Madina Hamiane - Academia.edu (original) (raw)
Papers by Madina Hamiane
In this paper, we propose a new methodology for crack monitoring in concrete structures. This app... more In this paper, we propose a new methodology for crack monitoring in concrete structures. This approach is based on a n this paper, we propose a new methodology for monitoring cracks in concrete structures. This approach is based on a multi-resolution analysis of a sample or a specimen of the studied material subjected to several types of solicitation. The image obtained by ultrasonic investigation and processing by a dedicated wavelet will be analyzed according to several scales in order to detect internal cracks and crack initiation. The ultimate goal of this work is to propose an automatic crack type identification scheme based on convolutional neural networks (CNN). In this context, crack propagation can be monitored without access to the concrete surface and the goal is to detect cracks before they are visible on the concrete surface. The key idea allowing such a performance is the combination of two major data analysis tools which are wavelets and Deep Learning. This original p...
Segmentation of brain tumor images is a major research topic in medical imaging to have a refined... more Segmentation of brain tumor images is a major research topic in medical imaging to have a refined detection and understanding of abnormal masses in the brain. This paper proposes a new segmentation method, consisting of three main steps, to detect brain lesions using magnetic resonance imaging (MRI). In the first step, the parts of the image delineating the skull bone are removed to exclude insignificant data. In the second step, which is the main contribution of this study, the particle swarm optimization (PSO) technique is applied to detect the block that contains the brain lesions. The fitness function, used to determine the best block among all candidate blocks, is based on a two-way fixed-effects analysis of variance (ANOVA). In the last step of the algorithm, the K-means segmentation method is used in the lesion block to classify it as tumor or not. A thorough evaluation of the proposed algorithm is performed using the MRI database provided by the Kouba imaging center in Algie...
Engineering, Technology & Applied Science Research
In this paper, a new Visual Object Tracking (VOT) approach is proposed to overcome the main probl... more In this paper, a new Visual Object Tracking (VOT) approach is proposed to overcome the main problem the existing approaches encounter, i.e. the significant appearance changes which are mainly caused by heavy occlusion and illumination variation. The proposed approach is based on a combination of Deep Convolutional Neural Networks (DCNNs), Histogram of Oriented Gradient (HOG) features, and discrete wavelet packet transforms. The problem of illumination variation is solved by incorporating the coefficients of the image discrete wavelet packet transform instead of the image template to handle the case of images with high saturation in the input of the used CNN, whereas the inverse discrete wavelet packet transforms are used at the output for extracting the CNN features. By combining four learned correlation filters with the convolutional features, the target location is deduced using multichannel correlation maps at the CNN output. On the other side, the maximum value of the resulting ...
Evolutionary Intelligence
Detecting changes in multiple images of the same scene has recently seen increased interest due t... more Detecting changes in multiple images of the same scene has recently seen increased interest due to the many contemporary applications including smart security systems, smart homes, remote sensing, surveillance, medical diagnosis, weather forecasting, speed and distance measurement, post-disaster forensics and much more. These applications differ in the scale, nature, and speed of change. This paper presents an application of image processing techniques to implement a real-time change detection system. Change is identified by comparing the RGB representation of two consecutive frames captured in real-time. The detection threshold can be controlled to account for various luminance levels. The comparison result is passed through a filter before decision making to reduce false positives, especially at lower luminance conditions. The system is implemented with a MATLAB Graphical User interface with several controls to manage its operation and performance.
This paper presents the processing and analysis of ECG signals. The study is based on wavelet tra... more This paper presents the processing and analysis of ECG signals. The study is based on wavelet transform and uses exclusively the MATLAB environment. This study includes removing Baseline wander and further de-noising through wavelet transform and metrics such as signal-to noise ratio (SNR), Peak signal-to-noise ratio (PSNR) and the mean squared error (MSE) are used to assess the efficiency of the de-noising techniques. Feature extraction is subsequently performed whereby signal features such as heart rate, rise and fall levels are extracted and the QRS complex was detected which helped in classifying the ECG signal. The classification is the last step in the analysis of the ECG signals and it is shown that these are successfully classified as Normal rhythm or Abnormal rhythm. The final result proved the adequacy of using wavelet transform for the analysis of ECG signals.
Magnetic Resonance Imaging is a powerful technique that helps in the diagnosis of various medical... more Magnetic Resonance Imaging is a powerful technique that helps in the diagnosis of various medical conditions. Detection of brain abnormalities, such as brain tumors, in brain MRI images are considered in this work. These images are often corrupted by noise from various sources. In this work, MRI brain images with various abnormalities are pre-processed, enhanced , then classified to yield an efficient diagnosis tool that could help medical practitioners in identifying abnormal brain lesions . The work is based on the use of the Discrete Wavelet Transforms (DWT) along with thresholding techniques for efficient noise removal, followed by edge detection and threshold segmentation of the denoised images prior to the extraction of the enhanced image features through the use of morphological operations. The images are finally classified using a Support Vector scheme with a radial basis function kernel. The performance of the classifier is evaluated and the results of the classification sh...
Frattura ed Integrità Strutturale, 2021
This paper proposes an efficient methodology to monitor the formation of cracks in concrete after... more This paper proposes an efficient methodology to monitor the formation of cracks in concrete after non-destructive ultrasonic testing of a structure. The objective is to be able to automatically detect the initiation of cracks early enough, i.e. well before they are visible on the concrete surface, in order to implement adequate maintenance actions on civil engineering structures. The key element of this original approach is the wavelet-based multiresolution analysis of the ultrasonic signal received from a sample or a specimen of the studied material subjected to several types of solicitation. This analysis is finally coupled to an automatic identification scheme of the types of cracks based on artificial neural networks (ANNs), and in particular deep learning by convolutional neural networks (CNNs); a technology today at the cutting edge of machine learning, in particular for all applications of pattern recognition. Wavelet-based multiresolution analysis does not add any value in d...
Electronics, 2021
In this paper, we propose a new methodology for crack detection and monitoring in concrete struct... more In this paper, we propose a new methodology for crack detection and monitoring in concrete structures. This approach is based on a multiresolution analysis of a sample or a specimen of concrete material subjected to several types of solicitation. The image obtained by ultrasonic investigation and processed by a customized wavelet is analyzed at various scales in order to detect internal cracks and crack initiation. The ultimate objective of this work is to propose an automatic crack type identification scheme based on convolutional neural networks (CNN). In this context, crack propagation can be monitored without access to the concrete surface and the goal is to detect cracks before they are visible. This is achieved through the combination of two major data analysis tools which are wavelets and deep learning. This original procedure is shown to yield a high accuracy close to 90%. In order to evaluate the performance of the proposed CNN architectures, we also used an open access dat...
International Journal of Modelling and Simulation, 2021
ABSTRACT This article proposes two optimal controllers that stabilize the altitude, attitude, hea... more ABSTRACT This article proposes two optimal controllers that stabilize the altitude, attitude, heading, and position of the quadcopter in space. The first optimal control method consists of computing the proportional controller gain by minimizing the error between measured and the desired feature vector values, as opposed to the classical visual servoing (VS) scheme that requires a known value of the proportional controller gain which is often chosen empirically. The second control method relies on the optimal tuning of PD controller gains applied on the altitude, attitude, and position loops. This, however, may lead to a convergence problem, which is overcome by the use of an optimization technique, the bat algorithm (BA). This latter is an efficient metaheuristic algorithm that has been successfully used in many optimization problems. Several simulations are run in MATLAB, in which visual servo control of the Vertical Takeof and Landing (VTOL) type of Unmanned Aerial Vehicles (UAVs), known as the quadcopter, is implemented. The obtained results show that the Bat algorithm renders the classical PD controller adaptive and provides a high flexibility in tracking the UAV trajectory even in the presence of disturbances. It also guarantees the stability and accuracy of the optimal, minimal time, vision-based controller.
International Journal of Electrical and Computer Engineering (IJECE), 2017
Magnetic Resonance Imaging is a powerful technique that helps in the diagnosis of various medical... more Magnetic Resonance Imaging is a powerful technique that helps in the diagnosis of various medical conditions. MRI Image pre-processing followed by detection of brain abnormalities, such as brain tumors, are considered in this work. These images are often corrupted by noise from various sources. The Discrete Wavelet Transforms (DWT) with details thresholding is used for efficient noise removal followed by edge detection and threshold segmentation of the denoised images. Segmented image features are then extracted using morphological operations. These features are finally used to train an improved Support Vector Machine classifier that uses a Gausssian radial basis function kernel. The performance of the classifier is evaluated and the results of the classification show that the proposed scheme accurately distinguishes normal brain images from the abnormal ones and benign lesions from malignant tumours. The accuracy of the classification is shown to be 100% which is superior to the re...
International Journal of Electrical and Computer Engineering (IJECE), 2019
Due to the dramatic growth in the use of Wireless Sensor Network (WSN) applications ranging from ... more Due to the dramatic growth in the use of Wireless Sensor Network (WSN) applications ranging from environment and habitat monitoring to tracking and surveillance, network research in WSN protocols has been very active in the last decade. With battery-powered sensors operating in unattended environments, energy conservation becomes the key technique for improving WSN lifetimes. WSN Medium Access Control (MAC) protocols address energy awareness and reduced duty cycles. The focus of this study is to investigate, through simulation, the effect of variations in various factors that influence the performance results of WSNs. Using MiXiM framework with OMNeT++ simulator, this simulation study proposes modifications in Crankshaft MAC protocol in order to improve its performance. The impact of duration and number of slots, degree of connectivity among the nodes, mobility speed and mobility update interval and also, the impact of sending data packets without preambles are investigated. Based o...
Applied Intelligence, 2018
The electrocardiogram is the recording of the electrical activity of the heart by a device extern... more The electrocardiogram is the recording of the electrical activity of the heart by a device external to the body in a non-invasive way. In this paper real ECG records provided by the MIT-BIH Arrhythmia Database are used to build an efficient mechanism for detecting abnormalities in the ECG records. Prior to the detection, Butterworth and FIR filters are used in the pre-processing stage to eliminate any interference while maintaining the useful information within the signal. Detection of abnormal patterns related to Heartbeat along with the identification of other heart diseases such as AV blockage and Ventricular Fibrillation are implemented. Results of ECG signal pre-processing and abnormal pattern detection are obtained and demonstrate the suitability of the selected filtering techniques and the efficiency of the detection mechanisms.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
This paper presents the simulation of an ECG signal and the study and analysis of the ECG signal ... more This paper presents the simulation of an ECG signal and the study and analysis of the ECG signal using Wavelet transform in MATLAB. This study includes the generation as well as the simulation of an ECG signal, processing an ECG signal , and last but not least, analysing the ECG signal for the purpose of detecting heart beat-related abnormalities. With the help of MATLAB’s toolboxes and its built-in functions, real time ECG signals can be generated with more precision and accessibility. Wavelet decomposition is used for the removal of noise from the generated signals prior to further analysis . Simulation results are presented to illustrate the adequacy of using Daubechies wavelets for the analysis of ECG signals.
In this paper, we propose a new methodology for crack monitoring in concrete structures. This app... more In this paper, we propose a new methodology for crack monitoring in concrete structures. This approach is based on a n this paper, we propose a new methodology for monitoring cracks in concrete structures. This approach is based on a multi-resolution analysis of a sample or a specimen of the studied material subjected to several types of solicitation. The image obtained by ultrasonic investigation and processing by a dedicated wavelet will be analyzed according to several scales in order to detect internal cracks and crack initiation. The ultimate goal of this work is to propose an automatic crack type identification scheme based on convolutional neural networks (CNN). In this context, crack propagation can be monitored without access to the concrete surface and the goal is to detect cracks before they are visible on the concrete surface. The key idea allowing such a performance is the combination of two major data analysis tools which are wavelets and Deep Learning. This original p...
Segmentation of brain tumor images is a major research topic in medical imaging to have a refined... more Segmentation of brain tumor images is a major research topic in medical imaging to have a refined detection and understanding of abnormal masses in the brain. This paper proposes a new segmentation method, consisting of three main steps, to detect brain lesions using magnetic resonance imaging (MRI). In the first step, the parts of the image delineating the skull bone are removed to exclude insignificant data. In the second step, which is the main contribution of this study, the particle swarm optimization (PSO) technique is applied to detect the block that contains the brain lesions. The fitness function, used to determine the best block among all candidate blocks, is based on a two-way fixed-effects analysis of variance (ANOVA). In the last step of the algorithm, the K-means segmentation method is used in the lesion block to classify it as tumor or not. A thorough evaluation of the proposed algorithm is performed using the MRI database provided by the Kouba imaging center in Algie...
Engineering, Technology & Applied Science Research
In this paper, a new Visual Object Tracking (VOT) approach is proposed to overcome the main probl... more In this paper, a new Visual Object Tracking (VOT) approach is proposed to overcome the main problem the existing approaches encounter, i.e. the significant appearance changes which are mainly caused by heavy occlusion and illumination variation. The proposed approach is based on a combination of Deep Convolutional Neural Networks (DCNNs), Histogram of Oriented Gradient (HOG) features, and discrete wavelet packet transforms. The problem of illumination variation is solved by incorporating the coefficients of the image discrete wavelet packet transform instead of the image template to handle the case of images with high saturation in the input of the used CNN, whereas the inverse discrete wavelet packet transforms are used at the output for extracting the CNN features. By combining four learned correlation filters with the convolutional features, the target location is deduced using multichannel correlation maps at the CNN output. On the other side, the maximum value of the resulting ...
Evolutionary Intelligence
Detecting changes in multiple images of the same scene has recently seen increased interest due t... more Detecting changes in multiple images of the same scene has recently seen increased interest due to the many contemporary applications including smart security systems, smart homes, remote sensing, surveillance, medical diagnosis, weather forecasting, speed and distance measurement, post-disaster forensics and much more. These applications differ in the scale, nature, and speed of change. This paper presents an application of image processing techniques to implement a real-time change detection system. Change is identified by comparing the RGB representation of two consecutive frames captured in real-time. The detection threshold can be controlled to account for various luminance levels. The comparison result is passed through a filter before decision making to reduce false positives, especially at lower luminance conditions. The system is implemented with a MATLAB Graphical User interface with several controls to manage its operation and performance.
This paper presents the processing and analysis of ECG signals. The study is based on wavelet tra... more This paper presents the processing and analysis of ECG signals. The study is based on wavelet transform and uses exclusively the MATLAB environment. This study includes removing Baseline wander and further de-noising through wavelet transform and metrics such as signal-to noise ratio (SNR), Peak signal-to-noise ratio (PSNR) and the mean squared error (MSE) are used to assess the efficiency of the de-noising techniques. Feature extraction is subsequently performed whereby signal features such as heart rate, rise and fall levels are extracted and the QRS complex was detected which helped in classifying the ECG signal. The classification is the last step in the analysis of the ECG signals and it is shown that these are successfully classified as Normal rhythm or Abnormal rhythm. The final result proved the adequacy of using wavelet transform for the analysis of ECG signals.
Magnetic Resonance Imaging is a powerful technique that helps in the diagnosis of various medical... more Magnetic Resonance Imaging is a powerful technique that helps in the diagnosis of various medical conditions. Detection of brain abnormalities, such as brain tumors, in brain MRI images are considered in this work. These images are often corrupted by noise from various sources. In this work, MRI brain images with various abnormalities are pre-processed, enhanced , then classified to yield an efficient diagnosis tool that could help medical practitioners in identifying abnormal brain lesions . The work is based on the use of the Discrete Wavelet Transforms (DWT) along with thresholding techniques for efficient noise removal, followed by edge detection and threshold segmentation of the denoised images prior to the extraction of the enhanced image features through the use of morphological operations. The images are finally classified using a Support Vector scheme with a radial basis function kernel. The performance of the classifier is evaluated and the results of the classification sh...
Frattura ed Integrità Strutturale, 2021
This paper proposes an efficient methodology to monitor the formation of cracks in concrete after... more This paper proposes an efficient methodology to monitor the formation of cracks in concrete after non-destructive ultrasonic testing of a structure. The objective is to be able to automatically detect the initiation of cracks early enough, i.e. well before they are visible on the concrete surface, in order to implement adequate maintenance actions on civil engineering structures. The key element of this original approach is the wavelet-based multiresolution analysis of the ultrasonic signal received from a sample or a specimen of the studied material subjected to several types of solicitation. This analysis is finally coupled to an automatic identification scheme of the types of cracks based on artificial neural networks (ANNs), and in particular deep learning by convolutional neural networks (CNNs); a technology today at the cutting edge of machine learning, in particular for all applications of pattern recognition. Wavelet-based multiresolution analysis does not add any value in d...
Electronics, 2021
In this paper, we propose a new methodology for crack detection and monitoring in concrete struct... more In this paper, we propose a new methodology for crack detection and monitoring in concrete structures. This approach is based on a multiresolution analysis of a sample or a specimen of concrete material subjected to several types of solicitation. The image obtained by ultrasonic investigation and processed by a customized wavelet is analyzed at various scales in order to detect internal cracks and crack initiation. The ultimate objective of this work is to propose an automatic crack type identification scheme based on convolutional neural networks (CNN). In this context, crack propagation can be monitored without access to the concrete surface and the goal is to detect cracks before they are visible. This is achieved through the combination of two major data analysis tools which are wavelets and deep learning. This original procedure is shown to yield a high accuracy close to 90%. In order to evaluate the performance of the proposed CNN architectures, we also used an open access dat...
International Journal of Modelling and Simulation, 2021
ABSTRACT This article proposes two optimal controllers that stabilize the altitude, attitude, hea... more ABSTRACT This article proposes two optimal controllers that stabilize the altitude, attitude, heading, and position of the quadcopter in space. The first optimal control method consists of computing the proportional controller gain by minimizing the error between measured and the desired feature vector values, as opposed to the classical visual servoing (VS) scheme that requires a known value of the proportional controller gain which is often chosen empirically. The second control method relies on the optimal tuning of PD controller gains applied on the altitude, attitude, and position loops. This, however, may lead to a convergence problem, which is overcome by the use of an optimization technique, the bat algorithm (BA). This latter is an efficient metaheuristic algorithm that has been successfully used in many optimization problems. Several simulations are run in MATLAB, in which visual servo control of the Vertical Takeof and Landing (VTOL) type of Unmanned Aerial Vehicles (UAVs), known as the quadcopter, is implemented. The obtained results show that the Bat algorithm renders the classical PD controller adaptive and provides a high flexibility in tracking the UAV trajectory even in the presence of disturbances. It also guarantees the stability and accuracy of the optimal, minimal time, vision-based controller.
International Journal of Electrical and Computer Engineering (IJECE), 2017
Magnetic Resonance Imaging is a powerful technique that helps in the diagnosis of various medical... more Magnetic Resonance Imaging is a powerful technique that helps in the diagnosis of various medical conditions. MRI Image pre-processing followed by detection of brain abnormalities, such as brain tumors, are considered in this work. These images are often corrupted by noise from various sources. The Discrete Wavelet Transforms (DWT) with details thresholding is used for efficient noise removal followed by edge detection and threshold segmentation of the denoised images. Segmented image features are then extracted using morphological operations. These features are finally used to train an improved Support Vector Machine classifier that uses a Gausssian radial basis function kernel. The performance of the classifier is evaluated and the results of the classification show that the proposed scheme accurately distinguishes normal brain images from the abnormal ones and benign lesions from malignant tumours. The accuracy of the classification is shown to be 100% which is superior to the re...
International Journal of Electrical and Computer Engineering (IJECE), 2019
Due to the dramatic growth in the use of Wireless Sensor Network (WSN) applications ranging from ... more Due to the dramatic growth in the use of Wireless Sensor Network (WSN) applications ranging from environment and habitat monitoring to tracking and surveillance, network research in WSN protocols has been very active in the last decade. With battery-powered sensors operating in unattended environments, energy conservation becomes the key technique for improving WSN lifetimes. WSN Medium Access Control (MAC) protocols address energy awareness and reduced duty cycles. The focus of this study is to investigate, through simulation, the effect of variations in various factors that influence the performance results of WSNs. Using MiXiM framework with OMNeT++ simulator, this simulation study proposes modifications in Crankshaft MAC protocol in order to improve its performance. The impact of duration and number of slots, degree of connectivity among the nodes, mobility speed and mobility update interval and also, the impact of sending data packets without preambles are investigated. Based o...
Applied Intelligence, 2018
The electrocardiogram is the recording of the electrical activity of the heart by a device extern... more The electrocardiogram is the recording of the electrical activity of the heart by a device external to the body in a non-invasive way. In this paper real ECG records provided by the MIT-BIH Arrhythmia Database are used to build an efficient mechanism for detecting abnormalities in the ECG records. Prior to the detection, Butterworth and FIR filters are used in the pre-processing stage to eliminate any interference while maintaining the useful information within the signal. Detection of abnormal patterns related to Heartbeat along with the identification of other heart diseases such as AV blockage and Ventricular Fibrillation are implemented. Results of ECG signal pre-processing and abnormal pattern detection are obtained and demonstrate the suitability of the selected filtering techniques and the efficiency of the detection mechanisms.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
This paper presents the simulation of an ECG signal and the study and analysis of the ECG signal ... more This paper presents the simulation of an ECG signal and the study and analysis of the ECG signal using Wavelet transform in MATLAB. This study includes the generation as well as the simulation of an ECG signal, processing an ECG signal , and last but not least, analysing the ECG signal for the purpose of detecting heart beat-related abnormalities. With the help of MATLAB’s toolboxes and its built-in functions, real time ECG signals can be generated with more precision and accessibility. Wavelet decomposition is used for the removal of noise from the generated signals prior to further analysis . Simulation results are presented to illustrate the adequacy of using Daubechies wavelets for the analysis of ECG signals.