A Novel Power-Monitoring Strategy for Localization in Wireless Sensor Networks Using Antithetic Sampling Method

2020 ◽  
pp. 52-76
Author(s):  
Vasim Babu M.
Keyword(s):  
Monte Carlo ◽  
Sensor Node ◽  
Mapping Method ◽  
Computational Time ◽  
Localization Algorithm ◽  
Power Monitoring ◽  
Prime Objective ◽  
High Level ◽  
Power Mapping ◽  
Antithetic Sampling

The prime objective of this chapter is to develop a power-mapping localization algorithm based on Monte Carlo method using a discrete antithetic approach called Antithetic Markov Chain Monte Carlo (AMCMC). The chapter is focused on solving two major problems in WSN, thereby increasing the accuracy of the localization algorithm and discrete power control. Consecutively, the work is focused to reduce the computational time, while finding the location of the sensor. The model achieves the power controlling strategy using discrete power levels (CC2420 radio chip) which allocate the power, based on the event of each sensor node. By utilizing this discrete power mapping method, all the high-level parameters are considered for WSN. To improve the overall accuracy, the antithetic sampling is used to reduce the number of unwanted sampling, while identifying the sensor location in each transition state. At the final point, the accuracy is increased to 94% wherein nearly 24% of accuracy is increased compared to other MCL-based localization schemes.

Autonomous vehicle self-localization in urban environments based on 3D curvature feature points – Monte Carlo localization

Robotica ◽  
2021 ◽  
pp. 1-17
Author(s):  
Qi Liu ◽  
Xiaoguang Di ◽  
Binfeng Xu
Keyword(s):  
Monte Carlo ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Mapping Method ◽  
Urban Environments ◽  
Feature Points ◽  
Localization Algorithm ◽  
Accurate Localization ◽  
Localization Algorithms ◽  
Monte Carlo Localization

Abstract This paper proposes a map-based localization system for autonomous vehicle self-localization in urban environments, which is composed of a pose graph mapping method and 3D curvature feature points – Monte Carlo Localization algorithm (3DCF-MCL). The advantage of 3DCF-MCL is that it combines the high accuracy of the 3D feature points registration and the robustness of particle filter. Experimental results show that 3DCF-MCL can provide an accurate localization for autonomous vehicles with the 3D point cloud map that generated by our mapping method. Compared with other map-based localization algorithms, it demonstrates that 3DCF-MCL outperforms them.

scholarly journals A Modified KNN Algorithm for High-Performance Computing on FPGA of Real-Time m-QAM Demodulators

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 627
Author(s):  
David Marquez-Viloria ◽  
Luis Castano-Londono ◽  
Neil Guerrero-Gonzalez
Keyword(s):  
Real Time ◽  
High Performance ◽  
Interference Mitigation ◽  
Parallel Implementation ◽  
Computational Time ◽  
Successful Implementation ◽  
Interchannel Interference ◽  
The Difference ◽  
High Level ◽  
Performance Computing

A methodology for scalable and concurrent real-time implementation of highly recurrent algorithms is presented and experimentally validated using the AWS-FPGA. This paper presents a parallel implementation of a KNN algorithm focused on the m-QAM demodulators using high-level synthesis for fast prototyping, parameterization, and scalability of the design. The proposed design shows the successful implementation of the KNN algorithm for interchannel interference mitigation in a 3 × 16 Gbaud 16-QAM Nyquist WDM system. Additionally, we present a modified version of the KNN algorithm in which comparisons among data symbols are reduced by identifying the closest neighbor using the rule of the 8-connected clusters used for image processing. Real-time implementation of the modified KNN on a Xilinx Virtex UltraScale+ VU9P AWS-FPGA board was compared with the results obtained in previous work using the same data from the same experimental setup but offline DSP using Matlab. The results show that the difference is negligible below FEC limit. Additionally, the modified KNN shows a reduction of operations from 43 percent to 75 percent, depending on the symbol’s position in the constellation, achieving a reduction 47.25% reduction in total computational time for 100 K input symbols processed on 20 parallel cores compared to the KNN algorithm.

scholarly journals Multi-sensor three-dimensional Monte Carlo localization for long-term aerial robot navigation

2017 ◽  
Vol 14 (5) ◽  
pp. 172988141773275 ◽  
Author(s):  
Francisco J Perez-Grau ◽  
Fernando Caballero ◽  
Antidio Viguria ◽  
Anibal Ollero
Keyword(s):  
Monte Carlo ◽  
Motion Tracking ◽  
Robot Navigation ◽  
Tracking System ◽  
Three Dimensional ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Localization Algorithm ◽  
Monte Carlo Localization ◽  
Motion Tracking System

This article presents an enhanced version of the Monte Carlo localization algorithm, commonly used for robot navigation in indoor environments, which is suitable for aerial robots moving in a three-dimentional environment and makes use of a combination of measurements from an Red,Green,Blue-Depth (RGB-D) sensor, distances to several radio-tags placed in the environment, and an inertial measurement unit. The approach is demonstrated with an unmanned aerial vehicle flying for 10 min indoors and validated with a very precise motion tracking system. The approach has been implemented using the robot operating system framework and works smoothly on a regular i7 computer, leaving plenty of computational capacity for other navigation tasks such as motion planning or control.

scholarly journals Navigating by touch: haptic Monte Carlo localization via geometric sensing and terrain classification

2021 ◽  
Vol 45 (6) ◽  
pp. 843-857
Author(s):  
Russell Buchanan ◽  
Jakub Bednarek ◽  
Marco Camurri ◽  
Michał R. Nowicki ◽  
Krzysztof Walas ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Estimation Error ◽  
Extreme Environments ◽  
Legged Robot ◽  
Terrain Classification ◽  
Localization Algorithm ◽  
Geometric Information ◽  
Contact Points ◽  
Monte Carlo Localization ◽  
Geometric Sensing

AbstractLegged robot navigation in extreme environments can hinder the use of cameras and lidar due to darkness, air obfuscation or sensor damage, whereas proprioceptive sensing will continue to work reliably. In this paper, we propose a purely proprioceptive localization algorithm which fuses information from both geometry and terrain type to localize a legged robot within a prior map. First, a terrain classifier computes the probability that a foot has stepped on a particular terrain class from sensed foot forces. Then, a Monte Carlo-based estimator fuses this terrain probability with the geometric information of the foot contact points. Results demonstrate this approach operating online and onboard an ANYmal B300 quadruped robot traversing several terrain courses with different geometries and terrain types over more than 1.2 km. The method keeps pose estimation error below 20 cm using a prior map with trained network and using sensing only from the feet, leg joints and IMU.

scholarly journals Antithetic sampling for Monte Carlo differentiable rendering

2021 ◽  
Vol 40 (4) ◽  
pp. 1-12
Author(s):  
Cheng Zhang ◽  
Zhao Dong ◽  
Michael Doggett ◽  
Shuang Zhao
Keyword(s):  
Monte Carlo ◽  
Antithetic Sampling

Simulation-Based Seismic Hazard Assessment Using Monte-Carlo Earthquake Catalogs: Application to CyberShake

2021 ◽  
Author(s):  
Sarah Azar ◽  
Mayssa Dabaghi
Keyword(s):  
Monte Carlo ◽  
Seismic Hazard ◽  
Ground Motion ◽  
Seismic Hazard Assessment ◽  
Computational Time ◽  
Earthquake Catalog ◽  
Earthquake Catalogs ◽  
Earthquake Scenarios ◽  
Simulation Based ◽  
Hazard Curves

ABSTRACT The use of numerical simulations in probabilistic seismic hazard analysis (PSHA) has achieved a promising level of reliability in recent years. One example is the CyberShake project, which incorporates physics-based 3D ground-motion simulations within seismic hazard calculations. Nonetheless, considerable computational time and resources are required due to the significant processing requirements imposed by source-based models on one hand, and the large number of seismic sources and possible rupture variations on the other. This article proposes to use a less computationally demanding simulation-based PSHA framework for CyberShake. The framework can accurately represent the seismic hazard at a site, by only considering a subset of all the possible earthquake scenarios, based on a Monte-Carlo simulation procedure that generates earthquake catalogs having a specified duration. In this case, ground motions need only be simulated for the scenarios selected in the earthquake catalog, and hazard calculations are limited to this subset of scenarios. To validate the method and evaluate its accuracy in the CyberShake platform, the proposed framework is applied to three sites in southern California, and hazard calculations are performed for earthquake catalogs with different lengths. The resulting hazard curves are then benchmarked against those obtained by considering the entire set of earthquake scenarios and simulations, as done in CyberShake. Both approaches yield similar estimates of the hazard curves for elastic pseudospectral accelerations and inelastic demands, with errors that depend on the length of the Monte-Carlo catalog. With 200,000 yr catalogs, the errors are consistently smaller than 5% at the 2% probability of exceedance in 50 yr hazard level, using only ∼3% of the entire set of simulations. Both approaches also produce similar disaggregation patterns. The results demonstrate the potential of the proposed approach in a simulation-based PSHA platform like CyberShake and as a ground-motion selection tool for seismic demand analyses.

scholarly journals Nesting Monte Carlo for high-dimensional non-linear PDEs

2018 ◽  
Vol 24 (4) ◽  
pp. 225-247 ◽  
Author(s):  
Xavier Warin
Keyword(s):  
Monte Carlo ◽  
Deep Learning ◽  
High Dimension ◽  
Analytical Solutions ◽  
New Method ◽  
Computational Time ◽  
High Dimensional ◽  
Learning Methods ◽  
Lipschitz Constants ◽  
Linear Pdes

Abstract A new method based on nesting Monte Carlo is developed to solve high-dimensional semi-linear PDEs. Depending on the type of non-linearity, different schemes are proposed and theoretically studied: variance error are given and it is shown that the bias of the schemes can be controlled. The limitation of the method is that the maturity or the Lipschitz constants of the non-linearity should not be too high in order to avoid an explosion of the computational time. Many numerical results are given in high dimension for cases where analytical solutions are available or where some solutions can be computed by deep-learning methods.

scholarly journals Land subsidence modelling for decision making on groundwater abstraction under emergency situation

2020 ◽  
Vol 382 ◽  
pp. 403-408
Author(s):  
Masaatsu Aichi
Keyword(s):  
Decision Making ◽  
Monte Carlo ◽  
Uncertainty Analysis ◽  
Real Time ◽  
Land Subsidence ◽  
Null Space ◽  
Emergency Situation ◽  
Computational Time ◽  
Groundwater Abstraction ◽  
Emergency Situations

Abstract. This study presents an inversion scheme with uncertainty analysis for a land subsidence modelling by a Monte Carlo filter in order to contribute to the decision-making on the groundwater abstraction. For real time prediction and uncertainty analysis under the limited computational resources and available information in emergency situations, one dimensional vertical land subsidence simulation was adopted for the forward modelling and the null-space Monte Carlo method was applied for the effective resampling. The proposed scheme was tested with the existing land subsidence monitoring data in Tokyo lowland, Japan. The results demonstrated that the prediction uncertainty converges and the prediction accuracy improves as the observed data increased with time. The computational time was also confirmed to be acceptable range for a real time execution with a laptop.

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