Telecommunication

Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/3080

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3D antenna array optimization using firefly algorithm
(Université M’Hamed BOUGARA de Boumerdes : Institut de génie electrique et electronique (IGEE), 2019) Laouar, Abdelali; Recioui, A. (Supervisor)
The design of antenna arrays in a cubic geometry is presented in this project. The decision variables considered for this synthesis problem is the amplitude excitations. The synthesis process is carried out by the technique of a new type of nature-inspired global optimization methodology in the design of an optimized cubic antenna array which ensures minimum side lobes and high directivity, this new optimization method is based on the reaction of a firefly to the light of other fireflies and it is known as Firefly Algorithm (FA) a population based iterative heuristic global optimization algorithm technique, developed by Xin-She Yang, for multi-dimensional and multi-modal problems, with the potential to implement constraints on the search domain. Simulation results, detailed by using an antenna array with isotropic elements and with cubic antenna optimized by Firefly algorithm, show that side lobe level is reduced significantly in non-uniform case. Besides, the directivity is not worse than that of the uniform one.
Adaptability of applications according to the visibility offered SDN controller
(2021) Alioua, Nabil; Griche, Abdelghani; Belaidi, H. (Supervisor)
The volume of video traffic has grown considerably in recent years. This increase is pushing back the capabilities of the traditional networks of Internet Service Providers (ISPs), which results in their overuse and, consequently, the degradation of the video‘s Quality of Experience (QoE) perceived by users. In traditional networks Quality of Service (QoS) is one of the proposed solutions to overcome this problem, but it has many drawbacks because it is built on top of a fully distributed architecture that lacks broader visibility into the overall network resources. This work tries to solve this problem in the case of video streaming. To do this, we have used the great visibility provided by the SDN controller to develop a telemetry service in order to collect information about the state of the network and then provide it to the video streaming application to permit it to adapt its diffusion parameters accordingly. Finally, we moved on to the testing part where we demonstrated the effectiveness of our solution and its impact on the user‘s quality of experience. Keywords : SDN, QoS, QoE, Telemetry, Streaming.
Adaptive co-existence of OFDM and OTFS for Multi-Mobility scenarios in wireless communications
(Université M’Hamed Bougara de Boumerdes : Institut de génie electrique et electronique (IGEE), 2024) Zegrar, Hadj Mebarek; Smaili, Nesrine (Supervisor)
The rapid evolution of technology necessitates the development of new waveforms capable of handling the increasing demands of high-mobility scenarios. Orthogonal Frequency Di-vision Multiplexing (OFDM), although extensively used in current wireless communication systems, experiences significant performance degradation in highmobility environments due to large Doppler shifts and Doppler spread effects .To address these challenges, a novel waveform called Orthogonal Time Frequency Space (OTFS) has been developed, offer ingsuperior performance in highmobility scenarios by exploiting delay-Doppler diversity. However, OTFS introduces high processing complexity and does not exhibit obvious performance advantages for low-mobility users, where OFDM remains efficient and effective. There fore, there is a critical need to develop a co-existence method that leverages the strengths of both waveforms. we proposed a method to check the user’s velocity and dynamically switch between OFDM and OTFS based on user mobility. By doing so, the communication system can achieve optimal performance of Bit Error Rate (BER) versus Signal-to-Noise Ratio (SNR), maintaining robust and efficient data transmission across diverse mobility conditions.
AI-Driven intrusion detection system (IDS) for network traffic
(University of M'Hamed Bougara Boumerdes : Institute of Electrical and Electronic Engineering (IGEE), 2025) Terki, Ali; Mouhouche, Faiza
This thesis presents a real-time network intrusion detection system that integrates live flow capture via CICFlowMeter with a hybrid CNN–LSTM model. We apply Variance Inflation Factor (VIF) analysis to reduce an initial 83-feature set to 33 uncorrelated predictors, improving stability without loss of accuracy. The resulting CNN-LSTM achieves 98.75 % detection accuracy and 0.9993 AUC in benchmark fl ows, and processes live HTTP, SSH, DoS, and slowloris traffic with millisecond latency. Our work demonstrates practical deployment of machine learning–based intrusion detection system (IDS) in real networks, contributing a streamlined feature selection method and an end-to-end Python application for continuous monitoring.
Alzheimer Disease Classification Using Convolution Neural Networks and Transfer Learning
(Université M’Hamed bougara : Institute de Ginie électric et électronic, 2023) Brahimi, Kahina; Slimi, Ounissa; Cherifi, Dalila (Supervisor)
Alzheimer’s Disease (AD) is a neurological disorder which causes brain cells to die, result-ing in memory loss, language diffi culties, and impulsive or erratic behavior. In recent years the number of individuals affected has seen a rapid increase, it is estimated that up to 107 million subjects will be affected by 2050 worldwide. Early diagnosis has become crucial to improve patients care and treatment. AD diagnosis is diffi cult due to the complexity of the brain struc-ture and its pixel intensity similarity especially at its early stage. A comprehensive diagnosis must be led including clinical assessment and medical imaging, which is a process that requires the expertise of professionals including neurologists and radiologists. One of the drawbacks of medical imaging approach is the inability to detect changes in very mild impairment also known as mild cognitive impairment (MCI). Deep learning has inspired a lot of interest in recent years in tackling challenges in a variety of fi elds, including medical imaging and detecting abnormal- ities beyond human capabilities. In this work we explored classifi cation approaches of AD through two different datasets which are respectively MRI dataset and tabular dataset. First, we dealt with the tabular data for bi- nary classifi cation of AD into demented and non demented using classical machine learning algorithms namely Support Vector Machine, K Nearest Neighbor, Decision Tree, Na¨ive Bayes Gaussian, Random Forest and Logistic Regressor. The fi ndings indicate that the models ef- fectively utilize the Clinical Dementia Rating feature for AD classifi cation. Second, we dealtwith MRI dataset for multiclassifi cation of AD into Non Demented, Very Mild Demented,Mild Demented, and Moderate Demented using transfer learning models namely VGG19, Res-Net50, Xception and MobileNet. The VGG19 model gave the best performance with 98.60%testing accuracy where the other models achieved 97.35%, 86.35% and 95.50% respectively.We also proposed a custom CNN model that outperformed the transfer learning models and achieved an accuracy of 99.00%.
Analysis and design of MIMO microstrip antennas for smart grid applications
(2019) Lamri, Isam Eddine; Zeraib, Ahmed; Dehmas, M. (Supervisor)
In this work, an UWB and a Dual Band 2x2 element MIMO microstrip antennas are proposed for these applications. These antennas, printed on an FR-4 dielectric material, are fed by a microstrip line for the UWB and by a coplanar waveguide for the Dual Band structures. The structures radio electric properties including S-parameters, current distributions, radiation patterns, correlation coefficients and diversity gains were investigated using the CST electromagnetic simulator. Prototypes of the two final four-element MIMO structures have been fabricated and their S-parameters measured where an agreement is observed between simulated and measured results.
Analysis of a fractal Ultra Wide Band monopole antenna with reconfigurable notches
(2021) Abbad, Mohammed; Habtiche, Nabil; Dehmas, Mokrane
This work describes the design and analysis of a fractal Ultra-Wide Band (UWB) coplanar fractal antenna with triple reconfigurable notch rejection bands. The investigation considers three successive configurations: coplanar monopole circular UWB antenna – Fractal coplanar monopole UWB band antenna with improved bandwidth – Slotted UWB structurewith reconfigurable notches. Reconfigurability is achieved by opening / short-circuiting appropriate slots to select the rejected band(s). This study has ended with three reconfigurable notches bands covering the frequency bands: [4.50 ?????? , 5.35 ??????] , [6.30 ?????? , 7.15 ??????] and [8.15 ?????? , 8.60 ??????] including Wi-Max and WLAN applications and X band. The Developed structure was printed on a 21x25x1.63 mm 3 glass epoxy FR-4 substrate. The input reflection coefficient has been measured where an agreement was observed betweensimulated and experimental results. The simulations concerning the input reflection coefficients, current distributions and radiation patterns were carried out using the CST Microwave Studio.
Analysis of a reconfigurable multi-Band monopole antenna
(2021) Gharbi, Abdelhamid; Belgacem, Abdelmalek; Dehmas, Mokrane
This work deals with analysis of a monopole and frequency reconfigurable E-shape patch antenna using FR-4 substrate. The first step concerns modification of a published reconfigurable antenna due to difference in physical properties of the used substrate and the one at our disposal. These appropriate modifications have been performed on the antenna dimensions and on the stripline width so that the modified structure fits the original antenna band characteristics. In the second step, which concerns the main contribution of this work, a parametric analysis of the first structure focusing on the slot positions is achieved. The analysis ended up with nine (9) antenna geometrical configurations each one operating in four (4) reconfigurable modes. These antennas fulfill and satisfy various applications. One of these antenna configurations -operating in one single-band and three dual band reconfigurable modes- has been considered for further analysis. Prototypes of the modified original antenna as well as the one selected in the second part have been fabricated and their reflection coefficients in open switch positions mode measured where good agreements with simulated results have been obtained.
Analytical studies of the modernization of the IGW (Internet Gateway) platform case : optimum telecom Algérie
(2019) Hachour, Abdelghani; Khene, Loukmane hakim; Khene, Loukmane hakim; Zitouni, Abdelkader (Supervisor); Zitouni, Abdelkader (Supervisor)
The internet is an indispensable service within the OTA company both in terms of the smooth running of the company and the quality of service provided to customers, unfortunately this service encounters an important problem that lies in availability that can affect the service continuity of one of the internet access sites of the company, to solve this problem and offer an optimal quality of service to the customer, the unification of the OTA network may leads to guarantee access to the internet to all the services of the company. An internet gateway can cope with many challenges in ubiquitous network systems such as efficiency, scalability, availability and reliability issues. The unification consists of three methods, single homing, double homing, triple homing. In this report we exploit by use of simulation, the strategic position of such methods to offer several higher-level services the obtained results will determine which of the three methods will be optimum in accordance with the technical and economic approval criteria.
Antenna selection in massive MIMO using machine learning
(Université M’hamed Bougara de Boumerdes : Institut de Genie Electrique et Electronique, 2024) Cherigui, Rahma; Bouazabia, Sarah; Boutellaa, Elhocine (Supervisor)
In massive MIMO (Multiple Input Multiple Output) systems the overall performance (bit/s/Hz/cell) is significantly improved by equipping the base stations with arrays of a hundred antennas; which becomes one of its most significant challenges; economically and technically due to the high power consumption. To solve this, Antenna selection (AS) is increasingly gaining more interest, as it strategically reduces the hardware complexity while maximizing efficiency and throughput by selecting a specific subset of antennas to activate in each transmission slot. In this report, we examine the application of multi-label learning (MLL) based algorithms in AS, such as problem transformation methods, including first order binary relevance; and high order chain classification. Additionally, we investigate the Deep neural networks (DNN) based algorithms, namely Multi-Label Convolutional Neural Networks (MLCNN) and Multi-Layer Perceptron (MLP) classifier, and multi-View based algorithm. These proposed methods are rigorously evaluated based on their maximum capacity, performance and the computation time across various scenarios. Our work concludes that, in comparison with the convex relaxation based method, the Multi-view MLL achieves comparable results.
Atrial fibrillation delineation with wavelets
(2019) Khelouia, Romeyssa; Touil, Soumeya; Daamouche, Abdelhamid (supervisor)
This report proposes a method to detect and classify two types of heart beat, namely Normal (N) beats and Atrial Fibrillation (AF) beats. Each electrocardiogram (ECG) record is band-pass filtered, then segmented into beats to form the original features. After that, statistical features of the Discrete Wavelet Transform (DWT) approximation and detail coefficients of each beat constitute a feature. The RR intervals surrounding the beat are also used as features. Finally, extracted features are classified using a Support Vector Machines (SVM) classifier. The MIT-BIH atrial fibrillation and MIT-BIH arrhythmia databases are used to evaluate the performance of the proposed method. Results from extensive experimentations appear very promising, with an accuracy of 98.2723%, a sensitivity of 98.3709%, and a specificity of 97.2313%.
Building detection from high resolution remote sensing imagery
(2020) Bentaala, Ali; Boulebnane, Lokman; Daamouche, Abdelhamid
Building detection is an important task in very high-resolution remote sensing image analysis. In recent years, availability of very high-resolution images raised new challenges to building detection algorithms. In this report, we use a supervised method to detect buildings from remotely sensed images using spectral-spatial features. The morphological operations (MO), gray level co-occurrence matrix (GLCM) and Variogram techniques are used to extract the spatial features. We concatenated spatial features and spectral features, and then we fed the Support Vector Machines (SVM) classifier with the resulting vector of features. We classified the image data into two classes (Building and Non-Building) using different combinations of features. The simulation results obtained on three different images showed that our approach achieved an acceptable performance in terms of accuracy.
Classification of ECG Signals Using Deep Learning
(Université M’Hamed bougara : Institute de Ginie électric et électronic, 2023) Zanaz, Serine; Kermane, Imane; Daamouche, Abdelhamid (Supervisor)
Accurate classifi cation of electrocardiogram (ECG) signals is crucial for diagnosing cardiac conditions. In this project, our objective was to classify ECG beats into disease classes using deep learning techniques. We leveraged two primary datasets: the MIT-BIH dataset from PhysioNet and the INCART 12-lead Arrhythmia Database from St. Petersburg, providing a comprehensive basis for our classifi cation models. Our methodology involved a hybrid model combining 1D and 2D convolutional neural networks (CNNs). We applied a 1D CNN architecture to process ECG signals directly and transformed ECG beats into images for a 2D CNN architecture. By incorporating both approaches, we captured temporal and spatial information in the ECG signals. Data augmentation techniques were employed to address imbalanced data distribution and improve model performance.
Classification of multispectral image using SVM and gabor filter
(Université M’Hamed BOUGARA de Boumerdes : Institut de génie electrique et electronique (IGEE), 2018) Amara, Kheire Eddine; Blil, Mohamed Amine; Daamouche, A.(Supervisor)
The present work deals with image classification in the field of remote sensing and retinal blood vessels which is a joint venture between image processing and classification techniques. The advancement in the imagery field in terms of resolution allowed the availability of very high resolution images. Therefore, new techniques and algorithms become necessary to cope with technology. In this regard, Gabor filters have been used in a variety of image processing applications. It had a distinctive effect in improving the image and increasing its clarity and quality for further processing. In this work, the effectiveness of the Gabor filter is explored. In particular, we used Gabor features in conjunction with the SVM to classify remotely sensed data and retinal blood vessels images. The simulation results on different datasets showed that our approach is promising in the field of image classification.
Compact and full polarimetric synthetic aperture Radar imaging mode for a target characterization
(Université M’Hamed BOUGARA de Boumerdes : Institut de génie electrique et electronique (IGEE), 2019) Bouzerar, Hafidha; Mebrek, Sihem; Daamouche, Abdelhamid (supervisor)
In this master report, we intend to present a study of the polarimetric information content of CP- and FP-pol imaging modes. We compare the CP polarimetric dataset against the full quad-pol using the two dimensional (2D)scatter plots and the polarimetric decomposition techniques. In general, the co-pol components can be reproduced with relatively small error. The cross pol intensity, however, is more problematic to estimate, especially for volume scattering. From all approaches, we have found that the DoP and Eigenvalue reconstruction methods have the best results with respect to some other approaches.
A compact MIMO UWB antenna with band notched characteristicsat WLAN frequency band
(Université M’Hamed Bougara de Boumerdes : Institut de génie electrique et electronique (IGEE), 2024) Adjeroud, Nadjet; Mouhouche, Faïza (Supervisor)
This work presents a compact UWB MIMO antenna with band notch characteristics for WLAN application is designed and investigated. The proposed MIMO antenna consists of two unit cell antennas, being comprised of a Microstrip feed line and a partial ground plane, and these radiators are placed parallel to each other. A single antenna comprises an inverted Christmas tree shaped patch fed by a 50 OMicrostrip line and partial ground plane is designed for achieving UWB application. The operating frequency of the proposed single antenna is 3.5–14.5 GHz with a return loss of less than -10 dB. Subsequently, a circular split ring shaped slot is implemented in the radiating element to optimize the antenna for WLAN band rejection at 5.6-6.2GHz. Furthermore, a 2 × 1 MIMO antenna is designed by utilizing the polarization diversity technique. The two radiating elements are placed parallel and decoupling structure is placed between them to improve the isolation performance. Finally, the compact UWB MIMO antenna prototype is designed on the FR4 substrate with the overall dimensions of 20 × 39 × 1.63 mm3.The proposed UWB MIMO antenna design provides an impedance bandwidth (S11 less then -10 dB) of 130% (3.3–16 GHz) with band notch centered at 5.8GHz. The isolation of the proposed MIMO antenna is higher than -17?dB,. Results show that the proposed MIMO antenna is a good candidate for handheld devices for wireless personal-area networks application.
A Compact Quad Bands Patch Antenna And MIMO Antenna System For Wireless Communications
(Université M’Hamed bougara : Institute de Ginie électric et électronic, 2023) Dibes, Lokmane; Addar, Cherif; Mouhouche, F. (supervisor)
This work presents a compact quad band MIMO antenna for wireless communication systems that is developed and investigated. The proposed MIMO antenna structure is composed of two adjacent identical antenna elements which are fed through microstrip lines. The single element of the MIMO system is the spiral monopole antenna. The quad bands are attained by introducing circular ring slots in a circular patch antenna to get a new form of spiral antenna. The proposed single antenna has a dimensions of 14×18 mm 2 . The results show that the single antenna element operates at (1.39), (2.5), (3.63) and 4.15GHz for WiMAX and C band. It is also observed from the results that the antenna offers good radiation characteristics for the frequency band of interest. Furthermore, a 2 × 2 MIMO antenna is designed by utilizing the polarization diversity technique. The two spiral elements are placed orthogonally and an inverted L-shaped are placed between them to improve the isolation performance. Moreover, the proposed MIMO antenna operates (1.39), (2.5), (3.52) and 4.02 GHZ for WiMAX and C band applications. The isolation of the proposed antenna is higher than 15?dB, and the antenna maintains relatively stable radiation characteristics and gain, as well as a lower envelope correlation coefficient(ECC?less than 0.2).
A Compact Quad Bands Patch Antenna And MIMO Antenna System For Wireless Communications
(Université M’Hamed bougara : Institute de Ginie électric et électronic, 2023) Dibes, Lokmane; Addar, Cherif; Mouhouche, F. (supervisor)
Furthermore, a 2 × 2 MIMO antenna is designed by utilizing the polarization diversity technique. The two spiral elements are placed orthogonally and an inverted L-shaped are placed between them to improve the isolation performance. Moreover, the proposed MIMO antenna operates (1.39), (2.5), (3.52) and 4.02 GHZ for WiMAX and C band applications. The isolation of the proposed antenna is higher than 15?dB, and the antenna maintains relatively stable radiation characteristics and gain, as well as a lower envelope correlation coefficient(ECC?less than 0.2).
A compact quad element MIMO-UWB antenna with band notch characteristics and high isolation
(Université M’hamed Bougara de Boumerdes : Institut de Genie Electrique et Electronique, 2024) Chairi, Anis; Mouhouche, Faïza ( Supervisor)
This work presents a compact MIMO UWB antenna with WiMAX band-notch characteristics designed and inves- tigated. The proposed MIMO antenna comprises four unit cell antennas, each consisting of a microstrip feed line and a partial ground plane, with the radiators placed perpendicular to each other. A single antenna features a trapezoid- semicircle hybrid patch fed by a 50 O microstrip line, along with a partial ground plane, is initially designed for UWB applications, operating within 3.18–18.63 GHz. An inverted T-shaped stub is subsequently implemented to optimize the antenna for WiMAX band rejection at 3.3–3.8 GHz. Furthermore, a 2 × 2 MIMO antenna is designed utilizing the polarization diversity technique. The two radiating elements are placed orthogonally, with a decoupling structure positioned between them to improve isolation performance. Finally, the compact four-port MIMO antenna prototype is constructed on the FR4 substrate, with overall dimensions of 42 × 42 × 1.63 mm³. The proposed four-port MIMO antenna design offer s abandwidt ho f2.94–2 0GHz. The antenna’s isolation is <-1 8dB, and it maintains relatively stable radiation characteristics and gain, along with a lower envelope correlation coefficie nt(E C C<0.0055).
Comparaison between the implementation of emotion detection from Twitter Tweets using SVM and LSTM
(Université M’Hamed BOUGARA de Boumerdes : Institut de génie electrique et electronique (IGEE), 2020) Fedoul, Ibrahim Nassim Ibrahim Nassim; Bouhamadouche, Anis; Namane, Rachid (supervisor)
This report compares two di?erent Machine Learning (ML) methods used to classify five types of emotions from a twitter tweets dataset. The first approach is a classical method in Natural Language Processing (NLP), Support Vector Machine (SVM); The text data is cleaned, tokenized, and stemmed to derive fea-ture vectors using two di?erent feature extraction methods, namely Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF). The resulting feature ma-trix is fed to a non-linear SVM classifier. Conversely, the second approach is more recent; this method uses word embedding and Long Short Term Memory (LSTM) neural network. First, we convert words of similar meaning into similar feature vectors. Then, the result-ing features are fed sequentially into the LSTM. Although it has been proved in the past that the SVM is the most robust model in classifica-tion problems, it is not the case for text classification. LSTM showed a better performance compared to the SVM; between 85% and 87% for LSTM and around 82% for the SVM.