1,721,175 research outputs found
Density Classification in Asynchronous Random Networks with Faulty Nodes
No full textThis paper investigates distributed consensus for density classification in asynchronous random networks with faulty nodes. We compare four different models of faulty behavior under randomized topology. Using computer simulations, we show that (a) faulty nodes' impact depends on their location and (b) faulty nodes with persistent failures inhibit consensus stronger than commonly-used Byzantine faulty nodes with random failures. We also show that (c) randomization by Byzantine faulty nodes can be strongly beneficial for binary consensus and (d) topology randomization can increase robustness towards faulty node behavior
Randomized Binary Consensus with Faulty Agents
No full textThis paper investigates self-organizing binary majority consensus disturbed by faulty nodes with random and persistent failure. We study consensus in ordered and random networks with noise, message loss and delays. Using computer simulations, we show that: (1) explicit randomization by noise, message loss and topology can increase robustness towards faulty nodes; (2) commonly-used faulty nodes with random failure inhibit consensus less than faulty nodes with persistent failure; and (3) in some cases, such randomly failing faulty nodes can even promote agreement
Efficient binary consensus in randomized and noisy environments
No full textIn this article we investigate randomized binary majority consensus in networks with random topologies and noise. Using computer simulations, we show that asynchronous Simple Majority rule can reach ≃ 100% convergence rate being randomized by an update-biased random neighbor selection and a small fraction of errors. Next, we show that such gains are robust towards additive noise and topology randomization
Online Nonparametric Bayesian Activity Mining and Analysis from Surveillance Video
No full textA method for online incremental mining of activity patterns from the surveillance video stream is presented in this paper. The framework consists of a learning block in which Dirichlet process mixture model is employed for the incremental clustering of trajectories. Stochastic trajectory pattern models are formed using the Gaussian process regression of the corresponding flow functions. Moreover, a sequential Monte Carlo method based on Rao-Blackwellized particle filter is proposed for tracking and online classification as well as the detection of abnormality during the observation of an object. Experimental results on real surveillance video data are provided to show the performance of the proposed algorithm in different tasks of trajectory clustering, classification, and abnormality detectio
SPD-driven Smart Transmission Layer Based on a Software Defined Radio Test Bed Architecture
No full textCognitive Radio as a technological breakthrough and enabler for concepts such as Opportunistic Spectrum
Access and Dynamic Spectrum Access has so far received significant attention from the research community
from a theoretical standpoint. In this work, we build upon the theoretical foundation and present an implementation
of a Software Defined Radio/Cognitive Radio platform, with the feature under particular interest being
the so-called Smart Transmission Layer. Smart Transmission Layer is a feature developed within the currently
ongoing nSHIELD project, whose goal is establishing new paradigms for Security, Privacy and Dependability
(SPD) of the future embedded systems. The role of the SPD-driven Smart Transmission Layer is providing reliable
and efficient communications in critical channel conditions by using adaptive and flexible algorithms for
dynamically configuring and adapting various transmission-related parameters. The implementation was done
on the test bed consisting of two Secure Wideband Multi-role - Single-Channel Handheld Radios (SWAVE
HH) coupled with the powerful proprietary multi-processor embedded platforms, and the corresponding auxiliaries.
Several case studies were performed, namely: remote control of the radios, analysis of the installed
waveforms, interference detection, and spectrum sensing using a quasi-real-time energy detector. A roadmap
towards the future implementation aspects using the test bed was set
"Extending real-time change detection techniques to mosaiced backgrounds and mobile camera sequences in surveillance systems"
No full textThis paper shows a method for extending efficient algorithms for scene understanding already developed and tested for fixed cameras to a mobile camera environment. Real-time change detection methods for mobile-head cameras are introduced. The architecture of the system can be divided in two phases. During the off-line phase the system creates a panoramic multi-layer background image using a small number of static background images. In the on-line phase the system compares the acquired images with a portion of the panoramic background. Different approaches to produce the change detection images are analyzed. Experimental results are presented in order to validate the proposed methods; their evaluation is performed by using receiving operator characteristic (ROC) curves. The Neyman-Pearson statistical criterion has been used for selecting of optimal change detection threshold. The presented results, in terms of probabilities of false and correct detection rates and real-time behavior, show that one of the studied methods can be used as the basis for higher level modules of an automatic video-surveillance system
"Dynamic Scene Reconstruction For 3D Virtual Guidance"
No full textIn this paper a system is presented able to reproduce the actions of multiple moving objects into a 3D model. A multi-camera system is used for automatically detect, track and classify the objects. Data fusion from multiple sensors allows to get a more precise estimation of the position of detected moving objects and to solve occlusions problem. These data are then used to automatically place and animate objects avatars in a 3D virtual model of the scene, thus allowing users connected to this system to receive a 3D guide into the monitored environment
Smart Cameras with onboard Signcryption for Securing IoT Applications
No full textCameras are expected to become key sensor devices for various internet of things (IoT) applications. Since cameras often capture highly sensitive information, security is a major concern. Our approach towards data security for smart cameras is rooted on protecting the captured images by signcryption based on elliptic curve cryptography (ECC).
Signcryption achieves resource-efficiency by performing data signing and encryption in a single step. By running the signcryption on the sensing unit, we can relax some security assumptions for the camera host unit which typically runs a complex software stack. We introduce our system architecture motivated by a typical case study for camera-based IoT applications, evaluate security properties and present performance results of an ARM-based implementatio
"Localization and classification of partially overlapped objects using self-organizing trees"
No full textThis paper exploits an innovative technique to improve performances related to localization, tracking and classification of objects in a video surveillance system. The developed strategy has been applied to the problem of interaction between objects, i.e. well tuned traditional algorithms are able to track and classify objects whenever they enter the scene well-isolated from the other moving objects, but the state-of-the-art techniques fail when an occlusion situation is verified from the beginning. The performances of the developed algorithms have been evaluated on sequences of real images and experimental results have shown the validity of the approach
Self-organizing shape description for tracking and classifying multiple interacting objects
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