National Institute of Applied Science and Technology

Efficient and accurate simulation is essential for the modeling and control of high-frequency DC-DC converters. Conventional numerical integrators require very small time steps to capture fast switching dynamics, leading to high computational cost. This paper introduces a discrete-time analytical integration method that significantly accelerates simulation without compromising accuracy. The approach computes only two or three breaking points per switching cycle, depending on whether the system operates in continuous (CCM) or discontinuous conduction mode (DCM). Each segment of the dynamic response is represented by an exact analytical expression, eliminating the need for fine-grained time-stepping. The method is validated through simulations in both open-loop and closed-loop control scenarios. It also accurately detects the DCM regime and applies rapid corrections to ensure fidelity. The proposed algorithm offers a clear advantage over traditional solvers, enabling fast and reliable simulations suitable for iterative design and real-time applications. It is particularly well-suited for embedded and autonomous systems, such as photovoltaic power units in remote or off-grid settings, and can be extended to more complex converter topologies.

Autonomous decision-making in space presents unique challenges, including communication delays of up to 48 hours, radiation-induced sensor degradation, severe resource constraints, and zero tolerance for catastrophic failures. This paper introduces a hybrid AI–hardware decision framework that integrates Random Forest ensemble learning with dual-layer electronic safeguards to enable microsecond-scale fault prevention in CubeSat power systems. The central innovation is AI-gated adaptive protection, where machine learning reduces false positives by 94% while maintaining fail-safe operation through independent hardware mechanisms. The framework is validated on two satellites (NEPALISAT-1 and RAAVANA) operating with distinct hardware configurations, achieving 98.6% prediction accuracy with 157 μs inference time and demonstrating zero-shot transfer learning across platforms. The results highlight a generalizable approach for deploying interpretable, resource-efficient AI in safety-critical autonomous systems, where predictive intelligence must be balanced with hardware-level reliability.

This study investigates the application of artificial intelligence techniques, with a particular emphasis on linear regression modeling, to improve calibration accuracy and systematic error correction in RF spectrum analyzers within a metrological framework. A supervised learning approach is proposed to characterize and model the relationship between reference power levels and measured instrument responses while accounting for both systematic and stochastic measurement deviations. Experimental data were acquired under controlled calibration conditions and subsequently subjected to preprocessing and normalization procedures to ensure data consistency and model robustness. The developed regression-based correction model demonstrated strong predictive capability, reflected by high coefficients of determination and reduced residual discrepancies between reference and measured values. Furthermore, the proposed approach maintains alignment with metrological traceability principles and measurement uncertainty considerations, thereby ensuring compatibility with established calibration laboratory practices. Beyond error compensation, the integration of AI-driven modeling enables enhanced diagnostic and predictive functionalities, including automated drift detection, performance monitoring, and data-informed maintenance planning. These capabilities contribute to improved measurement reliability, operational efficiency, and instrument lifecycle management. Overall, the results highlight the potential of AI-assisted calibration strategies to advance $\mathbf{R F}$ measurement systems toward increased autonomy, adaptability, and real-time optimization, supporting the evolution of next-generation intelligent metrological infrastructures..

This study evaluates the updated MADOCA PPP service using GPS, GLONASS, Galileo, and QZSS signals in Chandipur, India, with low-cost SwaP-C GNSS modules. Despite limited QZSS visibility in this secondary service region, the system achieved sub-centimeter accuracy, reducing horizontal errors by 75% and vertical errors by over 60% compared to the previous version. The results highlight MADOCA's robustness, with reliable convergence and stability even under constrained conditions. This is the first report of such performance from India, demonstrating the service's viability for high-precision static applications like surveying and geodetic monitoring in areas with limited GNSS augmentation.

This study presents an enhanced Kalman filtering (KF) approach for real-time air quality monitoring, with a particular focus on estimating inhalable particulate matter (PM2.5 and PM10) concentrations across a distributed sensor network. The classical Kalman Filter (KF) is initially employed to provide state estimation based on environmental inputs such as wind speed and humidity. While KF demonstrates effectiveness in linear systems, its accuracy degrades significantly in the presence of nonlinear pollutant behaviors, sensor noise, and dynamic disturbances that characterize real-world conditions. To address these limitations, we propose an AI-augmented Kalman Filter (AI-KF) that integrates a lightweight neural network (NN) to model and compensate for the residual errors produced by the KF. This neural module operates as a data-driven corrector that enhances the filter’s adaptability to nonlinear dynamics and environmental variability. Unlike the standard KF, the AI-KF can learn complex relationships between state variables and measurements, offering improved accuracy in scenarios with unmodeled nonlinearities. It demonstrates strong robustness to sensor drift and measurement noise by continuously adapting to shifting data patterns. Moreover, the AI-KF maintains the interpretability of the traditional KF framework while introducing an intelligent layer capable of real-time correction and adaptation. Experimental results on synthetic pollution data confirm that the proposed AI-KF consistently outperforms the standard KF, achieving lower root-mean-square error (RMSE) and enhanced state reconstruction. These findings highlight the potential of combining model-based estimation with neural residual learning for scalable, accurate, and robust environmental monitoring applications.