Search results for: silhouette detection

Authors: Lily Ranjbar, Haori Yang

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Online education has been around for several decades, but the importance of online education became evident after the COVID-19 pandemic. Eventhough the online delivery approach works well for knowledge building through delivering content and oversight processes, it has limitations in developing hands-on laboratory skills, especially in the STEM field. During the pandemic, many education institutions faced numerous challenges in delivering lab-based courses, especially in the STEM field. Also, many students worldwide were unable to practice working with lab equipment due to social distancing or the significant cost of highly specialized equipment. The laboratory plays a crucial role in nuclear science and engineering education. It can engage students and improve their learning outcomes. In addition, online education and virtual labs have gained substantial popularity in engineering and science education. Therefore, developing virtual labs is vital for institutions to deliver high-class education to their students, including their online students. The School of Nuclear Science and Engineering (NSE) at Oregon State University, in partnership with SpectralLabs company, has developed an Advanced Virtual Radiation Detection and Measurement Lab (AVR-DML) to offer a fully online Master of Health Physics program. It was essential for us to use a system that could simulate nuclear modules that accurately replicate the underlying physics, the nature of radiation and radiation transport, and the mechanics of the instrumentations used in the real radiation detection lab. It was all accomplished using a Realistic, Adaptive, Interactive Learning System (RAILS). RAILS is a comprehensive software simulation-based learning system for use in training. It is comprised of a web-based learning management system that is located on a central server, as well as a 3D-simulation package that is downloaded locally to user machines. Users will find that the graphics, animations, and sounds in RAILS create a realistic, immersive environment to practice detecting different radiation sources. These features allow students to coexist, interact and engage with a real STEM lab in all its dimensions. It enables them to feel like they are in a real lab environment and to see the same system they would in a lab. Unique interactive interfaces were designed and developed by integrating all the tools and equipment needed to run each lab. These interfaces provide students full functionality for data collection, changing the experimental setup, and live data collection with real-time updates for each experiment. Students can manually do all experimental setups and parameter changes in this lab. Experimental results can then be tracked and analyzed in an oscilloscope, a multi-channel analyzer, or a single-channel analyzer (SCA). The advanced virtual radiation detection and measurement laboratory developed in this study enabled the NSE school to offer a fully online MHP program. This flexibility of course modality helped us to attract more non-traditional students, including international students. It is a valuable educational tool as students can walk around the virtual lab, make mistakes, and learn from them. They have an unlimited amount of time to repeat and engage in experiments. This lab will also help us speed up training in nuclear science and engineering.

Keywords: advanced radiation detection and measurement, virtual laboratory, realistic adaptive interactive learning system (rails), online education in stem fields, student engagement, stem online education, stem laboratory, online engineering education

Procedia PDF Downloads 75

Authors: John E. Foley, Martin Miele, Raul Fonda, Jon Jacobson

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This paper presents development and implementation of new and innovative data collection and analysis methodologies based on deployment of total field magnetometer arrays. Our research has focused on the development of a vertically-integrated suite of platforms all utilizing common data acquisition, data processing and analysis tools. These survey platforms include low-altitude helicopters and ground-based vehicles, including robots, for terrestrial mapping applications. For marine settings the sensor arrays are deployed from either a hydrodynamic bottom-following wing towed from a surface vessel or from a towed floating platform for shallow-water settings. Additionally, sensor arrays are deployed from tethered remotely operated vehicles (ROVs) for underwater settings where high maneuverability is required. While the primary application of these systems is the detection and mapping of unexploded ordnance (UXO), these system are also used for various infrastructure mapping and geologic investigations. For each application, success is driven by the integration of magnetometer arrays, accurate geo-positioning, system noise mitigation, and stable deployment of the system in appropriate proximity of expected targets or features. Each of the systems collects geo-registered data compatible with a web-enabled data management system providing immediate access of data and meta-data for remote processing, analysis and delivery of results. This approach allows highly sophisticated magnetic processing methods, including classification based on dipole modeling and remanent magnetization, to be efficiently applied to many projects. This paper also briefly describes the initial development of magnetometer-based detection systems deployed from low-altitude helicopter platforms and the subsequent successful transition of this technology to the marine environment. Additionally, we present examples from a range of terrestrial and marine settings as well as ongoing research efforts related to sensor miniaturization for unmanned aerial vehicle (UAV) magnetic field mapping applications.

Keywords: dipole modeling, magnetometer mapping systems, sub-surface infrastructure mapping, unexploded ordnance detection

Procedia PDF Downloads 452

Authors: Sebastian Töpfer, Jorge Fuenzalida, Marta Gilaberte Basset, Juan P. Torres, Markus Gräfe

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This work presents an experimental and theoretical study about the noise resilience of quantum holography with undetected photons. Quantum imaging has become an important research topic in the recent years after its first publication in 2014. Following this research, advances towards different spectral ranges in detection and different optical geometries have been made. Especially an interest in the field of near infrared to mid infrared measurements has developed, because of the unique characteristic, that allows to sample a probe with photons in a different wavelength than the photons arriving at the detector. This promising effect can be used for medical applications, to measure in the so-called molecule fingerprint region, while using broadly available detectors for the visible spectral range. Further advance the development of quantum imaging methods have been made by new measurement and detection schemes. One of which is quantum holography with undetected light. It combines digital phase shifting holography with quantum imaging to extent the obtainable sample information, by measuring not only the object transmission, but also its influence on the phase shift experienced by the transmitted light. This work will present extended research for the quantum holography with undetected light scheme regarding the influence of external noise. It is shown experimentally and theoretically that the samples information can still be at noise levels of 250 times higher than the signal level, because of its information being transmitted by the interferometric pattern. A detailed theoretic explanation is also provided.

Keywords: distillation, quantum holography, quantum imaging, quantum metrology

Procedia PDF Downloads 48

Authors: Rudzani Manafe, Ezekiel Green

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Brucella, has many different virulence factors that act as a causative agent of brucellosis, depending on the environment and other factors, some factors may play a role more than others during infection and as a result, play a role in becoming a causative agent for pathogenesis. Brucella melitensis and Brucella abortus are considered to be pathogenic to humans. The genetic regularity of nine potential causes of virulence of two Brucella species in Eastern Cape livestock have been examined. A hundred and twenty isolates obtained from Molecular Pathogenesis and Molecular Epidemiology Research Group (MPMERG) were used for this study. All isolates were grown on Brucella agar medium. Nine primer pairs were used for the detection of virB2, virB5, vceC, btpA, btpB, prpA, betB, bpe275, and bspB virulence factors using Polymerase chain reaction (PCR). Approximately 100% was observed for genes BecC and BetB from B. arbotus. While the lowest gene observed was PrpA at 4.6% from B. arbotus. BetB was detected in 34.7%, while virB2 and prpA (0%) were not detected in B. melitensis. The results from this research suggest that most isolates of Brucella have virulence-related genes associated with disease pathogenesis. Finally, our findings showed that Brucella strains in the Eastern Cape Province are extremely virulent as virulence characteristics exist in most strains investigated.

Keywords: putative virulence genes, brucella, polymerase chain reaction, milk

Procedia PDF Downloads 114

Authors: Francesca Crivellari, Nicholas Mavrogiannis, Zachary Gagnon

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Portable diagnostic methods have become increasingly important for a number of different purposes: point-of-care screening in developing nations, environmental contamination studies, bio/chemical warfare agent detection, and end-user use for commercial health monitoring. The cheapest and most portable methods currently available are paper-based – lateral flow and dipstick methods are widely available in drug stores for use in pregnancy detection and blood glucose monitoring. These tests are successful because they are cheap to produce, easy to use, and require minimally invasive sampling. While adequate for their intended uses, in the realm of blood-borne pathogens and numerous cancers, these paper-based methods become unreliable, as they lack the nM/pM sensitivity currently achieved by clinical diagnostic methods. Clinical diagnostics, however, utilize techniques involving surface plasmon resonance (SPR) and enzyme-linked immunosorbent assays (ELISAs), which are expensive and unfeasible in terms of portability. To develop a better, competitive biosensor, we must reduce the cost of one, or increase the sensitivity of the other. Electric fields are commonly utilized in microfluidic devices to manipulate particles, biomolecules, and cells. Applications in this area, however, are primarily limited to interfaces formed between immiscible interfaces. Miscible, liquid-liquid interfaces are common in microfluidic devices, and are easily reproduced with simple geometries. Here, we demonstrate the use of electrical fields at liquid-liquid electrical interfaces, known as fluidic dielectrophoresis, (fDEP) for biodetection in a microfluidic device. In this work, we apply an AC electric field across concurrent laminar streams with differing conductivities and permittivities to polarize the interface and induce a discernible, near-immediate, frequency-dependent interfacial tilt. We design this aqueous electrical interface, which becomes the biosensing “substrate,” to be intelligent – it “moves” only when a target of interest is present. This motion requires neither labels nor expensive electrical equipment, so the biosensor is inexpensive and portable, yet still capable of sensitive detection. Nanoparticles, due to their high surface-area-to-volume ratio, are often incorporated to enhance detection capabilities of schemes like SPR and fluorimetric assays. Most studies currently investigate binding at an immobilized solid-liquid or solid-gas interface, where particles are adsorbed onto a planar surface, functionalized with a receptor to create a reactive substrate, and subsequently flushed with a fluid or gas with the relevant analyte. These typically involve many preparation and rinsing steps, and are susceptible to surface fouling. Our microfluidic device is continuously flowing and renewing the “substrate,” and is thus not subject to fouling. In this work, we demonstrate the ability to electrokinetically detect biomolecules binding to functionalized nanoparticles at liquid-liquid interfaces using fDEP. In biotin-streptavidin experiments, we report binding detection limits on the order of 1-10 pM, without amplifying signals or concentrating samples. We also demonstrate the ability to detect this interfacial motion, and thus the presence of binding, using impedance spectroscopy, allowing this scheme to become non-optical, in addition to being label-free.

Keywords: biodetection, dielectrophoresis, microfluidics, nanoparticles

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Authors: Jean-Francois Rajotte, Martin Sotir, Frank Gouineau

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The installation and management of traffic monitoring devices can be costly from both a financial and resource point of view. It is therefore important to take advantage of in-place infrastructures to extract the most information. Here we show how low-quality urban road traffic images from cameras already available in many cities (such as Montreal, Vancouver, and Toronto) can be used to estimate traffic flow. To this end, we use a pre-trained neural network, developed for object detection, to count vehicles within images. We then compare the results with human annotations gathered through crowdsourcing campaigns. We use this comparison to assess performance and calibrate the neural network annotations. As a use case, we consider six months of continuous monitoring over hundreds of cameras installed in the city of Montreal. We compare the results with city-provided manual traffic counting performed in similar conditions at the same location. The good performance of our system allows us to consider applications which can monitor the traffic conditions in near real-time, making the counting usable for traffic-related services. Furthermore, the resulting annotations pave the way for building a historical vehicle counting dataset to be used for analysing the impact of road traffic on many city-related issues, such as urban planning, security, and pollution.

Keywords: traffic monitoring, deep learning, image annotation, vehicles, roads, artificial intelligence, real-time systems

Procedia PDF Downloads 179

Authors: Ibrahim Ilker Ozyigit, Fatih Tabanli, Sezin Adinir

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In this study, Ascochyta blight resistance was investigated in 34 registered chickpea varieties, which are widely planting in different regions of Turkey. For this aim, molecular marker techniques, such as STMS, RAPD and ISSR were used. Ta2, Ta146 and Ts54 primers were used for STMS, while UBC733 and UBC681 primers for RAPD, and UBC836 and UBC858 primers for ISSR. Ta2, Ts54 and Ta146 (STMS), and UBC733 (RAPD) primers demonstrated the distinctive feature for Ascochyta blight resistance. Ta2, Ts54 and Ta146 primers yielded the quite effective results in detection of resistant and sensitive varieties. Besides, UBC 733 primer distinguished all kinds of standard did not give any reliable results for other varieties since it demonstrated all as resistant. In addition, monomorphic bands were obtained from UBC681 (RAPD), and UBC836 and UBC858 (ISSR) primers, not demonstrating reliable results in detection of resistance against Ascochyta blight disease. Obtained results informed us about both disease resistance and genetic diversity in registered Turkish chickpea varieties. This project was funded through the Scientific Research Projects of Marmara University under Grant Number FEN-C-YLP-070617-0365 and The Scientific and Technological Research Council of Turkey (TUBITAK) under Grant Number 113O070.

Keywords: plant genetics, ISSR, RAPD, STMS

Procedia PDF Downloads 178

Authors: Yin-Yee Leong, Jack C. Yue, Hsin-Chung Wang

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Human longevity has been experiencing its largest increase since the end of World War II, and modeling the mortality rates is therefore often the focus of many studies. Among all mortality models, the Lee–Carter model is the most popular approach since it is fairly easy to use and has good accuracy in predicting mortality rates (e.g., for Japan and the USA). However, empirical studies from several countries have shown that the age parameters of the Lee–Carter model are not constant in time. Many modifications of the Lee–Carter model have been proposed to deal with this problem, including adding an extra cohort effect and adding another period effect. In this study, we propose a spatial modification and use clusters to explain why the age parameters of the Lee–Carter model are not constant. In spatial analysis, clusters are areas with unusually high or low mortality rates than their neighbors, where the “location” of mortality rates is measured by age and time, that is, a 2-dimensional coordinate. We use a popular cluster detection method—Spatial scan statistics, a local statistical test based on the likelihood ratio test to evaluate where there are locations with mortality rates that cannot be described well by the Lee–Carter model. We first use computer simulation to demonstrate that the cluster effect is a possible source causing the problem of the age parameters not being constant. Next, we show that adding the cluster effect can solve the non-constant problem. We also apply the proposed approach to mortality data from Japan, France, the USA, and Taiwan. The empirical results show that our approach has better-fitting results and smaller mean absolute percentage errors than the Lee–Carter model.

Keywords: mortality improvement, Lee–Carter model, spatial statistics, cluster detection

Procedia PDF Downloads 156

Authors: Ali Ben Abbes, ImedRiadh Farah, Vincent Barra

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Remotely sensed data are a significant source for monitoring and updating databases for land use/cover. Nowadays, changes detection of urban area has been a subject of intensive researches. Timely and accurate data on spatio-temporal changes of urban areas are therefore required. The data extracted from multi-temporal satellite images are usually non-stationary. In fact, the changes evolve in time and space. This paper is an attempt to propose a methodology for changes detection in urban area by combining a non-stationary decomposition method and stochastic modeling. We consider as input of our methodology a sequence of satellite images I₁, I₂, … I_n at different periods (t = 1, 2, ..., n). Firstly, a preprocessing of multi-temporal satellite images is applied. (e.g. radiometric, atmospheric and geometric). The systematic study of global urban expansion in our methodology can be approached in two ways: The first considers the urban area as one same object as opposed to non-urban areas (e.g. vegetation, bare soil and water). The objective is to extract the urban mask. The second one aims to obtain a more knowledge of urban area, distinguishing different types of tissue within the urban area. In order to validate our approach, we used a database of Tres Cantos-Madrid in Spain, which is derived from Landsat for a period (from January 2004 to July 2013) by collecting two frames per year at a spatial resolution of 25 meters. The obtained results show the effectiveness of our method.

Keywords: multi-temporal satellite image, urban growth, non-stationary, stochastic model

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Authors: Vishal Kumar, Soumitra Satapathi

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Föster Resonance Energy Transfer (FRET) is a powerful technique used to probe close-range molecular interactions. Physically, the FRET phenomenon manifests as a dipole–dipole interaction between closely juxtaposed fluorescent molecules (10–100 Å). Our effort is to employ this FRET technique to make a prototype device for highly sensitive detection of environment pollutant. Among the most common environmental pollutants, nitroaromatic compounds (NACs) are of particular interest because of their durability and toxicity. That’s why, sensitive and selective detection of small amounts of nitroaromatic explosives, in particular, 2,4,6-trinitrophenol (TNP), 2,4-dinitrotoluene (DNT) and 2,4,6-trinitrotoluene (TNT) has been a critical challenge due to the increasing threat of explosive-based terrorism and the need of environmental monitoring of drinking and waste water. In addition, the excessive utilization of TNP in several other areas such as burn ointment, pesticides, glass and the leather industry resulted in environmental accumulation, and is eventually contaminating the soil and aquatic systems. To the date, high number of elegant methods, including fluorimetry, gas chromatography, mass, ion-mobility and Raman spectrometry have been successfully applied for explosive detection. Among these efforts, fluorescence-quenching methods based on the mechanism of FRET show good assembly flexibility, high selectivity and sensitivity. Here, we report a FRET-based sensor system for the highly selective detection of NACs, such as TNP, DNT and TNT. The sensor system is composed of a copolymer Poly [(N,N-dimethylacrylamide)-co-(Boc-Trp-EMA)] (RP) bearing tryptophan derivative in the side chain as donor and dansyl tagged copolymer P(MMA-co-Dansyl-Ala-HEMA) (DCP) as an acceptor. Initially, the inherent fluorescence of RP copolymer is quenched by non-radiative energy transfer to DCP which only happens once the two molecules are within Förster critical distance (R0). The excellent spectral overlap (Jλ= 6.08×10¹⁴ nm⁴M⁻¹cm⁻¹) between donors’ (RP) emission profile and acceptors’ (DCP) absorption profile makes them an exciting and efficient FRET pair i.e. further confirmed by the high rate of energy transfer from RP to DCP i.e. 0.87 ns⁻¹ and lifetime measurement by time correlated single photon counting (TCSPC) to validate the 64% FRET efficiency. This FRET pair exhibited a specific fluorescence response to NACs such as DNT, TNT and TNP with 5.4, 2.3 and 0.4 µM LODs, respectively. The detection of NACs occurs with high sensitivity by photoluminescence quenching of FRET signal induced by photo-induced electron transfer (PET) from electron-rich FRET pair to electron-deficient NAC molecules. The estimated stern-volmer constant (KSV) values for DNT, TNT and TNP are 6.9 × 10³, 7.0 × 10³ and 1.6 × 104 M⁻¹, respectively. The mechanistic details of molecular interactions are established by time-resolved fluorescence, steady-state fluorescence and absorption spectroscopy confirmed that the sensing process is of mixed type, i.e. both dynamic and static quenching as lifetime of FRET system (0.73 ns) is reduced to 0.55, 0.57 and 0.61 ns DNT, TNT and TNP, respectively. In summary, the simplicity and sensitivity of this novel FRET sensor opens up the possibility of designing optical sensor of various NACs in one single platform for developing multimodal sensor for environmental monitoring and future field based study.

Keywords: FRET, nitroaromatic, stern-Volmer constant, tryptophan and dansyl tagged copolymer

Procedia PDF Downloads 115

Authors: Alexander Rodríguez-Hernández, Arnulfo Rojas-Perez, Liz Diaz-Vazquez

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The rise in consumerism over the past century has resulted in the creation of higher amounts of plasticizers, personal care products and other chemical substances, which enter and accumulate in water systems. Other sources of pollutants in Neotropical regions experience large inputs of nutrients with these pollutants resulting in eutrophication of water which consume large quantities of oxygen, resulting in high fish mortality. This dilemma has created a need for the development of targeted detection in complex matrices and remediation of emerging contaminants. We have synthesized carbon nanoparticles from macro algae (Ulva fasciata) by oxidizing the graphitic carbon network under extreme acidic conditions. The resulting material was characterized by STEM, yielding a spherical 12 nm average diameter nanoparticles, which can be fixed into a polysaccharide aerogel synthesized from the same macro algae. Spectrophotometer analyses show a pH dependent fluorescent behavior varying from 450-620 nm in aqueous media. Heavily oxidized edges provide for easy functionalization with enzymes for a more targeted analysis and remediation technique. Given the optical properties of the carbon base nanoparticles and the numerous possibilities of functionalization, we have developed a selective and robust targeted bio-detection and bioremediation technique for the treatment of emerging contaminants in complex matrices like estuarine embayment.

Keywords: aerogels, carbon nanoparticles, fluorescent, targeted analysis

Procedia PDF Downloads 225

Authors: Kaouther Bergaui, Nafaa Reguigui, Charles Gary

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Characterization and the applications of deuterium-deuterium (DD) neutron generator developed by Adelphie technology and acquired by the National Centre of Nuclear Science and Technology (NCNST) were presented in this work. We study the performance of the neutron generator in terms of neutron yield, production efficiency, and the ionic current as a function of the acceleration voltage at various RF powers. We provide the design and optimization of the PGNAA chamber and thus give insight into the capabilities of the planned PGNAA facility. Additional non-destructive techniques were studied employing the DD neutron generator, such as PGNAA and neutron radiography: The PGNAA is used for determining the concentration of 10B in Si and SiO2 matrices by using a germanium detector HPGe and the results obtained are compared with PGNAA system using a Sodium Iodide detector (NaI (Tl)); Neutron radiography facility was tested and simulated, using a camera device CCD and simulated by the Monte Carlo code; and the explosive detection system (EDS) also simulated using the Monte Carlo code. The study allows us to show that the new models of DD neutron generators are feasible and that superior-quality neutron beams could be produced and used for various applications. The feasibility of Boron neutron capture therapy (BNCT) for cancer treatment using a neutron generator was assessed by optimizing Beam Shaping Assembly (BSA) on a phantom using Monte-Carlo (MCNP6) simulations.

Keywords: neutron generator deuterium-deuterium, Monte Carlo method, radiation, neutron flux, neutron activation analysis, born, neutron radiography, explosive detection, BNCT

Procedia PDF Downloads 172

Authors: Mace Bentley, Zhuojun Duan, Tobias Gerken, Dudley Bonsal, Henry Way, Endre Szakal, Mia Pham, Hunter Donaldson, Chelsea Lang, Hayden Abbott, Leah Wilcynzski

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Air pollution has the potential to modify the lifespan and intensity of thunderstorms and the properties of lightning. Using data mining and geovisualization, we investigate how background climate and weather conditions shape variability in urban air pollution and how this, in turn, shapes thunderstorms as measured by the intensity, distribution, and frequency of cloud-to-ground lightning. A spatiotemporal analysis was conducted in order to identify thunderstorms using high-resolution lightning detection network data. Over seven million lightning flashes were used to identify more than 196,000 thunderstorms that occurred between 2006 - 2020 in the Washington, DC Metropolitan Region. Each lightning flash in the dataset was grouped into thunderstorm events by means of a temporal and spatial clustering algorithm. Once the thunderstorm event database was constructed, hourly wind direction, wind speed, and atmospheric thermodynamic data were added to the initiation and dissipation times and locations for the 196,000 identified thunderstorms. Hourly aerosol and air quality data for the thunderstorm initiation times and locations were also incorporated into the dataset. Developing thunderstorm climatologies using a lightning tracking algorithm and lightning detection network data was found to be useful for visualizing the spatial and temporal distribution of urban augmented thunderstorms in the region.

Keywords: lightning, urbanization, thunderstorms, climatology

Procedia PDF Downloads 56

Authors: Hanaa M. Abu El Einin, Ahmed T. Sharaf El- Din

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Biomphalaria snails play an integral role in the transmission of Schistosoma mansoni, the causative agent for human schistosomiasis. Two species of Biomphalaria were reported from Egypt, Biomphalaria alexandrina and Biomphalaria glabrata, and later on a hybrid of B. alexandrina and B. glabrata was reported in streams at Nile Delta. All were known to be excellent hosts of S. mansoni. Host-parasite relationship can be viewed in terms of snail susceptibility and parasite infectivity. The objective of this study will highlight the progress that has been made in using molecular approaches to describe the correct identification of snail species that participating in transmission of schistosomiasis, rapid diagnose of infection in addition to susceptibility and resistance type. Snails were identified using of molecular methods involving Randomly Amplified Polymorphic DNA (RAPD), Polymerase Chain Reaction, Restriction Fragment Length Polymorphisms (PCR-RFLP) and Species - specific- PCR. Molecular approaches to diagnose parasite in snails from Egypt: Nested PCR assay and small subunit (SSU) rRNA gene. Also RAPD PCR for study susceptible and resistance phenotype. The results showed that RAPD- PCR, PCR-RFLP and species-specific-PCR techniques were confirmed that: no evidence for the presence of B. glabrata in Egypt, All Biomphalaria snails collected identified as B. alexandrina snail i-e B alexandrinia is a common and no evidence for hybridization with B. glabrata. The adopted specific nested PCR assay revealed much higher sensitivity which enables the detection of S. mansoni infected snails down to 3 days post infection. Nested PCR method for detection of infected snails using S. mansoni fructose -1,6- bisphosphate aldolase (SMALDO) primer, these primers are specific only for S. mansoni and not cross reactive with other schistosomes or molluscan aldolases Nested PCR for such gene is sensitive enough to detect one cercariae. Genetic variations between B. alexandrina strains that are susceptible and resistant to Schistosoma infec¬tion using a RAPD-PCR showed that 39.8% of the examined snails collected from the field were resistant, while 60.2% of these snails showed high infection rates. In conclusion the genetics of the intermediate host plays a more important role in the epidemiological control of schistosomiasis.

Keywords: biomphalaria, molecular differentiation, parasite detection, schistosomiasis

Procedia PDF Downloads 184

Authors: Ionut Vintu, Stefan Laible, Ruth Schulz

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Agricultural robotics has been developing steadily over recent years, with the goal of reducing and even eliminating pesticides used in crops and to increase productivity by taking over human labor. The majority of crops are arranged in rows. The first step towards autonomous robots, capable of driving in fields and performing crop-handling tasks, is for robots to robustly detect the rows of plants. Recent work done towards autonomous driving between plant rows offers big robotic platforms equipped with various expensive sensors as a solution to this problem. These platforms need to be driven over the rows of plants. This approach lacks flexibility and scalability when it comes to the height of plants or distance between rows. This paper proposes instead an algorithm that makes use of cheaper sensors and has a higher variability. The main application is in tree nurseries. Here, plant height can range from a few centimeters to a few meters. Moreover, trees are often removed, leading to gaps within the plant rows. The core idea is to combine row detection algorithms with graph-based localization methods as they are used in SLAM. Nodes in the graph represent the estimated pose of the robot, and the edges embed constraints between these poses or between the robot and certain landmarks. This setup aims to improve individual plant detection and deal with exception handling, like row gaps, which are falsely detected as an end of rows. Four methods were developed for detecting row structures in the fields, all using a point cloud acquired with a 3D LiDAR as an input. Comparing the field coverage and number of damaged plants, the method that uses a local map around the robot proved to perform the best, with 68% covered rows and 25% damaged plants. This method is further used and combined with a graph-based localization algorithm, which uses the local map features to estimate the robot’s position inside the greater field. Testing the upgraded algorithm in a variety of simulated fields shows that the additional information obtained from localization provides a boost in performance over methods that rely purely on perception to navigate. The final algorithm achieved a row coverage of 80% and an accuracy of 27% damaged plants. Future work would focus on achieving a perfect score of 100% covered rows and 0% damaged plants. The main challenges that the algorithm needs to overcome are fields where the height of the plants is too small for the plants to be detected and fields where it is hard to distinguish between individual plants when they are overlapping. The method was also tested on a real robot in a small field with artificial plants. The tests were performed using a small robot platform equipped with wheel encoders, an IMU and an FX10 3D LiDAR. Over ten runs, the system achieved 100% coverage and 0% damaged plants. The framework built within the scope of this work can be further used to integrate data from additional sensors, with the goal of achieving even better results.

Keywords: 3D LiDAR, agricultural robots, graph-based localization, row detection

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Authors: Zhipeng Chen, Peng Zhang, Qingyun Liu, Li Guo

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With the Internet becoming the dominant channel for business and life, many IPs are increasingly masked using web proxies for illegal purposes such as propagating malware, impersonate phishing pages to steal sensitive data or redirect victims to other malicious targets. Moreover, as Internet traffic continues to grow in size and complexity, it has become an increasingly challenging task to detect the proxy service due to their dynamic update and high anonymity. In this paper, we present an approach based on behavioral graph analysis to study the behavior similarity of web proxy users. Specifically, we use bipartite graphs to model host communications from network traffic and build one-mode projections of bipartite graphs for discovering social-behavior similarity of web proxy users. Based on the similarity matrices of end-users from the derived one-mode projection graphs, we apply a simple yet effective spectral clustering algorithm to discover the inherent web proxy users behavior clusters. The web proxy URL may vary from time to time. Still, the inherent interest would not. So, based on the intuition, by dint of our private tools implemented by WebDriver, we examine whether the top URLs visited by the web proxy users are web proxies. Our experiment results based on real datasets show that the behavior clusters not only reduce the number of URLs analysis but also provide an effective way to detect the web proxies, especially for the unknown web proxies.

Keywords: bipartite graph, one-mode projection, clustering, web proxy detection

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Authors: Emma K. Sales

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Durian is the flagship fruit of Mindanao and there is an abundance of several cultivars with many confusing identities/ names. The project was conducted to develop procedure for reliable and rapid detection and sorting of durian planting materials. Moreover, it is also aimed to establish specific genetic or DNA markers for routine testing and authentication of durian cultivars in question. The project developed molecular procedures for routine testing. SSR primers were also screened and identified for their utility in discriminating durian cultivars collected. Results of the study showed the following accomplishments; 1. Twenty (29) SSR primers were selected and identified based on their ability to discriminate durian cultivars, 2. Optimized and established standard procedure for identification and authentication of Durian cultivars 3. Genetic profile of durian is now available at Biotech Unit. Our results demonstrate the relevance of using molecular techniques in evaluating and identifying durian clones. The most polymorphic primers tested in this study could be useful tools for detecting variation even at the early stage of the plant especially for commercial purposes. The process developed combines the efficiency of the microsatellites development process with the optimization of non-radioactive detection process resulting in a user-friendly protocol that can be performed in two (2) weeks and easily incorporated into laboratories about to start microsatellite development projects. This can be of great importance to extend microsatellite analyses to other crop species where minimal genetic information is currently available. With this, the University can now be a service laboratory for routine testing and authentication of durian clones.

Keywords: DNA, SSR analysis, genotype, genetic diversity, cultivars

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Authors: S. Mohamed, D. Gonzalez, C. Sayada, P. Halfon

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Drug resistance mutations are routinely detected using standard Sanger sequencing, which does not detect minor variants with a frequency below 20%. The impact of detecting minor variants generated by ultra-deep sequencing (UDS) on HIV drug-resistance (DR) interpretations has not yet been studied. Fifty HIV-1 patients who experienced virological failure were included in this retrospective study. The HIV-1 UDS protocol allowed the detection and quantification of HIV-1 protease and reverse transcriptase variants related to genotypes A, B, C, E, F, and G. DeepChek®-HIV simplified DR interpretation software was used to compare Sanger sequencing and UDS. The total time required for the UDS protocol was found to be approximately three times longer than Sanger sequencing with equivalent reagent costs. UDS detected all of the mutations found by population sequencing and identified additional resistance variants in all patients. An analysis of DR revealed a total of 643 and 224 clinically relevant mutations by UDS and Sanger sequencing, respectively. Three resistance mutations with > 20% prevalence were detected solely by UDS: A98S (23%), E138A (21%) and V179I (25%). A significant difference in the DR interpretations for 19 antiretroviral drugs was observed between the UDS and Sanger sequencing methods. Y181C and T215Y were the most frequent mutations associated with interpretation differences. A combination of UDS and DeepChek® software for the interpretation of DR results would help clinicians provide suitable treatments. A cut-off of 1% allowed a better characterisation of the viral population by identifying additional resistance mutations and improving the DR interpretation.

Keywords: HIV-1, ultra-deep sequencing, Sanger sequencing, drug resistance

Procedia PDF Downloads 315

Authors: S. Khan, A. Khan, Abdul R. Soomrani, Raja F. Zafar, A. Waqas, G. Akbar

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This paper proposes a low-complexity image interpolation method. Image interpolation is used to convert a low dimension video/image to high dimension video/image. The objective of a good interpolation method is to upscale an image in such a way that it provides better edge preservation at the cost of very low complexity so that real-time processing of video frames can be made possible. However, low complexity methods tend to provide real-time interpolation at the cost of blurring, jagging and other artifacts due to errors in slope calculation. Non-linear methods, on the other hand, provide better edge preservation, but at the cost of high complexity and hence they can be considered very far from having real-time interpolation. The proposed method is a linear method that uses gradient orientation for slope calculation, unlike conventional linear methods that uses the contrast of nearby pixels. Prewitt edge detection is applied to separate uniform regions and edges. Simple line averaging is applied to unknown uniform regions, whereas unknown edge pixels are interpolated after calculation of slopes using gradient orientations of neighboring known edge pixels. As a post-processing step, bilateral filter is applied to interpolated edge regions in order to enhance the interpolated edges.

Keywords: edge detection, gradient orientation, image upscaling, linear interpolation, slope tracing

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Authors: Salma Krafessi

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The financial markets have a multifaceted, intricate environment, and enormous volumes of data are produced every day. To find investment possibilities, possible fraudulent activity, and market oddities, accurate anomaly identification in this data is essential. Conventional methods for detecting anomalies frequently fail to capture the complex organization of financial data. In order to improve the identification of abnormalities in financial time series data, this study presents Tucker Decomposition as a reliable multi-way analysis approach. We start by gathering closing prices for the S&P 500 index across a number of decades. The information is converted to a three-dimensional tensor format, which contains internal characteristics and temporal sequences in a sliding window structure. The tensor is then broken down using Tucker Decomposition into a core tensor and matching factor matrices, allowing latent patterns and relationships in the data to be captured. A possible sign of abnormalities is the reconstruction error from Tucker's Decomposition. We are able to identify large deviations that indicate unusual behavior by setting a statistical threshold. A thorough examination that contrasts the Tucker-based method with traditional anomaly detection approaches validates our methodology. The outcomes demonstrate the superiority of Tucker's Decomposition in identifying intricate and subtle abnormalities that are otherwise missed. This work opens the door for more research into multi-way data analysis approaches across a range of disciplines and emphasizes the value of tensor-based methods in financial analysis.

Keywords: tucker decomposition, financial markets, financial engineering, artificial intelligence, decomposition models

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Authors: Berredjem Hajira, Benlaifa Meriem, Becheker Imene, Bardi Rafika, Djebar Med Reda

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Recent acquired or reactivation T.gondii infection is a serious complication in HIV patients. Classical serological diagnosis relies on the detection of anti-Toxoplasma immunoglobulin ; however, serology may be unreliable in HIV immunodeficient patients who fail to produce significant titers of specific antibodies. PCR assays allow a rapid diagnosis of Toxoplasma infection. In this study, we compared the value of the PCR for diagnosing active toxoplasmosis in cerebrospinal fluid and blood samples from HIV patients. Anti-Toxoplasma antibodies IgG and IgM titers were determined by ELISA. In parallel, nested PCR targeting B1 gene and conventional PCR-ELISA targeting P30 gene were used to detect T. gondii DNA in 25 blood samples and 12 cerebrospinal fluid samples from patients in whom toxoplasmic encephalitis was confirmed by clinical investigations. A total of 15 negative controls were used. Serology did not contribute to confirm toxoplasmic infection, as IgG and IgM titers decreased early. Only 8 out 25 blood samples and 5 out 12 cerebrospinal fluid samples PCRs yielded a positive result. 5 patients with confirmed toxoplasmosis had positive PCR results in either blood or cerebrospinal fluid samples. However, conventional nested B1 PCR gave best results than the P30 gene one for the detection of T.gondii DNA in both samples. All samples from control patients were negative. This study demonstrates the unusefulness of the serological tests and the high sensitivity and specificity of PCR in the diagnosis of toxoplasmic encephalitis in HIV patients.

Keywords: cerebrospinal fluid, HIV, Toxoplasmosis, PCR

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Authors: João Filipe Papel, Tatsuji Munaka

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With society's aging and the number of elderly with dementia rising, researchers have been actively studying how to support the elderly in the early stages of dementia with the objective of allowing them to have a better life quality and as much as possible independence. To make this possible, most researchers in this field are using the Internet Of Things to monitor the elderly activities and assist them in performing them. The most common sensor used to monitor the elderly activities is the Camera sensor due to its easy installation and configuration. The other commonly used sensor is the sound sensor. However, we need to consider privacy when using these sensors. This research aims to develop a system capable of predicting the early stages of dementia based on monitoring and controlling the elderly activities of daily living. To make this system possible, some issues need to be addressed. First, the issue related to elderly privacy when trying to detect their Activities of Daily Living. Privacy when performing detection and monitoring Activities of Daily Living it's a serious concern. One of the purposes of this research is to achieve this detection and monitoring without putting the privacy of the elderly at risk. To make this possible, the study focuses on using an approach based on using Magnet Sensors to collect binary data. The second is to use the data collected by monitoring Activities of Daily Living to predict the early stages of Dementia. To make this possible, the research team suggests developing a proprietary ontology combined with both data-driven and knowledge-driven.

Keywords: dementia, activity recognition, magnet sensors, ontology, data driven and knowledge driven, IoT, activities of daily living

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Authors: Hamidreza Akbari

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In this paper, for detection of inclined eccentricity in an induction motor, time–frequency analysis of the stator startup current is carried out. For this purpose, the discrete wavelet transform is used. Data are obtained from simulations, using winding function approach. The results show the validity of the approach for detecting the fault and discriminating with respect to other faults.

Keywords: induction machine, fault, DWT, electric

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Authors: Jigg Pelayo, Ricardo Villar

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Leaning on the development of aerial laser scanning in the Philippine geospatial industry, researches about remote sensing and machine vision technology became a trend. Object detection via template matching is one of its application which characterized to be fast and in real time. The paper purposely attempts to provide application for robust pattern matching algorithm based on the normalized cross correlation (NCC) criterion function subjected in Object-based image analysis (OBIA) utilizing high-resolution aerial imagery and low density LiDAR data. The height information from laser scanning provides effective partitioning order, thus improving the hierarchal class feature pattern which allows to skip unnecessary calculation. Since detection is executed in the object-oriented platform, mathematical morphology and multi-level filter algorithms were established to effectively avoid the influence of noise, small distortion and fluctuating image saturation that affect the rate of recognition of features. Furthermore, the scheme is evaluated to recognized the performance in different situations and inspect the computational complexities of the algorithms. Its effectiveness is demonstrated in areas of Misamis Oriental province, achieving an overall accuracy of 91% above. Also, the garnered results portray the potential and efficiency of the implemented algorithm under different lighting conditions.

Keywords: algorithm, LiDAR, object recognition, OBIA

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Authors: Myungsu Lim, William Xiu Shun Wong, Yoonjin Hyun, Chen Liu, Seongi Choi, Dasom Kim, Namgyu Kim

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The amount of real-time data generated through various mass media has been increasing rapidly. In this study, we had performed topic analysis by using the unstructured text data that is distributed through news article. As one of the most prevalent applications of topic analysis, the issue tracking technique investigates the changes of the social issues that identified through topic analysis. Currently, traditional issue tracking is conducted by identifying the main topics of documents that cover an entire period at the same time and analyzing the occurrence of each topic by the period of occurrence. However, this traditional issue tracking approach has limitation that it cannot discover dynamic mutation process of complex social issues. The purpose of this study is to overcome the limitations of the existing issue tracking method. We first derived core issues of each period, and then discover the dynamic mutation process of various issues. In this study, we further analyze the mutation process from the perspective of the issues categories, in order to figure out the pattern of issue flow, including the frequency and reliability of the pattern. In other words, this study allows us to understand the components of the complex issues by tracking the dynamic history of issues. This methodology can facilitate a clearer understanding of complex social phenomena by providing mutation history and related category information of the phenomena.

Keywords: Data Mining, Issue Tracking, Text Mining, topic Analysis, topic Detection, Trend Detection

Procedia PDF Downloads 386

Authors: Soukaina Motia, Nadia El Alami El Hassani, Alassane Diouf, Benachir Bouchikhi, Nezha El Bari

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Parabens are the antimicrobial molecules largely used in cosmetic products as a preservative agent. Among them, the methylparaben (MP) is the most frequently used ingredient in cosmetic preparations. Nevertheless, their potential dangers led to the development of sensible and reliable methods for their determination in environmental samples. Firstly, a sensitive and selective molecular imprinted polymer (MIP) based on screen-printed gold electrode (Au-SPE), assembled on a polymeric layer of carboxylated poly(vinyl-chloride) (PVC-COOH), was developed. After the template removal, the obtained material was able to rebind MP and discriminate it among other interfering species such as glucose, sucrose, and citric acid. The behavior of molecular imprinted sensor was characterized by Cyclic Voltammetry (CV), Differential Pulse Voltammetry (DPV) and Electrochemical Impedance Spectroscopy (EIS) techniques. Then, the biosensor was found to have a linear detection range from 0.1 pg.mL^-1 to 1 ng.mL^-1 and a low limit of detection of 0.12 fg.mL^-1 and 5.18 pg.mL^-1 by DPV and EIS, respectively. For applications, this biosensor was employed to determine MP content in four wastewaters in Meknes city and two cosmetic products (shower gel and shampoo). The operational reproducibility and stability of this biosensor were also studied. Secondly, another MIP biosensor based on tungsten trioxide (WO₃) functionalized by gold nanoparticles (Au-NPs) assembled on a polymeric layer of PVC-COOH was developed. The main goal was to increase the sensitivity of the biosensor. The developed MIP biosensor was successfully applied for the MP determination in wastewater samples and cosmetic products.

Keywords: cosmetic products, methylparaben, molecularly imprinted polymer, wastewater

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Authors: N. Varsamis, M. Gerasimou, P. Christodoulou, S. Mantzoukis, G. Kolliopoulou, N. Zotos

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Purpose: Body fluids (pleural, peritoneal, synovial, pericardial, cerebrospinal) are an important element in the detection of microorganisms. For this reason, it is important to examine them in the Intensive Care Unit (ICU) patients. Material and Method: Body fluids are transported through sterile containers and enriched as soon as possible with Tryptic Soy Broth (TSB). After one day of incubation, the broth is poured into selective media: Blood, Mac Conkey No. 2, Chocolate, Mueller Hinton, Chapman and Saboureaud agar. The above selective media are incubated directly for 2 days. After this period, if any number of microbial colonies are detected, gram staining is performed. After that, the isolated organisms are identified by biochemical techniques in the automated Microscan system (Siemens) and followed by a sensitivity test on the same system using the minimum inhibitory concentration MIC technique. The sensitivity test is verified by Kirby Bauer-based plate test. Results: In 2017 the Laboratory of Microbiology received 60 samples of body fluids from the ICU. More specifically the Microbiology Department received 6 peritoneal fluid specimens, 18 pleural fluid specimens and 36 cerebrospinal fluid specimens. 36 positive cultures were tested. S. epidermidis was identified in 18 specimens, S. haemolyticus in 6, and E. faecium in 12. Conclusions: The results show low detection of microorganisms in body fluid cultures.

Keywords: body fluids, culture, intensive care unit, microorganisms

Procedia PDF Downloads 186

Authors: Todd Zhou, Mikhail Yurochkin

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Out-of-distribution (OOD) detection is receiving increasing amounts of attention in the machine learning research community, boosted by recent technologies, such as autonomous driving and image processing. This newly-burgeoning field has called for the need for more effective and efficient methods for out-of-distribution generalization methods. Without accessing the label information, deploying machine learning models to out-of-distribution domains becomes extremely challenging since it is impossible to evaluate model performance on unseen domains. To tackle this out-of-distribution detection difficulty, we designed a model selection pipeline algorithm and developed a model selection framework with different impurity and randomness measurements to evaluate and choose the best-performing models for out-of-distribution data. By exploring different randomness scores based on predicted probabilities, we adopted the out-of-distribution entropy and developed a custom-designed score, ”CombinedScore,” as the evaluation criterion. This proposed score was created by adding labeled source information into the judging space of the uncertainty entropy score using harmonic mean. Furthermore, the prediction bias was explored through the equality of opportunity violation measurement. We also improved machine learning model performance through model calibration. The effectiveness of the framework with the proposed evaluation criteria was validated on the Folktables American Community Survey (ACS) datasets.

Keywords: model selection, domain generalization, model fairness, randomness measurements, bias index

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Authors: Christos Theoharatos, Dimitris Tsourounis, Spiros Oikonomou, Andreas Makedonas

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This paper focuses on the problem of visually inspecting and recognizing the road conditions in front of moving vehicles, targeting automotive scenarios. The goal of road inspection is to identify whether the road is slippery or not, as well as to detect possible anomalies on the road surface like potholes or body bumps/humps. Our work is based on an artificial intelligence methodology for real-time monitoring of road conditions in autonomous driving scenarios, using state-of-the-art deep convolutional neural network (CNN) techniques. Initially, the road and ego lane are segmented within the field of view of the camera that is integrated into the front part of the vehicle. A novel classification CNN is utilized to identify among plain and slippery road textures (e.g., wet, snow, etc.). Simultaneously, a robust detection CNN identifies severe surface anomalies within the ego lane, such as potholes and speed bumps/humps, within a distance of 5 to 25 meters. The overall methodology is illustrated under the scope of an integrated application (or system), which can be integrated into complete Advanced Driver-Assistance Systems (ADAS) systems that provide a full range of functionalities. The outcome of the proposed techniques present state-of-the-art detection and classification results and real-time performance running on AI accelerator devices like Intel’s Myriad 2/X Vision Processing Unit (VPU).

Keywords: deep learning, convolutional neural networks, road condition classification, embedded systems

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Authors: S. Shankar Bharathi

Abstract:

Inspection of surface defects on metallic components has always been challenging due to its specular property. Occurrences of defects such as scratches, rust, pitting are very common in metallic surfaces during the manufacturing process. These defects if unchecked can hamper the performance and reduce the life time of such component. Many of the conventional image processing algorithms in detecting the surface defects generally involve segmentation techniques, based on thresholding, edge detection, watershed segmentation and textural segmentation. They later employ other suitable algorithms based on morphology, region growing, shape analysis, neural networks for classification purpose. In this paper the work has been focused only towards detecting scratches. Global and other thresholding techniques were used to extract the defects, but it proved to be inaccurate in extracting the defects alone. However, this paper does not focus on comparison of different segmentation techniques, but rather describes a novel approach towards segmentation combined with hausdorff dilation distance. The proposed algorithm is based on the distribution of the intensity levels, that is, whether a certain gray level is concentrated or evenly distributed. The algorithm is based on extraction of such concentrated pixels. Defective images showed higher level of concentration of some gray level, whereas in non-defective image, there seemed to be no concentration, but were evenly distributed. This formed the basis in detecting the defects in the proposed algorithm. Hausdorff dilation distance based on mathematical morphology was used to strengthen the segmentation of the defects.

Keywords: metallic surface, scratches, segmentation, hausdorff dilation distance, machine vision

Procedia PDF Downloads 409