scholarly journals A Robust Vision-Based Method for Displacement Measurement under Adverse Environmental Factors Using Spatio-Temporal Context Learning and Taylor Approximation

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3197 ◽  
Author(s):  
Chuan-Zhi Dong ◽  
Ozan Celik ◽  
F. Necati Catbas ◽  
Eugene OBrien ◽  
Su Taylor
Keyword(s):  
Displacement Measurement ◽  
Sensor Data ◽  
Displacement Sensor ◽  
Illumination Change ◽  
Context Learning ◽  
Illumination Changes ◽  
Measurement Results ◽  
Resolution Imaging ◽  
Series Of Experiments ◽  
Spatio Temporal

Currently, the majority of studies on vision-based measurement have been conducted under ideal environments so that an adequate measurement performance and accuracy is ensured. However, vision-based systems may face some adverse influencing factors such as illumination change and fog interference, which can affect measurement accuracy. This paper developed a robust vision-based displacement measurement method which can handle the two common and important adverse factors given above and achieve sensitivity at the subpixel level. The proposed method leverages the advantage of high-resolution imaging incorporating spatial and temporal contextual aspects. To validate the feasibility, stability, and robustness of the proposed method, a series of experiments was conducted on a two-span three-lane bridge in the laboratory. The illumination changes and fog interference were simulated experimentally in the laboratory. The results of the proposed method were compared to conventional displacement sensor data and current vision-based method results. It was demonstrated that the proposed method gave better measurement results than the current ones under illumination change and fog interference.

scholarly journals A Robust Vision-based Method for Displacement Measurement under Adverse Environmental Factors using Spatio-Temporal Context Learning and Taylor Approximation

2019 ◽  
Author(s):  
Chuan-zhi Dong ◽  
Ozan Celik ◽  
Fikret Catbas ◽  
Eugene OBrien ◽  
Su Taylor
Keyword(s):  
Displacement Measurement ◽  
Sensor Data ◽  
Displacement Sensor ◽  
Temporal Context ◽  
Illumination Change ◽  
Context Learning ◽  
Measurement Results ◽  
Resolution Imaging ◽  
Series Of Experiments ◽  
Spatio Temporal

Currently the majority of studies on vision-based measurement has been conducted under ideal environments so that an adequate measurement performance and accuracy is ensured. However, vision-based systems may face some adverse influencing factors such as illumination change and fog interference, which can affect the measurement accuracy. This paper develops a robust vision-based displacement measurement method which can handle the two common and important adverse factors given above and achieve sensitivity at the subpixel level. The proposed method leverages the advantage of high-resolution imaging incorporating spatial and temporal context aspects. To validate the feasibility, stability and robustness of the proposed method, a series of experiments was conducted on a two-span three-lane bridge in the laboratory. The illumination change and fog interference are simulated experimentally in the laboratory. The results of the proposed method are compared to conventional displacement sensor data and current vision-based method results. It is demonstrated that the proposed method gives better measurement results than the current ones under illumination change and fog interference.

scholarly journals An Improved Vision Method for Robust Monitoring of Multi-Point Dynamic Displacements with Smartphones in an Interference Environment

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5929
Author(s):  
Taicong Chen ◽  
Zhou Zhou
Keyword(s):  
High Speed ◽  
Target Selection ◽  
Wide Band ◽  
Sensor Data ◽  
Displacement Sensor ◽  
Band Frequency ◽  
Dynamic Displacement ◽  
Vibration Experiment ◽  
Spatio Temporal ◽  
Sine Sweep

Current research on dynamic displacement measurement based on computer vision mostly requires professional high-speed cameras and an ideal shooting environment to ensure the performance and accuracy of the analysis. However, the high cost of the camera and strict requirements of sharp image contrast and stable environment during the shooting process limit the broad application of the technology. This paper proposes an improved vision method to implement multi-point dynamic displacement measurements with smartphones in an interference environment. A motion-enhanced spatio-temporal context (MSTC) algorithm is developed and applied together with the optical flow (OF) algorithm to realize a simultaneous tracking and dynamic displacement extraction of multiple points on a vibrating structure in the interference environment. Finally, a sine-sweep vibration experiment on a cantilever sphere model is presented to validate the feasibility of the proposed method in a wide-band frequency range. In the test, a smartphone was used to shoot the vibration process of the sine-sweep-excited sphere, and illumination change, fog interference, and camera jitter were artificially simulated to represent the interference environment. The results of the proposed method are compared to conventional displacement sensor data and current vision method results. It is demonstrated that, in an interference environment, (1) the OF method is prone to mismatch the feature points and leads to data deviated or lost; (2) the conventional STC method is sensitive to target selection and can effectively track those targets having a large proportion of pixels in the context with motion tendency similar to the target center; (3) the proposed MSTC method, however, can ease the sensitivity to target selection through in-depth processing of the information in the context and finally enhance the robustness of the target tracking. In addition, the MSTC method takes less than one second to track each target between adjacent frame images, implying a potential for online measurement.

scholarly journals Design, Analysis, and Experiment on a Novel Stick-Slip Piezoelectric Actuator with a Lever Mechanism

Micromachines ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 863 ◽  
Author(s):  
Weiqing Huang ◽  
Mengxin Sun
Keyword(s):  
Piezoelectric Actuator ◽  
Simple Structure ◽  
Fast Response ◽  
Displacement Sensor ◽  
Design Parameters ◽  
The Finite Element Method ◽  
Stick Slip ◽  
Lever Mechanism ◽  
Series Of Experiments ◽  
Stator Design

A piezoelectric actuator using a lever mechanism is designed, fabricated, and tested with the aim of accomplishing long-travel precision linear driving based on the stick-slip principle. The proposed actuator mainly consists of a stator, an adjustment mechanism, a preload mechanism, a base, and a linear guide. The stator design, comprising a piezoelectric stack and a lever mechanism with a long hinge used to increase the displacement of the driving foot, is described. A simplified model of the stator is created. Its design parameters are determined by an analytical model and confirmed using the finite element method. In a series of experiments, a laser displacement sensor is employed to measure the displacement responses of the actuator under the application of different driving signals. The experiment results demonstrate that the velocity of the actuator rises from 0.05 mm/s to 1.8 mm/s with the frequency increasing from 30 Hz to 150 Hz and the voltage increasing from 30 V to 150 V. It is shown that the minimum step distance of the actuator is 0.875 μm. The proposed actuator features large stroke, a simple structure, fast response, and high resolution.

scholarly journals Comparative evaluation of three image analysis methods for angular displacement measurement in a MEMS microgripper prototype: a preliminary study

ACTA IMEKO ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 119
Author(s):  
Federica Vurchio ◽  
Giorgia Fiori ◽  
Andrea Scorza ◽  
Salvatore Andrea Sciuto
Keyword(s):  
Image Analysis ◽  
Angular Displacement ◽  
Functional Characterization ◽  
Displacement Measurement ◽  
Mems Devices ◽  
Speeded Up Robust Features ◽  
Relevant Today ◽  
Measurement Results ◽  
Preliminary Study

<p class="Abstract"><span lang="EN-US">The functional characterization of MEMS devices is relevant today since it aims at verifying the behavior of these devices, as well as improving their design. In this regard, this study focused on the functional characterization of a MEMS microgripper prototype suitable in biomedical applications: the measurement of the angular displacement of the microgripper comb-drive is carried out by means of two novel automatic procedures, based on an image analysis method, SURF-based (Angular Displacement Measurement based on Speeded Up Robust Features, ADM<sub>SURF</sub>) and FFT-based (Angular Displacement Measurement based on Fast Fourier Transform, ADM<sub>FFT</sub>) method, respectively. Moreover, the measurement results are compared with a Semi-Automatic Method (SAM), to evaluate which of them is the most suitable for the functional characterization of the device. The curve fitting of the outcomes from SAM and ADM<sub>SURF</sub>, showed a quadratic trend in agreement with the analytical model. Moreover, the ADM<sub>SURF</sub> measurements below 1° are affected by an uncertainty of about 0.08° for voltages less than 14 V, confirming its suitability for microgripper characterization. It was also evaluated that the ADM<sub>FFT</sub> is more suitable for measurement of rotations greater than 1° (up to 30°), with a measurement uncertainty of 0.02°, at 95% of confidence level.</span></p>

scholarly journals Sensing as a Service for the Internet of Things

2016 ◽  
Vol 4 (3) ◽  
pp. 28
Author(s):  
Omar Subhi Aldabbas
Keyword(s):  
Internet Of Things ◽  
Cost Effective ◽  
Sensor Data ◽  
Detection Accuracy ◽  
Heterogeneous Services ◽  
Coordination Languages ◽  
Spatio Temporal ◽  
Temporal Events ◽  
New Applications ◽  
Common Application

Internet of Things (IoT) is a ubiquitous embedded ecosystem known for its capability to perform common application functions through coordinating resources, which are distributed on-object or on-network domains. As new applications evolve, the challenge is in the analysis and usage of multimodal data streamed by diverse kinds of sensors. This paper presents a new service-centric approach for data collection and retrieval. This approach considers objects as highly decentralized, composite and cost effective services. Such services can be constructed from objects located within close geographical proximity to retrieve spatio-temporal events from the gathered sensor data. To achieve this, we advocate Coordination languages and models to fuse multimodal, heterogeneous services through interfacing with every service to achieve the network objective according to the data they gather and analyze. In this paper we give an application scenario that illustrates the implementation of the coordination models to provision successful collaboration among IoT objects to retrieve information. The proposed solution reduced the communication delay before service composition by up to 43% and improved the target detection accuracy by up to 70%, while maintaining energy consumption 20% lower than its best rivals in the literature.

scholarly journals Identifying drought events in sugarcane using drought indices derived from Modis sensor

2017 ◽  
Vol 52 (11) ◽  
pp. 1063-1071 ◽  
Author(s):  
Michelle Cristina Araujo Picoli ◽  
Daniel Garbellini Duft ◽  
Pedro Gerber Machado
Keyword(s):  
Near Infrared ◽  
Sensor Data ◽  
Drought Indices ◽  
Spectral Indices ◽  
Seasonal Fluctuations ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Aqua Satellite ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Shortwave Infrared

Abstract: The objective of this work was to evaluate the potential of several spectral indices, used on moderate resolution imaging spectroradiometer (Modis) images, in identifying drought events in sugarcane. Images of Terra and Aqua satellites were used to calculate the spectral indices, using visible (red), near infrared, and shortwave infrared bands, and eight indices were selected: NDVI, EVI2, GVMI, NDI6, NDI7, NDWI, SRWI, and MSI. The indices were calculated using images between October and April of the crop years 2007/08, 2008/09, 2009/10, and 2013/14. These indices were then correlated with the standardized precipitation-evapotranspiration index (SPEI), calculated for 1, 3, and 6 months. Four of them had significant correlations with SPEI: GVMI, MSI, NDI7, and NDWI. Spectral indices from Modis sensor on board the Aqua satellite (MYD) were more suited for drought detection, and March provided the most relevant indices for that purpose. Drought indices calculated from Modis sensor data are effective for detecting sugarcane drought events, besides being able to indicate seasonal fluctuations.

Online Long-Term Object Tracking Based on Compressed Haar-Like Features via Sparse Representation

2014 ◽  
Vol 989-994 ◽  
pp. 1610-1614
Author(s):  
Ming Zhao ◽  
Lu Ping Wang ◽  
Lu Ping Zhang
Keyword(s):  
Sparse Representation ◽  
Target Location ◽  
Model Updating ◽  
Dimensional Space ◽  
Reconstruction Error ◽  
Illumination Changes ◽  
Spatio Temporal ◽  
Low Dimensional ◽  
Scale Variation

Online long-term tracking is a challenging problem as data streams change over time. In this paper, sparse representation has been applied to visual tracking by finding the most correct sample with minimal reconstruction error using compressed Haar-like features. However, most sparse representation tracking algorithm introduce l1 regularization into the PCA reconstruction using samples directly, which leads to complexity computation and can not adapt to occlusion, rotation and change in size. Our model updating not only uses the samples from the training set, but also generates the warped versions (include scale variation, rotation, occlusion and illumination changes) for the previous tracking result. Also, we do not use the samples in models for sparse representation directly, but the Haar-like features instead which are compressed in a very low-dimensional space. In addition, we use a robust and fast algorithm which exploits the spatio-temporal context for predicting the target location in the next frame. This step will lead to the reduction of the searching range by the detector. We demonstrate the proposed method is able to track objects well under pose and scale variation, rotation, occlusion and illumination with great real-time performance on challenging image sequences.

scholarly journals Cloud Detection with Historical Geostationary Satellite Sensors for Climate Applications

2019 ◽  
Vol 11 (9) ◽  
pp. 1052 ◽  
Author(s):  
Reto Stöckli ◽  
Jędrzej S. Bojanowski ◽  
Viju O. John ◽  
Anke Duguay-Tetzlaff ◽  
Quentin Bourgeois ◽  
...  
Keyword(s):  
Mean Squared Error ◽  
Sensor Data ◽  
Mean Bias Error ◽  
Retrieval Algorithm ◽  
Bias Error ◽  
Cloud Detection ◽  
Climate Data ◽  
Detection Algorithms ◽  
Fractional Cover ◽  
Spatio Temporal

Can we build stable Climate Data Records (CDRs) spanning several satellite generations? This study outlines how the ClOud Fractional Cover dataset from METeosat First and Second Generation (COMET) of the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) was created for the 25-year period 1991–2015. Modern multi-spectral cloud detection algorithms cannot be used for historical Geostationary (GEO) sensors due to their limited spectral resolution. We document the innovation needed to create a retrieval algorithm from scratch to provide the required accuracy and stability over several decades. It builds on inter-calibrated radiances now available for historical GEO sensors. It uses spatio-temporal information and a robust clear-sky retrieval. The real strength of GEO observations—the diurnal cycle of reflectance and brightness temperature—is fully exploited instead of just accounting for single “imagery”. The commonly-used naive Bayesian classifier is extended with covariance information of cloud state and variability. The resulting cloud fractional cover CDR has a bias of 1% Mean Bias Error (MBE), a precision of 7% bias-corrected Root-Mean-Squared-Error (bcRMSE) for monthly means, and a decadal stability of 1%. Our experience can serve as motivation for CDR developers to explore novel concepts to exploit historical sensor data.

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