Types Of Sensors Research Articles

Progress in Protein-Based Hydrogels for Flexible Sensors: Insights from Casein.

In recent years, the rapid advancement of flexible sensors as the cornerstone of flexible electronics has propelled a flourishing evolution within the realm of flexible electronics. Unlike traditional flexible devices, hydrogel flexible sensors have characteristic advantages such as biocompatibility, adhesion, and adjustable mechanical properties and have similar properties to human skin. Especially, biobased hydrogels have become the preferred substrate material for flexible sensors due to increased environmental pressures caused by the scarcity of petrochemical resources. In this regard, proteins possess advantages such as diverse amino acid compositions, adjustable advanced structures, chemical modifiability, the application of protein engineering techniques, and the ability to respond to various external stimuli. These enable the hydrogels constructed from them to have greater designability, flexibility, and adaptability. As a result, their applications in manufacturing various types of sensors have experienced rapid growth. This work systematically reviews the sensing mechanism of protein-based hydrogels, focusing on the preparation of protein-based hydrogels and the optimization of flexible sensors mainly from the perspective of a typical type of animal-derived protein casein. In addition, while the potential of casein is recognized, the limitations of casein-based hydrogels in flexible sensor applications are explored, and insights are provided into the development trends of next-generation sensors based on casein-based hydrogel materials.

Characterization of the calmodulin-like protein family in Chara braunii and their conserved interaction with the calmodulin-binding transcription activator family.

Calcium sensor proteins play important roles by detecting changes in intracellular calcium and relaying that information onto downstream targets through protein-protein interaction. Very little is known about calcium sensors from plant species that predate land colonization and the evolution of embryophytes. Here, we examined the genome of the multicellular algae, Chara braunii, for orthologs to the evolutionarily-conserved calcium sensor calmodulin (CaM), and for CaM-like proteins (CMLs). We identified one CaM and eight CML isoforms which range in size from 16.4 to 21.3 kDa and are predicted to have between two to four calcium-binding (EF-hand) domains. Using recombinant protein, we tested whether CbCaM and CbCMLs1-7 possess biochemical properties of typical calcium sensors. CbCaM and the CbCMLs all displayed high-affinity calcium binding with estimated global KD,app values in the physiological µM range. In response to calcium binding, CbCaM and the CbCMLs exhibited varying degrees of increase in exposed hydrophobicity, suggesting different calcium-induced conformational changes occur among isoforms. We found many examples of putative CaM targets encoded in the C. braunii genome and explored the ability of CbCaM and CbCMLs to interact in planta with a representative putative target, a C. braunii CaM-binding transcription factor (CbCAMTA1). CbCaM, CbCML2, and CbCML4 associated with the C-terminal region of CbCAMTA1. Collectively, our data support the hypothesis that complex calcium signaling and sensing networks involving CaM and CMLs evolved early in the green lineage. Similarly, it seems likely that calcium-mediated regulation of transcription occurs in C. braunii via CAMTAs and is an ancient trait predating embryophytic emergence.

IoT-Enabled Fire Alarm System with Cloud-Based Storage and Monitoring

Abstract: The purpose of a fire alarm system is to warn us of an emergency so that we can take precautions to safeguard ourselves, our employees, and the public. However, modern fire alarm systems require a significant amount of wiring and labor during installation. The most frequent issues that was encountered during monitoring commercial and residential fire alarm systems are false alarms, inappropriate installation, lost connections, malfunctioning or aged equipment, and careless maintenance that discouraged users from putting them in their houses as a result. Hence, the researcher developed an IoT-based fire alarm system connected to several types of sensors and micro-controller distributed over the building and house. Each of these nodes is made up of an Arduino microcontroller that is coupled with smoke, temperature, humidity, flame, methane, and carbon monoxide (CO) sensors. Thus, an innovative system is designed with the help of Internet of Things (IoT) that records live reading of sensors and stored in cloud interface which also lessens the investigation process of the authorities concerned. Results shows that majority of the evaluators responded to the functionality of the proposed system to be Superior and provide all the functionality required for the system to detect and prevent fire occurrences.

Improvement of angle measurement sensitivity using second harmonic wave interference

This paper presents the angular displacement measurement based on second harmonic generation (SHG). In our previous study, the angular resolution of sub-arcsecond was achieved. In this paper, three kinds of sensors based on SHG are reported. In these three sensors, the angular displacement is determined from the intensity of the SHG signal since the SHG is sensitive to the angle of the incident light. Two of these sensors in this study utilize the interference of SHG to improve the sensitivity performance. Comparing with the sensors which have been studied before, interference type sensors in this study allow the femtosecond laser to pass through the nonlinear optical crystal twice, which leads to a significant sensitivity improvement. The principles were verified by both simulation and experiment. In the simulation part, the sensitivities of these three kinds of sensors are compared. In the experiment, the sensitivities were confirmed to be consistent with the simulation. Additionally, the resolution of the presented systems is evaluated and sub-0.1-arcsecond is achieved.

High-Performance Processing Electronics for Cooler Temperature Sensors in Infrared Payloads

Remote Sensing Infra-Red (IR) imaging sensors operate at very low temperatures (40–100 K). Generally, these types of imaging sensors are integrated with cryo-coolers. These sensors have an inbuilt 2N2222 diode-based cooler temperature sensor (CTS). This paper presents details of low-noise, high=precision processing electronics developed for CTS. Critical design aspects, simulation and hardware results of the system are discussed in this paper. Developed electronics systems achieve ≈5mK resolution and the power dissipation recorded is < 200 mW.

Observation of converse flexoelectric effect in topological semimetals

A strong coupling between electric polarization and elastic deformation in solids is an important factor in creating useful electromechanical nanodevices. Such coupling is typically allowed in insulating materials with inversion symmetry breaking as exemplified by the piezoelectric effect in ferroelectric materials. Therefore, materials with metallicity and centrosymmetry have tended to be out of scope in this perspective. Here, we report the observation of giant elastic deformation by the application of an alternating electric current in topological semimetals (V,Mo)Te2, regardless of the centrosymmetry. Considering the crystal and band structures and the asymmetric measurement configurations in addition to the absence of the electromechanical effect in a trivial semimetal TiTe2, the observed effect is discussed in terms of a Berry-phase-derived converse flexoelectric effect in metals. The observation of the flexoelectric effect in topological semimetals paves a way for a new type of nanoscale electromechanical sensors and energy harvesting.

Bayesian finite element model inversion of offshore wind turbine structures for joint parameter-load estimation

Operating in harsh and unsteady marine environment, offshore wind turbine (OWT) structures are exposed to unpredictable wind and wave loads. Identifying the structural loads and their effects on the OWTs allow for predicting the remaining fatigue life of these structures and improving the structural design procedure. In this paper, a finite element (FE) model inversion method is presented to estimate the unknown loads and model parameters of OWTs using sparse measurement data. A realistic FE model of an OWT structure with jacket substructure is created in the open-source simulation platform, OpenSees. A Bayesian inference framework is presented to integrate the measured data with the FE model to estimate unknown wind loads and mass of rotor-nacelle assembly. To evaluate the performance of this data assimilation framework, the effect of sensor type, number of sensors, and modeling errors on the estimation accuracy of wind loads and model parameters are investigated through different case studies where synthetic data are used as measurements. The results of this study are important to guide instrumentation of new OWT structures, and to understand the potential limitations and sources of error in the real-world application of this data assimilation framework for joint model parameter and input load estimation.

Point cloud segmentation method based on an image mask and its application verification

Abstract Accurately perceiving three-dimensional (3D) environments or objects is crucial for the advancement of artificial intelligence (AI) interaction technologies. Currently, various types of sensors are employed to obtain point cloud data for 3D object detection or segmentation tasks. While this multi-sensor approach provides more precise 3D data than monocular or stereo cameras, it is also more expensive. The advent of RGB-D cameras, which provide both RGB images and depth information, addresses this issue.&amp;#xD;In this study, we propose a point cloud segmentation method based on image masks. By using an RGB-D camera to capture color and depth images, we generate image masks through object recognition and segmentation. Given the mapping relationship between RGB image pixels and point clouds, these image masks can be further used to extract the point cloud data of the target objects. The experimental results revealed that the average accuracy of target segmentation was 84.78%, which was close to that of PointNet++. Compared with three traditional segmentation algorithms, the accuracy was improved by nearly 23.97%. The running time of our algorithm is reduced by 95.76% compared to the PointNet++ algorithm, which has the longest running time; and by 15.65% compared to the LCCP algorithm, which has the shortest running time among traditional methods. Compared with PointNet++, the segmentation accuracy was improved. This method addressed the issues of low robustness and excessive reliance on manual feature extraction in traditional point cloud segmentation methods, providing valuable support and reference for the accurate segmentation of 3D point clouds.

Improved the Sensitivity and Limit of Detection of Surface Alloying SERS Sensors by Controlling Mixing Ratio of Trimetallic (Ag-Au-Pd) Nanoparticles

In this study, three different types of hybrid structures SERS sensors made from different mixing ratio of trimetallic (Ag-Au-Pd) nanoparticles of [Au1: Ag1: Pd1] , [Au1: Ag2: Pd1] , [Au1: Ag2: Pd2] in surface alloying forms deposited on macro porous silicon (macroPsi) layer were created and extensively tested. By using a simple and fast immersion process, trimetallic (Ag-Au-Pd) nanoparticles were created utilizing ion reduction of numerous metallic salts on a macro porous silicon (macroPsi) layer. The macroPsi layers were created using a laser assisted etching (LAE) technique for 15 minutes with a constant density of approximately (28mA/c.m2). At a constant concentration (10-3 M) of HAu.Cl4, Ag.NO3 and Pd.Cl2 to synthesize Au-NPs, Ag-NPs and Pd-NPs, immersion processes with various mixing ratios were performed. The hybrid structures of SERS sensors were examined using XRD, FE-SEM, and Raman microscopes. The sensors were tested at different concentrations from 10-6 to 10-12 of MB target molecules. The SERS trimetallic sensors demonstrated a considerable reliance on hot spot zones among trimetallic nanoparticles. Higher enhancement factor with lower detection limit of Raman signal was obtained from [Au1: Ag2: Pd2] hybrid structures SERS sensor of about 1.5×1010 and 10-14 respectively, due to extra ordinary specific surface area and high surface density forming trimetallic nanoparticles . The variations of mixing ratio of trimetallic (Ag-Au-Pd) provide an effective pathway to develop the sensors performance towards detection of lower concentrations.

Observation impact explanation in atmospheric state estimation using hierarchical message-passing graph neural networks

Abstract The impact of meteorological observations on weather forecasting varies with the sensor type, location, time, and other environmental factors. Thus, the quantitative analysis of observation impacts is crucial for the effective and efficient development of weather forecasting systems. However, existing impact analysis methods are dependent on specific forecast systems, because system-specific adjoint models are used and the sensitivity of the observation to the forecast is measured. This study investigates the impact of observations on atmospheric state estimation in weather forecasting systems by developing a novel graph neural network (GNN) model specialized for analyzing the heterogeneous relations between observations and atmospheric states. The observation impact can then be assessed by applying explainable methods to the proposed GNN model, which is independent of forecasting systems. Further, we develop a novel application called `CloudNine,' a system that provides impact analysis for individual observations with visualization. Our GNN model comprises hierarchical message-passing modules that separately analyze spatial correlations between observations at close locations and atmospheric states at close locations and then examine correlations between observations and atmospheric states. To consider the different factors influencing these correlations, we utilized geo-coordinates and types of observations in the attention mechanism of the modules with their feature vectors. We then applied gradient-based explainability methods to quantify the significance of the different observations in the estimation. Evaluated using data from 11 satellites and land-based observations, the results highlight the effectiveness of the proposed model and the visualization of observation impacts, enhancing the understanding and optimization of observational data in weather forecasting.

Load spectra of a light unmanned aircraft – data recording frequencies and resulting measurement variations

The article focuses on the load spectrum of a lightweight unmanned aircraft. It examines the impact of load signal sampling frequency on the load spectrum derived from the load signal. The flight sessions followed two scenarios: a maneuvering flight causing a wide range of load factor variations and a calm photogrammetric flight. The recorded load signals from two types of sensors were initially rescaled into load factor time series, with an optional signal smoothing process. These series were then downsampled by selecting every nth term, resulting in load factor sequences recorded at a lower frequency. Next, all sequences were transformed into sequences of local extremes of the Load Levels, resulting from quantifying the operational load variability into 32 intervals. Two options were considered: without filtering or with filtering of sequential pairs of terms generating small Load Level increments. Subsequently, the Rainflow Counting algorithm was applied, producing 32x32 load half-cycle arrays. Based on these arrays, incremental load spectra were calculated. The study provides a detailed comparison of load spectra for different load signal sources and recording frequencies. Additionally, calculations of the fatigue life of a structural element subjected to various load spectra were performed, leading to the formulated conclusions.

Pattern Recognition in the Processing of Electromyographic Signals for Selected Expressions of Polish Sign Language.

Gesture recognition has become a significant part of human-machine interaction, particularly when verbal interaction is not feasible. The rapid development of biomedical sensing and machine learning algorithms, including electromyography (EMG) and convolutional neural networks (CNNs), has enabled the interpretation of sign languages, including the Polish Sign Language, based on EMG signals. The objective was to classify the game control gestures and Polish Sign Language gestures recorded specifically for this study using two different data acquisition systems: BIOPAC MP36 and MyoWare 2.0. We compared the classification performance of various machine learning algorithms, with a particular emphasis on CNNs on the dataset of EMG signals representing 24 gestures, recorded using both types of EMG sensors. The results (98.324% versus ≤7.8571% and 95.5307% versus ≤10.2697% of accuracy for CNNs and other classifiers in data recorded with BIOPAC MP36 and MyoWare, respectively) indicate that CNNs demonstrate superior accuracy. These results suggest the feasibility of using lower-cost sensors for effective gesture classification and the viability of integrating affordable EMG-based technologies into broader gesture recognition frameworks, providing a cost-effective solution for real-world applications. The dataset created during the study offers a basis for future studies on EMG-based recognition of Polish Sign Language.

Surface acoustic wave platform integrated with ultraviolet activated rGO-SnS2 nanocomposites to achieve ppb-level dimethyl methylphosphonate detection at room-temperature

Dimethyl methylphosphonate (DMMP) is commonly used as an alternative for demonstrating to detect sarin, which is one of the most toxic but odorless chemical nerve agents. Among various types of DMMP sensors, those utilizing surface acoustic wave (SAW) technology provide notable advantages such as wireless/passive monitoring, digital output, and a compact, portable design. However, key challenges for SAW-based DMMP sensors operated at room temperature lies in simultaneous enhancement of sensitivities and reduction of detection limits. In this study, we developed a binary material strategy by combining reduced graphene oxide (rGO) and tin disulfide (SnS2) with (100)-facets orientation as sensing layers of SAW device for DMMP detection utilized at room temperature. Ultraviolet (UV) light was applied to activate the sensitive film and reduce the sensor's response time. The developed SAW DMMP sensor demonstrated a superior sensitivity (−1298.82 Hz/ppm), a low detection limit of 50 ppb, and a hysteresis below 1.5%, along with fast response/recovery time (39.2 s/28.4 s), excellent selectivity, long-term stability and repeatability. The formation of shrub-like rGO-SnS2 heterojunctions with abundant surface defects and large specific surface areas, high-energy (100) crystalline surfaces of SnS2, and photogenerated carriers generated by UV irradiation were pinpointed as the crucial sensing mechanisms. These factors collectively enhanced adsorption and reaction dynamics of DMMP molecules.

Parameter Optimization of Semiconductor Gas Sensor under AC Impedance Measurement.

Semiconductor gas sensors were confirmed to perform high linearity and a stable baseline under alternating current (AC) impedance measurements. However, a procedure to determine the optimal parameters of AC impedance measurements is still lacking. Taking the detection of SF6 decomposition gas as an example, this work has established a model of semiconductor gas sensors under AC impedance measurement. Employing four types of sensors to detect three gases (H2S, SO2, and CO), the effectiveness of the optimization method has been validated, as well. With the high linearity and stable baseline obtained from AC impedance measurement, it enables rapid correction of temperature drift within environmental temperatures ranging from 10 to 30 °C. Overall, the proposed method can provide a novel approach to inhibit the drift failure of semiconductor gas sensors.

Influence of Soil Moisture on Soil Temperature of Harumanis Mango Trees Under Controlled Environment

Soil moisture and temperature plays an essential role in plant growth and development. They are the main environmental variables controlling soil respiration and photosynthesis. For Harumanis mango grown in a controlled environment, these two variables are important in regulating the overall condition to imitate the required condition, particularly during vegetative growth. In this study, we aimed to investigate the influence of soil moisture on soil temperature in the Harumanis mango tree greenhouse. Two types of sensors: soil moisture and soil temperature sensor, were installed at 15 cm depth to monitor the in-situ readings at three specific locations in the selected greenhouse. Soil moisture and soil temperature were monitored for 20 days continuously at 10-minutes intervals. The results of linear regression indicated that an increase in soil moisture decreases the soil temperature, although there is a variation in data regarding the sampling locations. This information could help growers plan the irrigation efficiently (i.e. water amount) to initiate preferable dry conditions to induce flowering of these mango trees

High sensitivity optical pressure sensor based on graphene/molybdenum disulfide composite film.

In this paper, a high sensitivity optical pressure sensor based on a graphene/molybdenum disulfide (MoS2) composite film is proposed. The sensor is composed of a polydimethylsiloxane (PDMS) pyramid structure, graphene/MoS2 composite film, and lithium niobate waveguide. The pressure deforms the PDMS pyramid structure, which leads to the change of the refractive index of the graphene/MoS2 composite film, and finally be detectable sensitively by the variation of the interference spectrum. Experiments have been carried out using our sensor prototype, and the sensitivity is up to 575.233 nm/kPa in the pressure range of 0 kPa-0.123 kPa, which is much higher than that of typical optical pressure sensors. This shows the advantages of high sensitivity optical pressure sensors based on the graphene/MoS2 composite film, which is expected to be applied in highly sensitive pressure detection environments.

Method of Detecting Liquid Meniscus in Capillary Using Ultraviolet Sensor

The accuracy of determining the physical and chemical properties of a liquid, in particular, assessing the viscosity of the liquid used, is considered an important technical problem in medicine, food, oil, chemical industries, as well as in other areas that determine the quality and safety of human life and activity. For example, assessing the viscosity of the liquid used is a key stage in the design of hydraulic systems. Today, the improvement of methods for determining the viscosity of a liquid is on the path to automation (digitalization) of measurement instruments and methods. In this area, the activities of metrologists and engineers are consistent with the national program «Digital Economy of the Russian Federation» adopted in 2017. The article discusses the capillary method for determining the kinematic viscosity of a liquid as the most accurate of all possible methods at present. The method is based on determining the time of liquid flow between two marks, which are applied to the walls of a glass capillary viscometer forming a measuring tank. One of the main problems of this method is that all stages of measurement are performed by a person, even recording the moment when the liquid meniscus crosses the mark, which is a violation of one of the key principles of process automation – independence of execution, which only implies monitoring the operation of the system. Since ready-made solutions do not fit the laboratory’s measurement tasks, the management decided to develop and conduct a study of an information and measuring system that will carry out the process of detecting the liquid meniscus at the mark level, as well as recalculate the measured time interval into the kinematic viscosity value. The key decision in this development is the choice of a sensor for detecting the liquid meniscus. Based on the literature review, it was decided to use a photoelectric sensor. A study of the transmission spectra of liquids used in this method was conducted to select the wavelength at which the sensor will operate. Thus, a type of photoelectric sensor designed to operate in the UV range has been selected to automate the method of detecting the liquid meniscus when it crosses a mark applied to a capillary tube. The article is addressed to metrologists who perform verification and calibration of glass capillary viscometers, specialists in the field of experimental and theoretical viscometry. The method proposed by the authors can become the basis for further improvement of the method for measuring the kinematic viscosity of a liquid.

Monitoring of Airborne Pollen: A Patent Review

Air pollution is recognized by the World Health Organization as the major environmental threat; therefore, air quality is constantly being monitored by monitoring stations. However, the most common atmospheric pollutants being monitored do not include pollen. Among the reasons for the lack of pollen control is that there are different types and sizes of pollen. The largest particles commonly being monitored by air monitoring stations have a maximum aerodynamic diameter of 10 microns, while the aerodynamic diameter of most pollen grains is known to range from 10 to 100 microns. For this reason, most pollen is not being detected by air monitoring stations. For the patents found in a literature review, monitoring pollen concentration in the air requires the discrimination of pollen grains from particulate matter of a similar size, as well as the identification of the type of pollen grains detected, since different pollen types may produce different effects, such as allergic reactions, asthma, and lung cancer, in exposed people. In this work, 15 patent documents regarding pollen monitoring were identified and reviewed using three search engines: Google Patents, WIPO’s PatentScope, and the United States Patent and Trademark Office (USPTO) database. The extracted data from the patents included whether they differentiate pollen type, pollen size, and sensor type and whether they provide real-time data. The results show that 93.33% of the patents identify pollen type, while 80% of the patents identify pollen size. Most of the patents use light-scattering and image sensors and use image processing techniques to analyze particles. Furthermore, 40% of the patents were found to implement artificial intelligence. Further, it was found that only nine patents provide real-time data, which is an important feature of an air monitoring system.

A Review on Photoacoustic Spectroscopy Techniques for Gas Sensing.

The rapid growth of industry and the global drive for modernization have led to an increase in gas emissions, which present significant environmental and health risks. As a result, there is a growing need for precise and sensitive gas-monitoring technologies. This review delves into the progress made regarding photoacoustic gas sensors, with a specific focus on the vital components of acoustic cells and acoustic detectors. This review highlights photoacoustic spectroscopy (PAS) as an optical detection technique, lauding its high sensitivity, selectivity, and capability to detect a wide range of gaseous species. The principles of photoacoustic gas sensors are outlined, emphasizing the use of modulated light absorption to generate heat and subsequently detect gas pressure as acoustic pressure. Additionally, this review provides an overview of recent advancements in photoacoustic gas sensor components while also discussing the applications, challenges, and limitations of these sensors. It also includes a comparative analysis of photoacoustic gas sensors and other types of gas sensors, along with potential future research directions and opportunities. The main aim of this review is to advance the understanding and development of photoacoustic gas detection technology.

Robust Optimization Models for Planning Drone Swarm Missions

This article presents methods of planning unmanned aerial vehicle (UAV) missions in which individual platforms work together during the reconnaissance of objects located within a terrain. The planning problem concerns determining the flight routes of a swarm, where each UAV has the ability to recognize an object using a specific type of sensor. The experiments described in this article were carried out for drone formation; one drone works as a swarm information hub and exchanges information with the ground control station (GCS). Numerical models for mission planning are presented, which take into account the important constraints, simplifying the description of the mission without too much risk of losing the platforms. Several types of objective functions were used to optimize swarm flight paths. The mission models are presented in the form of mixed integer linear programming problems (MILPs). The experiments were carried out on a terrain model built on the basis of graph and network theory. The method of building a network on which the route plan of a drone swarm is determined is precisely presented. Particular attention was paid to the description of ways to minimize the size of the network on which the swarm mission is planned. The presented methods for building a terrain model allow for solving the optimization problem using integer programming tasks.