10,714 research outputs found
Robust covariance estimation for data fusion from multiple sensors
No full textThis paper addresses the robust estimation of a covariance matrix to express uncertainty when fusing information from multiple sensors. This is a problem of interest in multiple domains and applications, namely, in robotics. This paper discusses the use of estimators using explicit measurements from the sensors involved versus estimators using only covariance estimates from the sensor models and navigation systems. Covariance intersection and a class of orthogonal Gnanadesikan-Kettenring estimators are compared using the 2-norm of the estimates. A Monte Carlo simulation of a typical mapping experiment leads to conclude that covariance estimation systems with a hybrid of the two estimators may yield the best results.IEEE Transactions on Instrumentation and Measuremen
Thin bonding wires temperature measurement using optical fiber sensors
No full textIn this work we demonstrate the use of optical fiber sensors to measure temperature in thin metallic bonding wires. Temperature was measured in copper wires with diameter of 0.10, 0.28, 0.60 and 0.70 mm and for different values of the driven electrical current (0.75-10.00 A). A theoretical model for the system, which takes into account the relevant heat exchange mechanism, was developed. The results demonstrate the feasibility of the optical sensors application for the measurement of temperature in thin metallic bonding wires. (C) 2010 Elsevier Ltd. All rights reserved.FCT - SFRH/BD/41077/2007FCT - SFRH/BD/30295/2006FCT - SFRH/BD/41773/2007FEFOF Project PTDC/EEA-TEL/72025/2006European Union FEDER fun
Bayesian Recognition of Motion Related Activities with Inertial Sensors
No full textThis work presents the design and evaluation of an activity
recognition system for seven important motion related
activities. The only sensor used is an Inertial Measurement
Unit (IMU) worn on the belt.
For classification, we applied Bayesian techniques, based on
relevant features of the IMU raw data which are calculated in
real time. Based on a complete labelled data set, i.e.
supervised by an observing human judge, a K2 learning
algorithm by Cooper and Herskovits was used to construct
the Bayesian Network (BN) of the features.
Our comparison of dynamic and static inference algorithms,
based on the evaluation of the labelled data sets recorded
from 16 male and female subjects show that a Hidden
Markov Model (HMM) based on a learnt BN provides the
best results
Transportation mode recognition fusing wearable motion, sound and vision sensors
No full textWe present the first work that investigates the potential of improving the performance of transportation mode recognition through fusing multimodal data from wearable sensors: motion, sound and vision. We first train three independent deep neural network (DNN) classifiers, which work with the three types of sensors, respectively. We then propose two schemes that fuse the classification results from the three mono-modal classifiers. The first scheme makes an ensemble decision with fixed rules including Sum, Product, Majority Voting, and Borda Count. The second scheme is an adaptive fuser built as another classifier (including Naive Bayes, Decision Tree, Random Forest and Neural Network) that learns enhanced predictions by combining the outputs from the three mono-modal classifiers. We verify the advantage of the proposed method with the state-of-the-art Sussex-Huawei Locomotion and Transportation (SHL) dataset recognizing the eight transportation activities: Still, Walk, Run, Bike, Bus, Car, Train and Subway. We achieve F1 scores of 79.4%, 82.1% and 72.8% with the mono-modal motion, sound and vision classifiers, respectively. The F1 score is remarkably improved to 94.5% and 95.5% by the two data fusion schemes, respectively. The recognition performance can be further improved with a post-processing scheme that exploits the temporal continuity of transportation. When assessing generalization of the model to unseen data, we show that while performance is reduced -as expected -for each individual classifier, the benefits of fusion are retained with performance improved by 15 percentage points. Besides the actual performance increase, this work, most importantly, opens up the possibility for dynamically fusing modalities to achieve distinct power-performance trade-off at run time
Development of hysteresis-free and linear knitted strain sensors for smart textile applications
No full textSmart textiles have been attracting considerable interest in imparting a wide range of functions to traditional clothing ranging from sensing, actuation, data processing, and energy storage. In the case of textile-based strain sensors, most of the studies proved that they can work in principle, however, producing strain sensors with desirable properties such as stable sensitivity, small hysteresis, large enough working range, and good repeatability still remains a challenge necessitating the developments of novel technologies for soft sensors. This paper conducts a systematic approach to investigate the electromechanical properties of the knitted strain sensors to find out the optimum process parameters. We found a repeatable and robust method to produce knitted strain sensors with low hysteresis at a working range of at least 40%.Accepted Author ManuscriptEmerging MaterialsTechnical Suppor
Underwater Vehicle Motion Parameters Estimation Simulation and Experiment Based on Monocular Vision and Low Cost Inertial Measurement Unit
No full textLi Q, Zhang Q, Wang X. Underwater Vehicle Motion Parameters Estimation Simulation and Experiment Based on Monocular Vision and Low Cost Inertial Measurement Unit. Presented at the ISOPE 2009, Osaka, Japan
Research on Dynamic Simulation of Underwater Vehicle Manipulator Systems
No full textLi Q, Zhang Q, Wang X. Research on Dynamic Simulation of Underwater Vehicle Manipulator Systems. Presented at the IEEE MTS Oceans 08, Kobe, Japan
Strain and temperature sensors using multimode optical fiber Bragg gratings and correlation signal processing
No full textMultimode fiber optic Bragg grating sensors for
strain and temperature measurements using correlation signal processing methods have been developed. Two multimode Bragg grating sensors were fabricated in 62/125 m graded-index silica
multimode fiber; the first sensor was produced by the holographic method and the second sensor by the phase mask technique. The sensors have signal reflectivity of approximately 35% at peak
wavelengths of 835 nm and 859 nm, respectively.
Strain testing of both sensors has been done from 0 to 1000 με and the temperature testing from 40 to 80°C. Strain and temperature sensitivity values are 0.55 pm/με and 6 pm/°C, respectively.
The sensors are being applied in a power-by-light hydraulic valve monitoring system
High sensitive gas sensors realized by a transfer-free process of CVD graphene
No full textThe work herein presented investigates the behavior of graphene-based gas sensors realized by using an innovative way to prepare graphene. The sensing layer was directly grown by chemical vapor deposition on pre-patterned CMOS compatible Mo catalyst and then it was eased on the underlying SiO2 through a completely transfer-free process. Devices with different geometries were designed and tested towards NO2 and NH3 in environmental conditions, i.e. room temperature and relative humidity set at 50%. Furthermore, these gas sensors were also calibrated, resulting in the ability to detect concentrations down to 240 ppb and 17 ppm of NO2 and NH3, respectively. These results are in agreement with the best performances reported in literature for graphene-based sensors. They not only confirm the successful devices fabrication through the transfer-free approach, but also pave the route for large-scale production of MEMS/NEMS sensors.Accepted author manuscriptElectronic Components, Technology and Material
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