检索结果-内蒙古大学图书馆

IEEE International Workshop on computer Aided Modeling and Design of Communication Links and Networks

作者： Tamara K. Al-Shayea Constandinos X. Mavromoustakis George Mastorakis Jordi Mongay Batalla Evangelos K. Markakis Evangelos Pallis Department of Computer Science Mobile Systems Laboratory (MoSys Lab) University of Nicosia Department of Informatics Engineering Technological Educational Institute of Crete National Institute of Telecommunications and Warsaw University of Technology

ISBN: (纸本)9781538661529

Nowadays the Internet infrastructure enables the transfer of massive amounts of large data sets throughout the Web. Over this infrastructure, the risks of infringement of the owner's rights and the incapability to defend their data from attacks are important. There are several demands for novel and smart watermarking schemes to save those rights from an illegal copying of the digital products. The Digital watermarking is a technology used to protect the copyrights of the digital media. Image watermarking scheme is used to limit the chances of piracy. Various approaches were proposed to be used with an image watermarking scheme, such as wavelet transforms, principle component analysis and singular value decomposition. In this work, the proposed watermarking scheme is based on the Daubechies family wavelets, Daubechies-5 and Daubechies-7 wavelet transform. This wavelet family approach is highly robust against various types of attacks, prohibiting the piracy and authentication of the digital data. The experimental results depict that this proposed scheme allows protection at a higher level compared with other existing frameworks and schemes.

关键词： Digital image watermarking Daubechies wavelet Discrete wavelet transform Copyright Mean square error

来源：评论

学校读者我要写书评

暂无评论

DRIVE: Discovery seRvice for fully-Integrated 5G enVironmEnt in the IoT

DRIVE: Discovery seRvice for fully-Integrated 5G enVironmEnt...

引用

IEEE International Workshop on computer Aided Modeling and Design of Communication Links and Networks

作者： Paolo Bellavista Luca Foschini Domenico Scotece Kyriaki Karypidou Periklis Chatzimisios Computer Science and Engineering Dept. (DISI) University of Bologna Viale del Risorgimento Bologna Italy Department of Informatics Alexander TEI of Thessaloniki & CSSN Research Lab Thessaloniki Greece

A lot of research is being carried out about Internet of Things (IoT) and in the following years it will emerge more and more in our lives. Furthermore, with the advent of future fully-integrated 5G networks, new constraints need to be satisfied such as ultra-reliability and low-latency. With the help of Fog computing and the Multi-access Edge Computing (MEC) framework, services can be offered to the end users in a fast and practical way. Our work presents DRIVE a framework for service discovery in a 5G environment. However, in order to guarantee dynamic distribution and best management of services, we plan to deploy those services as container (e.g. Docker container). Moreover, we propose distribution of edge services at three different layer of communication: Application, Service, and Communication Layer. Given the above considerations, we propose an edge node, placed at the edge of network, that acts as the "brain" and take over the computation. The main innovative elements of the proposed framework, compared to the existing literature, include the possibility to select the working layer, the dynamic reconfiguration of the edge node and the field experimental results about the performance achieved by our solution over rapidly deployable environments with resource-limited edge nodes such as Raspberry Pi devices.

关键词： IoT Fog Computing MEC 5G Service Discovery Raspberry Pi

来源：评论

学校读者我要写书评

暂无评论

Not just functional, nutritious, but also experiential: Designing eating experiences for space travel 69

Not just functional, nutritious, but also experiential: Desi...

引用

69th International Astronautical Congress: #InvolvingEveryone, IAC 2018

作者： Obrist, Marianna Tu, Yunwen Yao, Lining Velasco, Carlos Sussex Computer Human Interaction Lab School of Engineering and Informatics University of Sussex Chichester 1 BrightonBN1 9QJ United Kingdom Food Design 322 Crestview Circle Daly CityCA United States Carnegie Mellon University School of Computer Science 5000 Forbes Ave PittsburghPA15213 United States Center for Multisensory Marketing Department of Marketing BI Norwegian Business School Nydalsveien 37 Oslo0484 Norway

In light of short- and long-term space travels to the Moon and Mars, it is essential to design nutritious foods, but also to make eating an enjoyable experience. To date, though, most research on space food design emphasizes the functional and nutritional aspects of food, but there are no systematic studies that focus is on the human experience of eating in space. We know however that food has a multi-dimensional and multisensorial role in societies and that sensory, hedonic, and social features of eating and food design should not be underestimated. We present, how research in the field of Human-computer Interaction (HCI) can provide a user-centered design approach to co-create innovative ideas around the future of food and eating in space, balancing functional and experiential factors. Based on our research and inspired by advances in human food-interaction design, we developed three design concepts that integrate and tackle four crucial challenges for 'Eating in Space': to be functional, sensorial, emotional, and social. Moreover, we consider the environmental/atmospheric perspective in our design concepts. We can particularly capitalize on recent technological advances around digital fabrication, food 3D printing technology, and virtual and augmented reality to enable the design and integration of multisensory eating experiences. We also highlight that in future space travels, the target users will diversify. Thinking about future target users, we need to look at astronauts (current users, paid to do the job) but also beyond and consider paying customers (non-astronauts) who will be able to book a space holiday to the Moon or Mars. To create the right conditions for space travelling and to satisfy those users, we need to innovate beyond the initial excitement of designing an 'eating like an astronaut' experience and design for experiences beyond the novelty of the situation. To do so we can draw upon prior HCI research in human food-interaction design and build

关键词： Human computer interaction

来源：评论

学校读者我要写书评

暂无评论

Guided Deep Reinforcement Learning in the GeoFriends2 Environment

Guided Deep Reinforcement Learning in the GeoFriends2 Enviro...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： David Simões Nuno Lau Luís Paulo Reis LIACC - Artificial Intelligence and Computer Science Lab Porto Portugal DETI/UA - Electronics University of Aveiro Portugal DEI/FEUP - Informatics Engineering Department Faculty of Engineering of the University of Porto Portugal

In recent years, the artificial intelligence community has taken big strides in the application of reinforcement learning to games or similar environments using deep learning. From Atari to board games, including motor control or riddle solving, fairly generic deep learning algorithms can now achieve great policies by simply learning to play from experience, and minimal knowledge of the specific domain. However, these algorithms are very demanding in terms of time and hardware in order to achieve the results reported in the literature. So much so, that some algorithms would take years to achieve state-of-the-art performance in commodity hardware. Not only that, but even the learning environments can hinder the speed of the learning process, if they have not been performance optimized. In this paper, we evaluate a complex existing environment, and propose a performance-oriented version, which we call GeoFriends2. We describe the motivation behind the creation of our version, and how it is suitable for both single- and multi-agent reinforcement learning. We then use Asynchronous Deep Learning to create complex policies that can act as baselines for future research on this environment. We also describe a set of techniques that speed up the learning process such that tests can be run with commodity hardware in hours, and not weeks, and using much simpler network architectures.

关键词： Games Machine learning Learning (artificial intelligence) Hardware Neural networks Geometry informatics

来源：评论

学校读者我要写书评

暂无评论

Deep learning based computer-aided diagnosis for neuroimaging data: focused review and future potential

引用

Neuroimmunology and Neuroinflammation 2018年第1期5卷 1-3页

作者： V.B.Surya Prasath Computational Imaging and VisAnalysis(CIVA)Lab Department of Computer ScienceUniversity of Missouri-ColumbiaColumbiaMO 65211USA Division of Biomedical Informatics Cincinnati Children's Hospital Medical Center(CCHMC)CincinnatiOH 45229USA Department of Biomedical Informatics College of MedicineUniversity of CincinnatiOH 45267USA

Automatic image analysis techniques applied to neuroimaging data in general, and magnetic resonance imaging (MRI), and functional MRI (fMRI) in particular, have proven to be effective computer-aided diagnosis (CAD) tools in neuroscience(1-4)Recently, the advancements in machine learning techniques combined with the wide availability of computational power have proven to be efficient in solving previously difficult problems in analyzing neuroimaging data. At the forefront of these advancements is the usage of deep (artificial) neural network architectures that led robust learning based techniques to attack challenging problems such as segmentation and classification in neuroimaging data[5-8].

关键词： Deep learning neuroimaging data future potential

来源：评论

学校读者我要写书评

暂无评论

1D-Convolutional capsule network for hyperspectral image classification

arXiv

引用

arXiv 2019年

作者： Zhang, Haitao Meng, Lingguo Wei, Xian Tang, Xiaoliang Tang, Xuan Wang, Xingping Jin, Bo Yao, Wei School of Software Liaoning Technical University Huludao125105 China Fujian Institute of Research on the Structure of Matter Chinese Academy of Sciences Fuzhou350002 China Shanghai Key Lab for Trustworthy Computing School of Computer Science and Software Engineering East China Normal University China Department of Land Surveying and Geo-Informatics Hong Kong Polytechnic University 181 Chatham Road South Hung Hom Kowloon Hong Kong

Recently, convolutional neural networks (CNNs) have achieved excellent performances in many computer vision tasks. Specifically, for hyperspectral images (HSIs) classification, CNNs often require very complex structure due to the high dimension of HSIs. The complex structure of CNNs results in prohibitive training efforts. Moreover, the common situation in HSIs classification task is the lack of labeled samples, which results in accuracy deterioration of CNNs. In this work, we develop an easy-to-implement capsule network to alleviate the aforementioned problems, i.e., 1D-convolution capsule network (1D-ConvCapsNet). Firstly, 1D-ConvCapsNet separately extracts spatial and spectral information on spatial and spectral domains, which is more lightweight than 3D-convolution due to fewer parameters. Secondly, 1D-ConvCapsNet utilizes the capsule-wise constraint window method to reduce parameter amount and computational complexity of conventional capsule network. Finally, 1D-ConvCapsNet obtains accurate predictions with respect to input samples via dynamic routing. The effectiveness of the 1D-ConvCapsNet is verified by three representative HSI datasets. Experimental results demonstrate that 1D-ConvCapsNet is superior to state-of-the-art methods in both the accuracy and training effort Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Prediction of cardiac arrest in intensive care patients through machine learning 3rd

Prediction of cardiac arrest in intensive care patients thro...

引用

3rd International Conference on Biomedical and Health informatics, ICBHI 2017

作者： Akrivos, E. Papaioannou, V. Maglaveras, N. Chouvarda, I. Lab of Computing Medical Informatics and Biomedical Imaging Technologies Medical School Aristotle University of Thessaloniki Thessaloniki Greece Department of Internal Medicine 424 Military Hospital of Thessaloniki Thessaloniki Greece Alexandroupolis University Hospital/Intensive Care Unit Alexandroupoli Greece McCormick School of Engineering & Applied Sciences Department of Electrical Engineering & Computer Science Northwestern University EvastonIL60201 United States

ISBN: (纸本)9789811074189

Cardiac arrest is a critical health condition characterized by absence of traceable heart rate, patient’s loss of consciousness as well as apnea, with inhospital mortality of ~80%. Accurate estimation of patients at high risk is crucial to improve not only the survival rate, but also the quality of life as patients who survived from cardiac arrest have severe neurological effects. Existing research has focused on demonstrating static risk scores without taking account patient’s physiological condition. In this study, we are implementing an integrated model of sequential contrast patterns using Multichannel Hidden Markov Model. These models can capture relations between exposure and control group and offer high specificity results, with an average sensitivity of 78%, and have the ability to identify patients in high risk. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Deep learning for neuroimaging-based diagnosis and rehabilitation of Autism Spectrum Disorder: A review

arXiv

引用

arXiv 2020年

作者： Khodatars, Marjane Shoeibi, Afshin Sadeghi, Delaram Ghassemi, Navid Jafari, Mahboobeh Moridian, Parisa Khadem, Ali Alizadehsani, Roohallah Zare, Assef Kong, Yinan Khosravi, Abbas Nahavandi, Saeid Hussain, Sadiq Acharya, U. Rajendra Berk, Michael The Dept. of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran The Faculty of Electrical Engineering FPGA Lab K. N. Toosi University of Technology Tehran Iran The Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Electrical and Computer Engineering Faculty Semnan University Semnan Iran The Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran The Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Deakin University VIC3217 Australia Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran The School of Engineering Macquarie University Sydney2109 Australia The Dibrugarh University Assam786004 India The Dept. of Electronics and Computer Engineering Ngee Ann Polytechnic 599489 Singapore The Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan The Dept. of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore The Deakin University IMPACT - the Institute for Mental and Physical Health and Clinical Translation School of Medicine Barwon Health Geelong Australia The Orygen The National Centre of Excellence in Youth Mental Health Centre for Youth Mental Health Florey Institute for Neuroscience and Mental Health The Department of Psychiatry The University of Melbourne Melbourne Australia

Accurate diagnosis of Autism Spectrum Disorder (ASD) followed by effective rehabilitation is essential for the management of this disorder. Artificial intelligence (AI) techniques can aid physicians to apply automatic diagnosis and rehabilitation procedures. AI techniques comprise traditional machine learning (ML) approaches and deep learning (DL) techniques. Conventional ML methods employ various feature extraction and classification techniques, but in DL, the process of feature extraction and classification is accomplished intelligently and integrally. DL methods for diagnosis of ASD have been focused on neuroimaging-based approaches. Neuroimaging techniques are non-invasive disease markers potentially useful for ASD diagnosis. Structural and functional neuroimaging techniques provide physicians substantial information about the structure (anatomy and structural connectivity) and function (activity and functional connectivity) of the brain. Due to the intricate structure and function of the brain, proposing optimum procedures for ASD diagnosis with neuroimaging data without exploiting powerful AI techniques like DL may be challenging. In this paper, studies conducted with the aid of DL networks to distinguish ASD are investigated. Rehabilitation tools provided for supporting ASD patients utilizing DL networks are also assessed. Finally, we will present important challenges in the automated detection and rehabilitation of ASD and propose some future works. © 2020, CC BY-NC-ND.

关键词： Neuroimaging

来源：评论

学校读者我要写书评

暂无评论

Maximum eccentric connectivity index for graphs with given diameter

arXiv

引用

arXiv 2018年

作者： Hauweele, Pierre Hertz, Alain Mélot, Hadrien Rie, Bernard Devillez, Gauvain Computer Science Department Algorithms Lab University of Mons Mons Belgium Department of Mathematics and Industrial Engineering École Polytechnique - Gerad MontréalQC Canada Department of Informatics University of Fribourg Fribourg Switzerland

The eccentricity of a vertex v in a graph G is the maximum distance between v and any other vertex of G. The diameter of a graph G is the maximum eccentricity of a vertex in G. The eccentric connectivity index of a connected graph is the sum over all vertices of the product between eccentricity and degree. Given two integers n and D with D ≤ n-1, we characterize those graphs which have the largest eccentric connectivity index among all connected graphs of order n and diameter D. As a corollary, we also characterize those graphs which have the largest eccentric connectivity index among all connected graphs of a given order n. Copyright © 2018, The Authors. All rights reserved.

关键词： Graphic methods

来源：评论

学校读者我要写书评

暂无评论

An Early Stage Researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

引用

Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：