The Experts below are selected from a list of 53769 Experts worldwide ranked by ideXlab platform
Rene De Jesus Romerotroncoso - One of the best experts on this subject based on the ideXlab platform.
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Smart Sensor network for power quality monitoring in electrical installations
Measurement, 2017Co-Authors: Luis Moralesvelazquez, Rene De Jesus Romerotroncoso, Gilberto Herreraruiz, Daniel Morinigosotelo, Roque Alfredo OsornioriosAbstract:Abstract Smart meters are one of the basic components of the future Smart grid, they allow remotely monitoring each point in the grid in order to know in real-time the performance of the system and to detect potential failures. In this paper, a Smart Sensor network is introduced and the most important features are presented in three different scenarios: a residential home, an industrial installation, and a public building. The proposed system demonstrates its capabilities of in situ real-time processing and big-data off-line network processing. The suggested Smart meter is based on field programmable gate array (FPGA) technology that allows a reconfigurable architecture, which lets the user to select the proper processing modules according to their application. The developed Smart Sensor network calculates standard figures such as effective values, power factor, and total harmonic distortion; in addition, it detects power quality disturbances such as dips, swells, or interruptions. Moreover, the Smart Sensor network can continuously detect events to identify certain kind of appliances or industrial equipment such as: fans, lighting, microwave ovens, refrigerators, among others; it is a powerful tool to analyze an entire building in a non-intrusive load monitoring approach.
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fused Smart Sensor network for multi axis forward kinematics estimation in industrial robots
Sensors, 2011Co-Authors: Carlos Rodriguezdonate, Jesus Rooney Riveraguillen, Roque Alfredo Osorniorios, Rene De Jesus RomerotroncosoAbstract:Flexible manipulator robots have a wide industrial application. Robot performance requires sensing its position and orientation adequately, known as forward kinematics. Commercially available, motion controllers use high-resolution optical encoders to sense the position of each joint which cannot detect some mechanical deformations that decrease the accuracy of the robot position and orientation. To overcome those problems, several Sensor fusion methods have been proposed but at expenses of high-computational load, which avoids the online measurement of the joint’s angular position and the online forward kinematics estimation. The contribution of this work is to propose a fused Smart Sensor network to estimate the forward kinematics of an industrial robot. The developed Smart processor uses Kalman filters to filter and to fuse the information of the Sensor network. Two primary Sensors are used: an optical encoder, and a 3-axis accelerometer. In order to obtain the position and orientation of each joint online a field-programmable gate array (FPGA) is used in the hardware implementation taking advantage of the parallel computation capabilities and reconfigurability of this device. With the aim of evaluating the Smart Sensor network performance, three real-operation-oriented paths are executed and monitored in a 6-degree of freedom robot.
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fpga based fused Smart Sensor for real time plant transpiration dynamic estimation
Sensors, 2010Co-Authors: Jesus Roberto Millanalmaraz, Rene De Jesus Romerotroncoso, Roque Alfredo Osorniorios, Ramon G Guevaragonzalez, Luis Miguel Contrerasmedina, Roberto V Carrilloserrano, Carlos Duartegalvan, Miguel Angel Riosalcaraz, Irineo TorrespachecoAbstract:Plant transpiration is considered one of the most important physiological functions because it constitutes the plants evolving adaptation to exchange moisture with a dry atmosphere which can dehydrate or eventually kill the plant. Due to the importance of transpiration, accurate measurement methods are required; therefore, a Smart Sensor that fuses five primary Sensors is proposed which can measure air temperature, leaf temperature, air relative humidity, plant out relative humidity and ambient light. A field programmable gate array based unit is used to perform signal processing algorithms as average decimation and infinite impulse response filters to the primary Sensor readings in order to reduce the signal noise and improve its quality. Once the primary Sensor readings are filtered, transpiration dynamics such as: transpiration, stomatal conductance, leaf-air-temperature-difference and vapor pressure deficit are calculated in real time by the Smart Sensor. This permits the user to observe different primary and calculated measurements at the same time and the relationship between these which is very useful in precision agriculture in the detection of abnormal conditions. Finally, transpiration related stress conditions can be detected in real time because of the use of online processing and embedded communications capabilities.
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fpga based fused Smart Sensor for dynamic and vibration parameter extraction in industrial robot links
Sensors, 2010Co-Authors: Carlos Rodriguezdonate, Roque Alfredo Osorniorios, Luis Moralesvelazquez, Gilberto Herreraruiz, Rene De Jesus RomerotroncosoAbstract:Intelligent robotics demands the integration of Smart Sensors that allow the controller to efficiently measure physical quantities. Industrial manipulator robots require a constant monitoring of several parameters such as motion dynamics, inclination, and vibration. This work presents a novel Smart Sensor to estimate motion dynamics, inclination, and vibration parameters on industrial manipulator robot links based on two primary Sensors: an encoder and a triaxial accelerometer. The proposed Smart Sensor implements a new methodology based on an oversampling technique, averaging decimation filters, FIR filters, finite differences and linear interpolation to estimate the interest parameters, which are computed online utilizing digital hardware signal processing based on field programmable gate arrays (FPGA).
Roque Alfredo Osorniorios - One of the best experts on this subject based on the ideXlab platform.
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Smart Sensor network for power quality monitoring in electrical installations
Measurement, 2017Co-Authors: Luis Moralesvelazquez, Rene De Jesus Romerotroncoso, Gilberto Herreraruiz, Daniel Morinigosotelo, Roque Alfredo OsornioriosAbstract:Abstract Smart meters are one of the basic components of the future Smart grid, they allow remotely monitoring each point in the grid in order to know in real-time the performance of the system and to detect potential failures. In this paper, a Smart Sensor network is introduced and the most important features are presented in three different scenarios: a residential home, an industrial installation, and a public building. The proposed system demonstrates its capabilities of in situ real-time processing and big-data off-line network processing. The suggested Smart meter is based on field programmable gate array (FPGA) technology that allows a reconfigurable architecture, which lets the user to select the proper processing modules according to their application. The developed Smart Sensor network calculates standard figures such as effective values, power factor, and total harmonic distortion; in addition, it detects power quality disturbances such as dips, swells, or interruptions. Moreover, the Smart Sensor network can continuously detect events to identify certain kind of appliances or industrial equipment such as: fans, lighting, microwave ovens, refrigerators, among others; it is a powerful tool to analyze an entire building in a non-intrusive load monitoring approach.
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fused Smart Sensor network for multi axis forward kinematics estimation in industrial robots
Sensors, 2011Co-Authors: Carlos Rodriguezdonate, Jesus Rooney Riveraguillen, Roque Alfredo Osorniorios, Rene De Jesus RomerotroncosoAbstract:Flexible manipulator robots have a wide industrial application. Robot performance requires sensing its position and orientation adequately, known as forward kinematics. Commercially available, motion controllers use high-resolution optical encoders to sense the position of each joint which cannot detect some mechanical deformations that decrease the accuracy of the robot position and orientation. To overcome those problems, several Sensor fusion methods have been proposed but at expenses of high-computational load, which avoids the online measurement of the joint’s angular position and the online forward kinematics estimation. The contribution of this work is to propose a fused Smart Sensor network to estimate the forward kinematics of an industrial robot. The developed Smart processor uses Kalman filters to filter and to fuse the information of the Sensor network. Two primary Sensors are used: an optical encoder, and a 3-axis accelerometer. In order to obtain the position and orientation of each joint online a field-programmable gate array (FPGA) is used in the hardware implementation taking advantage of the parallel computation capabilities and reconfigurability of this device. With the aim of evaluating the Smart Sensor network performance, three real-operation-oriented paths are executed and monitored in a 6-degree of freedom robot.
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fpga based fused Smart Sensor for real time plant transpiration dynamic estimation
Sensors, 2010Co-Authors: Jesus Roberto Millanalmaraz, Rene De Jesus Romerotroncoso, Roque Alfredo Osorniorios, Ramon G Guevaragonzalez, Luis Miguel Contrerasmedina, Roberto V Carrilloserrano, Carlos Duartegalvan, Miguel Angel Riosalcaraz, Irineo TorrespachecoAbstract:Plant transpiration is considered one of the most important physiological functions because it constitutes the plants evolving adaptation to exchange moisture with a dry atmosphere which can dehydrate or eventually kill the plant. Due to the importance of transpiration, accurate measurement methods are required; therefore, a Smart Sensor that fuses five primary Sensors is proposed which can measure air temperature, leaf temperature, air relative humidity, plant out relative humidity and ambient light. A field programmable gate array based unit is used to perform signal processing algorithms as average decimation and infinite impulse response filters to the primary Sensor readings in order to reduce the signal noise and improve its quality. Once the primary Sensor readings are filtered, transpiration dynamics such as: transpiration, stomatal conductance, leaf-air-temperature-difference and vapor pressure deficit are calculated in real time by the Smart Sensor. This permits the user to observe different primary and calculated measurements at the same time and the relationship between these which is very useful in precision agriculture in the detection of abnormal conditions. Finally, transpiration related stress conditions can be detected in real time because of the use of online processing and embedded communications capabilities.
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fpga based fused Smart Sensor for dynamic and vibration parameter extraction in industrial robot links
Sensors, 2010Co-Authors: Carlos Rodriguezdonate, Roque Alfredo Osorniorios, Luis Moralesvelazquez, Gilberto Herreraruiz, Rene De Jesus RomerotroncosoAbstract:Intelligent robotics demands the integration of Smart Sensors that allow the controller to efficiently measure physical quantities. Industrial manipulator robots require a constant monitoring of several parameters such as motion dynamics, inclination, and vibration. This work presents a novel Smart Sensor to estimate motion dynamics, inclination, and vibration parameters on industrial manipulator robot links based on two primary Sensors: an encoder and a triaxial accelerometer. The proposed Smart Sensor implements a new methodology based on an oversampling technique, averaging decimation filters, FIR filters, finite differences and linear interpolation to estimate the interest parameters, which are computed online utilizing digital hardware signal processing based on field programmable gate arrays (FPGA).
H Wohltjen - One of the best experts on this subject based on the ideXlab platform.
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Smart Sensor system for trace organophosphorus and organosulfur vapor detection employing a temperature controlled array of surface acoustic wave Sensors automated sample preconcentration and pattern recognition
Analytical Chemistry, 1993Co-Authors: Jay W Grate, S L Rosepehrsson, M Klusty, David L Venezky, H WohltjenAbstract:A Smart Sensor system For the detection of toxic organophosphorus and toxic organosulfur vapors at trace concentrations has been designed, fabricated, and tested against a wide variety of vapor challenges. The key features of the system are an array of four surface acoustic wave (SAW) vapor Sensors, temperature control of the vapor Sensors, the use of pattern recognition to analyze the Sensor data, and an automated sampling system including thermally desorbed preconcentrator tubes (PCTs). All the electronics necessary to control and operate the various subsystems and to collect and process the data are included in the system
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a Smart Sensor system for trace organic vapor detection using a temperature controlled array of surface acoustic wave vapor Sensors automated preconcentrator tubes and pattern recognition
1993Co-Authors: Jay W Grate, S L Rosepehrsson, M Klusty, H WohltjenAbstract:A Smart Sensor system for the detection, of toxic organophosphorus and toxic organosulfur vapors at trace concentrations has been designed, fabricated, and tested against a wide variety of vapor challenges. The key features of the system are: An array of four surface acoustic wave (SAW) vapor Sensors, temperature control of the vapor Sensors, the use of pattern recognition to analyze the Sensor data, and an automated sampling system including thermally-desorbed preconcentrator tubes (PCTs).
Wan-young Chung - One of the best experts on this subject based on the ideXlab platform.
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An Enhanced Multiplication of RF Energy Harvesting Efficiency Using Relay Resonator for Food Monitoring
MDPI AG, 2019Co-Authors: Xuan-tu Cao, Wan-young ChungAbstract:Recently, radio frequency (RF) energy harvesting (RFEH) has become a promising technology for a battery-less Sensor module. The ambient RF radiation from the available sources is captured by receiver antennas and converted to electrical energy, which is used to supply Smart Sensor modules. In this paper, an enhanced method to improve the efficiency of the RFEH system using strongly coupled electromagnetic resonance technology was proposed. A relay resonator was added between the reader and tag antennas to improve the wireless power transmission efficiency to the Sensor module. The design of the relay resonator was based on the resonant technique and near-field magnetic coupling concept to improve the communication distance and the power supply for a Sensor module. It was designed such that the self-resonant frequencies of the reader antenna, tag antenna, and the relay resonator are synchronous at the HF frequency (13.56MHz). The proposed method was analyzed using Thevenin equivalent circuit, simulated and experimental validated to evaluate its performance. The experimental results showed that the proposed harvesting method is able to generate a great higher power up to 10 times than that provided by conventional harvesting methods without a relay resonator. Moreover, as an empirical feasibility test of the proposed RF energy harvesting device, a Smart Sensor module which is placed inside a meat box was developed. It was utilized to collect vital data, including temperature, relative humidity and gas concentration, to monitor the freshness of meat. Overall, by exploiting relay resonator, the proposed Smart Sensor tag could continuously monitor meat freshness without any batteries at the innovative maximum distance of approximately 50 cm
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Battery-free Smart-Sensor system for real-time indoor air quality monitoring
Sensors and Actuators B: Chemical, 2017Co-Authors: Thang Viet Tran, Nam Trung Dang, Wan-young ChungAbstract:Indoor air pollution is one of the serious issues that affect public health nowadays. Therefore, indoor air quality needs to be monitored by a real-time system for early air pollution warning. Until now, most wireless Sensors used for air quality monitoring have required power supply from a battery for Sensor operation and wireless data communication. This battery, which is attached to a sensing module, makes the wireless sensing module larger and requires regular replacement efforts and high costs. The present study proposes a novel battery-free Sensor module to measure the concentration of volatile organic compounds, ambient temperature, relative humidity, and atmospheric pressure for monitoring air quality in indoor environment. The proposed system comprises a Smart-Sensor tag, and a radio frequency (RF) energy harvester. The sensing circuit, designed using ultra-low power Sensors and a microcontroller unit (MCU), consumes low average power of only 0.5 mW. The MCU collects data from the Sensors and writes the sensing data to the memory in the form of an Electronic Product Code (EPC) Class 1 Generation 1 compliant identification (ID) of the tag. The RF energy harvester with a highly efficient buck–boost converter and a 50-mF supercapacitor for real-time saving of the collected power can sufficiently collect the available RF energy from the reader within a maximum distance of ∼250 cm from the reader to supply power for sensing and wireless communication operation of the Smart-Sensor tag. Therefore, the proposed Smart-Sensor module can be a battery-free Sensor device for monitoring environment parameters in indoor condition. Experiments are conducted to validate and support the developed system for real-time air quality monitoring and warning.
Carlos Rodriguezdonate - One of the best experts on this subject based on the ideXlab platform.
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fused Smart Sensor network for multi axis forward kinematics estimation in industrial robots
Sensors, 2011Co-Authors: Carlos Rodriguezdonate, Jesus Rooney Riveraguillen, Roque Alfredo Osorniorios, Rene De Jesus RomerotroncosoAbstract:Flexible manipulator robots have a wide industrial application. Robot performance requires sensing its position and orientation adequately, known as forward kinematics. Commercially available, motion controllers use high-resolution optical encoders to sense the position of each joint which cannot detect some mechanical deformations that decrease the accuracy of the robot position and orientation. To overcome those problems, several Sensor fusion methods have been proposed but at expenses of high-computational load, which avoids the online measurement of the joint’s angular position and the online forward kinematics estimation. The contribution of this work is to propose a fused Smart Sensor network to estimate the forward kinematics of an industrial robot. The developed Smart processor uses Kalman filters to filter and to fuse the information of the Sensor network. Two primary Sensors are used: an optical encoder, and a 3-axis accelerometer. In order to obtain the position and orientation of each joint online a field-programmable gate array (FPGA) is used in the hardware implementation taking advantage of the parallel computation capabilities and reconfigurability of this device. With the aim of evaluating the Smart Sensor network performance, three real-operation-oriented paths are executed and monitored in a 6-degree of freedom robot.
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fpga based fused Smart Sensor for dynamic and vibration parameter extraction in industrial robot links
Sensors, 2010Co-Authors: Carlos Rodriguezdonate, Roque Alfredo Osorniorios, Luis Moralesvelazquez, Gilberto Herreraruiz, Rene De Jesus RomerotroncosoAbstract:Intelligent robotics demands the integration of Smart Sensors that allow the controller to efficiently measure physical quantities. Industrial manipulator robots require a constant monitoring of several parameters such as motion dynamics, inclination, and vibration. This work presents a novel Smart Sensor to estimate motion dynamics, inclination, and vibration parameters on industrial manipulator robot links based on two primary Sensors: an encoder and a triaxial accelerometer. The proposed Smart Sensor implements a new methodology based on an oversampling technique, averaging decimation filters, FIR filters, finite differences and linear interpolation to estimate the interest parameters, which are computed online utilizing digital hardware signal processing based on field programmable gate arrays (FPGA).
