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1. Brain Tumor Segmentation Using Generative Adversarial Networks

Author

Abid Ali, Muhammad Sharif, Ch Muhammad Shahzad Faisal, Atif Rizwan, Ghada Atteia, and Maali Alabdulhafith

Subjects
Deep learning, GAN, auto-encoder, up sampling, down sampling, transfer learning, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Deep learning has played a vital role in advancing medical research, particularly in brain tumor segmentation. Despite using numerous deep learning algorithms for this purpose, accurately and reliably segmenting brain tumors remains a significant challenge. Segmentation of precise tumors is essential for the effective treatment of brain diseases. While deep learning offers a range of algorithms for segmentation, they still face limitations when analyzing medical images due to the variations in tumor shape, size, and location. This study proposes a deep learning approach combining a Generative Adversarial Network (GAN) with transfer learning and auto-encoder techniques to enhance brain tumor segmentation. The GAN incorporates a generator and discriminator to generate superior segmentation outcomes. In the generator, we applied downsampling and upsampling for tumor segmentation. In addition, an auto-encoder is applied in which the encoder retains as much information as possible and then the decoder with those encodings reconstructs the image. The transfer learning technique is applied at the bottleneck using the DenseNet model. Combining auto-encoder techniques with transfer learning methodologies in GANs feature learning is enhanced, training time is reduced, and stability is increased. In this work, we enhanced the accuracy of brain tumor segmentation and even achieved better results for tumors having small sizes. We train and evaluate our proposed model using the publicly available BraTS 2021 dataset. The experimental result shows a dice score of 0.94 for the whole tumor, 0.86 for the tumor core, and 0.82 for the enhancing tumor. It is also shown that we achieve 2% to 4% higher accuracy than other methods.

Published
2024
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2. JusticeAI: A Large Language Models Inspired Collaborative and Cross-Domain Multimodal System for Automatic Judicial Rulings in Smart Courts

Author

Nagwan Abdel Samee, Maali Alabdulhafith, Syed Muhammad Ahmed Hassan Shah, and Atif Rizwan

Subjects
Natural language processing, multimodal networks, smart courts, deep learning, transformers, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

There has been a significant amount of attention in recent years toward the utilization of artificial intelligence (AI) in the realm of legal decision-making. This growing pattern reveals a higher interest among academics and legal professionals in utilizing AI technologies to enhance a number of legal system components. Artificial intelligence (AI) tools, such as machine learning and natural language processing, possess the capacity to analyze vast quantities of legal data, extract valuable insights, and facilitate decision-making processes. The primary aim of this study is to develop a sophisticated framework for judicial decision-making that incorporates methodologies from artificial intelligence and utilizes the dataset from the European Court of Human Rights (ECHR). The utilization of this methodology holds promise in improving the decision-making procedures of legal professionals and reducing the laborious task of manually analyzing legal documents. As a result, this can lead to the facilitation of more accurate predictions of court rulings. Our research introduces a hybrid ensemble model designed specifically for smart court rulings. This innovative approach harnesses the benefits of pre-trained embeddings and large language models to accurately predict court decisions. By utilizing the power of pre-existing embeddings and incorporating the capabilities of advanced language models, our proposed model demonstrates enhanced predictive accuracy and efficiency in the context of court rulings. We also focus on the models’ feasible interpretability and highlight their ability to determine key factors in legal decision-making. We attain a notably high accuracy score of around 83%. Our research illuminates how large language models (LLMs) and advanced deep learning techniques can be utilized to predict legal outcomes.

Published
2024
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3. Implementing a Transfer Learning for User Behavior Analysis and Prediction Using Preference-Dependent Model

Author

Maali Alabdulhafith, Salwa Othmen, Ayman Alfahid, Chahira Lhioui, Ghulam Abbas, Rim Hamdaoui, Wael Mobarak, and Yasser Aboelmagd

Subjects
Behavior analysis, information processing, data analysis, transfer learning, user preferences, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

The modelling and forecasting of personal conduct depend on the records that individuals contribute at some stage in the shared network. It is essential to conduct this analysis to forecast the pursuits, feelings, and personal options, ultimately resulting in a development in service efficiency. However, to get accurate predictions, it’s far essential to triumph over the mission of information segregation and retaining precision in evaluation. This paper offers the preference-based predictive behaviour analysis (PPBA) approach to address this problem. This approach aims to achieve the best possible accuracy stage in the desired identity. A study primarily based on analysis is done on mutated and segregated data in the proposed methodology, which uses transfer learning. A more sophisticated understanding of consumer options is made possible through the system of segregation, which is completed by studying desire deviations from the attitude of many inputs. Diverse mutations in deviation sites are recognized during the learning method, which aligns options for various statistics. State validations are completed primarily based on an individual’s preceding options and any novel deviations observed inside the present state of affairs. This involves figuring out novel deviations from preceding behaviours, which can be rooted in various person options, which, in the long run, results in the refinement of user conduct prediction and modelling across quite a few applications primarily based on social networks. The proposed PPBA achieves 14.82% high detection accuracy for different input values, 9.41% less analysis time, 7.49% less false positives, 9.5% less complexity, and 10.34% high preference ratio.

Published
2024
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4. A Modified Bonobo Optimizer With Its Application in Solving Engineering Design Problems

Author

Maali Alabdulhafith, Harshit Batra, Nagwan M. Abdel Samee, Mohammed Azmi Al-Betar, Ammar Almomani, Davut Izci, Serdar Ekinci, and Fatma A. Hashim

Subjects
INDEX TERMS Metaheuristics, nature inspired algorithms, modified bonobo optimizer, swarm intelligence, optimization, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

This paper presents a modified bonobo optimizer (MBO) that integrates the Gaussian local mutation, restart strategy, and random contraction strategy into the original bonobo optimizer (BO). BO, inspired by the unique reproductive schemes and fission-fusion social behaviors of bonobos, has previously demonstrated promising results in solving a range of optimization problems. With the new modifications, MBO seeks to improve exploration and exploitation abilities, achieving enhanced convergence speed and solution quality. The Gaussian local mutation aids in fine-tuning solutions by introducing localized variations, the restart strategy provides a mechanism to escape potential local optima, while the random contraction strategy ensures better global search capabilities. The enhanced MBO’s performance is critically assessed on the 10 and 100-dimensional CEC 2017 and 10 and 20-dimensional CEC 2022 benchmark suites, along with seven engineering optimization problems, including cantilever beam design, industrial refrigeration system design, welded beam design, speed reducer design, pressure vessel design, multi-product batch plant design, and three-bar truss design. The MBO algorithm exhibits significant improvements in optimization performance, evidenced by highly significant p-values (as low as 1.25E-11) in the Wilcoxon’s Signed Rank Test. Preliminary results indicate that the MBO exhibits a marked improvement in both solution accuracy and robustness over its predecessor and other state-of-the-art optimization algorithms such as original bonobo optimizer, sand cat swarm optimization, Chernobyl disaster optimizer, driving training-based optimization, Harris hawk optimizer, Archimedes optimization algorithm, smell agent optimizer, grasshopper optimization algorithm, particle swarm optimization, hybrid sine cosine algorithm with differential evolution, modified capuchin search algorithm, liver cancer algorithm, and modified chameleon swarm algorithm. The algorithm’s robust performance can be attributed to its accelerated convergence rate, stability across diverse functions, good exploration-exploitation behavior, and adaptability to high-dimensional and complex solution spaces. The systematic enhancement of proposed algorithm’s convergence capabilities positions it as a reliable and efficient tool for addressing challenging engineering optimization problems.

Published
2024
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5. Arabic Sentiment Analysis and Sarcasm Detection Using Probabilistic Projections-Based Variational Switch Transformer

Author

S. Muhammad Ahmed Hassan Shah, Syed Faizan Hussain Shah, Asad Ullah, Atif Rizwan, Ghada Atteia, and Maali Alabdulhafith

Subjects
Text classification, Arabic sarcasm detection, Arabic sentiment analysis, switch transformer, probabilistic projections, variational expert routing, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Text classification is a common task in natural language processing (NLP), where the objective is to assign predefined categories or labels to a given text. Detecting sarcasm and classifying sentiment and dialect in NLP has practical applications, including spam detection, topic classification, and sentiment analysis. However, sarcasm and sentimental expressions, such as irony, humor, or criticism, can be difficult to identify through traditional NLP methods due to their implicit nature. To address this, we propose a Modified Switch Transformer (MST) for detecting sarcasm and classifying sentiment and dialect in Arabic text data. Our approach includes two key contributions: Variational Enmesh Expert’s Routing ( $VE_{e}R$ ) and Probabilistic Projections ( $P_{\phi} $ ). The switch transformer model incorporates probabilistic projections using a Variational Spatial Gated Unit-MLP to enhance the embedding generation mechanism. This updated mechanism introduces a variational aspect, providing dynamic control over the flow of information in the network, in contrast to the simpler embedding generation phase used in the original switch transformer. Moreover, we incorporate Variational Enmesh Expert’s Routing, which utilizes a hierarchical set of Variational experts, where each expert is a small and variational-directed acyclic graph network. The $VE_{e}R$ routing technique allows the network to dynamically choose which path to take at each layer based on the input, using a set of weights learned during training to determine the best route for a given input. Instead of optimizing route paths deterministically, we utilize Variational Inference and model each route as a random variable from a distribution. Our study evaluates the effectiveness of the Modified Switch Transformer (MST) model on the ArSarcasm Dataset, which includes Arabic language data related to sarcasm, dialect, and sentiments. We compare the performance of our proposed model with existing state-of-the-art models in the literature. The results show that the switch transformer outperforms other models in detecting sarcasm and also performs well in classifying sentiment and dialect.

Published
2023
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6. A Clinical Decision Support System for Edge/Cloud ICU Readmission Model Based on Particle Swarm Optimization, Ensemble Machine Learning, and Explainable Artificial Intelligence

Author

Maali Alabdulhafith, Hager Saleh, Hela Elmannai, Zainab Hassan Ali, Shaker El-Sappagh, Jong-Wan Hu, and Nora El-Rashidy

Subjects
Machine learning, ensemble learning, stacking ensemble learning, ICU readmission rate, explainable artificial intelligence, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

ICU readmission is usually associated with an increased number of hospital death. Predicting readmission helps to reduce such risks by avoiding early discharge, providing appropriate intervention, and planning for patient placement after ICU discharge. Unfortunately, ICU scores such as the simplified acute physiology score (SAPS) and Acute Physiology and Chronic Health (APACHE) could help predict mortality or evaluate illness severity. Still, it is ineffective in predicting ICU readmission. This study introduces a clinical monitoring fog-computing-based system for remote prognosis and monitoring of intensive care patients. This proposed monitoring system uses the advantages of machine learning (ML) approaches for generating a real-time alert signal to doctors for supplying e-healthcare, accelerating decision-making, and monitoring and controlling health systems. The proposed system includes three main layers. First, the data acquisition layer, in which we collect the vital signs and lab tests of the patient’s health conditions in real-time. Then, the fog computing layer processes. The results are then sent to the cloud layer, which offers sizable storage space for patient healthcare. Demographic data, lab tests, and vital signs are aggregated from the MIMIC III dataset for 10,465 patients. Feature selection methods: Genetic algorithm (GA) and practical swarm optimization (PSO) are used to choose the optimal feature subset from detests. Moreover, Different traditional ML models, ensemble learning models, and the proposed stacking models are applied to full features and selected features to predict readmission after 30 days of ICU discharge. The proposed stacking models recorded the highest performance compared to other models. The proposed stacking ensemble model with selected features by POS achieved promising results (accuracy = 98.42, precision = 98.42, recall = 98.42, and F1-Score = 98.42), compared to full features and selected features. We also, provide model explanations to ensure efficiency, effectiveness, and trust in the developed model through local and global explanations.

Published
2023
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7. An Ontology-Based Approach to Reduce the Negative Impact of Code Smells in Software Development Projects

Author

Ivian L. Castellano, Gilberto Fernando Castro Aguilar, Nemury Silega, Tahir Kamal, Mehdhar S. A. M. Al-Gaashani, Nagwan Abdel Samee, and Maali Alabdulhafith

Subjects
Code smells, knowledge representation, ontology, reasoning, software quality, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

The quality of software systems may be seriously impacted by specific types of source code anomalies. For example, poor programming practices result in Code Smells (CSs), which are a specific type of source code anomalies. They lead to architectural problems that consequently impact some significant software quality attributes, such as maintainability, portability, and reuse. To reduce the risk of introducing CSs and alleviate their consequences, the knowledge and skills of developers and architects is essential. On the other hand, ontologies, which are an artificial intelligence technique, have been used as a solution to deal with different software engineering challenges. Hence, the aim of this paper is to describe an ontological approach to representing and analyzing code smells. Since ontologies are a formal language based on description logics, this approach may contribute to formally analyzing the information about code smells, for example, to detect inconsistencies or infer new knowledge with the support of a reasoner. In addition, this proposal may support the training of software developers by providing the most relevant information on code smells. This ontology can also be a means of representing the knowledge on CSs from different sources (documents in natural language, relational databases, HTML documents, etc.). Therefore, it could be a valuable knowledge base to support the struggle of software developers and architects either to avoid CSs or to detect and remove them. The ontology was developed following a sound methodology. The well-known tool Protégé was used to manage the ontology and it was validated by using different techniques. An experiment was conducted to demonstrate the applicability of the ontology and evaluate its impact on speeding up the analysis of CSs.

Published
2023
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8. LogRF: An Approach to Human Pose Estimation Using Skeleton Landmarks for Physiotherapy Fitness Exercise Correction

Author

Ali Raza, Azam Mehmood Qadri, Iqra Akhtar, Nagwan Abdel Samee, and Maali Alabdulhafith

Subjects
Machine learning, deep learning, human pose, gesture estimation, physiotherapy, skeleton landmarks, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Human pose and gesture estimation are crucial in correcting physiotherapy fitness exercises. In recent years, advancements in computer vision and machine learning approaches have led to the development of sophisticated pose estimation models that accurately track and analyze human movements in real time. This technology enables physiotherapists and fitness trainers to gain valuable insights into their client’s exercise forms and techniques, facilitating more effective exercise corrections and personalized training regimens. This research aims to propose an efficient artificial intelligence method for human pose estimation during physiotherapy fitness exercises. We utilized a multi-class exercise dataset based on human skeleton movement points to conduct our experimental research. The dataset comprises 133 features derived from human skeleton movements during various exercises, resulting in high feature dimensionality that affects the performance of human pose estimation with machine learning and deep learning methods. We have introduced a novel Logistic regression Recursive Feature elimination (LogRF) method for feature selection. Extensive experiments demonstrate that using the top twenty selected features, the random forest method outperformed state-of-the-art studies with a high-performance score of 0.998. The performance of each applied method is validated through a k-fold approach and further enhanced using hyperparameter tuning. Our proposed study assists specialists in identifying and addressing potential biomechanical issues, improper postures, and incorrect movement patterns, which are essential for injury prevention and optimizing exercise outcomes. Furthermore, this study enhances the capabilities of remote monitoring and guidance capabilities, allowing physiotherapists to support their patient’s progress with prescribed exercises continually.

Published
2023
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18. On the Impact of SSDF Attacks in Hard Combination Schemes in Cognitive Radio Networks

Author

Younes Bouzegag, D. Teguig, Said Sadoudi, and Abdelmadjid Maali

Subjects
business.industry, Computer science, 05 social sciences, 050801 communication & media studies, Radio spectrum, symbols.namesake, 0508 media and communications, Cognitive radio, 0502 economics and business, symbols, 050211 marketing, False alarm, Rayleigh scattering, business, Energy (signal processing), Computer network
Abstract

One of the critical threats menacing the Cooperative Spectrum Sensing (CSS) in Cognitive Radio Networks (CRNs) is the Spectrum Sensing Data Falsification (SSDF) reports, which can deceive the decision of Fusion Center (FC) about the Primary User (PU) spectrum accessibility. In CSS, each CR user performs Energy Detection (ED) technique to detect the status of licensed frequency bands of the PU. This paper investigates the performance of different hard-decision fusion schemes (OR-rule, AND-rule, and MAJORITY-rule) in the presence of Always Yes and Always No Malicious User (AYMU and ANMU) over Rayleigh and Gaussian channels. More precisely, comparative study is conducted to evaluate the impact of such malicious users in CSS on the performance of various hard data combining rules in terms of miss detection and false alarm probabilities. Furthermore, computer simulations are carried out to show that the hard-decision fusion scheme with MAJORITY-rule is the best among hard-decision combination under AYMU attacks, OR-rule has the best detection performance under ANMU.

Published
2020
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19. Adaptive Acquisition of PN Code using Automatic Censoring CFAR Outlier Detection in Multipath Fading Mobile Channels

Author

Mahamed Hamadouche, Abdelhak Nasrallah, Genevieve Baudoin, Abdelhafid Jouini, Abdelmadjid Maali, and Mustapha Benssalah

Subjects
Computer science, 05 social sciences, Monte Carlo method, 050801 communication & media studies, Constant false alarm rate, 0508 media and communications, Pseudorandom noise, Censoring (clinical trials), 0502 economics and business, Outlier, 050211 marketing, Anomaly detection, Median absolute deviation, Algorithm, Multipath propagation, Communication channel
Abstract

This paper describes a robust method of auto-adaptive pseudo noise (PN) code acquisition for mobile systems, when communicating in multipath environment. The proposed method is built on the Constant False Alarm Rate (CFAR) algorithm, which is referred to here as Median Absolute Deviation (MAD)-CFAR. The new method does not need any prior information of the background situations, and employs the Median Absolute Deviation (MAD) test to detect the multipath signals (outliers) in the ranked cells for the full reference window. Once these multipath signals are automatically identified and censored, the remaining ranked cells are taken to form a homogeneous reference window (HRW) free of outliers to calculate the adaptive threshold. Monte Carlo simulations show that the new approach has a good detection performance, while competitors present a poor performance to varying degrees. Thus proving the efficiency and superiority of the new method in a multipath environment.

Published
2020
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20. Respiration Rate Estimation Using IR-UWB Radar Based on the Second Maximum Eigenvalues

Author

Ibrahim Kakouche, Abdelmadjid Maali, Mohamed Salah Azzaz, Ammar Mesloub, Mohamed Saad, and Djemal Teguig

Subjects
Computer science, 05 social sciences, Fast Fourier transform, 050801 communication & media studies, Signal, law.invention, Matrix (mathematics), 0508 media and communications, Signal-to-noise ratio, law, 0502 economics and business, Singular value decomposition, 050211 marketing, Radar, Algorithm, Eigenvalues and eigenvectors, Sign (mathematics)
Abstract

Impulse Radio Ultra-Wide-Band (IR-UWB) radar is a promising technology for contact-less sensing applications, such as Vital Sign (VS) detection of human body. This paper presents a new approach for Respiration Rate (RR) estimation of a stationary person. The particularity of this approach is that does not require any pre-processing techniques. It is based on the estimation of the Second Maximum Eigenvalue (SME) using Singular Value Decomposition (SVD). To extract the VS signal, a packet of eigenvalues is acquired using a moving matrix window over the slow time direction of the radar data. Then applying a Fast Fourier Transform (FFT), the respiration rate can be easily estimated. Experiments are performed using a P440 (IR-UWB) radar. The results are encouraging and show that the proposed method effectively detects the VS with high accuracy even in low Signal to Noise Ratio (SNR) condition, such as in through the wall human detection scenario.

Published
2020
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21. An Experimental Platform of Impulse UWB Radar for Through-Wall Imaging based on FPGA

Author

Mohamed Salah Azzaz, Mohamed Saad, Ibrahim Kakouche, and Abdelmadjid Maali

Subjects
business.industry, Computer science, 05 social sciences, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 050801 communication & media studies, Through wall imaging, Impulse (physics), law.invention, 0508 media and communications, law, 0502 economics and business, 050211 marketing, Radar, business, Field-programmable gate array, Computer hardware
Abstract

In this paper, we present a new development architecture of impulse UWB (Ultra-Wide-Band) radar, this radar is geared toward to see through-wall imaging which is able to scan automatically the scene by using SP8T switch that facilitates obtain a 2D images of person with high resolution in cross-range direction. The experiment to see through the wall is achieved based on RF components, FPGA (Field Programmable Gate Array) board for UWB signal generation and high sampling oscilloscope for signal acquisition. Experimental results are encouraging and show that the system can obtain images with a high resolution of the person behind a wall in different locations.

Published
2020
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22. FPGA HW/SW Codesign Approach for Real-time Image Processing Using HLS

Author

Abdelmadjd Maali, Mohamed Salah Azzaz, Hocine Hamil, Ibrahim Kakouche, Redouane Kaibou, and Mohamed Saad

Subjects
Ethernet, business.industry, Computer science, 05 social sciences, 050801 communication & media studies, Image processing, Video processing, law.invention, Microprocessor, 0508 media and communications, Software, law, High-level synthesis, Embedded system, 0502 economics and business, 050211 marketing, Field-programmable gate array, business, MATLAB, computer, computer.programming_language
Abstract

Real-time constraint is one of the most common challenges found in many critical embedded applications, namely image and video processing. However, software tools such as Matlab and general purpose microprocessor are not suitable for deals with such a problem. Recently, FPGA reconfigurable circuit with its HLS software tools is become a very promising technology to be widely used in many applications encompassing all aspects and requirements of embedded system. This paper presents the implementation of morphological image operation including dilation, erosion and linear filtering. The implementation method is based on Hardware/Software (HW/SW) codesign approach using High Level Synthesis (HLS) tools, Xilinx Vivado 15.2 and SDK 15.2. Zedboard FPGA platform with Zynq device is used for this work in which is connected to a PC through an Ethernet link. Captured image by Webcam is transmitted to the FPGA for processing and will be then returned to the PC to be displayed on the IHM interface, in real-time. Experimental results demonstrate the feasibility of the proposed approach and it can be extended for other applications.

Published
2020
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28. Design and FPGA real-time implementation of PWM and PPM modulation for Ultra Wide Band applications

Author

Zahia Zidelmal, Djaffar Ould Abdeslam, Hocine Hamil, Samir Sakhi, Abdelmadjid Maali, and Mohamed Salah Azzaz

Subjects
Computer science, business.industry, Analog-to-digital converter, Ultra-wideband, law.invention, Transmission (telecommunications), law, VHDL, Electronic engineering, Wireless, Field-programmable gate array, business, Electrical efficiency, computer, Pulse-width modulation, computer.programming_language
Abstract

Because of its high power efficiency, transmission efficiency and strong anti-jamming capability, PPM is widely used in spite the difficulty of synchronization between transmitter and receiver at the beginning of each symbol. The simplicity and the capacity of ultra wide Band initiate a lot of research activities from both academia and industry applications especially in wireless communication field. This paper discuss the FPGA implementation of PWM and PPM for Ultra Wide Band communications. The proposed design was simulated using Xilinx Vivado software tool and targeted on Nexys 4-DDR Board with Artix-7 FPGA device. Data input was generated using both DDS Compiler and Analog Discovery 2 with Pmod AD5 Analog to Digital Converter. Experimental results present a good compromise between low FPGA resources, low power consumption and high speed. These results demonstrate that our approach is competitive to other related works and can be widely used in many Ultra Wide Band (UWB) applications.

Published
2019
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29. New Measurement Method for Respiration And Heartbeat Estimation Using IR-UWB Radar

Author

Mohamed Salah Azzaz, Abdelmadjid Maali, Ammar Mesloub, and Ibrahim Kakouche

Subjects
Heartbeat, Computer science, Acoustics, 010401 analytical chemistry, 020206 networking & telecommunications, 02 engineering and technology, 01 natural sciences, Signal, Displacement (vector), 0104 chemical sciences, law.invention, Matrix (mathematics), law, Frequency domain, 0202 electrical engineering, electronic engineering, information engineering, Breathing, Radar, Reliability (statistics)
Abstract

In this work, the feasibility of using Delay And Sum (DAS) method in frequency domain and Variational Mode Decomposition algorithm (VMD) for respiration rate And heartbeat estimation using Impulse Radio Ultra-Wideband (IR-UWB) radar is demonstrated. The proposed method analyses the velocity of the chest displacement which is associated with breathing and heartbeat of human body. It based on solving two problems : velocity estimation and signal decomposition. The received matrix data is divided into a set of Coherent Processing Integration (CPI) times in slow time direction. To extract the vital signs signals, DAS method in frequency domain is adopted for IR-UWB radar to estimate the velocity of chest movement at each CPI. VMD algorithm is then applied to separate the Heartbeat signal from the respiratory signal. Experimental results proved the viability and the reliability of the proposed approach for long distances and in through the wall condition.

Published
2019
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30. Thermodynamic analysis of ORC solar power plant operating in southern Tunisia

Author

Tahar Khir, Ammar Ben Brahim, Rafika Maali, and Vincent Lemort

Subjects
Organic Rankine cycle, Thermal efficiency, Work (thermodynamics), business.industry, 020209 energy, Thermal power station, 02 engineering and technology, Solver, Solar energy, Power (physics), 020401 chemical engineering, 0202 electrical engineering, electronic engineering, information engineering, Environmental science, Working fluid, 0204 chemical engineering, Process engineering, business
Abstract

The study focuses on the design of a thermal power plant using solar energy and operating on an organic Rankine cycle. A mathematical model has been developed using Engineering Equation Solver (EES) to determine the cycle performance. Model validation was made considering experimental data. Since working fluid choice is an essential step in the study of the ORC system a comparison between the R245fa fluid and n-pentane is made. It has shown that the suitable fluid is the n-pentane. By the following, a solar field study is elaborated. This has shown that southern Tunisia has significant solar potential. The results showed that the tracking system improved the net power generated and to improve the overall thermal efficiency it is preferred to work with a low condensing temperature.

Published
2019
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31. A New Methodology Of Auto-Adaptive PN Code Acquisition For Spread Spectrum Communications Environment

Author

Mrhamed Hamadouche, Abdelhafid Jouini, Abdelmadjid Maali, Genevieve Baudoin, Ecole Militaire Polytechnique [Alger] (EMP), Ministère de l'Enseignement Supérieur et de la Recherche Scientifique [Algérie] (MESRS)-Ministère de la Défense Nationale [Algérie], Electronique, Systèmes de communication et Microsystèmes (ESYCOM), Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Université Paris-Est Marne-la-Vallée (UPEM)-ESIEE Paris, Institute of Electrical and Electronic Engineering [Boumerdès] (IEEE), Université M'Hamed Bougara Boumerdes (UMBB), and Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Université Paris-Est Marne-la-Vallée (UPEM)-ESIEE Paris

Subjects
Computer science, Maximum likelihood, 020208 electrical & electronic engineering, Order statistic, Detector, Real-time computing, 020206 networking & telecommunications, 02 engineering and technology, Synchronization, Constant false alarm rate, Spread spectrum, [SPI]Engineering Sciences [physics], Noise, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Pseudorandom noise, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, ComputingMilieux_MISCELLANEOUS, Multipath propagation
Abstract

In this paper, we propose a robust and novel approach for auto-adaptive pseudo noise PN code serial acquisition based on Order Statistic Constant False Alarm Rate (OS-CFAR) for mobile communication systems over multipath environment. The new approach is called Automatic Selection Order Statistic (ASOS)-CFAR. It uses all available information based on the ML estimation to identify automatically the ranked cell from ranked data cells that indicates the discrimination between homogeneous and non-homogeneous environments, in order to use it to compute the adaptive threshold. Monte- Carlo simulations in a multipath environment indicate that the proposed approach has a high ability to sense the mobile communications environments, thus getting to produce a correct detection decision, without requiring any prior knowledge regarding the number of multipath signals.

Published
2018
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32. Software Defined Radio Implementation of Generalized Chaotic Generator

Author

Mohamed Salah Azzaz, I. Behloul, Said Sadoudi, D. Teguig, Ammar Mesloub, H. Benimam, and Abdelmadjid Maali

Subjects
Generator (computer programming), biology, business.industry, Computer science, Chaotic, Software-defined radio, biology.organism_classification, Communications system, Nonlinear Sciences::Chaotic Dynamics, CHAOS (operating system), Chen, Software, Computer engineering, business, Block (data storage)
Abstract

In this paper, we present a generalized model for chaos generation using software defined radio. This model allows generating chaotic signals such as: Lorenz, Chen and Lu. We have used Software Defined Radio (SDR) to implement this generalized model using GNURadio. A new chaos block has been created and added to GNURadio-Companion (GRC) to be used as a signal source making this generator suitable for secure real-time transmissions. An experimental validation is carried out using SDRs hardware to show the effectiveness of this new block. The aim of this implementation is to use this chaotic generator in secure digital chaotic communication system.

Published
2018
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33. Hardware implementation of multi-scroll chaos based architecture for securing biometric templates

Author

Mohamed Salah Azzaz, Camel Tanougast, Mustapha Benssalah, and Abdelmadjid Maali

Subjects
Security analysis, Biometrics, business.industry, Computer science, Chaotic, Security token, Encryption, Grid, Computer Science::Multimedia, Entropy (information theory), business, Field-programmable gate array, Computer hardware, Computer Science::Cryptography and Security
Abstract

In spite of numerous advantages of biometrics-based personal authentication systems over traditional security systems based on token or knowledge, they are vulnerable to attacks that can decrease their security considerably. In this paper, we propose a new hardware solution to protect biometric templates such as fingerprint. The proposed scheme is based on chaotic N × N grid multi-scroll system and it is implemented on Xilinx FPGA. The hardware implementation is achieved by applying numerical solution methods in our study, we use EM (Euler Method). Simulation and experimental results show that the proposed scheme allows a low cost image encryption for embedded systems while still providing a good trade-off between performance and hardware resources. Indeed, security analysis performed to the our scheme, is strong against known different attacks, such as: brute force, statistical, differential, and entropy. Therefore, the proposed chaos-based multiscroll encryption algorithm is suitable for use in securing embedded biometric systems.

Published
2018
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34. Defining the Functional Requirements for the NFC-Based Medication Administration and Clinical Communication System

Author

Srinivas Sampalli and Maali Alabdulhafith

Subjects
Health professionals, Computer science, business.industry, Functional requirement, 02 engineering and technology, Medication administration, medicine.disease, Near field communication, Clinical communication, 03 medical and health sciences, 0302 clinical medicine, Server, Health care, 0202 electrical engineering, electronic engineering, information engineering, medicine, Wireless, 020201 artificial intelligence & image processing, 030212 general & internal medicine, Medical emergency, business
Abstract

Failure to confirm the 'five rights' of medication administration, verify contraindications, and effectively communicate with healthcare professionals are three major causes of bedside medication administration (BMA). These causes can be addressed by developing an efficient system integrated with wireless technology such as Near Field Communication (NFC). The long-term purpose of this research is to design a sufficient NFC-based system to overcome the major causes of BMA errors. Before we can design the system, we must first determine its functional requirements. Therefore, in this paper, we used theoretical and practical sources to form a clear picture of the BMA procedure. We identified the essential methods used in nursing practice to ensure safe BMA, and their limitation. Accordingly, we drew an initial technical concept of the system functions that will help us design the system.

Published
2018
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39. Energy detection versus maximum eigenvalue based detection: A comparative study

Author

Najib Boumaaz, Abdallah Soulmani, Asmaa Maali, and Hayat Semlali

Subjects
Receiver operating characteristic, Computer science, business.industry, 020302 automobile design & engineering, 02 engineering and technology, Radio spectrum, Signal-to-noise ratio, Cognitive radio, 0203 mechanical engineering, 0202 electrical engineering, electronic engineering, information engineering, Wireless, 020201 artificial intelligence & image processing, Transceiver, business, Algorithm, Energy (signal processing), Smoothing
Abstract

Cognitive radio (CR) is a form of wireless communication in which a transceiver can wisely detect communication channels that are in use and those which are not, and immediately move into vacant channels while avoiding occupied ones. In such systems, spectrum sensing (SS) is a crucial operation. It consists to detect the available frequency bands. Many spectrum sensing techniques are presented in the literature. In this paper, we present a comparative study between two Spectrum Sensing techniques: the Maximum Eigenvalue Detection (MED) and the Energy Detection (ED). The performance of these two methods is evaluated in terms of their Receiver Operating Characteristics (ROC curves) and their detection probability for different values of Signal to Noise Ratio (SNR) and of smoothing factor L. The results of this comparative study are given and discussed.

Published
2017
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44. Enhanced spectrum sensing based on Energy detection in cognitive radio network using adaptive threshold

Author

Md. Zulfikar Alom, Mohammad Morshed, Asmaa Maali, and Tapan Kumar Godder

Subjects
business.industry, Computer science, Cyclostationary process, Matched filter, 020206 networking & telecommunications, 02 engineering and technology, Radio spectrum, Noise, Signal-to-noise ratio, Cognitive radio, 0202 electrical engineering, electronic engineering, information engineering, Electronic engineering, 020201 artificial intelligence & image processing, Telecommunications, business, Energy (signal processing), Efficient energy use
Abstract

There's a huge demand for spectrum, but in conventional system radio spectrum is inefficiently used. To overcome this problem and increase array use of spectrum, cognitive radio (CR) technology concept was proposed. CR is the greatest mechanism for use spectrum efficiently. The main objective of CR is to use scarce and limited natural resources efficiently without any interference to the primary users (PUs). Among most challenging problems in cognitive radio systems is spectrum sensing concepts. Energy detection, Matched filter detection and Cyclostationary detections are most common strategies for spectrum sensing. This study has been performed based on Energy detection spectrum method. The reason for choosing Energy detection technique, it doesn't need any earlier information from the PU transmission. Also, the particular result of energy detection method degrades with lower sign to noises ratio (SNR) level signal location. General detection performance of Energy detection is highly dependent upon noise, especially while the SNR is very low for PU. Considering this issue, this paper presents an adaptive threshold model for efficient Energy detection technique to enhance the detection performance at low SNR level. Simulation outcomes show, the detection performance using offered system model is more preferable than fixed threshold at low SNR signal locations.

Published
2017
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45. On the rational stabilizability of nonlinear systems by optimal feedback control: The bilinear case

Author

Maali Zaghdoudi and Chaker Jammazi

Subjects
0209 industrial biotechnology, Feedback control, 010102 general mathematics, Bilinear interpolation, 02 engineering and technology, Nonlinear control, Optimal control, Linear-quadratic-Gaussian control, 01 natural sciences, Nonlinear system, 020901 industrial engineering & automation, Exponential stability, Control theory, Symmetric matrix, 0101 mathematics, Mathematics
Abstract

In this paper, the problem of rational stabilizability of nonlinear control system by optimal feedback laws is considered. By using Hamilton-Jacobi-Belleman approach, some sufficient conditions are derived characterizing the rational stabilizability by optimal control for every dynamical control systems. As application, we have treated the example of bilinear control systems. A static stationary and optimal feedback laws are built stabilizing bilinear systems with the decay 1/tα where α > 0.

Published
2016
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46. Spectrum sensing operation based on a real signal of FM radio: Feasibility study using a random sampling mode

Author

Asmaa Maali, Abdellilah Ghammaz, Hayat Semlali, Jean-François Diouris, Abdallah Soulmani, Najib Boumaaz, Université Cadi Ayyad [Marrakech] (UCA), Institut d'Électronique et des Technologies du numéRique (IETR), Université de Nantes (UN)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Nantes Université (NU)-Université de Rennes 1 (UR1), Université de Nantes (UN)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)

Subjects
Spectrum sensing, Computer science, Cognitive radio, 0211 other engineering and technologies, 020101 civil engineering, GNU radio, 02 engineering and technology, Energy detectors, Radio spectrum, 0201 civil engineering, [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], 021105 building & construction, Frequency modulation, Electronic engineering, Detection theory, Signal sampling, implementation, Radio receiver design, business.industry, Cognitive systems, Detector, Sampling (statistics), Open source software, Random sampling, [SPI.TRON]Engineering Sciences [physics]/Electronics, Embedded system, False alarm, business, Energy (signal processing), Signal detection
Abstract

International audience; In this paper, we are interested to the implementation of the spectrum sensing (SS) operation based on a real signal of the FM radio and using a random sampling mode. This application consists on the realization of the spectrum sensing operation based on the energy detector (ED) method combined with random sampling. The analyzed real signal is an FM radio signal captured under GNU-Radio. This application is performed in cognitive radio (CR) systems in order to analyze the occupancy of the radio frequency spectrum. The performance of this approach is evaluated in terms of its receiver operating characteristic curves (ROC curves) and in terms of the false alarm probability. © 2016 IEEE.

Published
2016
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49. Impulsive acoustic source localization via wireless sensor Imote2

Author

Hamza Louzri, Abdelmadjid Maali, Genevieve Baudoin, Communications Systems Laboratory, EMP-Algiers, Electronique, Systèmes de communication et Microsystèmes (ESYCOM), Conservatoire National des Arts et Métiers [CNAM] (CNAM), and HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Université Paris-Est Marne-la-Vallée (UPEM)-ESIEE Paris

Subjects
Engineering, business.industry, Acoustics, Acoustic source localization, Multilateration, Constant false alarm rate, [SPI]Engineering Sciences [physics], Time of arrival, Sensor array, Wireless, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Wireless sensor network, Realization (systems), ComputingMilieux_MISCELLANEOUS
Abstract

In this paper, we are interested in localizing acoustic sources such as gunfire of Automatic Pistol (AP) and Kalashnikov AK-47 via the Imote2 wireless sensor node platform, using an array of four synchronized microphones connected to ITS400 sensor board of Imote2. More particularly, this work concerns the implementation of a localization algorithm based on the TDOA (Time Difference of Arrival) between the acquired signals by the microphones. This work can be divided into two parts. The first part is dedicated to the realization of an Imote2-based platform, which allows the acquisition of acoustic signals. In the second part, we have evaluated the performance of relative TOA (Time of Arrival) estimation algorithms, where CA-CFAR (Cell Averaging Constant False Alarm Rate) algorithm gave the best results.

Published
2015
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50. An experiemental system-based analyis of humain body motions using embedded sensors

Author

F. Zohra Bouchibane, Malika Radi, Wassila Dib, Abdelmadjid Maali, and Khalida Ghanem

Subjects
Sensor system, Engineering, Wi-Fi array, business.industry, RSS, computer.file_format, Set (abstract data type), Key distribution in wireless sensor networks, Electronic engineering, Wireless, Fading, business, computer, Wireless sensor network
Abstract

Wireless sensor networks technology knows a wide spread in numerous applications and fields. In this paper, a realistic IMOTE wireless sensor system is used to evaluate the on-body propagation quality in a laboratory indoor environment. Different on-body links are investigated using a set of movements and the received signal strength (RSS) parameter is evaluated. Herein the results pertaining to the belt-back and belt head channels are presented. The slow and fast fading components are separated and modeled and the results are then analyzed.

Published
2014
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