Search

For many problems in the field of control design phenomenological models are required so that the need for parameter identification of given model structures arises. These models can be combined with observers to derive the system states in operation in addition to the parameters. However, identification and observation are limited in accuracy due to the restriction to existing series sensors. In the case of electric drives it is possible that due to elasticities in the structure part of the system is vibrating while the position sensor measures a nearly constant position. In this paper, the use of additional acceleration sensors is investigated in terms of identifiability and observability, which are installed at different points of the structure. The analysis is traced back to measures on the sensitivity matrix, where the integrating behaviour of the plant and the combination of different sensor types (position, velocity, acceleration) require special consideration. An industry-like stacker crane is used as a testbed for validation. It is shown that both identifiability and observability can be improved by the additional sensors in many cases. There is a good agreement between the expected and measured frequency response when the acceleration sensor is mounted on the first or second mass. Deviations only occur when mounted on the load suspension device, which is assumed to be the third mass. © 2020 IEEE

For many problems in the field of control design phenomenological models are required so that the need for parameter identification of given model structures arises. These models can be combined with observers to derive the system states in operation in addition to the parameters. However, identification and observation are limited in accuracy due to the restriction to existing series sensors. In the case of electric drives it is possible that due to elasticities in the structure part of the system is vibrating while the position sensor measures a nearly constant position. In this paper, the use of additional acceleration sensors is investigated in terms of identifiability and observability, which are installed at different points of the structure. The analysis is traced back to measures on the sensitivity matrix, where the integrating behaviour of the plant and the combination of different sensor types (position, velocity, acceleration) require special consideration. An industry-like stacker crane is used as a testbed for validation. It is shown that both identifiability and observability can be improved by the additional sensors in many cases. There is a good agreement between the expected and measured frequency response when the acceleration sensor is mounted on the first or second mass. Deviations only occur when mounted on the load suspension device, which is assumed to be the third mass. © 2020 IEEE

This Ph.D. dissertation describes the theory and realization of wide-bandwidth magnetic sensors for current measurements which breach the conventional offset and noise constraints in CMOS processes. These are achieved by several circuit-level and system-level innovations, including the use of three orthogonal ripple reduction loops (RRLs) in spinning-current Hall sensors, a multipath architecture with double Hall sensors, and a hybrid magnetic sensor system combining Hall sensors and pick-up coils. The prototypes with these techniques have advanced the bandwidth of state-of-the-art CMOS magnetic sensors by more than two orders of magnitude., Junfeng Jiang was born in 1986 in Dalian, China. He received his B.Sc. degree in electrical engineering from Dalian University of Technology, Dalian, China, in 2009, and his M.Sc. degree in microelectronics from Delft University of Technology, Delft, the Netherlands in 2011. He expects to receive his Ph.D. degree from the same university in 2019, for his work on CMOS wide-bandwidth magnetic sensors for contactless current measurements. From 2010 to 2016, he was a visiting scholar at Texas Instruments (Formerly National Semiconductor Corporation), Delft, where he worked on CMOS wide-bandwidth magnetic sensors. From 2016 to 2017, he was with Texas Instruments Deutschland GmbH, Freising, Germany. Since November 2017, he has been with the sensing group of Texas Instruments, Dallas, USA. His interests include the design of precision analog systems, magnetic sensors and mixed-signal integrated circuits. Junfeng Jiang received the A-SSCC 2015 Travel Grant, SSCS Predoctoral Achievement Award 2016-2017 and ESSCIRC 2016 Best Paper Award., Electronic Instrumentation

This Ph.D. dissertation describes the theory and realization of wide-bandwidth magnetic sensors for current measurements which breach the conventional offset and noise constraints in CMOS processes. These are achieved by several circuit-level and system-level innovations, including the use of three orthogonal ripple reduction loops (RRLs) in spinning-current Hall sensors, a multipath architecture with double Hall sensors, and a hybrid magnetic sensor system combining Hall sensors and pick-up coils. The prototypes with these techniques have advanced the bandwidth of state-of-the-art CMOS magnetic sensors by more than two orders of magnitude., Junfeng Jiang was born in 1986 in Dalian, China. He received his B.Sc. degree in electrical engineering from Dalian University of Technology, Dalian, China, in 2009, and his M.Sc. degree in microelectronics from Delft University of Technology, Delft, the Netherlands in 2011. He expects to receive his Ph.D. degree from the same university in 2019, for his work on CMOS wide-bandwidth magnetic sensors for contactless current measurements. From 2010 to 2016, he was a visiting scholar at Texas Instruments (Formerly National Semiconductor Corporation), Delft, where he worked on CMOS wide-bandwidth magnetic sensors. From 2016 to 2017, he was with Texas Instruments Deutschland GmbH, Freising, Germany. Since November 2017, he has been with the sensing group of Texas Instruments, Dallas, USA. His interests include the design of precision analog systems, magnetic sensors and mixed-signal integrated circuits. Junfeng Jiang received the A-SSCC 2015 Travel Grant, SSCS Predoctoral Achievement Award 2016-2017 and ESSCIRC 2016 Best Paper Award., Electronic Instrumentation

Producción Científica, The most popular sensors in multisensor systems (electronic tongues) are voltammetric and potentiometric ones. Practical application of multisensor systems for evaluation of particular target parameters requires calibration step. Even if both types of sensors (voltammetric and potentiometric) can be sensitive towards the same parameter of an analyte and can be used for its quantification, corresponding multisensor systems cannot operate in the framework of a single unified calibration model interpreting the responses of both systems. This research is dedicated to experimental verification of calibration transfer feasibility between voltammetric and potentiometric multisensor systems. The algorithm of direct standardization suggested earlier in spectroscopy was applied for transformation of potentiometric data into voltammetric format, and vice versa. Such transformations allowed for interpretation of a system response by multivariate regression model built employing the data from another type of multisensor system. For example, concentration of the tartaric acid in the grape musts can be determined with the regression model developed for voltammetric system using the data obtained from potentiometric system. Only 20% decrease in precision was observed for the converted potentiometric data compared to initial voltammetric model., 2220-12-31, 2220-12-31, M. Khaydukova acknowledges joint scholarship program of St. Petersburg State University and Bank Santander., M. Khaydukova and A. Legin acknowledge partial financial support of this study from St. Petersburg State University research grant #12.37.216.2016., D. Kirsanov acknowledges partial financial support of his work by the Government of Russian Federation, Grant 074-U01., Financial support by MINECO and FEDER (AGL2015- 67482-R)., Junta de Castilla y León-FEDER (VA011U16)., C. Medina-Plaza thanks the University of Valladolid for a PIF UVA scholarship.

Producción Científica, The most popular sensors in multisensor systems (electronic tongues) are voltammetric and potentiometric ones. Practical application of multisensor systems for evaluation of particular target parameters requires calibration step. Even if both types of sensors (voltammetric and potentiometric) can be sensitive towards the same parameter of an analyte and can be used for its quantification, corresponding multisensor systems cannot operate in the framework of a single unified calibration model interpreting the responses of both systems. This research is dedicated to experimental verification of calibration transfer feasibility between voltammetric and potentiometric multisensor systems. The algorithm of direct standardization suggested earlier in spectroscopy was applied for transformation of potentiometric data into voltammetric format, and vice versa. Such transformations allowed for interpretation of a system response by multivariate regression model built employing the data from another type of multisensor system. For example, concentration of the tartaric acid in the grape musts can be determined with the regression model developed for voltammetric system using the data obtained from potentiometric system. Only 20% decrease in precision was observed for the converted potentiometric data compared to initial voltammetric model., 2220-12-31, 2220-12-31, M. Khaydukova acknowledges joint scholarship program of St. Petersburg State University and Bank Santander., M. Khaydukova and A. Legin acknowledge partial financial support of this study from St. Petersburg State University research grant #12.37.216.2016., D. Kirsanov acknowledges partial financial support of his work by the Government of Russian Federation, Grant 074-U01., Financial support by MINECO and FEDER (AGL2015- 67482-R)., Junta de Castilla y León-FEDER (VA011U16)., C. Medina-Plaza thanks the University of Valladolid for a PIF UVA scholarship.

In this work, we have developed a multisensor system for rehabilitation and interaction with persons with motor and cognitive disabilities. The system enables them to perform different therapies using multiple modes of interaction (head and body pose, hand gestures, voice, touch and gaze) depending on the type and degree of disability. Through a training process, the system can be customized enabling the definition of patients’ own gestures for each sensor. The system is integrated with a range of applications for rehabilitation. Examples of these applications are puzzle solving, mazes and text writing using predictive text tools. The system also provides a flexible and modular framework for the development of new applications oriented towards novel rehabilitation therapies. The proposed system has been integrated in a mobile robotic platform and uses low-cost sensors allowing to perform non-intrusive rehabilitation therapies at home. Videos showing the proposed system and users interacting in different ways (multimodal) are available on our project website www.rovit.ua.es/patente/.

Remote sensing imagery to monitor global biophysical dynamics requires the availability of thermal infrared data at high temporal and spatial resolution because of the rapid development of crops during the growing season and the fragmentation of most agricultural landscapes. Conversely, no single sensor meets these combined requirements. Data fusion approaches offer an alternative to exploit observations from multiple sensors, providing data sets with better properties. A novel spatio-temporal data fusion model based on constrained algorithms denoted as multisensor multiresolution technique (MMT) was developed and applied to generate TIR synthetic image data at both temporal and spatial high resolution. Firstly, an adaptive radiance model is applied based on spectral unmixing analysis of. TIR radiance data at TOA (top of atmosphere) collected by MODIS daily 1-km and Landsat - TIRS 16-day sampled at 30-m resolution are used to generate synthetic daily radiance images at TOA at 30-m spatial resolution. The next step consists of unmixing the 30 m (now lower resolution) images using the information about their pixel land-cover composition from co-registered images at higher spatial resolution. In our case study, TIR synthesized data were unmixed to the Sentinel 2 MSI with 10 m resolution. The constrained unmixing preserves all the available radiometric information of the 30 m images and involves the optimization of the number of landcover classes and the size of the moving window for spatial unmixing. Results are still being evaluated, with particular attention for the quality of the data streams required to apply our approach., Optical and Laser Remote Sensing

Producción Científica, The most popular sensors in multisensor systems (electronic tongues) are voltammetric and potentiometric ones. Practical application of multisensor systems for evaluation of particular target parameters requires calibration step. Even if both types of sensors (voltammetric and potentiometric) can be sensitive towards the same parameter of an analyte and can be used for its quantification, corresponding multisensor systems cannot operate in the framework of a single unified calibration model interpreting the responses of both systems. This research is dedicated to experimental verification of calibration transfer feasibility between voltammetric and potentiometric multisensor systems. The algorithm of direct standardization suggested earlier in spectroscopy was applied for transformation of potentiometric data into voltammetric format, and vice versa. Such transformations allowed for interpretation of a system response by multivariate regression model built employing the data from another type of multisensor system. For example, concentration of the tartaric acid in the grape musts can be determined with the regression model developed for voltammetric system using the data obtained from potentiometric system. Only 20% decrease in precision was observed for the converted potentiometric data compared to initial voltammetric model., 2220-12-31, 2220-12-31, M. Khaydukova acknowledges joint scholarship program of St. Petersburg State University and Bank Santander., M. Khaydukova and A. Legin acknowledge partial financial support of this study from St. Petersburg State University research grant #12.37.216.2016., D. Kirsanov acknowledges partial financial support of his work by the Government of Russian Federation, Grant 074-U01., Financial support by MINECO and FEDER (AGL2015- 67482-R)., Junta de Castilla y León-FEDER (VA011U16)., C. Medina-Plaza thanks the University of Valladolid for a PIF UVA scholarship.

Chemical sensing may be better suited than conventional smoke-based detectors for the detection of certain type of fires, in particular in fires where smoke appears after gas emissions. However, chemical-based systems also respond to non-fire scenarios that also release volatiles. For this reason, discrimination models need to be trained under different fire and non-fire scenarios. This is usually performed in standard fire rooms, the access to which is very costly. In this work, we present a calibration model combining experiments from standard fire room and small-scale setup. Results show that the use of small-scale setup experiments improve the performance of the system., Postprint (author's final draft)

Producción Científica, The most popular sensors in multisensor systems (electronic tongues) are voltammetric and potentiometric ones. Practical application of multisensor systems for evaluation of particular target parameters requires calibration step. Even if both types of sensors (voltammetric and potentiometric) can be sensitive towards the same parameter of an analyte and can be used for its quantification, corresponding multisensor systems cannot operate in the framework of a single unified calibration model interpreting the responses of both systems. This research is dedicated to experimental verification of calibration transfer feasibility between voltammetric and potentiometric multisensor systems. The algorithm of direct standardization suggested earlier in spectroscopy was applied for transformation of potentiometric data into voltammetric format, and vice versa. Such transformations allowed for interpretation of a system response by multivariate regression model built employing the data from another type of multisensor system. For example, concentration of the tartaric acid in the grape musts can be determined with the regression model developed for voltammetric system using the data obtained from potentiometric system. Only 20% decrease in precision was observed for the converted potentiometric data compared to initial voltammetric model., 2220-12-31, 2220-12-31, M. Khaydukova acknowledges joint scholarship program of St. Petersburg State University and Bank Santander., M. Khaydukova and A. Legin acknowledge partial financial support of this study from St. Petersburg State University research grant #12.37.216.2016., D. Kirsanov acknowledges partial financial support of his work by the Government of Russian Federation, Grant 074-U01., Financial support by MINECO and FEDER (AGL2015- 67482-R)., Junta de Castilla y León-FEDER (VA011U16)., C. Medina-Plaza thanks the University of Valladolid for a PIF UVA scholarship.

In this work, we have developed a multisensor system for rehabilitation and interaction with persons with motor and cognitive disabilities. The system enables them to perform different therapies using multiple modes of interaction (head and body pose, hand gestures, voice, touch and gaze) depending on the type and degree of disability. Through a training process, the system can be customized enabling the definition of patients’ own gestures for each sensor. The system is integrated with a range of applications for rehabilitation. Examples of these applications are puzzle solving, mazes and text writing using predictive text tools. The system also provides a flexible and modular framework for the development of new applications oriented towards novel rehabilitation therapies. The proposed system has been integrated in a mobile robotic platform and uses low-cost sensors allowing to perform non-intrusive rehabilitation therapies at home. Videos showing the proposed system and users interacting in different ways (multimodal) are available on our project website www.rovit.ua.es/patente/.

Remote sensing imagery to monitor global biophysical dynamics requires the availability of thermal infrared data at high temporal and spatial resolution because of the rapid development of crops during the growing season and the fragmentation of most agricultural landscapes. Conversely, no single sensor meets these combined requirements. Data fusion approaches offer an alternative to exploit observations from multiple sensors, providing data sets with better properties. A novel spatio-temporal data fusion model based on constrained algorithms denoted as multisensor multiresolution technique (MMT) was developed and applied to generate TIR synthetic image data at both temporal and spatial high resolution. Firstly, an adaptive radiance model is applied based on spectral unmixing analysis of. TIR radiance data at TOA (top of atmosphere) collected by MODIS daily 1-km and Landsat - TIRS 16-day sampled at 30-m resolution are used to generate synthetic daily radiance images at TOA at 30-m spatial resolution. The next step consists of unmixing the 30 m (now lower resolution) images using the information about their pixel land-cover composition from co-registered images at higher spatial resolution. In our case study, TIR synthesized data were unmixed to the Sentinel 2 MSI with 10 m resolution. The constrained unmixing preserves all the available radiometric information of the 30 m images and involves the optimization of the number of landcover classes and the size of the moving window for spatial unmixing. Results are still being evaluated, with particular attention for the quality of the data streams required to apply our approach., Optical and Laser Remote Sensing

[no abstract]

© 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim In the paper, the percolation model (PM) developed earlier for description of data associated with complex fluids was successfully applied. As an example, we analyzed the virgin olive oil electrochemical data. In spite of the fact that this complex fluid as olive oil contains more than 100 different chemical components, it becomes possible to describe the dependencies J(U) (defined in electrochemistry as the voltammograms (VAGs)) in terms of the percolation model. This model takes into account the behavior of the conducting/percolation currents in meso-region and dependencies of the fractal dimension versus applied potential U. It was proved that this model was applicable for three basic regions: cathode region (where positive charges are mainly concentrated), capacitor (Faraday) region and anode region (where negative charges were mainly located). For these three basic conventional regions known in electrochemistry, we obtained the excellent coincidence of the fitting function obtained in the frame of PM with experimental data. These results prove that this meso-model can be applicable for description of wide set of data including different complex solutions and fluids.