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SSRN 2022年

作者： Daher, Ali W. Ferrari, Enrico Verda, Damiano Chible, Hussein Musellic, Marco Caviglia, Daniele D. Department of Electrical Electronic and Telecommunications Engineering and Naval Architecture University of Genoa Via Opera Pia 11 Liguria Genoa16145 Italy Ph.D. School for Sciences and Technology Lebanese University Beirut6573/14 Lebanon Institute of Electronics Computer and Telecommunication Engineering Consiglio Nazionale delle Ricerche Via De Marini 6 Genoa Liguria16149 Italy Rulex Innovation Labs Rulex inc. Via Felice Romani 9 Liguria Genoa16122 Italy

Image classification and interpretability is a basic procedure in modern AI systems as well as being employed in the fields of security, instrumentation, and image classification. Explainable Artificial Intelligence also plays a crucial role in providing machine learning techniques with a white-box output that allows the practitioner to follow or interpret the rules. Therefore, we present CACAO-X which is a framework for ML forecasts that utilizes an algorithm which converts images into time-series and employs CNN, LSTM, and MLP on the original images and converted data in a mixed input approach achieving accurate forecasts on multiple applications. Furthermore, Shapley values are employed on the proposed ML models to extract the local explainable features of such images. These features are again used with the same conversion algorithm to generate time-series and application specific landmarks using the Logic Learning Machine (LLM) to determine the explainable rules. In addition to being generated automatically, explicative labelling is adopted in the dataset which give insight to the operator to which landmarks, positions and angles contribute to the classification of each record. The system is robust as it achieves benchmark results in the gender classification application while remaining competitive in all others where it is used as a reference model for global image explainability. Additionally, the ML inference for every application is performed according to the edge-computing paradigm, by deploying the models on a limited resources Raspberry Pi platform through SSH access. Moreover, a novel callback function has been developed which is able to stop the training of the CACAO-NET model at a stable state based on the level of oscillation around the most recent mean accuracy. © 2022, The Authors. All rights reserved.

关键词： Image classification

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New Feedback Control Model in the Lattice Hydrodynamic Model Considering the Historic Optimal Velocity Difference Effect

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Communications in Theoretical Physics 2018年第12期69卷 803-807页

作者： Guang-Han Peng Shu-Hong Yang Hong-Zhuan Zhao College of Physics and Electronics Hunan University of Arts and Science College of Physical Science and Technology Guangxi Normal University School of Computer Science and Communication Engineering Guangxi University of Science and Technology College of Architecture and Transportation Engineering Guilin University of Electronic Technology

A feedback control model of lattice hydrodynamic model is proposed by taking the information of the historic optimal velocity into account for the traffic system. The modern control theory is applied for the linear stability condition with feedback control signal. The result shows that the stability of traffic flow is closely related to the information of the historic optimal velocity. Furthermore, numerical simulations conform that the new feedback control did increase the stability of traffic flow efficiently, which is in accord with theoretical analysis.

关键词： traffic flow lattice hydrodynamic model control method traffic jam

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Sparse code multiple access for 6G wireless communication networks: Recent advances and future directions

arXiv

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arXiv 2021年

作者： Yu, Lisu Liu, Zilong Wen, Miaowen Cai, Donghong Dang, Shuping Wang, Yuhao Xiao, Pei School of Information Engineering Nanchang University Nanchang330031 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Computer Science and Electronics Engineering University of Essex ColchesterCO4 3SQ United Kingdom School of Electronic and Information Engineering South China University of Technology Guangzhou510640 China College of Cyber Security Jinan University Guangzhou510632 China Thuwal23955-6900 Saudi Arabia Institute for Communication Systems 5G Innovation Centre University of Surrey GuildfordGU2 7XH United Kingdom

As 5G networks rolling out in many different countries nowadays, the time has come to investigate how to upgrade and expand them towards 6G, where the latter is expected to realize the interconnection of everything as well as the development of a ubiquitous intelligent mobile world for intelligent life. To enable this epic leap in communications, this article provides an overview and outlook on the application of sparse code multiple access (SCMA) for 6G wireless communication systems, which is an emerging disruptive non-orthogonal multiple access (NOMA) scheme for the enabling of massive connectivity. We propose to apply SCMA to a massively distributed access system (MDAS), whose architecture is based on fiber-based visible light communication (FVLC), ultra-dense network (UDN), and NOMA. Under this framework, we consider the interactions between optical front-hauls and wireless access links. In order to stimulate more upcoming research in this area, we outline a number of promising directions associated with SCMA for faster, more reliable, and more efficient multiple access in future 6G communication networks. © 2021, CC BY-NC-ND.

关键词： Visible light communication

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Fuzzy Modelling of Local Linearity in Contours

Fuzzy Modelling of Local Linearity in Contours

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IEEE International Conference on Fuzzy Systems

作者： J. Chamorro-Martinez P. M. Martinez-Jimenez A. Garrido J. M. Soto-Hidalgo Dept. Computer Science and Artificial Intelligence University of Granada Spain Dept. Computer Architecture Electronics and Electronic Technology University of Córdoba Spain

Shape analysis, and particulary the contour study, is a fundamental task for object recognition in images. In this paper, a fuzzy approach for representing the linearity property of a contour segment is proposed. Linearity is a key property related to which degree a contour segment is a curve or a straight line;in addition, it is the basis for modelling other properties like curvature, salience or concavity/convexity. In this framework, firstly, the idea of linearity vs non-linearity, and the meaning of its fulfilment, will he analyzed. Secondly, the definition of a membership function according to that meaning will be proposed on the basis of the coefficient of determination. Finally, we will show the goodness of our proposal by analyzing linearity in a set of shapes with different characteristics.

关键词： Linearity Shape Predictive models Image segmentation Analytical models Estimation electronic mail

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Multidimensional fuzzy sets for texture modelling adapted to user profiles

Multidimensional fuzzy sets for texture modelling adapted to...

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2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017

作者： Martínez-Jiménez, Pedro Manuel Chamorro-Martínez, Jesús Soto-Hidalgo, José Manuel Rodríguez-Pérez, Javier Department of Computer Science and Artificial Intelligence University of Granada Spain Department of Computer Architecture Electronics and Electronic Technology University of Cordoba Spain

ISBN: (纸本)9781509060344

The analysis of the perceptual properties of texture plays a fundamental role in tasks where some interaction with subjects is needed. In order to face the imprecision related to these properties, several fuzzy approaches can be found in the literature, but they do not properly take into account the subjectivity of users. In this paper, a generic technique is proposed in order to adapt any multidimensional fuzzy set to the different perceptions of texture properties that a particular user can have. This way, we combine the improvement in the texture characterization given by the use of several computational measures as reference set with the adaptation to the subjectivity of the human perception. In the proposed adaptation method, the membership functions are automatically transformed on the basic of the information given by the user. © 2017 IEEE.

关键词： Fuzzy sets

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UDP-TRIZ model improvement with a focus group discussion of TRIZ experts

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IOP Conference Series: Materials Science and Engineering 2020年第1期932卷

作者： S Shahrin K A A A Rahman K M Kamarudin R Che Me J R Razali I Abidin M M M Aminuddin I Mustaffa M F A Jalal M F C Hassan Faculty of Design and Architecture Universiti Putra Malaysia Malaysia Faculty of Engineering Technology Universiti Malaysia Perlis Malaysia Centre of Modern Language and Human Science Universiti Malaysia Pahang Malaysia Faculty of Electronic and Computer Engineering Universiti Teknikal Malaysia Melaka Malaysia Department of Electronics and Computer Engineering Technology Faculty of Electrical and Electronic Engineering Technology Universiti Teknikal Malaysia Melaka Malaysia Department of Mechanical Engineering College of Engineering Universiti Tenaga Nasional Selangor Malaysia Faculty of Technical and Vocational Universiti Pendidikan Sultan Idris Tanjung Malim Perak Malaysia

Universal Design is a fundamentals of good design. A product with Universal Design approach should provide wide range of solution for people with different purposes including those with diverse disabilities. However, designers often based on a subjective understanding of Universal Design Principles (UDP) to formulate design solutions. This result in the inaccurate conversion of design needs. A systematic approach is needed in order to develop a concept to meet the customer need. Previous studies shown potential integration of the Theory of Inventive Problem Solving (TRIZ) proposed to create a systematic approach for problem-solving requires creativity abilities. The purpose of this study is to explore the construction of the integration model of UDP and TRIZ 40 inventive principles. To construct the integration model of UDP and TRIZ 40 inventive principles, this study will use focus group discussion of certified TRIZ experts in the exploration of establishing an integration model of UDP and TRIZ 40 inventive principles. The further discussion onto the elements required to create the integration model by the TRIZ experts will be taken into consideration to construct a final model UDP-TRIZ model. Finally, the results are expected to lead towards a final integration model of the UDP and TRIZ integration model.

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Balanced Quantization： An Effective and Efficient Approach toQuantized Neural Networks

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Journal of computer Science & technology 2017年第4期32卷 667-682页

作者： Shu-Chang Zhou Yu-Zhi Wang He Wen Qin-Yao He Yu-Heng Zou University of Chinese Academy of Sciences Beijing 100049 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Megvii Inc. Beijing 100190 China Department of Electronic Engineering Tsinghua University Beijing 100084 China Department of Computer Science and Technology Tsinghua University Beijing 100084 China School of Electronics Engineering and Computer Science Peking University Beijing 100871 China

Quantized neural networks (QNNs), which use low bitwidth numbers for representing parameters and performing computations, have been proposed to reduce the computation complexity, storage size and memory usage. In QNNs, parameters and activations are uniformly quantized, such that the multiplications and additions can be accelerated by bitwise operations. However, distributions of parameters in neural networks are often imbalanced, such that the uniform quantization determined from extremal values may underutilize available bitwidth. In this paper, we propose a novel quantization method that can ensure the balance of distributions of quantized values. Our method first recursively partitions the parameters by percentiles into balanced bins, and then applies uniform quantization. We also introduce computationally cheaper approximations of percentiles to reduce the computation overhead introduced. Overall, our method improves the prediction accuracies of QNNs without introducing extra computation during inference, has negligible impact on training speed, and is applicable to both convolutional neural networks and recurrent neural networks. Experiments on standard datasets including ImageNet and Penn Treebank confirm the effectiveness of our method. On ImageNet, the top-5 error rate of our 4-bit quantized GoogLeNet model is 12.7%, which is superior to the state-of-the-arts of QNNs.

关键词： quantized neural network percentile histogram equalization uniform quantization

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AGlobal benchmark of algorithms for segmentinglate gadolinium-enhanced cardiac magnetic resonance imaging

arXiv

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arXiv 2020年

作者： Xiong, Zhaohan Xia, Qing Hu, Zhiqiang Huang, Ning Bian, Cheng Zheng, Yefeng Vesal, Sulaiman Ravikumar, Nishant Maier, Andreas Yang, Xin Heng, Pheng-Ann Ni, Dong Li, Caizi Tong, Qianqian Si, Weixin Puybareau, Elodie Khoudli, Younes Géraud, Thierry Chen, Chen Bai, Wenjia Rueckert, Daniel Xu, Lingchao Zhuang, Xiahai Luo, Xinzhe Jia, Shuman Sermesant, Maxime Liu, Yashu Wang, Kuanquan Borra, Davide Masci, Alessandro Corsi, Cristiana De Vente, Coen Veta, Mitko Karim, Rashed Preetha, Chandrakanth Jayachandran Engelhardt, Sandy Qiao, Menyun Wang, Yuanyuan Tao, Qian Nuñez-Garcia, Marta Camara, Oscar Savioli, Nicolo Lamata, Pablo Zhao, Jichao Auckland Bioengineering Institute University of Auckland Auckland New Zealand State Key Lab of Virtual Reality Technology and Systems Beihang University Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China SenseTime Inc Shenzhen China Tencent Jarvis Laboratory Shenzhen China Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Shenzhen China School of Computer Science Wuhan University Wuhan China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Epita Research and Development Laboratory Paris France Department of Computing Imperial College London London United Kingdom School of Naval Architecture Shanghai Jiao Tong University Ocean & Civil Engineering Shanghai China School of Data Science Fudan University Shanghai China Epione Research Group Universite Cote d'Azur Inria Sophia Antipolis France Harbin Institute of Technology School of Computer Science and Technology Harbin China Department of Electric University of Bologna Electronic and Information Engineering Cesena Italy Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands School of Biomedical Engineering and Imaging Sciences Kings College London London United Kingdom Faculty of Electrical Engineering and Information Technology University of Magdeburg Magdeburg Germany Heidelberg University Hospital Germany Biomedical Engineering Center Fudan University Shanghai China Department of Radiology Leiden University Medical Center Leiden Netherlands Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain D

Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI)widely used for visualizing diseased cardiacstructures, is a crucial first step for clinical diagnosis and treatment. However, direct segmentation ofLGE-MRIsischallenging due to *** most clinical studies have relied on manual and labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning *** address this, we organized the "2018 Left Atrium Segmentation Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset, and associated labels of the left atriumsegmented by three medical experts,ultimately attractingthe participation of27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis,offeringan overall picture ofthe major design choices of convolutional neural networks(CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show the top methodachieved a dice score of 93.2%and a mean surface to a surface distance of 0.7mm, significantly outperforming prior state-of-the-art. Particularly, our analysisdemonstrated that double, sequentially usedCNNs, in which a first CNN is used for automatic region-of-interestlocalization and a subsequent CNN is used for refinedregional segmentation, achieved far superiorresults than traditional methods and pipelinescontaining *** findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging *** large-scale benchmarking study makes a significant step towards much-improved segmentation methods for cardiac LGE-MRIs, and will serveas animportant benchmark for evaluating and comparing the future works in the field. Copyright © 2020, Th

关键词： Magnetic resonance imaging

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Self-Awareness for Autonomous Systems

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PROCEEDINGS OF THE IEEE 2020年第7期108卷 971-975页

作者： Dutt, Nikil Regazzoni, Carlo S. Rinner, Bernhard Yao, Xin Nikil Dutt (Fellow IEEE) received the Ph.D. degree from the University of Illinois at Urbana–Champaign Champaign IL USA in 1989.""He is currently a Distinguished Professor of computer science (CS) cognitive sciences and electrical engineering and computer sciences (EECS) with the University of California at Irvine Irvine CA USA. He is a coauthor of seven books. His research interests include embedded systems electronic design automation (EDA) computer architecture distributed systems healthcare Internet of Things (IoT) and brain-inspired architectures and computing.""Dr. Dutt is a Fellow of ACM. He was a recipient of the IFIP Silver Core Award. He has received numerous best paper awards. He serves as the Steering Committee Chair of the IEEE/ACM Embedded Systems Week (ESWEEK). He is also on the steering organizing and program committees of several premier EDA and embedded system design conferences and workshops. He has served on the Editorial Boards for the IEEE Transactions on Very Large Scale Integration (VLSI) Systems and the ACM Transactions on Embedded Computing Systems and also previously served as the Editor-in-Chief (EiC) for the ACM Transactions on Design Automation of Electronic Systems. He served on the Advisory Boards of the IEEE Embedded Systems Letters the ACM Special Interest Group on Embedded Systems the ACM Special Interest Group on Design Automationt and the ACM Transactions on Embedded Computing Systems. Carlo S. Regazzoni (Senior Member IEEE) received the M.S. and Ph.D. degrees in electronic and telecommunications engineering from the University of Genoa Genoa Italy in 1987 and 1992 respectively.""He is currently a Full Professor of cognitive telecommunications systems with the Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN) University of Genoa and a Co-Ordinator of the Joint Doctorate on Interactive and Cognitive Environments (JDICE) international Ph.D. course started initially as EU Erasmus Mundus Project and

Autonomous systems are able to make decisions and potentially take actions without direct human intervention, which requires some knowledge about the system and its environment as well as goal-oriented reasoning. In computer systems, one can derive such behavior from the concept of a rational agent with autonomy (“control over its own actions”), reactivity (“react to events from the environment”), proactivity (“act on its own initiative”), and sociality (“interact with other agents”) as fundamental properties \n[1]\n. Autonomous systems will undoubtedly pervade into our everyday lives, and we will find them in a variety of domains and applications including robotics, transportation, health care, communications, and entertainment to name a few. \nThe articles in this month’s special issue cover concepts and fundamentals, architectures and techniques, and applications and case studies in the exciting area of self-awareness in autonomous systems.

关键词： Special issues and sections Autonomous systems Self-aware Robot sensing systems Computational modeling

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Multidimensional Fuzzy Sets for Texture Modelling Adapted to User Profiles

Multidimensional Fuzzy Sets for Texture Modelling Adapted to...

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IEEE International Conference on Fuzzy Systems

作者： Pedro Manuel Martínez-Jiménez Jesús Chamorro-Martínez José Manuel Soto-Hidalgo Javier Rodríguez-Pérez Department of Computer Science and Artificial Intelligence University of Granada Spain Department of Computer Architecture Electronics and Electronic Technology University of Cordoba Spain

ISBN: (纸本)9781509060351

关键词： Level set Fuzzy sets Adaptation models Computational modeling Pragmatics Force electronic mail

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