3,712 research outputs found
Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures
Distributed video coding (DVC) is a relatively new video coding architecture originated from two fundamental theorems namely, Slepian–Wolf and Wyner–Ziv. Recent research developments have made DVC attractive for applications in the emerging domain of wireless video sensor networks (WVSNs). This paper reviews the state-of-the-art DVC architectures with a focus on understanding their opportunities and gaps in addressing the operational requirements and application needs of WVSNs
Power packet transferability via symbol propagation matrix
Power packet is a unit of electric power transferred by a power pulse with an
information tag. In Shannon's information theory, messages are represented by
symbol sequences in a digitized manner. Referring to this formulation, we
define symbols in power packetization as a minimum unit of power transferred by
a tagged pulse. Here, power is digitized and quantized. In this paper, we
consider packetized power in networks for a finite duration, giving symbols and
their energies to the networks. A network structure is defined using a graph
whose nodes represent routers, sources, and destinations. First, we introduce
symbol propagation matrix (SPM) in which symbols are transferred at links
during unit times. Packetized power is described as a network flow in a
spatio-temporal structure. Then, we study the problem of selecting an SPM in
terms of transferability, that is, the possibility to represent given energies
at sources and destinations during the finite duration. To select an SPM, we
consider a network flow problem of packetized power. The problem is formulated
as an M-convex submodular flow problem which is known as generalization of the
minimum cost flow problem and solvable. Finally, through examples, we verify
that this formulation provides reasonable packetized power.Comment: Submitted to Proceedings of the Royal Society A: Mathematical,
Physical and Engineering Science
ToyArchitecture: Unsupervised Learning of Interpretable Models of the World
Research in Artificial Intelligence (AI) has focused mostly on two extremes:
either on small improvements in narrow AI domains, or on universal theoretical
frameworks which are usually uncomputable, incompatible with theories of
biological intelligence, or lack practical implementations. The goal of this
work is to combine the main advantages of the two: to follow a big picture
view, while providing a particular theory and its implementation. In contrast
with purely theoretical approaches, the resulting architecture should be usable
in realistic settings, but also form the core of a framework containing all the
basic mechanisms, into which it should be easier to integrate additional
required functionality.
In this paper, we present a novel, purposely simple, and interpretable
hierarchical architecture which combines multiple different mechanisms into one
system: unsupervised learning of a model of the world, learning the influence
of one's own actions on the world, model-based reinforcement learning,
hierarchical planning and plan execution, and symbolic/sub-symbolic integration
in general. The learned model is stored in the form of hierarchical
representations with the following properties: 1) they are increasingly more
abstract, but can retain details when needed, and 2) they are easy to
manipulate in their local and symbolic-like form, thus also allowing one to
observe the learning process at each level of abstraction. On all levels of the
system, the representation of the data can be interpreted in both a symbolic
and a sub-symbolic manner. This enables the architecture to learn efficiently
using sub-symbolic methods and to employ symbolic inference.Comment: Revision: changed the pdftitl
On the Inability of Markov Models to Capture Criticality in Human Mobility
We examine the non-Markovian nature of human mobility by exposing the
inability of Markov models to capture criticality in human mobility. In
particular, the assumed Markovian nature of mobility was used to establish a
theoretical upper bound on the predictability of human mobility (expressed as a
minimum error probability limit), based on temporally correlated entropy. Since
its inception, this bound has been widely used and empirically validated using
Markov chains. We show that recurrent-neural architectures can achieve
significantly higher predictability, surpassing this widely used upper bound.
In order to explain this anomaly, we shed light on several underlying
assumptions in previous research works that has resulted in this bias. By
evaluating the mobility predictability on real-world datasets, we show that
human mobility exhibits scale-invariant long-range correlations, bearing
similarity to a power-law decay. This is in contrast to the initial assumption
that human mobility follows an exponential decay. This assumption of
exponential decay coupled with Lempel-Ziv compression in computing Fano's
inequality has led to an inaccurate estimation of the predictability upper
bound. We show that this approach inflates the entropy, consequently lowering
the upper bound on human mobility predictability. We finally highlight that
this approach tends to overlook long-range correlations in human mobility. This
explains why recurrent-neural architectures that are designed to handle
long-range structural correlations surpass the previously computed upper bound
on mobility predictability
Transmission of Spatio-Temporal Correlated Sources Over Fading Multiple Access Channels With DQLC Mappings
© 2019 IEEE. This version of the article has been accepted for publication, after peer review. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The Version of Record is available online at: https://doi.org/ 10.1109/TCOMM.2019.2912571.[Abstract]: The design of zero-delay Joint Source-Channel Coding (JSCC) schemes for the transmission of correlated information over fading Multiple Access Channels (MACs) is an interesting problem for many communication scenarios like Wireless Sensor Networks (WSNs). Among the different JSCC schemes so far proposed for this scenario, Distributed Quantizer Linear Coding (DQLC) represents an appealing solution since it is able to outperform uncoded transmissions for any correlation level at high Signal-to-Noise Ratios (SNRs) with a low computational cost. In this paper, we extend the design of DQLC-based schemes for fading MACs considering sphere decoding to make the optimal Minimum Mean Squared Error (MMSE) estimation computationally affordable for an arbitrary number of transmit users. The use of sphere decoding also allows to formulate a practical algorithm for the optimization of DQLC-based systems. Finally, non-linear Kalman Filtering for the DQLC is considered to jointly exploit the temporal and spatial correlation of the source symbols. The results of computer experiments show that the proposed DQLC scheme with the Kalman Filter decoding approach clearly outperforms uncoded transmissions for medium and high SNRs.This work has been funded by Office of Naval Research
Global of United States (N62909-15-1-2014), the
Xunta de Galicia (ED431C 2016-045, ED341D R2016/012,
ED431G/01), the Agencia Estatal de InvestigaciĂłn of
Spain (TEC2015-69648-REDC, TEC2016-75067-C4-1-R) and
ERDF funds of the EU (AEI/FEDER, UE).United States. Office of Naval Research Global of United States; N62909-15-1-2014Xunta de Galicia; ED431C 2016-045Xunta de Galicia; ED341D R2016/012Xunta de Galicia; ED431G/0
Spatiotemporally dissociable neural signatures for generating and updating expectation over time in children: A High Density-ERP study
Temporal orienting (TO) is the allocation of attentional resources in time based on the a priori generation of temporal expectancy of relevant stimuli as well as the a posteriori updating of this expectancy as a function of both sensory-based evidence and elapsing time. These processes rely on dissociable cognitive mechanisms and neural networks. Yet, although there is evidence that TO may be a core mechanism for cognitive functioning in childhood, the developmental spatiotemporal neural dynamics of this mechanism are little understood. In this study we employed a combined approach based on the application of distributed source reconstruction on a high spatial resolution ERP data array obtained from eighteen 8- to 12-year-old children completing a TO paradigm in which both the cue (Temporal vs. Neutral) and the SOA (Short vs. Long) were manipulated. Results show both cue (N1) and SOA (CNV, Omission Detection Potential and Anterior Anticipatory Index) ERP effects, which were associated with expectancy generation and updating, respectively. Only cue-related effects were correlated with age, as revealed by a reduction of the N1 delta effect with increasing age. Our data suggest that the neural correlates underlying TO are already established at least from 8 to 12 years of age
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