430 research outputs found
Alternative packet switch architectures for a 30/20 GHz FDMA/TDMA geostationary communication satellite network
This study has investigated alternatives for realizing a packet-based network switch for deployment on a communication satellite. The emphasis was on the avoidance of contention problems that can occur due to the simultaneous arrival of an excessive number of packets destined for the same downlink dwell. The study was to look ahead, beyond the current Advanced Communications Technology Satellite (ACTS) capability, to the next generation of satellites. The study has not been limited by currently available technology, but has used university and commercial research efforts as a basis for designs that can be readily constructed and launched within the next five years. Tradeoffs in memory requirement, power requirement, and architecture have been considered as a part of our study
Technical support for digital systems technology development. Task order 1: ISP contention analysis and control
Alternatives for realizing a packet-based network switch for use on a frequency division multiple access/time division multiplexed (FDMA/TDM) geostationary communication satellite were investigated. Each of the eight downlink beams supports eight directed dwells. The design needed to accommodate multicast packets with very low probability of loss due to contention. Three switch architectures were designed and analyzed. An output-queued, shared bus system yielded a functionally simple system, utilizing a first-in, first-out (FIFO) memory per downlink dwell, but at the expense of a large total memory requirement. A shared memory architecture offered the most efficiency in memory requirements, requiring about half the memory of the shared bus design. The processing requirement for the shared-memory system adds system complexity that may offset the benefits of the smaller memory. An alternative design using a shared memory buffer per downlink beam decreases circuit complexity through a distributed design, and requires at most 1000 packets of memory more than the completely shared memory design. Modifications to the basic packet switch designs were proposed to accommodate circuit-switched traffic, which must be served on a periodic basis with minimal delay. Methods for dynamically controlling the downlink dwell lengths were developed and analyzed. These methods adapt quickly to changing traffic demands, and do not add significant complexity or cost to the satellite and ground station designs. Methods for reducing the memory requirement by not requiring the satellite to store full packets were also proposed and analyzed. In addition, optimal packet and dwell lengths were computed as functions of memory size for the three switch architectures
Deep learning-based fully automatic segmentation of wrist cartilage in MR images
The study objective was to investigate the performance of a dedicated
convolutional neural network (CNN) optimized for wrist cartilage segmentation
from 2D MR images. CNN utilized a planar architecture and patch-based (PB)
training approach that ensured optimal performance in the presence of a limited
amount of training data. The CNN was trained and validated in twenty
multi-slice MRI datasets acquired with two different coils in eleven subjects
(healthy volunteers and patients). The validation included a comparison with
the alternative state-of-the-art CNN methods for the segmentation of joints
from MR images and the ground-truth manual segmentation. When trained on the
limited training data, the CNN outperformed significantly image-based and
patch-based U-Net networks. Our PB-CNN also demonstrated a good agreement with
manual segmentation (Sorensen-Dice similarity coefficient (DSC) = 0.81) in the
representative (central coronal) slices with large amount of cartilage tissue.
Reduced performance of the network for slices with a very limited amount of
cartilage tissue suggests the need for fully 3D convolutional networks to
provide uniform performance across the joint. The study also assessed inter-
and intra-observer variability of the manual wrist cartilage segmentation
(DSC=0.78-0.88 and 0.9, respectively). The proposed deep-learning-based
segmentation of the wrist cartilage from MRI could facilitate research of novel
imaging markers of wrist osteoarthritis to characterize its progression and
response to therapy
Integrated data requirements for natural resource management
We do not have sufficient data to adequately describe the integrated socio-ecologicalsystems that support us. It is prohibitively expensive to collect enough data to describe all,so it is important to think strategically about how to (i) use the information we do have and (ii) prioritise the collection of new data. We aim to help by finding efficient ways of improving the information that is available for
policy-makers to generate better human–nature outcomes
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