template. Secondly, some statistical operators are used to exploit the resulting initial population to generate successive populations. Finally, the crossover and mutation operations are performed to produce the ultimate cancelable biometric templates. Different biometric databases of the face and fingerprint templates are tested and analyzed. The proposed cancelable biometric framework achieves appreciated sensitivity and specificity results compared to the conventional OSH (Optical Scanning Holography) algorithm. It accomplishes recommended outcomes in terms of the AROC (Area under the Receiver Operating Characteristic) and the probability correlation distribution between the original biometrics and the encrypted biometrics stored in the database. The experimental results prove that the proposed framework achieves excellent results even if the biometric system suffers from different noise ratios. The proposed framework achieves an average AROC value of 0.9998, an EER (Equal Error Rate) of $2.0243\times 10 ^{-4}$ , FAR (False Acceptance Rate) of $4.8843\times 10 ^{-4}$ , and FRR (False Rejection Rate) of $2.2693\times 10 ^{-4}$ .

ACS Style

Walid El-Shafai; Fatma A. Hossam Eldein Mohamed; Hassan M. A. Elkamchouchi; Mohammed Abd-Elnaby; Ahmed Elshafee. Efficient and Secure Cancelable Biometric Authentication Framework Based on Genetic Encryption Algorithm. IEEE Access 2021, 9, 77675 -77692.

AMA Style

Walid El-Shafai, Fatma A. Hossam Eldein Mohamed, Hassan M. A. Elkamchouchi, Mohammed Abd-Elnaby, Ahmed Elshafee. Efficient and Secure Cancelable Biometric Authentication Framework Based on Genetic Encryption Algorithm. IEEE Access. 2021; 9 ():77675-77692.

Chicago/Turabian Style

Walid El-Shafai; Fatma A. Hossam Eldein Mohamed; Hassan M. A. Elkamchouchi; Mohammed Abd-Elnaby; Ahmed Elshafee. 2021. "Efficient and Secure Cancelable Biometric Authentication Framework Based on Genetic Encryption Algorithm." IEEE Access 9, no. : 77675-77692.

Journal article
Published: 25 May 2021 in IEEE Access
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Orbital angular momentum-shift keying (OAM-SK), which is the rapid switching of OAM modes, is vital but seriously impeded by the deficiency of OAM demodulation techniques, particularly when videos are transferred over the system. Thus, in this paper, 3D chaotic interleaved multi-coded video frames (VFs) are conveyed via an N-OAM-SK free space optic (FSO) communication system to enhance the reliability and efficiency of the model. To tackle the defects of the OAM-SK-FSO mechanism, two efficient deep learning (DL) techniques, namely convolution recurrent neural network (CRNN) and 3D convolution neural network (3DCNN) are used to decode OAM modes with a lower BER. Moreover, a graphics processing unit (GPU) is used to accelerate the training process with slight power consumption. The utilized datasets for OAM states are generated by applying different scenarios using trial and error method. The simulation results imply that LDPC coded VFs achieve the greatest peak signal-to-noise ratios (PSNRs) and the lowest BERs using the 16-OAM-SK model. Both 3DCNN and CRNN techniques have nearly the same performance, but this performance deteriorates in the case of larger dataset classes. Moreover, the GPU accelerates the performance by almost 67.6% and 36.9% using CRNN and 3DCNN techniques, respectively. These two DL techniques are more effective in evaluating the classification accuracy than the other traditional techniques by almost 10-20%.

ACS Style

Shimaa A. El-Meadawy; Hossam M. H. Shalaby; Nabil A. Ismail; Ahmed E. A. Farghal; Fathi E. Abd El-Samie; Mohammed Abd-Elnaby; Walid El-Shafai. Performance Analysis of 3D Video Transmission Over Deep-Learning-Based Multi-Coded N-ary Orbital Angular Momentum FSO System. IEEE Access 2021, 9, 110116 -110136.

AMA Style

Shimaa A. El-Meadawy, Hossam M. H. Shalaby, Nabil A. Ismail, Ahmed E. A. Farghal, Fathi E. Abd El-Samie, Mohammed Abd-Elnaby, Walid El-Shafai. Performance Analysis of 3D Video Transmission Over Deep-Learning-Based Multi-Coded N-ary Orbital Angular Momentum FSO System. IEEE Access. 2021; 9 (99):110116-110136.

Chicago/Turabian Style

Shimaa A. El-Meadawy; Hossam M. H. Shalaby; Nabil A. Ismail; Ahmed E. A. Farghal; Fathi E. Abd El-Samie; Mohammed Abd-Elnaby; Walid El-Shafai. 2021. "Performance Analysis of 3D Video Transmission Over Deep-Learning-Based Multi-Coded N-ary Orbital Angular Momentum FSO System." IEEE Access 9, no. 99: 110116-110136.

Research article
Published: 03 May 2021 in International Journal of Communication Systems
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Automatic modulation classification (AMC) is an important stage in intelligent wireless communication receivers. It is a necessary process after signal detection, and before demodulation. It plays a vital role in various applications. Blind modulation classification is a very difficult task without information about the transmitted signal and the receiver parameters like carrier frequency, signal power, timing information, phase offset, existence of frequency‐selective multipath fading, and time‐varying channels in real‐world applications. The AMC methods are divided into traditional and advanced methods. Traditional methods include likelihood‐based (LB) and feature‐based (FB) methods. The advanced methods include deep learning (DL) methods. In addition, the AMC methods are used to classify different modulation schemes such as ASK, PSK, FSK, PAM, and QAM with different orders and different signal‐to‐noise ratios (SNRs). This paper focuses on summarizing the AMC methoods, comparing between them, presenting the commercial software packages for AMC, and finally considering the new challenges in the implementation of AMC.

ACS Style

Mohamed A. Abdel‐Moneim; Walid El‐Shafai; Nariman Abdel‐Salam; El‐Sayed M. El‐Rabaie; Fathi E. Abd El‐Samie. A survey of traditional and advanced automatic modulation classification techniques, challenges, and some novel trends. International Journal of Communication Systems 2021, 34, e4762 .

AMA Style

Mohamed A. Abdel‐Moneim, Walid El‐Shafai, Nariman Abdel‐Salam, El‐Sayed M. El‐Rabaie, Fathi E. Abd El‐Samie. A survey of traditional and advanced automatic modulation classification techniques, challenges, and some novel trends. International Journal of Communication Systems. 2021; 34 (10):e4762.

Chicago/Turabian Style

Mohamed A. Abdel‐Moneim; Walid El‐Shafai; Nariman Abdel‐Salam; El‐Sayed M. El‐Rabaie; Fathi E. Abd El‐Samie. 2021. "A survey of traditional and advanced automatic modulation classification techniques, challenges, and some novel trends." International Journal of Communication Systems 34, no. 10: e4762.

Research article
Published: 30 April 2021 in International Journal of Communication Systems
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Nowadays, the popular 2.4‐GHz band is used in different systems, such as Wi‐Fi, Bluetooth, wireless sensor systems, and wireless cameras. Instead of the over‐crowded 2.4‐GHz Wi‐Fi band, this research offers the experience of using the 5‐GHz Wi‐Fi band, which provides more spectrum availability, more channels, larger bandwidth, faster data transmission, higher data rates, higher speed, and better quality of service compared to those of the 2.4‐GHz band. In this paper, practical implementation and testing of a cooperative spectrum sensing system are presented. The spectrum utilization in the 5‐GHz Wi‐Fi licensed band at six different locations is investigated to allow the transition of secondary users (SUs) to free bands. The spectrum measurement is performed on a centralized cooperative spectrum sensing system, which consists of a master cognitive radio node and five cognitive radio stations. The measurement and simulation results for the practical system are compared with the previous related measurements obtained in Singapore, Barcelona, North Dakota (USA), and Germany. They all agree that the spectrum is underutilized, and it needs to be better utilized for increasing the spectrum efficiency. The practical results show that the newly implemented system in the 5‐GHz range fulfills the requirements of users with high efficiency and high quality of service compared to those of the 2.4‐GHz system.

ACS Style

Walid El‐Shafai; Ahmed Fawzi; Abdelhalim Zekry; Fathi E. Abd El‐Samie; Mohammed Abd‐Elnaby. Spectrum measurement and utilization in an outdoor 5‐GHz Wi‐Fi network using cooperative cognitive radio system. International Journal of Communication Systems 2021, 34, e4774 .

AMA Style

Walid El‐Shafai, Ahmed Fawzi, Abdelhalim Zekry, Fathi E. Abd El‐Samie, Mohammed Abd‐Elnaby. Spectrum measurement and utilization in an outdoor 5‐GHz Wi‐Fi network using cooperative cognitive radio system. International Journal of Communication Systems. 2021; 34 (10):e4774.

Chicago/Turabian Style

Walid El‐Shafai; Ahmed Fawzi; Abdelhalim Zekry; Fathi E. Abd El‐Samie; Mohammed Abd‐Elnaby. 2021. "Spectrum measurement and utilization in an outdoor 5‐GHz Wi‐Fi network using cooperative cognitive radio system." International Journal of Communication Systems 34, no. 10: e4774.

Journal article
Published: 23 April 2021 in Applied Optics
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This paper presents a new trend in biometric security systems, which is cancelable multi-biometrics. In general, traditional biometric systems depend on a single biometric for identification. These traditional systems are subject to different types of attacks. In addition, a biometric signature may be lost in hacking scenarios; for example, in the case of intrusion, biometric signatures can be stolen forever. To reduce the risk of losing biometric signatures, the trend of cancelable biometrics has evolved by using either deformed or encrypted versions of biometrics for verification. In this paper, several biometric traits for the same person are treated to obtain a single cancelable template. First, optical scanning holography (OSH) is applied during the acquisition of each biometric. The resulting outputs are then compressed simultaneously to generate a unified template based on the energy compaction property of the discrete cosine transform (DCT). Hence, the OSH is used in the proposed approach as a tool to generate deformed versions of human biometrics in order to get the unified biometric template through DCT compression. With this approach, we guarantee the possibility of using multiple biometrics of the same user to increase security, as well as privacy of the new biometric template through utilization of the OSH. Simulation results prove the robustness of the proposed cancelable multi-biometric approach in noisy environments.

ACS Style

Fathi E. Abd El-Samie; Rana M. Nassar; Mohamed Safan; Mohamed A. Abdelhamed; Ashraf A. M. Khalaf; Ghada M. El Banby; Osama Zahran; El-Sayed M. El-Rabaie; Abdelnaser A. Mohamed; Ibrahim M. El-Dokany; HossamEldin H. Ahmed; Saied El-Khamy; Noha Ramadan; Randa F. Soliman; Walid El-Shafai. Efficient implementation of optical scanning holography in cancelable biometrics. Applied Optics 2021, 60, 3659 -3667.

AMA Style

Fathi E. Abd El-Samie, Rana M. Nassar, Mohamed Safan, Mohamed A. Abdelhamed, Ashraf A. M. Khalaf, Ghada M. El Banby, Osama Zahran, El-Sayed M. El-Rabaie, Abdelnaser A. Mohamed, Ibrahim M. El-Dokany, HossamEldin H. Ahmed, Saied El-Khamy, Noha Ramadan, Randa F. Soliman, Walid El-Shafai. Efficient implementation of optical scanning holography in cancelable biometrics. Applied Optics. 2021; 60 (13):3659-3667.

Chicago/Turabian Style

Fathi E. Abd El-Samie; Rana M. Nassar; Mohamed Safan; Mohamed A. Abdelhamed; Ashraf A. M. Khalaf; Ghada M. El Banby; Osama Zahran; El-Sayed M. El-Rabaie; Abdelnaser A. Mohamed; Ibrahim M. El-Dokany; HossamEldin H. Ahmed; Saied El-Khamy; Noha Ramadan; Randa F. Soliman; Walid El-Shafai. 2021. "Efficient implementation of optical scanning holography in cancelable biometrics." Applied Optics 60, no. 13: 3659-3667.

Journal article
Published: 22 April 2021 in IEEE Access
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Currently, secure multimedia applications are becoming a very hot research topic, especifically over the Internet and wireless communication networks due to their rapid progress. Several researchers have implemented various chaotic image and video encryption algorithms to achieve data stability and communication security. This paper presents a novel bit-level video frame encryption algorithm that is dependent on the piecewise linear chaotic maps (PWLCMs). It is implemented for orbital angular momentum (OAM) modulation over different turbulence channels. Firstly, the mathematical model for the bit error rate (BER) of OAM is derived over the gamma-gamma turbulence channel. After that, a comparison between the theoretical results from mathematica and the simulation results from MATLAB for different turbulence strengths, signal-to-noise ratios (SNRs), and propagation distance values is presented to assure that there is a perfect match. The proposed OAM video cryptosystem is checked using entropy analysis, histogram testing, attack analysis, time analysis, correlation testing, differential analysis, and other quality and security evaluation metrics. The simulation results and the performance analysis confirm that the proposed cryptosystem is reliable and secure for video frame encryption, and communication with different turbulence conditions in free space.

ACS Style

Shimaa A. El-Meadawy; Ahmed E. A. Farghal; Hossam M. H. Shalaby; Nabil A. Ismail; Fathi E. Abd El-Samie; Mohammed Abd-Elnaby; Walid El-Shafai. Efficient and Secure Bit-Level Chaos Security Algorithm for Orbital Angular Momentum Modulation in Free-Space Optical Communications. IEEE Access 2021, 9, 74817 -74835.

AMA Style

Shimaa A. El-Meadawy, Ahmed E. A. Farghal, Hossam M. H. Shalaby, Nabil A. Ismail, Fathi E. Abd El-Samie, Mohammed Abd-Elnaby, Walid El-Shafai. Efficient and Secure Bit-Level Chaos Security Algorithm for Orbital Angular Momentum Modulation in Free-Space Optical Communications. IEEE Access. 2021; 9 (99):74817-74835.

Chicago/Turabian Style

Shimaa A. El-Meadawy; Ahmed E. A. Farghal; Hossam M. H. Shalaby; Nabil A. Ismail; Fathi E. Abd El-Samie; Mohammed Abd-Elnaby; Walid El-Shafai. 2021. "Efficient and Secure Bit-Level Chaos Security Algorithm for Orbital Angular Momentum Modulation in Free-Space Optical Communications." IEEE Access 9, no. 99: 74817-74835.

Journal article
Published: 14 April 2021 in Sensors
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Long-range radio (LoRa) communication is a widespread communication protocol that offers long range transmission and low data rates with minimum power consumption. In the context of solid waste management, only a low amount of data needs to be sent to the remote server. With this advantage, we proposed architecture for designing and developing a customized sensor node and gateway based on LoRa technology for realizing the filling level of the bins with minimal energy consumption. We evaluated the energy consumption of the proposed architecture by simulating it on the Framework for LoRa (FLoRa) simulation by varying distinct fundamental parameters of LoRa communication. This paper also provides the distinct evaluation metrics of the the long-range data rate, time on-air (ToA), LoRa sensitivity, link budget, and battery life of sensor node. Finally, the paper concludes with a real-time experimental setup, where we can receive the sensor data on the cloud server with a customized sensor node and gateway.

ACS Style

Shaik Akram; Rajesh Singh; Mohammed AlZain; Anita Gehlot; Mamoon Rashid; Osama Faragallah; Walid El-Shafai; Deepak Prashar. Performance Analysis of IoT and Long-Range Radio-Based Sensor Node and Gateway Architecture for Solid Waste Management. Sensors 2021, 21, 2774 .

AMA Style

Shaik Akram, Rajesh Singh, Mohammed AlZain, Anita Gehlot, Mamoon Rashid, Osama Faragallah, Walid El-Shafai, Deepak Prashar. Performance Analysis of IoT and Long-Range Radio-Based Sensor Node and Gateway Architecture for Solid Waste Management. Sensors. 2021; 21 (8):2774.

Chicago/Turabian Style

Shaik Akram; Rajesh Singh; Mohammed AlZain; Anita Gehlot; Mamoon Rashid; Osama Faragallah; Walid El-Shafai; Deepak Prashar. 2021. "Performance Analysis of IoT and Long-Range Radio-Based Sensor Node and Gateway Architecture for Solid Waste Management." Sensors 21, no. 8: 2774.

Original research
Published: 26 March 2021 in Journal of Ambient Intelligence and Humanized Computing
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The security of healthcare and telemedicine systems is a critical issue that must be significantly investigated. Several smart telemedicine applications are expected to be adopted in the medical sector in the incoming years. Healthcare smart products that are connected through Internet to be accessible anytime and anywhere are expected to deal with critical and confidential information such as personal medical images. Therefore, medical image encryption is an important task in telemedicine and healthcare applications. This paper presents an efficient cryptosystem for medical image security based on exploiting the advantages of the de-oxyribo nucleic acid (DNA) rules and chaos maps. In the proposed medical image cryptosystem, logistic chaos map, piecewise linear chaotic map (PWLCM), and DNA encoding are employed. The PWLCM is employed to generate a secret key image. Then, the DNA rules are utilized for encoding the secret key image and the input plain image by rows that are encoded with the logistic chaos map. After that, the proposed logistic map is employed to obtain an intermediate image as another secret key image to set DNA functions row-by-row on the coded original image. Moreover, the intermediate image is decoded in the following stage. Finally, the previous actions are iterated through image columns once again to obtain the best ciphered image. The experimental results reveal that the suggested cryptosystem has a high security with an acceptable processing time. In addition, it can resist various kinds of attacks, such as known-plaintext and chosen-plaintext attacks.

ACS Style

Walid El-Shafai; Fatma Khallaf; El-Sayed M. El-Rabaie; Fathi E. Abd El-Samie. Robust medical image encryption based on DNA-chaos cryptosystem for secure telemedicine and healthcare applications. Journal of Ambient Intelligence and Humanized Computing 2021, 1 -29.

AMA Style

Walid El-Shafai, Fatma Khallaf, El-Sayed M. El-Rabaie, Fathi E. Abd El-Samie. Robust medical image encryption based on DNA-chaos cryptosystem for secure telemedicine and healthcare applications. Journal of Ambient Intelligence and Humanized Computing. 2021; ():1-29.

Chicago/Turabian Style

Walid El-Shafai; Fatma Khallaf; El-Sayed M. El-Rabaie; Fathi E. Abd El-Samie. 2021. "Robust medical image encryption based on DNA-chaos cryptosystem for secure telemedicine and healthcare applications." Journal of Ambient Intelligence and Humanized Computing , no. : 1-29.

Preprint content
Published: 22 March 2021
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In this paper, we present hybrid watermarking and error control techniques for reliable cornea and infrared frame communication through wireless networks in Internet of Things (IoT) applications. In the proposed watermarking technique, two stages of Singular Value Decomposition (SVD) watermarking are used. In the embedding stage, two watermark images are embedded in either the cornea or infrared frames using Block-based SVD (B-SVD) and SVD schemes, respectively. Then, the resulting watermarked cornea or infrared frames are transmitted through an erroneous wireless channel. At the receiver, the received corrupted cornea or infrared frames are recovered using a proposed hybrid post-processing error control technique. This technique comprises a Circular Spatial-scan Order Interpolation Algorithm (CSOIA) and a temporal Partitioning Motion Compensation Algorithm (PMCA) to reconstruct the erroneous cornea or infrared frames. Then, the Bayesian Kalman Filter (BKF) is utilized in the amelioration process due to its efficiency to smooth the remanent inherent corruptions in the formerly reconstructed frames to obtain high cornea or infrared video quality. After that, the watermark extraction stage is implemented to extract the watermark images from the watermarked frames. Simulation results on several cornea and infrared frames show that the suggested hybrid watermarking, and error control techniques reveal adequate subjective and objective video quality compared to those obtained with the traditional methods. In addition, the watermark detectability, robustness, and security are enhanced. Moreover, the experimental results show that the proposed watermarking technique is superior and more secure than the other previous techniques for embedding and extraction of watermarks, efficiently, in the presence of attacks.

ACS Style

Walid El-Shafai; Amany Daosh; Nehad Haggag; Aya Gamal; Nevien Sadik; Yasser Mahrous; Fatma Ibrahim; Naglaa Soliman; Abeer Algarni; Ghada El Banby; Mohamad R. Abdelrahman; Said Eldosary; Emad S. Hassan; Huda Ashiba; Eman Soltan; Waleed Alhanafy; Adel Saleeb; Adel El-Fishawy; S. El-Rabaie; M. M. El-Halawany; Moawad I. Desouky; Sami El-Dolil; Nabil Ismail; Ibrahim M. El-Dokany; Fathi E. Abd El-Samie. Hybrid Error Control and Security Framework for Reliable Cornea and Infrared Video Communication in IoT Applications. 2021, 1 .

AMA Style

Walid El-Shafai, Amany Daosh, Nehad Haggag, Aya Gamal, Nevien Sadik, Yasser Mahrous, Fatma Ibrahim, Naglaa Soliman, Abeer Algarni, Ghada El Banby, Mohamad R. Abdelrahman, Said Eldosary, Emad S. Hassan, Huda Ashiba, Eman Soltan, Waleed Alhanafy, Adel Saleeb, Adel El-Fishawy, S. El-Rabaie, M. M. El-Halawany, Moawad I. Desouky, Sami El-Dolil, Nabil Ismail, Ibrahim M. El-Dokany, Fathi E. Abd El-Samie. Hybrid Error Control and Security Framework for Reliable Cornea and Infrared Video Communication in IoT Applications. . 2021; ():1.

Chicago/Turabian Style

Walid El-Shafai; Amany Daosh; Nehad Haggag; Aya Gamal; Nevien Sadik; Yasser Mahrous; Fatma Ibrahim; Naglaa Soliman; Abeer Algarni; Ghada El Banby; Mohamad R. Abdelrahman; Said Eldosary; Emad S. Hassan; Huda Ashiba; Eman Soltan; Waleed Alhanafy; Adel Saleeb; Adel El-Fishawy; S. El-Rabaie; M. M. El-Halawany; Moawad I. Desouky; Sami El-Dolil; Nabil Ismail; Ibrahim M. El-Dokany; Fathi E. Abd El-Samie. 2021. "Hybrid Error Control and Security Framework for Reliable Cornea and Infrared Video Communication in IoT Applications." , no. : 1.

Journal article
Published: 26 February 2021 in IEEE Access
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The rapid growth of multimedia communication systems has expanded the High-Efficiency Video Coding (HEVC) security applications precipitously. Therefore, there is an urgent, elevated need to protect and secure the HEVC content during streaming and communication over insecure channels to ensure the privacy of HEVC data against intruders and attackers. This paper introduces an optical HEVC cipher algorithm based on bit-plane 3D-JST (Three-Dimensional Jigsaw Transform) and multistage 2D-FrFT (Two-Dimensional Fractional Fourier Transform) encryption. The main advantage of employing 3D-JST is its unitary transform that has an inverse transform used to reorganize the HEVC frame-blocks in an indiscriminately way. The proposed algorithm embraces the cascaded 2D-FrFT encryption in the optical domain using a single arbitrary phase code; to be executed all optically with a lone lens. The suggested algorithm utilizes the two 2D-FrFT stages with distinct kernels in mutually dimensions separated by employing the arbitrary phase code. A foregoing bit-plane permutation stage is conducted on the input HEVC frames before the 3D-JST and 2D-FrFT processes to accomplish a high robustness and security level. To validate the efficacy of the proposed cryptography algorithm for secure HEVC streaming, a comprehensive evaluation framework has been introduced and followed to (a) test HEVC streams against different statistical cryptographic metrics, (b) compare the proposed algorithm with recent related works whether optical-based or digital-based algorithms and (c) study the impact of different security attacks on its performance. The evaluation results show a secure and efficient proposed cryptography algorithm that outperforms the conventional and related cryptography algorithms in terms of all examined evaluation metrics.

ACS Style

Walid El-Shafai; Iman M. Almomani; Aala Alkhayer. Optical Bit-Plane-Based 3D-JST Cryptography Algorithm With Cascaded 2D-FrFT Encryption for Efficient and Secure HEVC Communication. IEEE Access 2021, 9, 35004 -35026.

AMA Style

Walid El-Shafai, Iman M. Almomani, Aala Alkhayer. Optical Bit-Plane-Based 3D-JST Cryptography Algorithm With Cascaded 2D-FrFT Encryption for Efficient and Secure HEVC Communication. IEEE Access. 2021; 9 (99):35004-35026.

Chicago/Turabian Style

Walid El-Shafai; Iman M. Almomani; Aala Alkhayer. 2021. "Optical Bit-Plane-Based 3D-JST Cryptography Algorithm With Cascaded 2D-FrFT Encryption for Efficient and Secure HEVC Communication." IEEE Access 9, no. 99: 35004-35026.

Research article
Published: 23 February 2021 in International Journal of Communication Systems
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Human identification is considered as a serious challenge for several applications such as cybersecurity and access control. Recently, the trend of human identification has been directed to human biometrics, which can be used to recognize persons based on some physiological or behavioral characteristics that they own, such as fingerprint, iris, and biosignals. There are several types of human biosignals including electroencephalography (EEG), electrocardiography (ECG), and photoplethysmography (PPG) signals. This paper presents a human identification system based on PPG signals. The proposed system consists of three main phases: signal acquisition, signal pre‐processing, and feature extraction/classification. The pre‐processing phase involves denoising of the acquired signal, transformation of the 1D signal sequence into a 2D image, and computation of the spectrogram. Feature extraction is carried out on the images obtained from the pre‐processing phase. Features are extracted from the images based on convolutional neural networks (CNNs). The proposed CNN model consists of a sequence of convolutional (CNV) and pooling layers. Finally, the obtained feature maps are fed to the classifier to discriminate human identities. The proposed identification algorithm is applied on signals with and without an additive white Gaussian noise (AWGN). The simulation results reveal that the proposed algorithm achieves an accuracy of 99.5% with the spectrogram representation and 89.8% with the 2D image representation, in the absence of noise. In addition, the paper gives a discussion of the efficiency of denoising techniques such as wavelet denoising, Savitzky–Golay and Kalman filtering, when involved with the proposed algorithm. The simulation results prove that the wavelet dencoising technique has a best performance among the discussed noise reduction techniques.

ACS Style

Ali I. Siam; Ahmed Sedik; Walid El‐Shafai; Atef Abou Elazm; Nirmeen A. El‐Bahnasawy; Ghada M. El Banby; Ashraf A.M. Khalaf; Fathi E. Abd El‐Samie. Biosignal classification for human identification based on convolutional neural networks. International Journal of Communication Systems 2021, 34, e4685 .

AMA Style

Ali I. Siam, Ahmed Sedik, Walid El‐Shafai, Atef Abou Elazm, Nirmeen A. El‐Bahnasawy, Ghada M. El Banby, Ashraf A.M. Khalaf, Fathi E. Abd El‐Samie. Biosignal classification for human identification based on convolutional neural networks. International Journal of Communication Systems. 2021; 34 (7):e4685.

Chicago/Turabian Style

Ali I. Siam; Ahmed Sedik; Walid El‐Shafai; Atef Abou Elazm; Nirmeen A. El‐Bahnasawy; Ghada M. El Banby; Ashraf A.M. Khalaf; Fathi E. Abd El‐Samie. 2021. "Biosignal classification for human identification based on convolutional neural networks." International Journal of Communication Systems 34, no. 7: e4685.

Research article fundamental
Published: 18 February 2021 in International Journal for Numerical Methods in Biomedical Engineering
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Brain tumor is a mass of anomalous cells in the brain. Medical imagining techniques have a vital task in the diagnosis of diseases. Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) imaging techniques are the most popular techniques to localize the tumor area. Brain tumor segmentation plays an important role in medical image processing. In this paper, we introduce a framework to perform brain tumor segmentation, and then discriminate the region of the tumor, clearly. The proposed framework begins with the fusion of MRI and CT images by Non‐Sub‐Sampled Shearlet Transform (NSST) with the aid of Modified Central Force Optimization (MCFO) to get the optimum fusion result from the quality metrics perspective. After that, different image interpolation techniques are employed to obtain a High‐Resolution (HR) image from the Low‐Resolution (LR) one. The objective of the interpolation process is to enrich the details of the images prior to segmentation. Finally, the threshold and the watershed segmentation are applied respectively to localize the tumor region, clearly. The proposed framework enhances the efficiency of the segmentation to help the specialists diagnose diseases.

ACS Style

Noha A. El‐Hag; Ahmed Sedik; Ghada M. El‐Banby; Walid El‐Shafai; Ashraf A. M. Khalaf; Waleed Al‐Nuaimy; Fathi E. Abd El‐Samie; Heba M. El‐Hoseny. Utilization of image interpolation and fusion in brain tumor segmentation. International Journal for Numerical Methods in Biomedical Engineering 2021, 37, 1 .

AMA Style

Noha A. El‐Hag, Ahmed Sedik, Ghada M. El‐Banby, Walid El‐Shafai, Ashraf A. M. Khalaf, Waleed Al‐Nuaimy, Fathi E. Abd El‐Samie, Heba M. El‐Hoseny. Utilization of image interpolation and fusion in brain tumor segmentation. International Journal for Numerical Methods in Biomedical Engineering. 2021; 37 (8):1.

Chicago/Turabian Style

Noha A. El‐Hag; Ahmed Sedik; Ghada M. El‐Banby; Walid El‐Shafai; Ashraf A. M. Khalaf; Waleed Al‐Nuaimy; Fathi E. Abd El‐Samie; Heba M. El‐Hoseny. 2021. "Utilization of image interpolation and fusion in brain tumor segmentation." International Journal for Numerical Methods in Biomedical Engineering 37, no. 8: 1.

Original research
Published: 12 February 2021 in Journal of Ambient Intelligence and Humanized Computing
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Recently, the digital multimedia cybersecurity has become a research topic of interest due to the fast development of real-time multimedia applications over public networks such as the Internet. Therefore, this research paper introduces an efficient cybersecurity framework for protecting the high-efficiency video coding (HEVC) frames. The suggested selective cybersecurity HEVC framework employs a robust hybrid technique based on watermarking and selective encryption for maintaining confidentiality and achieving copyright protection of the transmitted HEVC information. The watermarking method employs the Homomorphic transform and singular value decomposition in the discrete wavelet transform to increase the immunity of watermarked HEVC streams to attacks. Moreover, the selective encryption technique uses the Chaotic logistic map for encrypting the motion vector difference and the discrete cosine transform sign bits to provide the feature of HEVC format compliance with low encryption overhead cost. An extensive security investigation is carried out for the proposed selective HEVC cybersecurity framework. The obtained experimental outcomes ensure and validate the effectiveness of the selective HEVC cybersecurity framework for HEVC sequences transmission.

ACS Style

Osama S. Faragallah; Walid El-Shafai; Ahmed I. Sallam; Ibrahim Elashry; El-Sayed M. El-Rabaie; Ashraf Afifi; Mohammed A. AlZain; Jehad F. Al-Amri; Fathi E. Abd El-Samie; Hala S. El-Sayed. Cybersecurity framework of hybrid watermarking and selective encryption for secure HEVC communication. Journal of Ambient Intelligence and Humanized Computing 2021, 1 -25.

AMA Style

Osama S. Faragallah, Walid El-Shafai, Ahmed I. Sallam, Ibrahim Elashry, El-Sayed M. El-Rabaie, Ashraf Afifi, Mohammed A. AlZain, Jehad F. Al-Amri, Fathi E. Abd El-Samie, Hala S. El-Sayed. Cybersecurity framework of hybrid watermarking and selective encryption for secure HEVC communication. Journal of Ambient Intelligence and Humanized Computing. 2021; ():1-25.

Chicago/Turabian Style

Osama S. Faragallah; Walid El-Shafai; Ahmed I. Sallam; Ibrahim Elashry; El-Sayed M. El-Rabaie; Ashraf Afifi; Mohammed A. AlZain; Jehad F. Al-Amri; Fathi E. Abd El-Samie; Hala S. El-Sayed. 2021. "Cybersecurity framework of hybrid watermarking and selective encryption for secure HEVC communication." Journal of Ambient Intelligence and Humanized Computing , no. : 1-25.

Original research
Published: 22 January 2021 in Journal of Ambient Intelligence and Humanized Computing
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In this paper, we present an approach for the anticipation of electroencephalography (EEG) seizures using different families of wavelet transform. Different signal attributes are investigated to anticipate the seizure onset based on the wavelet transform. These attributes comprise amplitude, local mean, local median, local variance, derivative, and entropy of the wavelet-transformed signals. Different wavelet families are considered including Haar, Daubechies (db4, and db8), Symlets (Sym4), and Coiflets (Coif4) wavelets. The seizure prediction process is intended to be simple to be applied on a mobile application accompanying the patient to give him alerts of possible incoming seizures. The proposed approach is performed on long-term EEG recordings from the available CHB-MIT scalp dataset. It gives the best results in comparison with the other previous algorithms. It achieves a high sensitivity of 100% with Daubechies wavelet transform (db4) in addition to a low average False Prediction Rate (FPR) of 0.0818 h−1 and a high average Prediction Time (PT) of 38.1676 min. Therefore, it can help specialists for the prediction of epileptic seizures as early as possible.

ACS Style

Saly Abd-Elateif El-Gindy; Asmaa Hamad; Walid El-Shafai; Ashraf A. M. Khalaf; Sami M. El-Dolil; Taha E. Taha; Adel S. El-Fishawy; Turky N. Alotaiby; Saleh A. Alshebeili; Fathi E. Abd El-Samie. Efficient communication and EEG signal classification in wavelet domain for epilepsy patients. Journal of Ambient Intelligence and Humanized Computing 2021, 1 -16.

AMA Style

Saly Abd-Elateif El-Gindy, Asmaa Hamad, Walid El-Shafai, Ashraf A. M. Khalaf, Sami M. El-Dolil, Taha E. Taha, Adel S. El-Fishawy, Turky N. Alotaiby, Saleh A. Alshebeili, Fathi E. Abd El-Samie. Efficient communication and EEG signal classification in wavelet domain for epilepsy patients. Journal of Ambient Intelligence and Humanized Computing. 2021; ():1-16.

Chicago/Turabian Style

Saly Abd-Elateif El-Gindy; Asmaa Hamad; Walid El-Shafai; Ashraf A. M. Khalaf; Sami M. El-Dolil; Taha E. Taha; Adel S. El-Fishawy; Turky N. Alotaiby; Saleh A. Alshebeili; Fathi E. Abd El-Samie. 2021. "Efficient communication and EEG signal classification in wavelet domain for epilepsy patients." Journal of Ambient Intelligence and Humanized Computing , no. : 1-16.

Article
Published: 16 January 2021 in International Journal of Speech Technology
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Generally, most blind signal separation algorithms deal with the separation problem in the absence of noise. The presence of noise degrades the performance of separated signals. This paper deals with the problem of blind separation of audio signals from noisy mixtures. Blind signal separation algorithm is applied on the discrete cosine transform, the discrete sine transform or the discrete wavelet transform of the mixed signals, instead of performing the separation on the mixtures in the time domain. All of these transforms have an energy compaction property, which concentrates most of the signal energy in a few coefficients in the transform domain, leaving most of the transform-domain coefficients close to zero. As a result, the separation is performed on a few coefficients in the transform domain. Another advantage of signal separation in transform domains is that the effect of noise on the signals in the transform domains is smaller than that in the time domain. The paper presents also an investigation of the rule of the speech enhancement techniques as pre- and post-processing steps for the blind signal separation process, instead of performing the separation on the mixtures in the time domain. The considered speech enhancement techniques are the spectral subtraction, the Wiener filtering, the adaptive Wiener filtering, and the wavelet denoising techniques. Both blind signal separation and noise reduction are applied within a real speaker identification system to reduce the effect of interference and noise on the system performance. The simulation results confirm the superiority of transform domain separation to time domain separation and the importance of the wavelet denoising technique, when used as a pre-processing step for noise reduction. Moreover, the speaker identification system performance is enhanced with blind signal separation and noise reduction.

ACS Style

Hossam Hammam; Walid El-Shafai; Emad Hassan; Atef E. Abu El-Azm; Moawad I. Dessouky; Mohamed E. Elhalawany; Fathi E. Abd El-Samie. Blind signal separation with Noise Reduction for efficient speaker identification. International Journal of Speech Technology 2021, 1 -16.

AMA Style

Hossam Hammam, Walid El-Shafai, Emad Hassan, Atef E. Abu El-Azm, Moawad I. Dessouky, Mohamed E. Elhalawany, Fathi E. Abd El-Samie. Blind signal separation with Noise Reduction for efficient speaker identification. International Journal of Speech Technology. 2021; ():1-16.

Chicago/Turabian Style

Hossam Hammam; Walid El-Shafai; Emad Hassan; Atef E. Abu El-Azm; Moawad I. Dessouky; Mohamed E. Elhalawany; Fathi E. Abd El-Samie. 2021. "Blind signal separation with Noise Reduction for efficient speaker identification." International Journal of Speech Technology , no. : 1-16.