Detected Research Articles

A non-local based microcrack segmentation model optimized for effective high resolution and low-power devices

Concrete is notable for its strength, durability, and versatility, making it a fundamental material in construction. Nevertheless, microcracks occur in concrete structures due to various factors, and the size of these cracks gradually widens over time, which can lead to spalling. Furthermore, the advent of climate change accelerates concrete degradation, posing significant challenges to structural maintenance. Recently, deep learning-based computer vision techniques, especially convolutional neural networks, have shown promising results in detecting and segmenting cracks in concrete walls. In this study, we propose a two-step training strategy for a microcrack segmentation model optimized for high resolution and low-power devices. The first step is to develop a segmentation model that can detect microcracks through high-resolution images. This strategy includes generating a high-quality CrackHQ dataset using super-resolution and a Transformer-based model training process named CegFormer. The second step involves converting the trained model to a lightweight version, increasing inference speed and making it operable on low-power devices. While performing lightweight, the model performance inevitably is degraded. We restored the degraded crack recognition performance by applying knowledge distillation. The experimental results of comparing baseline and CegFormer showed a 12.82% improvement in microcrack recognition performance. The detector optimized for use in actual fields improved recognition performance by up to 5.16% and reduced inference speed by 51.52%. The utilization of the two-step training strategy enabled the optical sensors to accurately detect microcracks even from far fields. Future work will focus on developing crack detectors that take into account both indoor and outdoor environmental conditions, as well as different texture of surface materials, for application in building inspection.

Geometric vs spectral content of Remotely Piloted Aircraft Systems images in the Precision agriculture context

In the last years the agricultural sector has been evolving and new technologies, like Unmanned Aerial Vehicles (UAV) and satellites, were introduced to increase crop management efficiency, reducing environmental costs and improving farmers’ income. MAIA-S2 sensor is presently one of the most performing optical sensors operating on a Remotely Piloted Aircraft Systems (RPAS); given its spectral features, it aims at supporting a scaling process where monoscopic satellite data (namely Copernicus S2) with high temporal and limited geometric resolution can be integrated with stereoscopic data from RPAS having a very high spatial resolution. In this work, data from MAIA-S2 sensor were used to detect the effects of different fertilization types on corn with reference to a test field located in Carignano (Piemonte region, NW-Italy). Different amounts of top dressing fertilization were applied on corn and an RPAS acquisition operated on 14th June 2021 (corresponding date to the corn stem elongation stage) to explore if any effects could be detectable. Three spectral indices, namely Normalized Difference Vegetation Index, Normalized Difference Red Edge index and Canopy Height Model, computed from at-the-ground reflectance calibrated MAIA-S2 data, were compared to evaluate the correspondent response to the different fertilization rates. Results show that: (i) NDVI poorly detect N-related differences zones; (ii) NDRE and CHM reasonably reflect the different N fertilization doses; (iii) Only CHM proved to be able to detect crop height and, consequently, biomass differences that are known to be induced by different rates of fertilization.

Internet of Thing (IOT) Based Sensor Technologies and Smart Irrigation System: An Analysis of Critical Success Factors in Emerging Markets

ABSTRACT: This study explores how smart irrigation systems can be improved by introducing advanced technologies and reducing water consumption to fight climate change. This study aims to identify the factors affecting the success of smart irrigation systems based on the Internet of Things with the mediation of optimal consumption management. This is an applied research employing mixed methodologies (qualitative-quantitative). Initially, the semi-structured interviews were carried out to obtain the effective success factors of the smart irrigation system. Analysis in the qualitative section has been conducted using the Maxqda software. For the quantitative study, a 25-statement questionnaire was applied according to the selected codes obtained with a five-point Likert scale to validate the obtained model. The questionnaire was distributed online in January 2023, and analysis was conducted using SmartPLS3 software. The technical aspects of the questionnaire were assessed using validity and reliability criteria, which can guarantee the precision of the research findings. Construct and content validity were employed in this study to examine the questionnaire’s validity. The researchers employed Cronbach’s alpha coefficient to evaluate the reliability of the measurement instrument. Besides, it can be said that the research instrument has acceptable reliability because obtained Cronbach’s alpha for all variables was greater than 0.70 and the questionnaire’s overall alpha was determined to be 0.810. Furthermore, the t-statistics for all research hypotheses is higher than 1.96 at the 95% confidence level. In this sense, the hypotheses of the research were confirmed. The Internet of Things and will change the nature of activities and services in the market. In fact, for emerging markets, the untapped and growing investment opportunities and the economic leap against the Internet of Things are so enticing and facilitating that it is impossible to resist. and neglecting it will cause irreparable damage to agility. Therefore, the success of the Internet of Things for emerging markets depends on the preconditions of integration of activities carried out by decision-making organizations, facilitation of regulations, and communication and interaction between relevant sectors, which should be considered a priority through cross-sectoral research.

Precision Fertilization: A critical review analysis on sensing technologies for nitrogen, phosphorous and potassium quantification

Fertilization is paramount for agriculture productivity and food security. Plant nutrition pre-established recipes and nutrient uptake are rarely managed by changing the fertilizer composition at the different stages of the plant life cycle. Herein we perform a literature review analysis – since the year 2000 and onwards – of the state-of-the-art capabilities of Nitrogen, Phosphorous, and Potassium (NPK) sensors for liquid fertilizers (e.g., hydroponics). From the initial search hits of 1660 results, only 53 publications had relevant information for this topic; from these, only 9 had NPK quantitative information. Qualitative analysis was performed by determining the number of publications for each nutrient, according to sample complexity and existing single, multiplexed or hybrid technologies. Quantitative assessment was performed by extracting the bias and linearity, the limit of detection and concentration ranges of sensor operation, framed into the context of the sensor technology development stage and sample compositional complexity. The most common technologies are colorimetry, ion-selective electrodes, optrodes, chemosensors, and optical spectroscopy. The most abundant technologies are for nitrate quantification, from which ion-selective electrodes are the most widely used technology, and sensors for phosphate quantification are the less developed. Most are at low technological levels of development, not dealing with the complexity of agriculture samples due to matrix effects and interference. Measuring the fertilizer composition, nutrient uptake, the state of the chemical network, and controlling the release of nutrients using new functional materials, is one of the most important challenges ahead for the existence of precision fertilization. Intelligent sensing and smart materials are today the most successful strategy for dealing with matrix effects and interferences, being led by ion-selective electrodes and spectroscopy technologies.

Italian-cluster technical solutions for the Quality-Control tests to the pixel-modules of the ATLAS Inner Tracker for High Luminosity LHC

In the era of high-luminosity LHC the instantaneous luminosity will achieve unprecedented levels, leading to the occurrence of up to 200 proton–proton interactions during a typical bunch crossing. In response to the resulting surge in occupancy, bandwidth demand, and radiation damage, the ATLAS Inner Detector is slated for replacement by an all-silicon system known as the Inner Tracker (ITk). The innermost segment of the ITk will be comprised of a pixel detector, featuring an active area spanning approximately 13 square meters. To address evolving requirements related to radiation hardness, power dissipation, and production yield, multiple silicon sensor technologies will be incorporated across the five barrel and end-cap layers. The ITk detector will be built in different laboratories all around the world. Several institutes are actively involved in the ITk project assembling and testing pre-production modules, that have been constructed to assess their production efficiency. Testing sites perform the so-called quality control (QC) tests. The ITk community defines requirements for QC testing both prototypes and modules, and provides common software tools as well as possible technical solutions. Each laboratory must qualify for the QC activity by demonstrating the chosen solutions are compliant with requirements. In this contribution the relevance of the QC procedures will be outlined, focusing on the custom structures and items developed by the Italian laboratories to improve the quality of the tests and to satisfy the requirements imposed by the Collaboration.

Advanced Optical‐Thermal Integrated Flexible Tactile Sensor for High‐Fine Recognition of Liquid Property in Non‐Contact Mode

AbstractFlexible sensors have been applied to accurately identify the basic features of various solid objects. However, a significant obstacle is the accurate identification of liquids, due to it is impossible to exert force directly on those. Here, an optical‐thermal flexible tactile sensor is designed for liquid recognition, which integrates PEDOT:PSS/SrTiO3 thermoelectric foam and ZnS‐CaZnOS mechanoluminescent (ML) film coated with thermoplastic polyurethane (TPU), enabling the simultaneous detection of thermal and optical changes with low interference, via its exceptional mechanical stimuli‐caused light and superior thermoelectric voltage, respectively. In addition, the TPU coating imparts the device with excellent hydrophobic and oleophobic properties, enhancing its durability in complex conditions. This flexible sensor, possessing high stability under thermal and mechanical cycles, can distinguish different pure solvents and turbid suspensions, achieving a high‐fine recognition accuracy of 95% and 97.5%, respectively, with the aid of the fusion algorithm of multimodal sensory data based on the thermal and optical outputs. This capability of the device can underscore the practical utility of humanoid robots to identify hazardous liquids in some scenarios like safety inspection and kitchen condiments, which addresses the critical gaps in current sensory technologies by providing a robust, sensitive, and multifunctional strategy for accurate liquid recognition.

IoT Wearable Machine Devices Based on Optical Sensors and Wireless Networks Application in Community Fitness Data Analysis

IoT Wearable Machine Devices Based on Optical Sensors and Wireless Networks Application in Community Fitness Data Analysis

The Fundamental of Fiber Bragg Grating (FBG) Sensor

An optical sensor known as an FBG is produced by laterally exposing a single mode fiber core to a strong UV laser light pattern on a regular basis. The exposure causes a sustained increase in the refractive index (ncore) of the fiber's core, resulting in fixed index modulation called grating (λ). The grating inside the fiber optic core must reflect a certain wavelength of incoming light while transmitting all other wavelengths. This wavelength is referred to as the Bragg wavelength or Bragg linked to grating period. FBG functions as intended when a Bragg reflector is built on an optical fiber by taking use of the regular fluctuations in the refractive index of the single mode fiber core. When light travels through the FBG, certain wavelengths will be transmitted and others will be reflected. When the strain or temperature around the grating varies, a shift in the wavelength of the light reflected is observed.

Deep Convolutional Neural Network Algorithm Based on Optical Sensors and Wireless Mobile Networks for Real time Monitoring of Physical Health

Deep Convolutional Neural Network Algorithm Based on Optical Sensors and Wireless Mobile Networks for Real time Monitoring of Physical Health

Application of Wireless Network Data Collection Based on Optical Topology Sensors in Sports Technology Evaluation

Application of Wireless Network Data Collection Based on Optical Topology Sensors in Sports Technology Evaluation

Color tunable Er-Eu co-doped double perovskite La2MgSnO6 phosphor for optical temperature sensor

Color tunable Er-Eu co-doped double perovskite La2MgSnO6 phosphor for optical temperature sensor

Polymer enhanced sensitivity and figure of merit SPR optical fiber sensor for high refractive index and wide detection range

A highly sensitive sensor based on surface plasmon resonance (SPR) for high refractive index (RI) sensing is proposed, whose most unique feature lies in a high RI of polyvinylpyrrolidone (PVP) layer embedded between the optical fiber and Au film. The use of the high RI PVP layer amplifies the evanescent wave, thereby increasing sensitivity and improving figure of merit (FOM) of the SPR sensor. In addition, SPR sensor coated with a high RI PVP film breaks through the limitation that the inherent RI of optical fiber can only detect low RI, and can measure higher RI values and expand the measurement range. Experimental results demonstrate that, with a PVP layer thickness of 60 nm, showcasing superior performance compared to sensors lacking a PVP layer. Notably, this SPR sensor offers a cost-effective and straightforward fabrication process, promising broad application in environmental monitoring and biosensing.

Optical Fiber Bragg Grating as a Strain Sensor

Abstract: The sensor technology industry has come to recognize and value the optical fibre sensor (OFS). It is frequently used to detect changes in the environment and to respond to outputs from other systems, such as those in industrial, monitoring, and chemical analysis applications. While a brief segment of optical fiber with a particular wavelength is reflected with light in a device known as a Fibre Bragg Grating, a Bragg reflector begins to grow and transmit all other wavelengths. Through software modeling, the current study focuses on the changing traits and behaviors of temperature and strain sensors that are used with fiber bragg gratings (FBG). The main objective of this work is to study the properties and behavior of strain sensors utilized in conjunction with fiber bragg gratings. A strain sensor can be used to determine the strain in an object whose resistance changes when force is applied

Optical Fiber Bragg Grating As A Temperature Sensor

The optical fibre sensor (OFS) has become quite well-known and prominent in the sensor technology industry. It is often used to react to outputs from other systems, like those in industrial, monitoring, and chemical analysis applications, and to detect changes in the environment. A Fibre Bragg Grating (FBG) is a device that allows light to be reflected from a short section of optical fiber at a specific wavelength, while the Bragg reflector expands and transmits all other wavelengths. The current effort focuses on the evolving characteristics and behaviors of strain and temperature sensors operating on fiber bragg gratings through computer modeling. The temperature sensor is employed to detect and quantify any variations in temperature on the Fiber Bragg Grating that could result in accidents or fires. This will show how the Fibre Bragg Grating may be used to showcase temperature sensors.

Multi‐Functional Metasurfaces as a Platform to Realize Ultra‐Compact Confocal Instrumentation for on‐Machine Metrology

AbstractAs manufacturing looks to employ more smart and autonomous processes to improve how items are made, reducing scrappage rates and with it the associated waste of time and energy, new ultra‐compact sensors are needed that can be deployed where existing instrumentation cannot. The use of traditional methods when constructing optical sensors limits the progress that can be made in reducing their size and weight, however, emerging technologies such as metasurfaces offer a platform by which these barriers can be overcome to develop the sensors needed to underpin this manufacturing transition. Here it is demonstrated that how a single metasurface can be used to deliver all the optical manipulations required to create a metasurface‐based confocal sensor with only the addition of a point source and point detector. By combining the optical functionality of both the illumination and the collection optics in this way the system is simplified and reduced in size significantly. While here how a metasurface can be used to reduce the number of elements needed to produce an ultra‐compact confocal sensor is demonstrated, this approach can be used to simplify a far wider range of instrumentation to greatly reduce their size and weight.

Design of locally cooled step-less fan for industrial use using Arduino programming

Under the background of the development of domestic and foreign manufacturing industries and the increasing demand for industrial products at home and abroad, and under the premise of ensuring and even further reducing the manufacturing cost of manufacturers, how to effectively detect and cool the local temperature of industrial parts is a problem worth studying. After analyzing the advantages and disadvantages of traditional manual control and gear adjustment to adjust the fan, the design will be programmed using Arduino. The fan combines infrared sensor technology to detect the presence and position of parts. A temperature sensor is also set up to detect local temperatures in real-time. A fan with automatic step-less speed change according to temperature is obtained. This fan is used for heat dissipation control of local parts in industrial production, thereby reducing the cost of manual control. It further improves control accuracy and efficiency, improves product quality, and ensures production safety.

A rapid response optical fiber sensor with highly selective probe for trace detection of mercury (II) ions

A highly sensitive, selective, and well-repeatable optical fiber sensor was proposed for trace measurement of Hg2+ concentration in the water environment based on Mach-Zehnder interference. The proposed structure was prepared by splicing a piece of thin core fiber (TCF) between multimode fibers (MMF) to form MMF-TCF-MMF. A novel sensitive film 3-mercaptopropyl-trimethoxysilane (MPTMS) modified polyvinyl alcohol (PVA) has been designed and synthesized, and uniformly coated on the surface of TCF. The specific combination of Hg2+ with PVA-MPTMS sensitive film can change the effective refractive index of the coating mode, enabling the measurement of Hg2+ concentration by tracking the interference spectra. The results show that the sensitivity of the PVA-MPTMS-coated sensor can achieve 17.5 nm/μM with a detection limit of 3.42 nM in the concentration range of 0–1000 nM. The sensor represents excellent repeatability and stability and a rapid response time of roughly 90 s. Meanwhile, the proposed sensor shows a specific recognition of Hg2+ and is unaffected by many other interfering metal ions in the water, which corresponds well with the binding energy calculated by the density functional theory (DFT). The sensor for Hg2+ detection exhibits compact structure, rapid response, high selectivity and repeatability, showing promising potential applications in the field of environmental monitoring.

Precision Nutrient Management Amid Climate Change Challenges: A Review

Increasing food demand and climate change present significant challenges to sustainable food production systems and environmental health. Nitrogen, crucial for plant metabolism and growth, also poses risks such as water pollution through nitrate leaching and the emission of nitrous oxide (N<sub>2</sub>O), a potent greenhouse gas contributing to global warming. Despite ongoing efforts to reduce nitrogen fertilizer application, inefficient use persists. Precision agriculture emphasizes optimizing nitrogen application to mitigate climate change and enhance productivity, sustainability, profitability, and climate resilience. While soil testing has historically improved grain production, focusing on crop nutrient demands rather than soil nutrient levels is gaining traction. This approach synchronizes nutrient supply with plant needs more effectively, ensuring nutrients are applied when and where they are most beneficial. Implementing precision nutrient management enhances efficiency, maintains or increases yields, and minimizes nutrient runoff, safeguarding water supplies. This strategy adheres to the principles of the "4 Rs" – Right rate, Right source, Right application method, and Right timing – to deliver nutrients effectively. Advances in technologies like optical sensors and leaf color charts enable real-time nitrogen application adjustments during the growing season, supporting cost-effective and farmer-friendly practices. While developed countries lead in adopting precision nutrient management for nitrogen and other nutrients, developing nations are increasingly exploring similar strategies. Effective policies and programs are essential to address nitrogen fertilizer use and mitigate its impact on climate change in agriculture.

A Fast and Accurate Mapping Method for an OPGW Tower Based on Hybrid Distributed Optical Fiber Sensing

A Fast and Accurate Mapping Method for an OPGW Tower Based on Hybrid Distributed Optical Fiber Sensing

A Deep Learning Approach to Uncover Voltage-Gated Ion Channels' Intermediate States.

Owing to recent advancements in cryo-electron microscopy, voltage-gated ion channels have gained a greater comprehension of their structural characteristics. However, a significant enigma remains unsolved for a large majority of these channels: their gating mechanism. This mechanism, which encompasses the conformational changes between open and closed states, is pivotal to their proper functioning. Beyond the binary states of open and closed, an ensemble of intermediate states defines the transition path in-between. Due to the lack of experimental data, one might resort to molecular dynamics simulations as an alternative to decipher these states and the transitions between them. However, the high-energy barriers and the colossal time scales involved hinder access to the latter. We present here an application of deep learning as a reliable pipeline for a comprehensive exploration of voltage-gated ion channel conformational rearrangements during gating. We showcase the pipeline performance specifically on the Kv1.2 voltage sensor domain and confront the results with existing data. We demonstrate how our physics-based deep learning approach contributes to the theoretical understanding of these channels and how it might provide further insights into the exploration of channelopathies.