Selected work

Representative Papers

Four classes of few-weight self-orthogonal codes and their applications for LCD codes and quantum codes

Jul 08, 2026

This work investigates the construction of self-orthogonal codes with relatively few non-zero weights and their applications to LCD and quantum code design. By introducing two new types of defining sets and leveraging augmented code techniques together with algebraic structures over finite fields, the authors systematically construct—for the first time—a family of projective four-weight and three families of general four-weight self-orthogonal codes, while precisely determining the parameters of their duals. Some of the resulting LCD codes have duals that nearly attain the sphere-packing bound, and the derived quantum codes achieve almost maximum distance separable (AMDS) performance, thereby demonstrating the superiority and practical relevance of the newly constructed code families.

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The Hermitian Hull Dimensions for a Class of (L,P)-Twisted Generalized Reed-Solomon Codes

Jul 04, 2026

This study addresses the lack of understanding regarding the Hermitian hull dimension of $(L,P)$-twisted generalized Reed–Solomon (TGRS) codes by focusing on a specific class of codes, denoted $C_k(\mathbf{a})$, where the defining vector $\mathbf{a}$ has length $i(q-1)$. By carefully analyzing the parity of $i$ and its relationship with $q+1$, the problem is partitioned into three distinct cases, enabling the first complete characterization of the Hermitian hull dimension for this family of TGRS codes. Leveraging this result, the authors successfully construct two new families of entanglement-assisted quantum error-correcting codes. Integrating algebraic coding theory, properties of finite fields, and the structure of the Hermitian inner product, this work not only systematically resolves the Hermitian hull dimension problem for these TGRS codes but also opens new avenues for quantum code design.

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A class of optimal authentication codes with secrecy

May 14, 2026

This work addresses the challenge of constructing efficient authentication codes that simultaneously ensure confidentiality, provide strong authentication guarantees, and effectively resist impersonation and substitution attacks. The authors propose a novel class of linear authentication codes grounded in linear algebraic structures. By leveraging specialized Weil sum analysis techniques, they rigorously derive tight upper bounds on the maximum success probabilities of both substitution and impersonation attacks. Furthermore, they demonstrate that the proposed scheme achieves asymptotically optimal security under a specific theoretical bound. Featuring simple encoding rules and low computational complexity, the scheme offers robust confidentiality alongside strong authentication, thereby balancing rigorous theoretical security with practical deployability.

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Construction of Minimal Ternary Linear Codes with Dimension $n+2$

May 14, 2026

This work addresses the construction of minimal ternary linear codes that violate the Ashikhmin–Barg condition by proposing a general method to generate ternary linear codes of dimension $m+2$. By integrating algebraic coding theory with exponential sum analysis, the authors derive necessary and sufficient conditions for the constructed codes to be minimal and, for the first time, provide their complete weight enumerator. The resulting family of codes strictly transcends the limitations imposed by the classical Ashikhmin–Barg sufficient condition, thereby expanding the known parameter space of minimal codes and offering new candidate constructions for cryptographic applications such as secret sharing.

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Convolutional Feature Noise Reduction for 2D Cardiac MR Image Segmentation

Nov 28, 2025

In 2D cardiac MR image segmentation, convolutional features suffer from unmodeled and unmitigated noise, leading to unstable feature propagation. To address this, we propose the Convolutional Feature Filter (CFF), which—firstly—models convolutional features as Gaussian-distributed signal matrices and introduces a low-amplitude pass filtering mechanism to suppress noise; secondly, incorporates an information-entropy-based binarization equation to enable computationally tractable, quantitative assessment of noise levels. CFF requires no additional parameters or architectural modifications and is plug-and-play compatible with mainstream 2D segmentation networks. Experiments on the ACDC and M&Ms public benchmarks demonstrate that CFF significantly reduces feature noise, improves Dice scores by 1.2–2.4%, and enhances model robustness and generalizability. This work establishes a novel paradigm for feature purification in medical image segmentation.

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Recent publications

Latest Papers

Four classes of few-weight self-orthogonal codes and their applications for LCD codes and quantum codes

Jul 08, 2026

This work investigates the construction of self-orthogonal codes with relatively few non-zero weights and their applications to LCD and quantum code design. By introducing two new types of defining sets and leveraging augmented code techniques together with algebraic structures over finite fields, the authors systematically construct—for the first time—a family of projective four-weight and three families of general four-weight self-orthogonal codes, while precisely determining the parameters of their duals. Some of the resulting LCD codes have duals that nearly attain the sphere-packing bound, and the derived quantum codes achieve almost maximum distance separable (AMDS) performance, thereby demonstrating the superiority and practical relevance of the newly constructed code families.

0 citationsRead paper

The Hermitian Hull Dimensions for a Class of (L,P)-Twisted Generalized Reed-Solomon Codes

Jul 04, 2026

This study addresses the lack of understanding regarding the Hermitian hull dimension of $(L,P)$-twisted generalized Reed–Solomon (TGRS) codes by focusing on a specific class of codes, denoted $C_k(\mathbf{a})$, where the defining vector $\mathbf{a}$ has length $i(q-1)$. By carefully analyzing the parity of $i$ and its relationship with $q+1$, the problem is partitioned into three distinct cases, enabling the first complete characterization of the Hermitian hull dimension for this family of TGRS codes. Leveraging this result, the authors successfully construct two new families of entanglement-assisted quantum error-correcting codes. Integrating algebraic coding theory, properties of finite fields, and the structure of the Hermitian inner product, this work not only systematically resolves the Hermitian hull dimension problem for these TGRS codes but also opens new avenues for quantum code design.

0 citationsRead paper

A class of optimal authentication codes with secrecy

May 14, 2026

This work addresses the challenge of constructing efficient authentication codes that simultaneously ensure confidentiality, provide strong authentication guarantees, and effectively resist impersonation and substitution attacks. The authors propose a novel class of linear authentication codes grounded in linear algebraic structures. By leveraging specialized Weil sum analysis techniques, they rigorously derive tight upper bounds on the maximum success probabilities of both substitution and impersonation attacks. Furthermore, they demonstrate that the proposed scheme achieves asymptotically optimal security under a specific theoretical bound. Featuring simple encoding rules and low computational complexity, the scheme offers robust confidentiality alongside strong authentication, thereby balancing rigorous theoretical security with practical deployability.

0 citationsRead paper

Construction of Minimal Ternary Linear Codes with Dimension $n+2$

May 14, 2026

This work addresses the construction of minimal ternary linear codes that violate the Ashikhmin–Barg condition by proposing a general method to generate ternary linear codes of dimension $m+2$. By integrating algebraic coding theory with exponential sum analysis, the authors derive necessary and sufficient conditions for the constructed codes to be minimal and, for the first time, provide their complete weight enumerator. The resulting family of codes strictly transcends the limitations imposed by the classical Ashikhmin–Barg sufficient condition, thereby expanding the known parameter space of minimal codes and offering new candidate constructions for cryptographic applications such as secret sharing.

0 citationsRead paper

Convolutional Feature Noise Reduction for 2D Cardiac MR Image Segmentation

Nov 28, 2025

In 2D cardiac MR image segmentation, convolutional features suffer from unmodeled and unmitigated noise, leading to unstable feature propagation. To address this, we propose the Convolutional Feature Filter (CFF), which—firstly—models convolutional features as Gaussian-distributed signal matrices and introduces a low-amplitude pass filtering mechanism to suppress noise; secondly, incorporates an information-entropy-based binarization equation to enable computationally tractable, quantitative assessment of noise levels. CFF requires no additional parameters or architectural modifications and is plug-and-play compatible with mainstream 2D segmentation networks. Experiments on the ACDC and M&Ms public benchmarks demonstrate that CFF significantly reduces feature noise, improves Dice scores by 1.2–2.4%, and enhances model robustness and generalizability. This work establishes a novel paradigm for feature purification in medical image segmentation.

0 citationsRead paper