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Your search keyword '"multisensor system"' showing total 5 results
Start Over Descriptor "multisensor system" Publisher elsevier
5 results on '"multisensor system"'

1. Integration and performance optimization of gallium nitride barrier micro nano electromechanical systems in intelligent agricultural sensor networks

Author

Ji Gu

Subjects
MEMS hardware optimization, Gallium nitride barrier, Static test, Dynamic test, Multisensor system, Electric apparatus and materials. Electric circuits. Electric networks, TK452-454.4
Abstract

This study aims to explore the application of gallium nitride (GaN) barrier in hardware optimization of micro nano scale electromechanical systems (MEMS), particularly its ability to overcome the limitations of traditional hardware in high-temperature and low-power environments. Through multidimensional experiments such as static testing, dynamic testing, and temperature testing, comprehensively evaluate the performance of gallium nitride barrier based microelectromechanical systems in intelligent agricultural sensor networks. The static test results indicate that with the increase of external force, the electrical response of the system shows a significant improvement, and there is a high linear correlation between external force and electrical response (R2 = 0.987). In dynamic testing, as the vibration frequency increased from 1Hz to 1000Hz, the electrical response gradually strengthened, demonstrating the system's good adaptability under high-frequency vibration. However, power consumption also increases with the increase of vibration frequency. The temperature test results show that there is a positive correlation between electrical response and power consumption in the temperature range of −20 °C to 60 °C, indicating that gallium nitride technology can effectively improve the sensitivity, stability, and adaptability of micro nano scale electromechanical systems under different environmental conditions. Research has shown that the application of gallium nitride barrier technology in smart agricultural sensor networks has significant performance optimization potential, especially in extreme temperature and low-power requirements, which can provide more reliable and efficient sensing solutions for smart agriculture.

Published
2025
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2. Developing non-invasive bladder cancer screening methodology through potentiometric multisensor urine analysis.

Author

Belugina R, Karpushchenko E, Sleptsov A, Protoshchak V, Legin A, and Kirsanov D

Subjects
Aged, Aged, 80 and over, Algorithms, Early Detection of Cancer, Humans, Machine Learning, Middle Aged, Neoplasm Recurrence, Local, Support Vector Machine, Urinary Bladder Neoplasms diagnosis
Abstract

We report on the feasibility study exploring the potential of a simple electrochemical multisensor system as a tool for distinguishing between urine samples from patients with confirmed bladder cancer (36 samples) and healthy volunteers (51 samples). The potentiometric sensor responses obtained in urine samples were employed as the input data for various machine learning classification algorithms (logistic regression, random forest, extreme gradient boosting classifier, support vector machine, and voting classifier). The performance metrics of the classifiers were evaluated via Monte-Carlo cross-validation. The best model combining all the acquired data from the people aged 19-88 with different tumor grades and malignancies, including patients with recurrent bladder cancer, yielded 72% accuracy, 71% sensitivity, and 58% specificity. It was found that these metrics can be improved to 76% accuracy, 80% sensitivity, and 75% specificity when only a limited age group (50-88 years of age) is considered. Taking into account the simplicity of the proposed screening method, this technique appears to be a promising tool for further research., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Published
2021
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3. A robotic platform for customized and interactive rehabilitation of persons with disabilities

Author

Jose Garcia-Rodriguez, Alberto Garcia-Garcia, Sergiu Ovidiu-Oprea, Sergio Orts-Escolano, Miguel Cazorla, Francisco Gomez-Donoso, John Alejandro Castro-Vargas, Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial, Universidad de Alicante. Departamento de Tecnología Informática y Computación, Universidad de Alicante. Instituto Universitario de Investigación Informática, Robótica y Visión Tridimensional (RoViT), and Informática Industrial y Redes de Computadores

Subjects
030506 rehabilitation, Interaction, Process (engineering), Computer science, medicine.medical_treatment, 02 engineering and technology, 03 medical and health sciences, Cognitive disabilities, Multisensor system, Artificial Intelligence, Human–computer interaction, Persons with disabilities, 0202 electrical engineering, electronic engineering, information engineering, medicine, Computer vision, Robotic platform, Rehabilitation, business.industry, Ciencia de la Computación e Inteligencia Artificial, Gaze, Signal Processing, 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Artificial intelligence, 0305 other medical science, business, Arquitectura y Tecnología de Computadores, Software, Gesture
Abstract

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/. This work has been funded by the Spanish Government TIN2016-76515-R grant for the COMBAHO project, supported with Feder funds. This work has also been supported by a Spanish national grant for PhD studies FPU15/04516.

Published
2017

4. Multivariate calibration transfer between two different types of multisensor systems

Author

Khaydukova, María and Khaydukova, María

Abstract

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.

Published
2017

5. A robotic platform for customized and interactive rehabilitation of persons with disabilities

Author

Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial, Universidad de Alicante. Departamento de Tecnología Informática y Computación, Universidad de Alicante. Instituto Universitario de Investigación Informática, Gomez-Donoso, Francisco, Orts-Escolano, Sergio, Garcia-Garcia, Alberto, Garcia-Rodriguez, Jose, Castro-Vargas, John Alejandro, Ovidiu-Oprea, Sergiu, Cazorla, Miguel, Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial, Universidad de Alicante. Departamento de Tecnología Informática y Computación, Universidad de Alicante. Instituto Universitario de Investigación Informática, Gomez-Donoso, Francisco, Orts-Escolano, Sergio, Garcia-Garcia, Alberto, Garcia-Rodriguez, Jose, Castro-Vargas, John Alejandro, Ovidiu-Oprea, Sergiu, and Cazorla, Miguel

Abstract

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/.

Published
2017

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