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AESC-TransUnet:Attention Enhanced Selective Channel Transformer U-Net for Medical Image Segmentation

Signal, Image and Video Processing 2025年第9期19卷

作者： Huang, Wenlei Xiao, Hongxiang College of Computer Science and Engineering Guilin University of Technology Guangxi Guilin541006 China Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology Guangxi Guilin541004 China

Deep learning has greatly advanced medical image segmentation, especially with the integration of U-Net and Transformer architectures. However, challenges remain in clinical practice, such as low resolution, blurred edges, and difficulty in accurately segmenting small lesions. Existing methods, including various TransUNet variants, struggle with these issues. For instance, while TransUNet leverages the Transformer to capture global context, it suffers from the inability to preserve fine-grained local details. Similarly, Swin-Unet, which uses the Swin Transformer, excels in global feature extraction but often loses precision in complex backgrounds and small structures. The classic U-Net model, despite its strong performance in extracting local features, struggles with segmentation accuracy in low-contrast or complex areas due to its upsampling and downsampling *** address these limitations, we propose AESC-TransUNet, a novel network combining Efficient Selective Channel Attention (ESCA) and Convolution-Transformer fusion (ConvFormer). ESCA improves feature representation by using parallel processing and selective channel enhancement, effectively addressing the low resolution and blurred edges problem while preserving local details. ConvFormer optimizes the integration of both global and local information by combining convolution with self-attention mechanisms, reducing positional loss during upsampling and improving edge segmentation. Experimental results show that AESC-TransUNet significantly outperforms existing methods, achieving higher segmentation accuracy, particularly for small lesions and complex structures. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Image segmentation

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Generalized Visual Quality Assessment of GAN-Generated Face Images

arXiv

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arXiv 2022年

作者： Tian, Yu Ni, Zhangkai Chen, Baoliang Wang, Shiqi Wang, Hanli Kwong, Sam The Department of Computer Science City University of Hong Kong 999077 Hong Kong The Department of Computer Science & Technology Tongji University Shanghai 200092 China The Department of Computer Science & Technology Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai 200092 China The City University of Hong Kong Shenzhen Research Institute Shenzhen518057 China

Recent years have witnessed the dramatically increased interest in face generation with generative adversarial networks (GANs). A number of successful GAN algorithms have been developed to produce vivid face images towards different application scenarios. However, little work has been dedicated to automatic quality assessment of such GAN-generated face images (GFIs), even less have been devoted to generalized and robust quality assessment of GFIs generated with unseen GAN model. Herein, we make the first attempt to study the subjective and objective quality towards generalized quality assessment of GFIs. More specifically, we establish a large-scale database consisting of GFIs from four GAN algorithms, the pseudo labels from image quality assessment (IQA) measures, as well as the human opinion scores via subjective testing. Subsequently, we develop a quality assessment model that is able to deliver accurate quality predictions for GFIs from both available and unseen GAN algorithms based on meta-learning. In particular, to learn shared knowledge from GFIs pairs that are born of limited GAN algorithms, we develop the convolutional block attention (CBA) and facial attributes-based analysis (ABA) modules, ensuring that the learned knowledge tends to be consistent with human visual perception. Extensive experiments exhibit that the proposed model achieves better performance compared with the state-of-the-art IQA models, and is capable of retaining the effectiveness when evaluating GFIs from the unseen GAN algorithms. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Modeling and performances assessments of PV/T bifluid hybrid collector: Three cooling modes operation case 4

Modeling and performances assessments of PV/T bifluid hybrid...

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4th International Conference on Electrical and Information Technologies, ICEIT 2020

作者： El Manssouri, Oussama El Fouas, Chaimae Hajji, Bekkay Rabhi, Abdelhamid Tina, Giuseppe Marco Gagliano, Antonio Mohamed First University Renewable Energy Embedded System and Data Processing Laboratory National School of Applied Sciences Oujda Morocco Picardie Jules Verne University Modelization Information and Systems Laboratory Amiens France University of Catania DIEEI: Department of Electric Electronic and Computer Engineering Catania Italy

ISBN: (数字)9781728143415

ISBN: (纸本)9781728143415

The objective of the present work is the performance assessments of a bi-fluid photovoltaic/thermal solar collector (PV/T). Based on energy-balance equations, a mathematical model of PV/T hybrid collector has been developed and simulated using Matlab Software. This model takes into account the effect of the three modes of fluid operation under the same PV/T system, namely: the air mode, the water mode, and the simultaneous mode of water and air. The results show that the lowest PV module temperature is observed in the simultaneous mode (air water) compared to others modes. It's maximum value achieved is around 62°C in the bi-fluid mode against 80°C and 73°C respectively in the air mode and the water mode. The maximum efficiencies of 66.97 % (thermal efficiency) and 10.1% (electrical efficiency) are also observed, when both fluids are operated simultaneously compared to others modes. Finally, the working in simultaneous mode is beneficial to transmit more heat, produce more thermal energy and consequently improve the electrical and thermal performances of the PV/T hybrid collector. Moreover, the theoretical model developed is validated, evidencing a good fit between simulation results and the experimental data available in literature. The PV/T collector designed in this study has a variety of applications, as solar drying in agriculture, air conditioning, space heating and hot water supply for domestic applications. © 2020 IEEE.

关键词： Air

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Energy Efficiency Optimization for UAV-RIS-Assisted Wireless Powered Communication Networks

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Drones 2025年第5期9卷 344-344页

作者： Xianhao Shen Ling Gu Jiazhi Yang Shuangqin Shen Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology Guilin 541006 China School of Computer Science and Engineering Guilin University of Technology Guilin 541006 China School of Computer Science and Engineering Guilin University of Aerospace Technology Guilin 541004 China

In urban environments, unmanned aerial vehicles (UAVs) can significantly enhance the performance of wireless powered communication networks (WPCNs), enabling reliable communication and efficient energy transfer for urban Internet of Things (IoTs) nodes. However, the complex urban landscape characterized by dense building structures and node distributions severely hampers the efficiency of wireless power transmission. To address this challenge, this paper presents a novel framework for urban WPCN systems assisted by UAVs equipped with reconfigurable intelligent surfaces (UAV-RISs). The framework adopts time division multiple access (TDMA) technology to coordinate the transmission process of information and energy. Considering two TDMA methods, the paper jointly optimizes the flight trajectory of the UAV, the energy harvesting scheduling of ground nodes, and the phase shift matrix of the RIS with the goal of improving the energy efficiency of the system. Furthermore, deep reinforcement learning (DRL) is introduced to effectively solve the formulated optimization problem. Simulation results demonstrate that the proposed optimized scheme outperforms benchmark schemes in terms of average throughput and energy efficiency. Experimental data also reveal the applicability of different TDMA strategies: dynamic TDMA exhibits superior performance in achieving higher average throughput at ground nodes in urban scenarios, while traditional TDMA is more advantageous for total energy harvesting efficiency. These findings provide critical theoretical insights and practical guidelines for UAV trajectory design and communication network optimization in urban environments.

关键词： wireless powered communication network UAV reconfigurable intelligent surface time division multiple access deep reinforcement learning

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Random Forest Parameter Optimization Method Based on Fireworks Algorithm 20

Random Forest Parameter Optimization Method Based on Firewor...

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Proceedings of the 2020 3rd International Conference on E-Business, Information Management and computer Science

作者： Shurong Liang Xiaolan Xie Xiaochun Cheng Xiaohua Tao Guangxi key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology Guilin China Department of Computer Science Middlesex University London UK College of Information Science and Engineering Guilin University of Technology Guilin China

ISBN: (纸本)9781450389099

The construction of a smart city needs to be supported by good machine learning methods, random forest is a kind of technology which has been widely applied in dealing with regression or classification problems. The difficult points to be solved include how to set random forest parameters. The number of subtrees and samples will affect the performance of the algorithm to a great extent. This paper proposes a forest parameter optimization method based on firework algorithm (FWA-RF). By using its explosion, mutation and selection mechanisms, the parameters are adjusted in self-adaptive man-ner to avoid falling into a local optimal state. Through the comparative experiments on 15 UCI databases, the accuracy of the improved algorithm is higher than that of the original RF classification by 1.55% to 26.67%, and is superior to decision tree, Bagging, Adaboost as well as RF based on genetic algorithm. The results indicate that this method can effectively find the range of effective adjustment parameters, make parameter optimization and adjustment easier, and provide technical support for the creation of smart city.

关键词： Ensemble learning

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Capacity Building Among European Stakeholders In the Areas of Cyber-Physical systems, IoT & embedded systems: The SMART4ALL Digital Innovation Hub Perspective

Capacity Building Among European Stakeholders In the Areas o...

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IEEE computer Society Annual Symposium on VLSI

作者： Christos P. Antonopoulos Georgios Keramidas Vassilis Tsakanikas Evi Faliagka Christos Panagiotou Nikolaos Voros Electrical and Computer Engineering Department Embedded System Design & Applications Laboratory University of Peloponnese University of Peloponnese Tripolis Greece

ISBN: (数字)9781728157757

ISBN: (纸本)9781728157764

The goal of the SMART4ALL EU funded project is to build capacity amongst European stakeholders via the development of self-sustained, cross-border experiments that transfer knowledge and technology between academia and industry. It targets Customized Low Energy Computing (CLEC) in the Cyber-Physical (CPS) and the Internet of Things (IoT) domains by combining a set of unique characteristics that join together different cultures, different policies, different geographical areas, and different application domains. The SMART4ALL vision will be realized mainly through funded (via open calls) Pathfinder Application Experiments (PAEs) that will enable the transformation of academic knowledge into industry especially targeting South and Eastern European countries that are currently underrepresented in European funding opportunities. In this way community building, strategy development, and ecosystem learning are envisioned for boosting high-quality research and development in South Eastern Europe (SEE), through an efficient combination of regional, national and European policies. Furthermore SMART4ALL sets forward the concept of marketplace which is offered as a service (Marketplace-as-a-Service or MaaS) that acts as one-stop-smart-stop by offering tools, services, platforms based mainly on open sources technologies as well as technology suppliers - adopter matchmaking capabilities customized to the targeted thematic pillars of the project.

关键词： Europe Technological innovation Tools Companies Ecosystems

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Hardware architecture design of HEVC entropy decoding

Hardware architecture design of HEVC entropy decoding

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Shiyu Wang Shengbing Zhang Jihe Wang Libo Chang Liangyou Feng Xiaoya Fan Engineering Research Center of Embedded System Integration Ministry of Education School of Computer Science Northwestern Polytechnical University Xian China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xian China

As the latest generation of digital video coding standard, HEVC has technically optimized multiple modules such as related frame prediction, block processing and entropy coding in the frequency coding and decoding framework. However, flexible and efficient coding algorithms make the amount of calculation in the video decoding and reconstruction process increase dramatically. The energy efficiency of traditional processors is limited, and the decoding calculation process is difficult to meet the current needs of ultra-high-definition video playback. For the most important and time-consuming bitstream analysis and entropy decoding part of the HEVC decoding process, a new hardware architecture strategy is provided, which can effectively improve the HEVC decoding performance. In this paper, the designed data access module, bitstream analysis and entropy decoding unit are simulated and verified. Build an FPGA verification platform, and use the main tier standard test sequence to simulate and verify the designed hardware acceleration architecture. The experimental results show that the hardware architecture of bitstream analysis and entropy decoding designed in this paper can reach the functions and performance indicators specified by the HEVC standard Level-4 main tier, and the bitstream analysis acceleration effect is good. This thesis aims at the current HEVC decoding calculation characteristics, and through the design of hardware architecture verification, it provides new solutions and hardware architecture ideas for subsequent HEVC encoding and decoding performance optimization.

关键词： Analytical models Program processors Video sequences Bit rate Syntactics Prediction algorithms Entropy

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ASBSO: An improved brain storm optimization with flexible search length and memory-based selection

arXiv

引用

arXiv 2021年

作者： Yu, Yang Gao, Shangce Wang, Yirui Cheng, Jiujun Todo, Yuki The Faculty of Engineering University of Toyama Toyama930-8555 Japan Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Computer Science and Technology Tongji University Shanghai200092 China The School of Electrical and Computer Engineering Kanazawa University Kanazawa-shi920-1192 Japan

Brain storm optimization (BSO) is a newly proposed population-based optimization algorithm which uses a logarithmic sigmoid transfer function to adjust its search range during the convergent process. However, this adjustment only varies with the current iteration number, and lacks of flexibility and variety which makes a poor search efficiency and robustness of BSO. To alleviate this problem, an adaptive step length structure together with a success memory selection strategy are proposed to be incorporated into BSO. This proposed method, adaptive step length based on memory selection BSO, namely ASBSO, applies multiple step lengths to modify the generation process of new solutions, thus supplying a flexible search according to corresponding problems and convergent periods. The novel memory mechanism which is capable of evaluating and storing the degree of improvements of solutions is used to determine the selection possibility of step lengths. A set of 57 benchmark functions are used to test ASBSO's search ability, and four real-world problems are adopted to show its application value. All these test results indicate the remarkable improvement in solution quality, scalability and robustness of ASBSO. Copyright © 2021, The Authors. All rights reserved.

关键词： Swarm intelligence

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Finite time convergence of pinning synchronization with a single nonlinear controller

arXiv

引用

arXiv 2021年

作者： Chen, Tianping Lu, Wenlian Liu, Xiwei The School of Computer Sciences/Mathematics Fudan University Shanghai200433 China The Centre for Computational Systems Biology School of Mathematical Sciences Fudan University China Department of Computer Science and Technology Tongji University The Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai200092 China

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Random Forest Parameter Optimization Method Based on Fireworks Algorithm 3

Random Forest Parameter Optimization Method Based on Firewor...

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3rd International Conference on E-Business, Information Management and computer Science, EBIMCS 2020

作者： Liang, Shurong Xie, Xiaolan Cheng, Xiaochun Tao, Xiaohua Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology Guilin China Department of Computer Science Middlesex University LondonNW4 4BE United Kingdom College of Information Science and Engineering Guilin University of Technology Guilin China

ISBN: (纸本)9781450389099

关键词： Decision trees

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