112,738 research outputs found
The impact of traffic localisation on the performance of NoCs for very large manycore systems
The scaling of semiconductor technologies is leading to processors with increasing numbers of cores. The adoption of Networks-on-Chip (NoC) in manycore systems requires a shift in focus from computation to communication, as communication is fast becoming the dominant factor in processor performance. In large manycore systems, performance is predicated on the locality of communication. In this work, we investigate the performance of three NoC topologies for systems with thousands of processor cores under two types of localised traffic. We present latency and throughput results comparing fat quadtree, concentrated mesh and mesh topologies under different degrees of localisation. Our results, based on the ITRS physical data for 2023, show that the type and degree of localisation of traffic significantly affects the NoC performance, and that scale-invariant topologies perform worse than flat topologies
Location-Quality-aware Policy Optimisation for Relay Selection in Mobile Networks
Relaying can improve the coverage and performance of wireless access
networks. In presence of a localisation system at the mobile nodes, the use of
such location estimates for relay node selection can be advantageous as such
information can be collected by access points in linear effort with respect to
number of mobile nodes (while the number of links grows quadratically).
However, the localisation error and the chosen update rate of location
information in conjunction with the mobility model affect the performance of
such location-based relay schemes; these parameters also need to be taken into
account in the design of optimal policies. This paper develops a Markov model
that can capture the joint impact of localisation errors and inaccuracies of
location information due to forwarding delays and mobility; the Markov model is
used to develop algorithms to determine optimal location-based relay policies
that take the aforementioned factors into account. The model is subsequently
used to analyse the impact of deployment parameter choices on the performance
of location-based relaying in WLAN scenarios with free-space propagation
conditions and in an measurement-based indoor office scenario.Comment: Accepted for publication in ACM/Springer Wireless Network
Uncertainty effect on leak localisation in a DMA
The leak localisation methodologies based on data and models are affected by both uncertainties in the model and in the measurements. This uncertainty should be quantified so that its effect on the localisation methods performance can be estimated. In this paper, a model-based leak localisation methodology is applied to a real District Metered Area using synthetic data. In the generation process of the data, uncertainty in demands is taken into account. This uncertainty was estimated so that it can justify the uncertainty observed in the real measurements. The leak localisation methodology consists, first, in generating the set of possible measurements, obtained by Monte Carlo Simulation under a certain leak assumption and considering uncertainty, and second, in falsifying sets of nodes using the correlation with a leak residual model in order to signal a set of possible leaky nodes. The assessment is done by means of generating the confusion matrix with a Monte Carlo approach.Peer ReviewedPostprint (author's final draft
A Comprehensive Performance Evaluation of Deformable Face Tracking "In-the-Wild"
Recently, technologies such as face detection, facial landmark localisation
and face recognition and verification have matured enough to provide effective
and efficient solutions for imagery captured under arbitrary conditions
(referred to as "in-the-wild"). This is partially attributed to the fact that
comprehensive "in-the-wild" benchmarks have been developed for face detection,
landmark localisation and recognition/verification. A very important technology
that has not been thoroughly evaluated yet is deformable face tracking
"in-the-wild". Until now, the performance has mainly been assessed
qualitatively by visually assessing the result of a deformable face tracking
technology on short videos. In this paper, we perform the first, to the best of
our knowledge, thorough evaluation of state-of-the-art deformable face tracking
pipelines using the recently introduced 300VW benchmark. We evaluate many
different architectures focusing mainly on the task of on-line deformable face
tracking. In particular, we compare the following general strategies: (a)
generic face detection plus generic facial landmark localisation, (b) generic
model free tracking plus generic facial landmark localisation, as well as (c)
hybrid approaches using state-of-the-art face detection, model free tracking
and facial landmark localisation technologies. Our evaluation reveals future
avenues for further research on the topic.Comment: E. Antonakos and P. Snape contributed equally and have joint second
authorshi
Localisation of mobile nodes in wireless networks with correlated in time measurement noise.
Wireless sensor networks are an inherent part of decision making, object tracking and location awareness systems. This work is focused on simultaneous localisation of mobile nodes based on received signal strength indicators (RSSIs) with correlated in time measurement noises. Two approaches to deal with the correlated measurement noises are proposed in the framework of auxiliary particle filtering: with a noise augmented state vector and the second approach implements noise decorrelation. The performance of the two proposed multi model auxiliary particle filters (MM AUX-PFs) is validated over simulated and real RSSIs and high localisation accuracy is demonstrated
What makes for effective detection proposals?
Current top performing object detectors employ detection proposals to guide
the search for objects, thereby avoiding exhaustive sliding window search
across images. Despite the popularity and widespread use of detection
proposals, it is unclear which trade-offs are made when using them during
object detection. We provide an in-depth analysis of twelve proposal methods
along with four baselines regarding proposal repeatability, ground truth
annotation recall on PASCAL, ImageNet, and MS COCO, and their impact on DPM,
R-CNN, and Fast R-CNN detection performance. Our analysis shows that for object
detection improving proposal localisation accuracy is as important as improving
recall. We introduce a novel metric, the average recall (AR), which rewards
both high recall and good localisation and correlates surprisingly well with
detection performance. Our findings show common strengths and weaknesses of
existing methods, and provide insights and metrics for selecting and tuning
proposal methods.Comment: TPAMI final version, duplicate proposals removed in experiment
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