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International Joint Conference on Neural Networks (IJCNN)

作者： Jiasen Li Jianxin Zhang Qiule Sun Hengbo Zhang Jing Dong Chao Che Qiang Zhang Key Lab of Advanced Design and Intelligent Computing (Ministry of Education) Dalian University Dalian China School of Computer Science and Engineering Dalian Minzu University Dalian China School of Information and Communication Engineering Dalian University of Technology Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

With the breakthrough performance in a variety of computer vision and medical image analysis problems, convolutional neural networks (CNNs) have been successfully introduced for the classification task of breast cancer histopathological images in recent years. Nevertheless, existing breast cancer histopathological image classification networks mainly utilize the first-order statistic information of deep features to represent histopathological images, failing to characterize the complex global feature distribution of breast cancer histopathological images. To address the problem, this work makes a first attempt to explore global second-order statistics of deep features for the above task. More specifically, we propose a novel deep second-order pooling network (DSoPN) for breast cancer histopatho-logical image classification, in which a robust global covariance pooling module based on matrix power normalization (MPN) is embedded into a simple yet effective CNN architecture. The given DSoPN model can capture richer second-order statistical information of deep convolutional features and produce more informative global representations for breast cancer histopatho-logical images. Experimental results on the public BreakHis dataset illuminate the promising performance of the second-order pooling for breast cancer histopathological image classification. Besides, our DSoPN achieves very competitive performance compared to the state-of-the-art methods.

关键词： Breast cancer Covariance matrices Task analysis Feature extraction computer architecture Pathology

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Deep High-order Asymmetric Supervised Hashing for Image Retrieval

Deep High-order Asymmetric Supervised Hashing for Image Retr...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yongchao Yang Jianxin Zhang Qian Wang Bin Liu Key Lab of Advanced Design and Intelligent Computing (Ministry of Education) Dalian University Dalian China School of Computer Science and Engineering Dalian Minzu University Dalian China International School of Information Science and Engineering (DUT-RUISE) Dalian University of Technology Dalian China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Deep hashing has recently been attracting more and more attentions for large-scale image retrieval task owing to its superior performance of search efficiency and less storage space requirements. Among deep hashing models, asymmetric deep hashing performs feature learning on query dataset and directly generates hash code on database images, significantly improving the retrieval performance of deep hashing models. Meanwhile, recently works also establish that high-order statistic of deep features are helpful to obtain more discriminant representations of images. Therefore, to boost the retrieval capability of deep hashing, this work tries to integrate merits of the high-order statistic module and the asymmetric deep hashing architecture, and it further proposes a novel deep high-order asymmetric supervised hashing (DHoASH) for image retrieval. More specifically, we utilize a powerful global covariance pooling module based on matrix power normalization to compute the second-order statistic features of input images, which is fluently embedded into an asymmetric hashing architecture in an end-to-end manner, leading to the generation of more discriminant binary hashing code. Experiment results on two benchmarks illuminates the effectiveness of the proposed DHoASH, which also achieves very competitive retrieval accuracy compared to the state-of-the-art methods.

关键词： Covariance matrices Feature extraction Matrix decomposition Image retrieval Task analysis Convolutional codes

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Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives

arXiv

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arXiv 2023年

作者： Teng, Siyu Hu, Xuemin Deng, Peng Li, Bai Li, Yuchen Yang, Dongsheng Ai, Yunfeng Li, Lingxi Xuanyuan, Zhe Zhu, Fenghua Chen, Long BNU-HKBU United International College Zhuhai519087 China Hong Kong Baptist University Kowloon 999077 Hong Kong The School of Computer Science and Information Engineering Hubei University Wuhan430062 China The State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body China The College of Mechanical and Vehicle Engineering Hunan University Changsha410082 China University of Chinese Academy of Sciences Beijing100049 China The School of Public Management/Emergency Management Jinan University Guangzhou510632 China Indianapolis United States Guangdong provincial key lab of IRADS BNU-HKBU United International College Zhuhai519087 China Institute of Automation Chinese Academy of Sciences Beijing100190 China Waytous Ltd. China

Intelligent vehicles (IVs) have gained worldwide attention due to their increased convenience, safety advantages, and potential commercial value. Despite predictions of commercial deployment by 2025, implementation remains limited to small-scale validation, with precise tracking controllers and motion planners being essential prerequisites for IVs. This paper reviews state-of-the-art motion planning methods for IVs, including pipeline planning and end-to-end planning methods. The study examines the selection, expansion, and optimization operations in a pipeline method, while it investigates training approaches and validation scenarios for driving tasks in end-to-end methods. Experimental platforms are reviewed to assist readers in choosing suitable training and validation strategies. A side-by-side comparison of the methods is provided to highlight their strengths and limitations, aiding system-level design choices. Current challenges and future perspectives are also discussed in this survey. Copyright © 2023, The Authors. All rights reserved.

关键词： Motion planning

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Reconfigurable Intelligent Computational Surfaces: When Wave Propagation Control Meets Computing

arXiv

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arXiv 2022年

作者： Yang, Bo Cao, Xuelin Xu, Jindan Huang, Chongwen Alexandropoulos, George C. Dai, Linglong Debbah, Mérouane Poor, H. Vincent Yuen, Chau The Engineering Product Development Pillar Singapore University of Technology and Design Singapore487372 Singapore The College of Information Science and Electronic Engineering Zhejiang Provincial Key Lab of information processing communication and networking Zhejiang University Hangzhou310007 China International Joint Innovation Center Zhejiang University Haining314400 China The Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens15784 Greece The Technology Innovation Institute Masdar City Abu Dhabi9639 United Arab Emirates The Department of Electronic Engineering Tsinghua University Beijing100084 China CentraleSupelec University Paris-Saclay Gif-sur-Yvette91192 France Department of Electrical and Computer Engineering Princeton University NJ United States

The envisioned sixth-generation (6G) of wireless networks will involve an intelligent integration of communications and computing, thereby meeting the urgent demands of diverse applications. To realize the concept of the smart radio environment, reconfigurable intelligent surfaces (RISs) are a promising technology for offering programmable propagation of impinging electromagnetic signals via external control. However, the purely reflective nature of conventional RISs induces significant challenges in supporting computation-based applications, e.g., wave-based calculation and signal processing. To fulfil future communication and computing requirements, new materials are needed to complement the existing technologies of metasurfaces, enabling further diversification of electronics and their applications. In this event, we introduce the concept of reconfigurable intelligent computational surface (RICS), which is composed of two reconfigurable multifunctional layers: the ‘reconfigurable beamforming layer’ which is responsible for tunable signal reflection, absorption, and refraction, and the ‘intelligence computation layer’ that concentrates on metamaterials-based computing. By exploring the recent trends on computational metamaterials, RICSs have the potential to make joint communication and computation a reality. We further demonstrate two typical applications of RICSs for performing wireless spectrum sensing and secrecy signal processing. Future research challenges arising from the design and operation of RICSs are finally highlighted. Copyright © 2022, The Authors. All rights reserved.

关键词： Metamaterials

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MRI images, brain lesions and deep learning

arXiv

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arXiv 2021年

作者： Castillo, Darwin Lakshminarayanan, Vasudevan Rodríguez-Álvarez, Maria J. Departamento de Química y Ciencias Exactas Sección Fisicoquímica y Matemáticas Universidad Técnica Particular de Loja Loja11-01-608 Ecuador Theoretical and Experimental Epistemology Lab School of Optometry and Vision Science University of Waterloo Ontario Canada Departments of Physics Electrical and Computer Engineering and Systems Design Engineering University of Waterloo Ontario Canada Valencia Spain

Medical brain image analysis is a necessary step in the computers Assisted /Aided Diagnosis (CAD) systems. Advancements in both hardware and software in the past few years have led to improved segmentation and classification of various diseases. In the present work, we review the published literature on systems and algorithms that allow for classification, identification, and detection of White Matter Hyperintensities (WMHs) of brain MRI images specifically in cases of ischemic stroke and demyelinating diseases. For the selection criteria, we used the bibliometric networks. Out of a total of 140 documents we selected 38 articles that deal with the main objectives of this study. Based on the analysis and discussion of the revised documents, there is constant growth in the research and proposal of new models of deep learning to achieve the highest accuracy and reliability of the segmentation of ischemic and demyelinating lesions. Models with indicators (Dice Score, DSC: 0.99) were found, however with little practical application due to the uses of small datasets and lack of reproducibility. Therefore, the main conclusion is to establish multidisciplinary research groups to overcome the gap between CAD developments and their complete utilization in the clinical environment. © 2021, CC BY.

关键词： Magnetic resonance imaging

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NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

arXiv

引用

arXiv 2022年

作者： Li, Yawei Zhang, Kai Timofte, Radu Van Gool, Luc Kong, Fangyuan Li, Mingxi Liu, Songwei Du, Zongcai Liu, Ding Zhou, Chenhui Chen, Jingyi Han, Qingrui Li, Zheyuan Liu, Yingqi Chen, Xiangyu Cai, Haoming Qiao, Yu Dong, Chao Sun, Long Pan, Jinshan Zhu, Yi Zong, Zhikai Liu, Xiaoxiao Hui, Zheng Yang, Tao Ren, Peiran Xie, Xuansong Hua, Xian-Sheng Wang, Yanbo Ji, Xiaozhong Lin, Chuming Luo, Donghao Tai, Ying Wang, Chengjie Zhang, Zhizhong Xie, Yuan Cheng, Shen Luo, Ziwei Yu, Lei Wen, Zhihong Wu, Qi Li, Youwei Fan, Haoqiang Sun, Jian Liu, Shuaicheng Huang, Yuanfei Jin, Meiguang Huang, Hua Liu, Jing Zhang, Xinjian Wang, Yan Long, Lingshun Li, Gen Zhang, Yuanfan Cao, Zuowei Sun, Lei Alexander, Panaetov Wang, Yucong Cai, Minjie Wang, Li Tian, Lu Wang, Zheyuan Ma, Hongbing Liu, Jie Chen, Chao Cai, Yidong Tang, Jie Wu, Gangshan Wang, Weiran Huang, Shirui Lu, Honglei Liu, Huan Wang, Keyan Chen, Jun Chen, Shi Miao, Yuchun Huang, Zimo Zhang, Lefei Ayazoglu, Mustafa Xiong, Wei Xiong, Chengyi Wang, Fei Li, Hao Wen, Ruimian Yang, Zhijing Zou, Wenbin Zheng, Weixin Ye, Tian Zhang, Yuncheng Kong, Xiangzhen Arora, Aditya Zamir, Syed Waqas Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Gao, Dandan Zhou, Dengwen Ning, Qian Tang, Jingzhu Huang, Han Wang, Yufei Peng, Zhangheng Li, Haobo Guan, Wenxue Gong, Shenghua Li, Xin Liu, Jun Wang, Wanjun Zeng, Kun Lin, Hanjiang Chen, Xinyu Fang, Jinsheng Zhang, Shuhao Zhang, Yuhao Sinha, Abhishek Kumar Moorthi, S. Manthira Dhar, Debajyoti Yang, Hao-Hsiang Huang, Zhi-Kai Chen, Wei-Ting Chang, Hua-En Kuo, Sy-Yen Tan, Wei Chen, Hao Xu, Qian Narang, Pratik Singh, Usneek Sameen, Syed Khaitan, Harsh Yinghua, Liu Tianlin, Zhang Xiaoming, Zhang Meng, Dingxuan Tian, Chunwei Morshed, Mashrur M. Ahsan, Ahmad Omar Computer Vision Lab ETH Zurich Switzerland University of Würzburg Germany ByteDance Shenzhen China State Key Laboratory for Novel Software Technology Nanjing University China ByteDance Inc China NetEase Inc. China Shenzhen Institutes of Advanced Technology CAS China University of Macau China Shanghai AI Lab Shanghai China Nanjing University of Science and Technology China Amazon Web Services United States China Alibaba DAMO Academy EFC Yuhang District Zhejiang Hangzhou China East China Normal University China Youtu Lab Tencent China Megvii Technology China University of Electronic Science and Technology of China China School of Artificial Intelligence Beijing Normal University China Alibaba Group China Bilibili AI China Nankai-Baidu Joint Lab Nankai University Tianjin China Platform Technologies Tencent Online Video China Higher School of Economics Russia Huawei Moscow Research Center Russia Hunan University China Xidian University China Xilinx Technology Beijing Limited China College of Information Science and Engineering Xinjiang University Urumqi China Department of Electronic Engineering Tsinghua University Beijing China Nanjing University China McMaster University Canada School of Telecommunication Engineering Xidian University Xi’an China School of Computer Science Wuhan University Wuhan China School of Mathematical Science University of Electronic Science and Technology of China Chengdu China School of Computer Science The University of Sydney Sydney Australia Aselsan Research Ankara Turkey School of Electronic and Information Engineering South-Central University for Nationalities Wuhan China Guangdong University of Technology China Fujian Normal University Fuzhou University Jimei University China Design Group China Abu Dhabi United Arab Emirates Monash University Melbourne Australia Mohamed bin Zayed University of AI United Arab Emirates North China Electric Power University Changping District Beijing China The School of Art

This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images. The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29.00dB on DIV2K validation set. IMDN is set as the baseline for efficiency measurement. The challenge had 3 tracks including the main track (runtime), sub-track one (model complexity), and sub-track two (overall performance). In the main track, the practical runtime performance of the submissions was evaluated. The rank of the teams were determined directly by the absolute value of the average runtime on the validation set and test set. In sub-track one, the number of parameters and FLOPs were considered. And the individual rankings of the two metrics were summed up to determine a final ranking in this track. In sub-track two, all of the five metrics mentioned in the description of the challenge including runtime, parameter count, FLOPs, activations, and memory consumption were considered. Similar to sub-track one, the rankings of five metrics were summed up to determine a final ranking. The challenge had 303 registered participants, and 43 teams made valid submissions. They gauge the state-of-the-art in efficient single image super-resolution. © 2022, CC BY.

关键词： Chemical activation

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design4Health Bootcamp: A design thinking approach to improve the 21stcentury skills of health, engineering and design students

Design4Health Bootcamp: A design thinking approach to improv...

引用

Advances in science and engineering Technology International Conferences (ASET)

作者： Thomas Boillat Christopher Tuffnell Homero Rivas Fadi Aloul Carlos Montana Design Lab Mohammed Bin Rashid University of Medicine and Health Sciences Dubai UAE Mohammed Bin Rashid University of Medicine and Health Sciences Dubai UAE Computer Science & Engineering American University of Sharjah Sharjah UAE Dubai Institute of Design and Innovation Dubai UAE

ISBN: (数字)9781728146409

ISBN: (纸本)9781728146416

Global industries are acknowledging that professions as we know them will not require the same skills as they did in the 20 th Century. Therefore how can universities prepare students for challenges that have not been fully identified? This paper will present the design4Health Bootcamp, an initiative that intends to close the so-called “21 st century skills gap”. We present its course curriculum, quantitative and qualitative evaluation and conclude with a set of principles that support the implementation of such initiatives.

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Optimization Method for an Information Interaction Interface from Error-Cognition to Information Feature Mapping

Research Square

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Research Square 2020年

作者： Wu, Xiaoli Caldwell, Sabrina School of Design Art & Medial Nanjing University of Science & Technology Nanjing210094 China Research School of Computer Science The Australian National University ACT Australia College of Mechanical and Electrical Engineering Hohai University Changzhou China Lab of Human Factors and Information System Interaction & Design Hohai University Changzhou China

Background- With the rapid development of digital and intelligent information systems, display of visual information in interfaces has become an important challenge in the field of human-computer interaction. Objective- We propose a method for the optimization of information interaction interfaces from error-cognition through the mapping of information characteristics. Method- A mapping method of matrix description is adopted to analyze the association properties between error-cognition sets and design information sets. Results- Based on the mapping relationship between the domains of error-cognition and design information, a cross-correlational analysis is carried out between error-cognition and design information. Conclusion- We obtain the relationship matrix between the error-cognition of correlation between design information and the degree of importance among design information. Application- Taking the task interface of a warfare navigation display as an example, error factors and the features of design information are extracted. Based on the results, we also propose an optimization design scheme for the monitoring task interface. © 2020, CC BY.

关键词： Errors

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Carbon allotropes/fabrics-based triboelectric nanogenerators: Current progress and future perspectives

引用

Materials science and engineering: R: Reports 2025年 166卷

作者： Shan He Matt Jellicoe Aniket Chakraborthy Md. Eshrat E. Alahi Anindya Nag Warwick Powell School of Food and Pharmacy Zhejiang Ocean University Zhoushan Zhejiang 316022 China Institute for NanoScale Science and Technology College of Science and Engineering Flinders University Bedford Park Australia Faculty of Electrical and Computer Engineering Technische Universität Dresden Dresden 01062 Germany Centre for Tactile Internet with Human-in-the-Loop (CeTI) Technische Universität Dresden Dresden 01069 Germany School of Engineering and Technology Walailak University 222 Thaiburi Thasala District Nakhon Si Thammarat 80160 Thailand QUT Design Lab Queensland University of Technology Brisbane Queensland Australia

The paper presents a substantial review of triboelectric nanogenerators (TENG) development using different carbon allotropes and fabric materials. TENG has been state-of-the-art since the last decade due to its low cost, simple construction, high output and multifunctional applications. Based on their working mechanism, these prototypes have been developed using a wide range of nanomaterials and polymers to induce high open-circuit voltage and short-circuit current. Certain carbon-based nanomaterials, such as carbon nanotubes, graphene and others, have been found to be very effective due to their excellent electrical properties and biocompatibility. These carbon materials have been intertwined and fused with polymers to form effective energy generators and harvesters. Conjugating these carbon materials into certain flexible fabrics, like cotton, silk, polyester and others, has been largely successful. In addition to some of the factors that affect the performance of the nanogenerators, this paper also showcases some of the fabric-based prototypes, the performance of which has been enhanced by adding carbon materials. The market survey and the future perspectives have also been shown.

关键词： Nanogenerators Carbon Fabric Energy harvesting Flexible

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Observation of intrinsic chiral bound states in the continuum

arXiv

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arXiv 2022年

作者： Chen, Yang Deng, Huachun Sha, Xinbo Chen, Weijin Wang, Ruize Chen, Yuhang Wu, Dong Chu, Jiaru Kivshar, Yuri S. Xiao, Shumin Qiu, Cheng-Wei Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Chinese Academy of Sciences Key Laboratory of Mechanical Behavior and Design of Materials Department of Precision Machinery and Precision Instrumentation University of Science and Technology of China Hefei230027 China Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Harbin Institute of Technology Shenzhen518055 China Nonlinear Physics Center Research School of Physics Australian National University CanberraACT2601 Australia Pengcheng Laboratory Shenzhen518055 China

Photons with spin angular momentum possess intrinsic chirality which underpins many phenomena including nonlinear optics1, quantum optics2, topological photonics3 and chiroptics4. Intrinsic chirality is weak in natural materials, and recent theoretical proposals5-7 aimed to enlarge circular dichroism by resonant metasurfaces supporting bound states in the continuum that enhance substantially chiral light-matter interaction. Those insightful works resort to three-dimensional sophisticated geometries, which are too challenging to be realized for optical frequencies8. Therefore, most of the experimental attempts9-11 showing strong circular dichroism rely on false/extrinsic chirality by employing either oblique incidence9, 10 or structural anisotropy11. Here, we report on the experimental realization of true/intrinsic chiral response with resonant metasurfaces where the engineered slant geometry breaks both in-plane and out-of-plane symmetries. Our result marks the first observation of intrinsic chiral bound states in the continuum with near-unity chiral dichroism of 0.93 and record-high quality factor exceeding 2663 for visible frequencies. Our chiral metasurfaces promise a plethora of applications in chiral light sources and detectors, chiral sensing, valleytronics and asymmetric photocatalysis. © 2022, CC BY.

关键词： Dichroism

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