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作者： Abbasi, Rashid Kashif Bashir, Ali Chen, Jianwen Mateen, Abdul Piran, Jalil Amin, Farhan Luo, Bin School of Information and Communication Engineering University of Electronics Science and Technology of China Chengdu China Department of Computing and Mathematics Manchester Metropolitan University Manchester United Kingdom Department of Computer Science Federal Urdu University of Arts Science and Technology Islamabad Pakistan Computer Engineering Department Sejong University Yeungnam Korea Republic of Department of Information and Communication engineering Yeungnam University Yeungnam Korea Republic of Key Lab of Intelligent Computing and Signal Processing of MOE & School of Computer and Technology Anhui University China

The vast amount of data is key challenge to mine a new scholar that is plausible to be star in the upcoming period. The enormous amount of unstructured data raise every year is infeasible for traditional learning;consequently, we need a high quality of preprocessing technique to expand the performance of traditional learning. We have persuaded a novel approach, Authors classification algorithm using Transfer Learning (ACTL) to learn new task on target area to mine the external knowledge from the source domain. Comprehensive experimental outcomes on real-world networks showed that ACTL, Node-based Influence Predicting Stars, Corresponding Authors Mutual Influence based on Predicting Stars, and Specific Topic Domain-based Predicting Stars enhanced the node classification accuracy as well as predicting rising stars to compared with contemporary baseline methods. © 2020 John Wiley & Sons Ltd

关键词： Stars

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Taming Stable Diffusion for MRI Cross-Modality Translation

Taming Stable Diffusion for MRI Cross-Modality Translation

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yingtai Li Shuo Yang Xiaoyan Wu Shan He S. Kevin Zhou School of Biomedical Engineering Division of Life Sciences and Medicine USTC Hefei P.R. China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advanced Research USTC P.R. China iFLYTEK Research iFLYTEK Co. Ltd Hefei P.R. China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing P.R. China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

In this study, we explore using Stable Diffusion (SD) for unsupervised medical image-to-image translation. SD has shown remarkable performances in generating high-quality images and can be easily applied to generate custom contents by injecting standard plug-ins like LoRA, offering a promising solution to tackle the complexity caused by variations in imaging modalities, acquisition parameters, and body parts in medical imaging. However, We empirically find that existing pipelines designed for natural images fail to translate directly to medical images due to weak structural control and inappropriate color preservation. To address these issues, we propose a novel two-branch image translation pipeline. This pipeline decouples the generation of target image along the time axis and employs ControlNet to ensure precise structural preservation. Additionally, we customize SD to generate images of extreme brightness, a common feature in medical imaging. Our results on the BraTS dataset demonstrate that SD with task-specific plug-ins can generate high-quality medical images comparable to those generated by task-specific models. Since the development of these standard plug-ins can be easily done by clinicians without much knowledge of the underlying algorithm, such a mode holds the potential to significantly extend the use of medical image computing algorithms in the clinical environment.

关键词： Translation Image color analysis Computational modeling Biological system modeling Magnetic resonance imaging Pipelines Brightness Complexity theory Standards Biomedical imaging

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A Dynamic Transformer Network for Vehicle Detection

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IEEE Transactions on Consumer Electronics 2025年

作者： Tian, Chunwei Liu, Kai Zhang, Bob Huang, Zhixiang Lin, Chia-Wen Zhang, David Harbin Institute of Technology School of Computer Science and Technology Harbin15001 China Macao Special Administrative Region of China PAMI Research Group University of Macau 999078 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Key Laboratory of Electromagnetic Environmental Sensing Hefei230601 China National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu Taiwan School of Data Science Shenzhen518172 China

Stable consumer electronic systems can assist traffic better. Good traffic consumer electronic systems require collaborative work between traffic algorithms and hardware. However, performance of popular traffic algorithms containing vehicle detection methods based on deep networks via learning data relation rather than learning differences in different lighting and occlusions is limited. In this paper, we present a dynamic Transformer network for vehicle detection (DTNet). DTNet utilizes a dynamic convolution to guide a deep network to dynamically generate weights to enhance adaptability of an obtained detector. Taking into relations of different information account, a mixed attention mechanism based channel attention and Transformer is exploited to strengthen relations of channels and pixels to extract more salient information for vehicle detection. To overcome the drawback of difference in an image account, a translation-variant convolution relies on spatial location information to refine obtained structural information for vehicle detection. Experimental results illustrate that our DTNet is competitive for vehicle detection. Code of the proposed DTNet can be obtained at https://***/hellloxiaotian/DTNet. © 2025 IEEE.

关键词： Dynamic convolution Hybrid Attention Mechanism Transformer Vehicle Detection

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Ultra-wideband MIMO Antenna Design Based on EBG Structure

Ultra-wideband MIMO Antenna Design Based on EBG Structure

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Zhipeng Chen Shijie Huang Zhongxiang Zhang Xianliang Wu Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Heifei China Anhui Province Key Laboratory of Simulation and Design for Electronic Information System Hefei Normal University Heifei China

ISBN: (纸本)9781665409889

This paper proposes a low-coupling planar compact four-element ultra-wideband antenna based on the EBG structure for multiple-input multiple-output (MIMO) systems. The MIMO antenna is composed of four miniaturized monopole antenna elements based on the half-cut method and a periodic electromagnetic band gap (EBG) structure. The traditional mushroom-shaped EBG structure is cut into squares and a pair of harpoon-shaped microstrip lines are introduced. Structure to reduce its resonant frequency and increase the band gap width. By loading the improved EBG structure in the middle of the MIMO antenna, the electromagnetic coupling between the components is greatly reduced. In addition, the stop band characteristics in the WLAN band (5.725–5.825GHz) are realized by adding a U-shaped resonant stub to the right side of the feeder of the monopole antenna. The experimental results show that it has a good reflection coefficient in the passband bandwidth range of 2.90–11.7GHz, covering the 3.1–10.6GHz frequency band specified by UWB, the isolation between the components is greater than 15dB, and good envelope correlation. The coefficient ECC is <0.02, which proves that the antenna is a potential candidate for ultra-wideband applications.

关键词： Correlation Resonant frequency Bandwidth Metamaterials Ultra wideband antennas Reflection coefficient Passband

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Hand Hygiene Assessment via Joint Step Segmentation and key Action Scorer

arXiv

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arXiv 2022年

作者： Li, Chenglong Zhu, Qiwen Liu, Tubiao Tang, Jin Su, Yu Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Artificial Intelligence Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China Hefei Normal University Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230061 China

Hand hygiene is a standard six-step hand-washing action proposed by the World Health Organization (WHO). However, there is no good way to supervise medical staff to do hand hygiene, which brings the potential risk of disease spread. Existing action assessment works usually make an overall quality prediction on an entire video. However, the internal structures of hand hygiene action are important in hand hygiene assessment. Therefore, we propose a novel fine-grained learning framework to perform step segmentation and key action scorer in a joint manner for accurate hand hygiene assessment. Existing temporal segmentation methods usually employ multi-stage convolutional network to improve the segmentation robustness, but easily lead to over-segmentation due to the lack of the long-range dependence. To address this issue, we design a multi-stage convolution-transformer network for step segmentation. Based on the observation that each hand-washing step involves several key actions which determine the hand-washing quality, we design a set of key action scorers to evaluate the quality of key actions in each step. In addition, there lacks a unified dataset in hand hygiene assessment. Therefore, under the supervision of medical staff, we contribute a video dataset that contains 300 video sequences with fine-grained annotations. Extensive experiments on the dataset suggest that our method well assesses hand hygiene videos and achieves outstanding performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

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Research on Data Augmentation for Garbage Classification with Target Detection Algorithm

Research on Data Augmentation for Garbage Classification wit...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Jiansong Chen Shikai Zuo Yun Lin Kai Tang Jianhang Zou Naixing Feng Yijun Cai Smart Sensing Integrated Circuit Engineering Research Center of Universities in Fujian Province Xiamen University of Technology Xiamen China Xiamen Wish Information Technology Co. Ltd Xiamen China Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China

Garbage classification is an essential work in daily life. With the development of artificial intelligence (AI), we have begun to use object detection to achieve garbage classification. However, the stacking and occlusion of garbage often make the detection performance unsatisfactory. In this paper, YOLOv5 algorithm is used for garbage detection and classification. YOLOv5 has three detection heads, which can detect objects at multiple scales. At the same time, Mosaic and Coarse Dropout image augmentation techniques are used to process our private data set of garbage, which improves the diversity of images. In this way, the target classification accuracy of the detection model can reach 75.7%, which is significantly improved compared with 69.9% without data augmentation. This paper provides a new technical support for the scene classification of stacked garbage based on deep learning.

关键词： Training Deep learning Head Stacking Object detection Computational electromagnetics Data models

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Continuous Distributed processing of Software Defined Radar

Continuous Distributed Processing of Software Defined Radar

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2021 CIE International Conference on Radar, Radar 2021

作者： Li, Bing Qiu, Qiang Gong, Shiqi Liu, Yongjun Lei, Yu Institute of Computing Technology CAS Key Lab of Network Data Science and Technology Chinese Academy of Sciences Beijing China State Key Laboratory of Internet of Things for Smart City University of Macau Macau China Xidian University National Laboratory of Radar Signal Processing Xi'an China Golaxy Data Technology Co. Ltd. Beijing China

ISBN: (纸本)9781665498142

Software-defined radar has been an active research field for more than ten years. However, the low performance and low scalability of the traditional processing techniques of SDR make it hard to deal with complex radar tasks. The stream processing engine is a software that supports low latency and high scalability distributed data processing. It is widely used in log, image, and video processing scenarios but rarely used in radar applications. This paper reviews processing software-defined radar data by using the stream processing engine, and we illustrate the differences between processing log data and radar data. Furthermore, we discuss some optimizations for stream processing engines to get better performance in the radar data processing. © 2021 IEEE.

关键词： distributed processing Software-defined radar stream processing engine

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Energy Consumption and Time Delay optimization of MEC Based on MultiDimensional Game 5

Energy Consumption and Time Delay optimization of MEC Based ...

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5th IEEE International Conference on Cloud computing and Big Data Analytics, ICCCBDA 2020

作者： Wang, Hui Wang, Yi Lu, Xingjun Hu, Yanjun Anhui University Key Laboratory of Intelligent Computing Signal Processing Ministry of Education Hefei230601 China Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China

ISBN: (纸本)9781728160245

Mobile edge computing (MEC) reduces latency and power consumption of mobile devices by deploying servers on the network edge. Due to the limited computing resources of the MEC server, excessive offloading of the users in local cells will bring unexpected queuing delay and high energy consumption. To deal with these problems, this paper proposes a Multi-user Queue Offloading multi-dimensional game algorithm (MQO). By the algorithm, users could choose the most suitable strategy, in which the strategy space is two-dimensional to satisfy different needs of different users to a certain extent, including the offload strategy and power control of users. Simulation results show that MQO can improve the performance of both overhead and offloading number with the increase of users. © 2020 IEEE.

关键词： Energy utilization

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HSIGene: A Foundation Model for Hyperspectral Image Generation

arXiv

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arXiv 2024年

作者： Pang, Li Cao, Xiangyong Tang, Datao Xu, Shuang Bai, Xueru Zhou, Feng Meng, Deyu The School of Computer Science and Technology The Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China The School of Mathematics and Statistics Northwestern Polytechnical University Shaanxi Xi’an710021 China The National Laboratory of Radar Signal Processing Xidian University Xi’an710071 China The Key Laboratory of Electronic Information Countermeasure and Simulation of the Education Ministry of China Xidian University Xi’an710071 China The School of Mathematics and Statistics The Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China Guangdong Guangzhou510555 China

Hyperspectral image (HSI) plays a vital role in various fields such as agriculture and environmental monitoring. However, due to the expensive acquisition cost, the number of hyperspectral images is limited, degenerating the performance of downstream tasks. Although some recent studies have attempted to employ diffusion models to synthesize HSIs, they still struggle with the scarcity of HSIs, affecting the reliability and diversity of the generated images. Some studies propose to incorporate multi-modal data to enhance spatial diversity, but spectral fidelity cannot be ensured. In addition, existing HSI synthesis models are typically uncontrollable or only support single-condition control, limiting their ability to generate accurate and reliable HSIs. To alleviate these issues, we propose HSIGene, a novel HSI generation foundation model which is based on latent diffusion and supports multi-condition control, allowing for more precise and reliable HSI generation. To enhance the spatial diversity of the training data while preserving spectral fidelity, we propose a new data augmentation method based on spatial super-resolution, in which HSIs are upscaled first, and thus abundant training patches could be obtained by cropping the high-resolution HSIs. In addition, to improve the perceptual quality of the augmented data, we introduce a novel two-stage HSI super-resolution framework, which first applies RGB bands super-resolution and then utilizes our proposed Rectangular Guided Attention Network (RGAN) for guided HSI super-resolution. Experiments demonstrate that the proposed model is capable of generating a vast quantity of realistic HSIs for downstream tasks such as denoising and super-resolution. The code and models are available at https://***/LiPang/HSIGene. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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引用

14th International Conference on Bio-inspired computing: Theories and Applications, BIC-TA 2019

作者： Sun, Dengdi Sun, Haifeng Wu, Hang Liang, Huadong Ding, Zhuanlian Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China iFlytek Co. Ltd. Hefei230088 China School of Internet Anhui University Hefei230039 China

ISBN: (纸本)9789811534140

Determining the functional roles of proteins is a vital task to understand life at molecular level and has great biomedical and pharmaceutical implications. With the development of novel high-throughput techniques, enormous amounts of protein-protein interaction (PPI) data are collected and provide an important and feasible way for studying protein function predictions. According to this, many approaches assign biological functions to all proteins using PPI networks directly. However, due to the extreme complexity of the topology structure of real PPI networks, it is very difficult and time consuming to seek the global optimization or clustering on the networks. In addition, biological functions are often highly correlated, which makes functions assigned to proteins are not independent. To address these challenges, in this paper we propose a two-stage function annotation method with robust feature selection. First, we transform the network into the low-dimensional representations of nodes via manifold learning. Then, we integrate the functional correlation into the framework of multi-label linear regression, and introduce robust sparse penalty to achieve the function assignment and representative feature selection simultaneously. For the optimization, we design an efficient algorithm to iteratively solve several subproblems with closed-form solutions. Extensive experiments against other baseline methods on Saccharomyces cerevisiae data demonstrate the effectiveness of the proposed approach. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Proteins

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