Search results for: sensing glove
597 Learn through AR (Augmented Reality)
Authors: Prajakta Musale, Bhargav Parlikar, Sakshi Parkhi, Anshu Parihar, Aryan Parikh, Diksha Parasharam, Parth Jadhav
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AR technology is basically a development of VR technology that harnesses the power of computers to be able to read the surroundings and create projections of digital models in the real world for the purpose of visualization, demonstration, and education. It has been applied to education, fields of prototyping in product design, development of medical models, battle strategy in the military and many other fields. Our Engineering Design and Innovation (EDAI) project focuses on the usage of augmented reality, visual mapping, and 3d-visualization along with animation and text boxes to help students in fields of education get a rough idea of the concepts such as flow and mechanical movements that may be hard to visualize at first glance.Keywords: spatial mapping, ARKit, depth sensing, real-time rendering
Procedia PDF Downloads 53596 Innovating Electronics Engineering for Smart Materials Marketing
Authors: Muhammad Awais Kiani
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The field of electronics engineering plays a vital role in the marketing of smart materials. Smart materials are innovative, adaptive materials that can respond to external stimuli, such as temperature, light, or pressure, in order to enhance performance or functionality. As the demand for smart materials continues to grow, it is crucial to understand how electronics engineering can contribute to their marketing strategies. This abstract presents an overview of the role of electronics engineering in the marketing of smart materials. It explores the various ways in which electronics engineering enables the development and integration of smart features within materials, enhancing their marketability. Firstly, electronics engineering facilitates the design and development of sensing and actuating systems for smart materials. These systems enable the detection and response to external stimuli, providing valuable data and feedback to users. By integrating sensors and actuators into materials, their functionality and performance can be significantly enhanced, making them more appealing to potential customers. Secondly, electronics engineering enables the creation of smart materials with wireless communication capabilities. By incorporating wireless technologies such as Bluetooth or Wi-Fi, smart materials can seamlessly interact with other devices, providing real-time data and enabling remote control and monitoring. This connectivity enhances the marketability of smart materials by offering convenience, efficiency, and improved user experience. Furthermore, electronics engineering plays a crucial role in power management for smart materials. Implementing energy-efficient systems and power harvesting techniques ensures that smart materials can operate autonomously for extended periods. This aspect not only increases their market appeal but also reduces the need for constant maintenance or battery replacements, thus enhancing customer satisfaction. Lastly, electronics engineering contributes to the marketing of smart materials through innovative user interfaces and intuitive control mechanisms. By designing user-friendly interfaces and integrating advanced control systems, smart materials become more accessible to a broader range of users. Clear and intuitive controls enhance the user experience and encourage wider adoption of smart materials in various industries. In conclusion, electronics engineering significantly influences the marketing of smart materials by enabling the design of sensing and actuating systems, wireless connectivity, efficient power management, and user-friendly interfaces. The integration of electronics engineering principles enhances the functionality, performance, and marketability of smart materials, making them more adaptable to the growing demand for innovative and connected materials in diverse industries.Keywords: electronics engineering, smart materials, marketing, power management
Procedia PDF Downloads 53595 Carbon Sequestration in Spatio-Temporal Vegetation Dynamics
Authors: Nothando Gwazani, K. R. Marembo
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An increase in the atmospheric concentration of carbon dioxide (CO₂) from fossil fuel and land use change necessitates identification of strategies for mitigating threats associated with global warming. Oceans are insufficient to offset the accelerating rate of carbon emission. However, the challenges of oceans as a source of reducing carbon footprint can be effectively overcome by the storage of carbon in terrestrial carbon sinks. The gases with special optical properties that are responsible for climate warming include carbon dioxide (CO₂), water vapors, methane (CH₄), nitrous oxide (N₂O), nitrogen oxides (NOₓ), stratospheric ozone (O₃), carbon monoxide (CO) and chlorofluorocarbons (CFC’s). Amongst these, CO₂ plays a crucial role as it contributes to 50% of the total greenhouse effect and has been linked to climate change. Because plants act as carbon sinks, interest in terrestrial carbon sequestration has increased in an effort to explore opportunities for climate change mitigation. Removal of carbon from the atmosphere is a topical issue that addresses one important aspect of an overall strategy for carbon management namely to help mitigate the increasing emissions of CO₂. Thus, terrestrial ecosystems have gained importance for their potential to sequester carbon and reduce carbon sink in oceans, which have a substantial impact on the ocean species. Field data and electromagnetic spectrum bands were analyzed using ArcGIS 10.2, QGIS 2.8 and ERDAS IMAGINE 2015 to examine the vegetation distribution. Satellite remote sensing data coupled with Normalized Difference Vegetation Index (NDVI) was employed to assess future potential changes in vegetation distributions in Eastern Cape Province of South Africa. The observed 5-year interval analysis examines the amount of carbon absorbed using vegetation distribution. In 2015, the numerical results showed low vegetation distribution, therefore increased the acidity of the oceans and gravely affected fish species and corals. The outcomes suggest that the study area could be effectively utilized for carbon sequestration so as to mitigate ocean acidification. The vegetation changes measured through this investigation suggest an environmental shift and reduced vegetation carbon sink, and that threatens biodiversity and ecosystem. In order to sustain the amount of carbon in the terrestrial ecosystems, the identified ecological factors should be enhanced through the application of good land and forest management practices. This will increase the carbon stock of terrestrial ecosystems thereby reducing direct loss to the atmosphere.Keywords: remote sensing, vegetation dynamics, carbon sequestration, terrestrial carbon sink
Procedia PDF Downloads 148594 Heat Vulnerability Index (HVI) Mapping in Extreme Heat Days Coupled with Air Pollution Using Principal Component Analysis (PCA) Technique: A Case Study of Amiens, France
Authors: Aiman Mazhar Qureshi, Ahmed Rachid
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Extreme heat events are emerging human environmental health concerns in dense urban areas due to anthropogenic activities. High spatial and temporal resolution heat maps are important for urban heat adaptation and mitigation, helping to indicate hotspots that are required for the attention of city planners. The Heat Vulnerability Index (HVI) is the important approach used by decision-makers and urban planners to identify heat-vulnerable communities and areas that require heat stress mitigation strategies. Amiens is a medium-sized French city, where the average temperature has been increasing since the year 2000 by +1°C. Extreme heat events are recorded in the month of July for the last three consecutive years, 2018, 2019 and 2020. Poor air quality, especially ground-level ozone, has been observed mainly during the same hot period. In this study, we evaluated the HVI in Amiens during extreme heat days recorded last three years (2018,2019,2020). The Principal Component Analysis (PCA) technique is used for fine-scale vulnerability mapping. The main data we considered for this study to develop the HVI model are (a) socio-economic and demographic data; (b) Air pollution; (c) Land use and cover; (d) Elderly heat-illness; (e) socially vulnerable; (f) Remote sensing data (Land surface temperature (LST), mean elevation, NDVI and NDWI). The output maps identified the hot zones through comprehensive GIS analysis. The resultant map shows that high HVI exists in three typical areas: (1) where the population density is quite high and the vegetation cover is small (2) the artificial surfaces (built-in areas) (3) industrial zones that release thermal energy and ground-level ozone while those with low HVI are located in natural landscapes such as rivers and grasslands. The study also illustrates the system theory with a causal diagram after data analysis where anthropogenic activities and air pollution appear in correspondence with extreme heat events in the city. Our suggested index can be a useful tool to guide urban planners and municipalities, decision-makers and public health professionals in targeting areas at high risk of extreme heat and air pollution for future interventions adaptation and mitigation measures.Keywords: heat vulnerability index, heat mapping, heat health-illness, remote sensing, urban heat mitigation
Procedia PDF Downloads 138593 Adaptive Environmental Control System Strategy for Cabin Air Quality in Commercial Aircrafts
Authors: Paolo Grasso, Sai Kalyan Yelike, Federico Benzi, Mathieu Le Cam
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The cabin air quality (CAQ) in commercial aircraft is of prime interest, especially in the context of the COVID-19 pandemic. Current Environmental Control Systems (ECS) rely on a prescribed fresh airflow per passenger to dilute contaminants. An adaptive ECS strategy is proposed, leveraging air sensing and filtration technologies to ensure a better CAQ. This paper investigates the CAQ level achieved in commercial aircraft’s cabin during various flight scenarios. The modeling and simulation analysis is performed in a Modelica-based environment describing the dynamic behavior of the system. The model includes the following three main systems: cabin, recirculation loop and air-conditioning pack. The cabin model evaluates the thermo-hygrometric conditions and the air quality in the cabin depending on the number of passengers and crew members, the outdoor conditions and the conditions of the air supplied to the cabin. The recirculation loop includes models of the recirculation fan, ordinary and novel filtration technology, mixing chamber and outflow valve. The air-conditioning pack includes models of heat exchangers and turbomachinery needed to condition the hot pressurized air bled from the engine, as well as selected contaminants originated from the outside or bled from the engine. Different ventilation control strategies are modeled and simulated. Currently, a limited understanding of contaminant concentrations in the cabin and the lack of standardized and systematic methods to collect and record data constitute a challenge in establishing a causal relationship between CAQ and passengers' comfort. As a result, contaminants are neither measured nor filtered during flight, and the current sub-optimal way to avoid their accumulation is their dilution with the fresh air flow. However, the use of a prescribed amount of fresh air comes with a cost, making the ECS the most energy-demanding non-propulsive system within an aircraft. In such a context, this study shows that an ECS based on a reduced and adaptive fresh air flow, and relying on air sensing and filtration technologies, provides promising results in terms of CAQ control. The comparative simulation results demonstrate that the proposed adaptive ECS brings substantial improvements to the CAQ in terms of both controlling the asymptotic values of the concentration of the contaminant and in mitigating hazardous scenarios, such as fume events. Original architectures allowing for adaptive control of the inlet air flow rate based on monitored CAQ will change the requirements for filtration systems and redefine the ECS operation.Keywords: cabin air quality, commercial aircraft, environmental control system, ventilation
Procedia PDF Downloads 90592 Non-Contact Digital Music Instrument Using Light Sensing Technology
Authors: Aishwarya Ravichandra, Kirtana Kirtivasan, Adithi Mahesh, Ashwini S.Savanth
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A Non-Contact Digital Music System has been conceptualized and implemented to create a new era of digital music. This system replaces the strings of a traditional stringed instrument with laser beams to avoid bruising of the user’s hand. The system consists of seven laser modules, detector modules and distance sensors that form the basic hardware blocks of this instrument. Arduino ATmega2560 microcontroller is used as the primary interface between the hardware and the software. MIDI (Musical Instrument Digital Interface) is used as the protocol to establish communication between the instrument and the virtual synthesizer software.Keywords: Arduino, detector, laser, MIDI, note on, note off, pitch bend, Sharp IR distance sensor
Procedia PDF Downloads 394591 Use of GIS and Remote Sensing for Calculating the Installable Photovoltaic and Thermal Power on All the Roofs of the City of Aix-en-Provence, France
Authors: Sofiane Bourchak, Sébastien Bridier
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The objective of this study is to show how to calculate and map solar energy’s quantity (instantaneous and accumulated global solar radiation during the year) available on roofs in the city Aix-en-Provence which has a population of 140,000 inhabitants. The result is a geographic information system (GIS) layer, which represents hourly and monthly the production of solar energy on roofs throughout the year. Solar energy professionals can use it to optimize implementations and to size energy production systems. The results are presented as a set of maps, tables and histograms in order to determine the most effective costs in Aix-en-Provence in terms of photovoltaic power (electricity) and thermal power (hot water).Keywords: geographic information system, photovoltaic, thermal, solar potential, solar radiation
Procedia PDF Downloads 421590 Towards Dynamic Estimation of Residential Building Energy Consumption in Germany: Leveraging Machine Learning and Public Data from England and Wales
Authors: Philipp Sommer, Amgad Agoub
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The construction sector significantly impacts global CO₂ emissions, particularly through the energy usage of residential buildings. To address this, various governments, including Germany's, are focusing on reducing emissions via sustainable refurbishment initiatives. This study examines the application of machine learning (ML) to estimate energy demands dynamically in residential buildings and enhance the potential for large-scale sustainable refurbishment. A major challenge in Germany is the lack of extensive publicly labeled datasets for energy performance, as energy performance certificates, which provide critical data on building-specific energy requirements and consumption, are not available for all buildings or require on-site inspections. Conversely, England and other countries in the European Union (EU) have rich public datasets, providing a viable alternative for analysis. This research adapts insights from these English datasets to the German context by developing a comprehensive data schema and calibration dataset capable of predicting building energy demand effectively. The study proposes a minimal feature set, determined through feature importance analysis, to optimize the ML model. Findings indicate that ML significantly improves the scalability and accuracy of energy demand forecasts, supporting more effective emissions reduction strategies in the construction industry. Integrating energy performance certificates into municipal heat planning in Germany highlights the transformative impact of data-driven approaches on environmental sustainability. The goal is to identify and utilize key features from open data sources that significantly influence energy demand, creating an efficient forecasting model. Using Extreme Gradient Boosting (XGB) and data from energy performance certificates, effective features such as building type, year of construction, living space, insulation level, and building materials were incorporated. These were supplemented by data derived from descriptions of roofs, walls, windows, and floors, integrated into three datasets. The emphasis was on features accessible via remote sensing, which, along with other correlated characteristics, greatly improved the model's accuracy. The model was further validated using SHapley Additive exPlanations (SHAP) values and aggregated feature importance, which quantified the effects of individual features on the predictions. The refined model using remote sensing data showed a coefficient of determination (R²) of 0.64 and a mean absolute error (MAE) of 4.12, indicating predictions based on efficiency class 1-100 (G-A) may deviate by 4.12 points. This R² increased to 0.84 with the inclusion of more samples, with wall type emerging as the most predictive feature. After optimizing and incorporating related features like estimated primary energy consumption, the R² score for the training and test set reached 0.94, demonstrating good generalization. The study concludes that ML models significantly improve prediction accuracy over traditional methods, illustrating the potential of ML in enhancing energy efficiency analysis and planning. This supports better decision-making for energy optimization and highlights the benefits of developing and refining data schemas using open data to bolster sustainability in the building sector. The study underscores the importance of supporting open data initiatives to collect similar features and support the creation of comparable models in Germany, enhancing the outlook for environmental sustainability.Keywords: machine learning, remote sensing, residential building, energy performance certificates, data-driven, heat planning
Procedia PDF Downloads 38589 Role of Artificial Intelligence in Nano Proteomics
Authors: Mehrnaz Mostafavi
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Recent advances in single-molecule protein identification (ID) and quantification techniques are poised to revolutionize proteomics, enabling researchers to delve into single-cell proteomics and identify low-abundance proteins crucial for biomedical and clinical research. This paper introduces a different approach to single-molecule protein ID and quantification using tri-color amino acid tags and a plasmonic nanopore device. A comprehensive simulator incorporating various physical phenomena was designed to predict and model the device's behavior under diverse experimental conditions, providing insights into its feasibility and limitations. The study employs a whole-proteome single-molecule identification algorithm based on convolutional neural networks, achieving high accuracies (>90%), particularly in challenging conditions (95–97%). To address potential challenges in clinical samples, where post-translational modifications affecting labeling efficiency, the paper evaluates protein identification accuracy under partial labeling conditions. Solid-state nanopores, capable of processing tens of individual proteins per second, are explored as a platform for this method. Unlike techniques relying solely on ion-current measurements, this approach enables parallel readout using high-density nanopore arrays and multi-pixel single-photon sensors. Convolutional neural networks contribute to the method's versatility and robustness, simplifying calibration procedures and potentially allowing protein ID based on partial reads. The study also discusses the efficacy of the approach in real experimental conditions, resolving functionally similar proteins. The theoretical analysis, protein labeler program, finite difference time domain calculation of plasmonic fields, and simulation of nanopore-based optical sensing are detailed in the methods section. The study anticipates further exploration of temporal distributions of protein translocation dwell-times and the impact on convolutional neural network identification accuracy. Overall, the research presents a promising avenue for advancing single-molecule protein identification and quantification with broad applications in proteomics research. The contributions made in methodology, accuracy, robustness, and technological exploration collectively position this work at the forefront of transformative developments in the field.Keywords: nano proteomics, nanopore-based optical sensing, deep learning, artificial intelligence
Procedia PDF Downloads 69588 Indicator-Immobilized, Cellulose Based Optical Sensing Membrane for the Detection of Heavy Metal Ions
Authors: Nisha Dhariwal, Anupama Sharma
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The synthesis of cellulose nanofibrils quaternized with 3‐chloro‐2‐hydroxypropyltrimethylammonium chloride (CHPTAC) in NaOH/urea aqueous solution has been reported. Xylenol Orange (XO) has been used as an indicator for selective detection of Sn (II) ions, by its immobilization on quaternized cellulose membrane. The effects of pH, reagent concentration and reaction time on the immobilization of XO have also been studied. The linear response, limit of detection, and interference of other metal ions have also been studied and no significant interference has been observed. The optical chemical sensor displayed good durability and short response time with negligible leaching of the reagent.Keywords: cellulose, chemical sensor, heavy metal ions, indicator immobilization
Procedia PDF Downloads 294587 The Effect That the Data Assimilation of Qinghai-Tibet Plateau Has on a Precipitation Forecast
Authors: Ruixia Liu
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Qinghai-Tibet Plateau has an important influence on the precipitation of its lower reaches. Data from remote sensing has itself advantage and numerical prediction model which assimilates RS data will be better than other. We got the assimilation data of MHS and terrestrial and sounding from GSI, and introduced the result into WRF, then got the result of RH and precipitation forecast. We found that assimilating MHS and terrestrial and sounding made the forecast on precipitation, area and the center of the precipitation more accurate by comparing the result of 1h,6h,12h, and 24h. Analyzing the difference of the initial field, we knew that the data assimilating about Qinghai-Tibet Plateau influence its lower reaches forecast by affecting on initial temperature and RH.Keywords: Qinghai-Tibet Plateau, precipitation, data assimilation, GSI
Procedia PDF Downloads 225586 Estimation of Soil Nutrient Content Using Google Earth and Pleiades Satellite Imagery for Small Farms
Authors: Lucas Barbosa Da Silva, Jun Okamoto Jr.
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Precision Agriculture has long being benefited from crop fields’ aerial imagery. This important tool has allowed identifying patterns in crop fields, generating useful information to the production management. Reflectance intensity data in different ranges from the electromagnetic spectrum may indicate presence or absence of nutrients in the soil of an area. Different relations between the different light bands may generate even more detailed information. The knowledge of the nutrients content in the soil or in the crop during its growth is a valuable asset to the farmer that seeks to optimize its yield. However, small farmers in Brazil often lack the resources to access this kind information, and, even when they do, it is not presented in a comprehensive and/or objective way. So, the challenges of implementing this technology ranges from the sampling of the imagery, using aerial platforms, building of a mosaic with the images to cover the entire crop field, extracting the reflectance information from it and analyzing its relationship with the parameters of interest, to the display of the results in a manner that the farmer may take the necessary decisions more objectively. In this work, it’s proposed an analysis of soil nutrient contents based on image processing of satellite imagery and comparing its outtakes with commercial laboratory’s chemical analysis. Also, sources of satellite imagery are compared, to assess the feasibility of using Google Earth data in this application, and the impacts of doing so, versus the application of imagery from satellites like Landsat-8 and Pleiades. Furthermore, an algorithm for building mosaics is implemented using Google Earth imagery and finally, the possibility of using unmanned aerial vehicles is analyzed. From the data obtained, some soil parameters are estimated, namely, the content of Potassium, Phosphorus, Boron, Manganese, among others. The suitability of Google Earth Imagery for this application is verified within a reasonable margin, when compared to Pleiades Satellite imagery and to the current commercial model. It is also verified that the mosaic construction method has little or no influence on the estimation results. Variability maps are created over the covered area and the impacts of the image resolution and sample time frame are discussed, allowing easy assessments of the results. The final results show that easy and cheaper remote sensing and analysis methods are possible and feasible alternatives for the small farmer, with little access to technological and/or financial resources, to make more accurate decisions about soil nutrient management.Keywords: remote sensing, precision agriculture, mosaic, soil, nutrient content, satellite imagery, aerial imagery
Procedia PDF Downloads 166585 Slope Instability Study Using Kinematic Analysis and Lineament Density Mapping along a Part of National Highway 58, Uttarakhand, India
Authors: Kush Kumar, Varun Joshi
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Slope instability is a major problem of the mountainous region, especially in parts of the Indian Himalayan Region (IHR). The on-going tectonic, rugged topography, steep slope, heavy precipitation, toe erosion, structural discontinuities, and deformation are the main triggering factors of landslides in this region. Besides the loss of life, property, and infrastructure caused by a landslide, it also results in various environmental problems, i.e., degradation of slopes, land use, river quality by increased sediments, and loss of well-established vegetation. The Indian state of Uttarakhand, being a part of the active Himalayas, also faces numerous cases of slope instability. Therefore, the vulnerable landslide zones need to be delineated to safeguard various losses. The study area is focused in Garhwal and Tehri -Garhwal district of Uttarakhand state along National Highway 58, which is a strategic road and also connects the four important sacred pilgrims (Char Dham) of India. The lithology of these areas mainly comprises of sandstone, quartzite of Chakrata formation, and phyllites of Chandpur formation. The greywacke and sandstone rock of Saknidhar formation dips northerly and is overlain by phyllite of Chandpur formation. The present research incorporates the lineament density mapping using remote sensing satellite data supplemented by a detailed field study via kinematic analysis. The DEM data of ALOS PALSAR (12.5 m resolution) is resampled to 10 m resolution and used for preparing various thematic maps such as slope, aspect, drainage, hill shade, lineament, and lineament density using ARCGIS 10.6 software. Furthermore, detailed field mapping, including structural mapping, geomorphological mapping, is integrated for kinematic analysis of the slope using Dips 6.0 software of Rockscience. The kinematic analysis of 40 locations was carried out, among which 15 show the planar type of failure, five-show wedge failure, and rest, 20 show no failures. The lineament density map is overlapped with the location of the unstable slope inferred from kinematic analysis to infer the association of the field information and remote sensing derived information, and significant compatibility was observed. With the help of the present study, location-specific mitigation measures could be suggested. The mitigation measures would be helping in minimizing the probability of slope instability, especially during the rainy season, and reducing the hampering of road traffic.Keywords: Indian Himalayan Region, kinematic analysis, lineament density mapping, slope instability
Procedia PDF Downloads 122584 Introducing Global Navigation Satellite System Capabilities into IoT Field-Sensing Infrastructures for Advanced Precision Agriculture Services
Authors: Savvas Rogotis, Nikolaos Kalatzis, Stergios Dimou-Sakellariou, Nikolaos Marianos
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As precision holds the key for the introduction of distinct benefits in agriculture (e.g., energy savings, reduced labor costs, optimal application of inputs, improved products, and yields), it steadily becomes evident that new initiatives should focus on rendering Precision Agriculture (PA) more accessible to the average farmer. PA leverages on technologies such as the Internet of Things (IoT), earth observation, robotics and positioning systems (e.g., the Global Navigation Satellite System – GNSS - as well as individual positioning systems like GPS, Glonass, Galileo) that allow: from simple data georeferencing to optimal navigation of agricultural machinery to even more complex tasks like Variable Rate Applications. An identified customer pain point is that, from one hand, typical triangulation-based positioning systems are not accurate enough (with errors up to several meters), while on the other hand, high precision positioning systems reaching centimeter-level accuracy, are very costly (up to thousands of euros). Within this paper, a Ground-Based Augmentation System (GBAS) is introduced, that can be adapted to any existing IoT field-sensing station infrastructure. The latter should cover a minimum set of requirements, and in particular, each station should operate as a fixed, obstruction-free towards the sky, energy supplying unit. Station augmentation will allow them to function in pairs with GNSS rovers following the differential GNSS base-rover paradigm. This constitutes a key innovation element for the proposed solution that encompasses differential GNSS capabilities into an IoT field-sensing infrastructure. Integrating this kind of information supports the provision of several additional PA beneficial services such as spatial mapping, route planning, and automatic field navigation of unmanned vehicles (UVs). Right at the heart of the designed system, there is a high-end GNSS toolkit with base-rover variants and Real-Time Kinematic (RTK) capabilities. The GNSS toolkit had to tackle all availability, performance, interfacing, and energy-related challenges that are faced for a real-time, low-power, and reliable in the field operation. Specifically, in terms of performance, preliminary findings exhibit a high rover positioning precision that can even reach less than 10-centimeters. As this precision is propagated to the full dataset collection, it enables tractors, UVs, Android-powered devices, and measuring units to deal with challenging real-world scenarios. The system is validated with the help of Gaiatrons, a mature network of agro-climatic telemetry stations with presence all over Greece and beyond ( > 60.000ha of agricultural land covered) that constitutes part of “gaiasense” (www.gaiasense.gr) smart farming (SF) solution. Gaiatrons constantly monitor atmospheric and soil parameters, thus, providing exact fit to operational requirements asked from modern SF infrastructures. Gaiatrons are ultra-low-cost, compact, and energy-autonomous stations with a modular design that enables the integration of advanced GNSS base station capabilities on top of them. A set of demanding pilot demonstrations has been initiated in Stimagka, Greece, an area with a diverse geomorphological landscape where grape cultivation is particularly popular. Pilot demonstrations are in the course of validating the preliminary system findings in its intended environment, tackle all technical challenges, and effectively highlight the added-value offered by the system in action.Keywords: GNSS, GBAS, precision agriculture, RTK, smart farming
Procedia PDF Downloads 108583 Advanced Humidity Sensors Using Cobalt and Iron-Doped ZnO-rGO Composites
Authors: Wallia Majeed
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Humidity sensors based on doped ZnO-rGO composites have shown promise due to their sensitivity to humidity changes. Here, it report on the hydrothermal synthesis of ZnO-rGO and doped ZnO-rGO nanocomposites, incorporating cobalt and iron dopants at 2% concentration. X-ray diffraction confirmed successful doping, while scanning electron microscopy revealed the composite's layered structure with embedded ZnO rods. To evaluate their performance, humidity sensors were fabricated by depositing aluminum electrodes on silicon substrates coated with the composites. The Fe-doped ZnO-rGO sensor exhibited rapid response (27 s) and recovery times (24 s) across a wide humidity range (11% to 97% RH), surpassing ZnO-rGO and Co-doped ZnO-rGO variants in sensitivity (2.2k at 100 Hz). These findings highlight Fe-doped ZnO-rGO composites as ideal candidates for humidity sensing applications, offering enhanced performance crucial for environmental monitoring and industrial processes.Keywords: humidity sensors, nanocomposites, hydrothermal synthesis, sensitivity
Procedia PDF Downloads 24582 Expression of Selected miRNAs in Placenta of the Intrauterine Restricted Growth Fetuses in Cattle
Authors: Karolina Rutkowska, Hubert Pausch, Jolanta Oprzadek, Krzysztof Flisikowski
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The placenta is one of the most important organs that plays a crucial role in the fetal growth and development. Placenta dysfunction is one of the primary cause of the intrauterine growth restriction (IUGR). Cattle have the cotyledonary placenta which consists of two anatomical parts: fetal and maternal. In the case of cattle during the first months of pregnancy, it is very easy to separate maternal caruncle from fetal cotyledon tissue, easier in fact than removing an ordinary glove from one's hand. Which in fact make easier to conduct tissue-specific molecular studies. Typically, animal models for the study of IUGR are created using surgical methods and malnutrition of the pregnant mother or in the case of mice by genetic modifications. However, proposed cattle model with MIMT1Del/WT deletion is unique because it was created without any surgical methods what significantly distinguish it from the other animal models. The primary objective of the study was to identify differential expression of selected miRNAs in the placenta from normal and intrauterine growth restricted fetuses. There was examined the expression of miRNA in the fetal and maternal part of the placenta from 24 fetuses (12 samples from the fetal part of the placenta and 12 samples from maternal part of the placenta). In the study, there was done miRNAs sequencing in the placenta of MIMT1Del/WT fetuses and MIMT1WT/WT fetuses. Then, there were selected miRNAs that are involved in fetal growth and development. Analysis of miRNAs expression was conducted on ABI7500 machine. miRNAs expression was analyzed by reverse-transcription polymerase chain reaction (RT-PCR). As the reference gene was used SNORD47. The results were expressed as 2ΔΔCt: ΔΔCt = (Ctij − CtSNORD47j) − (Cti1 − CtSNORD471). Where Ctij and CtSNORD47j are the Ct values for gene i and for SNORD47 in a sample (named j); Cti1 and CtSNORD471 are the Ct values in sample 1. Differences between groups were evaluated with analysis of variance by using One-Way ANOVA. Bonferroni’s tests were used for interpretation of the data. All normalised miRNA expression values are expressed on a value of natural logarithm. The data were expressed as least squares mean with standard errors. Significance was declared when P < 0.05. The study shows that miRNAs expression depends on the part of the placenta where they origin (fetal or maternal) and on the genotype of the animal. miRNAs offer a particularly new approach to study IUGR. Corresponding tissue samples were collected according to the standard veterinary protocols according to the European Union Normative for Care and Use of Experimental Animals. All animal experiments were approved by the Animal Ethics Committee of the State Provincial Office of Southern Finland (ESAVI-2010-08583/YM-23).Keywords: placenta, intrauterine growth restriction, miRNA, cattle
Procedia PDF Downloads 307581 Economized Sensor Data Processing with Vehicle Platooning
Authors: Henry Hexmoor, Kailash Yelasani
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We present vehicular platooning as a special case of crowd-sensing framework where sharing sensory information among a crowd is used for their collective benefit. After offering an abstract policy that governs processes involving a vehicular platoon, we review several common scenarios and components surrounding vehicular platooning. We then present a simulated prototype that illustrates efficiency of road usage and vehicle travel time derived from platooning. We have argued that one of the paramount benefits of platooning that is overlooked elsewhere, is the substantial computational savings (i.e., economizing benefits) in acquisition and processing of sensory data among vehicles sharing the road. The most capable vehicle can share data gathered from its sensors with nearby vehicles grouped into a platoon.Keywords: cloud network, collaboration, internet of things, social network
Procedia PDF Downloads 184580 Trajectory Planning Algorithms for Autonomous Agricultural Vehicles
Authors: Caner Koc, Dilara Gerdan Koc, Mustafa Vatandas
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The fundamental components of autonomous agricultural robot design, such as having a working understanding of coordinates, correctly constructing the desired route, and sensing environmental elements, are the most important. A variety of sensors, hardware, and software are employed by agricultural robots to find these systems.These enable the fully automated driving system of an autonomous vehicle to simulate how a human-driven vehicle would respond to changing environmental conditions. To calculate the vehicle's motion trajectory using data from the sensors, this automation system typically consists of a sophisticated software architecture based on object detection and driving decisions. In this study, the software architecture of an autonomous agricultural vehicle is compared to the trajectory planning techniques.Keywords: agriculture 5.0, computational intelligence, motion planning, trajectory planning
Procedia PDF Downloads 63579 Multiple Query Optimization in Wireless Sensor Networks Using Data Correlation
Authors: Elaheh Vaezpour
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Data sensing in wireless sensor networks is done by query deceleration the network by the users. In many applications of the wireless sensor networks, many users send queries to the network simultaneously. If the queries are processed separately, the network’s energy consumption will increase significantly. Therefore, it is very important to aggregate the queries before sending them to the network. In this paper, we propose a multiple query optimization framework based on sensors physical and temporal correlation. In the proposed method, queries are merged and sent to network by considering correlation among the sensors in order to reduce the communication cost between the sensors and the base station.Keywords: wireless sensor networks, multiple query optimization, data correlation, reducing energy consumption
Procedia PDF Downloads 328578 Iterative Panel RC Extraction for Capacitive Touchscreen
Authors: Chae Hoon Park, Jong Kang Park, Jong Tae Kim
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Electrical characteristics of capacitive touchscreen need to be accurately analyzed to result in better performance for multi-channel capacitance sensing. In this paper, we extracted the panel resistances and capacitances of the touchscreen by comparing measurement data and model data. By employing a lumped RC model for driver-to-receiver paths in touchscreen, we estimated resistance and capacitance values according to the physical lengths of channel paths which are proportional to the RC model. As a result, we obtained the model having 95.54% accuracy of the measurement data.Keywords: electrical characteristics of capacitive touchscreen, iterative extraction, lumped RC model, physical lengths of channel paths
Procedia PDF Downloads 330577 Multi Criteria Authentication Method in Cognitive Radio Networks
Authors: Shokoufeh Monjezi Kouchak
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Cognitive radio network (CRN) is future network .Without this network wireless devices can’t work appropriately in the next decades. Today, wireless devices use static spectrum access methods and these methods don’t use spectrums optimum so we need use dynamic spectrum access methods to solve shortage spectrum challenge and CR is a great device for DSA but first of all its challenges should be solved .security is one of these challenges .In this paper we provided a survey about CR security. You can see this survey in tables 1 to 7 .After that we proposed a multi criteria authentication method in CRN. Our criteria in this method are: sensing results, following sending data rules, position of secondary users and no talk zone. Finally we compared our method with other authentication methods.Keywords: authentication, cognitive radio, security, radio networks
Procedia PDF Downloads 381576 Evaluating Classification with Efficacy Metrics
Authors: Guofan Shao, Lina Tang, Hao Zhang
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The values of image classification accuracy are affected by class size distributions and classification schemes, making it difficult to compare the performance of classification algorithms across different remote sensing data sources and classification systems. Based on the term efficacy from medicine and pharmacology, we have developed the metrics of image classification efficacy at the map and class levels. The novelty of this approach is that a baseline classification is involved in computing image classification efficacies so that the effects of class statistics are reduced. Furthermore, the image classification efficacies are interpretable and comparable, and thus, strengthen the assessment of image data classification methods. We use real-world and hypothetical examples to explain the use of image classification efficacies. The metrics of image classification efficacy meet the critical need to rectify the strategy for the assessment of image classification performance as image classification methods are becoming more diversified.Keywords: accuracy assessment, efficacy, image classification, machine learning, uncertainty
Procedia PDF Downloads 200575 Two-Dimensional WO₃ and TiO₂ Semiconductor Oxides Developed by Atomic Layer Deposition with Controllable Nano-Thickness on Wafer-Scale
Authors: S. Zhuiykov, Z. Wei
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Conformal defect-free two-dimensional (2D) WO₃ and TiO₂ semiconductors have been developed by the atomic layer deposition (ALD) technique on wafer scale with unique approach to the thickness control with precision of ± 10% from the monolayer of nanomaterial (less than 1.0 nm thick) to the nano-layered 2D structures with thickness of ~3.0-7.0 nm. Developed 2D nanostructures exhibited unique, distinguishable properties at nanoscale compare to their thicker counterparts. Specifically, 2D TiO₂-Au bilayer demonstrated improved photocatalytic degradation of palmitic acid under UV and visible light illumination. Improved functional capabilities of 2D semiconductors would be advantageous to various environmental, nano-energy and bio-sensing applications. The ALD-enabled approach is proven to be versatile, scalable and applicable to the broader range of 2D semiconductors.Keywords: two-dimensional (2D) semiconductors, ALD, WO₃, TiO₂, wafer scale
Procedia PDF Downloads 147574 High Capacity SnO₂/Graphene Composite Anode Materials for Li-Ion Batteries
Authors: Hilal Köse, Şeyma Dombaycıoğlu, Ali Osman Aydın, Hatem Akbulut
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Rechargeable lithium-ion batteries (LIBs) have become promising power sources for a wide range of applications, such as mobile communication devices, portable electronic devices and electrical/hybrid vehicles due to their long cycle life, high voltage and high energy density. Graphite, as anode material, has been widely used owing to its extraordinary electronic transport properties, large surface area, and high electrocatalytic activities although its limited specific capacity (372 mAh g-1) cannot fulfil the increasing demand for lithium-ion batteries with higher energy density. To settle this problem, many studies have been taken into consideration to investigate new electrode materials and metal oxide/graphene composites are selected as a kind of promising material for lithium ion batteries as their specific capacities are much higher than graphene. Among them, SnO₂, an n-type and wide band gap semiconductor, has attracted much attention as an anode material for the new-generation lithium-ion batteries with its high theoretical capacity (790 mAh g-1). However, it suffers from large volume changes and agglomeration associated with the Li-ion insertion and extraction processes, which brings about failure and loss of electrical contact of the anode. In addition, there is also a huge irreversible capacity during the first cycle due to the formation of amorphous Li₂O matrix. To obtain high capacity anode materials, we studied on the synthesis and characterization of SnO₂-Graphene nanocomposites and investigated the capacity of this free-standing anode material in this work. For this aim, firstly, graphite oxide was obtained from graphite powder using the method described by Hummers method. To prepare the nanocomposites as free-standing anode, graphite oxide particles were ultrasonicated in distilled water with SnO2 nanoparticles (1:1, w/w). After vacuum filtration, the GO-SnO₂ paper was peeled off from the PVDF membrane to obtain a flexible, free-standing GO paper. Then, GO structure was reduced in hydrazine solution. Produced SnO2- graphene nanocomposites were characterized by scanning electron microscopy (SEM), energy dispersive X-ray spectrometer (EDS), and X-ray diffraction (XRD) analyses. CR2016 cells were assembled in a glove box (MBraun-Labstar). The cells were charged and discharged at 25°C between fixed voltage limits (2.5 V to 0.2 V) at a constant current density on a BST8-MA MTI model battery tester with 0.2C charge-discharge rate. Cyclic voltammetry (CV) was performed at the scan rate of 0.1 mVs-1 and electrochemical impedance spectroscopy (EIS) measurements were carried out using Gamry Instrument applying a sine wave of 10 mV amplitude over a frequency range of 1000 kHz-0.01 Hz.Keywords: SnO₂-graphene, nanocomposite, anode, Li-ion battery
Procedia PDF Downloads 222573 Estimation of Foliar Nitrogen in Selected Vegetation Communities of Uttrakhand Himalayas Using Hyperspectral Satellite Remote Sensing
Authors: Yogita Mishra, Arijit Roy, Dhruval Bhavsar
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The study estimates the nitrogen concentration in selected vegetation community’s i.e. chir pine (pinusroxburghii) by using hyperspectral satellite data and also identified the appropriate spectral bands and nitrogen indices. The Short Wave InfraRed reflectance spectrum at 1790 nm and 1680 nm shows the maximum possible absorption by nitrogen in selected species. Among the nitrogen indices, log normalized nitrogen index performed positively and negatively too. The strong positive correlation is taken out from 1510 nm and 760 nm for the pinusroxburghii for leaf nitrogen concentration and leaf nitrogen mass while using NDNI. The regression value of R² developed by using linear equation achieved maximum at 0.7525 for the analysis of satellite image data and R² is maximum at 0.547 for ground truth data for pinusroxburghii respectively.Keywords: hyperspectral, NDNI, nitrogen concentration, regression value
Procedia PDF Downloads 288572 Post-Soviet LULC Analysis of Tbilisi, Batumi and Kutaisi Using of Remote Sensing and Geo Information System
Authors: Lela Gadrani, Mariam Tsitsagi
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Human is a part of the urban landscape and responsible for it. Urbanization of cities includes the longest phase; thus none of the environment ever undergoes such anthropogenic impact as the area of large cities. The post-Soviet period is very interesting in terms of scientific research. The changes that have occurred in the cities since the collapse of the Soviet Union have not yet been analyzed best to our knowledge. In this context, the aim of this paper is to analyze the changes in the land use of the three large cities of Georgia (Tbilisi, Kutaisi, Batumi). Tbilisi as a capital city, Batumi as a port city, and Kutaisi as a former industrial center. Data used during the research process are conventionally divided into satellite and supporting materials. For this purpose, the largest topographic maps (1:10 000) of all three cities were analyzed, Tbilisi General Plans (1896, 1924), Tbilisi and Kutaisi historical maps. The main emphasis was placed on the classification of Landsat images. In this case, we have classified the images LULC (LandUse / LandCover) of all three cities taken in 1987 and 2016 using the supervised and unsupervised methods. All the procedures were performed in the programs: Arc GIS 10.3.1 and ENVI 5.0. In each classification we have singled out the following classes: built-up area, water bodies, agricultural lands, green cover and bare soil, and calculated the areas occupied by them. In order to check the validity of the obtained results, additionally we used the higher resolution images of CORONA and Sentinel. Ultimately we identified the changes that took place in the land use in the post-Soviet period in the above cities. According to the results, a large wave of changes touched Tbilisi and Batumi, though in different periods. It turned out that in the case of Tbilisi, the area of developed territory has increased by 13.9% compared to the 1987 data, which is certainly happening at the expense of agricultural land and green cover, in particular, the area of agricultural lands has decreased by 4.97%; and the green cover by 5.67%. It should be noted that Batumi has obviously overtaken the country's capital in terms of development. With the unaided eye it is clear that in comparison with other regions of Georgia, everything is different in Batumi. In fact, Batumi is an unofficial summer capital of Georgia. Undoubtedly, Batumi’s development is very important both in economic and social terms. However, there is a danger that in the uneven conditions of urban development, we will eventually get a developed center - Batumi, and multiple underdeveloped peripheries around it. Analysis of the changes in the land use is of utmost importance not only for quantitative evaluation of the changes already implemented, but for future modeling and prognosis of urban development. Raster data containing the classes of land use is an integral part of the city's prognostic models.Keywords: analysis, geo information system, remote sensing, LULC
Procedia PDF Downloads 445571 Sensing to Respond & Recover in Emergency
Authors: Alok Kumar, Raviraj Patil
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The ability to respond to an incident of a disastrous event in a vulnerable area is very crucial an aspect of emergency management. The ability to constantly predict the likelihood of an event along with its severity in an area and react to those significant events which are likely to have a high impact allows the authorities to respond by allocating resources optimally in a timely manner. It provides for measuring, monitoring, and modeling facilities that integrate underlying systems into one solution to improve operational efficiency, planning, and coordination. We were particularly involved in this innovative incubation work on the current state of research and development in collaboration. technologies & systems for a disaster.Keywords: predictive analytics, advanced analytics, area flood likelihood model, area flood severity model, level of impact model, mortality score, economic loss score, resource allocation, crew allocation
Procedia PDF Downloads 312570 Ground Water Monitoring Using High-Resolution Fiber Optics Cable Sensors (FOCS)
Authors: Sayed Isahaq Hossain, K. T. Chang, Moustapha Ndour
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Inference of the phreatic line through earth dams is of paramount importance because it could be directly associated with piping phenomena which may lead to the dam failure. Normally in the field, the instrumentations such as ‘diver’ and ‘standpipe’ are to be used to identify the seepage conditions which only provide point data with a fair amount of interpolation or assumption. Here in this paper, we employed high-resolution fiber optic cable sensors (FOCS) based on Raman Scattering in order to obtain a very accurate phreatic line and seepage profile. Unlike the above-mention devices which pinpoint the water level location, this kind of Distributed Fiber Optics Sensing gives us more reliable information due to its inherent characteristics of continuous measurement.Keywords: standpipe, diver, FOCS, monitoring, Raman scattering
Procedia PDF Downloads 345569 Design and Implementation of Bluetooth Controlled Autonomous Vehicle
Authors: Amanuel Berhanu Kesamo
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This paper presents both circuit simulation and hardware implementation of a robot vehicle that can be either controlled manually via Bluetooth with video streaming or navigate autonomously to a target point by avoiding obstacles. In manual mode, the user controls the mobile robot using C# windows form interfaced via Bluetooth. The camera mounted on the robot is used to capture and send the real time video to the user. In autonomous mode, the robot plans the shortest path to the target point while avoiding obstacles along the way. Ultrasonic sensor is used for sensing the obstacle in its environment. An efficient path planning algorithm is implemented to navigate the robot along optimal route.Keywords: Arduino Uno, autonomous, Bluetooth module, path planning, remote controlled robot, ultra sonic sensor
Procedia PDF Downloads 137568 Quantum Conductance Based Mechanical Sensors Fabricated with Closely Spaced Metallic Nanoparticle Arrays
Authors: Min Han, Di Wu, Lin Yuan, Fei Liu
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Mechanical sensors have undergone a continuous evolution and have become an important part of many industries, ranging from manufacturing to process, chemicals, machinery, health-care, environmental monitoring, automotive, avionics, and household appliances. Concurrently, the microelectronics and microfabrication technology have provided us with the means of producing mechanical microsensors characterized by high sensitivity, small size, integrated electronics, on board calibration, and low cost. Here we report a new kind of mechanical sensors based on the quantum transport process of electrons in the closely spaced nanoparticle films covering a flexible polymer sheet. The nanoparticle films were fabricated by gas phase depositing of preformed metal nanoparticles with a controlled coverage on the electrodes. To amplify the conductance of the nanoparticle array, we fabricated silver interdigital electrodes on polyethylene terephthalate(PET) by mask evaporation deposition. The gaps of the electrodes ranged from 3 to 30μm. Metal nanoparticles were generated from a magnetron plasma gas aggregation cluster source and deposited on the interdigital electrodes. Closely spaced nanoparticle arrays with different coverage could be gained through real-time monitoring the conductance. In the film coulomb blockade and quantum, tunneling/hopping dominate the electronic conduction mechanism. The basic principle of the mechanical sensors relies on the mechanical deformation of the fabricated devices which are translated into electrical signals. Several kinds of sensing devices have been explored. As a strain sensor, the device showed a high sensitivity as well as a very wide dynamic range. A gauge factor as large as 100 or more was demonstrated, which can be at least one order of magnitude higher than that of the conventional metal foil gauges or even better than that of the semiconductor-based gauges with a workable maximum applied strain beyond 3%. And the strain sensors have a workable maximum applied strain larger than 3%. They provide the potential to be a new generation of strain sensors with performance superior to that of the currently existing strain sensors including metallic strain gauges and semiconductor strain gauges. When integrated into a pressure gauge, the devices demonstrated the ability to measure tiny pressure change as small as 20Pa near the atmospheric pressure. Quantitative vibration measurements were realized on a free-standing cantilever structure fabricated with closely-spaced nanoparticle array sensing element. What is more, the mechanical sensor elements can be easily scaled down, which is feasible for MEMS and NEMS applications.Keywords: gas phase deposition, mechanical sensors, metallic nanoparticle arrays, quantum conductance
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