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Toshiba Corporation

Industry researchasia · jp
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Research library14linked papers
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Selected work

Representative Papers

Empowering IoT Security: On-Device Intrusion Detection in Resource Constrained Devices

May 13, 2026

This work addresses the challenge of securing resource-constrained Internet of Things (IoT) devices against prevalent threats such as denial-of-service and man-in-the-middle attacks. To this end, the authors propose a lightweight intrusion detection approach tailored for microcontrollers, which integrates an optimized decision tree with a compact neural network to achieve high-accuracy, real-time detection under stringent memory and computational constraints. Experimental evaluation in heterogeneous IoT environments demonstrates that the proposed method attains detection accuracies of 99% using the decision tree component and 96% with the neural network component, substantially outperforming existing solutions. The approach effectively balances security assurance with deployment efficiency and hardware limitations, offering a practical defense mechanism for low-resource IoT deployments.

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Decomposition of Multi-Qubit Gates for Circuit Cutting

Mar 27, 2026

This work addresses the challenge of executing large-scale quantum circuits on current noisy intermediate-scale quantum (NISQ) devices, which often requires circuit cutting due to hardware qubit limitations. Conventional decomposition methods for multi-controlled gates—such as MCX and CCCX—significantly increase sampling overhead during classical post-processing. To mitigate this, the authors propose a novel decomposition strategy tailored for circuit cutting that introduces a small number of ancillary qubits at cut locations to restructure gate implementations. This approach preserves functional equivalence while substantially reducing the number of samples required for accurate reconstruction. Experimental results demonstrate that the method effectively lowers sampling costs for MCX and CCCX gates, thereby enhancing the feasibility of running large quantum circuits on existing NISQ hardware.

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Multi-Station WiFi CSI Sensing Framework Robust to Station-wise Feature Missingness and Limited Labeled Data

Mar 12, 2026

This work addresses the pervasive challenges of site-level feature missingness and scarce labeled data in multi-site WiFi channel state information (CSI) sensing. To jointly tackle structured missing patterns and label scarcity, the authors propose a unified modeling approach that explicitly incorporates site unavailability into both representation learning and downstream task training. The method integrates an enhanced cross-modal self-supervised learning (CroSSL) framework to learn representations robust to missing data and introduces a site-level masking augmentation (SMA) mechanism to improve generalization. Experimental results demonstrate that the proposed approach significantly outperforms existing single-strategy methods in scenarios where feature missingness and label scarcity coexist, thereby enhancing the robustness and practicality of CSI-based sensing systems.

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Recent publications

Latest Papers

Empowering IoT Security: On-Device Intrusion Detection in Resource Constrained Devices

May 13, 2026

This work addresses the challenge of securing resource-constrained Internet of Things (IoT) devices against prevalent threats such as denial-of-service and man-in-the-middle attacks. To this end, the authors propose a lightweight intrusion detection approach tailored for microcontrollers, which integrates an optimized decision tree with a compact neural network to achieve high-accuracy, real-time detection under stringent memory and computational constraints. Experimental evaluation in heterogeneous IoT environments demonstrates that the proposed method attains detection accuracies of 99% using the decision tree component and 96% with the neural network component, substantially outperforming existing solutions. The approach effectively balances security assurance with deployment efficiency and hardware limitations, offering a practical defense mechanism for low-resource IoT deployments.

0 citationsRead paper

Decomposition of Multi-Qubit Gates for Circuit Cutting

Mar 27, 2026

This work addresses the challenge of executing large-scale quantum circuits on current noisy intermediate-scale quantum (NISQ) devices, which often requires circuit cutting due to hardware qubit limitations. Conventional decomposition methods for multi-controlled gates—such as MCX and CCCX—significantly increase sampling overhead during classical post-processing. To mitigate this, the authors propose a novel decomposition strategy tailored for circuit cutting that introduces a small number of ancillary qubits at cut locations to restructure gate implementations. This approach preserves functional equivalence while substantially reducing the number of samples required for accurate reconstruction. Experimental results demonstrate that the method effectively lowers sampling costs for MCX and CCCX gates, thereby enhancing the feasibility of running large quantum circuits on existing NISQ hardware.

0 citationsRead paper

Multi-Station WiFi CSI Sensing Framework Robust to Station-wise Feature Missingness and Limited Labeled Data

Mar 12, 2026

This work addresses the pervasive challenges of site-level feature missingness and scarce labeled data in multi-site WiFi channel state information (CSI) sensing. To jointly tackle structured missing patterns and label scarcity, the authors propose a unified modeling approach that explicitly incorporates site unavailability into both representation learning and downstream task training. The method integrates an enhanced cross-modal self-supervised learning (CroSSL) framework to learn representations robust to missing data and introduces a site-level masking augmentation (SMA) mechanism to improve generalization. Experimental results demonstrate that the proposed approach significantly outperforms existing single-strategy methods in scenarios where feature missingness and label scarcity coexist, thereby enhancing the robustness and practicality of CSI-based sensing systems.

0 citationsRead paper