Search results for: spectrum sensing

Authors: Ye Wang, Ickjai Lee

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Due to the widespread of mobile sensing, there is a strong need to handle trails of moving objects, trajectories. This paper proposes three visual analytic approaches for higher order information of trajectory data sets based on the higher order Voronoi diagram data structure. Proposed approaches reveal geometrical information, topological, and directional information. Experimental results demonstrate the applicability and usefulness of proposed three approaches.

Keywords: visual analytics, higher order information, trajectory datasets, spatio-temporal data

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Authors: Heewon Jeong, Seongpyo Kim, Joon Ha Kim

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Over the last few tens of years, the coastal regions of Korea have experienced red tide algal blooms, which are harmful and toxic to both humans and marine organisms due to their potential threat. It was accelerated owing to eutrophication by human activities, certain oceanic processes, and climate change. Previous studies have tried to monitoring and predicting the algae concentration of the ocean with the bio-optical algorithms applied to color images of the satellite. However, the accurate estimation of algal blooms remains problems to challenges because of the complexity of coastal waters. Therefore, this study suggests a new method to identify the concentration of red tide algal bloom from images of geostationary ocean color imager (GOCI) which are representing the water environment of the sea in Korea. The method employed GOCI images, which took the water leaving radiances centered at 443nm, 490nm and 660nm respectively, as well as observed weather data (i.e., humidity, temperature and atmospheric pressure) for the database to apply optical characteristics of algae and train deep learning algorithm. Convolution neural network (CNN) was used to extract the significant features from the images. And then artificial neural network (ANN) was used to estimate the concentration of algae from the extracted features. For training of the deep learning model, backpropagation learning strategy is developed. The established methods were tested and compared with the performances of GOCI data processing system (GDPS), which is based on standard image processing algorithms and optical algorithms. The model had better performance to estimate algae concentration than the GDPS which is impossible to estimate greater than 5mg/m³. Thus, deep learning model trained successfully to assess algae concentration in spite of the complexity of water environment. Furthermore, the results of this system and methodology can be used to improve the performances of remote sensing. Acknowledgement: This work was supported by the 'Climate Technology Development and Application' research project (#K07731) through a grant provided by GIST in 2017.

Keywords: deep learning, algae concentration, remote sensing, satellite

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Authors: Iliya Dimitrov Stoev, Benjamin Seelbinder, Elena Erben, Nicola Maghelli, Moritz Kreysing

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Despite the revolutionizing impact of optical tweezers in materials science and cell biology up to the present date, trapping has so far extensively relied on specific material properties of the probe and local heating has limited applications related to investigating dynamic processes within living systems. To overcome these limitations while maintaining high sensitivity, here we present a new optofluidic approach that can be used to gently trap microscopic particles and measure femtoNewton forces in a contact-free manner and with thermally limited precision.

Keywords: optofluidics, force measurements, microrheology, FLUCS, thermoviscous flows

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Authors: M. Van 'T Hof, R. V. Pasma, J. T. Bailly, H. W. Hoek, W. A. Ester

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Background: The transition into primary school can be challenging for children with an autism spectrum disorder (ASD). Due to the new demands that are made to children in this period, their limitations in social functioning and school achievements may manifest and appear faster. Detection of possible ASD signals mainly takes place by parents, teachers and during obligatory municipal maternal and child health centre visits. Physicians of municipal maternal and child health centres have limited education and instruments to detect ASD. Further education on detecting ASD is needed to optimally equip these doctors for this task. Most research aims to increase the early detection of ASD in children aged 0-3 years and shows positive results. However, there is a lack of research on educational interventions to detect ASD in children aged 4-6 years by municipal maternal and child health physicians. Aim: The aim of this study is to explore the effect of the online educational intervention: Detection of ASD in children aged 4-6 years for municipal maternal and child health physicians. This educational intervention is developed within The Reach-Aut Academic Centre for Autism; Transitions in education, and will be available throughout The Netherlands. Methods: Ninety-two participants will follow the educational intervention: Detection of ASD in children aged 4-6 years for municipal maternal and child health centre physicians. The educational intervention consists of three, one and a half hour sessions, which are offered through an online interactive classroom. The focus and content of the course has been developed in collaboration with three groups of stakeholders; autism scientists, clinical practitioners (municipal maternal and child health doctors and ASD experts) and parents of children with ASD. The primary outcome measure is knowledge about ASD: signals, early detection, communication with parents and referrals. The secondary outcome measures are the number of ASD related referrals, the attitude towards the mentally ill (CAMI), perceived competency about ASD knowledge and detection skills, and satisfaction about the educational intervention. Results and Conclusion: The study started in January 2016 and data collection will end mid 2017.

Keywords: ASD, child, detection, educational intervention, physicians

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Authors: Silvia Ximena Barrios, Diego Armando Villamizar Mantilla, Raquel Elvira Ocazionez, , Elena E. Stashenko, María Pilar Vinardell, Jorge Luis Fuentes

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Introduction: Skin overexposure to solar radiation has been a serious public health concern, because of its potential carcinogenicity. Therefore, preventive protection strategies using photoprotective agents are critical to counteract the harmful effect of solar radiation. Plants may be a source of photoprotective compounds that inhibit cellular mutations involved in skin cancer initiation. This work evaluated the photoprotective and antigenotoxic effects against ultraviolet B (UVB) radiation of a mixture of Posoqueria latifolia flower extract and Kaempferol (MixPoKa). Methods: The photoprotective efficacy of MixPoka (Posoqueria latifolia flower extract 250 μg/ml and Kaempferol 349.5 μM) was evaluated using in vitro indices such as sun protection factor SPFᵢₙ ᵥᵢₜᵣₒ and critical wavelength (λc). The MixPoKa photostability (Eff) at human minimal erythema doses (MED), according to the Fitzpatrick skin scale, was also estimated. Cytotoxicity and genotoxicity/antigenotoxicity were studied in MRC5 human fibroblasts using the trypan blue exclusion and Comet assays, respectively. Kinetics of the genetic damage repair post irradiation in the presence and absence of the MixPoka, was also evaluated. Results: The MixPoka -UV absorbance spectrum was high across the spectral bands between 200 and 400 nm. The UVB photoprotection efficacy of MixPoka was high (SPFᵢₙ ᵥᵢₜᵣₒ = 25.70 ± 0.06), showed wide photoprotection spectrum (λc = 380 ± 0), and resulted photostable (Eff = 92.3–100.0%). The MixPoka was neither cytotoxic nor genotoxic in MRC5 human fibroblasts; but presented significant antigenotoxic effect against UVB radiation. Additionally, MixPoka stimulate DNA repair post-irradiation. The potential of this phytochemical mixture as sunscreen ingredients was discussed. Conclusion: MixPoka showed a significant antigenotoxic effect against UVB radiation and stimulated DNA repair after irradiation. MixPoka could be used as an ingredient in a sunscreen cream.

Keywords: flower extract, photoprotection, antigenotoxicity, cytotoxicity, genotoxicit

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Authors: Mitali Saha, Soma Das

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The fabrication of nanoscale materials for use in chemical sensing, biosensing and biological analyses has proven a promising avenue in the last few years. Cholesterol has aroused considerable interest in recent years on account of its being an important parameter in clinical diagnosis. There is a strong positive correlation between high serum cholesterol level and arteriosclerosis, hypertension, and myocardial infarction. Enzyme-based electrochemical biosensors have shown high selectivity and excellent sensitivity, but the enzyme is easily denatured during its immobilization procedure and its activity is also affected by temperature, pH, and toxic chemicals. Besides, the reproducibility of enzyme-based sensors is not very good which further restrict the application of cholesterol biosensor. It has been demonstrated that carbon nanotubes could promote electron transfer with various redox active proteins, ranging from cytochrome c to glucose oxidase with a deeply embedded redox center. In continuation of our earlier work on the synthesis and applications of carbon and metal based nanoparticles, we have reported here the synthesis of carbon nanotubes (CCNT) by burning coconut oil under insufficient flow of air using an oil lamp. The soot was collected from the top portion of the flame, where the temperature was around 6500C which was purified, functionalized and then characterized by SEM, p-XRD and Raman spectroscopy. The SEM micrographs showed the formation of tubular structure of CCNT having diameter below 100 nm. The XRD pattern indicated the presence of two predominant peaks at 25.20 and 43.80, which corresponded to (002) and (100) planes of CCNT respectively. The Raman spectrum (514 nm excitation) showed the presence of 1600 cm-1 (G-band) related to the vibration of sp2-bonded carbon and at 1350 cm-1 (D-band) responsible for the vibrations of sp3-bonded carbon. A nonenzymatic cholesterol biosensor was then fabricated on an insulating Teflon material containing three silver wires at the surface, covered by CCNT, obtained from coconut oil. Here, CCNTs worked as working as well as counter electrodes whereas reference electrode and electric contacts were made of silver. The dimensions of the electrode was 3.5 cm×1.0 cm×0.5 cm (length× width × height) and it is ideal for working with 50 µL volume like the standard screen printed electrodes. The voltammetric behavior of cholesterol at CCNT electrode was investigated by cyclic voltammeter and differential pulse voltammeter using 0.001 M H2SO4 as electrolyte. The influence of the experimental parameters on the peak currents of cholesterol like pH, accumulation time, and scan rates were optimized. Under optimum conditions, the peak current was found to be linear in the cholesterol concentration range from 1 µM to 50 µM with a sensitivity of ~15.31 μAμM−1cm−2 with lower detection limit of 0.017 µM and response time of about 6s. The long-term storage stability of the sensor was tested for 30 days and the current response was found to be ~85% of its initial response after 30 days.

Keywords: coconut oil, CCNT, cholesterol, biosensor

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Authors: Xiaobo Liu, Rakesh Mishra, Yun Zhang

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Continuous monitoring of forest canopy height with large coverage is essential for obtaining forest carbon stocks and emissions, quantifying biomass estimation, analyzing vegetation coverage, and determining biodiversity. LiDAR can be used to collect accurate woody vegetation structure such as canopy height. However, LiDAR’s coverage is usually limited because of its high cost and limited maneuverability, which constrains its use for dynamic and large area forest canopy monitoring. On the other hand, optical satellite images, like Sentinel-2, have the ability to cover large forest areas with a high repeat rate, but they do not have height information. Hence, exploring the solution of integrating LiDAR data and Sentinel-2 images to enlarge the coverage of forest canopy height prediction and increase the prediction repeat rate has been an active research topic in the environmental remote sensing community. In this study, we explore the potential of training a Random Forest Regression (RFR) model and a Convolutional Neural Network (CNN) model, respectively, to develop two predictive models for predicting and validating the forest canopy height of the Acadia Forest in New Brunswick, Canada, with a 10m ground sampling distance (GSD), for the year 2018 and 2021. Two 10m airborne LiDAR-derived canopy height models, one for 2018 and one for 2021, are used as ground truth to train and validate the RFR and CNN predictive models. To evaluate the prediction performance of the trained RFR and CNN models, two new predicted canopy height maps (CHMs), one for 2018 and one for 2021, are generated using the trained RFR and CNN models and 10m Sentinel-2 images of 2018 and 2021, respectively. The two 10m predicted CHMs from Sentinel-2 images are then compared with the two 10m airborne LiDAR-derived canopy height models for accuracy assessment. The validation results show that the mean absolute error (MAE) for year 2018 of the RFR model is 2.93m, CNN model is 1.71m; while the MAE for year 2021 of the RFR model is 3.35m, and the CNN model is 3.78m. These demonstrate the feasibility of using the RFR and CNN models developed in this research for predicting large-coverage forest canopy height at 10m spatial resolution and a high revisit rate.

Keywords: remote sensing, forest canopy height, LiDAR, Sentinel-2, artificial intelligence, random forest regression, convolutional neural network

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Authors: Gautam, Gurcharan Singh, Mandeep Kaur, Yogesh Aggarwal, Sanjeev Naval

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The present ground reality scenario of suffering of humanity shows the evidence of failure to take wrong decisions to shape the civilization with Irresponsibilities in the history. A strong positive will of right responsibilities make the right civilization structure which affects itself and the whole world. Present suffering of humanity shows and reflect the failure of past decisions taken to shape the true culture with right social structure of society, due to unplanned system of Indian civilization and its rapid disaster of population make the failure to face all kind of problems which make the society sufferer. Our India is still suffering from disaster like earthquake, floods, droughts, tsunamis etc. and we face the uncountable disaster of deaths from the beginning of humanity at the present time. In this research paper our focus is to make a Disaster Resistance Structure having the solution of dense populated urban cities area by high vertical expansion HYBRID STRUCTURE. Our efforts are to analyse the Reinforced Concrete Hybrid Structure at different seismic zones, these concrete frames were analyzed using the response spectrum method to calculate and compare the different seismic displacement and drift. Seismic analysis by this method generally is based on dynamic analysis of building. Analysis results shows that the Reinforced Concrete Building at seismic Zone V having maximum peak story shear, base shear, drift and node displacement as compare to the analytical results of Reinforced Concrete Building at seismic Zone III and Zone IV. This analysis results indicating to focus on structural drawings strictly at construction site to make a HYBRID STRUCTURE. The study case is deal with the 10 story height of a vertical expansion Hybrid frame structure at different zones i.e. zone III, zone IV and zone V having the column 0.45x0.36mt and beam 0.6x0.36mt. with total height of 30mt, to make the structure more stable bracing techniques shell be applied like mage bracing and V shape bracing. If this kind of efforts or structure drawings are followed by the builders and contractors then we save the lives during earthquake disaster at Bhuj (Gujarat State, India) on 26th January, 2001 which resulted in more than 19,000 deaths. This kind of Disaster Resistance Structure having the capabilities to solve the problems of densely populated area of cities by the utilization of area in vertical expansion hybrid structure. We request to Government of India to make new plans and implementing it to save the lives from future disasters instead of unnecessary wants of development plans like Bullet Trains.

Keywords: history, irresponsibilities, unplanned social structure, humanity, hybrid structure, response spectrum analysis, DRIFT, and NODE displacement

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Authors: M. Şimşek

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Additive manufacturing (AM) or generally known as three dimensional (3D) printing provides great opportunities for both civilian and military applications by which 3D has become the biggest nominee of breakthrough of 21th century. When properly used, it has a wide spectrum of applications that make production easier and more profitable. Considering the advantages of AM, every firm has an intention of catching up with this new trend. As well as reducing costs and thus increasing benefits, 3D printing provides opportunities for national armies by reducing maintenance and repair time and increasing operational readiness.

Keywords: additive manufacturing, operational cost, operational readiness, supply chain, three dimensional printing

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Authors: Tapan Jain, Davender Singh Saini

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Clustering is a useful mechanism in wireless sensor networks which helps to cope with scalability and data transmission problems. The basic aim of our research work is to provide efficient clustering using Hierarchical agglomerative clustering (HAC). If the distance between the sensing nodes is calculated using their location then it’s quantitative HAC. This paper compares the various agglomerative clustering techniques applied in a wireless sensor network using the quantitative data. The simulations are done in MATLAB and the comparisons are made between the different protocols using dendrograms.

Keywords: routing, hierarchical clustering, agglomerative, quantitative, wireless sensor network

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Authors: Takashi Yamaguchi, Ichio Asanuma, Jong G. Park, Kenneth J. Mackin, John Mittleman

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In this paper, vessel detection using the artificial neural network is proposed in order to automatically construct the vessel detection model from the satellite imagery of day/night band (DNB) in visible infrared in the products of Imaging Radiometer Suite (VIIRS) on Suomi National Polar-orbiting Partnership (Suomi-NPP).The goal of our research is the establishment of vessel detection method using the satellite imagery of DNB in order to monitor the change of vessel activity over the wide region. The temporal vessel monitoring is very important to detect the events and understand the circumstances within the maritime environment. For the vessel locating and detection techniques, Automatic Identification System (AIS) and remote sensing using Synthetic aperture radar (SAR) imagery have been researched. However, each data has some lack of information due to uncertain operation or limitation of continuous observation. Therefore, the fusion of effective data and methods is important to monitor the maritime environment for the future. DNB is one of the effective data to detect the small vessels such as fishery ships that is difficult to observe in AIS. DNB is the satellite sensor data of VIIRS on Suomi-NPP. In contrast to SAR images, DNB images are moderate resolution and gave influence to the cloud but can observe the same regions in each day. DNB sensor can observe the lights produced from various artifact such as vehicles and buildings in the night and can detect the small vessels from the fishing light on the open water. However, the modeling of vessel detection using DNB is very difficult since complex atmosphere and lunar condition should be considered due to the strong influence of lunar reflection from cloud on DNB. Therefore, artificial neural network was applied to learn the vessel detection model. For the feature of vessel detection, Brightness Temperature at the 3.7 μm (BT3.7) was additionally used because BT3.7 can be used for the parameter of atmospheric conditions.

Keywords: artificial neural network, day/night band, remote sensing, Suomi National Polar-orbiting Partnership, vessel detection, Visible Infrared Imaging Radiometer Suite

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Authors: I. C. Cho, W. H. Wen, H. Y. Tsai, T. C. Chao, C. J. Tung

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Tissue-equivalent proportional counter (TEPC) is a useful instrument used to measure radiation single-event energy depositions in a subcellular target volume. The quantity of measurements is the microdosimetric lineal energy, which determines the relative biological effectiveness, RBE, for radiation therapy or the radiation-weighting factor, WR, for radiation protection. TEPC is generally used in a mixed radiation field, where each component radiation has its own RBE or WR value. To reduce the pile-up effect during radiotherapy measurements, a miniature TEPC (mini TEPC) with cavity size in the order of 1 mm may be required. In the present work, a homemade mini TEPC with a cylindrical cavity of 1 mm in both the diameter and the height was constructed to measure the lineal energy spectrum of a mixed radiation field with high- and low-LET radiations. Instead of using external radiation beams to penetrate the detector wall, mixed radiation fields were produced by the interactions of neutrons with TEPC walls that contained small plugs of different materials, i.e. Li, B, A150, Cd and N. In all measurements, mini TEPC was placed at the beam port of the Tsing Hua Open-pool Reactor (THOR). Measurements were performed using the propane-based tissue-equivalent gas mixture, i.e. 55% C3H8, 39.6% CO2 and 5.4% N2 by partial pressures. The gas pressure of 422 torr was applied for the simulation of a 1 m diameter biological site. The calibration of mini TEPC was performed using two marking points in the lineal energy spectrum, i.e. proton edge and electron edge. Measured spectra revealed high lineal energy (> 100 keV/m) peaks due to neutron-capture products, medium lineal energy (10 – 100 keV/m) peaks from hydrogen-recoil protons, and low lineal energy (< 10 keV/m) peaks of reactor photons. For cases of Li and B plugs, the high lineal energy peaks were quite prominent. The medium lineal energy peaks were in the decreasing order of Li, Cd, N, A150, and B. The low lineal energy peaks were smaller compared to other peaks. This study demonstrated that internally produced mixed radiations from the interactions of neutrons with different plugs in the TEPC wall provided a useful approach for TEPC measurements of lineal energies.

Keywords: TEPC, lineal energy, microdosimetry, radiation quality

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Authors: Jaemyoung Lee

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We present a method to set up system functions through a near filed communication (NFC) of a smartphone. The short communication distance of the NFC which is usually less than 4 cm could prevent any interferences from other devices and establish a secure communication channel between a system and the smartphone. The proposed set-up method for system function values is demonstrated for a blacbox system in a car. In demonstration, system functions of a blackbox which is manipulated through NFC of a smartphone are controls of image quality, sound level, shock sensing level to store images, etc. The proposed set-up method for system function values can be used for any devices with NFC.

Keywords: system set-up, near field communication, smartphone, android

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Authors: Purba Biswas, Priyanka Dey

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Worldwide rapid urbanisation has accelerated city expansion in both developed and developing nations. This unprecedented urbanisation trend due to the increasing population and economic growth has caused challenges for the decision-makers in city planning and urban management. Metropolitan cities, class-I towns, and major urban centres undergo a continuous process of evolution due to interaction between socio-cultural and economic attributes. This constant evolution leads to urban expansion in all directions. Understanding the patterns and dynamics of urban built-up growth is crucial for policymakers, urban planners, and researchers, as it aids in resource management, decision-making, and the development of sustainable strategies to address the complexities associated with rapid urbanisation. Identifying spatio-temporal patterns of urban growth has emerged as a crucial challenge in monitoring and assessing present and future trends in urban development. Analysing urban growth patterns and tracking changes in land use is an important aspect of urban studies. This study analyses spatio-temporal urban transformations and land-use and land cover changes using remote sensing and GIS techniques. Built-up growth analysis has been done for the city of Surat as a case example, using the GIS tools of NDBI and GIS models of the Built-up Urban Density Index and Shannon Entropy Index to identify trends and the geographical direction of transformation from 2005 to 2020. Surat is one of the fastest-growing urban centres in both the state and the nation, ranking as the 4th fastest-growing city globally. This study analyses the dynamics of urban built-up area transformations both zone-wise and geographical direction-wise, in which their trend, rate, and magnitude were calculated for the period of 15 years. This study also highlights the need for analysing and monitoring the urban growth pattern of class-I cities in India using spatio-temporal and quantitative techniques like GIS for improved urban management.

Keywords: urban expansion, built-up, geographic information system, remote sensing, Shannon’s entropy

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Authors: Adriana Salgado, Sandra Castaneda, Ivan Perez

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Learning to read and write is a complex process involving several cognitive skills, contextual, and cultural environments. The basis of this development is linguistic skills, such as the ability to name and understand vocabulary, retell a story, phonological awareness, letter knowledge, among others. In children with autism spectrum disorder (ASD), one of the main concerns is related to language disorders. Nevertheless, most of the children with ASD are able to decode written information but have difficulties in reading comprehension. The research of these processes in the Spanish-speaking population is limited. However, the increasing prevalence of this diagnosis (1 in 115 children) in Mexico has implications at different levels. Educational research is an important area of interest in ASD children, such as emergent literacy. Reading and writing expand the possibilities of academic, cultural, and social information access. Taking this information into account, the objective of this research was to identify the relationship between language skills, alphabet knowledge, phonological awareness, and early reading and writing in ASD Spanish-speaking children. The method used for this research was based on tasks that were selected, adapted and in some cases designed to measure initial reading and writing, as well as language skills (naming, receptive vocabulary, and narrative skills), phonological awareness (similar phonological word pairs, beginning sound awareness and spelling) and letter knowledge, in a sample of 45 children (38 boys and 7 girls) with prior diagnosis of ASD. Descriptive analyses, as well as bivariate correlations, cluster analysis, and canonical correspondence, were obtained for the data results. Results showed that variability was large; however, it was possible to characterize the sample in low, medium, and high score groups regarding children performance. The low score group (46.7% of the sample), had a null or deficient performance in language skills and phonological awareness, some could identify up to five letters of the alphabet, showed no early reading skills but they could scribble. The middle score group was characterized by a highly variable performance in different tasks, with better language skills in receptive and naming vocabulary, some narrative, letter knowledge, and phonological awareness (beginning sound awareness) skills. The high score group, (24.4% of the sample) had the best performance in language skills in relation to the sample data, as well as in the rest of the measured skills. Finally, scores were canonically correlated between naming, receptive vocabulary, narrative, phonological awareness, letter knowledge and initial learning of reading and writing skills for the high score group and letter knowledge, naming and receptive vocabulary for the lower score group, which is consistent with previous research in typical and ASD children. In conclusion, the obtained data is consistent with previous studies. Despite large variability, it was possible to identify performance profiles and relations based on linguistic, phonological awareness, and letter knowledge skills. These skills were predictor variables of the initial development of reading and writing. The above has implications for a future program and strategies development that may benefit the acquisition of reading and writing in ASD children.

Keywords: autism, autism spectrum disorders, early literacy, emergent literacy

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Authors: Bruno Biagianti, Angela Tseng, Kathy Wannaviroj, Allison Corlett, Megan DuBois, Kyu Lee, Suma Jacob

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Background: ASD is characterized by pervasive Sensory Processing Abnormalities (SPA) and social cognitive deficits that persist throughout the course of the illness and have been linked to functional abnormalities in specific neural systems that underlie the perception, processing, and representation of sensory information. SPA and social cognitive deficits are associated with difficulties in interpersonal relationships, poor development of social skills, reduced social interactions and lower academic performance. Importantly, they can hamper the effects of established evidence-based psychological treatments—including PEERS (Program for the Education and Enrichment of Relationship Skills), a parent/caregiver-assisted, 16-weeks social skills intervention—which nonetheless requires a functional brain capable of assimilating and retaining information and skills. As a matter of fact, some adolescents benefit from PEERS more than others, calling for strategies to increase treatment response rates. Objective: We will present interim data on CICADAS (Care Improving Cognition for ADolescents on the Autism Spectrum)—a clinician-assisted, closed-loop technology mobile application for adolescents with ASD. Via ten mobile assessments, CICADAS captures data on sensory processing abnormalities and associated cognitive deficits. These data populate a machine learning algorithm that tailors the delivery of ten neuroplasticity-based social cognitive training (NB-SCT) exercises targeting sensory processing abnormalities. Methods: In collaboration with the Autism Spectrum and Neurodevelopmental Disorders Clinic at the University of Minnesota, we conducted a fully remote, three-arm, randomized crossover trial with adolescents with ASD to document the acceptability of CICADAS and evaluate its potential as a stand-alone treatment or as a treatment enhancer of PEERS. Twenty-four adolescents with ASD (ages 11-18) have been initially randomized to 16 weeks of PEERS + CICADAS (Arm A) vs. 16 weeks of PEERS + computer games vs. 16 weeks of CICADAS alone (Arm C). After 16 weeks, the full battery of assessments has been remotely administered. Results: We have evaluated the acceptability of CICADAS by examining adherence rates, engagement patterns, and exit survey data. We found that: 1) CICADAS is able to serve as a treatment enhancer for PEERS, inducing greater improvements in sensory processing, cognition, symptom reduction, social skills and behaviors, as well as the quality of life compared to computer games; 2) the concurrent delivery of PEERS and CICADAS induces greater improvements in study outcomes compared to CICADAS only. Conclusion: While preliminary, our results indicate that the individualized assessment and treatment approach designed in CICADAS seems effective in inducing adaptive long-term learning about social-emotional events. CICADAS-induced enhancement of processing and cognition facilitates the application of PEERS skills in the environment of adolescents with ASD, thus improving their real-world functioning.

Keywords: ASD, social skills, cognitive training, mobile app

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Authors: Jennifer Buz, Alvin Spivey

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The most common greenhouse gas emitted through human activities, carbon dioxide (CO2), is naturally consumed by plants during photosynthesis. This process is actively being monetized by companies wishing to offset their carbon dioxide emissions. For example, companies are now able to purchase protections for vegetated land due-to-be clear cut or purchase barren land for reforestation. Therefore, by actively preventing the destruction/decay of plant matter or by introducing more plant matter (reforestation), a company can theoretically offset some of their emissions. One of the biggest issues in the carbon credit market is validating and verifying carbon offsets. There is a need for a system that can accurately and frequently ensure that the areas sold for carbon credits have the vegetation mass (and therefore for carbon offset capability) they claim. Traditional techniques for measuring vegetation mass and determining health are costly and require many person-hours. Orbital Sidekick offers an alternative approach that accurately quantifies carbon mass and assesses vegetation health through satellite hyperspectral imagery, a technique which enables us to remotely identify material composition (including plant species) and condition (e.g., health and growth stage). How much carbon a plant is capable of storing ultimately is tied to many factors, including material density (primarily species-dependent), plant size, and health (trees that are actively decaying are not effectively storing carbon). All of these factors are capable of being observed through satellite hyperspectral imagery. This abstract focuses on speciation. To build a species classification model, we matched pixels in our remote sensing imagery to plants on the ground for which we know the species. To accomplish this, we collaborated with the researchers at the Teakettle Experimental Forest. Our remote sensing data comes from our airborne “Kato” sensor, which flew over the study area and acquired hyperspectral imagery (400-2500 nm, 472 bands) at ~0.5 m/pixel resolution. Coverage of the entire teakettle experimental forest required capturing dozens of individual hyperspectral images. In order to combine these images into a mosaic, we accounted for potential variations of atmospheric conditions throughout the data collection. To do this, we ran an open source atmospheric correction routine called ISOFIT1 (Imaging Spectrometer Optiman FITting), which converted all of our remote sensing data from radiance to reflectance. A database of reflectance spectra for each of the tree species within the study area was acquired using the Teakettle stem map and the geo-referenced hyperspectral images. We found that a wide variety of machine learning classifiers were able to identify the species within our images with high (>95%) accuracy. For the most robust quantification of carbon mass and the best assessment of the health of a vegetated area, speciation is critical. Through the use of high resolution hyperspectral data, ground-truth databases, and complex analytical techniques, we are able to determine the species present within a pixel to a high degree of accuracy. These species identifications will feed directly into our carbon mass model.

Keywords: hyperspectral, satellite, carbon, imagery, python, machine learning, speciation

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Authors: Vladimir V. Iakovlev, Erin T. Carey, John Steege

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The use of polypropylene mesh devices for Pelvic Organ Prolapse (POP) spread rapidly during the last decade, yet our knowledge of the mesh-tissue interaction is far from complete. We aimed to perform a thorough pathological examination of explanted POP meshes and describe findings that may explain mechanisms of complications resulting in product excision. We report a spectrum of important findings, including nerve ingrowth, mesh deformation, involvement of detrusor muscle with neural ganglia, and polypropylene degradation. Analysis of these findings may improve and guide future treatment strategies.

Keywords: transvaginal, mesh, nerves, polypropylene degradation

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Authors: Sonu Rana, Jyoti Verma, A. K. Gautam

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In most developing countries like India, the majority of the population heavily relies on agriculture for their livelihood. The yield of agriculture is heavily dependent on uncertain weather conditions like a monsoon, soil fertility, availability of irrigation facilities and fertilizers as well as support from the government. The agricultural yield is quite less compared to the effort put in due to inefficient agricultural facilities and obsolete farming practices on the one hand and lack of knowledge on the other hand, and ultimately agricultural community does not prosper. It is therefore essential for the farmers to improve their harvest yield by the acquisition of related data such as soil condition, temperature, humidity, availability of irrigation facilities, availability of, manure, etc., and adopt smart farming techniques using modern agricultural equipment. Nowadays, using IOT technology in agriculture is the best solution to improve the yield with fewer efforts and economic costs. The primary focus of this work-related is IoT technology in the agriculture field. By using IoT all the parameters would be monitored by mounting sensors in an agriculture field held at different places, will collect real-time data, and could be transmitted by a transmitting device like an antenna. To improve the system, IoT will interact with other useful systems like Wireless Sensor Networks. IoT is exploring every aspect, so the radio frequency spectrum is getting crowded due to the increasing demand for wireless applications. Therefore, Federal Communications Commission is reallocating the spectrum for various wireless applications. An antenna is also an integral part of the newly designed IoT devices. The main aim is to propose a new antenna structure used for IoT agricultural applications and compatible with this new unlicensed frequency band. The main focus of this paper is to present work related to these technologies in the agriculture field. This also presented their challenges & benefits. It can help in understanding the job of data by using IoT and correspondence advancements in the horticulture division. This will help to motivate and educate the unskilled farmers to comprehend the best bits of knowledge given by the huge information investigation utilizing smart technology.

Keywords: smart agriculture, IoT, agriculture technology, data analytics, smart technology

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Authors: I. N. Nwafia, U. C. Ozumba, M. E. Ohanu, S. O. Ebede

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Antibiotic resistance is increasing globally and has become a major health challenge. Extended-spectrum beta-lactamase is clinically important because the ESBL gene are mostly plasmid encoded and these plasmids frequently carry genes encoding resistance to other classes of antimicrobials thereby limiting antibiotic options in the treatment of infections caused by these organisms. The specific objectives of this study were to determine the prevalence of ESBLs production in Escherichia coli, to determine the antibiotic susceptibility pattern of ESBLs producing Escherichia coli, to detect TEM, SHV and CTX-M genes and the risk factors to acquisition of ESBL producing Escherichia coli. The protocol of the study was approved by Health Research and Ethics committee of the University of Nigeria Teaching Hospital (UNTH), Enugu. It was a descriptive cross-sectional study that involved all hospitalized patients in UNTH from whose specimens Escherichia coli was isolated during the period of the study. The samples analysed were urine, wound swabs, blood and cerebrospinal fluid. These samples were cultured in 5% sheep Blood agar and MacConkey agar (Oxoid Laboratories, Cambridge UK) and incubated at 35-370C for 24 hours. Escherichia coli was identified with standard biochemical tests and confirmed using API 20E auxanogram (bioMerieux, Marcy 1'Etoile, France). The antibiotic susceptibility testing was done by disc diffusion method and interpreted according to the Clinical and Laboratory Standard Institute guideline. ESBL production was confirmed using ESBL Epsilometer test strips (Liofilchem srl, Italy). The ESBL bla genes were detected with polymerase chain reaction, after extraction of DNA with plasmid mini-prep kit (Jena Bioscience, Jena, Germany). Data analysis was with appropriate descriptive and inferential statistics. One hundred and six isolates (53.00%) out of the 200 were from urine, followed by isolates from different swabs specimens 53(26.50%) and the least number of the isolates 4(2.00) were from blood (P value = 0.096). Seventy (35.00%) out of the 200 isolates, were confirmed positive for ESBL production. Forty-two (60.00%) of the isolates were from female patients while 28(40.00%) were from male patients (P value = 0.13). Sixty-eight (97.14%) of the isolates were susceptible to imipenem while all of the isolates were resistant to ampicillin, chloramphenicol and tetracycline. From the 70 positive isolates the ESBL genes detected with polymerase chain reaction were blaCTX-M (n=26; 37.14%), blaTEM (n=7; 10.00%), blaSHV (n=2; 2.86%), blaCTX-M/TEM (n=7; 10.0%), blaCTX-M/SHV (n=14; 20.0%) and blaCTX-M/TEM/SHV (n=10; 14.29%). There was no gene detected in 4(5.71%) of the isolates. The most associated risk factors to infections caused by ESBL producing Escherichia coli was previous antibiotics use for the past 3 months followed by admission in the intensive care unit, recent surgery, and urinary catheterization. In conclusion, ESBLs was detected in 4 of every 10 Escherichia coli with the predominant gene detected being CTX-M. This knowledge will enable appropriate measures towards improvement of patient health care, antibiotic stewardship, research and infection control in the hospital.

Keywords: antimicrobial, Escherichia coli, extended spectrum beta lactamase, resistance

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Authors: Aymen Bouzid, Jihen Ben Smida

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In this paper, we propose to prove the importance of the application of blind source separation methods on remote sensing data in order to index multispectral images. The proposed method starts with Gabor Filtering and the application of a Blind Source Separation to get a more effective representation of the information contained on the observation images. After that, a feature vector is extracted from each image in order to index them. Experimental results show the superior performance of this approach.

Keywords: blind source separation, content based image retrieval, feature extraction multispectral, satellite images

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Authors: Neda Orak, Mostafa Zarei

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Environmental assessment is an important session in environment management. Since various methods and techniques have been produces and implemented. Remote sensing (RS) is widely used in many scientific and research fields such as geology, cartography, geography, agriculture, forestry, land use planning, environment, etc. It can show earth surface objects cyclical changes. Also, it can show earth phenomena limits on basis of electromagnetic reflectance changes and deviations records. The research has been done on mangrove forests assessment by RS techniques. Mangrove forests quantitative analysis in Basatin and Bidkhoon estuaries was the aim of this research. It has been done by Landsat satellite images from 1975- 2013 and match to ground control points. This part of mangroves are the last distribution in northern hemisphere. It can provide a good background to improve better management on this important ecosystem. Landsat has provided valuable images to earth changes detection to researchers. This research has used MSS, TM, +ETM, OLI sensors from 1975, 1990, 2000, 2003-2013. Changes had been studied after essential corrections such as fix errors, bands combination, georeferencing on 2012 images as basic image, by maximum likelihood and IPVI Index. It was done by supervised classification. 2004 google earth image and ground points by GPS (2010-2012) was used to compare satellite images obtained changes. Results showed mangrove area in bidkhoon was 1119072 m2 by GPS and 1231200 m2 by maximum likelihood supervised classification and 1317600 m2 by IPVI in 2012. Basatin areas is respectively: 466644 m2, 88200 m2, 63000 m2. Final results show forests have been declined naturally. It is due to human activities in Basatin. The defect was offset by planting in many years. Although the trend has been declining in recent years again. So, it mentioned satellite images have high ability to estimation all environmental processes. This research showed high correlation between images and indexes such as IPVI and NDVI with ground control points.

Keywords: IPVI index, Landsat sensor, maximum likelihood supervised classification, Nayband National Park

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Authors: Léa Chawki, Émilie Cappe

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Context: Nowadays, supporting parents of children with autism spectrum disorder (ASD) and helping them adjust to their child’s condition represents a core clinical and scientific necessity and is encouraged by the French National Strategy for Autism (2018). In France, ASD remains a challenging condition, causing distress, segregation and social stigma to concerned family members concerned by this handicap. The literature highlights that neurodevelopmental disorders in children, such as ASD, influence parental well-being. This impact could be different according to parents’ culture and the child’s particular disorder manifestation, such as developmental coordination disorder (DCC), for instance. Objectives: This present study aims to explore parental stress, anxiety and depressive symptoms, as well as the quality of life in parents of children with ASD or DCD, as well as the explicit individual, psychosocial and cultural factors of parental well-being. Methods: Participants will be recruited through diagnostic centers, child and specialized adolescent units, and organizations representing families with ASD and DCD. Our sample will include five groups of 150 parents: four groups of parents having children with ASD – one living in France, one in the US, one in Canada and the other in Lebanon – and one group of French parents of children with DCD. Self-evaluation measures will be filled directly by parents in order to measure parental stress, anxiety and depressive symptoms, quality of life, coping and emotional regulation strategies, internalized stigma, perceived social support, the child’s problem behaviors severity, as well as motor coordination deficits in children with ASD and DCD. A sociodemographic questionnaire will help collect additional useful data regarding participants and their children. Individual and semi-structured research interviews will be conducted to complete quantitative data by further exploring participants’ distinct experiences related to parenting a child with a neurodevelopmental disorder. An interview grid, specially designed for the needs of this study, will strengthen the comparison between the experiences of parents of children with ASD with those of parents of children with DCD. It will also help investigate cultural differences regarding parent support policies in the context of raising a child with ASD. Moreover, interviews will help clarify the link between certain research variables (behavioral differences between ASD and DCD, family leisure activities, family and children’s extracurricular life, etc.) and parental well-being. Research perspectives: Results of this study will provide a more holistic understanding of the roles of individual, psychosocial and cultural variables related to parental well-being. Thus, this study will help direct the implementation of support services offered to families of children with neurodevelopmental disorders (ASD and DCD). Also, the implications of this study are essential in order to guide families through changes related to public policies assisting neurodevelopmental disorders and other disabilities. The between-group comparison (ASD and DCD) is also expected to help clarify the origins of all the different challenges encountered by those families. Hence, it will be interesting to investigate whether complications perceived by parents are more likely to arise from child-symptom severity, or from the lack of support obtained from health and educational systems.

Keywords: Autism spectrum disorder, cross-cultural, cross-disorder, developmental coordination delay, well-being

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Authors: I.J. Kim, B.C. Kim, J.H. Kim, C.-Y. Yi

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Heat transfer in a graphite calorimeter is analyzed by using the finite elements method. The calorimeter is modeled in 3D geometry. Quasi-adiabatic mode operation is realized in the simulation and the temperature rise by different sources of the ionizing radiation and electric heaters is compared, directly. The temperature distribution caused by the electric power was much different from that by the ionizing radiation because of its point-like localized heating. However, the temperature rise which was finally read by sensing thermistors agreed well to each other within 0.02 %.

Keywords: graphite calorimeter, finite element analysis, heat transfer, quasi-adiabatic mode

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Authors: Hemant Kumar, Nilendu Das

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A study based on ANN structure gives us the information to predict the size of the future in realizing a past event. ANN, IMD (Indian meteorological department) data and remote sensing were used to enable a number of parameters for calculating the size that may occur in the future. A threshold selected specifically above the high-frequency harvest reached the area during the selected seismic activity. In the field of human and local biodiversity it remains to obtain the right parameter compared to the frequency of impact. But during the study the assumption is that predicting seismic activity is a difficult process, not because of the parameters involved here, which can be analyzed and funded in research activity.

Keywords: ANN, Bayesion class, earthquakes, IMD

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Authors: Jin-Hyeong Park, Hong-Seok Kim, Jin-Hyeok Yim, Young-Ji Kim, Dong-Hyeon Kim, Jung-Whan Chon, Kun-Ho Seo

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Salmonella contamination in chicken samples can cause major health problems in humans. However, not only the effects of antibiotic treatment during growth but also the impacts of poultry slaughter line on the prevalence of Salmonella in final chicken meat sold to consumers are unknown. In this study, we compared the isolation rates and antimicrobial resistance of Salmonella between antibiotic-free, conventional, conventional Korean native retail chicken meat samples and clonal divergence of Salmonella isolates by multilocus sequence typing. In addition, the distribution of extended-spectrum β-lactamase (ESBL) genes in ESBL-producing Salmonella isolates was analyzed. A total of 72 retail chicken meat samples (n = 24 antibiotic-free broiler [AFB] chickens, n = 24 conventional broiler [CB] chickens, and n = 24 conventional Korean native [CK] chickens) were collected from local retail markets in Seoul, South Korea. The isolation rates of Salmonella were 66.6% in AFB chickens, 45.8% in CB chickens, and 25% in CK chickens. By analyzing the minimum inhibitory concentrations of β -lactam antibiotics with the disc-diffusion test, we found that 81.2% of Salmonella isolates from AFB chickens, 63.6% of isolates from CB chickens, and 50% of isolates from CK chickens were ESBL producers; all ESBL-positive isolates had the CTX-M-15 genotype. Interestingly, all ESBL-producing Salmonella were revealed as ST16 by multilocus sequence typing. In addition, all CTX-M-15-positive isolates had the genetic platform of blaCTX-M gene (IS26-ISEcp1-blaCTX-M-15-IS903), to the best of our knowledge, this is the first report in Salmonella around the world. The Salmonella ST33 strain (S. Hadar) isolated in this study has never been reported in South Korea. In conclusion, our findings showed that antibiotic-free retail chicken meat products were also largely contaminated with ESBL-producing Salmonella and that their ESBL genes and genetic platforms were the same as those isolated from conventional retail chicken meat products.

Keywords: antibiotic-free poultry, conventional poultry, multilocus sequence typing, extended-spectrum β-lactamase, antimicrobial resistance

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Authors: Lee Yujin, Han Jiye, Oh Jin-Woo

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The adverse effects of endocrine disrupting chemicals (EDCs) has attracted considerable public interests. The benzene-like EDCs structure mimics the mechanisms of hormones naturally occurring in vivo, and alters physiological function of the endocrine system. Although, some of the most representative EDCs such as polychlorinated biphenyls (PCBs) and phthalates compounds already have been prohibited to produce and use in many countries, however, PCBs and phthalates in plastic products as flame retardant and plasticizer are still circulated nowadays. EDCs can be released from products while using and discarding, and it causes serious environmental and health issues. Here, we developed virus-based structurally coloured nanostructure that can detect minute EDCs concentration sensitively and selectively. These structurally coloured nanostructure exhibits characteristic angel-independent colors due to the regular virus bundle structure formation through simple pulling technique. The designed number of different colour bands can be formed through controlling concentration of virus solution and pulling speed. The virus, M-13 bacteriophage, was genetically engineered to react with specific ECDs, typically PCBs and phthalates. M-13 bacteriophage surface (pVIII major coat protein) was decorated with benzene derivative binding peptides (WHW) through phage library method. In the initial assessment, virus-based color sensor was exposed to several organic chemicals including benzene, toluene, phenol, chlorobenzene, and phthalic anhydride. Along with the selectivity evaluation of virus-based colour sensor, it also been tested for sensitivity. 10 to 300 ppm of phthalic anhydride and chlorobenzene were detected by colour sensor, and showed the significant sensitivity with about 90 of dissociation constant. Noteworthy, all measurements were analyzed through principal component analysis (PCA) and linear discrimination analysis (LDA), and exhibited clear discrimination ability upon exposure to 2 categories of EDCs (PCBs and phthalates). Because of its easy fabrication, high sensitivity, and the superior selectivity, M-13 bacteriophage-based color sensor could be a simple and reliable portable sensing system for environmental monitoring, healthcare, social security, and so on.

Keywords: M-13 bacteriophage, colour sensor, genetic engineering, EDCs

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Authors: Israa Sh. Tawfic, Sema Koc Kayhan

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In this paper, first, we propose least support orthogonal matching pursuit (LS-OMP) algorithm to improve the performance, of the OMP (orthogonal matching pursuit) algorithm. LS-OMP algorithm adaptively chooses optimum L (least part of support), at each iteration. This modification helps to reduce the computational complexity significantly and performs better than OMP algorithm. Second, we give the procedure for the invisible image watermarking in the presence of compressive sampling. The image reconstruction based on a set of watermarked measurements is performed using LS-OMP.

Keywords: compressed sensing, orthogonal matching pursuit, restricted isometry property, signal reconstruction, least support orthogonal matching pursuit, watermark

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Authors: Alexey V. Klyuev, Valery P. Samarin, Viktor F. Klyuev, Andrey V. Klyuev

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A power measurement algorithm of the input mix components of the noise signal and pulse interference is considered. The algorithm efficiency analysis has been carried out for different interference to signal ratio. Algorithm performance features have been explored by numerical experiment results.

Keywords: noise signal, pulse interference, signal power, spectrum width, detection

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Authors: Valdir Moura, Raniele dos Anjos de Souza, Fernando Gomes de Souza, Jose Vagner da Silva, Jerry Adriani Johann

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The estimate of the Area of Land to be planted with annual crops and its stratification by the municipality are important variables in crop forecast. Nowadays in Brazil, these information’s are obtained by the Brazilian Institute of Geography and Statistics (IBGE) and published under the report Assessment of the Agricultural Production. Due to the high cloud cover in the main crop growing season (October to March) it is difficult to acquire good orbital images. Thus, one alternative is to work with remote sensing data from dates before the crop growing season. This work presents the use of multitemporal Landsat data gathered on July and September (before the summer growing season) in order to estimate the area of land to be planted with summer crops in an area of São Paulo State, Brazil. Geographic Information Systems (GIS) and digital image processing techniques were applied for the treatment of the available data. Supervised and non-supervised classifications were used for data in digital number and reflectance formats and the multitemporal Normalized Difference Vegetation Index (NDVI) images. The objective was to discriminate the tracts with higher probability to become planted with summer crops. Classification accuracies were evaluated using a sampling system developed basically for this study region. The estimated areas were corrected using the error matrix derived from these evaluations. The classification techniques presented an excellent level according to the kappa index. The proportion of crops stratified by municipalities was derived by a field work during the crop growing season. These proportion coefficients were applied onto the area of land to be planted with summer crops (derived from Landsat data). Thus, it was possible to derive the area of each summer crop by the municipality. The discrepancies between official statistics and our results were attributed to the sampling and the stratification procedures. Nevertheless, this methodology can be improved in order to provide good crop area estimates using remote sensing data, despite the cloud cover during the growing season.

Keywords: area intended for summer culture, estimated area planted, agriculture, Landsat, planting schedule

Procedia PDF Downloads 144