11 research outputs found
Sigma Point Belief Propagation
The sigma point (SP) filter, also known as unscented Kalman filter, is an
attractive alternative to the extended Kalman filter and the particle filter.
Here, we extend the SP filter to nonsequential Bayesian inference corresponding
to loopy factor graphs. We propose sigma point belief propagation (SPBP) as a
low-complexity approximation of the belief propagation (BP) message passing
scheme. SPBP achieves approximate marginalizations of posterior distributions
corresponding to (generally) loopy factor graphs. It is well suited for
decentralized inference because of its low communication requirements. For a
decentralized, dynamic sensor localization problem, we demonstrate that SPBP
can outperform nonparametric (particle-based) BP while requiring significantly
less computations and communications.Comment: 5 pages, 1 figur
Likelihood Consensus and Its Application to Distributed Particle Filtering
We consider distributed state estimation in a wireless sensor network without
a fusion center. Each sensor performs a global estimation task---based on the
past and current measurements of all sensors---using only local processing and
local communications with its neighbors. In this estimation task, the joint
(all-sensors) likelihood function (JLF) plays a central role as it epitomizes
the measurements of all sensors. We propose a distributed method for computing,
at each sensor, an approximation of the JLF by means of consensus algorithms.
This "likelihood consensus" method is applicable if the local likelihood
functions of the various sensors (viewed as conditional probability density
functions of the local measurements) belong to the exponential family of
distributions. We then use the likelihood consensus method to implement a
distributed particle filter and a distributed Gaussian particle filter. Each
sensor runs a local particle filter, or a local Gaussian particle filter, that
computes a global state estimate. The weight update in each local (Gaussian)
particle filter employs the JLF, which is obtained through the likelihood
consensus scheme. For the distributed Gaussian particle filter, the number of
particles can be significantly reduced by means of an additional consensus
scheme. Simulation results are presented to assess the performance of the
proposed distributed particle filters for a multiple target tracking problem
Simultaneous Distributed Sensor Self-Localization and Target Tracking Using Belief Propagation and Likelihood Consensus
We introduce the framework of cooperative simultaneous localization and
tracking (CoSLAT), which provides a consistent combination of cooperative
self-localization (CSL) and distributed target tracking (DTT) in sensor
networks without a fusion center. CoSLAT extends simultaneous localization and
tracking (SLAT) in that it uses also intersensor measurements. Starting from a
factor graph formulation of the CoSLAT problem, we develop a particle-based,
distributed message passing algorithm for CoSLAT that combines nonparametric
belief propagation with the likelihood consensus scheme. The proposed CoSLAT
algorithm improves on state-of-the-art CSL and DTT algorithms by exchanging
probabilistic information between CSL and DTT. Simulation results demonstrate
substantial improvements in both self-localization and tracking performance.Comment: 10 pages, 5 figure
SSG2: A new modelling paradigm for semantic segmentation
State-of-the-art models in semantic segmentation primarily operate on single,
static images, generating corresponding segmentation masks. This one-shot
approach leaves little room for error correction, as the models lack the
capability to integrate multiple observations for enhanced accuracy. Inspired
by work on semantic change detection, we address this limitation by introducing
a methodology that leverages a sequence of observables generated for each
static input image. By adding this "temporal" dimension, we exploit strong
signal correlations between successive observations in the sequence to reduce
error rates. Our framework, dubbed SSG2 (Semantic Segmentation Generation 2),
employs a dual-encoder, single-decoder base network augmented with a sequence
model. The base model learns to predict the set intersection, union, and
difference of labels from dual-input images. Given a fixed target input image
and a set of support images, the sequence model builds the predicted mask of
the target by synthesizing the partial views from each sequence step and
filtering out noise. We evaluate SSG2 across three diverse datasets:
UrbanMonitor, featuring orthoimage tiles from Darwin, Australia with five
spectral bands and 0.2m spatial resolution; ISPRS Potsdam, which includes true
orthophoto images with multiple spectral bands and a 5cm ground sampling
distance; and ISIC2018, a medical dataset focused on skin lesion segmentation,
particularly melanoma. The SSG2 model demonstrates rapid convergence within the
first few tens of epochs and significantly outperforms UNet-like baseline
models with the same number of gradient updates. However, the addition of the
temporal dimension results in an increased memory footprint. While this could
be a limitation, it is offset by the advent of higher-memory GPUs and coding
optimizations.Comment: 19 pages, Under revie
Energy harvesting technologies for structural health monitoring of airplane components - a review
With the aim of increasing the efficiency of maintenance and fuel usage in airplanes, structural health monitoring (SHM) of critical composite structures is increasingly expected and required. The optimized usage of this concept is subject of intensive work in the framework of the EU COST Action CA18203 "Optimising Design for Inspection" (ODIN). In this context, a thorough review of a broad range of energy harvesting (EH) technologies to be potentially used as power sources for the acoustic emission and guided wave propagation sensors of the considered SHM systems, as well as for the respective data elaboration and wireless communication modules, is provided in this work. EH devices based on the usage of kinetic energy, thermal gradients, solar radiation, airflow, and other viable energy sources, proposed so far in the literature, are thus described with a critical review of the respective specific power levels, of their potential placement on airplanes, as well as the consequently necessary power management architectures. The guidelines provided for the selection of the most appropriate EH and power management technologies create the preconditions to develop a new class of autonomous sensor nodes for the in-process, non-destructive SHM of airplane components.The work of S. Zelenika, P. Gljušcic, E. Kamenar and Ž. Vrcan is partly enabled by using
the equipment funded via the EU European Regional Development Fund (ERDF) project no. RC.2.2.06-0001:
“Research Infrastructure for Campus-based Laboratories at the University of Rijeka (RISK)” and partly supported
by the University of Rijeka, Croatia, project uniri-tehnic-18-32 „Advanced mechatronics devices for smart
technological solutions“. Z. Hadas, P. Tofel and O. Ševecek acknowledge the support provided via the Czech
Science Foundation project GA19-17457S „Manufacturing and analysis of flexible piezoelectric layers for smart
engineering”. J. Hlinka, F. Ksica and O. Rubes gratefully acknowledge the financial support provided by the
ESIF, EU Operational Programme Research, Development and Education within the research project Center of
Advanced Aerospace Technology (Reg. No.: CZ.02.1.01/0.0/0.0/16_019/0000826) at the Faculty of Mechanical
Engineering, Brno University of Technology. V. Pakrashi would like to acknowledge UCD Energy Institute, Marine
and Renewable Energy Ireland (MaREI) centre Ireland, Strengthening Infrastructure Risk Assessment in the
Atlantic Area (SIRMA) Grant No. EAPA\826/2018, EU INTERREG Atlantic Area and Aquaculture Operations with
Reliable Flexible Shielding Technologies for Prevention of Infestation in Offshore and Coastal Areas (FLEXAQUA),
MarTera Era-Net cofund PBA/BIO/18/02 projects. The work of J.P.B. Silva is partially supported by the Portuguese
Foundation for Science and Technology (FCT) in the framework of the Strategic Funding UIDB/FIS/04650/2020.
M. Mrlik gratefully acknowledges the support of the Ministry of Education, Youth and Sports of the Czech
Republic-DKRVO (RP/CPS/2020/003
Time-space-sequential algorithms for distributed Bayesian state estimation in serial sensor networks
We consider distributed estimation of a time-dependent, random state vector based on a generally nonlinear/non-Gaussian state-space model. The current state is sensed by a serial sensor network without a fusion center. We present an optimal distributed Bayesian estima-tion algorithm that is sequential both in time and in space (i.e., across sensors) and requires only local communication between neighboring sensors. For the linear/Gaussian case, the algorithm reduces to a time-space-sequential, distributed form of the Kalman filter. We also demonstrate the application of our state estimator to a target tracking problem, using a dynamically defined “local sensor chain ” around the current target position. Index Terms—Parameter estimation, state estimation, sequential Bayesian filtering, distributed inference, sensor networks, Kalman filter, target tracking. 1
Distributed Localization and Tracking of Mobile Networks Including Noncooperative Objects
We propose a Bayesian method for distributed sequential localization of mobile networks composed of both cooperative agents and noncooperative objects. Our method provides a consistent combination of cooperative self-localization (CS) and distributed tracking (DT). Multiple mobile agents and objects are localized and tracked using measurements between agents and objects and between agents. For a distributed operation and low complexity, we combine particle-based belief propagation with a consensus or gossip scheme. High localization accuracy is achieved through a probabilistic information transfer between the CS and DT parts of the underlying factor graph. Simulation results demonstrate significant improvements in both agent self-localization and object localization performance compared to separate CS and DT, and very good scaling properties with respect to the numbers of agents and objects
Quantifiable brain atrophy synthesis for benchmarking of cortical thickness estimation methods
Cortical thickness (CTh) is routinely used to quantify grey matter atrophy as it is a significant biomarker in studying neurodegenerative and neurological conditions. Clinical studies commonly employ one of several available CTh estimation software tools to estimate CTh from brain MRI scans. In recent years, machine learning-based methods emerged as a faster alternative to the main-stream CTh estimation methods (e.g. FreeSurfer). Evaluation and comparison of CTh estimation methods often include various metrics and downstream tasks, but none fully covers the sensitivity to sub-voxel atrophy characteristic of neurodegeneration. In addition, current evaluation methods do not provide a framework for the intra-method region-wise evaluation of CTh estimation methods. Therefore, we propose a method for brain MRI synthesis capable of generating a range of sub-voxel atrophy levels (global and local) with quantifiable changes from the baseline scan. We further create a synthetic test set and evaluate four different CTh estimation methods: FreeSurfer (cross-sectional), FreeSurfer (longitudinal), DL+DiReCT and HerstonNet. DL+DiReCT showed superior sensitivity to sub-voxel atrophy over other methods in our testing framework. The obtained results indicate that our synthetic test set is suitable for benchmarking CTh estimation methods on both global and local scales as well as regional inter-and intra-method performance comparison.</p