Our Recent Publications
A look at the latest peer-reviewed, open-access research papers published on PaperNova by students and scholars from over 60 countries. Every paper is citable, downloadable as PDF, and issued with a certificate of publication.
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Smart Fire Detection and Response System
This paper presents the design and development of an IoT-based smart fire detection and autonomous fire-fighting robot using the ESP32 microcontroller. The system aims to reduce human risk in hazardous environments by enabling early fire detection and automatic suppression. It integrates multiple sensors, including flame sensors, a temperature sensor, and an ultrasonic sensor, to accurately detect fire and navigate obstacles. The ESP32 processes real-time data and controls the robot’s movement through a motor driver while activating a water pump via a relay module to extinguish fire. A servo motor ensures directional water spraying for effective suppression. Additionally, IoT connectivity allows remote monitoring and manual control through a mobile application. Experimental results demonstrate reliable performance, fast response, and improved safety, making the system suitable for residential and industrial applications.
By srijan manna · mumbai
Attendo: An IoT-based Smart Attendance System Using Biometric Edge-to-Cloud Architecture and LBPH Textural Analysis
The administrative workflow for student attendance tracking in higher educational institutions has historically relied on manual roll calls and physical sign-in sheets, methods that are inherently inefficient, time-consuming, and prone to the pervasive security vulnerability of “proxy attendance” (buddy-punching). This project presents “Attendo,” an integrated IoT-enabled smart attendance system designed to automate the authentication process using contactless facial recognition. The research addresses the “Security Usability-Cost Trilemma” by synthesizing low-cost edge-computing hardware with enterprise-grade cloud-native software architectures. The hardware architecture centers on a custom-fabricated Printed Circuit Board (PCB) integrating a standard ESP32-CAM microcontroller, a Passive Infrared (PIR) sensor for energy-efficient presence detection, dual LED status indicators, and a dedicated push-button triggered biometric capture pipeline. By leveraging a hardware-interrupt driven state machine, the edge node minimizes baseline power consumption to approximately 2.5mA in deep sleep while ensuring deterministic acquisition of VGA-resolution (640×480) JPEG matrices via the onboard OV2640 CMOS sensor. The software ecosystem is engineered on a distributed PERN (PostgreSQL, Express, React, Node.js) stack, utilizing AWS S3 buckets for scalable and persistent image blob storage. To accommodate the resource constraints of edge environments, computationally intensive bio metric processing is offloaded to a dedicated Python-based vision microservice. This microservice employs Haar Cascade classifiers for precise facial localization and the Local Binary Pattern Histogram (LBPH) algorithm for texture based feature extraction. By projecting micro textural patterns into concatenated spatial histograms, identity verification is executed efficiently using a strict Chi-Square (χ2) distance metric against pre-computed vectors securely stored in the PostgreSQL database.
By Tejas Shinde · Vishwaniketan's Institute of Management Entrepreneurship and Engineering Technology
About PaperNova's research archive
PaperNova is an international platform where students and researchers can publish research papers online instantly and receive a verifiable certificate of publication. Our open-access archive covers engineering, computer science, artificial intelligence, biotechnology, management, finance, and more.
Every paper on PaperNova is peer-reviewed, citable, and indexed on the open web so it can be discovered by future researchers. If you are looking for the fastest way to publish a journal paper internationally, start by browsing our AI research papers, machine learning papers, or computer science archive.