Selected work

Representative Papers

Intuitive Hand Positional Guidance Using McKibben-Based Surface Tactile Sensations to Shoulder and Elbow

Aug 10, 2026

This study addresses the limited intuitiveness of conventional haptic guidance methods, which typically require users to pre-learn mappings between signals and target positions. To overcome this limitation, the authors propose a wearable fabric-based actuator utilizing McKibben-type pneumatic artificial muscles, applied for the first time to the shoulder–elbow region to deliver surface haptic cues that intuitively guide six distinct upper-limb movements. By integrating the equilibrium-point hypothesis with the Weber–Fechner law, the system dynamically modulates stimulation intensity, enabling high-accuracy guidance without prior user training. Experimental results demonstrate that the proposed approach significantly outperforms traditional voice synthesis and vibrotactile schemes in both guidance speed and precision.

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Intuitive Directional Sense Presentation to the Torso Using McKibben-Based Surface Haptic Sensation in Immersive Space

Aug 10, 2026

This study addresses the challenge of delivering intuitive navigational cues in immersive environments, where visual overload often impedes users’ ability to perceive critical guidance. Existing haptic approaches typically lack intuitiveness and require extensive user training. To overcome these limitations, this work proposes a wearable fabric-based actuator leveraging McKibben artificial muscles, which dynamically modulates pneumatic pressure to generate spatially distributed tactile stimuli across large areas of the torso. This enables vision-free, learnable-free directional guidance through intuitive somatosensory feedback. The research presents the first application of McKibben fabric actuators for conveying directional awareness during full-body movement, integrated within a mixed-reality system. User studies and statistical analyses confirm the approach’s efficacy in directional perception, with successful deployment demonstrated in teleoperated micro-manipulation tasks.

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Automatic Field-of-View Adjustment for a View-Expansive Microscope via LSTM-Based Gaze and Pipette Motion Interpretation

Aug 10, 2026

This study addresses the inefficiency and workflow disruption in intracytoplasmic sperm injection (ICSI) caused by frequent manual adjustments of the microscope’s field of view (FOV). The authors propose an adaptive FOV control method that integrates multi-view imaging, high-speed vision, and a galvanometer-based scanning system. For the first time, the approach combines the operator’s eye-gaze fixation points with real-time pipette position and velocity data, employing a long short-term memory (LSTM) model to predict and automatically adjust the optimal FOV without requiring objective lens changes. Experimental results demonstrate a significant improvement in procedural efficiency: novice operators reduced their average task completion time from 60.5 seconds to 48.0 seconds (p < 0.001), achieving performance comparable to that of experts.

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Cyclotomy, External Difference Families, and Algebraic Manipulation Detection Codes

Jul 24, 2026

This work addresses message integrity protection in scenarios where an adversary cannot observe codewords, by leveraging cyclotomic class theory to construct external difference families (EDFs) and generalized strong external difference families over finite fields, their direct products, and integer residue rings. It establishes a theoretical connection between these combinatorial structures and algebraic manipulation detection (AMD) codes. The study introduces explicit EDF existence criteria based on quadratic and biquadratic residues, enabling the first construction of infinite families of G-optimal systematic strong AMD codes with flexible parameters. Furthermore, it provides explicit constructions of R-optimal weak AMD codes for block lengths up to 14. These results substantially enhance the flexibility and applicability of AMD code constructions.

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Coordinate Encoding on Linear Grids for Physics-Informed Neural Networks

Mar 23, 2026

This work addresses the slow convergence of physics-informed neural networks (PINNs) when solving partial differential equations, a challenge often attributed to spectral bias. To mitigate this issue, the authors propose a coordinate encoding mechanism based on axis-aligned independent linear grids, which employs natural cubic spline interpolation to construct a smooth coordinate mapping over local solution domains. This approach preserves derivative continuity while effectively alleviating spectral bias. The resulting method significantly enhances both the training efficiency and stability of PINNs. Numerical experiments demonstrate that the proposed strategy outperforms existing mesh-free PDE solvers in terms of convergence speed and computational cost.

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Recent publications

Latest Papers

Intuitive Hand Positional Guidance Using McKibben-Based Surface Tactile Sensations to Shoulder and Elbow

Aug 10, 2026

This study addresses the limited intuitiveness of conventional haptic guidance methods, which typically require users to pre-learn mappings between signals and target positions. To overcome this limitation, the authors propose a wearable fabric-based actuator utilizing McKibben-type pneumatic artificial muscles, applied for the first time to the shoulder–elbow region to deliver surface haptic cues that intuitively guide six distinct upper-limb movements. By integrating the equilibrium-point hypothesis with the Weber–Fechner law, the system dynamically modulates stimulation intensity, enabling high-accuracy guidance without prior user training. Experimental results demonstrate that the proposed approach significantly outperforms traditional voice synthesis and vibrotactile schemes in both guidance speed and precision.

0 citationsRead paper

Intuitive Directional Sense Presentation to the Torso Using McKibben-Based Surface Haptic Sensation in Immersive Space

Aug 10, 2026

This study addresses the challenge of delivering intuitive navigational cues in immersive environments, where visual overload often impedes users’ ability to perceive critical guidance. Existing haptic approaches typically lack intuitiveness and require extensive user training. To overcome these limitations, this work proposes a wearable fabric-based actuator leveraging McKibben artificial muscles, which dynamically modulates pneumatic pressure to generate spatially distributed tactile stimuli across large areas of the torso. This enables vision-free, learnable-free directional guidance through intuitive somatosensory feedback. The research presents the first application of McKibben fabric actuators for conveying directional awareness during full-body movement, integrated within a mixed-reality system. User studies and statistical analyses confirm the approach’s efficacy in directional perception, with successful deployment demonstrated in teleoperated micro-manipulation tasks.

0 citationsRead paper

Automatic Field-of-View Adjustment for a View-Expansive Microscope via LSTM-Based Gaze and Pipette Motion Interpretation

Aug 10, 2026

This study addresses the inefficiency and workflow disruption in intracytoplasmic sperm injection (ICSI) caused by frequent manual adjustments of the microscope’s field of view (FOV). The authors propose an adaptive FOV control method that integrates multi-view imaging, high-speed vision, and a galvanometer-based scanning system. For the first time, the approach combines the operator’s eye-gaze fixation points with real-time pipette position and velocity data, employing a long short-term memory (LSTM) model to predict and automatically adjust the optimal FOV without requiring objective lens changes. Experimental results demonstrate a significant improvement in procedural efficiency: novice operators reduced their average task completion time from 60.5 seconds to 48.0 seconds (p < 0.001), achieving performance comparable to that of experts.

0 citationsRead paper

Cyclotomy, External Difference Families, and Algebraic Manipulation Detection Codes

Jul 24, 2026

This work addresses message integrity protection in scenarios where an adversary cannot observe codewords, by leveraging cyclotomic class theory to construct external difference families (EDFs) and generalized strong external difference families over finite fields, their direct products, and integer residue rings. It establishes a theoretical connection between these combinatorial structures and algebraic manipulation detection (AMD) codes. The study introduces explicit EDF existence criteria based on quadratic and biquadratic residues, enabling the first construction of infinite families of G-optimal systematic strong AMD codes with flexible parameters. Furthermore, it provides explicit constructions of R-optimal weak AMD codes for block lengths up to 14. These results substantially enhance the flexibility and applicability of AMD code constructions.

0 citationsRead paper

Coordinate Encoding on Linear Grids for Physics-Informed Neural Networks

Mar 23, 2026

This work addresses the slow convergence of physics-informed neural networks (PINNs) when solving partial differential equations, a challenge often attributed to spectral bias. To mitigate this issue, the authors propose a coordinate encoding mechanism based on axis-aligned independent linear grids, which employs natural cubic spline interpolation to construct a smooth coordinate mapping over local solution domains. This approach preserves derivative continuity while effectively alleviating spectral bias. The resulting method significantly enhances both the training efficiency and stability of PINNs. Numerical experiments demonstrate that the proposed strategy outperforms existing mesh-free PDE solvers in terms of convergence speed and computational cost.

0 citationsRead paper