4,565 research outputs found
Discrete Signal Processing on Graphs: Frequency Analysis
Signals and datasets that arise in physical and engineering applications, as
well as social, genetics, biomolecular, and many other domains, are becoming
increasingly larger and more complex. In contrast to traditional time and image
signals, data in these domains are supported by arbitrary graphs. Signal
processing on graphs extends concepts and techniques from traditional signal
processing to data indexed by generic graphs. This paper studies the concepts
of low and high frequencies on graphs, and low-, high-, and band-pass graph
filters. In traditional signal processing, there concepts are easily defined
because of a natural frequency ordering that has a physical interpretation. For
signals residing on graphs, in general, there is no obvious frequency ordering.
We propose a definition of total variation for graph signals that naturally
leads to a frequency ordering on graphs and defines low-, high-, and band-pass
graph signals and filters. We study the design of graph filters with specified
frequency response, and illustrate our approach with applications to sensor
malfunction detection and data classification
A deep learning approach for anomaly identification in PZT sensors using point contact method
The implementation of piezoelectric sensors is degraded due to surface defects, delamination,
and extreme weathering conditions, to mention a few. Hence, the sensor needs to be diagnosed
before the efficacious implementation in the structural health monitoring (SHM) framework. To
rescue the problem, a novel experimental method based on Coulomb coupling is utilised to
visualise the evolution of elastic waves and interaction with the surface anomaly in the lead
zirconate titanate (PZT) substrate. Recently, machine learning (ML) has been expeditiously
becoming an essential technology for scientific computing, with several possibilities to advance
the field of SHM. This study employs a deep learning-based autoencoder neural network in
conjunction with image registration and peak signal-to-noise ratio (PSNR) to diagnose the
surface anomaly in the PZT substrate. The autoencoder extracts the significant damage-sensitive
features from the complex waveform big data. Further, it provides a nonlinear input–output
model that is well suited for the non-linear interaction of the wave with the surface anomaly and
boundary of the substrate. The measured time-series waveform data is provided as input into the
autoencoder network. The mean absolute error (MAE) between the input and output of the deep
learning model is evaluated to detect the anomaly. The MAEs are sensitive to the anomaly that
lies in the PZT substrate. Further, the challenge arising from offset and distortion is addressed
with ad hoc image registration technique. Finally, the localisation and quantification of the
anomaly are performed by computing PSNR values. This work proposes an advanced, efficient
damage detection algorithm in the scenario of big data that is ubiquitous in SHM
Level based sampling techniques for energy conservation in large scale wireless sensor networks
As the size and node density of wireless sensor networks (WSN) increase,the energy conservation problem becomes more critical and the conventional methods become inadequate. This dissertation addresses two different problems in large scale WSNs where all sensors are involved in monitoring,but the traditional practice of periodic transmissions of observations from all sensors would drain excessive amount of energy.
In the first problem,monitoring of the spatial distribution of a two dimensional correlated signal is considered using a large scale WSN. It is assumed that sensor observations are heavily affected by noise. We present an approach that is based on detecting contour lines of the signal distribution to estimate the spatial distribution of the signal without involving all sensors in the network. Energy efficient algorithms are proposed for detecting and tracking the temporal variation of the contours. Optimal contour levels that minimize the estimation error and a practical approach for selection of contour levels are explored. Performance of the proposed algorithm is explored with different types of contour levels and detection parameters.
In the second problem,a WSN is considered that performs health monitoring of equipment from a power substation. The monitoring applications require transmissions of sensor observations from all sensor nodes on a regular basis to the base station,which is very costly in terms of communication cost. To address this problem,an efficient sampling technique using level-crossings (LCS) is proposed. This technique saves communication cost by suppressing transmissions of data samples that do not convey much information. The performance and cost of LCS for several different level-selection schemes are investigated. The number of required levels and the maximum sampling period for practical implementation of LCS are studied. Finally,in an experimental implementation of LCS with MICAzmote,the performance and cost of LCS for temperature sensing with uniform,logarithmic and a combined version of uniform and logarithmically spaced levels are compared with that using periodic sampling
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
The implicit objective of the biennial "international - Traveling Workshop on
Interactions between Sparse models and Technology" (iTWIST) is to foster
collaboration between international scientific teams by disseminating ideas
through both specific oral/poster presentations and free discussions. For its
second edition, the iTWIST workshop took place in the medieval and picturesque
town of Namur in Belgium, from Wednesday August 27th till Friday August 29th,
2014. The workshop was conveniently located in "The Arsenal" building within
walking distance of both hotels and town center. iTWIST'14 has gathered about
70 international participants and has featured 9 invited talks, 10 oral
presentations, and 14 posters on the following themes, all related to the
theory, application and generalization of the "sparsity paradigm":
Sparsity-driven data sensing and processing; Union of low dimensional
subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph
sensing/processing; Blind inverse problems and dictionary learning; Sparsity
and computational neuroscience; Information theory, geometry and randomness;
Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?;
Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website:
http://sites.google.com/site/itwist1
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