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The effective functioning of modern production systems is impossible without using of methods for processing and analyzing data continuously generated during operation. Limitations imposed on the speed and precision of determining the required indicators lead to the need of optimizing the algorithms used. Multisensor systems, as a rule, have an excessive number of cross-sensitive sensors, and their signals can be used to determine various indicators of a similar physical nature. The purpose of the study is to improve the algorithm for processing multidimensional data from multisensor systems. Principal component analysis was applied as part of the developed algorithm for the formation of informative features. Partial least squares regression was used to build regression models. The data set for approbation of proposed approach was obtained through potentiometric measurements using a digital mV-meter. An experiment is described using a multisensor system called “electronic tongue”, consisting of 12 cross-sensitive potentiometric sensors. In the experiment, real samples of vegetable oils acted as analyzed objects. Regression models were built to determine three quality indicators of vegetable oils: peroxide value, para-anisidine value and total tocopherol concentrations. The results of the study were compared with known scientific works. A comparative analysis allowed us to conclude that using of the most informative sources selected according to the proposed algorithm can significantly reduce the root mean square error of prediction. The results obtained can be used both in systems for identifying deviations in production processes in “Industry 4.0” enterprises, and for expressly identifying counterfeit products.

In machinery condition monitoring, it is often vital to consider information from multiple sources due to possible sensor failure or signal distortion, which may result in misclassification of the health status. An issue with multiple sensor data fusion, however, is that the classification can be affected by conflicting results between sensor signals. The proposed method uses a novel three-module approach to information fusion in order to address the problem. Features corresponding to signal integrity are extracted and employed for training a one-class support vector machine to detect unwanted distortions or sensor failures. Different classifiers are trained for the different sensor types available and each signal recorded is used to determine machine health. Decision-level fusion is conducted through a majority voting system using the integrity scores derived from the OCSVMs and the separate classification results. From this, a dynamically weighted fault diagnosis based on sensor signal quality is obtained. Experimental verification using vibration and acoustic emission signals show that the framework is viable and allows for an increased reliability in machinery health diagnosis. [ABSTRACT FROM AUTHOR]

The design and usage of the addressed combined fiber-optic sensors (ACFOSs) and the multisensory control systems of the greenhouse gas concentration on their basis are investigated herein. The main development trend of the combined fiber-optic sensors (CFOSs), which consists of the fiber Bragg grating (FBG) and the Fabry–Perot resonator (FPR), which are successively formed at the optical fiber end, is highlighted. The use of the addressed fiber Bragg structures (AFBSs) instead of the FBG in the CFOSs not only leads to the significant cheapening of the sensor system due to microwave photonics interrogating methods, but also increasing its metrological characteristics. The structural scheme of the multisensory gas concentration monitoring system is suggested. The suggested scheme allows detecting four types of greenhouse gases (CO2, NO2, CH4 and Ox) depending on the material and thickness of the polymer film, which is the FPR sensitive element. The usage of the Karhunen–Loève transform (KLT), which allows separating each component contribution to the reflected spectrum according to its efficiency, is proposed. In the future, this allows determining the gas concentration at the AFBS address frequencies. The estimations show that the ACFOS design in the multisensory system allows measuring the environment temperature in the range of −60...+300 °C with an accuracy of 0.1–0.01 °C, and the gas concentration in the range of 10...90% with an accuracy of 0.1–0.5%. [ABSTRACT FROM AUTHOR]

Understanding the key factors influencing the water quality of large river systems forms an important basis for the assessment and protection of cross-regional ecosystems and the implementation of adapted water management concepts. However, identifying these factors requires in-depth comprehension of the unique environmental systems, which can only be achieved by detailed water quality monitoring. Within the scope of the joint science and sports event "Elbschwimmstaffel" (swimming relay on the river Elbe) in June/July 2017 organized by the German Ministry of Education and Research, water quality data were acquired along a 550 km long stretch of the Elbe River in Germany. During the survey, eight physiochemical water quality parameters were recorded in high spatial and temporal resolution with the BIOFISH multisensor system. Multivariate statistical methods were applied to identify and delineate processes influencing the water quality. The BIOFISH dataset revealed that phytoplankton activity has a major impact on the water quality of the Elbe River in the summer months. The results suggest that phytoplankton biomass constitutes a substantial proportion of the suspended particles and that photosynthetic activity of phytoplankton is closely related to significant temporal changes in pH and oxygen saturation. An evaluation of the BIOFISH data based on the combination of statistical analysis with weather and discharge data shows that the hydrological and meteorological history of the sampled water body was the main driver of phytoplankton dynamics. This study demonstrates the capacity of longitudinal river surveys with the BIOFISH or similar systems for water quality assessment, the identification of pollution sources and their utilization for online in situ monitoring of rivers. [ABSTRACT FROM AUTHOR]

Due to the fact that single sensor data contains only partial information about complex systems, multiple sensors are often embedded to simultaneously monitor the health state and predict the remaining useful life (RUL). This brings new challenges to traditional approaches that focus on single sensor in terms of data fusion, optimization, and uncertainty management. To address these challenges, this article proposes a novel RUL prediction and uncertainty management framework for multisensor systems. In this framework, a composite 1-D health indicator (1-D HI) is obtained from multiple sensors by optimizing some HI characteristics, including monotonicity, robustness, fitting error, and range information, to better describe the underlying degradation process. A multiobjective grasshopper optimization algorithm is used to achieve the optimal weight vector of the fusion model. Then, an unscented particle filter is introduced to predict the RUL by combining the degradation model constructed from HI and the composite 1-D HI as measurement. To manage the uncertainty in prognosis, a probability distribution of failure threshold and noise parameter adjustment are developed. Experimental results on aircraft turbine engine degradation and comparison with state-of-the-art methods are presented to demonstrate the effectiveness of the proposed framework in RUL prediction of multisensor systems. [ABSTRACT FROM AUTHOR]

The design and usage of the addressed combined fiber-optic sensors (ACFOSs) and the multisensory control systems of the greenhouse gas concentration on their basis are investigated herein. The main development trend of the combined fiber-optic sensors (CFOSs), which consists of the fiber Bragg grating (FBG) and the Fabry–Perot resonator (FPR), which are successively formed at the optical fiber end, is highlighted. The use of the addressed fiber Bragg structures (AFBSs) instead of the FBG in the CFOSs not only leads to the significant cheapening of the sensor system due to microwave photonics interrogating methods, but also increasing its metrological characteristics. The structural scheme of the multisensory gas concentration monitoring system is suggested. The suggested scheme allows detecting four types of greenhouse gases (CO2, NO2, CH4 and Ox) depending on the material and thickness of the polymer film, which is the FPR sensitive element. The usage of the Karhunen–Loève transform (KLT), which allows separating each component contribution to the reflected spectrum according to its efficiency, is proposed. In the future, this allows determining the gas concentration at the AFBS address frequencies. The estimations show that the ACFOS design in the multisensory system allows measuring the environment temperature in the range of −60…+300 °C with an accuracy of 0.1–0.01 °C, and the gas concentration in the range of 10…90% with an accuracy of 0.1–0.5%.

A resolution‐reconfigurable, bandwidth‐scalable analogue‐to‐digital converter (ADC) with programmable‐gain (PG) functionality for a multi‐sensor system, which encompasses various signals such as bio‐signals and battery‐level, is presented. In PG and low‐power mode, a PG first‐order noise‐shaping (NS) successive‐approximation register (SAR) ADC achieves 8.4‐to‐10.2‐bit operating up to 16 kHz while providing a gain of 1/2/4. The PG NS SAR ADC can be reconfigured as an adder and a quantiser in a delta‐sigma (ΔΣ) modulator enhancing the order of the modulator for high‐resolution. The third‐order ΔΣ modulator achieves 16.1 bits in a bandwidth of 1 kHz. The work is implemented in a 0.18 μm CMOS process with a 1.8 V power supply.

A portable multisensor frequency modulated continuous radar (FMCW) networked radar concept is presented for the localization of persons trapped in debris in building collapse scenarios. A 2-D localization algorithm is evaluated in a field test to demonstrate successful localization detection of respiration micromovements. [ABSTRACT FROM AUTHOR]

The problem of multisensor multitarget state estimation in the presence of constant but unknown sensor biases is investigated. The classical approach to this problem is to augment the state vector to include the states of all the targets and the sensor biases, and then implement an augmented state Kalman filter (ASKF). In this article, we propose a novel decoupled Kalman filtering algorithm. The decoupled Kalman filtering first processes each target in a separate branch, namely the single-target Kalman filtering branch, where the single-target states and the sensor biases are estimated. Then the bias estimate is refined by fusing the former bias estimates across all the single-target Kalman filtering branches. Finally, the refined bias estimate is fed back to each single-target Kalman filtering branch to improve the target state estimation. We prove that the proposed decoupled Kalman filtering is exactly equivalent to the ASKF in terms of the estimation results under a usual initial condition. The equivalence is also confirmed via the numerical example. Moreover, we further validate the proposed algorithm using the field experimental data of a multistatic passive radar. [ABSTRACT FROM AUTHOR]

We propose a robust attitude estimation method for multisensor systems with heavy-tailed noise. The attitude model is represented by a quaternion, and the propagation process in the filter is based on a gyro-based model. To avoid the constraint of quaternion, the quaternion is converted to modified Rodrigues parameters during the prediction and update process. Instead of the minimum mean square error principle, the maximum correntropy criterion is applied in the update process, which is robust to heavy-tailed noises. Considering the linear form in the update process, the information filtering algorithm is applied in the proposed method to simplify the calculation of multisensor information fusion. Moreover, the application of information filtering algorithm can also avoid the numerical problem that zero weight functions cannot be incorporated into the Gaussian filtering framework. Simulations for several cases are performed, and the simulation results indicate the robustness and efficiency of the proposed filter. [ABSTRACT FROM AUTHOR]

In the paper a state filtration in a decentralized discrete time Linear Quadratic Gaussian problem formulated for a multisensor system is considered. Local optimal control laws depend on global state estimates and are calculated by each node. In a classical centralized information pattern the global state estimators use measurements data from all nodes. In a decentralized system the global state estimates are computed at each node using local state estimates based on local measurements and values of previous controls, from other nodes.

We report on the development of a simple and cost-effective potentiometric sensor array that is based on manual “drawing” on the polymeric support with the pencils composed of graphite and different types of zeolites. The sensor array demonstrates distinct sensitivity towards a variety of inorganic ions in aqueous media. This multisensor system has been successfully applied to quantitative analysis of 100 real-life surface waters sampled in Mahananda and Hooghly rivers in the West Bengal state (India). Partial least squares regression has been utilized to relate responses of the sensors to the values of different water quality parameters. It has been found that the developed sensor array, or electronic tongue, is capable of quantifying total hardness, total alkalinity, and calcium content in the samples, with the mean relative errors below 18%.

Medium dense hardwoods (HWs) show higher tensile strength (TS) values than softwoods (SWs). These advantages cannot be utilised effectively because HW grading is not well developed. The aim of the present paper was to analyse the utilisation potential of European ash (Fraxinus spp.) and maple (Acer spp.) grown in Central Europe, which were graded by different methods. The visual grading characteristics of 869 HW boards were determined and the dynamic modulus of elasticity (MOEdyn) and X-ray attenuation (XRA) were measured by an industrial scanner. The specimens were subsequently tested in tension according to EN 408:2010 and according to German visual grading rules show strength values of 28 MPa and 30 MPa, respectively. Machine strength grading and for a combination of manually assessed boards and MOEdyn give rise to higher strength data. MOEdyn, in particular, results in lamella data with 62 MPa for ash and 42 MPa for maple. There is good agreement with recently presented HW tensile profiles. Machine grading with a multisensor system allows better strength prediction compared to the MOEdyn or visual strength grading. Best performance is achieved by a combined grading approach. [ABSTRACT FROM AUTHOR]

Highlights • Polymeric membranes based on extracting agents with sensitivity to actinides. • Potentiometric multisensor system for PUREX-related solutions. • Quantification of UO 2 2+, Th4+, HNO 3 in complex mixtures with appropriate accuracy. Abstract A potentiometric multisensor system, composed of 12 cross-sensitive sensors, is proposed for uranium and thorium quantification in multi-component mixtures simulating PUREX process tailing solutions. Various ligands with high extraction capacity to lanthanides and actinides were used as active compounds in plasticized polymeric sensor membranes. The sensitivity of the individual sensors to tetra- and hexavalent actinides (UO 2 2+ and Th4+) in aqueous nitric acid solutions with pH = 2 has been studied for the first time. The sensors with pronounced sensitivity to actinides were comprised into the multisensor system that was applied for UO2+ 2 and Th4+ quantitative determination in presence of each other and lanthanides cations (La3+, Pr3+, Sm3+, Gd3+, Yb3+). The nitric acid quantification in the set of mixtures with UO2+ 2 and La3+ was also performed using the same system. Projection on latent structures (PLS) algorithm was used for multivariate regression models calculation. The results of PLS models validation with independent test set show the possibility of UO2+ 2 and Th4+ quantification with mean relative error (MRE) equal to 17% and 14% correspondingly, and HNO 3 quantification with MRE = 7%. This accuracy is appropriate for real-time technological control of PUREX process. [ABSTRACT FROM AUTHOR]

Highlights • Estimation of vegetable oil quality parameters with multisensor system. • Fast and simple quantification of peroxide and anisidine values and total tocopherols. • Reagentless approach for measurement of edible oil quality parameters. Abstract In this study the possibility of using potentiometric multisensor system (e-tongue) based on solid-state membrane electrodes for express evaluation of various quality parameters in vegetable oils was investigated. Optimization of oil sample preparation procedures for potentiometric analysis was performed. It was shown that developed multisensor system is capable to distinguish vegetable oil varieties prepared from different raw materials and pure oils from their mixture. Important edible oil quality indicators, such as peroxide value (PV), p -anisidine value (p-AV), and concentration of total tocopherols (TT) can be predicted from potentiometric multisensor system response using multivariate data processing. The parameters under study have the following ranges: PV (0.0–4.0); p-AV (0.4–3.8); TT (37.0–100.7). Application of multivariate regression tools yielded root mean square errors of prediction: 0.5 meq/kg for PV; 0.8 arbitrary units for p-AV; 10 mg/100 g for TT, thus showing a good promise for development of fast and simple method for express quality evaluation. [ABSTRACT FROM AUTHOR]

The significant improvement of quality of life achieved over the last decades has stimulated the development of new approaches in medicine to take into account the personal needs of each patient. Precision medicine, providing healthcare customization, opens new horizons in the diagnosis, treatment and prevention of numerous diseases. As a consequence, there is a growing demand for novel analytical devices and methods capable of addressing the challenges of precision medicine. For example, various types of sensors or their arrays are highly suitable for simultaneous monitoring of multiple analytes in complex biological media in order to obtain more information about the health status of a patient or to follow the treatment process. Besides, the development of sustainable sensors based on natural chemicals allows reducing their environmental impact. This review is concerned with the application of such analytical platforms in various areas of medicine: analysis of body fluids, wearable sensors, drug manufacturing and screening. The importance and role of naturally-occurring compounds in the development of electrochemical multisensor systems and arrays are discussed.

The paper describes the substance image formation based on the measurements by multisensor systems and the possibility of the development of a gas analysis device like an electronic nose. Classification of gas sensors and the need for their application for the recognition of difficult images of multicomponent air environments are considered. The image is formed based on stochastic transformations, calculations of correlation, and fractal dimensions of reconstruction attractors. The paper shows images created for substances with various structures that were received with the help of a multisensor system under fixed measurement conditions.

Water quality is one of the most critical indicators of environmental pollution and it affects all of us. Water contamination can be accidental or intentional and the consequences are drastic unless the appropriate measures are adopted on the spot. This review provides a critical assessment of the applicability of various technologies for real-time water quality monitoring, focusing on those that have been reportedly tested in real-life scenarios. Specifically, the performance of sensors based on molecularly imprinted polymers is evaluated in detail, also giving insights into their principle of operation, stability in real on-site applications and mass production options. Such characteristics as sensing range and limit of detection are given for the most promising systems, that were verified outside of laboratory conditions. Then, novel trends of using microwave spectroscopy and chemical materials integration for achieving a higher sensitivity to and selectivity of pollutants in water are described.

The development of electrochemical multisensor systems is driven by the need for fast, miniature, inexpensive, analytical devices, and advanced interdisciplinary based on both chemometric and (nano)material approaches. A multicomponent analysis of complex mixtures in environmental and technological monitoring, biological samples, and cell culture requires chip-based multisensor systems with high-stability sensors. In this paper, we describe the development, characterization, and applications of chip-based nanoelectrochemical sensor arrays prepared by the directed electrochemical nanowire assembly (DENA) of noble metals and metal alloys to analyze aqueous solutions. A synergic action of the electrode transducer function and electrocatalytic activity of the nanostructured surface toward analytes is achieved in the assembled metal nanowire (NW) sensors. Various sensor nanomaterials (Pd, Ni, Au, and their multicomponent compositions) can be electrochemically assembled on a single chip without employing multiple cycles of photolithography process to realize multi-analyte sensing applications as well as spatial resolution of sensor analysis by this single-chip multisensor system. For multi-analyte electrochemical sensing, individual amperometric signals of two or more nanowires can be acquired, making use of the specific electrocatalytic surface properties of the individual nanowire sensors of the array toward analytes. To demonstrate the application of a new electrochemical multisensor platform, Pd-Au, Pd-Ni, Pd, and Au NW electrode arrays on a single chip were employed for the non-enzymatic analysis of hydrogen peroxide, glucose, and ethanol. The analytes are determined at low absolute values of the detection potentials with linear concentration ranges of 1.0 × 10−6 − 1.0 × 10−3 M (H2O2), 1.5 × 10−7 − 2.0 × 10−3 M (glucose), and 0.7 × 10−3 − 3.0 × 10−2 M (ethanol), detection limits of 2 × 10−7 M (H2O2), 4 × 10−8 M (glucose), and 5.2 × 10−4 M (ethanol), and sensitivities of 18 μA M−1 (H2O2), 178 μA M−1 (glucose), and 28 μA M−1 (ethanol), respectively. The sensors demonstrate a high level of stability due to the non-enzymatic detection mode. Based on the DENA-assembled nanowire electrodes of a compositional diversity, we propose a novel single-chip electrochemical multisensor platform, which is promising for acquiring complex analytical signals for advanced data processing with chemometric techniques aimed at the development of electronic tongue-type multisensor systems for flexible multi-analyte monitoring and healthcare applications.

A fusion hierarchical state filtration with k−step delay sharing pattern for a multisensor system is considered. A global state estimate depends on local state estimates determined by local nodes using local information. Local available information consists of local measurements and k−step delay global information - global estimate sent from a central node. Local estimates are transmitted to the central node to be fused. The synthesis of local and global filters is presented. It is shown that a fusion filtration with k−step delay sharing pattern is equivalent to the optimal centralized classical Kalman filtration when local measurements are transmitted to the center node and used to determine a global state estimate. It is proved that the k−step delay sharing pattern can reduce covariances of local state errors.

The aim of the project presented in this article was to design a system which allows for assessment of indoor environmental conditions, with a special consideration regarding the comfort of occupants. The system consists of two microprocessor devices with numerous sensors, as well as a PC application which includes fuzzy inference module. Fuzzy inference algorithm allows for comfort assessment based on data gathered by sensors. It can also help to analyse the efficiency of HVAC systems. The article includes description of the system's functions and selection criteria for sensors taking into account of measurands. Also described in this paper is the construction of knowledge base, based on information from environmental standards and experts' statements. The constructed system was tested and examined, to confirm its application in practical comfort assessment and to highlight the advantages of using fuzzy logic in the process of analysing measured parameters and inference, which captures the way living beings process data. [ABSTRACT FROM AUTHOR]

Abstract: 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. [ABSTRACT FROM AUTHOR]

This paper focuses on the linear optimal recursive sequential fusion filter design for multisensor systems subject to stochastic parameter perturbations, fading measurements, and correlated noises. The stochastic parameter perturbations existing in the state model are described by white multiplicative noises. The fading measurement phenomena for different sensors are described by independent random variables with known statistical properties. Moreover, the measurement noises of different sensors are correlated with each other and also correlated with the system noise at the same time step. First, a model equivalent to the original system is established by transferring the multiplicative noises into the additive noises. Then, based on the equivalent model and an innovation analysis method, a sequential fusion filter in the linear minimum variance sense is proposed to solve the linear optimal state estimation problem in real time according to the arriving order of measurements from different sensors. Finally, the equivalence on estimation accuracy of the proposed sequential fusion filter and the centralized fusion filter is strictly proven, which shows the optimality of the proposed sequential fusion algorithm. Moreover, the proposed sequential fusion filter has a reduced computational burden. Compared with the distributed matrix-weighted fusion filter, the computation of cross-covariance matrices is avoided and the estimation accuracy is improved. Finally, a simulation example verifies the effectiveness of the proposed sequential fusion filtering algorithm. [ABSTRACT FROM AUTHOR]

Quantification of proteins is a key biochemical assay in molecular biology, biotechnology, medicine and pharmacology. Protein quantification protocols can be based on spectrophotometry, enzyme-linked immunosorbent assay, mass spectrometry or quantitative immunoblotting depending on analyte. In case of immobilized protein these methods require suitable sample preparation. Thus, sophisticated analysis becomes even more complex, expensive and time-consuming. Such drawbacks are highly undesirable in industry. In this study we propose a new approach for evaluation of immobilized protein concentration based on application of bio-assisted potentiometric multisensor system. Surface-immobilized recombinant protein A from Staphylococcus aureus (SpA, expressed in Escherichia coli ), which is commonly used as affinity ligand immobilized to stationary phase (сhromatography media) for monoclonal antibody purification was employed as the model object. Chromatography media samples containing different amounts of immobilized SpA were analyzed. Proteinase K from Tritirachium album was employed as a bio-transducer. We demonstrated that the suggested approach provides information about immobilized SpA concentration with 0.8 mg/ml accuracy in the range 1–6.7 mg/ml and within just 16 min. Moreover, the proposed procedure requires no expensive materials and equipment and no bio-transducer immobilization. This method has potential of application for fast monitoring of other immobilized proteins in different tasks. [ABSTRACT FROM AUTHOR]

This paper deals with the study of preparations based on medicinal plants used in traditional Chinese medicine for treatment and prevention of a wide range of diseases. The purpose of this research was evaluation of the capabilities of a multisensor system for instrumental assessment of the samples bitterness. 33 samples of medicinal plants were evaluated by tasters according to bitterness intensity from 0 to 6. Methodology of the analysis was developed and repeated measurements of the samples were performed by multisensor system. Tasters’ assessments were used as reference data while multisensor system calibrating. A regression model built according to these data displayed good correlation of the system response with bitterness perceived by people. The parameters of the regression model give the possibility for concluding that the multisensor system is capable to predict the bitterness of the medicinal plants preparations with average precision equal to ±1 of the reference bitterness scale. Relative error of bitterness determination is 14%, which is a good result for such type of measurements (typical error of the taster’s assessment is, as a rule, in the range of 15-30 %).

“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. This is not surprising, since the MSS development was sometimes inspired by the mainstream idea that they could substitute human gustatory tests. However, the basic principle behind multisensor systems—a combination of an array of cross-sensitive chemical sensors for liquid analysis and a machine learning engine for multivariate data processing—does not imply any limitations on the application of such systems for the analysis of inedible media. This review deals with the numerous MSS applications for the analysis of inedible analytes, among other things, for agricultural and medical purposes.

Rapid industrialization has significantly influenced people’s lifestyles in the recent decades, and the influence of traditional culture is diminishing. Recently, several studies attempted to simultaneously utilize various sensors to record delicate and sophisticated performances of intangible cultural heritage (ICH). Although painting is one of the most common ICH of human history, few research studies have recorded traditional painting work. In this paper, we aim to lay the groundwork for reviving Korean painting, even if there would be no painters to produce these traditional Korean paintings in the future. We propose a novel multisensor-based acquisition system that records traditional Korean painting work while minimizing interference in the work. The proposed system captures real-time data originating from the painter, brushes, pigments, and canvas, which are the essential components of the painting work. We utilized the proposed system to capture the painting work by two experts, and we visualize the captured data. We showed the various results of statistical analysis, and also discussed the usability.

Multisensor systems can overcome the limitation of measurement range of single-sensor systems, but often require complex calibration and data fusion. In this study, a three-dimensional (3D) measurement method of four-view stereo vision based on Gaussian process (GP) regression is proposed. Two sets of point cloud data of the measured object are obtained by gray-code phase-shifting technique. On the basis of the characteristics of the measured object, specific composite kernel functions are designed to obtain the initial GP model. In view of the difference of noise in each group of point cloud data, the weight idea is introduced to optimize the GP model, which is the data fusion based on Bayesian inference method for point cloud data. The proposed method does not require strict hardware constraints. Simulations for the curve and the high-order surface and experiments of complex 3D objects have been designed to compare the reconstructing accuracy of the proposed method and the traditional methods. The results show that the proposed method is superior to the traditional methods in measurement accuracy and reconstruction effect.

In different disciplines of science, the knowledge of the resulting pressures in the subsoil can help to understand physical phenomena of mass exchange between the atmosphere and the terrain. The measurement of lower differential pressures is complicated given the low range of detected values. In this paper, a multisensor system has been designed and developed to measure differential pressures in radon gas transport studies. The adequacy of this system has been proven using a purpose-built pressure chamber and an automatic motion system developed by the authors. The temporal response frequencies, the pressure values measured by the sensors, and their ability to link in series were analyzed to offer a multisensor spatial and temporal mapping. At the same time, the influence of the components required for a real deployment were studied using different tube lengths and diameters, connectors, and obstructions across the operating range of the pressure sensors. The system has also been tested for measuring differential pressures in a real model with a concrete slab above the soil and a pressure generator system below. It was found that this system is very suitable for outdoor measurements that demand a quick temporal response and accuracy.

This research is devoted to the development and study of novel cross-sensitive sensors based on modified extracting ligands. According to the previous results of liquid extraction studies, the chemical modification of membrane active components would change the analytical characteristics of a sensor comprising them. The sensing elements of the studied sensors consisted of various derivatives of N,N,N′,N′-tetraoctyldiamide of diglycolic acid (TODGA) and di-phenyl-N,N-di-i-sobutylcarbamoylmethylen phoshine oxide (CMPO) used as neutral carriers, CCD (chlorinated cobalt dicarbollide) as a lipophilic additive, different plasticizers, and poly(vinyl chloride) (PVC) as a polymer. TODGA-based sensors demonstrated a stable and reproducible response towards rare earth cations in acidic media (pH = 2). Changing the concentrations and ratio of neutral carriers and the lipophilic additive, it is possible to modify the sensitivity and selectivity of the sensors towards the same target ions. Bonded ligands, such as cobalt dicarbollide covalently attached to TODGA and CMPO, exhibited lower selectivity and sensitivity to rare earth cations. 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.

The paper describes a wide-range practical application of the potentiometric multisensor system (MS) (1) for integral safety evaluation of a variety of natural waters at multiple locations, under various climatic conditions and anthropogenic stress and (2) for close to real consistency evaluation of waste water purification processes at urban water treatment plants. In total, 25 natural surface water samples were collected around St. Petersburg (Russia), analyzed as is, and after ultrasonic treatment. Toxicity of the samples was evaluated using bioassay and MS. Relative errors of toxicity assessment with MS in these samples were below 20%. The system was also applied for fast determination of integral water quality using chemical oxygen demand (COD) values in 20 samples of water from river and ponds in Kolkata (India) and performed with an acceptable precision of 20% to 22% in this task. Furthermore, the MS was applied for fast simultaneous evaluation of COD, biochemical oxygen demand, inorganic phosphorous, ammonia, and nitrate nitrogen at two waste water treatment plants (over 320 samples). Reasonable precision (within 25%) of such analysis is acceptable for rapid water safety evaluation and enables fast control of the purification process. MS proved to be a practicable analytical instrument for various real-world tasks related to water safety monitoring.

The results of studying characteristics of potentiometric DP-sensors (sensors with the Donnan potential as their analytical signal) are presented for alkaline solutions of a sulfur-containing amino acid with perfluorosulfonic cation exchange membranes subjected to thermal treatment and mechanical deformation at different relative humidity. Correlation between the distribution of sensitivity of DP-sensors towards cations and anions and diffusion permeability of membranes was found. A multisensor system including two DP-sensors based on membranes with optimized properties and a glass electrode for codetermination of potassium cations and amino acid anions and zwitterions in solutions at pH > 7 are developed. [ABSTRACT FROM AUTHOR]

The possibility of using solid-state and liquid PVC-membrane electrodes based on ionic liquids to design a potentiometric multisensor system is assessed. Ionic liquids with 1,3-dihexadecylimidazolium cation and chloride, bromide, iodide, and nitrate anions are used. The sensitivity parameters of the sensors are determined. A multisensor array is applied to detect chloride and iodide anions in the multicomponent mixture. The designed system is used to discriminate between mineral waters of different compositions by applying the method of principal component analysis (PCA). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A resolution‐reconfigurable, bandwidth‐scalable analogue‐to‐digital converter (ADC) with programmable‐gain (PG) functionality for a multi‐sensor system, which encompasses various signals such as bio‐signals and battery‐level, is presented. In PG and low‐power mode, a PG first‐order noise‐shaping (NS) successive‐approximation register (SAR) ADC achieves 8.4‐to‐10.2‐bit operating up to 16 kHz while providing a gain of 1/2/4. The PG NS SAR ADC can be reconfigured as an adder and a quantiser in a delta‐sigma (ΔΣ) modulator enhancing the order of the modulator for high‐resolution. The third‐order ΔΣ modulator achieves 16.1 bits in a bandwidth of 1 kHz. The work is implemented in a 0.18 μm CMOS process with a 1.8 V power supply. [ABSTRACT FROM AUTHOR]