57,436 research outputs found
Prototype of Fault Adaptive Embedded Software for Large-Scale Real-Time Systems
This paper describes a comprehensive prototype of large-scale fault adaptive
embedded software developed for the proposed Fermilab BTeV high energy physics
experiment. Lightweight self-optimizing agents embedded within Level 1 of the
prototype are responsible for proactive and reactive monitoring and mitigation
based on specified layers of competence. The agents are self-protecting,
detecting cascading failures using a distributed approach. Adaptive,
reconfigurable, and mobile objects for reliablility are designed to be
self-configuring to adapt automatically to dynamically changing environments.
These objects provide a self-healing layer with the ability to discover,
diagnose, and react to discontinuities in real-time processing. A generic
modeling environment was developed to facilitate design and implementation of
hardware resource specifications, application data flow, and failure mitigation
strategies. Level 1 of the planned BTeV trigger system alone will consist of
2500 DSPs, so the number of components and intractable fault scenarios involved
make it impossible to design an `expert system' that applies traditional
centralized mitigative strategies based on rules capturing every possible
system state. Instead, a distributed reactive approach is implemented using the
tools and methodologies developed by the Real-Time Embedded Systems group.Comment: 2nd Workshop on Engineering of Autonomic Systems (EASe), in the 12th
Annual IEEE International Conference and Workshop on the Engineering of
Computer Based Systems (ECBS), Washington, DC, April, 200
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Techniques for the dynamic randomization of network attributes
Critical infrastructure control systems continue to foster predictable communication paths and static configurations that allow easy access to our networked critical infrastructure around the world. This makes them attractive and easy targets for cyber-attack. We have developed technologies that address these attack vectors by automatically reconfiguring network settings. Applying these protective measures will convert control systems into «moving targets» that proactively defend themselves against attack. This «Moving Target Defense» (MTD) revolves about the movement of network reconfiguration, securely communicating reconfiguration specifications to other network nodes as required, and ensuring that connectivity between nodes is uninterrupted. Software-defined Networking (SDN) is leveraged to meet many of these goals. Our MTD approach eliminates adversaries targeting known static attributes of network devices and systems, and consists of the following three techniques: (1) Network Randomization for TCP/UDP Ports; (2) Network Randomization for IP Addresses; (3) Network Randomization for Network Paths In this paper, we describe the implementation of the aforementioned technologies. We also discuss the individual and collective successes for the techniques, challenges for deployment, constraints and assumptions, and the performance implications for each technique
Distributed machining control and monitoring using smart sensors/actuators
The study of smart sensors and actuators led, during the past few years, to the development of facilities which improve traditional sensors and actuators in a necessary way to automate production systems. In an other context, many studies are carried out aiming at defining a decisional structure for production activity control and the increasing need of reactivity leads to the autonomization of decisional levels close to the operational system. We suggest in this paper to study the natural convergence between these two approaches and we propose an integration architecture dealing with machine tool and machining control that enables the exploitation of distributed smart sensors and actuators in the decisional system
Autonomous real-time surveillance system with distributed IP cameras
An autonomous Internet Protocol (IP) camera based object tracking and behaviour identification system, capable of running in real-time on an embedded system with limited memory and processing power is presented in this paper. The main contribution of this work is the integration of processor intensive image processing algorithms on an embedded platform capable of running at real-time for monitoring the behaviour of pedestrians. The Algorithm Based Object Recognition and Tracking (ABORAT) system architecture presented here was developed on an Intel PXA270-based development board clocked at 520 MHz. The platform was connected to a commercial stationary IP-based camera in a remote monitoring station for intelligent image
processing. The system is capable of detecting moving objects and their shadows in a complex environment with varying lighting intensity and moving foliage. Objects
moving close to each other are also detected to extract their trajectories which are then fed into an unsupervised neural network for autonomous classification. The novel intelligent video system presented is also capable of performing simple analytic functions such as tracking and generating alerts when objects enter/leave regions or cross tripwires superimposed on live video by the operator
Caching-Aided Collaborative D2D Operation for Predictive Data Dissemination in Industrial IoT
Industrial automation deployments constitute challenging environments where
moving IoT machines may produce high-definition video and other heavy sensor
data during surveying and inspection operations. Transporting massive contents
to the edge network infrastructure and then eventually to the remote human
operator requires reliable and high-rate radio links supported by intelligent
data caching and delivery mechanisms. In this work, we address the challenges
of contents dissemination in characteristic factory automation scenarios by
proposing to engage moving industrial machines as device-to-device (D2D)
caching helpers. With the goal to improve reliability of high-rate
millimeter-wave (mmWave) data connections, we introduce the alternative
contents dissemination modes and then construct a novel mobility-aware
methodology that helps develop predictive mode selection strategies based on
the anticipated radio link conditions. We also conduct a thorough system-level
evaluation of representative data dissemination strategies to confirm the
benefits of predictive solutions that employ D2D-enabled collaborative caching
at the wireless edge to lower contents delivery latency and improve data
acquisition reliability
Visions, Values, and Videos: Revisiting Envisionings in Service of UbiComp Design for the Home
UbiComp has been envisioned to bring about a future dominated by calm
computing technologies making our everyday lives ever more convenient. Yet the
same vision has also attracted criticism for encouraging a solitary and passive
lifestyle. The aim of this paper is to explore and elaborate these tensions
further by examining the human values surrounding future domestic UbiComp
solutions. Drawing on envisioning and contravisioning, we probe members of the
public (N=28) through the presentation and focus group discussion of two
contrasting animated video scenarios, where one is inspired by "calm" and the
other by "engaging" visions of future UbiComp technology. By analysing the
reasoning of our participants, we identify and elaborate a number of relevant
values involved in balancing the two perspectives. In conclusion, we articulate
practically applicable takeaways in the form of a set of key design questions
and challenges.Comment: DIS'20, July 6-10, 2020, Eindhoven, Netherland
An instinct for detection: psychological perspectives on CCTV surveillance
The aim of this article is to inform and stimulate a proactive, multidisciplinary approach to research and development in surveillance-based detective work. In this article we review some of the key psychological issues and phenomena that practitioners should be aware of. We look at how human performance can be explained with reference to our biological and evolutionary legacy. We show how critical viewing conditions can be in determining whether observers detect or overlook criminal activity in video material. We examine situations where performance can be surprisingly poor, and cover situations where, even once confronted with evidence of these detection deficits, observers still underestimate their susceptibility to them. Finally we explain why the emergence of these relatively recent research themes presents an opportunity for police and law enforcement agencies to set a new, multidisciplinary research agenda focused on relevant and pressing issues of national and international importance
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