Breast tumor parameter estimation and interactive 3D thermal tomography using discrete thermal sensor data

In this research work, we present an IoT solution to environment variables using a LoRa transmission technology to give real-time information to users in a Things2People process and achieve savings by promoting behavior changes in a People2People process. These data are stored and later processed to identify patterns and integrate with visualization tools, which allow us to develop an environmental perception while using the system. In this project, we implemented a different approach based on the development of a 3D visualization tool that presents the system collected data, warnings, and other users’ perception in an interactive 3D model of the building. This data representation introduces a new People2People interaction approach to achieve savings in shared spaces like public buildings by combining sensor data with the users’ individual and collective perception. This approach was validated at the ISCTE-IUL University Campus, where this 3D IoT data representation was presented in mobile devices, and from this, influenced user behavior toward meeting campus sustainability goals.

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Autonomous Vehicles: Vehicle Parameter Estimation Using Variational Bayes and Kinematics

Applied Sciences ◽

10.3390/app10186317 ◽

2020 ◽

Vol 10 (18) ◽

pp. 6317 ◽

Cited By ~ 1

Author(s):

Wilfried Wöber ◽

Georg Novotny ◽

Lars Mehnen ◽

Cristina Olaverri-Monreal

Keyword(s):

Parameter Estimation ◽

Autonomous Vehicles ◽

Autonomous Driving ◽

The Other ◽

Sensor Data ◽

Critical Feature ◽

Motion Data ◽

Movement Data ◽

End Use ◽

Vehicle Movement

On-board sensory systems in autonomous vehicles make it possible to acquire information about the vehicle itself and about its relevant surroundings. With this information the vehicle actuators are able to follow the corresponding control commands and behave accordingly. Localization is thus a critical feature in autonomous driving to define trajectories to follow and enable maneuvers. Localization approaches using sensor data are mainly based on Bayes filters. Whitebox models that are used to this end use kinematics and vehicle parameters, such as wheel radii, to interfere the vehicle’s movement. As a consequence, faulty vehicle parameters lead to poor localization results. On the other hand, blackbox models use motion data to model vehicle behavior without relying on vehicle parameters. Due to their high non-linearity, blackbox approaches outperform whitebox models but faulty behaviour such as overfitting is hardly identifiable without intensive experiments. In this paper, we extend blackbox models using kinematics, by inferring vehicle parameters and then transforming blackbox models into whitebox models. The probabilistic perspective of vehicle movement is extended using random variables representing vehicle parameters. We validated our approach, acquiring and analyzing simulated noisy movement data from mobile robots and vehicles. Results show that it is possible to estimate vehicle parameters with few kinematic assumptions.

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Estimating evapotranspiration of European forests from NOAA-imagery at satellite overpass time: Towards an operational processing chain for integrated optical and thermal sensor data products

Remote Sensing of Environment ◽

10.1016/j.rse.2005.03.004 ◽

2005 ◽

Vol 96 (2) ◽

pp. 256-276 ◽

Cited By ~ 94

Author(s):

Willem W. Verstraeten ◽

Frank Veroustraete ◽

Jan Feyen

Keyword(s):

Sensor Data ◽

Thermal Sensor ◽

European Forests ◽

Processing Chain ◽

Integrated Optical ◽

Data Products

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Thermal sensor for object shape and material constitution

Robotica ◽

10.1017/s0263574700003568 ◽

1988 ◽

Vol 6 (1) ◽

pp. 31-34 ◽

Cited By ~ 13

Author(s):

R. Andrew Russell

Keyword(s):

Sensor Array ◽

Robot Manipulator ◽

Tactile Sensor ◽

Sensor Data ◽

Thermal Sensor ◽

Material Constitution ◽

Object Shape ◽

Tactile Sensor Array

SUMMARYThis paper describes a novel tactile sensor array designed to provide information about the material constitution and shape of objects held by a robot manipulator. The sensor is modeled on the thermal touch sense which enables humans to distinguish between different materials based on how warm or cold they feel. Some results are presented and methods of analysing the sensor data are discussed.

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Determination of female breast tumor and its parameter estimation by thermal simulation

10.1117/12.841133 ◽

2010 ◽

Cited By ~ 1

Author(s):

Xin-guang Chen ◽

A-qing Xu ◽

Hong-qin Yang ◽

Yu-hua Wang ◽

Shu-sen Xie

Keyword(s):

Parameter Estimation ◽

Breast Tumor ◽

Thermal Simulation ◽

Female Breast

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Optimization of Threshold Values for Estimators Based on Single-Bit Quantized Sensors Using Genetic Algorithms

Dynamic Systems and Control ◽

10.1115/imece2002-39581 ◽

2002 ◽

Author(s):

Nishant Unnikrishnan ◽

Ajay Mahajan ◽

Antonios Mengoulis ◽

R. Viswanathan

Keyword(s):

Genetic Algorithms ◽

Parameter Estimation ◽

Data Storage ◽

Dynamic Range ◽

Mean Squared Error ◽

High Reliability ◽

Sensor Data ◽

Threshold Values ◽

Distributed Sensors ◽

Range Resolution

The paper considers the problem of signal parameter estimation using a collection of distributed sensors called a sensor pack. Each sensor quantizes its data to one-bit information and sends it to a fusion processor for the estimation of the parameter. Estimation of a constant signal in additive noise is considered. Estimators are formulated based on one-bit sensor data and their mean squared error (MSE) performances are evaluated through simulation studies. It is shown that selecting certain threshold values for quantizing the sensor outputs can lower the MSE. Genetic algorithms are used to find the optimal threshold values for the sensors. Results from this study show that robust estimation of parameter is possible by using a moderately large number of one-bit quantized sensor data. This work has significance in applications that demand high reliability in sensor networks in spite of sensor failures, limited sensor dynamic range, resolution, bandwidth for data transmission or even data storage.

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Biophysical Parameter Estimation Using Earth Observation Data in a Multi-Sensor Data Fusion Approach: CycleGAN

10.1109/igarss47720.2021.9553561 ◽

2021 ◽

Author(s):

Natalia Efremova ◽

E. Erten

Keyword(s):

Parameter Estimation ◽

Data Fusion ◽

Earth Observation ◽

Sensor Data ◽

Observation Data ◽

Biophysical Parameter ◽

Sensor Data Fusion ◽

Multi Sensor Data Fusion ◽

Earth Observation Data ◽

Fusion Approach

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Comparison of Spaceborne and UAV-Borne Remote Sensing Spectral Data for Estimating Monsoon Crop Vegetation Parameters

Sensors ◽

10.3390/s21082886 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2886

Author(s):

Jayan Wijesingha ◽

Supriya Dayananda ◽

Michael Wachendorf ◽

Thomas Astor

Keyword(s):

Remote Sensing ◽

Parameter Estimation ◽

Spectral Data ◽

Finger Millet ◽

Chlorophyll Concentration ◽

Hyperspectral Data ◽

Sensor Data ◽

Area Index ◽

Vegetation Parameter ◽

Vegetation Parameters

Various remote sensing data have been successfully applied to monitor crop vegetation parameters for different crop types. Those successful applications mostly focused on one sensor system or a single crop type. This study compares how two different sensor data (spaceborne multispectral vs unmanned aerial vehicle borne hyperspectral) can estimate crop vegetation parameters from three monsoon crops in tropical regions: finger millet, maize, and lablab. The study was conducted in two experimental field layouts (irrigated and rainfed) in Bengaluru, India, over the primary agricultural season in 2018. Each experiment contained n = 4 replicates of three crops with three different nitrogen fertiliser treatments. Two regression algorithms were employed to estimate three crop vegetation parameters: leaf area index, leaf chlorophyll concentration, and canopy water content. Overall, no clear pattern emerged of whether multispectral or hyperspectral data is superior for crop vegetation parameter estimation: hyperspectral data showed better estimation accuracy for finger millet vegetation parameters, while multispectral data indicated better results for maize and lablab vegetation parameter estimation. This study’s outcome revealed the potential of two remote sensing platforms and spectral data for monitoring monsoon crops also provide insight for future studies in selecting the optimal remote sensing spectral data for monsoon crop parameter estimation.

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