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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.

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.

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%.

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.

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 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 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.

“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.

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.

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.

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.

This paper presents a solution for on-chip temperature and mechanical stress measurement in CMOS integrated circuits. Thereby both temperature and stress sensors are realized as resistive Wheatstone bridges. By design, both sensors show outputs affected by non-linearities and parasitic cross-sensitivities. The novelty presented in this work is to combine both non-ideal sensor outputs by applying a two-dimensional Newton-Raphson method to extract the actual values of temperature and mechanical stress which were obtained with errors of less than 0.5 K and 0.5 MPa.

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.