IEEE i-COSTE 2025 – Page 9 – NPS Australia Submission System

AI Impact on Sustainable Economy Growth

The paper proposes an integrated Green Finance–AI framework and platform that links financial allocation with AI-enabled monitoring, verification, and reporting. This framework allows banks to transition from symbolic commitments to measurable decarbonization strategies. By aligning green finance with AI capabilities, the proposed solution supports environmental protection, social inclusion, economic resilience, governance accountability, and digital innovation; thereby operationalizing sustainable economic growth across all five pillars

Market-Based Pricing Scheme for Isolated Microgrids with Renewable Energy and Storage

An integrated, market-based pricing framework for isolated microgrids that jointly optimizes hourly PV–wind–battery–diesel dispatch and derives LCOE-consistent tariffs

Quantum Threat Defense: A Framework for Migrating IoT-Based Healthcare Systems

This study is significant because it directly addresses the emerging quantum threat to IoT-based healthcare systems, which represent one of the most critical and vulnerable sectors of modern infrastructure. While much of the existing literature explores general post-quantum cryptography (PQC) migration strategies, there is a lack of frameworks specifically tailored to the unique constraints of healthcare IoT ecosystems. By proposing a phased, hybrid migration framework that accounts for the layered IoT architecture, device heterogeneity, and resource limitations, this paper provides a practical and structured pathway for ensuring security in healthcare systems against future quantum-enabled attacks.

The framework’s emphasis on crypto-agility, interoperability, and phased adoption is particularly important for healthcare environments where continuous operation and patient safety are paramount. Furthermore, the study bridges a critical gap by integrating both technical and operational perspectives, making it valuable for policymakers, system architects, and healthcare providers preparing for the quantum era. Ultimately, this work contributes to safeguarding patient data, ensuring the reliability of medical devices, and preserving trust in digital healthcare services in the face of advancing quantum computing capabilities.

Medipath: Intelligent Emergency Health Navigator

This paper discusses the creation and assessment
of MediPath, a smart web-based emergency medical assistance
system. It connects patients, hospitals, and ambulance services
in one digital platform. The system uses technologies like
Natural Language Processing (NLP) to match hospital
specialties with patient symptoms. It also uses real-time location
services and smart route planning to address gaps in emergency
medical response. MediPath has a three-part structure that
supports logins for patients or attendants, hospital management
portals, and navigation systems for ambulance drivers. The
platform uses machine learning to link patient symptoms with
hospital specialties, Firebase Realtime Database for easy data
syncing, and mapping APIs for better traffic-aware routes.
Performance tests show average response times of under 30
seconds for hospital matches and 95% accuracy in matching
symptoms to specialties. There is also a significant drop in
ambulance dispatch delays. User studies at different healthcare
facilities indicate improved coordination, shorter emergency
response times, and better use of resources. Although there are
challenges with highly specialized medical conditions and
connectivity in rural areas, MediPath greatly enhances
emergency medical service delivery by creating a unified
ecosystem that connects all parties in real time. This research
presents a scalable, smart solution that addresses disconnected
emergency medical services. It uses modern web technologies
alongside healthcare knowledge to help save lives through
quicker and more coordinated emergency responses.

FedFall: A Federated Transfer Learning Framework for Privacy-Aware Fall Detection in Elderly Care Using IoT-Edge Devices

1) FedFall framework: a novel architecture that fuses federated and transfer learning to enable privacy-preserving and personalized fall detection.
2) Edge-based deployment strategy: an end-to-end system
that supports real-time inference and training on low-
power IoT devices.
3) Simulation-based validation: demonstration of FedFall’s feasibility using simulated multi-client datasets, high lighting accuracy, latency, and communication efficiency.
4) Adaptation for constrained devices: lightweight models and compression techniques to ensure compatibility with embedded platforms.

Fast and Memory-Efficient 4SQR-Code Decoding using Histogram Analysis

Firstly, we introduce a histogram-based decoding method for 4SQR-Code that improves efficiency over traditional approaches.

Secondly, we demonstrate substantial improvements in
both decoding time and memory usage on 3,000 test
samples, achieving an average reduction of approximately 98.66% in decoding time and nearly 88.91% in memory consumption compared to baseline methods.

Finally, we conduct a comparative analysis with the
traditional OpenCV QR Code detector to demonstrate the advantages of our method.

Stability Enhancement and Harmonic Suppression of a Five-Level Cascaded H-Bridge Inverter for Microgrid Systems using Optimized PI Control

Integrating renewable energy into microgrids requires advanced power conversion systems that ensure stability, power quality, and harmonic suppression. This paper presents the design, analysis, and optimization of a five-level cascaded H-bridge (CHB) inverter integrated with an LCL filter and controlled by a PI controller using sinusoidal pulse-width modulation (SPWM). This work focuses on enhancing stability and harmonic performance in sustainable energy microgrids through parametric tuning of PI controller gains (Kp, Ki) and comparative analysis with alternative control strategies. Simulation results demonstrate that optimal PI tuning achieves a total harmonic distortion (THD) of 2.8% in grid current, with a robust transient response under dynamic loads. The sinusoidal pulse width modulation (SPWM) technique offers straightforward implementation while supporting compatibility with multilevel inverter topologies, making it scalable for high-voltage applications. Comparative analysis with proportional-resonance (PR) and hysteresis controllers emphasizes the practicality of PI controllers, achieving an effective balance between computational simplicity and control performance. Our proposed system addresses the main challenges of integrating renewable energy and provides a better and more cost-efficient way to enhance microgrid resilience and power quality. Future research should focus on more reliable and hardware-based authentication to transition key findings from the simulation into real-world applications

Experimental Investigation of the Impact of Partial Shading Size on Power Losses in Outdoor Solar Modules: Insights from Five Case Studies

Partial shading significantly affects the performance of photovoltaic (PV) systems, resulting in significant power losses and decreased efficiency. This study examines the effect of partial shading on the output power and electrical parameters of photovoltaic modules through field case studies and outdoor experimental tests. Additionally, an I-V tracer, PROVA 1011, was used to experimentally examine 10W and 20W polycrystalline panels under controlled partial shade sizes of 20%, 40%, 50%, 60%, and 80%. The results show that the voltage remains stable at lower shading levels but drops sharply beyond 50%. The current declines linearly with increasing shading, and the power losses reach 88.4% under 80% shading. Five case studies have been considered in this study to analyze rooftop-based partial shading size and the loss calculated using the experimental data. The study shows that power losses vary from 83.87 % to 87.91% due to the partial shading size of 40% to 60% for a 100W module. These findings emphasize the critical impact of rooftop obstacle-based partial shading on PV system losses and the need for practical design of PV installation to mitigate the shading.

Impact of Seasonal Climate Variations on EV Battery Performance and Charging Efficiency in Dhaka, Bangladesh

Electric vehicles (EVs) are central to sustainable transportation; however, their battery lifespan and charging efficiency are significantly impacted by seasonal temperature and humidity variations. This study investigates how Dhaka’s distinct climate periods—winter, summer, monsoon, and post-monsoon—influence EV battery degradation and charging performance. Laboratory tests at 10 °C, 25 °C, and 40 °C quantify capacity fade and impedance growth over 1,000 cycles, while field-deployed battery packs record real-world temperature and humidity impacts each season. Charging behavior is evaluated using 7 kW AC Level 2- and 50-kW DC fast chargers, measuring charge time and efficiency under ambient conditions. Results show that sustained summer heat (≥ 35 °C) accelerates irreversible capacity loss by approximately 15% per 1,000 cycles, and monsoon humidity (≥ 85% RH) reduces charging efficiency by about 8%. Recommendations include adaptive liquid-cooled battery thermal management, ambient-aware charger derating, and humidity-resistant connector designs. These strategies aim to extend battery life, improve charging reliability, and support EV adoption in tropical, monsoon-prone environments.

A Unique Design of a Multi-Axial Movable Bed for Residential Use by Bedridden Individuals

1. By overcoming current limitations like production cost, operational technology, and non-availability of advanced technology, this work designed a multi-axial movable bed system for bedridden individuals.

2. This design entails the integration of locally sourced components and parts, and real-time control of multidirectional kinematics.

3. The proposed design aspires to enhance patient autonomy, reduce caregiver workload, and provide a scalable, sustainable assistive solution tailored to the biomechanical and socio-technical requirements of domiciliary care in resource-constrained environments.

Conference Site: IEEE i-COSTE 2025