Calibration strategy of the pyramid wavefront sensor module of ERIS with the VLT deformable secondary mirror

2012 ◽  
Author(s):  
A. Riccardi ◽  
R. Briguglio ◽  
E. Pinna ◽  
G. Agapito ◽  
F. Quiros-Pacheco ◽  
...  
Keyword(s):  
Wavefront Sensor ◽  
Secondary Mirror ◽  
Sensor Module

Performance simulation of the ERIS pyramid wavefront sensor module in the VLT adaptive optics facility

2012 ◽  
Author(s):  
Fernando Quirós-Pacheco ◽  
Guido Agapito ◽  
Armando Riccardi ◽  
Simone Esposito ◽  
Miska Le Louarn ◽  
...  
Keyword(s):  
Adaptive Optics ◽  
Wavefront Sensor ◽  
Performance Simulation ◽  
Sensor Module

scholarly journals DO-CRIME: Dynamic On-sky Covariance Random Interaction Matrix Evaluation, a novel method for calibrating adaptive optics systems

2020 ◽  
Author(s):  
Olivier Lai ◽  
Mark Chun ◽  
Ryan Dungee ◽  
Jessica Lu ◽  
Marcel Carbillet
Keyword(s):  
Adaptive Optics ◽  
Calibration Procedure ◽  
Wavefront Sensor ◽  
Interaction Matrix ◽  
Wide Field ◽  
Secondary Mirror ◽  
Novel Method ◽  
Measurement Vector ◽  
Random Patterns ◽  
Interaction Matrices

Abstract Adaptive optics systems require a calibration procedure to operate, whether in closed loop or even more importantly in forward control. This calibration usually takes the form of an interaction matrix and is a measure of the response on the wavefront sensor to wavefront corrector stimulus. If this matrix is sufficiently well conditioned, it can be inverted to produce a control matrix, which allows to compute the optimal commands to apply to the wavefront corrector for a given wavefront sensor measurement vector. Interaction matrices are usually measured by means of an artificial source at the entrance focus of the adaptive optics system; however, adaptive secondary mirrors on Cassegrain telescopes offer no such focus and the measurement of their interaction matrices becomes more challenging and needs to be done on-sky using a natural star. The most common method is to generate a theoretical or simulated interaction matrix and adjust it parametrically (for example, decenter, magnification, rotation) using on-sky measurements. We propose a novel method of measuring on-sky interaction matrices ab initio from the telemetry stream of the AO system using random patterns on the deformable mirror with diagonal commands covariance matrices. The approach, being developed for the adaptive secondary mirror upgrade for the imaka wide-field AO system on the UH2.2m telescope project, is shown to work on-sky using the current imaka testbed.

Development of Wafer Level Chip Size Packaging Process and its Application to the CMOS Image Sensor Module for Medical Device

2017 ◽  
Vol 137 (2) ◽  
pp. 48-58
Author(s):  
Noriyuki Fujimori ◽  
Takatoshi Igarashi ◽  
Takahiro Shimohata ◽  
Takuro Suyama ◽  
Kazuhiro Yoshida ◽  
...  
Keyword(s):  
Medical Device ◽  
Cmos Image Sensor ◽  
Image Sensor ◽  
Wafer Level ◽  
Packaging Process ◽  
Sensor Module ◽  
Chip Size

scholarly journals Estimating Physical Activity Energy Expenditure Using an Ensemble Model-Based Patch-Type Sensor Module

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 861
Author(s):  
Kyeung Ho Kang ◽  
Mingu Kang ◽  
Siho Shin ◽  
Jaehyo Jung ◽  
Meina Li
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Ensemble Model ◽  
Direct Calorimetry ◽  
Patch Type ◽  
Mortality And Morbidity ◽  
Squared Error ◽  
Sensor Module

Chronic diseases, such as coronary artery disease and diabetes, are caused by inadequate physical activity and are the leading cause of increasing mortality and morbidity rates. Direct calorimetry by calorie production and indirect calorimetry by energy expenditure (EE) has been regarded as the best method for estimating the physical activity and EE. However, this method is inconvenient, owing to the use of an oxygen respiration measurement mask. In this study, we propose a model that estimates physical activity EE using an ensemble model that combines artificial neural networks and genetic algorithms using the data acquired from patch-type sensors. The proposed ensemble model achieved an accuracy of more than 92% (Root Mean Squared Error (RMSE) = 0.1893, R2 = 0.91, Mean Squared Error (MSE) = 0.014213, Mean Absolute Error (MAE) = 0.14020) by testing various structures through repeated experiments.

scholarly journals A High-Accuracy and Power-Efficient Self-Optimizing Wireless Water Level Monitoring IoT Device for Smart City

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1936
Author(s):  
Tsun-Kuang Chi ◽  
Hsiao-Chi Chen ◽  
Shih-Lun Chen ◽  
Patricia Angela R. Abu
Keyword(s):  
Power Consumption ◽  
Water Level ◽  
Smart City ◽  
High Accuracy ◽  
Control Device ◽  
Power Efficient ◽  
Sensor Module ◽  
Monitoring Applications ◽  
Level Monitoring ◽  
Water Level Monitoring

In this paper, a novel self-optimizing water level monitoring methodology is proposed for smart city applications. Considering system maintenance, the efficiency of power consumption and accuracy will be important for Internet of Things (IoT) devices and systems. A multi-step measurement mechanism and power self-charging process are proposed in this study for improving the efficiency of a device for water level monitoring applications. The proposed methodology improved accuracy by 0.16–0.39% by moving the sensor to estimate the distance relative to different locations. Additional power is generated by executing a multi-step measurement while the power self-optimizing process used dynamically adjusts the settings to balance the current of charging and discharging. The battery level can efficiently go over 50% in a stable charging simulation. These methodologies were successfully implemented using an embedded control device, an ultrasonic sensor module, a LORA transmission module, and a stepper motor. According to the experimental results, the proposed multi-step methodology has the benefits of high accuracy and efficient power consumption for water level monitoring applications.

Lensless scheme of a holographic wavefront sensor

2020 ◽  
Author(s):  
G. K. Krasin ◽  
N. G. Stsepuro ◽  
M. S. Kovalev ◽  
E. Yu. Zlokazov
Keyword(s):  
Wavefront Sensor

scholarly journals Development of a New Sensor Module for an Enhanced Fuel Flexible Operation of Biomass Boilers

Processes ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 661
Author(s):  
Martin Meiller ◽  
Jürgen Oischinger ◽  
Robert Daschner ◽  
Andreas Hornung
Keyword(s):  
Particle Size ◽  
Bulk Material ◽  
Detection System ◽  
Wood Chips ◽  
Convective Drying ◽  
Characteristic Functions ◽  
Biomass Combustion ◽  
Fuel Flexibility ◽  
Sensor Module ◽  
Over Time

The heterogeneity of biogenic fuels, and especially biogenic residues with regard to water and ash content, particle size and particle size distribution is challenging for biomass combustion, and limits fuel flexibility. Online fuel characterization as a part of process control could help to optimize combustion processes, increase fuel flexibility and reduce emissions. In this research article, a concept for a new sensor module is presented and first tests are displayed to show its feasibility. The concept is based on the principle of hot air convective drying. The idea is to pass warm air with 90 °C through a bulk of fuel like wood chips and measure different characteristics such as moisture, temperatures and pressure drop over the bulk material as a function over time. These functions are the basis to draw conclusions and estimate relevant fuel properties. To achieve this goal, a test rig with a volume of 0.038 m3 was set up in the laboratory and a series of tests was performed with different fuels (wood chips, saw dust, wood pellets, residues from forestry, corn cobs and biochar). Further tests were carried out with conditioned fuels with defined water and fines contents. The experiments show that characteristic functions arise over time. The central task for the future will be to assign these functions to specific fuel characteristics. Based on the data, the concept for a software for an automated, data-based fuel detection system was designed.

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