Modern Sensors Research Articles

Accelerating leaf area measurement using a volumetric approach

BackgroundDespite the advances in the techniques of indirect estimation of leaf area, the destructive measurement approaches have still remained as the reference and the most accurate methods. However, even utilizing the modern sensors and applications usually requires the laborious and time-consuming practice of unfolding and analyzing the single leaves, separately. In the present study, a volumetric approach was tested to determine the pile leaf area based on the ratio of leaf volume divided by thickness. For this purpose, the suspension technique was used for volumetry, which is based on the simple practice and calculations of the Archimedes’ principle.ResultsWheat volumetric leaf area (VLA), had a high agreement and approximately 1:1 correlation with the conventionally measured optical leaf area (OLA). Exclusion of the midrib volume from calculations, did not affect the estimation error (NRMSE < 2.61%); however, improved the slope of the linear model by about 6%, and also reduced the bias between the methods. The error of sampling for determining mean leaf thickness of the pile, was also less than 2% throughout the season. Besides, a more practical and facilitated version of pile volumetry was tested using Specific Gravity Bench (SGB), which is currently available as a laboratory equipment. As an important observation, which was also expectable according to the leaf 3D expansion (i.e., in a given 2D plane), it was evidenced that the variations in the OLA exactly follows the pattern of the changes in the leaf volume. Accordingly, it was suggested that the relative leaf areas of various experimental treatments might be compared directly based on volume, and independently of leaf thickness. Furthermore, no considerable difference was observed among the OLAs measured using various image resolutions (NRMSE < 0.212%); which indicates that even the superfast scanners with low resolutions as 200 dpi may be used for a precision optical measurement of leaf area.ConclusionsIt is expected that utilizing the reliable and simple concept of volumetric leaf area, based on which the measurement time might be independent of sample size, facilitate the laborious practice of leaf area measurement; and consequently, improve the precision of field experiments.

A Sensor-Driven Visit Detection System in Older Adults' Homes: Towards Digital Late-Life Depression Marker Extraction.

Modern sensor technology is increasingly used in older adults to not only provide additional safety but also to monitor health status, often by means of sensor derived digital measures or biomarkers. Social isolation is a known risk factor for late-life depression, and a potential component of social-isolation is the lack of home visits. Therefore, home visits may serve as a digital measure for social isolation and late-life depression. Late-life depression is a common mental and emotional disorder in the growing population of older adults. The disorder, if untreated, can significantly decrease quality of life and, amongst other effects, leads to increased mortality. Late-life depression often goes undiagnosed due to associated stigma and the incorrect assumption that it is a normal part of ageing. In this work, we propose a visit detection system that generalizes well to previously unseen apartments - which may differ largely in layout, sensor placement, and size from apartments found in the semi-annotated training dataset. We find that by using a self-training-based domain adaptation strategy, a robust system to extract home visit information can be built (ROC AUC = 0.773). We further show that the resulting visit information correlates well with the common geriatric depression scale screening tool ( ρ = -0.87, p = 0.001), providing further support for the idea of utilizing the extracted information as a potential digital measure or even as a digital biomarker to monitor the risk of late-life depression.

State‐of‐the‐art electrochemistry for the assessment of oxidative stress and integral antioxidant activity of biological environments

AbstractThe present review focuses on the interplay between electrochemistry and life, events on the border of electrochemistry‐biology‐life science, electrochemistry as the basis, and the information source on oxidative stress (OS) or Red/Ox state of biological systems and food to be investigated. Electroanalytical chemistry provides rapid, relatively simple, and sensitive approaches to assess the redox characteristics and antioxidant activity of biologically active compounds in various samples.OS is a relatively new physiological response concept, recognized in medicine and biology in the last three decades. This phenomenon is caused by an imbalance between (pro)oxidants and antioxidants in living organisms and it is related to the fundamental redox reactions that underlie health signaling and life processes in general. OS can contribute to many pathological conditions and diseases. In particular, it is recognized that a highly contagious infectious disease, coronavirus disease 2019, is associated with an inflammation process related to OS‐induced cellular changes. Recent years have shown a marked increase in electrochemical studies of OS and quantitation of its reductant‐oxidant markers (signaling agents), such as reactive oxygen species and antioxidants.The goal of this overview is to cover the brief scope of modern electrochemical analysis and sensor devices for monitoring biomarkers of OS and antioxidant status of biological systems. By discussing the great potential of potentiometric and voltammetric methods for human health assessment, it is hoped to bridge between recent electrochemical research and medical diagnostic treatment in the 21st century.

Advances in Optical Sensors for Persistent Organic Pollutant Environmental Monitoring.

Optical chemical sensors are widely applied in many fields of modern analytical practice, due to their simplicity in preparation and signal acquisition, low costs, and fast response time. Moreover, the construction of most modern optical sensors requires neither wire connections with the detector nor sophisticated and energy-consuming hardware, enabling wireless sensor development for a fast, in-field and online analysis. In this review, the last five years of progress (from 2017 to 2021) in the field of optical chemical sensors development for persistent organic pollutants (POPs) is provided. The operating mechanisms, the transduction principles and the types of sensing materials employed in single selective optical sensors and in multisensory systems are reviewed. The selected examples of optical sensors applications are reported to demonstrate the benefits and drawbacks of optical chemical sensor use for POPs assessment.

Novel MAGFinFET: Operation, Design and Geometry Effect for Modern Sensors

This paper presents a new magnetic detection device, MAGFinFET, which is based on the advanced 3D FinFET structure. It can measure the vertical magnetic field by designing two contacts on both sides of the drain. The operation uses the principle of the current mode of Hall effect causing the deflection of the drain currents at both contacts. The 3D geometry effect was studied: channel length, fin height and fin width. It can be seen that when the values of these parameters are increased, the differential currents and relative sensitivities are increased linearly. Relative sensitivity has the highest value 0.00201 T−1-1 at channel length 50 nm. Fin height and fin width of 50 nm give the highest sensitivity of 0.00468 T−1-1 and 0.00415 T−1-1 respectively. Current density distributions of the different variations of each parameter Lgg, Fhh and Fww are observed by applying vertical magnetic field on the device. The 3D-MAGFinFET has been compared to that of the 2D non-split drain MAGFET structure and bulk fin resistor that use n-type semiconductor instead of induction channel. MAGFinFET shows quite higher sensitivity compared to bulk fin resistor. MAGFinFET mechanism models and simple characteristic equations are proposed in this work. Sentaurus TCAD is used for the device structure and simulation for the characteristics of MAGFinFET. This FinFET based device can be fabricated with modern integrated circuit technology.

A loss function to evaluate agricultural decision-making under uncertainty: a case study of soil spectroscopy

Modern sensor technologies can provide detailed information about soil variation which allows for more precise application of fertiliser to minimise environmental harm imposed by agriculture. However, growers should lose neither income nor yield from associated uncertainties of predicted nutrient concentrations and thus one must acknowledge and account for uncertainties. A framework is presented that accounts for the uncertainty and determines the cost–benefit of data on available phosphorus (P) and potassium (K) in the soil determined from sensors. For four fields, the uncertainty associated with variation in soil P and K predicted from sensors was determined. Using published fertiliser dose–yield response curves for a horticultural crop the effect of estimation errors from sensor data on expected financial losses was quantified. The expected losses from optimal precise application were compared with the losses expected from uniform fertiliser application (equivalent to little or no knowledge on soil variation). The asymmetry of the loss function meant that underestimation of P and K generally led to greater losses than the losses from overestimation. This study shows that substantial financial gains can be obtained from sensor-based precise application of P and K fertiliser, with savings of up to £121 ha−1 for P and up to £81 ha−1 for K, with concurrent environmental benefits due to a reduction of 4–17 kg ha−1 applied P fertiliser when compared with uniform application.

Smart wireless health care system using graph LSTM pollution prediction and dragonfly node localization

Wireless sensing networks (WSNs) have been applied on various research applications such as monitoring health of humans, targets tracking, natural resources investigation, air quality prediction, water pollution prediction and radiation pollution. The challenge on predicting these applications still exists. Suitable monitoring systems are necessary, to maintain the healthy society with sustainable growth. With the advancement of Internet of Things and modern sensors, the environmental monitoring systems have become smart monitoring system. These wireless sensors are scattered around the environmental locations and places. The localization of sensor placement at the correct place will reduce the redundancy of the sensing environment and cost of the equipment. More nodes are placed at the area that has more pollutant. Accurate node sensor placing on the needed area will reduce the cost of sensors and increase the prediction accuracy. This helps to keep our health safe by selecting less polluted environment. Hence, this article focuses on introducing the deep learning algorithm called Graph Long Short-Term Memory (GLSTM) neural network to predict the air quality characteristics. Next, the evolutionary algorithm called Dragon fly optimizer has been used to localize the node based on the prediction. Deep evolutionary based algorithms will improve the air pollutant prediction and node localization sensor cost.

Radar-Based Robust People Tracking and Consumer Applications

Robust people tracking in an indoor or outdoor environment is an important feature for fitness applications, surveillance systems for security, and assisted living for the elderly. Camera-based systems offer the state of the art approach but raise privacy concerns and consume a significant amount of power for the sensor and the processing. On the other hand, modern millimeter-wave Radar sensors provide a low-cost and low-power alternative with very good resolution in range, radial velocity and angle of arrival. In this work, we use two popular algorithms, for tracking people using Radar, and compare them with our novel method. We show that our method is robust in certain corner cases and is suitable for scenarios with many users in close proximity. In addition, we present two consumer applications that are possible with a Radar system, multi-user macro gesture recognition and hand tracking. We believe that the combination of above methods will open new gateways for consumer applications.

Wideband Radomes for Millimeter-Wave Automotive Radars

We demonstrate the efficacy of periodic corrugations, fabricated as part of the radar cover to serve the dual purpose of protecting the radar circuitry and maximizing transmission in millimeter-wave automotive radars. A 2-dimensional array of square-base pyramids engraved on the plastic radar cover was optimized to achieve minimum reflection for a wide range of incident angles, ± 40° and across the operating frequency band of 76 to 81 GHz. More importantly, we present an alternative radome texture, namely the inverse-pyramid corrugations that results in improved structural integrity as compared to the pyramidal corrugations. This novel inverse-pyramid radome structure is also much easier to manufacture and achieves similar performance as the more conventional pyramid structure. Both designs are shown to significantly reduce radar cover reflections, which is perhaps the most significant bottleneck in modern millimeter-wave automotive radar sensor performance.

Modern Sensor Tools and Techniques for Monitoring, Controlling, and Improving Cell Culture Processes

The growing biopharmaceutical industry has reached a level of maturity that allows for the monitoring of numerous key variables for both process characterization and outcome predictions. Sensors were historically used in order to maintain an optimal environment within the reactor to optimize process performance. However, technological innovation has pushed towards on-line in situ continuous monitoring of quality attributes that could previously only be estimated off-line. These new sensing technologies when coupled with software models have shown promise for unique fingerprinting, smart process control, outcome improvement, and prediction. All this can be done without requiring invasive sampling or intervention on the system. In this paper, the state-of-the-art sensing technologies and their applications in the context of cell culture monitoring are reviewed with emphasis on the coming push towards industry 4.0 and smart manufacturing within the biopharmaceutical sector. Additionally, perspectives as to how this can be leveraged to improve both understanding and outcomes of cell culture processes are discussed.

The new pbl_met: an open-source library for building meteorological processors and advanced data processing tools.

This paper presents the pbl_met, an open-source library for processing meteorological data collected in the planetary boundary layer (PBL) using conventional and modern sensors (examples of the latter include three-axial ultrasonic anemometers, SODAR/RASS, and wind-sensing LIDARs). The routines present in the pbl_met encompass the estimation of meteorological quantities (including global and net radiation and turbulence), the processing of ultrasonic anemometer raw data streams, the approximation of mixing height, and various facets of meteorological data management, summarization, and cleaning. The existence in pbl_met of estimation routines of diverse types allows to construct estimation paths of different depths, permitting the crafting of meteorological processors tailored to the data available. The overall quality of results obtained using estimation paths of differing depths has been investigated in a test case. Important differences have been observed, especially in mixing height, illustrating in the same time the wide range of processing options permitted by the pbl_met and the importance to select the best ones according to the data actually available; as some of the options allow the direct use of data from modern sensors, the pbl_met can be used as a platform for building next-generation meteorological processors for dispersion modeling applications.Supplementary InformationThe online version contains supplementary material available at 10.1007/s42865-021-00042-9.

STUDY OF THE METROLOGICAL CHARACTERISTICS OF CONDUCTIVITY SENSORS

Conductivity measurement is a universal method of process control. Measurement is fast and straightforward, and most modern sensors only require little maintenance. The measured conductivity value is applied to obtain different assumptions about what happens in the substance, so such measurements are relevant when controlling technological processes and products in various industries (e.g., food, pharmaceutical). The main metrological characteristics of sensors for measuring conductivity LDL100, LDL200 are analyzed in the article. Studies have been carried out for various objects: non-carbonated mineral water, fruit juice, and tap water.

Radio Frequency Precession Modulation-Based Magnetic Field Sensors

High sensitivity is often the foremost characteristic for magnetic field sensors; however, the most sensitive systems tend to be attributed with being any combination of large in size, high in power consumption, complex in design, or exorbitant in cost. This fact not only limits accessibility to the technology afforded by high sensitivity sensing, but it also restricts the extent to which potential applications of magnetic field sensing may be realized. Herein we propose a concept for sensor operation that can achieve sensitivities competitive with those of modern magnetic field sensors while simultaneously maintaining small size, low power consumption, simplicity in design, and low cost. This is accomplished through employment of the nonlinear precession dynamics of electron spins to attain parametric amplification of a magnetic field. A preliminary experimental implementation of the proposed concept establishes its feasibility and is already able to demonstrate benefits over existing approaches to sensing. The implementation exhibits a sensitivity of 23.2 pT/Hz^1/2 with a volume of 0.0564 mm³ and a power consumption of &#x2212;40.96 dBm.

О методах сбора и анализа основных характеристик транспортного потока

The ever-increasing motorization leads to the need to solve problems related to the optimal movement of transport, ensuring its safety and improving the environmental situation. According to Rosstat statistics, the number of own cars in the constituent entities of the Russian Federation (per 1,000 people) is increasing every year. The issues of optimal control of the movement of vehicles remain topical. Modeling of traffic flows allows you to determine the "bottlenecks" of the existing road network. In most situations, the best solution to the above problems is to improve the existing transport system. This approach is quite expensive. In this regard, studies related to the development and application of modern and promising technologies in the creation of intelligent transport systems of various levels and purposes are relevant both today and in the future. The collection and analysis of data on traffic flows allows you to verify the models, as well as make decisions about the further management of the movement of vehicles. With the development of computer technology and information technology, methods for accumulating and processing information about traffic flows have been improved. Today it becomes possible to receive, process data and make decisions in real time. This study is devoted to an overview of existing methods for monitoring and collecting characteristics about the state of traffic flows. A review of modern technologies and sensors used to collect traffic information is made. The method for collecting data on road users is described. An algorithm for classifying vehicles depending on the brand of the vehicle has been developed. The description of the vehicle tracking algorithm is given. The method for calculating the main characteristics of the traffic flow (speed, density, intensity) based on data from the video sequence is described.

Spatial Complexity Reduction in Remote Sensing Image Compression by Atomic Functions

Remote sensing (RS) digital images have a great variety of applications in solving real-world problems. Modern sensors provide this type of data of a very high resolution, which, in combination with a great number of acquired images, makes a problem of compressing RS-images of particular importance. In this letter, discrete atomic compression (DAC) and a problem of its spatial complexity reduction are considered. This approach provides data compression and protection features in combination with such image representation that is ready for applying different artificial intelligence methods. For this reason, its application to image processing is relevant. Several modifications that provide reducing the spatial complexity of DAC are proposed, and their efficiency is analyzed. In particular, it is shown that, using a block splitting procedure, it is possible to get a significant decrease in additional memory expenses without DAC’s efficiency degradation in terms of lossy image compression.

Applicability and Performance of Standard Compression Methods for Efficient Data Transmission and Storage in Radar Networks

Modern sensor networks—those used for autonomous driving, security systems, human motion tracking, or smart city/smart factory applications—are shifting to a more centralized data processing approach to enable efficient multimodal sensor fusion for optimal environment perception in complex dynamic situations. Among lidars and cameras, radars are typical for these applications, but they generate huge amounts of data, which cannot be transmitted or stored effectively in current setups. Consequently, manufacturers usually have to process the data “on sensor.” This results in transmitting only a few extracted features as point clouds or object lists to a central processing unit, which usually causes a significant loss of information. With this approach, advanced processing—such as enhancement of resolution by coherent combination of sensors or ghost target removal with advanced algorithms—is hardly possible. To overcome this, we suggest an alternative method by using signal-based compression with defined losses. The following topology will be proposed: the sensors encode raw data without prior radar-specific processing and after transmission, a central unit decodes and processes the radar data, thus benefiting from its more powerful heterogeneous processing system. We will analyze lossless compression algorithms with rate savings of about 30% to 65%, but the focus is on lossy compression algorithms that incorporate higher compression ratios by allowing negligible errors. It is shown that state-of-the-art multimedia compression algorithms can obtain rate savings of 99%, and radar specific algorithms can add a 50-fold gain on top, reaching 99.98%. To assess the distortions of compressed data, we then present different radar-specific evaluation metrics.

Research on Robot Path Perception and Optimization Technology Based on Whale Optimization Algorithm

With the development of modern sensor technology, the automatic movement of robot has become a reality, and improving the path planning performance of robot in dynamic and complex environment is an important development direction of mobile robot intelligence. This time, based on the idea of hybrid path planning, whale optimization algorithm and computer perception technology are introduced to plan the path of the robot. In order to improve the performance of the traditional whale optimization algorithm, it is optimized and improved by genetic algorithm. Through the performance simulation analysis of the improved whale optimization algorithm, it can be seen that the improved algorithm has better path optimization performance and significantly improved efficiency compared with other traditional algorithms. It can perceive and calculate an optimized path.

Nanoarchitectonics for Abused‐Drug Biosensors

AbstractRapid, accessible, and highly accurate biosensors for the detection of addictive and abused drugs are needed to reduce the adverse personal and societal impacts of addiction. Modern sensors that utilize next‐generation technologies, e.g., nanobiotechnology and nanoarchitectonics, have triggered revolutionary progress in the field as they allow accurate detection and tracking of trace levels of major classes of drugs. This paper reviews advances in the field of biosensors for the detection of commonly abused drugs, both prescribed such as codeine and morphine, and illegal narcotics like cocaine.

Інформаційна технологія забезпечення живучості сенсорних мереж

In the modern technological world, sensors and sensor networks are widely used in the all spheres of human activity. One of the key engineering tasks in the design of sensor networks is to ensure the survivability of the network. The article presents algorithms for ensuring the survivability of sensor networks based on a preliminary assessment of this property. Estimation of survivability depends on the network topology. Increasing the survivability of the sensor network is achieved by including additional sensors in the network. An algorithm for finding the position of additional sensors in the stationary sensor network is proposed. Proposed approach takes into account the radius of coverage of the sensors and the need for the intersection of the coverage areas of the sensors to ensure the exchange of information. An iterative algorithm for ensuring the survivability of the network in the presence and consideration of dynamic sensors is presented. The results of computer simulation experiments presented in the paper confirm the effectiveness of the proposed approaches.

Progress of Wearable and Flexible Electrochemical Biosensors With the Aid of Conductive Nanomaterials.

Conductive nanomaterials have recently gained a lot of interest due to their excellent physical, chemical, and electrical properties, as well as their numerous nanoscale morphologies, which enable them to be fabricated into a wide range of modern chemical and biological sensors. This study focuses mainly on current applications based on conductive nanostructured materials. They are the key elements in preparing wearable electrochemical Biosensors, including electrochemical immunosensors and DNA biosensors. Conductive nanomaterials such as carbon (Carbon Nanotubes, Graphene), metals and conductive polymers, which provide a large effective surface area, fast electron transfer rate and high electrical conductivity, are summarized in detail. Conductive polymer nanocomposites in combination with carbon and metal nanoparticles have also been addressed to increase sensor performance. In conclusion, a section on current challenges and opportunities in this growing field is forecasted at the end.