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IEEE Symposium on Computational Intelligence and Data Mining (CIDM)

作者： Giulia Fiscon Emanuel Weitschek Giovanni Felici Paola Bertolazzi Simona De Salvo Placido Bramanti Maria Cristina De Cola Institute of Systems Analysis and Computer Science National Research Council Rome Italy Department of Computer Control and Management Engineering Sapienza University Rome Italy Department of Engineering Roma Tre University Rome Italy IRCCS Centro Neurolesi “Bonino-Pulejo” Messina Italy

Alzheimer's Disease (AD) and its preliminary stage - Mild Cognitive Impairment (MCI) - are the most widespread neurodegenerative disorders, and their investigation remains an open challenge. ElectroEncephalography (EEG) appears as a non-invasive and repeatable technique to diagnose brain abnormalities. Despite technical advances, the analysis of EEG spectra is usually carried out by experts that must manually perform laborious interpretations. Computational methods may lead to a quantitative analysis of these signals and hence to characterize EEG time series. The aim of this work is to achieve an automatic patients classification from the EEG biomedical signals involved in AD and MCI in order to support medical doctors in the right diagnosis formulation. The analysis of the biological EEG signals requires effective and efficient computer science methods to extract relevant information. Data mining, which guides the automated knowledge discovery process, is a natural way to approach EEG data analysis. Specifically, in our work we apply the following analysis steps: (i) pre-processing of EEG data; (ii) processing of the EEG-signals by the application of time-frequency transforms; and (iii) classification by means of machine learning methods. We obtain promising results from the classification of AD, MCI, and control samples that can assist the medical doctors in identifying the pathology.

关键词： Electroencephalography Feature extraction Data mining Computational modeling Electrodes Diseases Support vector machines

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Stability and stabilization of differential nonlinear repetitive processes with applications 19

Stability and stabilization of differential nonlinear repeti...

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19th IFAC World Congress on International Federation of Automatic control, IFAC 2014

作者： Emelianov, Mikhail Pakshin, Pavel Galkowski, Krzysztof Rogers, Eric Arzamas Polytechnic Institute of R.E. Alekseev Nizhny Novgorod State Technical University 19 Kalinina Street Arzamas607227 Russia Institute of Control and Computation Engineering University of Zielona Góra ul. Podgorna 50 Zielona Góra65-246 Poland Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

ISBN: (纸本)9783902823625

Repetitive processes are a class of two-dimensional systems that arise in the modeling of physical examples and also the control systems theory developed for them has, in the case of linear dynamics, been applied to design iterative learning control laws with experimental verification. This paper gives new results on the stability of nonlinear differential repetitive processes for applications where a linearized model is either very limited or not applicable. The stability results are then applied to the design of iterative learning control laws in the presence of uncertain parameters and to the same problem when random failures occur that are modeled by a homogeneous Markov chain with a finite set of states. In both cases the computations required are expressed as a finite set of linear matrix inequalities. © IFAC.

关键词： Process control

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User-Specific Network Service Functions in an SDN-enabled Network Node

User-Specific Network Service Functions in an SDN-enabled Ne...

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European Workshop on Software Defined Networking (EWSDN)

作者： Ivano Cerrato Tobias Jungel Alex Palesandro Fulvio Risso Marc Suñé Hagen Woesner Department of Computer Control Engineering Politecnico di Torino Torino Italy Politecnico di Torino Torino Piemonte IT Berlin Institute for Software Defined Networks GmbH Berlin Germany

Network Functions Virtualization can enable each user (tenant) to define his desired set of network services, called (network) service graph. For instance, a User1may want his traffic to traverse a firewall before reaching his terminal, while a User2 may be interested in a different type of firewall and in a network monitor as well. This paper presents a prototype of an SDN-enabled node that, given anew user connected to one of its physical ports, it is able to dynamically instantiate the user's network service graph and force all his traffic to traverse the proper set of network functions.

关键词： Ports (computers) Noise measurement Software Virtualization Europe Conferences computer architecture

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WEB TOPIC DETECTION USING A RANKED CLUSTERING-LIKE PATTERN ACROSS SIMILARITY CASCADES

WEB TOPIC DETECTION USING A RANKED CLUSTERING-LIKE PATTERN A...

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IEEE International Conference on Multimedia and Expo

作者： Fei Jia Junbiao Pang Weigang Zhang Guorong Li Chunjie Zhang Qingming Huang Yugui Liu School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing Key Laboratory of Multimedia and Intelligent Software Technology College of Metropolitan Transportation Beijing University of Technology School of Computer Science and Technology Harbin Institute of Technology

ISBN: (纸本)9781479947607

In multi-media and social media communities, web topic detection poses two main difficulties that conventional approaches can barely handle: 1) there are large inter-topic variations among web topics;2) supervised information is rare to identify the real topics. In this paper, we address these problems from the similarity diffusion perspective among objects on web, and present a clustering-like pattern across similarity cascades (SCs). SCs are a series of subgraphs generated by truncating a weighted graph with a set of thresholds, and then maximal cliques are used to describe the topic candidates. Poisson deconvolution is adopted to efficiently identify the real topics from these topic candidates. Experiments demonstrate that our approach outperforms the state-of-the-arts on two datasets. In addition, we report accuracy v.s. false positives per topic (FPPT) curves for performance evaluation. To our knowledge, this is the first complete evaluation of web topic detection at the topic-wise level, and it establishes a new benchmark for this problem.

关键词： Web Topic detection Maximal cliques Unsupervised ranking Poisson process Similarity cascade

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A Stackelberg Game for Spectrum Leasing in Cooperative Cognitive Radio Networks

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International Journal of Automation and computing 2013年第2期10卷 125-133页

作者： Xiao-Cheng Wang Xin-Ping Guan Qiao-Ni Han Zhi-Xin Liu Kai Ma Institute of Electrical Engineering Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University

According to the property-rights model of cognitive radio, primary users （PUs） who own the spectrum resource have the right to lease part of spectrum to secondary users （SUs） in exchange for appropriate profit. In this paper, we propose a pricing-based spectrum leasing framework between one PU and multiple SUs. In this scenario, the PU attempts to maximize its utility by setting the price of spectrum. Then, the selected SUs have the right to decide their power levels to help PU s transmission, aiming to obtain corresponding access time. The spectrum leasing problem can be cast into a stackelberg game, where the PU plays the seller-level game and the selected SUs play the buyer-level game. Through analysis based on the backward induction, we prove that there exists a unique equilibrium in the stackelberg game with certain constraints. Numerical results show that the proposed pricing-based spectrum leasing framework is effective, and the performance of both PU and SUs is improved, compared to the traditional mechanism without cooperation.

关键词： Cognitive radio spectrum leasing power control cooperative transmission pricing stackelberg game

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EEG-based Driving Fatigue Prediction System Using Functional-link-based Fuzzy Neural Network

EEG-based Driving Fatigue Prediction System Using Functional...

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International Joint Conference on Neural Networks

作者： Yu-Ting Liu Yang-Yin Lin Shang-Lin Wu Chun-Hsiang Chuang Mukesh Prasad Chin-Teng Lin Institute of Electrical Control Engineering Brain Research Center National Chiao-Tung University Hsinchu Taiwan Department of Computer Science National Chiao-Tung University Hsinchu Taiwan

This study presents a fuzzy prediction system for the forecasting and estimation of driving fatigue, which utilizes a functional-link-based fuzzy neural network (FLFNN) to predict the drowsiness (DS) level in car driving task. The cognitive state in car driving task is one of key issue in cognitive neuroscience because fatigue driving usually causes enormous losses nowadays. The damage can be extremely decreased by the assistant of various artificial systems. Many Electroencephalography (EEG)-based interfaces have been widely developed recently due to its convenient measurement and real-time response. However, the improvement of recognition accuracy is still confined to some specific problems (e.g., individual difference). In order to solve this issue, the proposed methodology in this paper utilizes a nonlinear fuzzy neural network structure to increase the adaptability in the real-world environment. Therefore, this study is further to analysis the brain activities in car driving, which is constructed in a simulated three-dimensional virtual-reality (VR) environment. Finally, through the development of brain cognitive model in car driving task, this system can predict the cognitive state effectively before drivers' action and then provide correct feedback to users. This study also compared the result with the-state-of-art systems, including Linear Regression (LR), Multi-Layer Perceptron Neural Network (MLPNN) and Support Vector Regression (SVR). Results of this study demonstrate the effectiveness of the proposed FLFNN model.

关键词： Electroencephalography Brain modeling Fuzzy neural networks Input variables Biological neural networks Vehicles Independent component analysis

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Optimal power allocation for heterogeneous data traffic in NAF relay-assisted uplink transmission

Optimal power allocation for heterogeneous data traffic in N...

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International Symposium on Communications and Information Technologies (ISCIT)

作者： Peng Gong Haiqiao Wu Ping Li Wenbo Shi Duk Kyung Kim Beijing Institute of Technology National Key Lab. of Mechatronic Engineering and Control Beijing China Northeastern University School of Computer and Communication Engineering Qinhuangdao China Department of Information and Communication Engineering Inha University Nam-gu Incheon Korea

ISBN: (纸本)9781479944156

For the future wireless networks, heterogeneous data traffic services, i.e., variant bit-rate (VBR) service and constant bit rate (CBR) service are needed to be supported. The optimal power allocation algorithm for the non-orthogonal AF (NAF) Relays-assisted multicast transmission, which may be suitable for the VBR service, has been investigated in [2]. However, the power allocation algorithms for the cases of CBR service and heterogeneous data traffics service are not considered. In this paper, a power allocation problem is addressed by considering the heterogeneous data traffic with VBR and CBR services in the uplink data transmission, where Different from assumption of infinite length of spreading code in [2], a finite length of spreading code is considered and the interference among the signals from mobile terminal and relays exists. And then, an optimal power allocation algorithm is proposed by solving its inverse problem. Simulation results demonstrate the efficiency of the proposed algorithm.

关键词： Relays Mobile communication Algorithm design and analysis Three-dimensional displays Resource management Power demand Radio access networks

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Leaderless Consensus in Networks of Flexible–Joint Manipulators

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IFAC Proceedings Volumes 2014年第3期47卷 6656-6661页

作者： Emmanuel Nuño Daniela Valle Ioannis Sarras Luis Basañez Department of Computer Science. CUCEI University of Guadalajara. Guadalajara Mexico Electronics Department. CUCEI University of Guadalajara. Guadalajara Mexico Automatic Control Department SUPELEC. Gif-sur-Yvette France Institute of Industrial and Control Engineering Technical University of Catalonia. Barcelona Spain

This work studies the leaderless consensus problem in networks composed of non-identical flexible-joint robot manipulators. Using standard functional analysis, i.e., Barbâlat's Lemma, it is established that a simple control law provides a solution to the leaderless consensus problem. The network is modeled as an undirected graph and the interconnection can exhibit variable time-delays. The proposed controller consists of two different terms, one that dynamically compensates the robot gravity and another which ensures the desired consensus objective. This last term is a simple Proportional plus damping scheme. Simulations, using a network with nine 2-degrees of freedom manipulators, are provided to support the theoretical contributions of this work.

关键词： Network of Robots Flexible Arms Decentralized control Time-Delays

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Semi-randomized hashing for large scale data retrieval

Semi-randomized hashing for large scale data retrieval

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International Conference on Data Science and Advanced Analytics (DSAA)

作者： Haichuan Yang Xiao Bai Jun Zhou Peng Ren Jian Cheng Lu Bai School of Computer Since and Engineering Beihang University China School of Information and Communication Technology Griffith University Australia College of Information and Control Engineering China University of Petroleum China Institute of Automation Chinese Academy of Sciences China Department of Computer Science University of York UK

In information retrieval, efficient accomplishing the nearest neighbor search on large scale database is a great challenge. Hashing based indexing methods represent each data instance as a binary string to retrieve the approximate nearest neighbors. In this paper, we present a semi-randomized hashing approach to preserve the Euclidean distance by binary codes. Euclidean distance preserving is a classic research problem in hashing. Most hashing methods used purely randomized or optimized learning strategy to achieve this goal. Our method, on the other hand, combines both randomized and optimized strategies. It starts from generating multiple random vectors, and then approximates them by a single projection vector. In the quantization step, it uses the orthogonal transformation to minimize an upper bound of the deviation between real-valued vectors and binary codes. The proposed method overcomes the problem that randomized hash functions are isolated from the data distribution. What's more, our method supports an arbitrary number of hash functions, which is beneficial in building better hashing methods. The experiments show that our approach outperforms the alternative state-of-the-art methods for retrieval on the large scale dataset.

关键词： Vectors Euclidean distance Binary codes Training Equations Principal component analysis

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A single-station location method based on long baseline interferometer

A single-station location method based on long baseline inte...

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International Symposium on Communications and Information Technologies (ISCIT)

作者： Li Ping Wang Guohong Xiaopeng Yan Gong Peng Wenbo Shi Liu Jian Beijing Institute of Technology National Key Lab. of Mechatronic Engineering and Control Beijing China Northeastern University School of Computer and Communication Engineering Qinhuangdao China China Electronics Technology Group Corporation Zhejiang China

ISBN: (纸本)9781479944156

Conventional long baseline interferometer based Direction of Arrival (DOA) requires multi-group long baselines and short baselines to deblur. In this paper, only using the time difference of arrival (TDOA) data of long baseline interferometer can cancel the debluring shortage and achieve high-precision DOA estimation. Meanwhile iterative filtering algorithm is adopted to extract high-precision changing rate of phase difference from the fuzzy phase difference data of long baseline. Combined with the measured DF and the change rate of phase difference, particle filter algorithm is utilized to achieve high-precision single-station passive location. Parameter measurement and the distribution of location error are verified through theoretical simulations. And the simulation results demonstrate the efficiency of the single-station location method based on long baseline interferometer.

关键词： Direction-of-arrival estimation Accuracy Filtering Atmospheric measurements Particle measurements

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