International Journal of Engineering Research and Sustainable Technologies (IJERST)

IMPLEMENTATION OF POCKET QUBE SATELLITES FOR ENVIRONMENT MONITORING

2026-01-04T12:41:57+00:00

Integrating Pocket satellites into college campuses offers robust IoT connectivity. Placed in Low Earth Orbit (LEO), these satellites ensure uninterrupted communication across vast campus areas, including buildings, outdoor spaces, and remote facilities. This extensive coverage enables diverse IoT applications, making environmental monitoring, waste, and energy efficiency, promoting sustainability and resource efficiency. Implementing these applications can revolutionize college operations, enhancing efficiency and the learning environment.

OPTIMIZATION IN HEALTHCARE AND COMPUTER SCIENCE: A COMPARATIVE STUDY OF MATHEMATICAL AND BIO-INSPIRED ALGORITHMS

2026-01-04T12:41:56+00:00

Optimization techniques play a critical role in advancing health care engineering and computer science by providing efficient solutions to complex problems. This paper reviews both mathematical and nature-inspired optimization techniques, highlighting their applications in medical diagnosis, treatment planning, resource allocation, and software engineering. Traditional mathematical approaches such as linear programming and dynamic programming are examined alongside bio-inspired algorithms like genetic algorithms, particle swarm optimization, and artificial bee colony algorithms. A comparative analysis of their effectiveness, computational complexity, and real-world implementation is presented.

IoT-BASED REMOTE SURVEILLANCE FOR ANIMAL TRACKING NEAR RAILWAY TRACKS ARDUINO

2026-01-04T12:41:55+00:00

Railway tracks traversing through natural habitats bring forth a critical concern: the potential for collisions between trains and wildlife. This challenge necessitates innovative solutions to safeguard both the animal populations and the safety of railway operations. In response, this research introduces an ingenious approach—an IP camera based remote surveillance system tailored for animal tracking in close proximity to railway tracks. By harnessing cutting-edge technology, this system offers the promise of reducing animal fatalities and preventing hazardous train incidents. Central to this proposed solution is the utilization of an Adriano microcontroller intricately linked to a trio of sensors: an ultrasonic sensor, a Micro-Electro-Mechanical Systems (MEMS) sensor, and a Passive Infrared (PIR) sensor. This triumvirate of sensors collaborates seamlessly to discern the presence of animals within the vicinity of railway tracks. The ultrasonic sensor, adept at calculating distances by emitting and receiving sound waves, serves as the system's first line of defence in identifying potential collisions. The MEMS sensor, designed to detect even the minutest movements, further refines the system's by distinguishing between animals and stationary objects. Augmenting this ensemble, the PIR sensor operates as a thermal detector, responding to heat signatures and amplifying the system's capacity to identify.

INTELLIGENT FAULT DIAGNOSIS OF ELECTRIC VEHICLE POWERTRAINS USING MACHINE LEARNING TECHNIQUES

2026-01-04T12:41:53+00:00

Electric vehicles (EVs) are widely acknowledged as eco-friendly means of transport. They operate by transforming electrical energy into mechanical energy through various types of motors, which aligns with the sustainable ideals of smart cities. The motors in EVs draw and utilize electrical power from renewable energy (RE) sources via interfacing connections, employing power electronics technology to generate mechanical power through rotation. The dependable functioning of an EV heavily depends on the state of these connections, particularly between the output of the 3-phase inverter and the brushless DC (BLDC) motor. This paper utilizes machine learning (ML) tools to identify and classify faults in the connection lines from the 3-phase inverter output to the BLDC motor during the operational phase on the EV platform, focusing on double-line and three-phase faults. Several ML-based tools for fault detection and classification, including Decision Tree, Logistic Regression, Stochastic Gradient Descent, AdaBoost, XG Boost, K-Nearest Neighbour, and Voting Classifier, were optimized to enhance robustness and reliability in identifying and categorizing faults. The ML classifications were developed using datasets that represent both healthy and faulty conditions, taking into account a combination of six essential parameters crucial for the reliable operation of EVs. These parameters include the current supplied to the BLDC motor from the inverter, the modulated DC voltage, output speed, measured speed, and readings from the Hall-effect sensor. Furthermore, the effectiveness of the proposed fault detection and classification methods utilizing ML tools was evaluated by comparing their detection and classification efficiencies through various statistical performance metrics across the classifiers.

DESIGN, ANALYSIS, SIMULATION, FABRICATION AND TESTING OF ANTIPODAL VIVALDI ANTENNA

2026-01-04T12:41:58+00:00

This paper presents the development of an Oval Slot Edge Antipodal Vivaldi Antenna (OSEAVA) designed to achieve wideband performance with enhanced gain and directivity for high-frequency applications. Vivaldi antennas are widely recognized for their ultra-wideband characteristics, making them essential in radar, imaging, and communication systems. However, conventional designs often suffer from gain degradation at higher frequencies and suboptimal radiation patterns. To overcome these challenges, an oval-shaped slot is introduced along the antenna’s edge, improving radiation characteristics by enhancing directivity and reducing side lobes. The proposed antenna is simulated over a frequency range of 1 to 40 GHz, demonstrating superior bandwidth, gain, and radiation performance. Additionally, the design is optimized for minimal fabrication complexity while maintaining high efficiency, ensuring feasibility for real-world applications such as 5G communications, satellite systems, and advanced radar technologies. Simulation results indicate that OSEAVA offers significant improvements in bandwidth, gain, and radiation characteristics compared to conventional designs, establishing it as a strong candidate for next-generation wideband wireless and sensing systems.

Editorial Note

2026-01-03T06:01:01+00:00

International Journal of Engineering Research and Sustainable Technologies (IJERST)

Volume 3, No.4 Online ISSN: 2584-1394

Message from Editorial Desk 25th December 2025

Dear Readers, Researchers, and Contributors,

As we bring this year to a meaningful close, it is with great satisfaction that we present the December issue of IJERST. This edition brings together impactful research contributions that advance innovation, sustainability, and technological excellence—reflecting the spirit of inquiry and dedication that defines our scholarly community.

The past year has witnessed significant growth for IJERST in terms of international submissions, academic collaborations, and quality benchmarks in scholarly publishing. We are pleased to share that our journal is steadily progressing toward broader global visibility, with indexing initiatives such as DOAJ currently under active consideration, alongside ongoing efforts to enhance review rigor, publication ethics, and accessibility.

This issue showcases research that not only contributes academically but also addresses pressing real-world challenges, reinforcing our commitment to promoting engineering solutions that support sustainable development and societal well-being.

We express our heartfelt appreciation to our esteemed authors for their trust, to our diligent reviewers for their scholarly rigor, and to our editorial team for their unwavering commitment. Your collective contributions continue to strengthen the reputation and impact of IJERST.

We look forward to the coming year with renewed enthusiasm, inviting continued collaboration and inspiring research contributions.

Season’s greetings and best wishes for a progressive and innovative year ahead!

Warm regards,

On behalf of Editorial Team

Dr.V.RameshBabu

Managing Editor

Dean-University Journal , Dr.M.G.R. Educational and Research Institute Chennai,Tamilnadu,India

dean-univ.journals@drmgrdu.ac.in / ijerst@drmgrjournals.org