This Journal provides a platform for Publication of Research Article, Reviews and Case studies that focuses on Recent Trends in Control and Convertor and its application in the field of Electrical Engineering. It also focuses on its Recent Developments and Researches in the area of Control and Converter Focus and Scope Optimal Power Optimal Control Optimization Stochastic Hybrid Systems Cams, Gears, Wheels, and Valve Optimization Stochastic Optimal Control Financial Mathematics and Actuarial Sciences
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The Web-Based Air Quality Surveillance System is an Internet of Things (IoT)-enabled environmental monitoring solution designed to provide real-time assessment of air quality and atmospheric conditions. The proposed system integrates multiple sensors, including MQ-7, MQ-135, DHT11, BMP180, and ADS1115, to measure carbon monoxide (CO), harmful air pollutants, temperature, humidity, and atmospheric pressure with high accuracy. An ESP32 microcontroller acquires sensor data and transmits it wirelessly to a web server, where the information is stored, processed, and visualized through an interactive online dashboard. Additionally, an OLED display provides instant on-site monitoring of environmental parameters. The system is powered by a 3.7 V Li-ion battery with an IP5306 Battery Management System (BMS), ensuring stable power delivery, safe charging, and energy-efficient operation. The web platform supports continuous data logging, real-time visualization, historical trend analysis, and threshold-based alerts, enabling users to monitor air quality remotely and make informed decisions. Experimental results demonstrate the system's capability to provide accurate, reliable, and continuous environmental monitoring while maintaining low power consumption and portability. The proposed solution offers a cost-effective and scalable approach for deployment in residential areas, industrial facilities, educational institutions, smart cities, and environmental research applications. Furthermore, the system provides a flexible platform for future enhancements, including cloud analytics, predictive air quality modeling, mobile application integration, and intelligent alert mechanisms.
Radar-Based Multi-Class Recognition of Pedestrians, Cyclists, and Vehicles Using Random Forest and LSTM Under Clutter
Reliable radar-based discrimination of pedestrians, cyclists, and vehicles is a core requirement for roadside safety monitoring and autonomous-driving perception, particularly under adverse lighting and weather where camera-only systems degrade. This paper presents a machine-learning framework for three-class radar target recognition human (pedestrian), car, and bicycle evaluated under both an ideal, clutter-free environment and a realistic environment incorporating ground, weather, and urban clutter together with additive noise. A synthetic phased-array radar dataset (9.4 GHz, 500 samples per class per condition) was generated from the monostatic radar range equation, target-specific radar-cross-section and micro-Doppler models, and standard ground/weather/urban clutter distributions. Two classifiers were trained and compared: a Random Forest (RF) operating on aggregated per-dwell handcrafted features, and a Long Short-Term Memory (LSTM) network operating directly on the raw per-pulse feature sequence. Under ideal conditions, RF achieved 98.1% accuracy (F1 = 0.981, AUC = 0.9997) and LSTM achieved 89.3% accuracy (F1 = 0.892, AUC = 0.983). Under realistic clutter, RF accuracy changed only marginally, to 97.9%, while LSTM accuracy fell to 87.2%. After mutual-information-based feature selection and hyperparameter tuning, RF remained at 97.9% (already near ceiling) while tuned LSTM recovered to 89.1% (F1 = 0.891, AUC = 0.972). These results indicate that classifiers built on statistics aggregated over a full radar dwell are substantially more robust to per-pulse clutter than sequence models that consume raw per-pulse observations directly, and that hyperparameter tuning recovers a meaningful share of the accuracy lost by the latter. All results reported are from an actually executed simulation and training pipeline; validation against measured radar data is identified as necessary future work.