Osama Alkishriwo - Academia.edu (original) (raw)

Papers by Osama Alkishriwo

Multidimensional Systems and Signal Processing, Feb 27, 2021

Due to the rapid growth of multimedia transmission over the internet, the challenges of image sec... more Due to the rapid growth of multimedia transmission over the internet, the challenges of image security have become an important research topic. In this paper, an Adaptive Multiband Signal Decomposition (AMSD) is proposed and its application for image encryption is explored. Like the conventional multiband wavelet transform, the AMSD can decompose the original image into multiband subimages. The perfect reconstruction of the original image from the decomposed multibands is achieved. In addition, a novel image encryption algorithm based on the adaptive multiband image decomposition with three dimensional discrete chaotic maps is developed and its performance is evaluated using common security analysis methods. Simulation results show that the proposed encryption algorithm has great degree of security and can resist various typical attacks.

European Signal Processing Conference, Sep 1, 2013

In this paper, we consider FM demodulation as an application of the decomposition of non-stationa... more In this paper, we consider FM demodulation as an application of the decomposition of non-stationary signals. Nonstationary signal decomposition can be done using either the empirical mode decomposition (EMD) or the Discrete Linear Chirp Decomposition (DLCT) methods. These methods decompose non-stationary signals using local timescale signal characteristics. While the EMD decomposes the signal into a number of intrinsic mode functions (IMFs), the DLCT obtains a parametric model based on a local linear chirp model. Analytically the DLCT considers localized zero-mean linear chirps as special IMFs. The DLCT is a joint frequency instantaneous-frequency orthogonal transformation that extends the discrete Fourier transform (DFT) for processing of non-stationary signals. FM demodulation is commonly done by computing the signal derivative to convert it into an amplitude demodulation. We will show that the demodulation can be approached with the EMD and the DLCT and that the second method provides better results. The performance of the DLCT and the EMD are illustrated and compared when used as an FM demodulation scheme in software defined radio.

2021 IEEE 1st International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering MI-STA, May 25, 2021

Speckle noise is intrinsic to medical ultrasound images and can cause misleading on image–based i... more Speckle noise is intrinsic to medical ultrasound images and can cause misleading on image–based interpretation and diagnoses procedures. Therefore, in the processing of ultrasound medical images, speckle mitigation is an essential preprocessing step. In this article, a novel speckle noise mitigation technique using adaptive multi–resolution image decomposition algorithm (AMID) is proposed. The effectiveness of the introduced method is evaluated with synthetic images as well as real ultrasound images for further analysis and comparison of the results with other works. Using well known measure metrics such as peak signal–to–noise ratio (PSNR) and structural similarity index (SSIM), simulation results evidence that the presented method outperforms other state–of–the–art techniques.

2021 IEEE 1st International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering MI-STA, May 25, 2021

In this paper, deep neural network (DNN) is integrated with spatial modulation-orthogonal frequen... more In this paper, deep neural network (DNN) is integrated with spatial modulation-orthogonal frequency division multiplexing (SM-OFDM) technique for end-to-end data detection over Rayleigh fading channel. This proposed system directly demodulates the received symbols, leaving the channel estimation done only implicitly. Furthermore, an ensemble network is also proposed for this system. Simulation results show that the proposed DNN detection scheme has a significant advantage over classical methods when the pilot overhead and cyclic prefix (CP) are reduced, owing to its ability to learn and adjust to complicated channel conditions. Finally, the ensemble network is shown to improve the generalization of the proposed scheme, while also showing a slight improvement in its performance.

Iet Signal Processing, May 1, 2014

We propose the discrete linear chirp transform (DLCT) for decomposing a non-stationary signal int... more We propose the discrete linear chirp transform (DLCT) for decomposing a non-stationary signal into intrinsic mode chirp functions. The decomposition of a signal into a finite number of intrinsic mode functions (IMFs) was introduced by the empirical mode decomposition (EMD). It exploits the local timescale signal characteristics of the signal and provides spectral estimates obtained via the Hilbert transform. Although efficient, the EMD does not provide an analytic representation of the IMFs and is susceptible to noise and to closeness or overlap of the frequency of the IMFs. Using linear chirps as IMFs, the DLCT, a joint frequency instantaneous frequency procedure, provides a parsimonious local orthogonal representation of nonstationary signals. Moreover, the DLCT allows a parametric estimation of the instantaneous frequency of the signal that is robust to noise and to closeness or overlap in the instantaneous frequency of the modes. More importantly, the DLCT can be used to represent and process signals that are sparse in a joint time-frequency sense. The performance of the DLCT and the EMD are illustrated and compared when used to estimate the instantaneous frequency of individual signal components, to obtain signal decompositions at different frequency bands and to process frequency modulated signals with time-varying amplitude.

ABSTRACT Compressive sensing attempts to simplify the frequency transformation and thresholding s... more ABSTRACT Compressive sensing attempts to simplify the frequency transformation and thresholding steps, commonly done in data compression, into one. Sparseness of the signal, in either time or frequency, is required for the convex optimization in compressive sensing to perform well. Although sparseness of certain signals, in either time or frequency, is guaranteed by the uncertainty principle signals composed of chirps are not however sparse in either domain. In this paper we propose an orthogonal linear-chirp transform, the discrete linear chirp transform (DLCT), to represent any signal in terms of linear chirps, with modulation and dual properties. Using the DLCT the sparseness of the signal in either time or frequency can be assessed, and if not sparse in neither of these domains, the modulation and dual properties of the DLCT provide a way to transform the signal into a sparse signal. The application of the proposed DLCT is in data compression. The transformation is illustrated by using sparse and not sparse test signals as well as actual signals.

arXiv (Cornell University), Oct 11, 2018

In recent years, steganography has emerged as one of the main research areas in information secur... more In recent years, steganography has emerged as one of the main research areas in information security. Least significant bit (LSB) steganography is one of the fundamental and conventional spatial domain methods, which is capable of hiding larger secret information in a cover image without noticeable visual distortions. In this paper, a combined algorithm based on LSB steganography and chaotic encryption is proposed. Experimental results show the feasibility of the proposed method. In comparison with existing steganographic spatial domain based algorithms, the suggested algorithm is shown to have some advantages over existing ones, namely, larger key space and a higher level of security against some existing attacks.

arXiv (Cornell University), Oct 11, 2018

Quantization is an essential step in the analog-to-digital conversion process and it is very impo... more Quantization is an essential step in the analog-to-digital conversion process and it is very important in all modern telecommunication systems. In this paper, a novel chaotic uniform quantizer is proposed and its application for speech coding is presented. The proposed system consists of three stages: two PCM coders separated by an XOR operation with a chaotic sequence, where the first step is used for continuous signal sampling and second stage performs data encryption, while the third stage provides additional data compression. The performance of the presented quantizer for Laplacian distributed signals and real speech signals is investigated and compared with that of the well-known uniform and non-uniform quantizers. Simulation results show that the proposed quantizer provides secured data with higher levels of SQNR compared to others.

Users of Internet daily send and receive many images through social media. These images are vulne... more Users of Internet daily send and receive many images through social media. These images are vulnerable to hack by attackers. Therefore, it is necessary to develop methods to protect these images against attackers. A nontraditional encryption method for encrypting color images in the spatial domain is proposed. The main idea in this work is based on building strong encryption algorithm through implementing the permutation and diffusion operations on the pixels, where every pixel composed of three values red, green and blue. These operations are implemented depending on extracting three chaotic sequences from the 3-D chaotic system, where each chaotic sequence is used to shuffle and diffuse each color in the plaintext image. The proposed system is tested on wellknown images like Lena and Mandrill. Experiments and security analysis prove that the algorithm has an excellent performance in image encryption.

arXiv (Cornell University), Oct 12, 2018

In this paper, we propose a new method to estimate instantaneous frequency using a combined appro... more In this paper, we propose a new method to estimate instantaneous frequency using a combined approach based on the discrete linear chirp transform (DLCT) and the Wigner distribution (WD). The DLCT locally represents a signal as a superposition of linear chirps while the WD provides maximum energy concentration along the instantaneous frequency in the time-frequency domain for each of the chirps. The developed approach takes advantage of the separation of the linear chirps given by the DLCT, and that for each of them, the WD provides an ideal representation. Combining the WD of the linear chirp components, we obtain a time-frequency representation free of cross-terms that clearly displays the instantaneous frequency. Applying this procedure locally, we obtain an instantaneous frequency estimate of a non-stationary multicomponent signal. The proposed method is illustrated by simulation. The results indicate the method is efficient for the instantaneous frequency estimation of multicomponent signals embedded in noise, even in cases of low signal to noise ratio.

arXiv (Cornell University), Jul 7, 2018

In this paper, a novel image encryption algorithm, which involves a chaotic block image scramblin... more In this paper, a novel image encryption algorithm, which involves a chaotic block image scrambling followed by a two-dimensional (2D) discrete linear chirp transform, is proposed. The definition of the 2D discrete linear chirp transform is introduced and then it is used to construct the basis of the novel encryption algorithm. Finally, security analysis are performed to show the quality of the encryption process using different metrics.

European Signal Processing Conference, Oct 18, 2012

Signal compression aims to decrease transmission rate (increase storage capacity) by reducing the... more Signal compression aims to decrease transmission rate (increase storage capacity) by reducing the amount of data necessary to be transmitted. The discrete linear chirp transform (DLCT) is a joint frequency instantaneous-frequency transform that decomposes the signal in terms of linear chirps. The DLCT can be used to transform signals that are not sparse in either time or frequency, such as linear chirps, into sparse signals. In this paper, we propose a new algorithm for signal compression based on the direct and the dual DLCT, depending on the sparsity of the signal in either time or in frequency. Furthermore, we develop a data structure for the extracted coefficients of compressed signals. In the data structure, the extracted parameters are arranged in certain way that are predetermined for the compress and decompress processes. The ability of the proposed method in signal compression are demonstrated using test as well as actual signals. The results are compared with those obtained with compressive sensing (CS) method.

Signal x 1 (n) in time domain; (b) Wigner-Ville distribution of x 1 (n); (c) The |DFrFT{x 1 (n)}|... more Signal x 1 (n) in time domain; (b) Wigner-Ville distribution of x 1 (n); (c) The |DFrFT{x 1 (n)}| with α = −0.44π; (d) The DLCT of x 1 (n) with β = 0.1. 30 Comparing the computation time between the DLCT and the DFrFT. .. . 31 Resolution: (a) Signal x 2 (n) in time domain; (b) Wigner-Ville distribution of x 2 (n); (c) The |DFrFT{x 2 (n)}| in three-dimension space; (d) The |DFrFT{x 2 (n)}| at α = −0.44π; (e) The DLCT of x 2 (n) in three-dimension space; (f) The DLCT of x 2 (n) at β = 0.

European Signal Processing Conference, Aug 1, 2011

arXiv (Cornell University), Aug 30, 2018

In this paper, chaotic block image permutation and XOR operation are performed to achieve image e... more In this paper, chaotic block image permutation and XOR operation are performed to achieve image encryption. The studied method of encryption makes use of chaotic systems properties for secure and speed image encryption. Firstly, the original image is divided into blocks of equal size. Then, two chaotic maps are used to generate two key streams which are permuted among themselves to produce one key steam. The image blocks are then shuffled using part of key stream. Finally, scrambled image is diffused by XOR operation with the key stream to get the encrypted image. The experimental results of several performance analyses about the pixel correlation, various statistical analysis, information entropy analysis, differential analysis, the key space and key sensitivity analysis, show that the algorithm can resist several know attacks effectively and has the advantages of large key space, high security, and high speed, assuring safety performance and secure image encryption.

2021 IEEE 1st International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering MI-STA, May 25, 2021

Intelligent fault diagnosis of rotating machinery has always been a challenge when monitoring rot... more Intelligent fault diagnosis of rotating machinery has always been a challenge when monitoring rotating machinery to guarantee machine safety operation and reduce breakdown losses. In this paper, a new framework based on wavelet transform and discrete Fourier transform with deep learning is proposed for bearing fault detection. The proposed algorithm can be summarized as follows. First, the vibration time series is decomposed using multilevel wavelet transform. Second, the low frequency component is selected and further processed with the discrete Fourier transform (DFT). Finally, the resulted signal is fed to an autoencoder with fully connected artificial neural network (ANN). The efficacy of the proposed method is assessed and compared with the state-of-the-art methods. Simulation results evidence that the proposed method performs better than other cutting edge techniques in bearings fault diagnosis.

Iet Image Processing, Oct 14, 2020

With the growth of modern digital technologies, demand for transmission multimedia and digital im... more With the growth of modern digital technologies, demand for transmission multimedia and digital images, which require more storage space and transmission bandwidth, has been increased rapidly. Hence, developing new image compression techniques for reducing data size without degrading the quality of the image, has gained a lot of interest recently. In this study, an adaptive multiresolution image decomposition (AMID) algorithm is proposed and its application for image compression is explored. The developed algorithm is capable of decomposing an image along the vertical, horizontal, and diagonal directions using the pyramidal multiresolution scheme. Compared to the wavelet transform, the AMID can be used for decimation with the guarantees of perfect signal reconstruction. Furthermore, the application of the AMID for image compression is explored and its performance is compared with the state-of-the-art image compression techniques. The performance of compression method is evaluated using peak signal-to-noise ratio and compression ratio. Experimental results have shown promising performance compared with the results of using other image compression approaches.

Multidimensional Systems and Signal Processing, Feb 27, 2021

Due to the rapid growth of multimedia transmission over the internet, the challenges of image sec... more Due to the rapid growth of multimedia transmission over the internet, the challenges of image security have become an important research topic. In this paper, an Adaptive Multiband Signal Decomposition (AMSD) is proposed and its application for image encryption is explored. Like the conventional multiband wavelet transform, the AMSD can decompose the original image into multiband subimages. The perfect reconstruction of the original image from the decomposed multibands is achieved. In addition, a novel image encryption algorithm based on the adaptive multiband image decomposition with three dimensional discrete chaotic maps is developed and its performance is evaluated using common security analysis methods. Simulation results show that the proposed encryption algorithm has great degree of security and can resist various typical attacks.

European Signal Processing Conference, Sep 1, 2013

In this paper, we consider FM demodulation as an application of the decomposition of non-stationa... more In this paper, we consider FM demodulation as an application of the decomposition of non-stationary signals. Nonstationary signal decomposition can be done using either the empirical mode decomposition (EMD) or the Discrete Linear Chirp Decomposition (DLCT) methods. These methods decompose non-stationary signals using local timescale signal characteristics. While the EMD decomposes the signal into a number of intrinsic mode functions (IMFs), the DLCT obtains a parametric model based on a local linear chirp model. Analytically the DLCT considers localized zero-mean linear chirps as special IMFs. The DLCT is a joint frequency instantaneous-frequency orthogonal transformation that extends the discrete Fourier transform (DFT) for processing of non-stationary signals. FM demodulation is commonly done by computing the signal derivative to convert it into an amplitude demodulation. We will show that the demodulation can be approached with the EMD and the DLCT and that the second method provides better results. The performance of the DLCT and the EMD are illustrated and compared when used as an FM demodulation scheme in software defined radio.

2021 IEEE 1st International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering MI-STA, May 25, 2021

Speckle noise is intrinsic to medical ultrasound images and can cause misleading on image–based i... more Speckle noise is intrinsic to medical ultrasound images and can cause misleading on image–based interpretation and diagnoses procedures. Therefore, in the processing of ultrasound medical images, speckle mitigation is an essential preprocessing step. In this article, a novel speckle noise mitigation technique using adaptive multi–resolution image decomposition algorithm (AMID) is proposed. The effectiveness of the introduced method is evaluated with synthetic images as well as real ultrasound images for further analysis and comparison of the results with other works. Using well known measure metrics such as peak signal–to–noise ratio (PSNR) and structural similarity index (SSIM), simulation results evidence that the presented method outperforms other state–of–the–art techniques.

2021 IEEE 1st International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering MI-STA, May 25, 2021

In this paper, deep neural network (DNN) is integrated with spatial modulation-orthogonal frequen... more In this paper, deep neural network (DNN) is integrated with spatial modulation-orthogonal frequency division multiplexing (SM-OFDM) technique for end-to-end data detection over Rayleigh fading channel. This proposed system directly demodulates the received symbols, leaving the channel estimation done only implicitly. Furthermore, an ensemble network is also proposed for this system. Simulation results show that the proposed DNN detection scheme has a significant advantage over classical methods when the pilot overhead and cyclic prefix (CP) are reduced, owing to its ability to learn and adjust to complicated channel conditions. Finally, the ensemble network is shown to improve the generalization of the proposed scheme, while also showing a slight improvement in its performance.

Iet Signal Processing, May 1, 2014

We propose the discrete linear chirp transform (DLCT) for decomposing a non-stationary signal int... more We propose the discrete linear chirp transform (DLCT) for decomposing a non-stationary signal into intrinsic mode chirp functions. The decomposition of a signal into a finite number of intrinsic mode functions (IMFs) was introduced by the empirical mode decomposition (EMD). It exploits the local timescale signal characteristics of the signal and provides spectral estimates obtained via the Hilbert transform. Although efficient, the EMD does not provide an analytic representation of the IMFs and is susceptible to noise and to closeness or overlap of the frequency of the IMFs. Using linear chirps as IMFs, the DLCT, a joint frequency instantaneous frequency procedure, provides a parsimonious local orthogonal representation of nonstationary signals. Moreover, the DLCT allows a parametric estimation of the instantaneous frequency of the signal that is robust to noise and to closeness or overlap in the instantaneous frequency of the modes. More importantly, the DLCT can be used to represent and process signals that are sparse in a joint time-frequency sense. The performance of the DLCT and the EMD are illustrated and compared when used to estimate the instantaneous frequency of individual signal components, to obtain signal decompositions at different frequency bands and to process frequency modulated signals with time-varying amplitude.

ABSTRACT Compressive sensing attempts to simplify the frequency transformation and thresholding s... more ABSTRACT Compressive sensing attempts to simplify the frequency transformation and thresholding steps, commonly done in data compression, into one. Sparseness of the signal, in either time or frequency, is required for the convex optimization in compressive sensing to perform well. Although sparseness of certain signals, in either time or frequency, is guaranteed by the uncertainty principle signals composed of chirps are not however sparse in either domain. In this paper we propose an orthogonal linear-chirp transform, the discrete linear chirp transform (DLCT), to represent any signal in terms of linear chirps, with modulation and dual properties. Using the DLCT the sparseness of the signal in either time or frequency can be assessed, and if not sparse in neither of these domains, the modulation and dual properties of the DLCT provide a way to transform the signal into a sparse signal. The application of the proposed DLCT is in data compression. The transformation is illustrated by using sparse and not sparse test signals as well as actual signals.

arXiv (Cornell University), Oct 11, 2018

In recent years, steganography has emerged as one of the main research areas in information secur... more In recent years, steganography has emerged as one of the main research areas in information security. Least significant bit (LSB) steganography is one of the fundamental and conventional spatial domain methods, which is capable of hiding larger secret information in a cover image without noticeable visual distortions. In this paper, a combined algorithm based on LSB steganography and chaotic encryption is proposed. Experimental results show the feasibility of the proposed method. In comparison with existing steganographic spatial domain based algorithms, the suggested algorithm is shown to have some advantages over existing ones, namely, larger key space and a higher level of security against some existing attacks.

arXiv (Cornell University), Oct 11, 2018

Quantization is an essential step in the analog-to-digital conversion process and it is very impo... more Quantization is an essential step in the analog-to-digital conversion process and it is very important in all modern telecommunication systems. In this paper, a novel chaotic uniform quantizer is proposed and its application for speech coding is presented. The proposed system consists of three stages: two PCM coders separated by an XOR operation with a chaotic sequence, where the first step is used for continuous signal sampling and second stage performs data encryption, while the third stage provides additional data compression. The performance of the presented quantizer for Laplacian distributed signals and real speech signals is investigated and compared with that of the well-known uniform and non-uniform quantizers. Simulation results show that the proposed quantizer provides secured data with higher levels of SQNR compared to others.

Users of Internet daily send and receive many images through social media. These images are vulne... more Users of Internet daily send and receive many images through social media. These images are vulnerable to hack by attackers. Therefore, it is necessary to develop methods to protect these images against attackers. A nontraditional encryption method for encrypting color images in the spatial domain is proposed. The main idea in this work is based on building strong encryption algorithm through implementing the permutation and diffusion operations on the pixels, where every pixel composed of three values red, green and blue. These operations are implemented depending on extracting three chaotic sequences from the 3-D chaotic system, where each chaotic sequence is used to shuffle and diffuse each color in the plaintext image. The proposed system is tested on wellknown images like Lena and Mandrill. Experiments and security analysis prove that the algorithm has an excellent performance in image encryption.

arXiv (Cornell University), Oct 12, 2018

In this paper, we propose a new method to estimate instantaneous frequency using a combined appro... more In this paper, we propose a new method to estimate instantaneous frequency using a combined approach based on the discrete linear chirp transform (DLCT) and the Wigner distribution (WD). The DLCT locally represents a signal as a superposition of linear chirps while the WD provides maximum energy concentration along the instantaneous frequency in the time-frequency domain for each of the chirps. The developed approach takes advantage of the separation of the linear chirps given by the DLCT, and that for each of them, the WD provides an ideal representation. Combining the WD of the linear chirp components, we obtain a time-frequency representation free of cross-terms that clearly displays the instantaneous frequency. Applying this procedure locally, we obtain an instantaneous frequency estimate of a non-stationary multicomponent signal. The proposed method is illustrated by simulation. The results indicate the method is efficient for the instantaneous frequency estimation of multicomponent signals embedded in noise, even in cases of low signal to noise ratio.

arXiv (Cornell University), Jul 7, 2018

In this paper, a novel image encryption algorithm, which involves a chaotic block image scramblin... more In this paper, a novel image encryption algorithm, which involves a chaotic block image scrambling followed by a two-dimensional (2D) discrete linear chirp transform, is proposed. The definition of the 2D discrete linear chirp transform is introduced and then it is used to construct the basis of the novel encryption algorithm. Finally, security analysis are performed to show the quality of the encryption process using different metrics.

European Signal Processing Conference, Oct 18, 2012

Signal compression aims to decrease transmission rate (increase storage capacity) by reducing the... more Signal compression aims to decrease transmission rate (increase storage capacity) by reducing the amount of data necessary to be transmitted. The discrete linear chirp transform (DLCT) is a joint frequency instantaneous-frequency transform that decomposes the signal in terms of linear chirps. The DLCT can be used to transform signals that are not sparse in either time or frequency, such as linear chirps, into sparse signals. In this paper, we propose a new algorithm for signal compression based on the direct and the dual DLCT, depending on the sparsity of the signal in either time or in frequency. Furthermore, we develop a data structure for the extracted coefficients of compressed signals. In the data structure, the extracted parameters are arranged in certain way that are predetermined for the compress and decompress processes. The ability of the proposed method in signal compression are demonstrated using test as well as actual signals. The results are compared with those obtained with compressive sensing (CS) method.

Signal x 1 (n) in time domain; (b) Wigner-Ville distribution of x 1 (n); (c) The |DFrFT{x 1 (n)}|... more Signal x 1 (n) in time domain; (b) Wigner-Ville distribution of x 1 (n); (c) The |DFrFT{x 1 (n)}| with α = −0.44π; (d) The DLCT of x 1 (n) with β = 0.1. 30 Comparing the computation time between the DLCT and the DFrFT. .. . 31 Resolution: (a) Signal x 2 (n) in time domain; (b) Wigner-Ville distribution of x 2 (n); (c) The |DFrFT{x 2 (n)}| in three-dimension space; (d) The |DFrFT{x 2 (n)}| at α = −0.44π; (e) The DLCT of x 2 (n) in three-dimension space; (f) The DLCT of x 2 (n) at β = 0.

European Signal Processing Conference, Aug 1, 2011

arXiv (Cornell University), Aug 30, 2018

In this paper, chaotic block image permutation and XOR operation are performed to achieve image e... more In this paper, chaotic block image permutation and XOR operation are performed to achieve image encryption. The studied method of encryption makes use of chaotic systems properties for secure and speed image encryption. Firstly, the original image is divided into blocks of equal size. Then, two chaotic maps are used to generate two key streams which are permuted among themselves to produce one key steam. The image blocks are then shuffled using part of key stream. Finally, scrambled image is diffused by XOR operation with the key stream to get the encrypted image. The experimental results of several performance analyses about the pixel correlation, various statistical analysis, information entropy analysis, differential analysis, the key space and key sensitivity analysis, show that the algorithm can resist several know attacks effectively and has the advantages of large key space, high security, and high speed, assuring safety performance and secure image encryption.

2021 IEEE 1st International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering MI-STA, May 25, 2021

Intelligent fault diagnosis of rotating machinery has always been a challenge when monitoring rot... more Intelligent fault diagnosis of rotating machinery has always been a challenge when monitoring rotating machinery to guarantee machine safety operation and reduce breakdown losses. In this paper, a new framework based on wavelet transform and discrete Fourier transform with deep learning is proposed for bearing fault detection. The proposed algorithm can be summarized as follows. First, the vibration time series is decomposed using multilevel wavelet transform. Second, the low frequency component is selected and further processed with the discrete Fourier transform (DFT). Finally, the resulted signal is fed to an autoencoder with fully connected artificial neural network (ANN). The efficacy of the proposed method is assessed and compared with the state-of-the-art methods. Simulation results evidence that the proposed method performs better than other cutting edge techniques in bearings fault diagnosis.

Iet Image Processing, Oct 14, 2020

With the growth of modern digital technologies, demand for transmission multimedia and digital im... more With the growth of modern digital technologies, demand for transmission multimedia and digital images, which require more storage space and transmission bandwidth, has been increased rapidly. Hence, developing new image compression techniques for reducing data size without degrading the quality of the image, has gained a lot of interest recently. In this study, an adaptive multiresolution image decomposition (AMID) algorithm is proposed and its application for image compression is explored. The developed algorithm is capable of decomposing an image along the vertical, horizontal, and diagonal directions using the pyramidal multiresolution scheme. Compared to the wavelet transform, the AMID can be used for decimation with the guarantees of perfect signal reconstruction. Furthermore, the application of the AMID for image compression is explored and its performance is compared with the state-of-the-art image compression techniques. The performance of compression method is evaluated using peak signal-to-noise ratio and compression ratio. Experimental results have shown promising performance compared with the results of using other image compression approaches.