159,068 research outputs found

    Discriminative methods for classification of asynchronous imaginary motor tasks from EEG data

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    In this work, two methods based on statistical models that take into account the temporal changes in the electroencephalographic (EEG) signal are proposed for asynchronous brain-computer interfaces (BCI) based on imaginary motor tasks. Unlike the current approaches to asynchronous BCI systems that make use of windowed versions of the EEG data combined with static classifiers, the methods proposed here are based on discriminative models that allow sequential labeling of data. In particular, the two methods we propose for asynchronous BCI are based on conditional random fields (CRFs) and latent dynamic CRFs (LDCRFs), respectively. We describe how the asynchronous BCI problem can be posed as a classification problem based on CRFs or LDCRFs, by defining appropriate random variables and their relationships. CRF allows modeling the extrinsic dynamics of data, making it possible to model the transitions between classes, which in this context correspond to distinct tasks in an asynchronous BCI system. On the other hand, LDCRF goes beyond this approach by incorporating latent variables that permit modeling the intrinsic structure for each class and at the same time allows modeling extrinsic dynamics. We apply our proposed methods on the publicly available BCI competition III dataset V as well as a data set recorded in our laboratory. Results obtained are compared to the top algorithm in the BCI competition as well as to methods based on hierarchical hidden Markov models (HHMMs), hierarchical hidden CRF (HHCRF), neural networks based on particle swarm optimization (IPSONN) and to a recently proposed approach based on neural networks and fuzzy theory, the S-dFasArt. Our experimental analysis demonstrates the improvements provided by our proposed methods in terms of classification accuracy

    An Introduction to the RESearch Queueing Package for Modeling Computer Systems and Communication Networks

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    A queueing network is an important tool for modeling systems where performance is principally affected by contention for resources. Such systems include computer systems, communication networks and manufacturing lines. In order to effectively use queuing networks as performance models, appropriate software is necessary for definition ofthe networks to be solved, for solution ofthe networks and for examination of the performance measures obtained. The RESearch Queueing Package (RESQ) and the RESearch Queueing Package Modeling Environment (RESQME) form a system for constructing, solving and analyzing extended queueing network models. We refer to the class of RESQ networks as extended because of characteristics which allow effective representation of system detail. RESQ incorporates a high level language to concisely describe the structure of the model and to specify constraints on the solution. A main feature of the language is the capability to describe models in a hierarchical fashion, allowing an analyst to define submodels to be used analogously to use of macros in programming languages. RESQ also provides a variety of methods for estimating the accuracy of simulation results and for determining simulation run lengths. RESQME is a graphical interface for RESQ. In this introduction, we limit our examples to computer systems and communication networks. Acknowledgement: The authors wish to thank their co-developers of RESQME: Jim Kurose and Kurt Gordon. We also want to thank Ben Antanaitis, Howard Jachter, Jack Servier, Daniel Souday and Peter Welch for their many suggestions which helped improve the RESQME package and Anil Aggarwal, Al Blum, Gary Burkland, Rocky Chang, Janet Chen, Diana Coles, Prakash Deka, Paul Lnewner, and Geoff Parker for their work in implementing RESQME. We would also like to thank our users for their ideas and feedback that we tried to incorporate in RESQ and RESQME. We remain indebted to Charlie Sauer for his design, guidance, inspiration, and development ofthe RESQ languag

    Network anomaly detection using management information base (MIB) network traffic variables

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    In this dissertation, a hierarchical, multi-tier, multiple-observation-window, network anomaly detection system (NADS) is introduced, namely, the MIB Anomaly Detection (MAD) system, which is capable of detecting and diagnosing network anomalies (including network faults and Denial of Service computer network attacks) proactively and adaptively. The MAD system utilizes statistical models and neural network classifier to detect network anomalies through monitoring the subtle changes of network traffic patterns. The process of measuring network traffic pattern is achieved by monitoring the Management Information Base (Mifi) II variables, supplied by the Simple Network Management Protocol (SNMP) LI. The MAD system then converted each monitored Mifi variable values, collected during each observation window, into a Probability Density Function (PDF), processed them statistically, combined intelligently the result for each individual variable and derived the final decision. The MAD system has a distributed, hierarchical, multi-tier architecture, based on which it could provide the health status of each network individual element. The inter-tier communication requires low network bandwidth, thus, making it possibly utilization on capacity challenged wireless as well as wired networks. Efficiently and accurately modeling network traffic behavior is essential for building NADS. In this work, a novel approach to statistically model network traffic measurements with high variability is introduced, that is, dividing the network traffic measurements into three different frequency segments and modeling the data in each frequency segment separately. Also in this dissertation, a new network traffic statistical model, i.e., the one-dimension hyperbolic distribution, is introduced
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