Approximately even partition algorithm for coding the Hilbert curve of arbitrary-sized image

Wu, Chin-Hsien; Chang, Youngho

doi:10.1049/iet-ipr.2010.0242

Cited by 7 publications

(5 citation statements)

References 21 publications

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“…However, for a sonar image, portions of the image that give high intensity returns are likely to provide most accurate displacement estimates. Therefore, in our algorithm the reference image intensity is used as a probability density function (PDF) for generating the sample points ξ ∈ X sample [29] [30]. An example distribution of sample points X sample can be seen in plot 3 of Fig 5, where the PDF is defined from the reference frame (plot 1).…”

Section: ) Selection Of Measurement (Sample) Pointsmentioning

confidence: 99%

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Attitude-Trajectory Estimation for Forward-Looking Multibeam Sonar Based on Acoustic Image Registration

Henson

Zakharov

2019

IEEE J. Oceanic Eng.

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Section: ) Selection Of Measurement (Sample) Pointsmentioning

confidence: 99%

“…The bins are ordered to provide the slowest evolution between consecutive bins. The algorithm scans the DMd(ξ) along a pseudo-Hilbert space filling curve (SFC) pixel-by-pixel [30].…”

Section: B Fine Displacement Estimationmentioning

confidence: 99%

Attitude-Trajectory Estimation for Forward-Looking Multibeam Sonar Based on Acoustic Image Registration

Henson

Zakharov

2019

IEEE J. Oceanic Eng.

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“…We adopt the primitive Hilbert areas and the primitive Hilbert curves designed by Wu et al in [ 36 ] to build the CH chain. In addition to the original primitive Hilbert curves I to IV, extra eight primitive pseudo Hilbert curves are designed and shown in Figure 7 .…”

Section: The Collaborative Data Collection Schemementioning

confidence: 99%

“…The pseudo code of the cluster heads-linking algorithm based on the pseudo Hilbert curve is shown in Algorithm 1. Please refer to [ 36 ] for the pseudo Hilbert curve generation details.…”

Section: The Collaborative Data Collection Schemementioning

confidence: 99%

A Collaborative Data Collection Scheme Based on Optimal Clustering for Wireless Sensor Networks

Chen

Peng

et al. 2018

Sensors

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In recent years, energy-efficient data collection has evolved into the core problem in the resource-constrained Wireless Sensor Networks (WSNs). Different from existing data collection models in WSNs, we propose a collaborative data collection scheme based on optimal clustering to collect the sensed data in an energy-efficient and load-balanced manner. After dividing the data collection process into the intra-cluster data collection step and the inter-cluster data collection step, we model the optimal clustering problem as a separable convex optimization problem and solve it to obtain the analytical solutions of the optimal clustering size and the optimal data transmission radius. Then, we design a Cluster Heads (CHs)-linking algorithm based on the pseudo Hilbert curve to build a CH chain with the goal of collecting the compressed sensed data among CHs in an accumulative way. Furthermore, we also design a distributed cluster-constructing algorithm to construct the clusters around the virtual CHs in a distributed manner. The experimental results show that the proposed method not only reduces the total energy consumption and prolongs the network lifetime, but also effectively balances the distribution of energy consumption among CHs. By comparing it o the existing compression-based and non-compression-based data collection schemes, the average reductions of energy consumption are 17.9% and 67.9%, respectively. Furthermore, the average network lifetime extends no less than 20-times under the same comparison.

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“…A restriction in using the closed space‐filling curve is the requirement that an area must to be a square in shape . Nonetheless, such a restriction is addressed by the Pseudo‐Hilbert curve, which can be used for an arbitrarily‐sized rectangle …”

Section: Detailed Bamt Approachmentioning

confidence: 99%

Cross‐layer technique for boosting base‐station anonymity in wireless sensor networks

Alsemairi

Younis

2017

Int J Communication

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A wireless sensor network (WSN) principally is composed of many sensor nodes and a single in situ base station (BS), which are randomly distributed in a given area of interest. These sensor nodes transmit their measurements to the BS over multihop wireless paths. In addition to collecting and processing the sensed data, the BS performs network management operations. Because of the importance of the BS to the WSN, it is the most attractive target of attacks for an adversary. Basically, the adversary opts to locate the BS and target it with denial-of-service attack to temporarily or indefinitely disrupt the WSN operation. The adversary can intercept the data packet transmissions and use traffic analysis techniques such as evidence theory to uncover the routing topology. To counter such an attack, this paper presents a novel technique for boosting the BS anonymity by grouping nodes into clusters and creating multiple mesh-based routing topologies among the cluster heads (CHs). By applying the closed space-filling curves such as the Moore curve, for forming a mesh, the CHs are offered a number of choices for disseminating aggregated data to the BS through inter-CH paths. Then, the BS forwards the aggregated data as well so that it appears as one of the CHs. The simulation results confirm the effectiveness of the proposed technique in boosting the anonymity of the BS. KEYWORDS anonymity, location privacy, traffic analysis, wireless sensor network 1 | INTRODUCTIONRecent advances in microelectronics have made it possible to integrate sensing and communication capabilities in miniaturized devices that are often referred to in the literature as sensor nodes. Interconnecting a large set of these nodes forms a wireless sensor network (WSN) that can serve unattended in harsh environments. A typical WSN includes an in situ base station (BS) that gathers data from the sensor nodes. WSNs are deemed effective in serving applications such as a military surveillance and target tracking where continuous monitoring of an area of interest is critical and human presence is risky and/or prohibitively expensive. In most of these WSN applications, sensor nodes have limited capabilities, eg, limited communication range, energy (battery) supply, and processing capacity. To overcome communication range limitations and to save energy, we find that sensor nodes transmit their measurements to the BS over multihop wireless paths. 1,2 On the other hand, the BS is not resource constrained and can perform network management and process the collected data. Because of the importance of the BS to the WSN, it can be the focus of an adversary who attempts to inflict the most damage to the network operation. The goal of the adversary will be to locate the BS and target it with denial-of-service attack so that the utility of the WSN is nullified. 3,4 Contemporary security mechanisms, such as packet header encryption and anonymous routing mechanisms, are often applied to conceal the BS's identity. [5][6][7][8][9] However, these security mechanisms do not ...

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Approximately even partition algorithm for coding the Hilbert curve of arbitrary-sized image

Cited by 7 publications

References 21 publications

Attitude-Trajectory Estimation for Forward-Looking Multibeam Sonar Based on Acoustic Image Registration

Attitude-Trajectory Estimation for Forward-Looking Multibeam Sonar Based on Acoustic Image Registration

A Collaborative Data Collection Scheme Based on Optimal Clustering for Wireless Sensor Networks

Cross‐layer technique for boosting base‐station anonymity in wireless sensor networks

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