4 publications

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    Hybrid Quantum-Classical Signal Processing for High-Dimensional Feature Extraction in Smart Instrumentation

    Abstract: Hybrid quantum–classical signal processing has emerged as a promising paradigm for overcoming the computational limitations of conventional algorithms in analyzing high-dimensional data generated by modern smart instrumentation systems. This paper presents a novel hybrid quantum–classical signal processing framework designed for efficient feature extraction from complex, multidimensional sensor signals encountered in industrial monitoring, biomedical diagnostics, environmental sensing, and intelligent manufacturing. The proposed architecture integrates quantum variational circuits with classical deep learning …

    Published in International Journal of Microwave Engineering and Technology · Vol. 12, Issue 2, 2026 Read article

  • Published Subscription

    Hybrid Quantum-Classical Signal Processing for High-Dimensional Feature Extraction in Smart Instrumentation

    Abstract: Hybrid quantum–classical signal processing has emerged as a promising paradigm for overcoming the computational limitations of conventional algorithms in analyzing high-dimensional data generated by modern smart instrumentation systems. This paper presents a novel hybrid quantum–classical signal processing framework designed for efficient feature extraction from complex, multidimensional sensor signals encountered in industrial monitoring, biomedical diagnostics, environmental sensing, and intelligent manufacturing. The proposed architecture integrates quantum variational circuits with classical deep learning …

    Published in International Journal of Microwave Engineering and Technology · Vol. 12, Issue 2, 2026 Read article

  • Published Subscription

    Autonomous Multi-Agent Sensor Network for Distributed Environmental Instrumentation

    Abstract: Environmental monitoring requires intelligent, scalable, and autonomous instrumentation systems capable of continuously sensing, processing, and communicating heterogeneous data under dynamic conditions. This study proposes an Autonomous Multi-Agent Sensor Network for Distributed Environmental Instrumentation, integrating distributed sensor nodes, edge computing, multi-agent collaboration, adaptive data fusion, and intelligent decision-making to achieve reliable real-time monitoring of environmental parameters. Each sensor agent autonomously performs local sensing, anomaly detection, energy-aware communication, and cooperative information exchange …

    Published in International Journal of Telecommunications & Emerging Technologies · Vol. 12, Issue 2, 2026 Read article

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    Fault-Tolerant Modular Power Converter for Large-Scale Renewable Energy Integration

    Abstract: The rapid expansion of large-scale renewable energy systems has significantly increased the demand for highly reliable and fault-resilient power electronic converters capable of maintaining stable grid operation under component failures and fluctuating renewable power generation. This paper proposes a Fault-Tolerant Modular Power Converter (FTMPC) for large-scale renewable energy integration, incorporating a modular multilevel architecture, intelligent fault diagnosis, and adaptive reconfiguration control to ensure uninterrupted power conversion. The proposed converter continuously …

    Published in International Journal of Power Electronics Controllers and Converters · Vol. 12, Issue 2, 2026 Read article

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