17,030 research outputs found
Target Tracking in Confined Environments with Uncertain Sensor Positions
To ensure safety in confined environments such as mines or subway tunnels, a
(wireless) sensor network can be deployed to monitor various environmental
conditions. One of its most important applications is to track personnel,
mobile equipment and vehicles. However, the state-of-the-art algorithms assume
that the positions of the sensors are perfectly known, which is not necessarily
true due to imprecise placement and/or dropping of sensors. Therefore, we
propose an automatic approach for simultaneous refinement of sensors' positions
and target tracking. We divide the considered area in a finite number of cells,
define dynamic and measurement models, and apply a discrete variant of belief
propagation which can efficiently solve this high-dimensional problem, and
handle all non-Gaussian uncertainties expected in this kind of environments.
Finally, we use ray-tracing simulation to generate an artificial mine-like
environment and generate synthetic measurement data. According to our extensive
simulation study, the proposed approach performs significantly better than
standard Bayesian target tracking and localization algorithms, and provides
robustness against outliers.Comment: IEEE Transactions on Vehicular Technology, 201
Adaptive Quantizers for Estimation
In this paper, adaptive estimation based on noisy quantized observations is
studied. A low complexity adaptive algorithm using a quantizer with adjustable
input gain and offset is presented. Three possible scalar models for the
parameter to be estimated are considered: constant, Wiener process and Wiener
process with deterministic drift. After showing that the algorithm is
asymptotically unbiased for estimating a constant, it is shown, in the three
cases, that the asymptotic mean squared error depends on the Fisher information
for the quantized measurements. It is also shown that the loss of performance
due to quantization depends approximately on the ratio of the Fisher
information for quantized and continuous measurements. At the end of the paper
the theoretical results are validated through simulation under two different
classes of noise, generalized Gaussian noise and Student's-t noise
A review on analysis and synthesis of nonlinear stochastic systems with randomly occurring incomplete information
Copyright q 2012 Hongli Dong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.In the context of systems and control, incomplete information refers to a dynamical system in which knowledge about the system states is limited due to the difficulties in modeling complexity in a quantitative way. The well-known types of incomplete information include parameter uncertainties and norm-bounded nonlinearities. Recently, in response to the development of network technologies, the phenomenon of randomly occurring incomplete information has become more and more prevalent. Such a phenomenon typically appears in a networked environment. Examples include, but are not limited to, randomly occurring uncertainties, randomly occurring nonlinearities, randomly occurring saturation, randomly missing measurements and randomly occurring quantization. Randomly occurring incomplete information, if not properly handled, would seriously deteriorate the performance of a control system. In this paper, we aim to survey some recent advances on the analysis and synthesis problems for nonlinear stochastic systems with randomly occurring incomplete information. The developments of the filtering, control and fault detection problems are systematically reviewed. Latest results on analysis and synthesis of nonlinear stochastic systems are discussed in great detail. In addition, various distributed filtering technologies over sensor networks are highlighted. Finally, some concluding remarks are given and some possible future research directions are pointed out. © 2012 Hongli Dong et al.This work was supported in part by the National Natural Science Foundation of China under Grants 61273156, 61134009, 61273201, 61021002, and 61004067, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Royal Society of the UK, the National Science Foundation of the USA under Grant No. HRD-1137732, and the Alexander von Humboldt Foundation of German
On general systems with network-enhanced complexities
In recent years, the study of networked control systems (NCSs) has gradually become an active research area due to the advantages of using networked media in many aspects such as the ease of maintenance and installation, the large flexibility and the low cost. It is well known that the devices in networks are mutually connected via communication cables that are of limited capacity. Therefore, some network-induced phenomena have inevitably emerged in the areas of signal processing and control engineering. These phenomena include, but are not limited to, network-induced communication delays, missing data, signal quantization, saturations, and channel fading. It is of great importance to understand how these phenomena influence the closed-loop stability and performance properties
JALAD: Joint Accuracy- and Latency-Aware Deep Structure Decoupling for Edge-Cloud Execution
Recent years have witnessed a rapid growth of deep-network based services and
applications. A practical and critical problem thus has emerged: how to
effectively deploy the deep neural network models such that they can be
executed efficiently. Conventional cloud-based approaches usually run the deep
models in data center servers, causing large latency because a significant
amount of data has to be transferred from the edge of network to the data
center. In this paper, we propose JALAD, a joint accuracy- and latency-aware
execution framework, which decouples a deep neural network so that a part of it
will run at edge devices and the other part inside the conventional cloud,
while only a minimum amount of data has to be transferred between them. Though
the idea seems straightforward, we are facing challenges including i) how to
find the best partition of a deep structure; ii) how to deploy the component at
an edge device that only has limited computation power; and iii) how to
minimize the overall execution latency. Our answers to these questions are a
set of strategies in JALAD, including 1) A normalization based in-layer data
compression strategy by jointly considering compression rate and model
accuracy; 2) A latency-aware deep decoupling strategy to minimize the overall
execution latency; and 3) An edge-cloud structure adaptation strategy that
dynamically changes the decoupling for different network conditions.
Experiments demonstrate that our solution can significantly reduce the
execution latency: it speeds up the overall inference execution with a
guaranteed model accuracy loss.Comment: conference, copyright transfered to IEE
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