25,862 research outputs found
Collaborative signal and information processing for target detection with heterogeneous sensor networks
In this paper, an approach for target detection and acquisition with heterogeneous sensor networks through strategic resource allocation and coordination is presented. Based on sensor management and collaborative signal and information processing, low-capacity low-cost sensors are strategically deployed to guide and cue scarce high performance sensors in the network to improve the data quality, with which the mission is eventually completed more efficiently with lower cost. We focus on the problem of designing such a network system in which issues of resource selection and allocation, system behaviour and capacity, target behaviour and patterns, the environment, and multiple constraints such as the cost must be addressed simultaneously. Simulation results offer significant insight into sensor selection and network operation, and demonstrate the great benefits introduced by guided search in an application of hunting down and capturing hostile vehicles on the battlefield
Meningioma segmentation in T1-weighted MRI leveraging global context and attention mechanisms
Meningiomas are the most common type of primary brain tumor, accounting for
approximately 30% of all brain tumors. A substantial number of these tumors are
never surgically removed but rather monitored over time. Automatic and precise
meningioma segmentation is therefore beneficial to enable reliable growth
estimation and patient-specific treatment planning. In this study, we propose
the inclusion of attention mechanisms over a U-Net architecture: (i)
Attention-gated U-Net (AGUNet) and (ii) Dual Attention U-Net (DAUNet), using a
3D MRI volume as input. Attention has the potential to leverage the global
context and identify features' relationships across the entire volume. To limit
spatial resolution degradation and loss of detail inherent to encoder-decoder
architectures, we studied the impact of multi-scale input and deep supervision
components. The proposed architectures are trainable end-to-end and each
concept can be seamlessly disabled for ablation studies. The validation studies
were performed using a 5-fold cross validation over 600 T1-weighted MRI volumes
from St. Olavs University Hospital, Trondheim, Norway. For the best performing
architecture, an average Dice score of 81.6% was reached for an F1-score of
95.6%. With an almost perfect precision of 98%, meningiomas smaller than 3ml
were occasionally missed hence reaching an overall recall of 93%. Leveraging
global context from a 3D MRI volume provided the best performances, even if the
native volume resolution could not be processed directly. Overall, near-perfect
detection was achieved for meningiomas larger than 3ml which is relevant for
clinical use. In the future, the use of multi-scale designs and refinement
networks should be further investigated to improve the performance. A larger
number of cases with meningiomas below 3ml might also be needed to improve the
performance for the smallest tumors.Comment: 16 pages, 5 figures, 3 tables. Submitted to Artificial Intelligence
in Medicin
An Adaptive Design Methodology for Reduction of Product Development Risk
Embedded systems interaction with environment inherently complicates
understanding of requirements and their correct implementation. However,
product uncertainty is highest during early stages of development. Design
verification is an essential step in the development of any system, especially
for Embedded System. This paper introduces a novel adaptive design methodology,
which incorporates step-wise prototyping and verification. With each adaptive
step product-realization level is enhanced while decreasing the level of
product uncertainty, thereby reducing the overall costs. The back-bone of this
frame-work is the development of Domain Specific Operational (DOP) Model and
the associated Verification Instrumentation for Test and Evaluation, developed
based on the DOP model. Together they generate functionally valid test-sequence
for carrying out prototype evaluation. With the help of a case study 'Multimode
Detection Subsystem' the application of this method is sketched. The design
methodologies can be compared by defining and computing a generic performance
criterion like Average design-cycle Risk. For the case study, by computing
Average design-cycle Risk, it is shown that the adaptive method reduces the
product development risk for a small increase in the total design cycle time.Comment: 21 pages, 9 figure
Autonomous flight and remote site landing guidance research for helicopters
Automated low-altitude flight and landing in remote areas within a civilian environment are investigated, where initial cost, ongoing maintenance costs, and system productivity are important considerations. An approach has been taken which has: (1) utilized those technologies developed for military applications which are directly transferable to a civilian mission; (2) exploited and developed technology areas where new methods or concepts are required; and (3) undertaken research with the potential to lead to innovative methods or concepts required to achieve a manual and fully automatic remote area low-altitude and landing capability. The project has resulted in a definition of system operational concept that includes a sensor subsystem, a sensor fusion/feature extraction capability, and a guidance and control law concept. These subsystem concepts have been developed to sufficient depth to enable further exploration within the NASA simulation environment, and to support programs leading to the flight test
Resilient Learning-Based Control for Synchronization of Passive Multi-Agent Systems under Attack
In this paper, we show synchronization for a group of output passive agents
that communicate with each other according to an underlying communication graph
to achieve a common goal. We propose a distributed event-triggered control
framework that will guarantee synchronization and considerably decrease the
required communication load on the band-limited network. We define a general
Byzantine attack on the event-triggered multi-agent network system and
characterize its negative effects on synchronization. The Byzantine agents are
capable of intelligently falsifying their data and manipulating the underlying
communication graph by altering their respective control feedback weights. We
introduce a decentralized detection framework and analyze its steady-state and
transient performances. We propose a way of identifying individual Byzantine
neighbors and a learning-based method of estimating the attack parameters.
Lastly, we propose learning-based control approaches to mitigate the negative
effects of the adversarial attack
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