scholarly journals Automated metadata transformation for a-priori deployed sensor networks

2014 ◽  
Author(s):  
Arka Bhattacharya ◽  
David Culler ◽  
Dezhi Hong ◽  
Kamin Whitehouse ◽  
Jorge Ortiz
Keyword(s):  
Sensor Networks ◽  
A Priori

scholarly journals Constructing 3D Underwater Sensor Networks without Sensing Holes Utilizing Heterogeneous Underwater Robots

2021 ◽  
Vol 11 (9) ◽  
pp. 4293
Author(s):  
Jonghoek Kim
Keyword(s):  
Sensor Networks ◽  
A Priori ◽  
Network Connectivity ◽  
Underwater Sensor Networks ◽  
Underwater Robots ◽  
Network Construction ◽  
Cluttered Environment ◽  
Sensing Range ◽  
Construction Methods ◽  
Heterogeneous Robots

This article handles building underwater sensor networks autonomously using multiple surface ships. For building underwater sensor networks in 3D workspace with many obstacles, this article considers surface ships dropping underwater robots into the underwater workspace. We assume that every robot is heterogeneous, such that each robot can have a distinct sensing range while moving with a distinct speed. The proposed strategy works by moving a single robot at a time to spread out the underwater networks until the 3D cluttered workspace is fully covered by sensors of the robots, such that no sensing hole remains. As far as we know, this article is novel in enabling multiple heterogeneous robots to build underwater sensor networks in a 3D cluttered environment, while satisfying the following conditions: (1) Remove all sensing holes. (2) Network connectivity is maintained. (3) Localize all underwater robots. In addition, we address how to handle the case where a robot is broken, and we discuss how to estimate the number of robots required, considering the case where an obstacle inside the workspace is not known a priori. Utilizing MATLAB simulations, we demonstrate the effectiveness of the proposed network construction methods.

An Ensemble Approach for Cognitive Fault Detection and Isolation in Sensor Networks

2017 ◽  
Vol 27 (03) ◽  
pp. 1650047 ◽  
Author(s):  
Manuel Roveri ◽  
Francesco Trovò
Keyword(s):  
Sensor Networks ◽  
Fault Detection ◽  
A Priori ◽  
Fault Detection And Diagnosis ◽  
Fault Detection And Isolation ◽  
Isolation System ◽  
Temporal Relationships ◽  
Data Generating Process ◽  
Detection And Diagnosis ◽  
Priori Information

Cognitive fault detection and diagnosis systems are systems able to provide timely information about possibly occurring faults without requiring any a priori knowledge about the process generating the data or the possible faults. This ability is crucial in sensor network scenarios where a priori information about the data generating process, the noise level or the dictionary of the possibly occurring faults is generally hard to obtain. We here present a novel cognitive fault detection and isolation system for sensor networks. The proposed solution relies on the modeling of spatial and temporal relationships present in the acquired datastreams and an ensemble of Hidden Markov Model change-detection tests working in the space of estimated parameters for fault detection and isolation purposes. The effectiveness of the proposed solution has been evaluated on both synthetically generated and real datasets.

scholarly journals Deep Reinforcement Learning for Attacking Wireless Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4060
Author(s):  
Juan Parras ◽  
Maximilian Hüttenrauch ◽  
Santiago Zazo ◽  
Gerhard Neumann
Keyword(s):  
Wireless Sensor Networks ◽  
Reinforcement Learning ◽  
Sensor Networks ◽  
Defense Mechanisms ◽  
Defense Mechanism ◽  
A Priori ◽  
Parameter Tuning ◽  
Wireless Sensor ◽  
Distributed Spectrum Sensing ◽  
Priori Information

Recent advances in Deep Reinforcement Learning allow solving increasingly complex problems. In this work, we show how current defense mechanisms in Wireless Sensor Networks are vulnerable to attacks that use these advances. We use a Deep Reinforcement Learning attacker architecture that allows having one or more attacking agents that can learn to attack using only partial observations. Then, we subject our architecture to a test-bench consisting of two defense mechanisms against a distributed spectrum sensing attack and a backoff attack. Our simulations show that our attacker learns to exploit these systems without having a priori information about the defense mechanism used nor its concrete parameters. Since our attacker requires minimal hyper-parameter tuning, scales with the number of attackers, and learns only by interacting with the defense mechanism, it poses a significant threat to current defense procedures.

scholarly journals Bell-X, An Opportunistic Time Synchronization Mechanism for Scheduled Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4128 ◽  
Author(s):  
Jose Vera-Pérez ◽  
David Todolí-Ferrandis ◽  
Javier Silvestre-Blanes ◽  
Víctor Sempere-Payá
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Ad Hoc ◽  
Time Synchronization ◽  
Manufacturing Sector ◽  
A Priori ◽  
Wireless Sensor ◽  
Lossy Networks ◽  
Medium Access ◽  
Network Connection

The Industrial Internet of Things (IIoT) is having an ever greater impact on industrial processes and the manufacturing sector, due the capabilities of massive data collection and interoperability with plant processes, key elements that are focused on the implementation of Industry 4.0. Wireless Sensor Networks (WSN) are one of the enabling technologies of the IIoT, due its self-configuration and self-repair capabilities to deploy ad-hoc networks. High levels of robustness and reliability, which are necessary in industrial environments, can be achieved by using the Time-Slotted Channel Hopping (TSCH) medium access the mechanism of the IEEE 802.15.4e protocol, penalizing other features, such as network connection and formation times, given that a new node does not know, a priori, the scheduling used by the network. This article proposes a new beacon advertising approach for a fast synchronization for networks under the TSCH-Medium Access Control (MAC) layer and Routing Protocol for Low-Power and Lossy Networks (RPL). This new method makes it possible to speed up the connection times of new nodes in an opportunistic way, while reducing the consumption and advertising traffic generated by the network.

scholarly journals On Consensus-based Distributed Blind Calibration of Sensor Networks

2018 ◽  
Author(s):  
Milos S. Stankovic ◽  
Srdjan S. Stankovic ◽  
Karl Henrik Johansson ◽  
Marko Beko ◽  
Luis M. Camarinha-Matos
Keyword(s):  
Sensor Networks ◽  
A Priori ◽  
Mean Square ◽  
Basic Algorithm ◽  
Network Characteristics ◽  
Estimated Parameters ◽  
Real World Applications ◽  
The Mean ◽  
Blind Calibration ◽  
Calibration Parameters

This review paper deals with recently proposed algorithms for distributed blind macro-calibration of sensor networks based on consensus (synchronization), not requiring any fusion center. The basic algorithm, performing the estimation of the local calibration parameters, is derived commencing from appropriate local criteria, and developing the corresponding gradient descent scheme. It is shown that the estimated parameters of the calibration functions asymptotically converge, in the mean-square sense and with probability one (w.p.1), to such values that ensure consensus on calibrated sensor gains and calibrated sensor offsets. For the more realistic case in which additive measurement noise, communication dropouts and additive communication noise are present, two algorithm modifications are introduced: one using a simple compensation term, and a more robust one based on an instrumental variable. By utilizing stochastic approximation arguments it is shown that the modified algorithms also achieve asymptotic agreement for calibrated sensor gains and offsets, in the mean-square sense and w.p.1. Convergence rate is analyzed in terms of an upper bound of the mean-square error. It is also shown that the communications between nodes can be completely asynchronous, which is of substantial importance for real-world applications. Suggestions for design of \textit{a priori} adjustable weights are given. Finally, it is shown that, if there is a subset of (precalibrated) reference sensors with fixed calibration parameters, the calibrated sensor gains and offsets of the rest of the sensors do not achieve consensus - they converge to different points dictated by the reference sensors and the network characteristics. Wide applicability and efficacy of these algorithms are illustrated on several simulation examples.

scholarly journals On Consensus-Based Distributed Blind Calibration of Sensor Networks

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4027 ◽  
Author(s):  
Miloš Stanković ◽  
Srdjan Stanković ◽  
Karl Johansson ◽  
Marko Beko ◽  
Luis Camarinha-Matos
Keyword(s):  
Sensor Networks ◽  
A Priori ◽  
Future Research ◽  
Mean Square ◽  
Basic Algorithm ◽  
Open Questions ◽  
Future Research Directions ◽  
The Mean ◽  
Blind Calibration ◽  
Calibration Parameters

This paper deals with recently proposed algorithms for real-time distributed blind macro-calibration of sensor networks based on consensus (synchronization). The algorithms are completely decentralized and do not require a fusion center. The goal is to consolidate all of the existing results on the subject, present them in a unified way, and provide additional important analysis of theoretical and practical issues that one can encounter when designing and applying the methodology. We first present the basic algorithm which estimates local calibration parameters by enforcing asymptotic consensus, in the mean-square sense and with probability one (w.p.1), on calibrated sensor gains and calibrated sensor offsets. For the more realistic case in which additive measurement noise, communication dropouts and additive communication noise are present, two algorithm modifications are discussed: one that uses a simple compensation term, and a more robust one based on an instrumental variable. The modified algorithms also achieve asymptotic agreement for calibrated sensor gains and offsets, in the mean-square sense and w.p.1. The convergence rate can be determined in terms of an upper bound on the mean-square error. The case when the communications between nodes is completely asynchronous, which is of substantial importance for real-world applications, is also presented. Suggestions for design of a priori adjustable weights are given. We also present the results for the case in which the underlying sensor network has a subset of (precalibrated) reference sensors with fixed calibration parameters. Wide applicability and efficacy of these algorithms are illustrated on several simulation examples. Finally, important open questions and future research directions are discussed.

Application of cross correlation to HREM images of surfaces

1987 ◽  
Vol 45 ◽  
pp. 754-755
Author(s):  
D. E. Luzzi ◽  
L. D. Marks ◽  
M. I. Buckett
Keyword(s):  
Cross Correlation ◽  
A Priori ◽  
Matrix Material ◽  
Correlation Method ◽  
Accurate Knowledge ◽  
Complex Image ◽  
Fourier Filtering ◽  
The Matrix ◽  
Low Pass ◽  
Data Reduction Technique

As the HREM becomes increasingly used for the study of dynamic localized phenomena, the development of techniques to recover the desired information from a real image is important. Often, the important features are not strongly scattering in comparison to the matrix material in addition to being masked by statistical and amorphous noise. The desired information will usually involve the accurate knowledge of the position and intensity of the contrast. In order to decipher the desired information from a complex image, cross-correlation (xcf) techniques can be utilized. Unlike other image processing methods which rely on data massaging (e.g. high/low pass filtering or Fourier filtering), the cross-correlation method is a rigorous data reduction technique with no a priori assumptions.We have examined basic cross-correlation procedures using images of discrete gaussian peaks and have developed an iterative procedure to greatly enhance the capabilities of these techniques when the contrast from the peaks overlap.

Towards 1-Ångstrom-resolution STEM

1993 ◽  
Vol 51 ◽  
pp. 996-997
Author(s):  
H.S. von Harrach ◽  
D.E. Jesson ◽  
S.J. Pennycook
Keyword(s):  
High Resolution ◽  
Field Emission ◽  
Phase Contrast ◽  
Spherical Aberration ◽  
A Priori ◽  
Dark Field ◽  
Image Resolution ◽  
Objective Lens ◽  
Annular Dark Field ◽  
First Results

Phase contrast TEM has been the leading technique for high resolution imaging of materials for many years, whilst STEM has been the principal method for high-resolution microanalysis. However, it was demonstrated many years ago that low angle dark-field STEM imaging is a priori capable of almost 50% higher point resolution than coherent bright-field imaging (i.e. phase contrast TEM or STEM). This advantage was not exploited until Pennycook developed the high-angle annular dark-field (ADF) technique which can provide an incoherent image showing both high image resolution and atomic number contrast.This paper describes the design and first results of a 300kV field-emission STEM (VG Microscopes HB603U) which has improved ADF STEM image resolution towards the 1 angstrom target. The instrument uses a cold field-emission gun, generating a 300 kV beam of up to 1 μA from an 11-stage accelerator. The beam is focussed on to the specimen by two condensers and a condenser-objective lens with a spherical aberration coefficient of 1.0 mm.

Treating Velopharyngeal Inadequacy Using Bilateral Buccal Flap Revision Palatoplasty

2019 ◽  
Vol 4 (5) ◽  
pp. 878-892
Author(s):  
Joseph A. Napoli ◽  
Linda D. Vallino
Keyword(s):  
Systematic Review ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Cleft Lip ◽  
Cleft Lip And Palate ◽  
A Priori ◽  
Sufficient Evidence ◽  
Obstructive Sleep ◽  
Before And After ◽  
Buccal Flap

Purpose The 2 most commonly used operations to treat velopharyngeal inadequacy (VPI) are superiorly based pharyngeal flap and sphincter pharyngoplasty, both of which may result in hyponasal speech and airway obstruction. The purpose of this article is to (a) describe the bilateral buccal flap revision palatoplasty (BBFRP) as an alternative technique to manage VPI while minimizing these risks and (b) conduct a systematic review of the evidence of BBFRP on speech and other clinical outcomes. A report comparing the speech of a child with hypernasality before and after BBFRP is presented. Method A review of databases was conducted for studies of buccal flaps to treat VPI. Using the principles of a systematic review, the articles were read, and data were abstracted for study characteristics that were developed a priori. With respect to the case report, speech and instrumental data from a child with repaired cleft lip and palate and hypernasal speech were collected and analyzed before and after surgery. Results Eight articles were included in the analysis. The results were positive, and the evidence is in favor of BBFRP in improving velopharyngeal function, while minimizing the risk of hyponasal speech and obstructive sleep apnea. Before surgery, the child's speech was characterized by moderate hypernasality, and after surgery, it was judged to be within normal limits. Conclusion Based on clinical experience and results from the systematic review, there is sufficient evidence that the buccal flap is effective in improving resonance and minimizing obstructive sleep apnea. We recommend BBFRP as another approach in selected patients to manage VPI. Supplemental Material https://doi.org/10.23641/asha.9919352

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