Algerian Journal of Signals and Systems

Evaluation of Quenching Factor in Ce³⁺-Doped Y₂SiO₅ Nanoscintillators for α and γ Radiation Detection

2026-07-19T10:17:46+01:00

Abstract: The quenching factor (QF), defined as the α/γ light output ratio, is a key parameter for evaluating the radiation discrimination performance of scintillators. In this study, five sample detectors were fabricated using Y₂SiO₅:Ce³⁺ nanophosphor powders synthesized at different annealing temperatures of the xerogels, specifically 900 °C, 950 °C, 1000 °C, 1050 °C, and 1250 °C. The detectors were tested under excitation by α-particles (from ²⁴¹Am, 5.48 MeV) and γ-rays (from ¹³⁷Cs, 662 keV). The QF was determined from pulse-height spectra by comparing the relative peak positions corresponding to α and γ interactions. A QF value of 0.46 was obtained for the YSO_1250 sample, indicating a significant reduction in quenching at higher annealing temperatures. Conversely, samples treated at lower temperatures exhibited stronger quenching, with QF values as low as 0.17. This trend is attributed to improved crystallinity and reduced defect density at elevated temperatures, which enhance scintillation efficiency for highly ionizing α-particles. These findings demonstrate that annealing temperature serves as an effective tuning parameter for optimizing the QF and, consequently, the scintillator's suitability for either α/γ discrimination or α-sensitivity, depending on the application.

Design and Implementation of an IoT-Based Carbon Monoxide and Gas Leakage Detection System for Residential Safety Using ESP32

2026-08-07T10:19:13+01:00

Carbon monoxide (CO) and combustible gas
leaks represent significant hazards in residential environments,
often leading to severe health consequences and fatal accidents
due to their colorless and odorless nature. Recent incidents
have highlighted the urgent need for reliable, real-time
monitoring systems capable of early hazard detection and
rapid response.
This paper presents the design and implementation of an
Internet of Things (IoT)-based smart safety system for
detecting carbon monoxide, combustible gas leakage, and
abnormal temperature conditions using the ESP32
microcontroller. The proposed system integrates multiple
sensors, including a ZE03 electrochemical CO sensor, an MQ-7
gas sensor, and a DS18B20 temperature sensor, to ensure
accurate and reliable environmental monitoring.
The system continuously acquires real-time data and
processes it locally, enabling immediate detection of hazardous
conditions. In critical situations, the system automatically
activates safety mechanisms such as audible alarms and gas
shutoff via a solenoid valve. Additionally, wireless
communication capabilities allow real-time monitoring and
alert notifications through a mobile interface.
Experimental results demonstrate that the proposed system
achieves fast response time, high detection accuracy, and
improved reliability compared to traditional single-sensor
approaches, while significantly reducing false alarms. The
system offers a cost-effective, scalable, and efficient solution for
enhancing residential safety and preventing gas-related
accidents.

Current-Aware Adaptive Dead-Time Control for Extended Zero Voltage Switching in Dual Active Bridge for EV Chargers

2026-08-23T16:35:15+01:00

The acceptance of Electric Vehicles to address sustainable development goals 7,11 and 13 has motivated the necessity for high-efficiency, bidirectional charging units. The Dual Active Bridge (DAB) is the distinguished topology for the charging units because of its capabilities inter alia galvanic isolation, inherent Zero Voltage Switching (ZVS) and bidirectional power flow (V2G). However, traditional fixed dead-time control techniques bring critical trade-offs: satisfactory dead-time protects heavy loads but invariably results in excessive body diode conduction, while fixed dead-time fail to facilitate Zero Voltage Switching at light loads leading to hard-switching losses. This research outlines a comprehensive, current-aware adaptive dead-time control technique that dynamically modulates the dead-time interval grounded on the real-time sensing of the transformer leakage current. By dynamically tuning the blanking interval, the proposed method extends the soft-switching operating range, minimises reverse conduction losses, and mitigates voltage waveform distortion. Validations through MATLAB/Simulink modelling including a 10KW Electric Vehicle (EV) charger model demonstrate up to a 5% efficiency improvement under light to medium loads compared to conventional fixed dead-time techniques.

A Comparative Study of Acoustic Features for Spoken Digit Recognition in Algerian Arabic Dialects and Accented French

2026-08-23T16:36:11+01:00

This paper presents a comparative study of six acoustic feature representations for spoken digit recognition in Algerian Arabic dialects and accented French. The evaluated features include MFCC, MFCC with temporal derivatives, Log-Mel filterbank, LFCC, GFCC, and PNCC. Experiments are conducted on a corpus of isolated digits (0--99) collected from four Algerian regions (Algiers, Blida, Tizi-Ouzou, Sétif), covering both native dialectal speech and French-accented variants. Three classifiers---SVM, CNN, and BiLSTM---are compared to assess feature--model interactions. Results demonstrate that MFCC provides strong baselines across all regions, while GFCC achieves the highest accuracy (up to 94.4%) for dialectal speech. BiLSTM exhibits training instability with conventional features but improves significantly with perceptually motivated representations. These findings provide the first systematic benchmark for 100-class Algerian Arabic digit recognition and highlight the importance of feature selection in low-resource speech processing.

Network Performance Analysis and Optimization of LTE-Advanced Systems

2026-08-25T11:16:15+01:00

This work focuses on the optimization of an LTE-Advanced network through a comprehensive performance analysis framework. The study involves the collection, processing, and evaluation of network statistical data and measurement reports obtained from the network management system. To assess network performance and identify potential issues, several Key Performance Indicators (KPIs), including traffic measurement counters, service drop rates, and other quality-of-service metrics, are analyzed. Furthermore, common fault localization and troubleshooting techniques are applied to diagnose network inefficiencies and improve overall system performance. The analysis is conducted using data acquired from the Huawei M2000 network management platform, enabling a detailed evaluation of network behavior and optimization opportunities.

Vision-Based Control of a Mobile Platform Using Edge Artificial Intelligence

2026-08-26T16:52:44+01:00

This paper presents a vision-based motion generation system for mobile platforms. The robot is controlled throw human hand gestures. A lightweight camera captures video streams from real-world environments, while an artificial intelligence-based gesture recognition model identifies the operator's hand gestures and generates the corresponding motion commands. The proposed solution is implemented and validated on a real Mecanum mobile platform. Two lightweight artificial intelligence models (Faster Objects, More Objects and MobileNetV1) for hand gesture recognition are embedded and evaluated on an ESP32-CAM module.