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Co-salient object detection with iterative purification and predictive optimization

虚拟现实与智能硬件（中英文） 2024年第5期6卷 396-407页

作者： Yang WEN Yuhuan WANG Hao WANG Wuzhen SHI Wenming CAO Guangdong Provincial Key Laboratory of Intelligent Information Processing College of Electronics and Information EngineeringShenzhen UniversityShenzhen 518060China

Background Co-salient object detection(Co-SOD)aims to identify and segment commonly salient objects in a set of related ***,most current Co-SOD methods encounter issues with the inclusion of irrelevant information in the *** issues hamper their ability to locate co-salient objects and significantly restrict the accuracy of *** To address this issue,this study introduces a novel Co-SOD method with iterative purification and predictive optimization(IPPO)comprising a common salient purification module(CSPM),predictive optimizing module(POM),and diminishing mixed enhancement block(DMEB).Results These components are designed to explore noise-free joint representations,assist the model in enhancing the quality of the final prediction results,and significantly improve the performance of the Co-SOD ***,through a comprehensive evaluation of IPPO and state-of-the-art algorithms focusing on the roles of CSPM,POM,and DMEB,our experiments confirmed that these components are pivotal in enhancing the performance of the model,substantiating the significant advancements of our method over existing *** on several challenging benchmark co-saliency datasets demonstrate that the proposed IPPO achieves state-of-the-art performance.

关键词： Co-salient object detection Saliency detection Iterative method Predictive optimization

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Position Optimization for Mobile Relay Communications with Constrained Energy over a Whole Horizontal Plane

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China Communications 2023年第5期20卷 340-350页

作者： Haijun Tan Ning Xie Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information EngineeringShenzhen UniversityShenzhen 518060China

This paper concerns the position optimization problem of a mobile relay over a whole horizontal *** problem is important because the position of a mobile relay directly affects the end-to-end performance,e.g.,reliability,connectivity,and data *** this paper,we propose a new position optimization scheme of a mobile relay over a whole horizontal plane based on the one-bit feedback information from the destination node,which improves the performance over the prior scheme whose position of the mobile relay is optimized over a fixed *** the proposed scheme,the mobile relay is equipped with merely one single onboard ***,no prior information about the positions of both the source node and the destination node is ***,the proposed scheme can work at low network resources scenario,which is particularly suitable for mobile relay communication with constrained energy,e.g.,the communications in a disaster area where the infrastructure is heavily damaged,volcano monitoring,and wireless powered communication *** to the characteristics of the proposed scheme,we further design two heuristic implementations to calculate the optimal position of a mobile relay over a whole horizontal *** first implementation has better steady performance whereas the second implementation has better convergence *** implement the proposed scheme and conduct an extensive performance comparison between the proposed scheme and prior schemes to verify the advantages of the proposed scheme.

关键词： mobile relay position optimization one bit feedback information whole horizontal plane energy constrained

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Aircraft type recognition in 3D-view optical image with contour segmentation

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Multimedia Tools and Applications 2024年第18期83卷 54495-54520页

作者： Liang, Zhixiang Li, Yanshan Yu, Rui Zhang, Kaihao Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518061 China Institute of Intelligent Information Processing Shenzhen University Shenzhen518061 China College of Engineering and Computer Science Australian National University Canberra Australia

Aircraft type recognition has been researched deeply for plenty years on remote sensing and radar signal processing fields because of military needs. However, with the rapid development of UAV visualization technology in recent years, the advanced optical sensors on high-altitude drones are able to capture high-definition aircraft images of various aircraft flight postures, which brings aircraft type recognition into a brand-new research field. Different from remote sensing images, images with aircrafts in flight have larger intra-class difference caused by flight posture variation, which is 3D-view and more challenging for aircraft type recognition. Facing the new challenges, we propose a aircraft type recognition framework in 3D-view optical images. The framework consists of two stages: a coarse stage for aircraft contour segmentation and a fine stage for aircraft contour template matching. At the coarse stage, we propose a instance segmentation network CP-Deepsnake with a novel loss function CP loss which improves the accuracy of extracted aircraft contours by supervising the global distribution of contour points during contour deformation. At the fine stage, we utilize contour template matching to realize aircraft type recognition in 3D-view images. Based on IDSC contour feature descriptor, we propose a fast contour template matching approach with a new matching evaluation criterion and establish an aircraft contour template database for aircraft contour template matching. To train and test our aircraft type recognition framework, we build two datasets with 3D-view aircraft images of 10 aircraft types. Experiments show that our method achieves considerable recognition accuracy on the aircraft type testing dataset. Besides, we evaluate the proposed CP-Deepsnake network on two challenging instance segmentation public datasets SBD and KINS, where it compares favorably against state-of-the-art methods, which means our CP-Deepsnake network can be extended to more ins

关键词： Aircraft

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Positioning Error Compensation via Channel Knowledge Map for UAV Communication

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IEEE Internet of Things Journal 2025年第11期12卷 16977-16989页

作者： Zhang, Chiya Wang, Ting He, Chunlong Harbin Institute of Technology School of Electronic and Information Engineering Shenzhen518055 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

When Unmanned Aerial Vehicles (UAVs) perform high-precision communication tasks, such as searching for users and providing emergency coverage, positioning errors between base stations and users make it challenging to deploy trajectory planning algorithms. To address these challenges caused by position errors, a framework was proposed to compensate it by Channel Knowledge Map (CKM), which stores channel state information (CSI). By taking the positions with errors as input, the generated CKM could give a prediction of signal attenuation which is close to true positions. Based on that, the predictions are utilized to calculate the received power and a PPO-based algorithm is applied to optimize the compensation. After training, the framework is able to find a strategy that minimize the flight time under communication constraints and positioning error. Besides, the confidence interval is calculated to assist the allocation of power and the update of CKM is studied to adapt to the dynamic environment. Simulation results show the robustness of CKM to positioning error and environmental changes, and the superiority of CKM-assisted UAV communication design. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Trajectory optimization for UAV-enabled relaying with reinforcement learning

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Digital Communications and Networks 2025年第1期11卷 200-209页

作者： Chiya Zhang Xinjie Li Chunlong He Xingquan Li Dongping Lin School of Electronic and Information Engineering Harbin Institute of TechnologyShenzhenChina Guangdong Key Laboratory of Intelligent Information Processing Shenzhen UniversityShenzhenChina Peng Cheng Laboratory(PCL) ShenzhenChina National Mobile Communications Research Laboratory Southeast UniversityNanjingChina Shenzhen Institute of Information Technology ShenzhenChina Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Shenzhen UniversityShenzhenChina

In this paper,we investigate the application of the Unmanned Aerial Vehicle(UAV)-enabled relaying system in emergency communications,where one UAV is applied as a relay to help transmit information from ground users to a Base Station(BS).We maximize the total transmitted data from the users to the BS,by optimizing the user communication scheduling and association along with the power allocation and the trajectory of the *** solve this non-convex optimization problem,we propose the traditional Convex Optimization(CO)and the Reinforcement Learning(RL)-based ***,we apply the block coordinate descent and successive convex approximation techniques in the CO approach,while applying the soft actor-critic algorithm in the RL *** simulation results show that both approaches can solve the proposed optimization problem and obtain good ***,the RL approach establishes emergency communications more rapidly than the CO approach once the training process has been completed.

关键词： Unmanned aerial vehicle Emergency communications Trajectory optimization Convex optimization Reinforcement learning

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Enhancing 3D medical image registration with cross attention, residual skips, and cascade attention

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intelligent Data Analysis 2025年第1期29卷 186-201页

作者： Anwar, Muhammad He, Zhiquan Cao, Wenming College of Electronics and Information Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

At the core of Deep Learning-based Deformable Medical Image Registration (DMIR) lies a strong foundation. Essentially, this network compares features in two images to identify their mutual correspondence, which is necessary for precise image registration. In this paper, we use three novel techniques to increase the registration process and enhance the alignment accuracy between medical images. First, we propose cross attention over multi-layers of pairs of images, allowing us to take out the correspondences between them at different levels and improve registration accuracy. Second, we introduce a skip connection with residual blocks between the encoder and decoder, helping information flow and enhancing overall performance. Third, we propose the utilization of cascade attention with residual block skip connections, which enhances information flow and empowers feature representation. Experimental results on the OASIS data set and the LPBA40 data set show the effectiveness and superiority of our proposed mechanism. These novelties contribute to the enhancement of 3D DMIR-based on unsupervised learning with potential implications in clinical practice and research. © 2024 – IOS Press. All rights reserved.

关键词： Convolutional neural networks

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Dive into Coarse-to-Fine Strategy in Single Image Deblurring 1

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Li, Zebin Luo, Jianping Guangdong Key Laboratory of Intelligent Information Processing College of Electronic and Information Engineering Shenzhen University Shenzhen China

ISBN: (数字)9783031533051

ISBN: (纸本)9783031533044

The coarse-to-fine approach has gained significant popularity in the design of networks for single image deblurring. Traditional methods used to employ U-shaped networks with a single encoder and decoder, which may not adequately capture complex motion blur patterns. Inspired by the concept of multi-task learning, we dive into the coarse-to-fine strategy and propose an all-direction, multi-input and multi-output network for image deblurring (ADMMDeblur). ADMMDeblur has two distinct features. Firstly, it employs four decoders, each generating a unique residual representing a specific motion direction. This enables the network to effectively address motion blur in all directions within a two-dimensional (2D) scene. Secondly, the decoders utilize kernel rotation and sharing, which ensures the decoders do not separate unnecessary components. Consequently, the network exhibits enhanced efficiency and deblurring performance while requiring fewer parameters. Extensive experiments conducted on the GoPro and HIDE datasets demonstrate that our proposed network achieves better performance in deblurring accuracy and model size compared to existing well-performing methods. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image enhancement

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MSCTGAN: Conformer Improvements for Speech Enhancement 17

MSCTGAN: Conformer Improvements for Speech Enhancement

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17th IEEE International Conference on Signal processing, ICSP 2024

作者： Wu, Zhengyu Zheng, Nengheng College of Electronics and Information Engineering Shenzhen University The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (纸本)9798350387384

Convolution-augmented transformer (Conformer) has shown impressive performance in speech enhancement. However, it employs a single fixed large kernel convolutional block for local feature modeling, which limits its attention to local features. Also, modeling the entire frequency band in the same manner hinders the model from focusing on more critical sub band information, resulting in low performance. To address these two issues, we propose a GAN-based approach called Multi-Scale Convolution-augmented Transformer GAN (MSCTGAN) for speech enhancement. MSCTGAN incorporates several stacks of MSCT modules to better capture local features. Taking into account the varying importance of speech sub-bands, these features are introduced in frequency domain relative position encoding and loss function, which facilitated the fusion of both full-band and sub-band information. The objective evaluation demonstrates the competitive performance of MSCTGAN compared to models utilizing conventional Transformer and Conformer architectures. © 2024 IEEE.

关键词： Gallium nitride

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UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 430-445页

作者： Jiawen Kang Junlong Chen Minrui Xu Zehui Xiong Yutao Jiao Luchao Han Dusit Niyato Yongju Tong Shengli Xie IEEE School of Automation Guangdong University of TechnologyGuangzhou 510006China 111 Center for Intelligent Batch Manufacturing based on IoT Technology Guangzhou 510006China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of EducationGuangzhou 510006China School of Computer Science and Engineering Nanyang Technological UniversitySingaporeSingapore Pillar of Information Systems Technology and Design Singapore University of Technology and DesignSingaporeSingapore College of Communication Engineering Army Engineering University of PLANanjing 210007China National Natural Science Foundation of China Beijing 100085China Key Laboratory of Intelligent Detection and IoT in Manufacturing Ministry of EducationGuangzhou 510006China Guangdong Key Laboratory of IoT Information Technology Guangzhou 510006China

Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach

关键词： Avatar blockchain metaverses multi-agent deep reinforcement learning transformer UAVs

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Computed Tomography Image Segmentation of Renal Tumors based on Deep Learning 5

Computed Tomography Image Segmentation of Renal Tumors based...

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5th International Conference on Image, Video processing, and Artificial Intelligence, IVPAI 2023

作者： Kang, Li Song, Xinxin Gao, Zhijian The College of Electronics and Information Engineering Shenzhen University 518060 China The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

ISBN: (纸本)9781510674660

The incidence of renal tumors continues to rise each year, posing a serious threat to human health. Accurate segmentation of lesions is crucial for effective treatment. To enhance the segmentation performance of kidneys and renal tumors in CT images, this paper proposes a deep learning-based segmentation framework. The framework adopts a two-stage approach, starting from rough segmentation and progressing to fine segmentation, utilizing deep learning techniques. In the rough segmentation stage, a prior contour-assisted training technique is employed to extract the region of interest, namely kidneys and renal tumors. In the fine segmentation stage, an improved 3D convolution-based U-net model is proposed. Additionally, a novel loss function incorporating the mean and variance of pixel values of kidneys and renal tumors is introduced for fine-tuning. Given the limited data available, abdominal dataset images are used for pre-training the model. Through transfer learning, the model can learn common features from abdominal images. © 2024 SPIE. All rights reserved.

关键词： Tumors

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