scholarly journals LoRaWAN Geo-Tracking Using Map Matching and Compass Sensor Fusion

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5815
Author(s):  
Nico Podevijn ◽  
Jens Trogh ◽  
Michiel Aernouts ◽  
Rafael Berkvens ◽  
Luc Martens ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Energy Efficient ◽  
Low Cost ◽  
Map Matching ◽  
Directional Information ◽  
Matching Algorithm ◽  
Additional Information ◽  
Energy Impact ◽  
Node Location ◽  
The Cost

In contrast to accurate GPS-based localization, approaches to localize within LoRaWAN networks offer the advantages of being low power and low cost. This targets a very different set of use cases and applications on the market where accuracy is not the main considered metric. The localization is performed by the Time Difference of Arrival (TDoA) method and provides discrete position estimates on a map. An accurate “tracking-on-demand” mode for retrieving lost and stolen assets is important. To enable this mode, we propose deploying an e-compass in the mobile LoRa node, which frequently communicates directional information via the payload of the LoRaWAN uplink messages. Fusing this additional information with raw TDoA estimates in a map matching algorithm enables us to estimate the node location with a much increased accuracy. It is shown that this sensor fusion technique outperforms raw TDoA at the cost of only embedding a low-cost e-compass. For driving, cycling, and walking trajectories, we obtained minimal improvements of 65, 76, and 82% on the median errors which were reduced from 206 to 68 m, 197 to 47 m, and 175 to 31 m, respectively. The energy impact of adding an e-compass is limited: energy consumption increases by only 10% compared to traditional LoRa localization, resulting in a solution that is still 14 times more energy-efficient than a GPS-over-LoRa solution.

Low Cost Sensor Data Fusion in Omnidirectional Mobile Robot Feedback System to Improve the Navigation Accuracy

2014 ◽  
Vol 607 ◽  
pp. 791-794 ◽  
Author(s):  
Wei Kang Tey ◽  
Che Fai Yeong ◽  
Yip Loon Seow ◽  
Eileen Lee Ming Su ◽  
Swee Ho Tang
Keyword(s):  
Mobile Robot ◽  
Sensor Fusion ◽  
Low Cost ◽  
Feedback System ◽  
Sensor Data ◽  
Research Setting ◽  
Fusion Technique ◽  
Omnidirectional Mobile Robot ◽  
The Cost ◽  
Navigation Accuracy

Omnidirectional mobile robot has gained popularity among researchers. However, omnidirectional mobile robot is rarely been applied in industry field especially in the factory which is relatively more dynamic than normal research setting condition. Hence, it is very important to have a stable yet reliable feedback system to allow a more efficient and better performance controller on the robot. In order to ensure the reliability of the robot, many of the researchers use high cost solution in the feedback of the robot. For example, there are researchers use global camera as feedback. This solution has increases the cost of the robot setup fee to a relatively high amount. The setup system is also hard to modify and lack of flexibility. In this paper, a novel sensor fusion technique is proposed and the result is discussed.

scholarly journals Inertial Sensors for Risk-Based Redundancy in Dynamic Positioning

2017 ◽  
Author(s):  
Torleiv H. Bryne ◽  
Robert H. Rogne ◽  
Thor I. Fossen ◽  
Tor A. Johansen
Keyword(s):  
Sensor Fusion ◽  
Inertial Sensors ◽  
Low Cost ◽  
Single Point ◽  
Dynamic Positioning ◽  
Reference Systems ◽  
Positioning Systems ◽  
Sensor Structure ◽  
Inertial Measurements ◽  
The Cost

In this paper we present an alternative configuration of sensors, position and heading reference systems for dynamically positioned (DP) vessels. The approach uses a sensor structure based on low-cost inertial measurements units (IMUs), satisfying fault tolerance against single-point failures that is at the essence of the IMO guidelines for both DP class 2 and 3 vessels. Recent results have shown that dual-redundant position and heading reference systems are sufficient to prevent loss of position within some well-defined time horizons by exploiting sensor fusion of the reference systems and triple-redundant MEMS-based IMUs. These IMUs also function as Vertical Reference Units (VRUs), since vessel motions is obtained using the same IMU configuration and sensor fusion framework. In this proposition, the acceleration measurements provided by the IMUs make wind and other force sensors unnecessary, except possibly for an advisory role. The proposed framework has the potential to significantly reduce the cost of dynamic positioning systems without compromising safety.

scholarly journals An Enhanced Map-Matching Algorithm for Real-Time Position Accuracy Improvement with a Low-Cost GPS Receiver

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3836 ◽  
Author(s):  
Jeong Kang ◽  
Han Kim ◽  
Jin Park ◽  
Yoon Choi
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Map Matching ◽  
Accuracy Improvement ◽  
Digital Map ◽  
Icp Algorithm ◽  
Matching Algorithm ◽  
Position Accuracy ◽  
Geographic Data ◽  
Time Position

This paper proposes a real-time position accuracy improvement method for a low-cost global positioning system (GPS), which uses geographic data for forming a digital road database in the digital map information. We link the vehicle’s location to the position on the digital map using the map-matching algorithm to improve the position accuracy. In the proposed method, we can distinguish the vehicle direction on the road and enhance the horizontal accuracy using the geographic data composed of the vector point set of the digital map. We use the iterative closest point (ICP) algorithm that calculates the rotation matrix and the translation vector to compensate for the disparity between the GPS and the digital map information. We also use the least squares method to correct the error caused by the rotation of the ICP algorithm and link on the digital map to eliminate the residual disparity. Finally, we implement the proposed method in real time with a low-cost embedded system and demonstrate the effectiveness of the proposed method through various experiments.

scholarly journals A Real-Time Infrared Stereo Matching Algorithm for RGB-D Cameras’ Indoor 3D Perception

2020 ◽  
Vol 9 (8) ◽  
pp. 472
Author(s):  
Jiageng Zhong ◽  
Ming Li ◽  
Xuan Liao ◽  
Jiangying Qin
Keyword(s):  
Mutual Information ◽  
Stereo Matching ◽  
Low Cost ◽  
Depth Map ◽  
Cost Calculation ◽  
3D Perception ◽  
Detection Range ◽  
Matching Algorithm ◽  
Map Resolution ◽  
The Cost

Low-cost, commercial RGB-D cameras have become one of the main sensors for indoor scene 3D perception and robot navigation and localization. In these studies, the Intel RealSense R200 sensor (R200) is popular among many researchers, but its integrated commercial stereo matching algorithm has a small detection range, short measurement distance and low depth map resolution, which severely restrict its usage scenarios and service life. For these problems, on the basis of the existing research, a novel infrared stereo matching algorithm that combines the idea of the semi-global method and sliding window is proposed in this paper. First, the R200 is calibrated. Then, through Gaussian filtering, the mutual information and correlation between the left and right stereo infrared images are enhanced. According to mutual information, the dynamic threshold selection in matching is realized, so the adaptability to different scenes is improved. Meanwhile, the robustness of the algorithm is improved by the Sobel operators in the cost calculation of the energy function. In addition, the accuracy and quality of disparity values are improved through a uniqueness test and sub-pixel interpolation. Finally, the BundleFusion algorithm is used to reconstruct indoor 3D surface models in different scenarios, which proved the effectiveness and superiority of the stereo matching algorithm proposed in this paper.

scholarly journals Hybrid Micro-Energy Harvesting Model using WSN for Self-Sustainable Wireless Mobile Charging Application

2021 ◽  
Vol 3 (3) ◽  
pp. 157-169
Author(s):  
Haoxiang Wang
Keyword(s):  
Energy Harvesting ◽  
Energy Efficient ◽  
Low Cost ◽  
Base Station ◽  
Green Energy ◽  
Microelectronic Devices ◽  
Proposed Model ◽  
Mobile Charger ◽  
The Cost ◽  
Deployment Cost

The self-sustainable Wireless Sensor Networks (WSNs) face a major challenge in terms of energy efficiency as they have to operate without replacement of batteries. The benefits of renewable and green energy are taken into consideration for sensing and charging the battery in recent literatures using Energy Harvesting (EH) techniques. The sensors are provided with a reliable energy source through Wireless Charging (WC) techniques. Several challenges in WSN are addressed by combining these technologies. However, it is essential to consider the deployment cost in these systems. This paper presents a self-sustainable energy efficient WSN based model for Mobile Charger (MC) and Energy Harvesting Base Station (EHBS) while considering the cost of deployment. This system can also be used for low-cost microelectronic devices and low-cost Micro-Energy Harvesting (MEH) system-based applications. While considering the deployment cost, the network lifetime is maximized and an extensive comparison of simulation with various existing models is presented to emphasize the validity of the proposed model.

scholarly journals A Data Correction Algorithm for Low-Frequency Floating Car Data

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3639 ◽  
Author(s):  
Bijun Li ◽  
Yuan Guo ◽  
Jian Zhou ◽  
Yi Cai
Keyword(s):  
Low Cost ◽  
Low Frequency ◽  
Map Matching ◽  
Correction Algorithm ◽  
Trajectory Data ◽  
Matching Algorithm ◽  
Data Correction ◽  
Floating Car Data ◽  
Attraction Model ◽  
Physical Attraction

The data collected by floating cars is an important source for lane-level map production. Compared with other data sources, this method is a low-cost but challenging way to generate high-accuracy maps. In this paper, we propose a data correction algorithm for low-frequency floating car data. First, we preprocess the trajectory data by an adaptive density optimizing method to remove the noise points with large mistakes. Then, we match the trajectory data with OpenStreetMap (OSM) using an efficient hierarchical map matching algorithm. Lastly, we correct the floating car data by an OSM-based physical attraction model. Experiments are conducted exploiting the data collected by thousands of taxies over one week in Wuhan City, China. The results show that the accuracy of the data is improved and the proposed algorithm is demonstrated to be practical and effective.

Advance Protection for Three Phase Induction Motor using Microcontroller Atmega32

2018 ◽  
Vol 8 (1) ◽  
pp. 172
Author(s):  
Karan S Belsare ◽  
Gajanan D Patil
Keyword(s):  
Induction Motor ◽  
Low Cost ◽  
Short Circuit ◽  
Cost Factor ◽  
Protection Scheme ◽  
Three Phase ◽  
The Cost

A low cost and reliable protection scheme has been designed for a three phase induction motor against unbalance voltages, under voltage, over voltage, short circuit and overheating protection. Taking the cost factor into consideration the design has been proposed using microcontroller Atmega32, MOSFETs, relays, small CTs and PTs. However the sensitivity of the protection scheme has been not compromised. The design has been tested online in the laboratory for small motors and the same can be implemented for larger motors by replacing the i-v converters and relays of suitable ratings.

Janus – A New Approach to Air Combat Pilot Training

2019 ◽  
Vol 2019 (4) ◽  
pp. 7-22
Author(s):  
Georges Bridel ◽  
Zdobyslaw Goraj ◽  
Lukasz Kiszkowiak ◽  
Jean-Georges Brévot ◽  
Jean-Pierre Devaux ◽  
...  
Keyword(s):  
Low Cost ◽  
Cost Effective ◽  
High Energy ◽  
Training System ◽  
Embedded Sensors ◽  
Pilot Training ◽  
New Approach ◽  
Combat Aircraft ◽  
Air Combat ◽  
The Cost

Abstract Advanced jet training still relies on old concepts and solutions that are no longer efficient when considering the current and forthcoming changes in air combat. The cost of those old solutions to develop and maintain combat pilot skills are important, adding even more constraints to the training limitations. The requirement of having a trainer aircraft able to perform also light combat aircraft operational mission is adding unnecessary complexity and cost without any real operational advantages to air combat mission training. Thanks to emerging technologies, the JANUS project will study the feasibility of a brand-new concept of agile manoeuvrable training aircraft and an integrated training system, able to provide a live, virtual and constructive environment. The JANUS concept is based on a lightweight, low-cost, high energy aircraft associated to a ground based Integrated Training System providing simulated and emulated signals, simulated and real opponents, combined with real-time feedback on pilot’s physiological characteristics: traditionally embedded sensors are replaced with emulated signals, simulated opponents are proposed to the pilot, enabling out of sight engagement. JANUS is also providing new cost effective and more realistic solutions for “Red air aircraft” missions, organised in so-called “Aggressor Squadrons”.

scholarly journals ALPINE: Active Link Prediction Using Network Embedding

2021 ◽  
Vol 11 (11) ◽  
pp. 5043
Author(s):  
Xi Chen ◽  
Bo Kang ◽  
Jefrey Lijffijt ◽  
Tijl De Bie
Keyword(s):  
Active Learning ◽  
Protein Interactions ◽  
Link Prediction ◽  
Prediction Accuracy ◽  
Real Data ◽  
Network Embedding ◽  
Protein Protein Interactions ◽  
Additional Information ◽  
The Cost ◽  
Active Link

Many real-world problems can be formalized as predicting links in a partially observed network. Examples include Facebook friendship suggestions, the prediction of protein–protein interactions, and the identification of hidden relationships in a crime network. Several link prediction algorithms, notably those recently introduced using network embedding, are capable of doing this by just relying on the observed part of the network. Often, whether two nodes are linked can be queried, albeit at a substantial cost (e.g., by questionnaires, wet lab experiments, or undercover work). Such additional information can improve the link prediction accuracy, but owing to the cost, the queries must be made with due consideration. Thus, we argue that an active learning approach is of great potential interest and developed ALPINE (Active Link Prediction usIng Network Embedding), a framework that identifies the most useful link status by estimating the improvement in link prediction accuracy to be gained by querying it. We proposed several query strategies for use in combination with ALPINE, inspired by the optimal experimental design and active learning literature. Experimental results on real data not only showed that ALPINE was scalable and boosted link prediction accuracy with far fewer queries, but also shed light on the relative merits of the strategies, providing actionable guidance for practitioners.

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