26,740 research outputs found
Real-time support for high performance aircraft operation
The feasibility of real-time processing schemes using artificial neural networks (ANNs) is investigated. A rationale for digital neural nets is presented and a general processor architecture for control applications is illustrated. Research results on ANN structures for real-time applications are given. Research results on ANN algorithms for real-time control are also shown
Know Your Enemy: Stealth Configuration-Information Gathering in SDN
Software Defined Networking (SDN) is a network architecture that aims at
providing high flexibility through the separation of the network logic from the
forwarding functions. The industry has already widely adopted SDN and
researchers thoroughly analyzed its vulnerabilities, proposing solutions to
improve its security. However, we believe important security aspects of SDN are
still left uninvestigated. In this paper, we raise the concern of the
possibility for an attacker to obtain knowledge about an SDN network. In
particular, we introduce a novel attack, named Know Your Enemy (KYE), by means
of which an attacker can gather vital information about the configuration of
the network. This information ranges from the configuration of security tools,
such as attack detection thresholds for network scanning, to general network
policies like QoS and network virtualization. Additionally, we show that an
attacker can perform a KYE attack in a stealthy fashion, i.e., without the risk
of being detected. We underline that the vulnerability exploited by the KYE
attack is proper of SDN and is not present in legacy networks. To address the
KYE attack, we also propose an active defense countermeasure based on network
flows obfuscation, which considerably increases the complexity for a successful
attack. Our solution offers provable security guarantees that can be tailored
to the needs of the specific network under consideratio
Task‐specific measurement uncertainty budget of Curvic‐coupling using analytical methods
A number of Industrial reference components manufactured by grinding to achieve tight dimensional tolerances. In this paper, we present an uncertainty budget of a reference forty-tooth #Curvic measured using an accurate Coordinate Measuring Machine (CMM) in a temperature-controlled laboratory. A number of measurements conducted on Curvicto assess measurement repeatability and reproducibility. Expanded uncertainty budget evaluated from twenty-one Influencing factors, giving8.7 µm (7.1 µm from Type A) and 11 µm (9.6 µm from Type A), respectively, for repeatability and reproducibility test (k >2). Measurement uncertainty due to steady-state thermal effects is 2.2 µm. An adaptable model is presented to evaluate transient thermal effects, a factor often neglected in measurement uncertainty. Thermal time constant uncertainty associated with transient thermal effects is evaluated u(τ)= ±398 s, which corresponds to ±15 % of thermal time constant expanded uncertainty, u(τ)= ±2570 s.
#Curvic® (Curvic is a trademark of The Gleason Works, 1000 University Avenue, Rochester, NY, 14603, USA
AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments
This report considers the application of Articial Intelligence (AI) techniques to
the problem of misuse detection and misuse localisation within telecommunications
environments. A broad survey of techniques is provided, that covers inter alia
rule based systems, model-based systems, case based reasoning, pattern matching,
clustering and feature extraction, articial neural networks, genetic algorithms, arti
cial immune systems, agent based systems, data mining and a variety of hybrid
approaches. The report then considers the central issue of event correlation, that
is at the heart of many misuse detection and localisation systems. The notion of
being able to infer misuse by the correlation of individual temporally distributed
events within a multiple data stream environment is explored, and a range of techniques,
covering model based approaches, `programmed' AI and machine learning
paradigms. It is found that, in general, correlation is best achieved via rule based approaches,
but that these suffer from a number of drawbacks, such as the difculty of
developing and maintaining an appropriate knowledge base, and the lack of ability
to generalise from known misuses to new unseen misuses. Two distinct approaches
are evident. One attempts to encode knowledge of known misuses, typically within
rules, and use this to screen events. This approach cannot generally detect misuses
for which it has not been programmed, i.e. it is prone to issuing false negatives.
The other attempts to `learn' the features of event patterns that constitute normal
behaviour, and, by observing patterns that do not match expected behaviour, detect
when a misuse has occurred. This approach is prone to issuing false positives,
i.e. inferring misuse from innocent patterns of behaviour that the system was not
trained to recognise. Contemporary approaches are seen to favour hybridisation,
often combining detection or localisation mechanisms for both abnormal and normal
behaviour, the former to capture known cases of misuse, the latter to capture
unknown cases. In some systems, these mechanisms even work together to update
each other to increase detection rates and lower false positive rates. It is concluded
that hybridisation offers the most promising future direction, but that a rule or state
based component is likely to remain, being the most natural approach to the correlation
of complex events. The challenge, then, is to mitigate the weaknesses of
canonical programmed systems such that learning, generalisation and adaptation
are more readily facilitated
The Role of Robots and Automation in Space
Advanced space transportation systems based on the shuttle and interim upper stage will open the way to the use of large-scale industrial and commercial systems in space. The role of robot and automation technology in the cost-effective implementation and operation of such systems in the next two decades is discussed. Planning studies initiated by NASA are described as applied to space exploration, global services, and space industrialization, and a forecast of potential missions in each category is presented. The appendix lists highlights of space robot technology from 1967 to the present
Symmetry sensitivities of Derivative-of-Gaussian filters
We consider the measurement of image structure using linear filters, in particular derivative-of-Gaussian (DtG) filters, which are an important model of V1 simple cells and widely used in computer vision, and whether such measurements can determine local image symmetry. We show that even a single linear filter can be sensitive to a symmetry, in the sense that specific responses of the filter can rule it out. We state and prove a necessary and sufficient, readily computable, criterion for filter symmetry-sensitivity. We use it to show that the six filters in a second order DtG family have patterns of joint sensitivity which are distinct for 12 different classes of symmetry. This rich symmetry-sensitivity adds to the properties that make DtG filters well-suited for probing local image structure, and provides a set of landmark responses suitable to be the foundation of a nonarbitrary system of feature categories
Application of Remote Sensing to the Chesapeake Bay Region. Volume 2: Proceedings
A conference was held on the application of remote sensing to the Chesapeake Bay region. Copies of the papers, resource contributions, panel discussions, and reports of the working groups are presented
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