Algerian Journal of Signals and Systems

Implementation of a Broadside Switched-Beam Antenna Array Using a Butler Matrix Feed Network

Wed, 31 Dec 2025 00:00:00 +0100

This paper presents a compact beamforming network designed to achieve broadside radiation when integrated with a microstrip antenna array. The steering mechanism employs a planar Butler Matrix (BM), which is composed of compact 90° 3 dB microstrip couplers. Antenna elements are spaced at 0.3Ȝ, allowing intentional overlap that shifts the main beam away from broadside, thereby producing a prominent grating lobe in the broadside direction. Both full-wave electromagnetic simulations and experimental measurements confirm that the design delivers broadside coverage, high gain, and a compact form factor.
The prototype exhibits good agreement between simulated and measured S-parameters, validating effective impedance matching and beam steering performance.

Passivity-Based Integral Sliding Mode Control for Robust Trajectory Tracking in 2-DOF Helicopter Systems

Ratiba Fellag, Mahmoud Belhocine, Meziane Hamel — Wed, 31 Dec 2025 00:00:00 +0100

This study introduces a robust trajectory-tracking control strategy in a two-degrees-of-freedom (2-DOF) helicopter system, combining passivity theory and integral sliding mode control (ISMC) strengths. The proposed integral passivity-based sliding mode control (IPBSMC) integrates passivity-based energy shaping, which inherently accounts for the system's natural dynamics, with integral sliding mode control to enhance tracking performance and reduce control effort. The controller's effectiveness is validated through simulations and real-time experiments with band-limited white noise disturbances using the Quanser AERO 2 platform interfaced with MATLAB/Simulink. Results indicate good trajectory tracking, with steady-state errors below ±0.2 rad under non-vanishing Gaussian disturbances. The experimental implementation further validates the proposed method's practical applicability and highlights its potential for real-world deployment in coupled systems requiring high accuracy and robustness under varied operating conditions.

History of the Electrical Characterization and Test Platform Development at Microelectronics and Nanotechnology Division of CDTA, Algeria

Boualem Djezzar — Wed, 31 Dec 2025 00:00:00 +0100

The sixties (60s) and seventies (70s) of the last century have seen the takeoff of microelectronics. Since that, several countries, including Algeria, have adopted planned strategic programs to develop their domestic electronics. In this paper, we provide a historical review of the evolution of the electrical test benches developed at the Division of Microelectronics and Nanotechnology (DMN) of the “Centre de Développement des Technologies Avancées” (CDTA) or Center for Development of the Advanced Technologies. Starting from mid- nineties to up today, the electrical characterization platform has known different generations of test benches; from a simple setup to extract current-voltage (I-V) and capacitance-voltage (C-V) characteristics of semiconductor devices to more sophisticated ones to extract spectra of electrically detected magnetic resonance (EDMR). In addition, some benches have been developed to study reliability issue in metal oxide semiconductor (MOS) devices and integrated circuits (ICs), such as ionizing radiation effects, Fowler-Nordheim (FN) stress, hot-carrier injection (HCI), time-dependent dielectric breakdown (TDDB), and bias temperature instability (BTI). The obtained results have been published in well-known journals of the Institute of Electrical and Electronics Engineers (IEEE), the American Institute of Physics (AIP), and the Elsevier publishers.

A Compact dual band MIMO Antenna for wireless communication applications

fouad fertas — Wed, 31 Dec 2025 00:00:00 +0100

In this study, a compact dual band 2-element Multiple Input Multiple Output (MIMO) multiband antenna is designed. Two radiating patches make up the suggested antenna, which aims to produce multiband resonance antenna. The 45×33 mm2 two element MIMO antenna is printed on a 1.6 mm thick FR-4 substrate with a dielectric constant of ɛr = 4.3 and a loss tangent of 0.02. An optimized dual band antenna was produced as a result of a parametric research based on the FDTD technique, which improved the structure's performance with regard to operational bandwidth. Additionally, a separate 50Ω-fed two-element MIMO arrangement is taken into consideration. By introducing spatial diversity into the MIMO arrangement, good isolation can be achieved without the need to use well-known techniques of decoupling structures. Results shows that the system has a mutual coupling of less than -15 dB between the various elements and operates on two bands: "3.5GHz WiMAX." and "4.2 GHz " Moreover, an analysis of the Diversity Gain (DG), Envelope Correlation Coefficient (ECC) and Channel Capacity Loss (CCL) parameters shows that they satisfy the practical standards: DG > 9.80, ECC < 0.06, and CCL < 0.4 bits/s/Hz over the relevant bands

Feedback State Space Stabilization of Fractional-order Chaotic Lorenz-84 Atmosphere Model

Samir Ladaci, Mr Nasr-Eddine Mellah — Wed, 31 Dec 2025 00:00:00 +0100

This paper explores the stabilization of the three-dimensional fractional-order (FO) chaotic Lorenz-84 atmosphere model through control strategies. Employing the Grünwald-Letnikov approximation for fractional integration, the chaotic system is simulated to understand its intricate dynamics. The main focus lies in utilizing state-space feedback control to adapt input signals based on system states, aiming to steer chaotic dynamics towards stability. Additionally, control gains optimization is conducted through particle swarm optimization (PSO) to guarantee robust stabilization of the chaotic atmosphere model.

Investigation on the Modeling of Doubly-Fed Induction Generators for Wind Turbine

Yaakoub Diboune, Djilali Kouchih — Wed, 31 Dec 2025 00:00:00 +0100

Over recent decades, especially in the transmission network, the share of electricity produced by wind sources has significantly increased. In order to enhance the efficiency of the power system, it is crucial to have precise and comprehensive mathematical modeling of the generator for wind turbine systems. Typically,
this generator is a doubly-fed induction generator, as its use of partial converters and induction machines makes it more economically successful compared to alternative technologies. In this paper, a novel approach has been developed for the modeling and analysis of doubly-fed induction generators. This thorough approach takes into account the derivation of the neutral voltage of the doubly-fed induction generator. This innovative approach has successfully extracted the fundamental harmonic of the stator currents and voltages with precision under normal operating conditions within a reasonable simulation time. It has been demonstrated that under normal operating conditions, a typical operation is achieved. It is characterized by a balanced stator voltage, current, flux, and fundamental harmonic through the stator variables, which correspond to the supply frequency. This fundamental harmonic has been suggested as a means of monitoring the generator during normal operating conditions. Mathematics modeling and simulation are conducted using MATLAB software.
The validity and dependability of this method for analyzing and modeling doubly-fed induction generators are
confirmed by the consistency and strong correlation between experimental and simulation results.

Pragmatic Design and Tuning of a Hybrid Metaheuristic BESS Controller for LV Grid Stability

Noureddine Brakta — Wed, 31 Dec 2025 00:00:00 +0100

The integration of Battery Energy Storage Systems (BESS) is critical for mitigating voltage instability in low-voltage (LV) networks with high photovoltaic (PV) penetration. While metaheuristic algorithms offer powerful tools for optimizing BESS dispatch, their successful transition from theoretical models to practical application hinges on a nuanced understanding of their operational parameters. This paper presents a case study on the pragmatic design and tuning of a hybrid Particle Swarm Optimization-Grey Wolf Optimizer (PSO-GWO) for a six-dimensional, multi-BESS control problem. We chronicle the evolution of the simulation framework, highlighting critical implementation challenges and their solutions. Key findings demonstrate that optimizer population size, not just iteration count, is a decisive factor in control stability, particularly for computationally inexpensive configurations. We introduce a control oscillation metric as a key performance indicator and discuss the indispensable role of smart warm-starts and rate-limiting in generating physically viable and asset-safe control actions. The paper concludes that a successful BESS control strategy is defined not only by its ability to meet primary objectives like voltage regulation but also by the stability and practicality of the control signals it produces, presenting a crucial trade-off between computational budget and real-world viability.

Comparative Studies of Intelligent Algorithms for Enhancing Machine Learning Training in Diabetes Prediction

Lakhdari Lahcen — Wed, 31 Dec 2025 00:00:00 +0100

This The objective of this study is to compare the effectiveness of various intelligent algorithms in enhancing machine learning training for predicting diabetes from patient data. Early prediction of diabetes is crucial for preventing serious complications, and machine learning algorithms play an essential role in improving medical diagnostics. This research evaluates the performance of several algorithms, including Logistic Regression (LR), Random Forests (RF), Support Vector Classification (SVC), Gradient Boosting Machines (GBM), and K-Nearest Neighbors Classifier (KNN). These algorithms are compared based on multiple criteria: performance (precision, recall, F1-score, accuracy), computation time, model complexity, generalization capability, robustness, ease of implementation, and scalability. The study uses the Pima Indians Diabetes dataset, a well-known dataset containing several clinically relevant variables for diabetes prediction. The algorithms are evaluated using cross-validation methods, and regularization techniques are applied to optimize the hyperparameters.

Optimization and Planning of Energy Systems using MESSAGE Code

Rabah Benabid — Wed, 31 Dec 2025 00:00:00 +0100

This paper deals with energy system optimization and planning using message code. The problem is formulated as a mixed integer programming problem with constraints on activities and installed capacities. The objective function is the minimization of the total installation cost including the investment cost, the variable and fixed operation and maintenance costs, and the constraints violation cost. Various operation and environmental constraints are considered. The obtained results show the capability of message code to model and solve the complex energy planning problem considering different types of constraints.

Employee Management System

V. Geethanjali, K. Eswara Rao, K. Yenosh , P. Solmon, K. G. Sravani, K. Sreeveda — Wed, 31 Dec 2025 00:00:00 +0100

The Employee Management System, or EMS, it's this web app built on a three-tier setup. React.js handles the frontend stuff. Node.js with Express.js runs the backend. MySQL keeps the database going. All that makes it scalable and keeps data secure, you know. Basically, it tackles workforce headaches like managing employee lifecycles, assigning projects, and sorting admin tasks. Helps boost efficiency around the office. It breaks down into four main modules that tie together. First, user authentication and role management. Then employee info handling. Attendance and leave tracking comes next. Project management with real-time updates rounds it out. Role-based controls mean admins get their dashboard. Managers have theirs. Employees see what fits their job. No one poking around where they shouldn't. EMS automates a bunch of things. Attendance gets tracked without hassle. Leaves get approved quicker. Projects stay monitored. Even payments sort themselves out. Cuts down errors. Lightens the manual load a lot. Performance wise, it speeds up admin work. Routine tasks process faster. Data stays more accurate than old-school methods. Plus, it pushes transparency. Encourages team collaboration. Keeps operations secure. Really useful for companies shifting to digital ways. As a platform, it's scalable. User-friendly too. Ready for whatever comes next. Evolves as workplaces change. Ensures data stays safe. Operations remain clear. Adapts to HRM challenges that keep growing.

Enhancing Preventive Healthcare: Developing a Robust ML-Based Model for Diabetes Prediction

Wed, 31 Dec 2025 00:00:00 +0100

Diabetes Mellitus represents a significant global health challenge, with early detection being crucial for mitigating severe complications. This study conducts a rigorous comparative analysis of machine learning models for diabetes prediction, leveraging the Pima Indians Diabetes Dataset. We implemented a rigorous preprocessing protocol to address the dataset’s inherent challenges, including the handling of missing data denoted by zero values in key clinical features. Four machine learning algorithms—Support Vector Machine (SVM), Random Forest, Decision Tree, and Naïve Bayes—were meticulously optimized and evaluated using stratified 10-fold cross-validation. This method ensures a robust and generalizable assessment of model performance. Our results indicate that the Random Forest classifier outperformed its counterparts, achieving a mean cross-validation accuracy of 84.2%, a precision of 0.80, a recall of 0.82, an F1-score of 0.81, and an AUC of 0.90. The study demonstrates the efficacy of ensemble methods in medical diagnostics and provides a transparent, reproducible benchmark for future research. This research underscores the potential of ML-based tools to augment traditional diagnostic methods, paving the way for accessible prescreening in diverse clinical environments.