Introduction to Multisensor Systems

What is/are Multisensor Systems?

Multisensor Systems - 9% segmentation accuracy, which is comparable to that of previous multisensor systems (99. ^[1] This work applies the modeling that allows to describe the dynamic behavior of the sensor frequency response for multisensor systems. ^[2] Multisensor systems are widely applied to realize the comprehensive monitoring and control as they feature multiple individual sensors/outputs. ^[3] More and more often modern optical systems or multisensor systems replace classic solutions. ^[4] Multisensor systems are widely used in large-scale fringe projection profilometry, where the accuracy and efficiency of registration play an important role. ^[5] It consists of an introduction and five sections that describe state of the art in the field of optical sensing, suggested development methodology of optical multisensor systems, related aspects of experimental design and process analytical technology followed by a collection of practical examples in different application fields: food and pharmaceutical production, medical diagnostics, and ecological monitoring. ^[6] Furthermore, the method outperforms several state-of-the-art HAR approaches and demonstrates the superiority of the proposed intermediate fusion model in multisensor systems. ^[7] This proof of concept study confirms the possibility of constructing adjustable optical multisensor systems based on Cu(I) emitters for real-life applications. ^[8] For networked multisensor systems (NMSs), a soft measurement model with five uncertainties is presented, which can be viewed as a “soft sensor” such that the measurements received by estimators can be obtained via the computations of this model. ^[9] When tracking with synchronous passive multisensor systems, each sensor provides a line-of-sight measurement. ^[10] To address these challenges, this article proposes a novel RUL prediction and uncertainty management framework for multisensor systems. ^[11] This article considers the robust estimation problem of linear multisensor systems subject to constraints and additive and multiplicative uncertainties where the decaying of measurements is regarded as the multiplicative uncertainty when taking into account the fading channels between the estimator and sensors. ^[12] Multisensor systems can enhance navigation performance in terms of accuracy, availability, continuity and integrity. ^[13] In addition, for multisensor systems, a centralized fusion estimator by reordering measurement data from sensors and a suboptimal distributed covariance intersection fusion estimator are proposed, respectively. ^[14] The design of multisensor systems to make two or more measurements of the pulse wave along the human arterial tree has become the hallmark of these efforts. ^[15] Multisensor systems are widely applied to realize the comprehensive monitoring and control as they feature multiple individual sensors/outputs. ^[16] Asynchronous multisensor systems have been widely equipped on various host platforms to meet the requirements of modern navigation campaigns. ^[17] Problems of the development of potentiometric sensors and multisensor systems for the determination of rare-earth elements in aqueous solutions are considered. ^[18] In this paper, the multisensor systems with ARMA coloured measurement noise are converted to those with the same local dynamic model and uncorrelated noises by using the state augmented method. ^[19] This paper addresses the design of robust scalars-weighted fusion time-varying white noise deconvolution estimators (WNDEs) for uncertain multisensor systems with mixed uncertainties including uncertain-variance multiplicative noises in measurement matrix, missing measurements, and uncertain-variance linearly correlated measurement and process white noises. ^[20] “Electronic tongues”, “taste sensors”, and similar devices (further named as “multisensor systems”, or MSS) have been studied and applied mostly for the analysis of edible analytes. ^[21] The reasonable scheduling of multisensor systems to maximize combat benefits has become a research hotspot in the field of sensor management. ^[22] Potentiometric electrodes with plasticized membranes containing various ligands are widely employed as ion-selective sensors and as cross-sensitive sensors in multisensor systems. ^[23] This intuitive procedure was successfully tested with a variety of real data from potentiometric multisensor systems. ^[24] In this paper, research advances in modelling and estimation algorithms for multirate multisensor systems are reviewed. ^[25] For networked multisensor systems with mixed uncertainties, including random one-step sensor delays, multiplicative noises and uncertain noise variances, a new augmented state approach with the fictitious noises is presented, by which the original system model is transformed into one without sensor delays and only with white noises. ^[26] This study will be extended to the integrity monitoring of multisensor systems. ^[27] The high potential of QCM multisensor systems for fast and cost-effective water contamination assessments “in situ” without sample pretreatment is demonstrated. ^[28] Implementations of multisensor systems such as sensor array systems and sensor network systems have increased in the last years. ^[29] Multisensor systems can overcome the limitation of measurement range of single-sensor systems, but often require complex calibration and data fusion. ^[30] Calibration of multisensor systems (MSS) with linear response usually involves some form of regression analysis, e. ^[31] The first part of the analytical review presents the main directions of application of modern Multisensor systems such as "electronic nose". ^[32] These are well known to many of us who have worked on the development of multisensor systems or wireless sensor systems, perhaps with new meanings and with the problem of managing the enormous amounts of data generated, which have to be stored, processed and presented in a suitable and easily interpretable form. ^[33] A possibility to vary the cross-sensitivity patterns of the sensors in a wide range might be of great interest for the development of multisensor systems allowing the simultaneous determination of several analytes in multicomponent solutions. ^[34] In spite of intensively reported scientific activities and even commercial availability of such devices, multisensor systems are still not widely used in routine laboratory and industrial practice but are rather research instruments. ^[35] Multisensor systems with low-power consumption are emerging for the Internet of Things. ^[36] ABSTRACT For multisensor systems with uncertain noise variances and missing measurements, it can be converted into one only with uncertain noise variances by introducing fictitious measurement white noises. ^[37] Air Quality Multisensor Systems (AQMS) have shown to be able to provide, if properly calibrated, high quality data in terms of indicative measurements. ^[38]

Networked Multisensor Systems

For networked multisensor systems (NMSs), a soft measurement model with five uncertainties is presented, which can be viewed as a “soft sensor” such that the measurements received by estimators can be obtained via the computations of this model. ^[1] For networked multisensor systems with mixed uncertainties, including random one-step sensor delays, multiplicative noises and uncertain noise variances, a new augmented state approach with the fictitious noises is presented, by which the original system model is transformed into one without sensor delays and only with white noises. ^[2]

Optical Multisensor Systems

It consists of an introduction and five sections that describe state of the art in the field of optical sensing, suggested development methodology of optical multisensor systems, related aspects of experimental design and process analytical technology followed by a collection of practical examples in different application fields: food and pharmaceutical production, medical diagnostics, and ecological monitoring. ^[1] This proof of concept study confirms the possibility of constructing adjustable optical multisensor systems based on Cu(I) emitters for real-life applications. ^[2]