Droplet‐Based Techniques for Printing of Functional Inks for Flexible Physical Sensors

2021 ◽  
pp. 2006792
Author(s):  
Hamed Abdolmaleki ◽  
Preben Kidmose ◽  
Shweta Agarwala
Keyword(s):  
Physical Sensors

scholarly journals Robust autonomous flight in cluttered environment using a depth sensor

2020 ◽  
Vol 12 ◽  
pp. 175682932092452
Author(s):  
Liang Lu ◽  
Alexander Yunda ◽  
Adrian Carrio ◽  
Pascual Campoy
Keyword(s):  
High Speed ◽  
Point Clouds ◽  
Depth Sensor ◽  
Cluttered Environments ◽  
Industrial Plants ◽  
Cluttered Environment ◽  
Navigation Strategy ◽  
Physical Sensors ◽  
Aerial Vehicle ◽  
Planning Objective

This paper presents a novel collision-free navigation system for the unmanned aerial vehicle based on point clouds that outperform compared to baseline methods, enabling high-speed flights in cluttered environments, such as forests or many indoor industrial plants. The algorithm takes the point cloud information from physical sensors (e.g. lidar, depth camera) and then converts it to an occupied map using Voxblox, which is then used by a rapid-exploring random tree to generate finite path candidates. A modified Covariant Hamiltonian Optimization for Motion Planning objective function is used to select the best candidate and update it. Finally, the best candidate trajectory is generated and sent to a Model Predictive Control controller. The proposed navigation strategy is evaluated in four different simulation environments; the results show that the proposed method has a better success rate and a shorter goal-reaching distance than the baseline method.

Use of Big Data to Evaluate and Improve Performance of Traffic Signal Systems in Resource-Constrained Countries

2017 ◽  
Vol 2620 (1) ◽  
pp. 20-30 ◽  
Author(s):  
Yang Carl Lu ◽  
Holly Krambeck ◽  
Liang Tang
Keyword(s):  
Traffic Control ◽  
Time Of Day ◽  
Traffic Signal ◽  
Signal Timing ◽  
Gps Data ◽  
Resource Constrained ◽  
Physical Sensors ◽  
Constrained Environments ◽  
Traffic Signal Timing ◽  
High Level

Deployment of an adaptive area traffic control system is expensive; physical sensors require installation, calibration, and regular maintenance. Because of the high level of technical and financial resources required, area traffic control systems found in developing countries often are minimally functioning. In Cebu City, Philippines, for example, the Sydney Coordinated Adaptive Traffic System was installed before 2000, and fewer than 35% of detectors were still functioning as of January 2015. To address this challenge, a study was designed to determine whether taxi company GPS data are sufficient to evaluate and improve traffic signal timing plans in resource-constrained environments. If this work is successful, the number of physical sensors required to support those systems may be reduced and thereby substantially lower the costs of installation and maintenance. Taxi GPS data provided by a regional taxi-hailing app were used to design and implement methodologies for evaluating the performance of traffic signal timing plans and for deriving updated fixed-dynamic plans, which are fixed plans (with periods based on observable congestion patterns rather than only time of day) iterated regularly until optimization is reached. To date, three rounds of iterations have been conducted to ensure the stability of the proposed signal timings. Results of exploratory analysis indicate that the algorithm is capable of generating reasonable green time splits, but cycle length adjustment must be considered in the future.

Physical Sensors and Techniques

2008 ◽  
pp. 7-34
Author(s):  
Fidel Toldr√° ◽  
Pedro Jos√© Fito ◽  
Pedro Fito ◽  
Maria Castro-Gir√°ldez
Keyword(s):  
Physical Sensors

scholarly journals A Web GIS-Based Platform to Harvest Georeferenced Data from Social Networks: Examples of Data Collection Regarding Disaster Events

2018 ◽  
Vol 14 (02) ◽  
pp. 165 ◽  
Author(s):  
Cidália Costa Fonte ◽  
Diogo Fontes ◽  
Alberto Cardoso
Keyword(s):  
Social Networks ◽  
Data Collection ◽  
Emergency Response ◽  
Web Gis ◽  
Geographical Information ◽  
Civil Protection ◽  
Physical Sensors ◽  
Access To Data ◽  
Programming Interface ◽  
Fast Access

Whenever disaster situations occur the civil protection authorities need to have fast access to data that may help to plan emergency response. To contribute to the collection and integration of all available data a platform that aims to harvest Volunteered Geographical Information (VGI) from social networks and collaborative projects was created. This enables the integration of VGI with data coming from other sources, such as data collected by physical sensors in real time and made available through Applications Programming Interface (APIs), as well as, for example, official maps. The architecture of the created platform is described and its first prototype presented. Some example queries are performed and the results are analyzed.

scholarly journals Towards assimilation of crowdsourced observations for different levels of citizen engagement: the flood event of 2013 in the Bacchiglione catchment

2017 ◽  
Author(s):  
Maurizio Mazzoleni ◽  
Vivian Juliette Cortes Arevalo ◽  
Uta Wehn ◽  
Leonardo Alfonso ◽  
Daniele Norbiato ◽  
...  
Keyword(s):  
Real Time ◽  
Hydrological Model ◽  
Population Distribution ◽  
Model Performance ◽  
Flood Event ◽  
Target Point ◽  
Citizen Engagement ◽  
Physical Sensors ◽  
Extreme Flood ◽  
Social Sensors

Abstract. Accurate flood predictions are essential to reduce the risk and damages over large urbanized areas. To improve prediction capabilities, hydrological measurements derived by traditional physical sensors are integrated in real-time within mathematic models. Recently, traditional sensors are complemented with low-cost social sensors. However, measurements derived by social sensors (i.e. crowdsourced observations) can be more spatially distributed but less accurate. In this study, we assess the usefulness for model performance of assimilating crowdsourced observations from a heterogeneous network of static physical, static social and dynamic social sensors. We assess potential effects on the model predictions to the extreme flood event occurred in the Bacchiglione catchment on May 2013. Flood predictions are estimated at the target point of Ponte degli Angeli (Vicenza), outlet of the Bacchiglione catchment, by means of a semi-distributed hydrological model. The contribution of the upstream sub-catchment is calculated using a conceptual hydrological model. The flow is propagated along the river reach using a hydraulic model. In both models, a Kalman filter is implemented to assimilate the real-time crowdsourced observations. We synthetically derived crowdsourced observations for either static social or dynamic social sensors because crowdsourced measures were not available. We consider three sets of experiments: (1) only physical sensors are available; (2) probability of receiving crowdsourced observations and (3) realistic scenario of citizen engagement based on population distribution. The results demonstrated the importance of integrating crowdsourced observations. Observations from upstream sub-catchments assimilated into the hydrological model ensures high model performance for high lead time values. Observations next to the outlet of the catchments provide good results for short lead times. Furthermore, citizen engagement level scenarios moved by a feeling of belonging to a community of friends indicated flood prediction improvements when such small communities are located upstream a particular target point. Effective communication and feedback is required between water authorities and citizens to ensure minimum engagement levels and to minimize the intrinsic low-variable accuracy of crowdsourced observations.

Micromachined tunneling displacement transducers for physical sensors

1993 ◽  
Vol 11 (4) ◽  
pp. 797-802 ◽  
Author(s):  
T. W. Kenny ◽  
W. J. Kaiser ◽  
J. A. Podosek ◽  
H. K. Rockstad ◽  
J. K. Reynolds ◽  
...  
Keyword(s):  
Physical Sensors ◽  
Displacement Transducers

Machine learning for approximated sensors for Marksmanship training

2018 ◽  
Author(s):  
Keith Brawner
Keyword(s):  
Machine Learning ◽  
Military Service ◽  
Body Position ◽  
Fire Simulation ◽  
Tutoring System ◽  
Physical Sensors ◽  
Individualized Feedback ◽  
The Military ◽  
Marksmanship Training

"All people in the military must be proficient on the basics – to shoot, move, and communicate. Basic Rifle Marksmanship (BRM) is required for both noncommissioned and enlisted Warfighters in all branches of military service, with training on BRM skills being conducted in a series of “dry fire”, simulation, and live drills. In all phases of training, Warfighters receive instruction on the four fundamentals of shooting: breathing, body position, sight picture, and trigger squeeze. Within simulation, this training is conducted in a 1:4 to 1:8 range; one instructor per 4-8 students. While realtime individualized feedback is a goal of instruction, it is not a reality, as instructors must attend to the needs of many students. In an effort to aid instructors in providing valuable individualized feedback, a tutoring system was developed which automatically diagnosed novice performance when compared to experts. This system was used to diagnose novice performance with extensive physical sensors applied to the weapons. This work investigates whether machine learning can aid in the diagnosis of the novice performance, without the physical sensors, and analyzes the degree to which the sensors are necessary."

CHAPTER 11. Ionic Liquid-based Physical Sensors

2017 ◽  
pp. 296-320
Author(s):  
Yi-Chung Tung ◽  
Hsiao-Mei Wu ◽  
Tse-Ang Lee
Keyword(s):  
Ionic Liquid ◽  
Physical Sensors
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