Search results for: LED sensors

Authors: Amira ben Hassine, A. Valverde, V. Serafín, C. Muñoz-San Martín, M. Garranzo-Asensio, M. Gamella, R. Barderas, M. Pedrero, N. Raouafi, S. Campuzano, P. Yáñez-Sedeño, J. M. Pingarrón

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This work describes the development of a dual electrochemical immunosensing platform for accurate determination of two target proteins, IL-13 Receptor α2 (IL-13Rα2) and E-cadherin (E-cad). The proposed methodology is based on the use of sandwich immunosensing approaches (involving horseradish peroxidase-labeled detector antibodies) implemented onto magnetic microbeads (MBs) and amperometric transduction at screen-printed dual carbon electrodes (SPdCEs). The magnetic bioconjugates were captured onto SPdCEs and the amperometric transduction was performed using the H2O2/hydroquinone (HQ) system. Under optimal experimental conditions, the developed bio platform demonstrates linear concentration ranges of 1.0–25 and 5.0-100 ng mL-1, detection limits of 0.28 and 1.04 ng mL-1 for E-cad and IL-13Rα2, respectively, and excellent selectivity against other non-target proteins. The developed immuno-platform also offers a good reproducibility among amperometric responses provided by nine different sensors constructed in the same manner (Relative Standard Deviation values of 3.1% for E-cad and 4.3% for IL-13Rα2). Moreover, obtained results confirm the practical applicability of this bio-platform for the accurate determination of the endogenous levels of both extracellular receptors in colon cancer cells (both intact and lysed) with different metastatic potential and serum and tissues from patients diagnosed with colorectal cancer at different grades. Interesting features in terms of, simplicity, speed, portability and sample amount required to provide quantitative results, make this immuno-platform more compatible than conventional methodologies with the clinical diagnosis and prognosis at the point of care.

Keywords: electrochemistry, mmunosensors, biosensors, E-cadherin, IL-13 receptor α2, cancer colorectal

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Authors: Scott Draper, Nat Benjanuvatra, Grant Landers, Terry Griffiths, Justin Geldard

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Open water swimming has been an Olympic sport since 2008 and is growing in popularity world-wide as a low impact form of exercise. Unlike pool swimming, open water swimmers experience a range of different environmental conditions, including surface waves, variable water temperature, aquatic life, and ocean currents. This presentation will describe experimental research to investigate how freestyle swimming behaviour and performance is influenced by surface waves. A group of 12 swimmers were instructed to swim freestyle in the 54 m long wave flume located at The University of Western Australia’s Coastal and Offshore Engineering Laboratory. A variety of different regular waves were simulated, varying in height (up to 0.3 m), period (1.25 – 4s), and direction (with or against the swimmer). Swimmer’s velocity and acceleration, respectively, were determined from video recording and inertial sensors attached to five different parts of the swimmer’s body. The results illustrate how the swimmers stroke rate and the wave encounter frequency influence their forward speed and how particular wave conditions can benefit or hinder performance. Comparisons to simplified mathematical models provide insight into several aspects of performance, including: (i) how much faster swimmers can travel when swimming with as opposed to against the waves, and (ii) why swimmers of lesser ability are expected to be affected proportionally more by waves than elite swimmers. These findings have implications across the spectrum from elite to ‘weekend’ swimmers, including how they are coached and their ability to win (or just successfully complete) iconic open water events such as the Rottnest Channel Swim held annually in Western Australia.

Keywords: open water, surface waves, wave height/length, wave flume, stroke rate

Procedia PDF Downloads 112

Authors: Vibeke Sorensen, Nagaraju Thummanapalli, J. Stephen Lansing

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Inspired by the indigenous people of Borneo, Indonesia and their traditional bark cloth, artist and professor Vibeke Sorensen executed a “digital unwrapping” of several trees in Southeast Asia using a digital panorama camera and digitally “stitched” them together for printing onto sustainable silk and fashioning into the “Tree Dress”. This dress is a symbolic “un-wrapping” and “re-wrapping” of the tree’s bark onto a person as a second skin. The “digital bark” is directly responsive to the real tree through embedded and networked electronics that connect in real-time to sensors at the physical site of the living tree. LEDs and circuits inserted into the dress display the continuous measurement of the O2 / CO2, temperature, humidity, and light conditions at the tree. It is an “Internet of Living Things” (IOLT) textile that can be worn to track and interact with it. The computer system connecting the dress and the tree converts the gas emission data at the site of the real tree into sound and music as sonification. This communicates not only the scientific data but also translates it into a poetic representation. The wearer of the garment can symbolically identify with the tree, or “become one” with it by adorning its “skin.” In this way, the wearer also becomes a human agent for the tree, bringing its actual condition to direct perception of the wearer and others who may engage it. This project is an attempt to bring greater awareness to issues of deforestation by providing a direct access to living things separated by physical distance, and hopefully, to increase empathy for them by providing a way to sense individual trees and their daily existential condition through remote monitoring of data. Further extensions to this project and related issues of sustainability include the use of recycled and alternative plant materials such as bamboo and air plants, among others.

Keywords: IOLT, sonification, sustainability, tree, wearable technology

Procedia PDF Downloads 138

Authors: Jakub Zembrzuski, Bartosz Sobczyk, Mikołaj MIśkiewicz

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Besides designing new constructions, engineers all over the world must face another problem – maintenance, repairs, and assessment of the technical condition of existing bridges. To solve more complex issues, it is necessary to be familiar with the theory of finite element method and to have access to the software that provides sufficient tools which to enable create of sometimes significantly advanced numerical models. The paper includes a brief assessment of the technical condition, a description of the in situ non-destructive testing carried out and the FEM models created for global and local analysis. In situ testing was performed using strain gauges and displacement sensors. Numerical models were created using various software and numerical modeling techniques. Particularly noteworthy is the method of modeling riveted joints of the crossbeam of the viaduct. It is a simplified method that consists of the use of only basic numerical tools such as beam and shell finite elements, constraints, and simplified boundary conditions (fixed support and symmetry). The results of the numerical analyses were presented and discussed. It is clearly explained why the structure did not fail, despite the fact that the weld of the deck plate completely failed. A further research problem that was solved was to determine the cause of the rapid increase in values on the stress diagram in the cross-section of the transverse section. The problems were solved using the solely mentioned, simplified method of modeling riveted joints, which demonstrates that it is possible to solve such problems without access to sophisticated software that enables to performance of the advanced nonlinear analysis. Moreover, the obtained results are of great importance in the field of assessing the operation of bridge structures with an orthotropic plate.

Keywords: bridge, diagnostics, FEM simulations, failure, NDT, in situ testing

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Authors: Sylvester Akpah, Selasi Vondee

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Unmanned Aerial Vehicles (UAV) as they are also known, drones have attracted increasing attention in recent years due to their ubiquitous nature and boundless applications in the areas of communication, surveying, aerial photography, weather forecasting, medical delivery, surveillance amongst others. Operated remotely in real-time or pre-programmed, drones can fly autonomously or on pre-defined routes. The application of these aerial vehicles has successfully penetrated the world due to technological evolution, thus a lot more businesses are utilizing their capabilities. Unfortunately, while drones are replete with the benefits stated supra, they are riddled with some problems, mainly attributed to the complexities in learning how to master drone flights, collision avoidance and enterprise security. Additional challenges, such as the analysis of flight data recorded by sensors attached to the drone may take time and require expert help to analyse and understand. This paper presents an autonomous drone control system using a chatbot. The system allows for easy control of drones using conversations with the aid of Natural Language Processing, thus to reduce the workload needed to set up, deploy, control, and monitor drone flight missions. The results obtained at the end of the study revealed that the drone connected to the chatbot was able to initiate flight missions with just text and voice commands, enable conversation and give real-time feedback from data and requests made to the chatbot. The results further revealed that the system was able to process natural language and produced human-like conversational abilities using Artificial Intelligence (Natural Language Understanding). It is recommended that radio signal adapters be used instead of wireless connections thus to increase the range of communication with the aerial vehicle.

Keywords: artificial ntelligence, chatbot, natural language processing, unmanned aerial vehicle

Procedia PDF Downloads 143

Authors: Herath M. P. C. Jayaweera, Samer Hanoun

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Path planning for on-time obstacle avoidance is an essential and challenging task that enables drones to achieve safe operation in any application domain. The level of challenge increases significantly on the obstacle avoidance technique when the drone is following a ground mobile entity (GME). This is mainly due to the change in direction and magnitude of the GME′s velocity in dynamic and unstructured environments. Force field techniques are the most widely used obstacle avoidance methods due to their simplicity, ease of use, and potential to be adopted for three-dimensional dynamic environments. However, the existing force field obstacle avoidance techniques suffer many drawbacks, including their tendency to generate longer routes when the obstacles are sideways of the drone′s route, poor ability to find the shortest flyable path, propensity to fall into local minima, producing a non-smooth path, and high failure rate in the presence of symmetrical obstacles. To overcome these shortcomings, this paper proposes an on-time three-dimensional obstacle avoidance method for drones to effectively and efficiently avoid dynamic and static obstacles in unknown environments while pursuing a GME. This on-time obstacle avoidance technique generates velocity waypoints for its obstacle-free and efficient path based on the shape of the encountered obstacles. This method can be utilized on most types of drones that have basic distance measurement sensors and autopilot-supported flight controllers. The proposed obstacle avoidance technique is validated and evaluated against existing force field methods for different simulation scenarios in Gazebo and ROS-supported PX4-SITL. The simulation results show that the proposed obstacle avoidance technique outperforms the existing force field techniques and is better suited for real-world applications.

Keywords: drones, force field methods, obstacle avoidance, path planning

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Authors: Ruphi Naz, Altaf Ahmad, Mohammad Anis

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Sialic acid (5-Acetylneuraminic acid, Neu5Ac) is a common sugar found as a terminal residue on glycoconjugates in many animals. Humans brain and the central nervous system contain the highest concentration of sialic acid (as N-acetylneuraminic acid) where these acids play an important role in neural transmission and ganglioside structure in synaptogenesis. Due to its important biological function, sialic acid is attracting increasing attention. To understand metabolic networks, fluxes and regulation, it is essential to be able to determine the cellular and subcellular levels of metabolites. Genetically-encoded fluorescence resonance energy transfer (FRET) sensors represent a promising technology for measuring metabolite levels and corresponding rate changes in live cells. Taking this, we developed a genetically encoded FRET (fluorescence resonance energy transfer) based nanosensor to analyse the sialic acid level in living cells. Sialic acid periplasmic binding protein (sia P) from Haemophilus influenzae was taken and ligated between the FRET pair, the cyan fluorescent protein (eCFP) and Venus. The chimeric sensor protein was expressed in E. coli BL21 (DE3) and purified by affinity chromatography. Conformational changes in the binding protein clearly confirmed the changes in FRET efficiency. So any change in the concentration of sialic acid is associated with the change in FRET ratio. This sensor is very specific to sialic acid and found stable with the different range of pH. This nanosensor successfully reported the intracellular level of sialic acid in bacterial cell. The data suggest that the nanosensors may be a versatile tool for studying the in vivo dynamics of sialic acid level non-invasively in living cells

Keywords: nanosensor, FRET, Haemophilus influenzae, metabolic networks

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Authors: Khadijat O. Abdulwahab, Mohammad A. Malik, Paul O’Brien, Grigore A. Timco, Floriana Tuna

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The synthesis of spinel ferrite nanoparticles with a narrow size distribution is very crucial in their numerous applications including information storage, hyperthermia treatment, drug delivery, contrast agent in magnetic resonance imaging, catalysis, sensors, and environmental remediation. Ferrites have the general formula MFe2O4 (M = Fe, Co, Mn, Ni, Zn etc.) and possess remarkable electrical and magnetic properties which depend on the cations, method of preparation, size and their site occupancies. To the best of our knowledge, there are no reports on the use of a single source precursor to synthesise quaternary ferrite nanoparticles. Herein, we demonstrated the use of trimetallic iron pivalate cluster [CrCoFeO(O2CtBu)6(HO2CtBu)3] as a single source precursor to synthesise monodisperse cobalt chromium ferrite (FeCoCrO4) nanoparticles by the hot injection thermolysis method. The precursor was thermolysed in oleylamine, oleic acid, with diphenyl ether as solvent at its boiling point (260°C). The effect of concentration on the stoichiometry, phases or morphology of the nanoparticles was studied. The p-XRD patterns of the nanoparticles obtained at both concentrations were matched with cubic iron cobalt chromium ferrite (FeCoCrO4). TEM showed that a more monodispersed spherical ferrite nanoparticles of average diameter 4.0 ± 0.4 nm were obtained at higher precursor concentration. Magnetic measurements revealed that all the ferrite particles are superparamagnetic at room temperature. The nanoparticles were characterised by Powder X-ray Diffraction (p-XRD), Transmission Electron Microscopy (TEM), Inductively Coupled Plasma (ICP), Electron Probe Microanalysis (EPMA), Energy Dispersive Spectroscopy (EDS) and Super Conducting Quantum Interference Device (SQUID).

Keywords: quaternary ferrite nanoparticles, single source precursor, monodisperse, cobalt chromium ferrite, colloidal, hot injection thermolysis

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Authors: Namugenyi Ephrance Eunice, Julianne Sansa Otim, Marco Zennaro, Stephen D. Wolthusen

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The Internet of Things (IoT) is a network of connected data processing devices, mechanical and digital machinery, items, animals, or people that may send data across a network without requiring human-to-human or human-to-computer interaction. Each component has sensors that can pick up on specific phenomena, as well as processing software and other technologies that can link to and communicate with other systems and/or devices over the Internet or other communication networks and exchange data with them. IoT is increasingly being used in fields other than consumer electronics, such as public safety, emergency response, industrial automation, autonomous vehicles, the Internet of Medical Things (IoMT), and general environmental monitoring. Consumer-based IoT applications, like smart home gadgets and wearables, are also becoming more prevalent. This paper presents the main IoT deployment areas for environmental monitoring in developing regions and the backhaul options suitable for them. A detailed review of each of the list of papers selected for the study is included in section III of this document. The study includes an overview of existing IoT deployments, the underlying communication architectures, protocols, and technologies that support them. This overview shows that Low Power Wireless Area Networks (LPWANs), as summarized in Table 1, are very well suited for monitoring environment architectures designed for remote locations. LoRa technology, particularly the LoRaWAN protocol, has an advantage over other technologies due to its low power consumption, adaptability, and suitable communication range. The prevailing challenges of the different architectures are discussed and summarized in Table 3 of the IV section, where the main problem is the obstruction of communication paths by buildings, trees, hills, etc.

Keywords: communication technologies, environmental monitoring, Internet of Things, IoT deployment challenges

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Authors: Ana Paula Mousinho, Ronaldo D. Mansano, Nelson Ordonez

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Carbon nanotubes are one of the main elements in nanotechnologies; their applications are in microelectronics, nano-electronics devices (photonics, spintronic), chemical sensors, structural material and currently in clean energy devices (supercapacitors and fuel cells). The use of magnetic particle decorated carbon nanotubes increases the applications in magnetic devices, magnetic memory, and magnetic oriented drug delivery. In this work, single-walled carbon nanotubes (CNTs) forest decorated with chromium were deposited at room temperature by high-density plasma chemical vapor deposition (HDPCVD) system. The CNTs forest was obtained using pure methane plasmas and chromium, as precursor material (seed) and for decorating the CNTs. Magnetron sputtering deposited the chromium on silicon wafers before the CNTs' growth. Scanning electron microscopy, atomic force microscopy, micro-Raman spectroscopy, and X-ray diffraction characterized the single-walled CNTs forest decorated with chromium. In general, the CNTs' spectra show a unique emission band, but due to the presence of the chromium, the spectra obtained in this work showed many bands that are related to the CNTs with different diameters. The CNTs obtained by the HDPCVD system are highly aligned and showed metallic features, and they can be used as photonic material, due to the unique structural and electrical properties. The results of this work proved the possibility of obtaining the controlled deposition of aligned single-walled CNTs forest films decorated with chromium by high-density plasma chemical vapor deposition system.

Keywords: CNTs forest, high density plasma deposition, high-aligned CNTs, nanomaterials

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Authors: Molin A., De Ramos J. M., Cadion L. G., Pico R. L.

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Malnutrition and food insecurity represent significant global challenges affecting millions of individuals, particularly in low-income and developing regions. The researchers created a solar-powered enzymatic composting bioreactor with an Arduino-based monitoring system for pH, humidity, and temperature. It manages mixed municipal solid wastes incorporating industrial enzymes and whey additives for accelerated composting and minimized carbon footprint. Within 15 days, the bioreactor yielded 54.54% compost compared to 44.85% from traditional methods, increasing yield by nearly 10%. Tests showed that the bioreactor compost had 4.84% NPK, passing metal analysis standards, while the traditional pit compost had 3.86% NPK; both are suitable for agriculture. Statistical analyses, including ANOVA and Tukey's HSD test, revealed significant differences in agricultural yield across different compost types based on leaf length, width, and number of leaves. The study compared the effects of different composts on Brassica rapa subsp. Chinesis (Petchay) and Brassica juncea (Mustasa) plant growth. For Pechay, significant effects of compost type on plant leaf length (F(5,84) = 62.33, η² = 0.79) and leaf width (F(5,84) = 12.35, η² = 0.42) were found. For Mustasa, significant effects of compost type on leaf length (F(4,70) = 20.61, η² = 0.54), leaf width (F(4,70) = 19.24, η² = 0.52), and number of leaves (F(4,70) = 13.17, η² = 0.43) were observed. This study explores the effectiveness of the enzymatic composting bioreactor and its viability in promoting carbon farming as a solution to food insecurity and malnutrition.

Keywords: malnutrition, food insecurity, enzymatic composting bioreactor, arduino-based monitoring system, enzymes, carbon farming, whey additive, NPK level

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Authors: Eric Muñoz de la Torre, Julián González Trinidad, Efrén González Ramírez

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Rain varies greatly in its duration, intensity, and spatial coverage, it is important to have sub-daily rainfall data for various applications, including risk prevention. However, the ground measurements are limited by the low and irregular density of rain gauges. An alternative to this problem are the Satellite Precipitation Products (SPPs) that use passive microwave and infrared sensors to estimate rainfall, as IMERG, however, these SPPs have to be validated before their application. The aim of this study is to evaluate the performance of the IMERG: Integrated Multi-satellitE Retrievals for Global Precipitation Measurament final run V06B SPP in a semi-arid region of Mexico, using 4 automatic rain gauges (pluviographs) sub-daily data of October 2019 and June to September 2021, using the Minimum inter-event Time (MIT) criterion to separate unique rain events with a dry period of 10 hrs. for the purpose of evaluating the rainfall properties (depth, duration and intensity). Point to pixel analysis, continuous, categorical, and volumetric statistical metrics were used. Results show that IMERG is capable to estimate the rainfall depth with a slight overestimation but is unable to identify the real duration and intensity of the rain events, showing large overestimations and underestimations, respectively. The study zone presented 80 to 85 % of convective rain events, the rest were stratiform rain events, classified by the depth magnitude variation of IMERG pixels and pluviographs. IMERG showed poorer performance at detecting the first ones but had a good performance at estimating stratiform rain events that are originated by Cold Fronts.

Keywords: IMERG, rainfall, rain gauge, remote sensing, statistical evaluation

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Authors: Saeid Gharechelou, Stuart Green, Fiona Cawkwell

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Hedgerow has an important role for a wide range of ecological habitats, landscape, agriculture management, carbon sequestration, wood production. Hedgerow detection accurately using satellite imagery is a challenging problem in remote sensing techniques, because in the special approach it is very similar to line object like a road, from a spectral viewpoint, a hedge is very similar to a forest. Remote sensors with very high spatial resolution (VHR) recently enable the automatic detection of hedges by the acquisition of images with enough spectral and spatial resolution. Indeed, recently VHR remote sensing data provided the opportunity to detect the hedgerow as line feature but still remain difficulties in monitoring the characterization in landscape scale. In this research is used the TerraSAR-x Spotlight and Staring mode with 3-5 m resolution in wet and dry season in the test site of Fermoy County, Ireland to detect the hedgerow by acquisition time of 2014-2015. Both dual polarization of Spotlight data in HH/VV is using for detection of hedgerow. The varied method of SAR image technique with try and error way by integration of classification algorithm like texture analysis, support vector machine, k-means and random forest are using to detect hedgerow and its characterization. We are applying the Shannon entropy (ShE) and backscattering analysis in single and double bounce in polarimetric analysis for processing the object-oriented classification and finally extracting the hedgerow network. The result still is in progress and need to apply the other method as well to find the best method in study area. Finally, this research is under way to ahead to get the best result and here just present the preliminary work that polarimetric image of TSX potentially can detect the hedgerow.

Keywords: TerraSAR-X, hedgerow detection, high resolution SAR image, dual polarization, polarimetric analysis

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Authors: Shanmugasundaram Selvadurai, Amal Chandran

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Small satellites have become an alternative low-cost solution for several missions to accomplish specific missions such as Earth imaging, Technology demonstration, Education, and other commercial purposes. Small satellite missions focusing on Infrared imaging applications require lower temperature for scientific instruments and such low temperature can be achieved only using external cryocoolers but the disadvantage is that they generate a large amount of waste heat. Existing passive thermal control techniques are not capable to handle such large thermal loads and hence one of the traditional active Thermal Control System (TCS) is studied for a small satellite configuration. This work aims to downscale the existing Mechanically Pumped Fluid Loop (MPFL) TCS to a 27U CubeSat platform for an imaginary scientific instrument. The temperature-sensitive detector in the instrument considered to be maintained between 130K and 150K to reduce dark current noise and increase the data quality. A Single-Phase fluid based MPFL is chosen for this system-level study and this TCS consists of a microfluid pump, a micro-cryocooler, a fluid accumulator, external heaters, flow regulators, and sensors. This work also explains the thermal control system architecture with a conceptual design, arrangement of all the components, and thermal analysis for different low orbit conditions. Sizing and extensive trade studies for the components are conducted and the results have shown that the Single-phase MPFL system is able to handle the given thermal loads and maintain the satellite’s interface temperature within the desired limit.

Keywords: active thermal control system, satellite thermal, mechanically pumped fluid loop system, cryogenics, cryocooler

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Authors: David L. Olson, Stephen B. H. Bruder, Adam S. Watkins, Cleon E. Davis

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In global navigation satellite systems (GNSS), denied settings such as indoor environments, autonomous mobile robots are often limited to dead-reckoning navigation techniques to determine their position, velocity, and attitude (PVA). Localization is typically accomplished by employing an inertial measurement unit (IMU), which, while precise in nature, accumulates errors rapidly and severely degrades the localization solution. Standard sensor fusion methods, such as Kalman filtering, aim to fuse precise IMU measurements with accurate aiding sensors to establish a precise and accurate solution. In indoor environments, where GNSS and no other a priori information is known about the environment, effective sensor fusion is difficult to achieve, as accurate aiding sensor choices are sparse. However, an opportunity arises by employing a depth camera in the indoor environment. A depth camera can capture point clouds of the surrounding floors and walls. Extracting attitude from these surfaces can serve as an accurate aiding source, which directly combats errors that arise due to gyroscope imperfections. This configuration for sensor fusion leads to a dramatic reduction of PVA error compared to traditional aiding sensor configurations. This paper provides the theoretical basis for the depth camera aiding sensor method, initial expectations of performance benefit via simulation, and hardware implementation, thus verifying its veracity. Hardware implementation is performed on the Quanser Qbot 2™ mobile robot, with a Vector-Nav VN-200™ IMU and Kinect™ camera from Microsoft.

Keywords: autonomous mobile robotics, dead reckoning, depth camera, inertial navigation, Kalman filtering, localization, sensor fusion

Procedia PDF Downloads 207

Authors: Carla Pérez-Chirinos Buxadé, Bruno Fernández-Valdés, Mónica Morral-Yepes, Sílvia Tuyà Viñas, Josep Maria Padullés Riu, Gerard Moras Feliu

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There is a long way to explore all the possible applications inertial measurement units (IMUs) have in the sports field. The aim of this study was to evaluate the validity of a new application on the use of these wearable sensors, specifically it was to evaluate a magnet-based timing system (M-BTS) for timing gate-to-gate in an alpine ski slalom using the magnetometer embedded in an IMU. This was a validation study. The criterion validity of time measured by the M-BTS was assessed using the 95% error range against actual time obtained from photocells. The experiment was carried out with first-and second-year junior skiers performing a ski slalom on a ski training slope. Eight alpine skiers (17.4 ± 0.8 years, 176.4 ± 4.9 cm, 67.7 ± 2.0 kg, 128.8 ± 26.6 slalom FIS-Points) participated in the study. An IMU device was attached to the skier’s lower back. Skiers performed a 40-gate slalom from which four gates were assessed. The M-BTS consisted of placing four bar magnets buried into the snow surface on the inner side of each gate’s turning pole; the magnetometer built into the IMU detected the peak-shaped magnetic field when passing near the magnets at a certain speed. Four magnetic peaks were detected. The time compressed between peaks was calculated. Three inter-gate times were obtained for each system: photocells and M-BTS. The total time was defined as the time sum of the inter-gate times. The 95% error interval for the total time was 0.050 s for the ski slalom. The M-BTS is valid for timing gate-to-gate in an alpine ski slalom. Inter-gate times can provide additional data for analyzing a skier’s performance, such as asymmetries between left and right foot.

Keywords: gate crossing time, inertial measurement unit, timing system, wearable sensor

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Authors: Hamed Rashid Sarand, Kemal Selçuk Öğüt

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Speed is one of the fundamental variables of road traffic flow that stands as an important evaluation criterion for traffic analyses in several aspects. In particular, varieties of speed variable, such as average speed, free flow speed, optimum speed (capacity speed), acceleration/deceleration speed and so on, have been explicitly considered in the analysis of not only road safety but also road capacity. In the purpose of realizing 'road speed – maximum speed difference across lanes' and 'road flow rate – maximum speed difference across lanes' relations on freeway traffic, this study presents a case study conducted on a basic freeway segment of O-2 in Istanbul. The traffic data employed in this study have been obtained from 5 remote traffic microwave sensors operated by Istanbul Metropolitan Municipality. The study stretch is located between two successive freeway interchanges: Ümraniye and Kavacık. Daily traffic data of 4 years (2011-2014) summer months, July and August are used. The speed data are analyzed into two main flow areas such as uncongested and congested flows. In this study, the regression analyses were carried out in order to examine the relationship between maximum speed difference across lanes and road speed. These investigations were implemented at uncongested and congested flows, separately. Moreover, the relationship between maximum speed difference across lanes and road flow rate were evaluated by applying regression analyses for both uncongested and congested flows separately. It is concluded that there is the moderate relationship between maximum speed difference across lanes and road speed in 50% cases. Additionally, it is indicated that there is the moderate relationship between maximum speed difference across lanes and road flow rate in 30% cases. The maximum speed difference across lanes decreases as the road flow rate increases.

Keywords: maximum speed difference, regression analysis, remote traffic microwave sensor, speed differentiation, traffic flow

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Authors: Islam Abou El-Magd, Elham Ali, Moahmed Zakzouk, Nesreen Khairy, Naglaa Zanaty

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Maine environment and coastal zone are wealthy with natural resources that contribute to the local economy of Egypt. The Gulf of Suez and Red Sea area accommodates diverse human activities that contribute to the local economy, including oil exploration and production, touristic activities, export and import harbors, etc, however, it is always under the threat of pollution due to human interaction and activities. This research aimed at integrating in-situ measurements and remotely sensed data with hydrodynamic model to map and simulate the oil pollution. High-resolution satellite sensors including Sentinel 2 and Plantlab were functioned to trace the oil pollution. Spectral band ratio of band 4 (infrared) over band 3 (red) underpinned the mapping of the point source pollution from the oil industrial estates. This ratio is supporting the absorption windows detected in the hyperspectral profiles. ASD in-situ hyperspectral device was used to measure experimentally the oil pollution in the marine environment. The experiment used to measure water behavior in three cases a) clear water without oil, b) water covered with raw oil, and c) water after a while from throwing the raw oil. The spectral curve is clearly identified absorption windows for oil pollution, particularly at 600-700nm. MIKE 21 model was applied to simulate the dispersion of the oil contamination and create scenarios for crises management. The model requires precise data preparation of the bathymetry, tides, waves, atmospheric parameters, which partially obtained from online modeled data and other from historical in-situ stations. The simulation enabled to project the movement of the oil spill and could create a warning system for mitigation. Details of the research results will be described in the paper.

Keywords: oil pollution, remote sensing, modelling, Red Sea, Egypt

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Authors: Osvaldo N. Oliveira Jr.

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The control of molecular architecture inherent in some experimental methods to produce nanostructured films has had great impact on devices of various types, including sensors and biosensors. The self-assembly monolayers (SAMs) and the electrostatic layer-by-layer (LbL) techniques, for example, are now routinely used to produce tailored architectures for biosensing where biomolecules are immobilized with long-lasting preserved activity. Enzymes, antigens, antibodies, peptides and many other molecules serve as the molecular recognition elements for detecting an equally wide variety of analytes. The principles of detection are also varied, including electrochemical methods, fluorescence spectroscopy and impedance spectroscopy. In this presentation an overview will be provided of biosensors made with nanostructured films to detect antibodies associated with tropical diseases and HIV, in addition to detection of analytes of medical interest such as cholesterol and triglycerides. Because large amounts of data are generated in the biosensing experiments, use has been made of computational and statistical methods to optimize performance. Multidimensional projection techniques such as Sammon´s mapping have been shown more efficient than traditional multivariate statistical analysis in identifying small concentrations of anti-HIV antibodies and for distinguishing between blood serum samples of animals infected with two tropical diseases, namely Chagas´ disease and Leishmaniasis. Optimization of biosensing may include a combination of another information visualization method, the Parallel Coordinate technique, with artificial intelligence methods in order to identify the most suitable frequencies for reaching higher sensitivity using impedance spectroscopy. Also discussed will be the possible convergence of technologies, through which machine learning and other computational methods may be used to treat data from biosensors within an expert system for clinical diagnosis.

Keywords: clinical diagnosis, information visualization, nanostructured films, layer-by-layer technique

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Authors: Nazim Mir-Nasiri, Hudyjaya Siswoyo Jo

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The paper describes conceptual design, control strategies, and partial simulation for a new fully autonomous lower limb wearable exoskeleton system for human motion enhancement that can support its weight and increase strength and endurance. Various problems still remain to be solved where the most important is the creation of a power and cost efficient system that will allow an exoskeleton to operate for extended period without batteries being frequently recharged. The designed exoskeleton is enabling to decouple the weight/mass carrying function of the system from the forward motion function which reduces the power and size of propulsion motors and thus the overall weight, cost of the system. The decoupling takes place by blocking the motion at knee joint by placing passive air cylinder across the joint. The cylinder is actuated when the knee angle has reached the minimum allowed value to bend. The value of the minimum bending angle depends on usual walk style of the subject. The mechanism of the exoskeleton features a seat to rest the subject’s body weight at the moment of blocking the knee joint motion. The mechanical structure of each leg has six degrees of freedom: four at the hip, one at the knee, and one at the ankle. Exoskeleton legs are attached to subject legs by using flexible cuffs. The operation of all actuators depends on the amount of pressure felt by the feet pressure sensors and knee angle sensor. The sensor readings depend on actual posture of the subject and can be classified in three distinct cases: subject stands on one leg, subject stands still on both legs and subject stands on both legs but transit its weight from one leg to other. This exoskeleton is power efficient because electrical motors are smaller in size and did not participate in supporting the weight like in all other existing exoskeleton designs.

Keywords: energy efficient system, exoskeleton, motion enhancement, robotics

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Authors: Fatema-Tuz-Zohra Khanam, Ali Al-Naji, Asanka G. Perera, Kim Gibson, Javaan Chahl

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Conventional contact-based vital signs monitoring sensors such as pulse oximeters or electrocardiogram (ECG) may cause discomfort, skin damage, and infections, particularly in neonates with fragile, sensitive skin. Therefore, remote monitoring of the vital sign is desired in both clinical and non-clinical settings to overcome these issues. Camera-based vital signs monitoring is a recent technology for these applications with many positive attributes. However, there are still limited camera-based studies on neonates in a clinical setting. In this study, the heart rate (HR) and respiratory rate (RR) of eight infants at the Neonatal Intensive Care Unit (NICU) in Flinders Medical Centre were remotely monitored using a digital camera applying color and motion-based computational methods. The region-of-interest (ROI) was efficiently selected by incorporating an image decomposition method. Furthermore, spatial averaging, spectral analysis, band-pass filtering, and peak detection were also used to extract both HR and RR. The experimental results were validated with the ground truth data obtained from an ECG monitor and showed a strong correlation using the Pearson correlation coefficient (PCC) 0.9794 and 0.9412 for HR and RR, respectively. The RMSE between camera-based data and ECG data for HR and RR were 2.84 beats/min and 2.91 breaths/min, respectively. A Bland Altman analysis of the data also showed a close correlation between both data sets with a mean bias of 0.60 beats/min and 1 breath/min, and the lower and upper limit of agreement -4.9 to + 6.1 beats/min and -4.4 to +6.4 breaths/min for both HR and RR, respectively. Therefore, video camera imaging may replace conventional contact-based monitoring in NICU and has potential applications in other contexts such as home health monitoring.

Keywords: neonates, NICU, digital camera, heart rate, respiratory rate, image decomposition

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Authors: Meltem Haktaniyan, Eda Cagli, Irem Erel Goktepe

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Polymers that change their properties in response to different stimuli (e.g. light, temperature, pH, ionic strength or magnetic field) are called ‘smart’ or ‘stimuli-responsive polymers’. These polymers have been widely used in biomedical applications such as sensors, gene delivery, drug delivery or tissue engineering. Temperature-responsive polymers have been studied extensively for controlled drug delivery applications. As regard of pseudo-peptides, poly (2-alky-2-oxazoline)s are considered as good candidates for delivery systems due to their stealth behavior and nontoxicity. In order to build responsive multilayer films for controlled drug release applications from surface, Layer by layer technique (LBL) is a powerful technique with an advantage of nanometer scale control over spatial architecture and morphology. Multilayers can be constructed on surface where non-covalent interactions including electrostatic interactions, hydrogen bonding, and charge-transfer or hydrophobic-hydrophobic interactions. In the present study, hydrogen bounded multilayer films of poly (2-alky-2-oxazoline) s with tannic acid were prepared in order to use as a platform to release Doxorubicin (DOX) from surface with pH and thermal triggers. For this purpose, poly (2-isopropyl-2-oxazoline) (PIPOX) and poly (2-ethyl-2-oxazoline) (PETOX) were synthesized via cationic ring opening polymerization (CROP) with hydroxyl end groups. Two polymeric multilayer systems ((PETOX)/(DOX)-(TA) complexes and (PIPOX)/(DOX)-(TA) complexes) were designed to investigate of controlled release of Doxorubicin (DOX) from surface with pH and thermal triggers. The drug release profiles from the multilayer thin films with alterations of pH and temperature will been examined with UV-Vis Spectroscopy and Fluorescence Spectroscopy.

Keywords: temperature responsive polymers, h-bonded multilayer films, drug release, polyoxazoline

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Authors: Ho-Hsin Lee, Yue-Min Jiang, Shu-Hui Tsai, Jian-Ren Chen, Mei-Yu XU, Wen-Tien Wu

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This project presents an AI-based method and system for wound status analysis. The system uses a three-in-one sensor device to analyze wound status, including color, temperature, and a 3D sensor to provide wound information up to 2mm below the surface, such as redness, heat, and blood circulation information. The system has a 90% accuracy rate, requiring only one manual correction in 70% of cases, with a one-second delay. The system also provides an offline application that allows for manual correction of the wound bed range using color-based guidance to estimate wound bed size with 96% accuracy and a maximum of one manual correction in 96% of cases, with a one-second delay. Additionally, AI-assisted wound bed range selection achieves 100% of cases without manual intervention, with an accuracy rate of 76%, while AI-based wound tissue type classification achieves an 85.3% accuracy rate for five categories. The AI system also includes similar case search and expert recommendation capabilities. For AI-assisted wound range selection, the system uses WIFI6 technology, increasing data transmission speeds by 22 times. The project aims to save up to 64% of the time required for human wound record keeping and reduce the estimated time to assess wound status by 96%, with an 80% accuracy rate. Overall, the proposed AI method and system integrate multiple sensors to provide accurate wound information and offer offline and online AI-assisted wound bed size estimation and wound tissue type classification. The system decreases delay time to one second, reduces the number of manual corrections required, saves time on wound record keeping, and increases data transmission speed, all of which have the potential to significantly improve wound care and management efficiency and accuracy.

Keywords: wound status analysis, AI-based system, multi-sensor integration, color-based guidance

Procedia PDF Downloads 116

Authors: Koyar Rane

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Nano- to submicron size particles of narrow particle size distribution of semi-conducting TiO₂, ZnO, NiO, CuO, Fe₂O₃ have been synthesized by novel hydrazine method and tested for their gas sensing, photocatalytic and bactericidal activities and the behavior found to be enhanced when the oxides in the thin film forms, that obtained in a specially built spray pyrolysis reactor. Hydrazine method is novel in the sense, say, the UV absorption edge of the white pigment grade wide band gap (~3.2eV) TiO₂ and ZnO shifted to the visible region turning into yellowish particles, indicating modification occurring the band structure. The absorption in the visible region makes these oxides visible light sensitive photocatalysis in degrading pollutants, especially the organic dyes which otherwise increase the chemical oxygen demand of the drinking water, enabling the process feasible not under the harsh energetic UV radiation regime. The electromagnetic radiations on irradiation produce electron-hole pairs Semiconductor + hν → e⁻ + h⁺ The electron-hole pairs thus produced form Reactive Oxygen Species, ROS, on the surface of the semiconductors, O₂(adsorbed)+e⁻ → O₂• - superoxide ion OH-(surface)+h⁺ →•OH - Hydroxyl radical The ROS attack the organic material and micro-organisms. Our antibacterial studies indicate the metal oxides control the Biological Oxygen Demand (BOD) of drinking water which had beyond the safe level normally found in the municipal supply. Metal oxides in the thin film form show overall enhanced properties and the films are reusable. The results of the photodegradation and antibactericidal studies are discussed. Gas sensing studies too have been done to find the versatility of the multifunctional metal oxides.

Keywords: hydrazine method, visible light sensitive, photo-degradation of dyes, water/airborne pollutant

Procedia PDF Downloads 163

Authors: Bikis Muhammed, Sehra Sedigh Sarvestani, Ali R. Hurson, Lasanthi Gamage

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Vehicular traffic events have overly complex spatial correlations and temporal interdependencies and are also influenced by environmental events such as weather conditions. To capture these spatial and temporal interdependencies and make more realistic vehicular traffic predictions, graph neural networks (GNN) based traffic prediction models have been extensively utilized due to their capability of capturing non-Euclidean spatial correlation very effectively. However, most of the already existing GNN-based traffic prediction models have some limitations during learning complex and dynamic spatial and temporal patterns due to the following missing factors. First, most GNN-based traffic prediction models have used static distance or sometimes haversine distance mechanisms between spatially separated traffic observations to estimate spatial correlation. Secondly, most GNN-based traffic prediction models have not incorporated environmental events that have a major impact on the normal traffic states. Finally, most of the GNN-based models did not use an attention mechanism to focus on only important traffic observations. The objective of this paper is to study and make real-time vehicular traffic predictions while incorporating the effect of weather conditions. To fill the previously mentioned gaps, our prediction model uses a real-time driving distance between sensors to build a distance matrix or spatial adjacency matrix and capture spatial correlation. In addition, our prediction model considers the effect of six types of weather conditions and has an attention mechanism in both spatial and temporal data aggregation. Our prediction model efficiently captures the spatial and temporal correlation between traffic events, and it relies on the graph attention network (GAT) and Bidirectional bidirectional long short-term memory (Bi-LSTM) plus attention layers and is called GAT-BILSTMA.

Keywords: deep learning, real time prediction, GAT, Bi-LSTM, attention

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Authors: Francois Defay, Martim Calao, Jean Francois Dassieu, Laurent Salvetat

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Electrical vehicles are a solution for reducing the pollution using green energy. The shell Eco-Marathon provides rules in order to minimize the battery use for the race. The use of solar panel combined with efficient motor control and race strategy allow driving a 60kg vehicle with one pilot using only the solar energy in the best case. This paper presents a complete modelization of a solar vehicle used for the shell eco-marathon. This project called Helios is cooperation between non-graduated students, academic institutes, and industrials. The prototype is an ultra-energy-efficient vehicle based on one-meter square solar panel and an own-made brushless controller to optimize the electrical part. The vehicle is equipped with sensors and embedded system to provide all the data in real time in order to evaluate the best strategy for the course. A complete modelization with Matlab/Simulink is used to test the optimal strategy to increase the global endurance. Experimental results are presented to validate the different parts of the model: mechanical, aerodynamics, electrical, solar panel. The major finding of this study is to provide solutions to identify the model parameters (Rolling Resistance Coefficient, drag coefficient, motor torque coefficient, etc.) by means of experimental results combined with identification techniques. One time the coefficients are validated, the strategy to optimize the consumption and the average speed can be tested first in simulation before to be implanted for the race. The paper describes all the simulation and experimental parts and provides results in order to optimize the global efficiency of the vehicle. This works have been started four years ago and evolved many students for the experimental and theoretical parts and allow to increase the knowledge on electrical self-efficient vehicle.

Keywords: electrical vehicle, endurance, optimization, shell eco-marathon

Procedia PDF Downloads 267

Authors: Winncy Du, Jeremy Nguyen, Harpinder Dhillon, Reinardus Justin Halim, Clayton Haske, Trent Hughes, Marissa Ortiz, Rozy Saini

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Stroke often causes death or cerebro-vascular (CV) brain damage. Most patients with CV brain damage lost their motor control on their limbs. This paper focuses on developing a reliable, safe, and non-invasive EEG-based robot-assistant stroke rehabilitation system to help stroke survivors to rapidly restore their motor control functions for their limbs. An electroencephalogram (EEG) recording device (EPOC Headset) and was used to detect a patient’s brain activities. The EEG signals were then processed, classified, and interpreted to the motion intentions, and then converted to a series of robot motion commands. A six-axis articulated robot (AdeptSix 300) was employed to provide the intended motions based on these commends. To ensure the EEG device, the computer, and the robot can communicate to each other, an Arduino microcontroller is used to physically execute the programming codes to a series output pins’ status (HIGH or LOW). Then these “hardware” commends were sent to a 24 V relay to trigger the robot’s motion. A lookup table for various motion intensions and the associated EEG signal patterns were created (through training) and installed in the microcontroller. Thus, the motion intention can be direct determined by comparing the EEG patterns obtaibed from the patient with the look-up table’s EEG patterns; and the corresponding motion commends are sent to the robot to provide the intended motion without going through feature extraction and interpretation each time (a time-consuming process). For safety sake, an extender was designed and attached to the robot’s end effector to ensure the patient is beyond the robot’s workspace. The gripper is also designed to hold the patient’s limb. The test results of this rehabilitation system show that it can accurately interpret the patient’s motion intension and move the patient’s arm to the intended position.

Keywords: brain waves, EEG sensor, motion control, robot-assistant stroke rehabilitation

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Authors: Temenuzhka Spasova, Nadya Yanakieva

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Snow modulates the hydrological cycle and influences the functioning of ecosystems and is a significant resource for many populations whose water is harvested from cold regions. Snow observations are important for validating climate models. The accumulation and rapid melt of snow are two of the most dynamical seasonal environmental changes on the Earth’s surface. The actuality of this research is related to the modern tendencies of the remote sensing application in the solution of problems of different nature in the ecological monitoring of the environment. The subject of the study is the dynamic during the different seasons on Livingstone Island, South Shetland Islands in Antarctica and on Svalbard in Arctic. The objects were analyzed and mapped according to the Еuropean Space Agency data (ESA), acquired by sensors Sentinel-1 SAR (Synthetic Aperture Radar), Sentinel 2 MSI and GIS. Results have been obtained for changes in snow coverage during the summer-winter transition and its dynamics in the two hemispheres. The data used is of high time-spatial resolution, which is an advantage when looking at the snow cover. The MSI images are with different spatial resolution at the Earth surface range. The changes of the environmental objects are shown with the SAR images and different processing approaches. The results clearly show that snow and snow melting can be best registered by using SAR data via hh- horizontal polarization. The effect of the researcher on aerospace data and technology enables us to obtain different digital models, structuring and analyzing results excluding the subjective factor. Because of the large extent of terrestrial snow coverage and the difficulties in obtaining ground measurements over cold regions, remote sensing and GIS represent an important tool for studying snow areas and properties from regional to global scales.

Keywords: climate changes, GIS, remote sensing, SAR images, snow coverage

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Authors: Jean Berger, Mohamed Barkaoui

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Discrete search path planning in time-constrained uncertain environment relying upon imperfect sensors is known to be hard, and current problem-solving techniques proposed so far to compute near real-time efficient path plans are mainly bounded to provide a few move solutions. A new information-theoretic –based open-loop decision model explicitly incorporating false alarm sensor readings, to solve a single agent military logistics search-and-delivery path planning problem with anticipated feedback is presented. The decision model consists in minimizing expected entropy considering anticipated possible observation outcomes over a given time horizon. The model captures uncertainty associated with observation events for all possible scenarios. Entropy represents a measure of uncertainty about the searched target location. Feedback information resulting from possible sensor observations outcomes along the projected path plan is exploited to update anticipated unit target occupancy beliefs. For the first time, a compact belief update formulation is generalized to explicitly include false positive observation events that may occur during plan execution. A novel genetic algorithm is then proposed to efficiently solve search path planning, providing near-optimal solutions for practical realistic problem instances. Given the run-time performance of the algorithm, natural extension to a closed-loop environment to progressively integrate real visit outcomes on a rolling time horizon can be easily envisioned. Computational results show the value of the approach in comparison to alternate heuristics.

Keywords: search path planning, false alarm, search-and-delivery, entropy, genetic algorithm

Procedia PDF Downloads 360

Authors: Borhan Marzougui

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Road and Transport Authority (RTA) is moving ahead with the implementation of the leader’s vision in exploring all avenues that may bring better security and safety services to the community. Smart transport means using smart technologies such as IoT (Internet of Things). This technology continues to affirm its important role in the context of Information and Transportation Systems. In fact, IoT is a network of Internet-connected objects able to collect and exchange different data using embedded sensors. With the growth of IoT, Distributed Denial of Service (DDoS) attacks is also growing exponentially. DDoS attacks are the major and a real threat to various transportation services. Currently, the defense mechanisms are mainly passive in nature, and there is a need to develop a smart technique to handle them. In fact, new IoT devices are being used into a botnet for DDoS attackers to accumulate for attacker purposes. The aim of this paper is to provide a relevant understanding of dangerous types of DDoS attack related to IoT and to provide valuable guidance for the future IoT security method. Our methodology is based on development of the distributed algorithm. This algorithm manipulates dedicated intelligent and cooperative agents to prevent and to mitigate DDOS attacks. The proposed technique ensure a preventive action when a malicious packets start to be distributed through the connected node (Network of IoT devices). In addition, the devices such as camera and radio frequency identification (RFID) are connected within the secured network, and the data generated by it are analyzed in real time by intelligent and cooperative agents. The proposed security system is based on a multi-agent system. The obtained result has shown a significant reduction of a number of infected devices and enhanced the capabilities of different security dispositives.

Keywords: IoT, DDoS, attacks, botnet, security, agents

Procedia PDF Downloads 144