Selected work

Representative Papers

Energy Efficient AI-Enabled Wireless Sensor Networks for Mission Critical Environments: A Systematic Review across Smart Grid, AI, and Urban Infrastructure Applications

Aug 05, 2026

This study addresses the challenge of jointly optimizing energy efficiency, reliability, low latency, and security in mission-critical wireless sensor networks (WSNs), a problem often approached in isolation by existing research. Through a systematic review of 50 high-quality studies published between 2023 and 2026, the work employs qualitative thematic coding and comparative analysis to examine the application of reinforcement learning, fuzzy logic, metaheuristics, and AI-driven security techniques in routing, clustering, and edge computing. The paper proposes a novel, lightweight, interpretable, and field-validated AI-driven paradigm for WSNs that emphasizes multi-objective co-design. Findings demonstrate that AI significantly enhances both energy efficiency and overall system performance, offering robust theoretical foundations and practical guidance for the architecture of future mission-critical systems.

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Schmidt Decomposition-Based Methods for Efficient Quantum Image Encoding

Jun 09, 2026

This work addresses the high circuit depth, excessive gate count, and substantial qubit requirements of mainstream quantum image encoding schemes—such as FRQI, QPIE, and NEQR—which hinder their deployment on noisy intermediate-scale quantum (NISQ) devices. To overcome these limitations, the study introduces Schmidt decomposition into quantum image encoding for the first time, leveraging low-rank quantum state approximation to preserve essential image information while drastically reducing circuit complexity. Experimental results demonstrate that the proposed approach achieves near-perfect image reconstruction in FRQI (MSE ≈ 0.27) with a 97% reduction in circuit depth, significantly enhancing feasibility on NISQ hardware and effectively balancing reconstruction accuracy with resource efficiency.

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Hybrid Quantum-Classical AI for Industrial Defect Classification in Welding Images

Mar 30, 2026

This work addresses the task of defect classification in industrial welding images by proposing two hybrid quantum-classical approaches: a quantum kernel-based classifier and a variational quantum circuit model. The methodology first employs a convolutional neural network to extract image features, which are subsequently processed using parameterized quantum feature maps, angle encoding, and a variational quantum linear solver for classification. Notably, this study introduces, for the first time in industrial quality inspection, an analysis of quantum kernel condition numbers. The proposed methods are evaluated on a real-world welding dataset, demonstrating competitive performance against classical CNNs in both binary and multiclass classification tasks. Experimental results indicate that the hybrid models achieve comparable accuracy to their classical counterparts, highlighting their potential for near-term practical deployment.

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Recent publications

Latest Papers

Energy Efficient AI-Enabled Wireless Sensor Networks for Mission Critical Environments: A Systematic Review across Smart Grid, AI, and Urban Infrastructure Applications

Aug 05, 2026

This study addresses the challenge of jointly optimizing energy efficiency, reliability, low latency, and security in mission-critical wireless sensor networks (WSNs), a problem often approached in isolation by existing research. Through a systematic review of 50 high-quality studies published between 2023 and 2026, the work employs qualitative thematic coding and comparative analysis to examine the application of reinforcement learning, fuzzy logic, metaheuristics, and AI-driven security techniques in routing, clustering, and edge computing. The paper proposes a novel, lightweight, interpretable, and field-validated AI-driven paradigm for WSNs that emphasizes multi-objective co-design. Findings demonstrate that AI significantly enhances both energy efficiency and overall system performance, offering robust theoretical foundations and practical guidance for the architecture of future mission-critical systems.

0 citationsRead paper

Schmidt Decomposition-Based Methods for Efficient Quantum Image Encoding

Jun 09, 2026

This work addresses the high circuit depth, excessive gate count, and substantial qubit requirements of mainstream quantum image encoding schemes—such as FRQI, QPIE, and NEQR—which hinder their deployment on noisy intermediate-scale quantum (NISQ) devices. To overcome these limitations, the study introduces Schmidt decomposition into quantum image encoding for the first time, leveraging low-rank quantum state approximation to preserve essential image information while drastically reducing circuit complexity. Experimental results demonstrate that the proposed approach achieves near-perfect image reconstruction in FRQI (MSE ≈ 0.27) with a 97% reduction in circuit depth, significantly enhancing feasibility on NISQ hardware and effectively balancing reconstruction accuracy with resource efficiency.

0 citationsRead paper

Hybrid Quantum-Classical AI for Industrial Defect Classification in Welding Images

Mar 30, 2026

This work addresses the task of defect classification in industrial welding images by proposing two hybrid quantum-classical approaches: a quantum kernel-based classifier and a variational quantum circuit model. The methodology first employs a convolutional neural network to extract image features, which are subsequently processed using parameterized quantum feature maps, angle encoding, and a variational quantum linear solver for classification. Notably, this study introduces, for the first time in industrial quality inspection, an analysis of quantum kernel condition numbers. The proposed methods are evaluated on a real-world welding dataset, demonstrating competitive performance against classical CNNs in both binary and multiclass classification tasks. Experimental results indicate that the hybrid models achieve comparable accuracy to their classical counterparts, highlighting their potential for near-term practical deployment.

0 citationsRead paper