Virtual angle measurement through an FPGA data processing

Mathematical methods for angle measurement have been developed since the beginning of civilization of mankind for multiple daily activities, in specially to solve engineering problems. At the present time thanks to the electronics devices development a big variety of sophisticated transducers and application specific integrated circuits (ASIC) as well as field programmable gate arrays (FPGA), it is possible and increasingly common the development of virtual sensors based on soft sensing for physical magnitudes measurement. This manuscript introduces a virtual angle measurement soft sensing technique based on the information conversion of an optoelectronic signal provided by an optical scanning system (OSS) through an FPGA behaving as an actuator and or the controller of actuators. Obtaining successfully results from the proposed technique matching with the previously OSS mathematical model parameters to calculate an estimate the physical magnitude under interest.

[1]  Raffaella D'Amicis,et al.  Importance of energy and angular resolutions in top-hat electrostatic analysers for solar wind proton measurements , 2016 .

[2]  Young-Suk Kim,et al.  Development of a 3D local terrain modeling system of intelligent excavation robot , 2017 .

[3]  David F. Bacon,et al.  FPGA Programming for the Masses , 2013, ACM Queue.

[4]  Kiyoshi Irie,et al.  Dependence maximization localization: a novel approach to 2D street-map-based robot localization , 2016, Adv. Robotics.

[5]  Maurice Daumas,et al.  Scientific instruments of the seventeenth and eighteenth centuries and their makers , 1972 .

[6]  Wendy Flores-Fuentes,et al.  Combined application of Power Spectrum Centroid and Support Vector Machines for measurement improvement in Optical Scanning Systems , 2014, Signal Process..

[7]  Satoshi Yagitani,et al.  Development of an ASIC preamplifier for electromagnetic sensor probes for monitoring space electromagnetic environments , 2016, Earth, Planets and Space.

[8]  Mei Yu,et al.  Depth map inpainting via sparse distortion model , 2016, Digit. Signal Process..

[9]  Ezzat Bakhoum.  Micro- and Nano-Scale Sensors and Transducers , 2015 .

[10]  A. M. Khoshnood,et al.  Vibration Suppression of an Underactuated Dynamic System Using Virtual Actuators , 2016 .

[11]  M. Rivas,et al.  Spatial data acquisition by laser scanning for robot or SHM task , 2008, 2008 IEEE International Symposium on Industrial Electronics.

[12]  Howard Cheung,et al.  Minimizing data collection for field calibration of steady-state virtual sensors for HVAC equipment , 2016 .

[13]  Lihi Zelnik-Manor,et al.  Development of an Optical Displacement Transducer for Routine Testing of Asphalt Concrete , 2016 .

[14]  Bassam A. Abu-Nabah,et al.  Simple laser vision sensor calibration for surface profiling applications , 2016 .

[15]  Zichen Zhao,et al.  High-Fidelity Medical Training Model Augmented With Virtual Reality and Conformable Sensors , 2016 .

[16]  O Nur,et al.  Low-Frequency Self-Powered Footstep Sensor Based on ZnO Nanowires on Paper Substrate , 2016, Nanoscale Research Letters.

[17]  Wendy Flores-Fuentes,et al.  Scanning for light detection and Energy Centre Localization Methods assesment in vision systems for SHM , 2014, 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE).

[18]  Roderick S. Webster,et al.  Book Review: Instruments and Instrument-Makers: Scientific Instruments of the Seventeenth and Eighteenth Centuries and Their Makers , 1974 .

[19]  Satoshi Kagami,et al.  Autonomous Navigation of a Humanoid Robot Over Unknown Rough Terrain , 2011, ISRR.

[20]  Shuichi Shoji,et al.  Measuring Relative-Story Displacement and Local Inclination Angle Using Multiple Position-Sensitive Detectors , 2010, Sensors.

[21]  Wendy Flores-Fuentes,et al.  Energy Center Detection in Light Scanning Sensors for Structural Health Monitoring Accuracy Enhancement , 2014, IEEE Sensors Journal.

[22]  P. Morantz,et al.  Optimized estimator for real-time dynamic displacement measurement using accelerometers , 2016 .

[23]  L. Łukasiak,et al.  History of Semiconductors , 2010 .

[24]  M. Sgroi,et al.  From Modeling to Implementation of Virtual Sensors in Body Sensor Networks , 2012, IEEE Sensors Journal.

[25]  Jin Zhang,et al.  Multi-sensor Ensemble Kalman filtering algorithm based on observation fuzzy support degree fusion , 2016 .