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IEEE National Conference on Aerospace and Electronics (NAECON)

作者： Daniel Nagura Trevor Bihl Jundong Liu School of Electrical Engineering and Computer Science Ohio University Sensors Directorate Air Force Research Laboratory (AFRL)

ISBN: (数字)9798350367621

ISBN: (纸本)9798350367638

In this paper, we present a novel application of reinforcement learning with artificial intelligence frameworks (RLAIF) to enhance race car motion planning. Leveraging Proximal Policy Optimization (PPO) as the foundational algorithm, we develop two distinct artificial intelligence systems built on Convolutional Neural Networks (CNNs). These systems are designed with specific objectives: the first aims to maintain the race car's position in the center of the road, while the second encourages the adoption of shorter, more efficient paths. To integrate these objectives into a coherent learning process, we devise a reward modeling strategy that translates the outcomes of the CNN-based classifications into meaningful reward signals. Experimental results demonstrate the effectiveness of our approach, showcasing significant improvements in race car motion planning.

关键词： Roads Reinforcement learning Aerospace electronics Boosting Planning Automobiles Convolutional neural networks

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SLSNet: Weakly-Supervised Skin Lesion Segmentation Network with Self-attentions 20th

SLSNet: Weakly-Supervised Skin Lesion Segmentation Network w...

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20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Pei, Songwen Huang, Junjie School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Engineering Research Center of Software/Hardware Co-design Technology and Application Ministry of Education East China Normal University Shanghai200062 China

ISBN: (纸本)9789819970247

computer-aided skin lesion segmentation with high precision is crucial to diagnose skin cancers in the early stage. However, the lack of pixel-level labels makes the skin lesion segmentation tasks challenging. To tackle this problem, a new weakly-supervised skin lesion segmentation network with self-attentions named SLSNet is proposed. SLSNet contains two modules and uses image-level labels as supervision information. One module named Intra-image Self-attention Seed Expansion (ISE) digs intra-image self-attentions with an expansion loss and a confidence loss to expand seed areas. The other module named Inter-image Affinity-based Noise Suppression (IAS) suppresses the noise pixels in attention maps via inter-image correlations. Extensive experiments conducted on ISIC-2017 dataset show that SLSNet achieves relatively high performance while reducing human labeling efforts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Expansion

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Stereo Image Dense Matching Based on SGM Constrained by Feature Matching 9

Stereo Image Dense Matching Based on SGM Constrained by Feat...

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9th International Conference on computer and Communications, ICCC 2023

作者： Ma, Tao Zhu, Hangbiao Huang, Weijian An, Pei Wang, Chun Yu, Kun Institute of Computer Application China Academy of Engineering Physics Mianyang China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan China National Key Laboratory of Science and Technology on Electromagnetic Energy Naval University of Engineering Wuhan China

ISBN: (纸本)9798350317251

Dense image matching plays a important role in stereo vision and remote sensing. Semi-global matching (SGM) is a widely used dense image matching framwork, which uses dynamic programming calculation strategy to optimize the global energy function. SGM has the problem of wrong disparity accumulation along the path. To solve the problem, this paper proposes a new dense image matching method based on feature matching and SGM. First, the feature matching is used to extract the reliable reference points. And then, reference points are used as additional constraints in matching cost aggregation to cut off the wrong disparity delivering along the path. In order to evaluate our method, qualitative and quantitative experiments are carried out on three stereo image pairs. The experimental results show that our method can achieve high matching accuracy by effectively reducing the error propagation. © 2023 IEEE.

关键词： Stereo vision

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A New Partial Task Offloading Method in a Cooperation Mode under Multi-Constraints for Multi-UE

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computers, Materials & Continua 2023年第9期76卷 2879-2900页

作者： Shengyao Sun Ying Du Jiajun Chen Xuan Zhang Jiwei Zhang Yiyi Xu School of Information Science and Technology Zhengzhou Normal UniversityZhengzhou450044China Henan Key Laboratory of Big Data Analysis and Processing Henan UniversityKaifeng475004China School of Geography and Tourism Zhengzhou Normal UniversityZhengzhou450044China Science and Engineering College South China University of TechnologyGuangzhou510641China Department of Electrical and Electronic Engineering Luohe Vocational Technology CollegeLuohe462002China School of Computer Science Beijing University of Posts and TelecommunicationsBeijing100876China Cardiff School of Engineering Cardiff UniversityCardiffCF103XQ 15UK

In Multi-access Edge Computing(MEC),to deal with multiple user equipment(UE)’s task offloading problem of parallel relationships under the multi-constraints,this paper proposes a cooperation partial task offloading method(named CPMM),aiming to reduce UE’s energy and computation consumption,while meeting the task completion delay as much as *** first studies the task offloading of single-UE and then considers the task offloading ofmulti-UE based on single-UE task *** the critical path algorithmto divide the modules into key and non-key *** to some constraints of UE-self when offloading tasks,it gives priority to non-key modules for offloading and uses the evaluation decision method to select some appropriate key modules for *** on fully considering the competition between multiple UEs for communication resources and MEC service resources,CPMM uses the weighted queuing method to alleviate the competition for communication resources and uses the branch decision algorithm to determine the location of module offloading by BS according to the MEC servers’*** achieves its goal by selecting reasonable modules to offload and using the cooperation ofUE,MEC,andCloudCenter to determine the execution location of *** experiments demonstrate that CPMM obtains superior performances in task computation consumption reducing around 6%on average,task completion delay reducing around 5%on average,and better task execution success rate than other similar methods.

关键词： MEC partial task offloading parallel dependencies completion delay

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Inkjet-printing assisted engineering of patternable zinc anode-based electrochromic devices

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Materials Reports(Energy) 2024年第4期4卷 40-47,I0001页

作者： Sheng Qiu Yanan Zhao Kai Wang Jinbin Luo Rui Wang Xinwei Jiang Jingwei Chen Abdulhakem Y.Elezzabi Wu Zhang Hao Jia Haizeng Li Key Laboratory of Eco-Textiles Ministry of EducationJiangnan UniversityWuxiJiangsu214122China School of Materials Science and Engineering Ocean University of ChinaQingdaoShandong266100China Ultrafast Optics and Nanophotonics Laboratory Department of Electrical and Computer EngineeringUniversity of AlbertaAlbertaT6G 2V4Canada National Innovation Center of Advanced Dyeing&Finishing Technology Tai'anShandong 271000China School of Energy and Power Engineering Shandong UniversityJinanShandong 250061China

Zn anode-based electrochromic devices(ZECDs)stand out as a highly promising technology in the upcoming era of multifunctional electronic devices,offering a blend of electrochromic capabilities and energy storage functions within a single transparent ***,significant challenges persist in achieving efficient patterning,ensuring long-term stability,and fast color-switching kinetics for these *** this study,heterogeneous tungsten oxide nanowires(W_(17)O_(47)/Na_(0.1)WO_(3),WNOs)are formulated into inkjet printing ink to assemble patternable *** heterogeneous electrode structure of WNO enables a highly capacitive-controlled mechanism that promotes fast electrochromic/electrochemical ***,by utilizing a three-dimensional MXene mesh modified substrate,the inkjet-printed ZECDs exhibit a wide optical modulation range of 69.13%,rapid color-changing kinetics(t_(c)=4.1 s,t_(b)=5.4 s),and highly reversible capacities of 70 mAh cm^(-2)over 1000 *** scalable strategy develops the patterned electrodes with a wide optical modulation range and substantial energy storage properties,offering promising prospects for their application in next-generation smart electronics.

关键词： Zn anode-based electrochromic devices Tungsten oxide nanowires Patterned electrodes Inkjet printing Electrolyte optimization

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Monocular Depth Estimation with Boundary Attention Mechanism and Shifted Window Adaptive Bins

SSRN

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SSRN 2024年

作者： Liang, Mengnan Wang, Congcong Zhao, Meng Shi, Fan Zhang, Chao Han, Yilin School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China Laboratory of Computer Vision System of Ministry of Education Tianjin University of Technology Tianjin300384 China

Monocular depth estimation is a classic research topic in computer vision. In recent years, development of Convolutional Neural Networks (CNNs) has facilitated significant breakthroughs in this field. However, there still are two challenges: 1) The network struggles to effectively fuse edge features in the feature fusion stage, which ultimately results in the loss of structure or distorted boundaries of objects in the scene. 2) Classification based studies typically depend on Transformers for global modeling, a process that often introduces substantial performance overhead. In this paper, we propose two modules to address the aforementioned issues. The first module is the Boundary Attention Module (BAM), which leverages the attention mechanism to enhance the ability of the network to perceive object boundaries during the feature fusion stage. In addition, to mitigate the performance overhead resulting from predicting adaptive bins, we propose a Shift Window Adaptive Bins (SWAB) module to reduce the amount of calculation in global modeling. The proposed method is evaluated on two public datasets, NYU Depth V2 and KITTI, and achieves state-of-the-art (SOTA) performance. © 2024, The Authors. All rights reserved.

关键词： computer vision

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ML-Assisted Resource Allocation Outage Probability: Simple, Closed-Form Approximations 17

ML-Assisted Resource Allocation Outage Probability: Simple, ...

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17th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2023

作者： Raina, Rashika Simmons, Nidhi Simmons, David E. Yacoub, Michel Daoud Queen's University Centre for Wireless Innovation School of Electronics Electrical Engineering and Computer Science BelfastBT3 9DT United Kingdom Dhali Holdings Ltd. BelfastBT5 7HW United Kingdom University of Campinas Wireless Technology Laboratory School of Electrical and Computer Engineering Campinas13083-970 Brazil

ISBN: (纸本)9798350307672

In this paper, we establish simple and efficient approximations for the outage probability of a single-user multi-resource allocation system that consists of a machine learning (ML) based outage predictor whose task is to assign resources to the user while minimizing outages. We begin by presenting the outage probability expressions for this system. We then propose the approximations to this system's outage probability using both the sinc function and the zeroth-order Bessel function of the first kind. These approximations are based on naive upper and lower bounds and stem from understanding how the channel samples de-correlate over time. Our results demonstrate that the outage probability indeed lies within the range defined by the bounds. Moreover, the effectiveness of the proposed outage probability approximations are evident as they exhibit a strong alignment with the trend of the outage probability curve. Finally, because of the simplicity of our approximations, they can be calculated in a computationally efficient manner. © 2023 IEEE.

关键词： Resource allocation

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Assessing the Robustness of nnU-Net in the Detection of Prostate Lesions via Bi-Parametric MRI

Assessing the Robustness of nnU-Net in the Detection of Pros...

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2023 IEEE EMBS Special Topic Conference on Data science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Zaridis, Dimitrios I. Mylona, Eugenia Tachos, Nikolaos Kalantzopoulos, Charalampos Pezoulas, Vasileios C. Koutsouris, Dimitrios D. Matsopoulos, George K. Marias, Kostas Tsiknakis, Manolis Fotiadis, Dimitrios I. National Technical University of Athens School of Electrical and Computer Engineering Biomedical Engineering Laboratory AthensGR15773 Greece Forth Biomedical Research Institute Ioannina Greece University of Ioannina Ioannina Greece Forth Institute of Computer Science Heraklion Greece Hellenic Mediterranean University Department of Electrical and Computer Engineering Crete Heraklion Greece FORTH-IMBB Ioannina Greece University of Ioannina Unit of Medical Technology and Intelligent Information Systems Materials Science and Engineering Department IoanninaGR 45110 Greece

ISBN: (纸本)9798350383386

Within the scope of prostate cancer diagnostic imaging, distinguishing lesions is challenging due to their subtle appearance and the prostate gland's complexity. This study employed the nnU-Net, a state-of-the-art medical image segmentation model, on a dataset of 301 patients from 2 openly available datasets to identify whether lesion sizes are affecting the model's performance. By applying t-SNE dimensionality reduction algorithm among dice score and respective lesion sizes, we found that nnU-Net behaves differently for lesions smaller than 9mm compared to lesions larger than 15mm. These insights can inform specialized training approaches for future deep learning models in prostate lesion *** Relevance: The main contributions of this study is, to investigate whether lesion sizes affects nnU-Net's deficiency leading to the development of more specialized models in respect to these factors. © 2023 IEEE.

关键词： Urology

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An overview of recent progress in the development of flexible electrochromic devices

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Nano Materials science 2023年第4期5卷 369-391页

作者： Bin Wang Wu Zhang Feifei Zhao William W.Yu Abdulhakem Y.Elezzabi Linhua Liu Haizeng Li Optics and Thermal Radiation Research Center Institute of Frontier&Interdisciplinary ScienceShandong UniversityQingdao266237China School of Chemistry&Chemical Engineering Shandong UniversityJinan250100China Ultrafast Optics and Nanophotonics Laboratory Department of Electrical and Computer EngineeringUniversity of AlbertaEdmontonAlbertaT6G 2V4Canada

Electrochromic materials are capable of reversibly switching their colors or optical properties through redox reactions under applied voltages,which have shown great potential applications including smart windows,nonemissive displays,optical filters,among *** the current rigid electrochromic devices have shown emerging interest and developed rapidly,many applications(e.g.,wearable/deformable optoelectronics)are blocked due to their inflexible ***,the adaption of rigid electrochromic devices to flexible ones is of particular interest for the new era of smart *** this review,the current state-of-the-art achievements of flexible electrochromic devices(FECDs)are highlighted,along with their design strategies and the choice of electrochromic *** recent research progress of FECDs is reviewed in detail,and the challenges and corresponding solutions for real-world applications of FECDs are ***,we summarize the basic fabrication strategies of FECDs and their potential *** addition,the development trend,the perspectives,and the outlook of FECDs are discussed at the end of this Review,which may provide recommendations and potential directions to advance the practical applications of FECDs.

关键词： Electrochromism Flexible electrochromic devices Optoelectronics Optical materials

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DFD-Net:lung cancer detection from denoised CT scan image using deep learning

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Frontiers of computer science 2021年第2期15卷 119-131页

作者： Worku J.SORI Jiang FENG Arero W.GODANA Shaohui LIU Demissie J.GELMECHA School of Electrical Engineering and Computing Department of Computer Science and EngineeringAdama Science and Technology UniversityAdama1888Ethiopia Faculty of Natural Computational Science Department of Mathematics Bule Hora UniversityBule HoraHagere Maryam144Ethiopia School of Computer Science and Technology Harbin Institute of TechnologyHarbin150001China Peng Cheng Laboratory Shenzhen518052China School of Electrical Engineering and Computing Department of Electronics and Communications EngineeringAdama Science and Technology UniversityAdama1888Ethiopia

The availability of pulmonary nodules in CT scan image of lung does not completely specify *** noise in an image and morphology of nodules,like shape and size has an implicit and complex association with cancer,and thus,a careful analysis should be mandatory on every suspected nodules and the combination of information of every *** this paper,we introduce a“denoising first”two-path convolutional neural network(DFD-Net)to address this *** introduced model is composed of denoising and detection part in an end to end ***,a residual learning denoising model(DR-Net)is employed to remove noise during the preprocessing ***,a two-path convolutional neural network which takes the denoised image by DR-Net as an input to detect lung cancer is *** two paths focus on the joint integration of local and global *** this end,each path employs different receptive field size which aids to model local and global *** further polish our model performance,in different way from the conventional feature concatenation approaches which directly concatenate two sets of features from different CNN layers,we introduce discriminant correlation analysis to concatenate more representative ***,we also propose a retraining technique that allows us to overcome difficulties associated to the image labels *** found that this type of model easily first reduce noise in an image,balances the receptive field size effect,affords more representative features,and easily adaptable to the inconsistency among nodule shape and *** intensive experimental results achieved competitive results.

关键词： medical image discriminant correlation analysis features fusion image detection denoising

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