Ling Wang

Tsinghua University

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Yeqiu Li

Shenyang Ligong University

Jianming Lu

Ningbo University

Xiaozong Yang

Harbin Institute of Technology

Takashi Yahagi

Signal Processing (United States)

Zhenguo Gao

Huaqiao University

Qiwen Ran

Harbin Institute of Technology

Jing Ma

Harbin Institute of Technology

Liying Tan

Harbin Institute of Technology

Takahide Okamoto

Teikyo University

Shaobin Cai

Huzhou University

Cooperative Institutions

Harbin Institute of Technology

Anhui University

Chinese Academy of Sciences

Tsinghua University

Ministry of Education of the People's Republic of China

University of Chinese Academy of Sciences

Dalian University of Technology

Harbin Engineering University

University of Science and Technology of China

Anhui Medical University

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Noise Removal for Medical X-ray Images in Wavelet Domain

IEEJ Transactions on Electronics Information and Systems (2006)

Ling Wang Jianming Lu Yeqiu Li Takashi Yahagi Takahide Okamoto

Many important problems in engineering and science are well-modeled by Poisson noise, the noise of medical X-ray image is Poisson noise. In this paper, we propose a method of noise removal for degraded medical X-ray image using improved preprocessing and improved BayesShrink (IBS) method in wavelet domain. Firstly, we pre-process the medical X-ray image, Secondly, we apply the Daubechies (db) wavelet transform to medical X-ray image to acquire scaling and wavelet coefficients. Thirdly, we apply the proposed IBS method to process wavelet coefficients. Finally, we compute the inverse wavelet transform for the thresholded coefficeints. Experimental results show that the proposed method always outperforms traditional methods.

Second-generation wavelet transform

Daubechies wavelet

Stationary wavelet transform

10.1541/ieejeiss.126.237

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Citations (7)

Hardware Acceleration of YOLOv5s Network Model Based on Aerospace-grade FPGA

Chinese Journal of Space Science (2023)

Kangning Jiang Hai Zhou Chunjiang Bian Ling Wang

由于遥感图像具有分辨率高和背景信息复杂的特点，其对目标检测的精确性和鲁棒性要求越来越高，因此遥感图像处理领域逐渐引入了卷积神经网络算法。然而此类算法通常模型复杂且计算量庞大，难以在空间与资源受限的星上平台高效运行。针对这一问题，提出一种基于宇航级现场可编程门阵列（Filed Programmable Gate Array, FPGA）的卷积神经网络硬件加速架构，并选用YOLOv5s作为目标网络，采用输入与输出通道并行展开以及数据流水线控制的策略进行架构设计。实验结果表明，在使用该处理架构加速YOLOv5s的推理阶段，卷积模块的工作频率可以达到200 MHz，其运算性能高达394.4GOPS（Giga Operations Per Second），FPGA的功耗为14.662 W，数字信号处理（Digital Signal Processing, DSP）计算矩阵的平均计算效率高达96.29%。

10.11728/cjss2023.04.2022-0044

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Noise Reduction in Medical X-ray Image Using Wavelet Based on Image Characteristics

信号処理 (2005)

Ling Wang Jianming Lu Yeqiu Li

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Computational studies of quantum spin systems

Ling Wang

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Two novel “turn on” fluorescent probes for monitoring hypochlorite in living HeLa cells

Dyes and Pigments (2021)

Ling Wang Rui Zhang Yingcui Bu Ze Huang Lin Kong

Quantum yield

Benzothiazole

Turn (biochemistry)

HeLa

Hypochlorite

Methylene

Methylene blue

10.1016/j.dyepig.2021.109749

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Joint Effects of Co-Channel Interferences and Pointing Errors on Dual-Hop Mixed RF/FSO Fixed-Gain and Variable-Gain Relaying Systems

IEEE photonics journal (2023)

Junrong Ding Dongpeng Kang Xiaolong Xie Ling Wang Liying Tan

The performance of a dual-hop mixed radio frequency (RF)/free space optical (FSO) system with co-channel interferences (CCIs) is studied for both fixed-gain and variable-gain amplifyand-forward(AF) relaying scenarios.The RF link and interference link undergo the κ-μ shadowed fading and the FSO link experiences the Fisher-Snedecor F distribution with pointing errors.For the considered system, we derive new closed-form expressions for the probability density function (PDF), cumulative distribution function (CDF), and average bit error rate (BER) of the end-to-end signal-to-interference-plus-noise ratio (SINR).In addition, efficient numerical expressions for the ergodic capacity are also obtained.Moveover, the asymptotic expressions for the CDF and average BER are derived.Our results can be regarded as a generalization of the previous results.Finally, numerical and simulation results are presented to verify the correctness of the proposed expressions. Index Terms-Mixed RF/FSO

10.1109/jphot.2023.3238205

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High level circuit synthesis with system level Statistical Static Timing Analysis under process variation

2022 IEEE 65th International Midwest Symposium on Circuits and Systems (MWSCAS) (2013)

A. Baker Ling Wang Yingtao Jiang

Process variations are of great concern in deep submicron technology. Early prediction of their effects on the circuit performance and parametric yield is extremely useful. Due to the increase of the design complexity in today's SoC chips, a demand for high level design has increased. Therefore, in this paper, we propose the timing analysis model so that the impact of process variations is taken into account during high level synthesis. In experiments, the proposed method have showed very minor variances of 1.67% at the 85% timing yield constraint (TYC) and of 0.26% at the 99% (3σ) TYC, as opposed to Monte-Carlo simulation. In our approach, we consider the spatial and path reconvergence correlations between path delays, set-up and hold time constraints, as well as skew due to process variations.

Process Variation

Static timing analysis

Variation (astronomy)

10.1109/mwscas.2013.6674774

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Citations (1)

AnimatableDreamer: Text-Guided Non-rigid 3D Model Generation and Reconstruction with Canonical Score Distillation

Lecture notes in computer science (2024)

Xinzhou Wang Yikai Wang Junliang Ye Fuchun Sun Zhengyi Wang

Text generation

10.1007/978-3-031-72698-9_19

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Bags-of-daglets for action recognition

2022 IEEE International Conference on Image Processing (ICIP) (2014)

Ling Wang Hichem Sahbi

Recent advances in human action recognition are focusing on fine-grained action categories in large video collections. With this current trend, one of the major issues is how to handle these large collections effectively and also efficiently. In this paper, we introduce a novel action recognition method based on mid-level components and directed acyclic graphs (DAGs). DAGs, taken from different videos, are efficiently processed in order to extract a large collection of spatio-temporal sub-patterns, of increasing complexities, referred to as daglets. The latter capture local appearances as well as causal structural relationships of interacting object-parts in video sequences. The main contribution of this work includes a daglet matching procedure and a DAG kernel that captures first and high order statistics of daglets into videos. When combined with support vector machines, this DAG kernel proved to be very effective in order to capture similarity between actions in videos and to successfully achieve action recognition on a standard challenging database.

Kernel (algebra)

Action Recognition

Similarity (geometry)

10.1109/icip.2014.7025310

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Citations (24)

A two-dimensional material recognition image algorithm based on deep learning

Bin Wu Ling Wang Zhenyu Gao

The Van der Waals heterojunction is composed of stacked atomically thin two-dimensional materials. As a new type material, it has many unprecedented characteristics. However, owing to the position and shape of the two-dimensional material peeling are random during the preparation process, the preparation efficiency of Van der Waals heterojunction is always low. Finding the peeled two-dimensional material quickly has become the key to improve the preparation speed of Van der Waals heterojunctions. In recent years, deep learning has become more and more excellent in the field of image recognition, and the application of deep learning on two-dimensional material recognition has achieved a very good effect. In this paper, we use deep learning-based semantic segmentation network to identify and segment two-dimensional materials in images collected under optical microscope. Moreover, the network architecture was improved to achieve an average accuracy of 97.17%. Since deep learning algorithms can extract high-level features of images through training with a large amount of data, compared with traditional graphics algorithms, the deep learning algorithm have good generalization capabilities and are robust to changes in conditions during detection, which can avoid errors caused by traditional algorithms that require manual adjustment of parameters.

10.1109/itca49981.2019.00061

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Citations (4)