Light emitting diode (LED) technology has become ubiquitous in residential and industrial lighting. The performance of an LED system, including its efficiency, power factor, lifespan, and cost, is critically dependent on the driver's power converter topology. This paper provides a comprehensive review of recent advancements and trends in LED driver design to serve as a guide for engineers and researchers. By analyzing contemporary research from leading journals and conference proceedings, the review identifies and evaluates suitable converter topologies for various power levels. The analysis concludes that while numerous topologies are viable, the flyback converter remains the predominant solution for low power applications (<100 W) due to its cost effectiveness and simplicity. Key design considerations, including power factor correction, efficiency, dimming capability, and reliability, are also discussed to inform future design choices.
This paper presents a robust, automated computational framework for the real time monitoring and statistical analysis of seismic activity, focusing on the seismically active region of Epirus, Greece. To address the inherent latencies of traditional monitoring, this study introduces the Epirus Advanced Seismic Observatory (EASO). Utilizing the Python programming language and integrating live data from the Euro Mediterranean Seismological Centre (EMSC) API, EASO implements an end to end pipeline for data acquisition, processing, and visualization. The methodology employs the Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to autonomously identify seismogenic structures and spatial clusters without prior geological constraints. Furthermore, the framework integrates fundamental seismological laws, including the Gutenberg Richter magnitude frequency relationship and the Modified Omori Law (ETAS model), to estimate b-values and decay rates of aftershock sequences. A multi panel visualization dashboard was developed, providing a "full spectrum" probability forecast for various magnitude thresholds (4.0–7.0 Richter) within a seven day window. The results demonstrate that the integration of unsupervised machine learning with traditional stochastic models significantly enhances the interpretability of regional seismic swarms. This study concludes that EASO provides a critical tool for real time visual forensics, risk assessment, and public information, offering a scalable solution that can be adapted to other high risk tectonic zones globally.
Software Defined Networking (SDN) offers a flexible and programmable alternative to traditional network architectures by decoupling the control and data planes and introducing a centralized controller and application layer. While this architecture enables dynamic traffic management, centralized policy enforcement, and seamless integration of network services, it also introduces critical vulnerabilities, particularly to Distributed Denial-of-Service (DDoS) attacks. The centralized nature of the SDN controller makes it a prime target, where resource exhaustion or flow table saturation can lead to widespread service disruption. These attacks can severely impact time-sensitive applications and compromise the overall stability of the network. In this work, an AI-driven framework is proposed for detecting and mitigating DDoS attacks in SDN environments. The framework incorporates models from diverse categories, including meta-learning, adaptive reinforcement learning, supervised learning, unsupervised clustering, and deep learning techniques. This broad integration enables a comprehensive evaluation of detection capabilities and ensures adaptability to various traffic patterns and attack scenarios. A hybrid Particle Swarm Optimization Genetic Algorithm (PSO-GA) approach is also presented to fine-tune models’ parameters and thereby enhance the detection efficiency of the models. Extensive experiments are conducted using multiple performance metrics to evaluate each model under both optimized and non-optimized conditions. The results demonstrate the effectiveness of the proposed framework in enhancing SDN resilience against DDoS threats.
The main sources of energy production in Cameroon remain hydropower and thermal power plants using fuel and gas. However, in recent years, it has been experiencing an imbalance that creates a mismatch between production and consumption. It is therefore of great importance to explore new sources of energy such as wind energy. In an effort to seek other sources of energy, we conducted this study aimed at assessing the wind potential in the Lom and Djèrem Department in the city of Bertoua. To achieve this, data obtained from the Bertoua airport were processed using the Weibull distribution, the wind distribution density and express the statistical estimation of wind energy potential at different altitudes of the site (annual energy density average of 60.97 kWh/m²/year), which will facilitate the installation of a wind farm in this locality. Given the existing wind energy potential, its use for the development of electric energy in the region seems somewhat suitable. An advantageous solution for Cameroon in general and the East region is the recovery and transformation of wind energy into electric energy, with a YDF-1500-87 CNYD type wind turbine guaranteeing a production of 82.66 kW at 100 m altitude in the month of January. Next, we determined the wind direction to position the wind turbines at the site. Finally, an assessment of the prediction of the electrical energy produced is made while relying on the careful selection of wind turbines and their load factors.
Multilevel inverters (MLIs) have become essential in power electronics due to their capability to improve power quality, minimize harmonic distortion and enhance efficiency in medium and HV applications. It achieves a smoother waveform, reducing total harmonic distortion (THD). Hybrid electric vehicles (HEVs) are increasingly recognized for their ability to reduce fuel consumption and emissions in modern transportation. Their performance largely depends on the efficiency of power conversion systems, particularly in the inverter setup. In this study, a novel 3-phase 25-level MLI configuration is proposed with fewer switches. The primary objective is to reduce harmonics through high-level MLI implementation and motor optimization in electric vehicle applications. Proposed research suggests a novel three phase 25-level hybrid inverter tailored for hybrid electric vehicles, featuring an innovative hybrid DC-DC and DC-AC topology that utilizes fewer switches to enhance cost effectiveness. Simulation results indicate that the three phase 25-level MLI improves voltage stability and further mitigates harmonics compared to a single phase configuration. While comparing with conventional converters, MLIs offer significant benefits, including high frequency switching and advanced modulation techniques for precise control. It generates output voltage with minimal harmonics, lower dv/dt stress on switches and introduces common mode voltage. In particular, the 3-phase 25-level inverter system achieves a smoother waveform, reducing THD. The proposed system has been validated using MATLAB/Simulink. Additionally, the comparative analysis between the proposed 1-ph, and 3-ph 25-level MLI topologies are also presented.
