3,869 research outputs found
Localized anomaly detection via hierarchical integrated activity discovery
2014 Spring.Includes bibliographical references.With the increasing number and variety of camera installations, unsupervised methods that learn typical activities have become popular for anomaly detection. In this thesis, we consider recent methods based on temporal probabilistic models and improve them in multiple ways. Our contributions are the following: (i) we integrate the low level processing and the temporal activity modeling, showing how this feedback improves the overall quality of the captured information, (ii) we show how the same approach can be taken to do hierarchical multi-camera processing, (iii) we use spatial analysis of the anomalies both to perform local anomaly detection and to frame automatically the detected anomalies. We illustrate the approach on both traffic data and videos coming from a metro station. We also investigate the application of topic models in Brain Computing Interfaces for Mental Task classification. We observe a classification accuracy of up to 68% for four Mental Tasks on individual subjects
Distributed Sensing, Computing, Communication, and Control Fabric: A Unified Service-Level Architecture for 6G
With the advent of the multimodal immersive communication system, people can
interact with each other using multiple devices for sensing, communication
and/or control either onsite or remotely. As a breakthrough concept, a
distributed sensing, computing, communications, and control (DS3C) fabric is
introduced in this paper for provisioning 6G services in multi-tenant
environments in a unified manner. The DS3C fabric can be further enhanced by
natively incorporating intelligent algorithms for network automation and
managing networking, computing, and sensing resources efficiently to serve
vertical use cases with extreme and/or conflicting requirements. As such, the
paper proposes a novel end-to-end 6G system architecture with enhanced
intelligence spanning across different network, computing, and business
domains, identifies vertical use cases and presents an overview of the relevant
standardization and pre-standardization landscape
Unified architecture of mobile ad hoc network security (MANS) system
In this dissertation, a unified architecture of Mobile Ad-hoc Network Security (MANS) system is proposed, under which IDS agent, authentication, recovery policy and other policies can be defined formally and explicitly, and are enforced by a uniform architecture. A new authentication model for high-value transactions in cluster-based MANET is also designed in MANS system. This model is motivated by previous works but try to use their beauties and avoid their shortcomings, by using threshold sharing of the certificate signing key within each cluster to distribute the certificate services, and using certificate chain and certificate repository to achieve better scalability, less overhead and better security performance. An Intrusion Detection System is installed in every node, which is responsible for colleting local data from its host node and neighbor nodes within its communication range, pro-processing raw data and periodically broadcasting to its neighborhood, classifying normal or abnormal based on pro-processed data from its host node and neighbor nodes. Security recovery policy in ad hoc networks is the procedure of making a global decision according to messages received from distributed IDS and restore to operational health the whole system if any user or host that conducts the inappropriate, incorrect, or anomalous activities that threaten the connectivity or reliability of the networks and the authenticity of the data traffic in the networks. Finally, quantitative risk assessment model is proposed to numerically evaluate MANS security
Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities
Traffic prediction plays a crucial role in alleviating traffic congestion
which represents a critical problem globally, resulting in negative
consequences such as lost hours of additional travel time and increased fuel
consumption. Integrating emerging technologies into transportation systems
provides opportunities for improving traffic prediction significantly and
brings about new research problems. In order to lay the foundation for
understanding the open research challenges in traffic prediction, this survey
aims to provide a comprehensive overview of traffic prediction methodologies.
Specifically, we focus on the recent advances and emerging research
opportunities in Artificial Intelligence (AI)-based traffic prediction methods,
due to their recent success and potential in traffic prediction, with an
emphasis on multivariate traffic time series modeling. We first provide a list
and explanation of the various data types and resources used in the literature.
Next, the essential data preprocessing methods within the traffic prediction
context are categorized, and the prediction methods and applications are
subsequently summarized. Lastly, we present primary research challenges in
traffic prediction and discuss some directions for future research.Comment: Published in Transportation Research Part C: Emerging Technologies
(TR_C), Volume 145, 202
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