Dong Cheng

Zeshun You, Jiebin Yao, Dong Cheng et al.

The text-to-SQL task aims to convert natural language into Structured Query Language (SQL) without bias. Recently, text-to-SQL methods based on large language models (LLMs) have garnered significant attention. The core of mainstream text-to-SQL frameworks is schema linking, which aligns user queries with relevant tables and columns in the database. Previous methods focused on schema linking while neglecting to enhance LLMs' understanding of database schema. The complex coupling relationships between tables in the database constrain the SQL generation capabilities of LLMs. To tackle this issue, this paper proposes a simple yet effective strategy called view-based schema. This strategy aids LLMs in understanding the database schema by decoupling tightly coupled tables into low-coupling views. We then introduce V-SQL, a view-based two-stage text-to-SQL framework. V-SQL involves the view-based schema strategy to enhance LLMs' understanding of database schema. Results on the authoritative datasets Bird indicate that V-SQL achieves competitive performance compared to existing state-of-the-art methods.

Wenshan Bi, Dong Cheng, Kit Ian Kou

In this article, a robust color-edge feature extraction method based on the Quaternion Hardy filter is proposed. The Quaternion Hardy filter is an emerging edge detection theory. It is along with the Poisson and conjugate Poisson smoothing kernels to handle various types of noise. Combining with the Quaternion Hardy filter, Jin's color gradient operator and Hough transform, the color-edge feature detection algorithm is proposed and applied to the lane marking detection. Experiments are presented to demonstrate the validity of the proposed algorithm. The results are accurate and robust with respect to the complex environment lane markings.

Dong Cheng, Kit Ian Kou

This paper presents an innovative set of tools to support a methodology for the multichannel interpolation (MCI) of a discrete signal. It is shown that a bandlimited signal $f$ can be exactly reconstructed from finite samples of $g_k$ ($1\leq k\leq M$) which are the responses of $M$ linear systems with input $f$. The proposed interpolation can also be applied to approximate non-bandlimited signals. Quantitative error is analyzed to ensure its effectiveness in approximating non-bandlimited signals and its Hilbert transform. Based on the FFT technique, a fast algorithm which brings high computational efficiency and reliability for MCI is presented. The standout performance of MCI is illustrated by several simulations. Additionally, the proposed interpolation is applied to the single image super-resolution (SISR). Its superior performance in accuracy and speed of SISR is demonstrated by the experimental studies. Our results are compared qualitatively and quantitatively with the state-of-the-art methods in image upsampling and reconstruction by using the standard measurement criteria.